INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(68636, 2012, 'bib', 'Toward more accurate pan-specific MHC-peptide binding prediction: a review of current methods and tools', 'Zhang, Lianming|Udaka, Keiko|Mamitsuka, Hiroshi|Zhu, Shanfeng', 'School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai 200433, China.|School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai 200433, China.|School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai 200433, China.|School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai 200433, China.', 'China|China|China|China', 68, 'Algorithms|Alleles|Binding Sites|Databases, Protein|Epitopes|Major Histocompatibility Complex|Peptides', 'Journal Article', '10.1093/bib/bbr060', '21949215', 'Binding of short antigenic peptides to major histocompatibility complex (MHC) molecules is a core step in adaptive immune response. Precise identification of MHC-restricted peptides is of great significance for understanding the mechanism of immune response and promoting the discovery of immunogenic epitopes. However, due to the extremely high MHC polymorphism and huge cost of biochemical experiments, there is no experimentally measured binding data for most MHC molecules. To address the problem of predicting peptides binding to these MHC molecules, recently computational approaches, called pan-specific methods, have received keen interest. Pan-specific methods make use of experimentally obtained binding data of multiple alleles, by which binding peptides (binders) of not only these alleles but also those alleles with no known binders can be predicted. To investigate the possibility of further improvement in performance and usability of pan-specific methods, this article extensively reviews existing pan-specific methods and their web servers. We first present a general framework of pan-specific methods. Then, the strategies and performance as well as utilities of web servers are compared. Finally, we discuss the future direction to improve pan-specific methods for MHC-peptide binding prediction.'),
(68637, 2012, 'bib', 'Lessons from a decade of integrating cancer copy number alterations with gene expression profiles', 'Huang, Norman|Shah, Parantu K|Li, Cheng', 'Department of Biostatistics and Computational Biology, CLS-11075, Dana-Farber Cancer Institute, Harvard School of Public Health, CLS-11075 3 Blackfan Circle, Boston, MA 02115, USA.|Department of Biostatistics and Computational Biology, CLS-11075, Dana-Farber Cancer Institute, Harvard School of Public Health, CLS-11075 3 Blackfan Circle, Boston, MA 02115, USA.|Department of Biostatistics and Computational Biology, CLS-11075, Dana-Farber Cancer Institute, Harvard School of Public Health, CLS-11075 3 Blackfan Circle, Boston, MA 02115, USA.', 'United States|United States|United States', 39, 'Algorithms|Data Interpretation, Statistical|Gene Dosage|Gene Expression Profiling|Genomics|Neoplasms|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bib/bbr056', '21949216', 'Over the last decade, multiple functional genomic datasets studying chromosomal aberrations and their downstream effects on gene expression have accumulated for several cancer types. A vast majority of them are in the form of paired gene expression profiles and somatic copy number alterations (CNA) information on the same patients identified using microarray platforms. In response, many algorithms and software packages are available for integrating these paired data. Surprisingly, there has been no serious attempt to review the currently available methodologies or the novel insights brought using them. In this work, we discuss the quantitative relationships observed between CNA and gene expression in multiple cancer types and biological milestones achieved using the available methodologies. We discuss the conceptual evolution of both, the step-wise and the joint data integration methodologies over the last decade. We conclude by providing suggestions for building efficient data integration methodologies and asking further biological questions.'),
(68638, 2012, 'bib', 'The relationship between rational drug design and drug side effects', 'Wang, Juan|Li, Zhi-xin|Qiu, Cheng-xiang|Wang, Dong|Cui, Qing-hua', 'Department of Biomedical Informatics, MOE Key Lab of Molecular Cardiovascular Sciences, Peking University Health Science Center, Beijing, China. wjuan@hsc.pku.edu.cn|Department of Biomedical Informatics, MOE Key Lab of Molecular Cardiovascular Sciences, Peking University Health Science Center, Beijing, China. wjuan@hsc.pku.edu.cn|Department of Biomedical Informatics, MOE Key Lab of Molecular Cardiovascular Sciences, Peking University Health Science Center, Beijing, China. wjuan@hsc.pku.edu.cn|Department of Biomedical Informatics, MOE Key Lab of Molecular Cardiovascular Sciences, Peking University Health Science Center, Beijing, China. wjuan@hsc.pku.edu.cn|Department of Biomedical Informatics, MOE Key Lab of Molecular Cardiovascular Sciences, Peking University Health Science Center, Beijing, China. wjuan@hsc.pku.edu.cn', 'China|China|China|China|China', 24, 'Cell Membrane|Cytoplasm|Databases, Factual|Drug Delivery Systems|Drug Design|Drug Discovery|Drug-Related Side Effects and Adverse Reactions|Proteins', 'Journal Article', '10.1093/bib/bbr061', '21949217', 'Previous analysis of systems pharmacology has revealed a tendency of rational drug design in the pharmaceutical industry. The targets of new drugs tend to be close with the corresponding disease genes in the biological networks. However, it remains unclear whether the rational drug design introduces disadvantages, i.e. side effects. Therefore, it is important to dissect the relationship between rational drug design and drug side effects. Based on a recently released drug side effect database, SIDER, here we analyzed the relationship between drug side effects and the rational drug design. We revealed that the incidence drug side effect is significantly associated with the network distance of drug targets and diseases genes. Drugs with the distances of three or four have the smallest incidence of side effects, whereas drugs with the distances of more than four or smaller than three show significantly greater incidence of side effects. Furthermore, protein drugs and small molecule drugs show significant differences. Drugs hitting membrane targets and drugs hitting cytoplasm targets also show differences. Failure drugs because of severe side effects show smaller network distances than approved drugs. These results suggest that researchers should be prudent on rationalizing the drug design. Too small distances between drug targets and diseases genes may not always be advantageous for rational design for drug discovery.'),
(68639, 2012, 'bib', 'Detecting coevolving positions in a molecule: why and how to account for phylogeny', 'Dutheil, Julien Y', 'Institut des Sciences de lEvolution - Montpellier (I.S.E.-M.) Unité Mixte de Recherche UMII - CNRS (UMR 5554) Université de Montpellier II - CC 065 34095 Montpellier Cedex 05. julien.dutheil@univ-montp2.fr', '-', 21, 'Computer Simulation|Evolution, Molecular|Phylogeny|RNA, Ribosomal|Sequence Alignment', 'Journal Article', '10.1093/bib/bbr048', '21949241', 'Positions in a molecule that share a common constraint do not evolve independently, and therefore leave a signature in the patterns of homologous sequences. Exhibiting such positions with a coevolution pattern from a sequence alignment has great potential for predicting functional and structural properties of molecules through comparative analysis. This task is complicated by the existence of additional correlation sources, leading to false predictions. The nature of the data is a major source of noise correlation: sequences are taken from individuals with different degrees of relatedness, and who therefore are intrinsically correlated. This has led to several method developments in different fields that are potentially confusing for non-expert users interested in these methodologies. It also explains why coevolution detection methods are largely unemployed despite the importance of the biological questions they address. In this article, I focus on the role of shared ancestry for understanding molecular coevolution patterns. I review and classify existing coevolution detection methods according to their ability to handle shared ancestry. Using a ribosomal RNA benchmark data set, for which detailed knowledge of the structure and coevolution patterns is available, I demonstrate and explain why taking the underlying evolutionary history of sequences into account is the only way to extract the full coevolution signal in the data. I also evaluate, using rigorous statistical procedures, the best approaches to do so, and discuss several important biological aspects to consider when performing coevolution analyses.'),
(68640, 2011, 'bib', 'Editorial: Orthology and applications', 'Dessimoz, Christophe', 'Institut des Sciences de lEvolution - Montpellier (I.S.E.-M.) Unité Mixte de Recherche UMII - CNRS (UMR 5554) Université de Montpellier II - CC 065 34095 Montpellier Cedex 05. julien.dutheil@univ-montp2.fr', '-', 3, 'Genome|Genomics|Phylogeny', 'Editorial', '10.1093/bib/bbr057', '21949264', '-'),
(68641, 2011, 'bib', 'Obituary: Walter Fitch and the orthology paradigm', 'Koonin, Eugene V', 'Institut des Sciences de lEvolution - Montpellier (I.S.E.-M.) Unité Mixte de Recherche UMII - CNRS (UMR 5554) Université de Montpellier II - CC 065 34095 Montpellier Cedex 05. julien.dutheil@univ-montp2.fr', '-', 1, 'Algorithms|Evolution, Molecular|History, 20th Century|History, 21st Century|Humans|Phylogeny', 'Biography', '10.1093/bib/bbr058', '21949265', '-'),
(68642, 2011, 'bib', 'Models, algorithms and programs for phylogeny reconciliation', 'Doyon, Jean-Philippe|Ranwez, Vincent|Daubin, Vincent|Berry, Vincent', 'Université Montpellier 2, Laboratoire dInformatique, de Robotique et de Microélectronique de Montpellier, France.|Université Montpellier 2, Laboratoire dInformatique, de Robotique et de Microélectronique de Montpellier, France.|Université Montpellier 2, Laboratoire dInformatique, de Robotique et de Microélectronique de Montpellier, France.|Université Montpellier 2, Laboratoire dInformatique, de Robotique et de Microélectronique de Montpellier, France.', 'France|France|France|France', 96, 'Algorithms|Animals|Evolution, Molecular|Gene Duplication|Gene Transfer, Horizontal|Genome|Genomics|Humans|Models, Genetic|Phylogeny|Species Specificity', 'Journal Article', '10.1093/bib/bbr045', '21949266', 'Gene sequences contain a gold mine of phylogenetic information. But unfortunately for taxonomists this information does not only tell the story of the species from which it was collected. Genes have their own complex histories which record speciation events, of course, but also many other events. Among them, gene duplications, transfers and losses are especially important to identify. These events are crucial to account for when reconstructing the history of species, and they play a fundamental role in the evolution of genomes, the diversification of organisms and the emergence of new cellular functions. We review reconciliations between gene and species trees, which are rigorous approaches for identifying duplications, transfers and losses that mark the evolution of a gene family. Existing reconciliation models and algorithms are reviewed and difficulties in modeling gene transfers are discussed. We also compare different reconciliation programs along with their advantages and disadvantages.'),
(68643, 2011, 'bib', 'Monovalent and divalent salt correction algorithms for Tm prediction--recommendations for Primer3 usage', 'von Ahsen, Nicolas|Wittwer, Carl T|Schütz, Ekkehard', 'University Hospital Göttingen, Germany.|University Hospital Göttingen, Germany.|University Hospital Göttingen, Germany.', 'Germany|Germany|Germany', 3, 'Algorithms|Cations, Divalent|Cations, Monovalent|DNA Primers|Polymerase Chain Reaction|Salts|Transition Temperature', 'Journal Article', '10.1093/bib/bbq081', '21949267', 'Primer3 is a widely used program for selection of oligonucleotide primers for PCR. The websites used for implementation of Primer3 have recently been updated. PCR requires Mg(2+)(,) which has a significant dsDNA stabilizing effect that must be taken into account when designing PCR primers. The data sets and formulas used to correct for salt concentrations have been updated in Primer3 to give better prediction of melting temperature (T(m)). The liberal combination of different formulas for monovalent and divalent salt correction can lead to different results, depending on the formula chosen by the user. Using published T(m) for 475 different oligonucleotides, it is shown that the combination of the implemented conversion of divalent to monovalent cation concentration works well with one salt correction formula but not with an alternative one. Use of a more recently described alternative formula would lead to equally good T(m) predictions if divalent cations are present. The proper selection of compatible primer pairs depends on the choice of a good combination of salt correction formulas. Currently the SantaLucia salt correction formula should be used if Mg(2+) is present. The alternative formula should be updated to its recent form for future releases.'),
(68644, 2011, 'bib', 'Evolution and applications of plant pathway resources and databases', 'Sucaet, Yves|Deva, Taru', 'Iowa State University, USA.|Iowa State University, USA.', 'United States|United States', 7, 'Databases, Factual|Evolution, Molecular|Plants|Signal Transduction|Systems Biology', 'Journal Article', '10.1093/bib/bbq083', '21949268', 'Plants are important sources of food and plant products are essential for modern human life. Plants are increasingly gaining importance as drug and fuel resources, bioremediation tools and as tools for recombinant technology. Considering these applications, database infrastructure for plant model systems deserves much more attention. Study of plant biological pathways, the interconnection between these pathways and plant systems biology on the whole has in general lagged behind human systems biology. In this article we review plant pathway databases and the resources that are currently available. We lay out trends and challenges in the ongoing efforts to integrate plant pathway databases and the applications of database integration. We also discuss how progress in non-plant communities can serve as an example for the improvement of the plant pathway database landscape and thereby allow quantitative modeling of plant biosystems. We propose Good Database Practice as a possible model for collaboration and to ease future integration efforts.'),
(68645, 2011, 'bib', 'Knowledge sharing and collaboration in translational research, and the DC-THERA Directory', 'Splendiani, Andrea|Gündel, Michaela|Austyn, Jonathan M|Cavalieri, Duccio|Scognamiglio, Ciro|Brandizi, Marco', 'IntelliLeaf Ltd, Cambridge CB1 3UF, UK. andrea@leafbioscience.com|IntelliLeaf Ltd, Cambridge CB1 3UF, UK. andrea@leafbioscience.com|IntelliLeaf Ltd, Cambridge CB1 3UF, UK. andrea@leafbioscience.com|IntelliLeaf Ltd, Cambridge CB1 3UF, UK. andrea@leafbioscience.com|IntelliLeaf Ltd, Cambridge CB1 3UF, UK. andrea@leafbioscience.com|IntelliLeaf Ltd, Cambridge CB1 3UF, UK. andrea@leafbioscience.com', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 8, 'Database Management Systems|Information Dissemination|Information Storage and Retrieval|Internet|Semantics|Translational Medical Research', 'Journal Article', '10.1093/bib/bbr051', '21969471', 'Biomedical research relies increasingly on large collections of data sets and knowledge whose generation, representation and analysis often require large collaborative and interdisciplinary efforts. This dimension of big data research calls for the development of computational tools to manage such a vast amount of data, as well as tools that can improve communication and access to information from collaborating researchers and from the wider community. Whenever research projects have a defined temporal scope, an additional issue of data management arises, namely how the knowledge generated within the project can be made available beyond its boundaries and life-time. DC-THERA is a European Network of Excellence (NoE) that spawned a very large collaborative and interdisciplinary research community, focusing on the development of novel immunotherapies derived from fundamental research in dendritic cell immunobiology. In this article we introduce the DC-THERA Directory, which is an information system designed to support knowledge management for this research community and beyond. We present how the use of metadata and Semantic Web technologies can effectively help to organize the knowledge generated by modern collaborative research, how these technologies can enable effective data management solutions during and beyond the project lifecycle, and how resources such as the DC-THERA Directory fit into the larger context of e-science.'),
(68646, 2011, 'bib', 'Tools and collaborative environments for bioinformatics research', 'Romano, Paolo|Giugno, Rosalba|Pulvirenti, Alfredo', 'Bioinformatics, National Cancer Research Institute (IST), Genoa, Italy. paolo.romano@istge.it|Bioinformatics, National Cancer Research Institute (IST), Genoa, Italy. paolo.romano@istge.it|Bioinformatics, National Cancer Research Institute (IST), Genoa, Italy. paolo.romano@istge.it', 'Italy|Italy|Italy', 13, 'Computational Biology|Cooperative Behavior|Internet|Research|Social Networking', 'Journal Article', '10.1093/bib/bbr055', '21984743', 'Advanced research requires intensive interaction among a multitude of actors, often possessing different expertise and usually working at a distance from each other. The field of collaborative research aims to establish suitable models and technologies to properly support these interactions. In this article, we first present the reasons for an interest of Bioinformatics in this context by also suggesting some research domains that could benefit from collaborative research. We then review the principles and some of the most relevant applications of social networking, with a special attention to networks supporting scientific collaboration, by also highlighting some critical issues, such as identification of users and standardization of formats. We then introduce some systems for collaborative document creation, including wiki systems and tools for ontology development, and review some of the most interesting biological wikis. We also review the principles of Collaborative Development Environments for software and show some examples in Bioinformatics. Finally, we present the principles and some examples of Learning Management Systems. In conclusion, we try to devise some of the goals to be achieved in the short term for the exploitation of these technologies.'),
(68647, 2012, 'bib', 'Integration of Boolean models exemplified on hepatocyte signal transduction', 'Schlatter, Rebekka|Philippi, Nicole|Wangorsch, Gaby|Pick, Robert|Sawodny, Oliver|Borner, Christoph|Timmer, Jens|Ederer, Michael|Dandekar, Thomas', 'Institute for System Dynamics, University of Stuttgart, Stuttgart, Germany.|Institute for System Dynamics, University of Stuttgart, Stuttgart, Germany.|Institute for System Dynamics, University of Stuttgart, Stuttgart, Germany.|Institute for System Dynamics, University of Stuttgart, Stuttgart, Germany.|Institute for System Dynamics, University of Stuttgart, Stuttgart, Germany.|Institute for System Dynamics, University of Stuttgart, Stuttgart, Germany.|Institute for System Dynamics, University of Stuttgart, Stuttgart, Germany.|Institute for System Dynamics, University of Stuttgart, Stuttgart, Germany.|Institute for System Dynamics, University of Stuttgart, Stuttgart, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 27, 'Apoptosis|Hepatocytes|Liver|Models, Theoretical|Signal Transduction|Systems Biology', 'Journal Article', '10.1093/bib/bbr065', '22016404', 'The number of mathematical models for biological pathways is rapidly growing. In particular, Boolean modelling proved to be suited to describe large cellular signalling networks. Systems biology is at the threshold to holistic understanding of comprehensive networks. In order to reach this goal, connection and integration of existing models of parts of cellular networks into more comprehensive network models is necessary. We discuss model combination approaches for Boolean models. Boolean modelling is qualitative rather than quantitative and does not require detailed kinetic information. We show that these models are useful precursors for large-scale quantitative models and that they are comparatively easy to combine. We propose modelling standards for Boolean models as a prerequisite for smooth model integration. Using these standards, we demonstrate the coupling of two logical models on two different examples concerning cellular interactions in the liver. In the first example, we show the integration of two Boolean models of two cell types in order to describe their interaction. In the second example, we demonstrate the combination of two models describing different parts of the network of a single cell type. Combination of partial models into comprehensive network models will take systems biology to the next level of understanding. The combination of logical models facilitated by modelling standards is a valuable example for the next step towards this goal.'),
(68648, 2012, 'bib', 'Comparative analysis of algorithms for whole-genome assembly of pyrosequencing data', 'Finotello, Francesca|Lavezzo, Enrico|Fontana, Paolo|Peruzzo, Denis|Albiero, Alessandro|Barzon, Luisa|Falda, Marco|Di Camillo, Barbara|Toppo, Stefano', 'Information Engineering Department of the University of Padova, Padova, Italy.|Information Engineering Department of the University of Padova, Padova, Italy.|Information Engineering Department of the University of Padova, Padova, Italy.|Information Engineering Department of the University of Padova, Padova, Italy.|Information Engineering Department of the University of Padova, Padova, Italy.|Information Engineering Department of the University of Padova, Padova, Italy.|Information Engineering Department of the University of Padova, Padova, Italy.|Information Engineering Department of the University of Padova, Padova, Italy.|Information Engineering Department of the University of Padova, Padova, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 21, 'Algorithms|Animals|Genome|Genomics|Humans|Sequence Analysis, DNA', 'Comparative Study', '10.1093/bib/bbr063', '22021898', 'Next-generation sequencing technologies have fostered an unprecedented proliferation of high-throughput sequencing projects and a concomitant development of novel algorithms for the assembly of short reads. In this context, an important issue is the need of a careful assessment of the accuracy of the assembly process. Here, we review the efficiency of a panel of assemblers, specifically designed to handle data from GS FLX 454 platform, on three bacterial data sets with different characteristics in terms of reads coverage and repeats content. Our aim is to investigate their strengths and weaknesses in the reconstruction of the reference genomes. In our benchmarking, we assess assemblers performance, quantifying and characterizing assembly gaps and errors, and evaluating their ability to solve complex genomic regions containing repeats. The final goal of this analysis is to highlight pros and cons of each method, in order to provide the final user with general criteria for the right choice of the appropriate assembly strategy, depending on the specific needs. A further aspect we have explored is the relationship between coverage of a sequencing project and quality of the obtained results. The final outcome suggests that, for a good tradeoff between costs and results, the planned genome coverage of an experiment should not exceed 20-30 ×.'),
(68649, 2011, 'bib', 'myMIR: a genome-wide microRNA targets identification and annotation tool', 'Corrada, Dario|Viti, Federica|Merelli, Ivan|Battaglia, Cristina|Milanesi, Luciano', 'Institute for Biomedical Technologies - National Research Council (ITB-CNR), Segrate, Italy. dario.corrada@itb.cnr.it|Institute for Biomedical Technologies - National Research Council (ITB-CNR), Segrate, Italy. dario.corrada@itb.cnr.it|Institute for Biomedical Technologies - National Research Council (ITB-CNR), Segrate, Italy. dario.corrada@itb.cnr.it|Institute for Biomedical Technologies - National Research Council (ITB-CNR), Segrate, Italy. dario.corrada@itb.cnr.it|Institute for Biomedical Technologies - National Research Council (ITB-CNR), Segrate, Italy. dario.corrada@itb.cnr.it', 'Italy|Italy|Italy|Italy|Italy', 18, 'Computational Biology|Genome|MicroRNAs|Molecular Sequence Annotation|Reproducibility of Results|Software', 'Journal Article', '10.1093/bib/bbr062', '22021901', 'miRNA target genes prediction represents a crucial step in miRNAs functional characterization. In this context, the challenging issue remains predictions accuracy and recognition of false positive results. In this article myMIR, a web based system for increasing reliability of miRNAs predicted targets lists, is presented. myMIR implements an integrated pipeline for computing ranked miRNA::target lists and provides annotations for narrowing them down. The system relies on knowledge base data, suitably integrated in order to extend the functional characterization of targeted genes to miRNAs, by highlighting the search on over-represented annotation terms. Validation results show a dramatic reduction in the quantity of predictions and an increase in the sensitivity, when compared to other methods. This improves the predictions accuracy and allows the formulation of novel hypotheses on miRNAs functional involvement.'),
(68650, 2011, 'bib', 'The NETTAB 2009 workshop', 'Romano, Paolo|Giugno, Rosalba|Pulvirenti, Alfredo', 'Institute for Biomedical Technologies - National Research Council (ITB-CNR), Segrate, Italy. dario.corrada@itb.cnr.it|Institute for Biomedical Technologies - National Research Council (ITB-CNR), Segrate, Italy. dario.corrada@itb.cnr.it|Institute for Biomedical Technologies - National Research Council (ITB-CNR), Segrate, Italy. dario.corrada@itb.cnr.it', 'Italy|Italy|Italy', 0, 'Computational Biology|Database Management Systems|Metagenomics|RNA', 'Editorial', '10.1093/bib/bbr068', '22101028', '-'),
(68651, 2011, 'bib', 'Bioinformatics tools for the structural elucidation of multi-subunit protein complexes by mass spectrometric analysis of protein-protein cross-links', 'Mayne, Shannon L N|Patterton, Hugh-George', 'Department of Biotechnology, University of the Free State, Bloemfontein 9300, South Africa.|Department of Biotechnology, University of the Free State, Bloemfontein 9300, South Africa.', 'South Africa|South Africa', 12, 'Computational Biology|Cryoelectron Microscopy|Mass Spectrometry|Multiprotein Complexes|Peptides|Protein Conformation|Protein Subunits|Proteins|Proteomics', 'Journal Article', '10.1093/bib/bbq087', '22101029', 'Multi-subunit protein complexes are involved in many essential biochemical processes including signal transduction, protein synthesis, RNA synthesis, DNA replication and protein degradation. An accurate description of the relative structural arrangement of the constituent subunits in such complexes is crucial for an understanding of the molecular mechanism of the complex as a whole. Many complexes, however, lie in the mega-Dalton range, and are not amenable to X-ray crystallographic or nuclear magnetic resonance analysis. Techniques that are suited to structural studies of such large complexes, such as cryo-electron microscopy, do not provide the resolution required for a mechanistic insight. Mass spectrometry (MS) has increasingly been applied to identify the residues that are involved in chemical cross-links in compound protein assemblies, and have provided valuable insight into the molecular arrangement, orientation and contact surfaces of subunits within such large complexes. This approach is known as MS3D, and involves the MS analysis of cross-linked di-peptides following the enzymatic cleavage of a chemically cross-linked complex. A major challenge of this approach is the identification of the cross-linked di-peptides in a composite mixture of peptides, as well as the identification of the residues involved in the cross-link. These analyses require bioinformatics tools with capabilities beyond that of general, MS-based proteomic analysis software. Many MS3D software tools have appeared, often designed for very specific experimental methods. Here, we provide a review of all major MS3D bioinformatics programmes, reviewing their applicability to different workflows, specific experimental requirements and the computational approach taken by each.'),
(68652, 2012, 'bib', 'Bioinformatics Training Network (BTN): a community resource for bioinformatics trainers', 'Schneider, Maria V|Walter, Peter|Blatter, Marie-Claude|Watson, James|Brazas, Michelle D|Rother, Kristian|Budd, Aidan|Via, Allegra|van Gelder, Celia W G|Jacob, Joachim|Fernandes, Pedro|Nyrönen, Tommi H|De Las Rivas, Javier|Blicher, Thomas|Jimenez, Rafael C|Loveland, Jane|McDowall, Jennifer|Jones, Phil|Vaughan, Brendan W|Lopez, Rodrigo|Attwood, Teresa K|Brooksbank, Catherine', 'EMBL Outstation, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. vicky@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. vicky@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. vicky@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. vicky@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. vicky@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. vicky@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. vicky@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. vicky@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. vicky@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. vicky@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. vicky@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. vicky@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. vicky@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. vicky@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. vicky@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. vicky@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. vicky@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. vicky@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. vicky@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. vicky@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. vicky@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. vicky@ebi.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|', 19, 'Community Networks|Computational Biology|Humans|Research Personnel', 'Journal Article', '10.1093/bib/bbr064', '22110242', 'Funding bodies are increasingly recognizing the need to provide graduates and researchers with access to short intensive courses in a variety of disciplines, in order both to improve the general skills base and to provide solid foundations on which researchers may build their careers. In response to the development of high-throughput biology, the need for training in the field of bioinformatics, in particular, is seeing a resurgence: it has been defined as a key priority by many Institutions and research programmes and is now an important component of many grant proposals. Nevertheless, when it comes to planning and preparing to meet such training needs, tension arises between the reward structures that predominate in the scientific community which compel individuals to publish or perish, and the time that must be devoted to the design, delivery and maintenance of high-quality training materials. Conversely, there is much relevant teaching material and training expertise available worldwide that, were it properly organized, could be exploited by anyone who needs to provide training or needs to set up a new course. To do this, however, the materials would have to be centralized in a database and clearly tagged in relation to target audiences, learning objectives, etc. Ideally, they would also be peer reviewed, and easily and efficiently accessible for downloading. Here, we present the Bioinformatics Training Network (BTN), a new enterprise that has been initiated to address these needs and review it, respectively, to similar initiatives and collections.'),
(68653, 2012, 'bib', 'Semantic similarity analysis of protein data: assessment with biological features and issues', 'Guzzi, Pietro H|Mina, Marco|Guerra, Concettina|Cannataro, Mario', 'University Magna Gracia of Catanzaro, Italy. hguzzi@unicz.it|University Magna Gracia of Catanzaro, Italy. hguzzi@unicz.it|University Magna Gracia of Catanzaro, Italy. hguzzi@unicz.it|University Magna Gracia of Catanzaro, Italy. hguzzi@unicz.it', 'Italy|Italy|Italy|Italy', 47, 'Algorithms|Data Mining|Databases, Protein|Molecular Sequence Annotation|Natural Language Processing|Proteins|Proteome|Proteomics|Semantics|Vocabulary, Controlled', 'Journal Article', '10.1093/bib/bbr066', '22138322', 'The integration of proteomics data with biological knowledge is a recent trend in bioinformatics. A lot of biological information is available and is spread on different sources and encoded in different ontologies (e.g. Gene Ontology). Annotating existing protein data with biological information may enable the use (and the development) of algorithms that use biological ontologies as framework to mine annotated data. Recently many methodologies and algorithms that use ontologies to extract knowledge from data, as well as to analyse ontologies themselves have been proposed and applied to other fields. Conversely, the use of such annotations for the analysis of protein data is a relatively novel research area that is currently becoming more and more central in research. Existing approaches span from the definition of the similarity among genes and proteins on the basis of the annotating terms, to the definition of novel algorithms that use such similarities for mining protein data on a proteome-wide scale. This work, after the definition of main concept of such analysis, presents a systematic discussion and comparison of main approaches. Finally, remaining challenges, as well as possible future directions of research are presented.'),
(68654, 2012, 'bib', 'A review of statistical methods for prediction of proteolytic cleavage', 'duVerle, David A|Mamitsuka, Hiroshi', 'Bioinformatics Center, Kyoto University, Uji, Kyoto 611-0011, Japan. dave@kuicr.kyoto-u.ac.jp|Bioinformatics Center, Kyoto University, Uji, Kyoto 611-0011, Japan. dave@kuicr.kyoto-u.ac.jp', 'Japan|Japan', 22, 'Algorithms|Binding Sites|Calpain|Caspases|Markov Chains|Mathematical Computing|Proteolysis|Support Vector Machine', 'Journal Article', '10.1093/bib/bbr059', '22138323', 'A fundamental component of systems biology, proteolytic cleavage is involved in nearly all aspects of cellular activities: from gene regulation to cell lifecycle regulation. Current sequencing technologies have made it possible to compile large amount of cleavage data and brought greater understanding of the underlying protein interactions. However, the practical impossibility to exhaustively retrieve substrate sequences through experimentation alone has long highlighted the need for efficient computational prediction methods. Such methods must be able to quickly mark substrate candidates and putative cleavage sites for further analysis. Available methods and expected reliability depend heavily on the type and complexity of proteolytic action, as well as the availability of well-labelled experimental data sets: factors varying greatly across enzyme families. For this review, we chose to give a quick overview of the general issues and challenges in cleavage prediction methods followed by a more in-depth presentation of major techniques and implementations, with a focus on two particular families of cysteine proteases: caspases and calpains. Through their respective differences in proteolytic specificity (high for caspases, broader for calpains) and data availability (much lower for calpains), we aimed to illustrate the strengths and limitations of techniques ranging from position-based matrices and decision trees to more flexible machine-learning methods such as hidden Markov models and Support Vector Machines. In addition to a technical overview for each family of algorithms, we tried to provide elements of evaluation and performance comparison across methods.'),
(68655, 2012, 'bib', 'Functional mapping of ontogeny in flowering plants', 'Zhao, Xiyang|Tong, Chunfa|Pang, Xiaoming|Wang, Zhong|Guo, Yunqian|Du, Fang|Wu, Rongling', 'National Engineering Laboratory for Tree Breeding, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Beijing Forestry University, Beijing 100083, China.|National Engineering Laboratory for Tree Breeding, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Beijing Forestry University, Beijing 100083, China.|National Engineering Laboratory for Tree Breeding, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Beijing Forestry University, Beijing 100083, China.|National Engineering Laboratory for Tree Breeding, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Beijing Forestry University, Beijing 100083, China.|National Engineering Laboratory for Tree Breeding, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Beijing Forestry University, Beijing 100083, China.|National Engineering Laboratory for Tree Breeding, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Beijing Forestry University, Beijing 100083, China.|National Engineering Laboratory for Tree Breeding, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Beijing Forestry University, Beijing 100083, China.', 'China|China|China|China|China|China|China', 2, 'Flowers|Genes, Plant|Plant Development|Plant Proteins|Plants|Quantitative Trait Loci', 'Journal Article', '10.1093/bib/bbr054', '22138324', 'All organisms face the problem of how to perform a sequence of developmental changes and transitions during ontogeny. We revise functional mapping, a statistical model originally derived to map genes that determine developmental dynamics, to take into account the entire process of ontogenetic growth from embryo to adult and from the vegetative to reproductive phase. The revised model provides a framework that reconciles the genetic architecture of development at different stages and elucidates a comprehensive picture of the genetic control mechanisms of growth that change gradually from a simple to a more complex level. We use an annual flowering plant, as an example, to demonstrate our model by which to map genes and their interactions involved in embryo and postembryonic growth. The model provides a useful tool to study the genetic control of ontogenetic growth in flowering plants and any other organisms through proper modifications based on their biological characteristics.'),
(68656, 2012, 'bib', 'Hive plots--rational approach to visualizing networks', 'Krzywinski, Martin|Birol, Inanc|Jones, Steven J M|Marra, Marco A', 'BC Cancer Research Center, Vancouver, Canada. martink@bcgsc.ca|BC Cancer Research Center, Vancouver, Canada. martink@bcgsc.ca|BC Cancer Research Center, Vancouver, Canada. martink@bcgsc.ca|BC Cancer Research Center, Vancouver, Canada. martink@bcgsc.ca', 'Canada|Canada|Canada|Canada', 54, 'Algorithms|Disease|Escherichia coli|Gene Regulatory Networks|Humans|Software', 'Journal Article', '10.1093/bib/bbr069', '22155641', 'Networks are typically visualized with force-based or spectral layouts. These algorithms lack reproducibility and perceptual uniformity because they do not use a node coordinate system. The layouts can be difficult to interpret and are unsuitable for assessing differences in networks. To address these issues, we introduce hive plots (http://www.hiveplot.com) for generating informative, quantitative and comparable network layouts. Hive plots depict network structure transparently, are simple to understand and can be easily tuned to identify patterns of interest. The method is computationally straightforward, scales well and is amenable to a plugin for existing tools.'),
(68657, 2012, 'bib', 'Generic framework for high-dimensional fixed-effects ANOVA', 'Smilde, Age K|Timmerman, Marieke E|Hendriks, Margriet M W B|Jansen, Jeroen J|Hoefsloot, Huub C J', 'Department of Biosystems Data Analysis, University of Amsterdam, The Netherlands. a.k.smilde@uva.nl|Department of Biosystems Data Analysis, University of Amsterdam, The Netherlands. a.k.smilde@uva.nl|Department of Biosystems Data Analysis, University of Amsterdam, The Netherlands. a.k.smilde@uva.nl|Department of Biosystems Data Analysis, University of Amsterdam, The Netherlands. a.k.smilde@uva.nl|Department of Biosystems Data Analysis, University of Amsterdam, The Netherlands. a.k.smilde@uva.nl', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 12, 'Algorithms|Analysis of Variance|Databases, Factual|Genomics|Humans|Mathematical Concepts|Metabolomics|Research Design', 'Journal Article', '10.1093/bib/bbr071', '22199378', 'In functional genomics it is more rule than exception that experimental designs are used to generate the data. The samples of the resulting data sets are thus organized according to this design and for each sample many biochemical compounds are measured, e.g. typically thousands of gene-expressions or hundreds of metabolites. This results in high-dimensional data sets with an underlying experimental design. Several methods have recently become available for analyzing such data while utilizing the underlying design. We review these methods by putting them in a unifying and general framework to facilitate understanding the (dis-)similarities between the methods. The biological question dictates which method to use and the framework allows for building new methods to accommodate a range of such biological questions. The framework is built on well known fixed-effect ANOVA models and subsequent dimension reduction. We present the framework both in matrix algebra as well as in more insightful geometrical terms. We show the workings of the different special cases of our framework with a real-life metabolomics example from nutritional research and a gene-expression example from the field of virology.'),
(68658, 2013, 'bib', 'Hawkeye and AMOS: visualizing and assessing the quality of genome assemblies', 'Schatz, Michael C|Phillippy, Adam M|Sommer, Daniel D|Delcher, Arthur L|Puiu, Daniela|Narzisi, Giuseppe|Salzberg, Steven L|Pop, Mihai', 'Cold Spring Harbor, New York, NY 11724, USA. mschatz@cshl.edu|Cold Spring Harbor, New York, NY 11724, USA. mschatz@cshl.edu|Cold Spring Harbor, New York, NY 11724, USA. mschatz@cshl.edu|Cold Spring Harbor, New York, NY 11724, USA. mschatz@cshl.edu|Cold Spring Harbor, New York, NY 11724, USA. mschatz@cshl.edu|Cold Spring Harbor, New York, NY 11724, USA. mschatz@cshl.edu|Cold Spring Harbor, New York, NY 11724, USA. mschatz@cshl.edu|Cold Spring Harbor, New York, NY 11724, USA. mschatz@cshl.edu', 'United States|United States|United States|United States|United States|United States|United States|United States', 28, 'Animals|Computational Biology|Computer Graphics|Data Display|Genomics|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bib/bbr074', '22199379', 'Since its launch in 2004, the open-source AMOS project has released several innovative DNA sequence analysis applications including: Hawkeye, a visual analytics tool for inspecting the structure of genome assemblies, the Assembly Forensics and FRCurve pipelines for systematically evaluating the quality of a genome assembly, and AMOScmp, the first comparative genome assembler. These applications have been used to assemble and analyze dozens of genomes ranging in complexity from simple microbial species through mammalian genomes. Recent efforts have been focused on enhancing support for new data characteristics brought on by second- and now third-generation sequencing. This review describes the major components of AMOS in light of these challenges, with an emphasis on methods for assessing assembly quality and the visual analytics capabilities of Hawkeye. These interactive graphical aspects are essential for navigating and understanding the complexities of a genome assembly, from the overall genome structure down to individual bases. Hawkeye and AMOS are available open source at http://amos.sourceforge.net.'),
(68659, 2012, 'bib', 'Probe mapping across multiple microarray platforms', 'Allen, Jeffrey D|Wang, Siling|Chen, Min|Girard, Luc|Minna, John D|Xie, Yang|Xiao, Guanghua', 'UT Southwestern Medical Center, Dallas, TX 75390, USA.|UT Southwestern Medical Center, Dallas, TX 75390, USA.|UT Southwestern Medical Center, Dallas, TX 75390, USA.|UT Southwestern Medical Center, Dallas, TX 75390, USA.|UT Southwestern Medical Center, Dallas, TX 75390, USA.|UT Southwestern Medical Center, Dallas, TX 75390, USA.|UT Southwestern Medical Center, Dallas, TX 75390, USA.', 'United States|United States|United States|United States|United States|United States|United States', 18, 'Databases, Genetic|Gene Expression Profiling|Internet|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1093/bib/bbr076', '22199380', 'Access to gene expression data has become increasingly common in recent years, however, analysis has become more difficult as it is often desirable to integrate data from different platforms. Probe mapping across microarray platforms is the first and most crucial step for data integration. In this article, we systematically review and compare different approaches to map probes across seven platforms from different vendors: U95A, U133A and U133 Plus 2.0 from Affymetrix, Inc., HT-12 v1, HT-12v2 and HT-12v3 from Illumina, Inc., and 4112A from Agilent, Inc. We use a unique data set, which contains 56 lung cancer cell line samples-each of which has been measured by two different microarray platforms-to evaluate the consistency of expression measurement across platforms using different approaches. Based on the evaluation from the empirical data set, the BLAST alignment of the probe sequences to a recent revision of the Transcriptome generated better results than using annotations provided by Vendors or from Bioconductors Annotate package. However, a combination of all three methods (deemed the Consensus Annotation) yielded the most consistent expression measurement across platforms. To facilitate data integration across microarray platforms for the research community, we develop a user-friendly web-based tool, an API and an R package to map data across different microarray platforms from Affymetrix, Illumina and Agilent. Information on all three can be found at http://qbrc.swmed.edu/software/probemapper/.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(68660, 2012, 'bib', 'Affymetrix GeneChip microarray preprocessing for multivariate analyses', 'McCall, Matthew N|Almudevar, Anthony', 'Department of Biostatistics and Computational Biology, University of Rochester Medical Center, NY, USA. mccallm@gmail.com|Department of Biostatistics and Computational Biology, University of Rochester Medical Center, NY, USA. mccallm@gmail.com', 'United States|United States', 5, 'Cluster Analysis|Gene Expression|Gene Expression Profiling|Gene Regulatory Networks|Multivariate Analysis|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bib/bbr072', '22210854', 'Affymetrix GeneChip microarrays are the most widely used high-throughput technology to measure gene expression, and a wide variety of preprocessing methods have been developed to transform probe intensities reported by a microarray scanner into gene expression estimates. There have been numerous comparisons of these preprocessing methods, focusing on the most common analyses-detection of differential expression and gene or sample clustering. Recently, more complex multivariate analyses, such as gene co-expression, differential co-expression, gene set analysis and network modeling, are becoming more common, however, the same preprocessing methods are typically applied. In this article, we examine the effect of preprocessing methods on some of these multivariate analyses and provide guidance to the user as to which methods are most appropriate.'),
(68661, 2012, 'bib', 'Building an HIV data mashup using Bio2RDF', 'Nolin, Marc-Alexandre|Dumontier, Michel|Belleau, Franxc3xa7ois|Corbeil, Jacques', 'Department of Biology, Carleton University, 1125 Colonel By Drive, Ottawa, Ontario, Canada K1S 5B6.|Department of Biology, Carleton University, 1125 Colonel By Drive, Ottawa, Ontario, Canada K1S 5B6.|Department of Biology, Carleton University, 1125 Colonel By Drive, Ottawa, Ontario, Canada K1S 5B6.|Department of Biology, Carleton University, 1125 Colonel By Drive, Ottawa, Ontario, Canada K1S 5B6.', 'Canada|Canada|Canada|Canada', 12, 'Computational Biology|Databases, Protein|HIV Infections|HIV-1|Humans|Macrophages|Microarray Analysis', 'Journal Article', '10.1093/bib/bbr003', '22223742', 'We present an update to the Bio2RDF Linked Data Network, which now comprises xe2x88xbc30 billion statements across 30 data sets. Significant changes to the framework include the accommodation of global mirrors, offline data processing and new search and integration services. The utility of this new network of knowledge is illustrated through a Bio2RDF-based mashup with microarray gene expression results and interaction data obtained from the HIV-1, Human Protein Interaction Database (HHPID) with respect to the infection of human macrophages with the human immunodeficiency virus type 1 (HIV-1).'),
(68662, 2012, 'bib', 'Multiscale modeling of macromolecular biosystems', 'Flores, Samuel C|Bernauer, Julie|Shin, Seokmin|Zhou, Ruhong|Huang, Xuhui', 'Biomedical Center, Box 596, SE-751 24 Uppsala, Sweden. samuelfloresc@gmail.com|Biomedical Center, Box 596, SE-751 24 Uppsala, Sweden. samuelfloresc@gmail.com|Biomedical Center, Box 596, SE-751 24 Uppsala, Sweden. samuelfloresc@gmail.com|Biomedical Center, Box 596, SE-751 24 Uppsala, Sweden. samuelfloresc@gmail.com|Biomedical Center, Box 596, SE-751 24 Uppsala, Sweden. samuelfloresc@gmail.com', 'Sweden|Sweden|Sweden|Sweden|Sweden', 10, 'Computer Simulation|Kinetics|Macromolecular Substances|Models, Theoretical|Proteins|RNA', 'Journal Article', '10.1093/bib/bbr077', '22228511', 'In this article, we review the recent progress in multiresolution modeling of structure and dynamics of protein, RNA and their complexes. Many approaches using both physics-based and knowledge-based potentials have been developed at multiple granularities to model both protein and RNA. Coarse graining can be achieved not only in the length, but also in the time domain using discrete time and discrete state kinetic network models. Models with different resolutions can be combined either in a sequential or parallel fashion. Similarly, the modeling of assemblies is also often achieved using multiple granularities. The progress shows that a multiresolution approach has considerable potential to continue extending the length and time scales of macromolecular modeling.'),
(68663, 2012, 'bib', 'Bioinformatics for personal genome interpretation', 'Capriotti, Emidio|Nehrt, Nathan L|Kann, Maricel G|Bromberg, Yana', 'Department of Mathematics and Computer Science, University of Balearic Islands, ctra. de Valldemossa Km 7.5, Palma de Mallorca, 07122 Spain. emidio.capriotti@uib.es|Department of Mathematics and Computer Science, University of Balearic Islands, ctra. de Valldemossa Km 7.5, Palma de Mallorca, 07122 Spain. emidio.capriotti@uib.es|Department of Mathematics and Computer Science, University of Balearic Islands, ctra. de Valldemossa Km 7.5, Palma de Mallorca, 07122 Spain. emidio.capriotti@uib.es|Department of Mathematics and Computer Science, University of Balearic Islands, ctra. de Valldemossa Km 7.5, Palma de Mallorca, 07122 Spain. emidio.capriotti@uib.es', 'Spain|Spain|Spain|Spain', 37, 'Computational Biology|Databases, Genetic|Genetic Variation|Genome|Genotype|Human Genome Project|Humans|Phenotype', 'Journal Article', '10.1093/bib/bbr070', '22247263', 'An international consortium released the first draft sequence of the human genome 10 years ago. Although the analysis of this data has suggested the genetic underpinnings of many diseases, we have not yet been able to fully quantify the relationship between genotype and phenotype. Thus, a major current effort of the scientific community focuses on evaluating individual predispositions to specific phenotypic traits given their genetic backgrounds. Many resources aim to identify and annotate the specific genes responsible for the observed phenotypes. Some of these use intra-species genetic variability as a means for better understanding this relationship. In addition, several online resources are now dedicated to collecting single nucleotide variants and other types of variants, and annotating their functional effects and associations with phenotypic traits. This information has enabled researchers to develop bioinformatics tools to analyze the rapidly increasing amount of newly extracted variation data and to predict the effect of uncharacterized variants. In this work, we review the most important developments in the field--the databases and bioinformatics tools that will be of utmost importance in our concerted effort to interpret the human variome.'),
(68664, 2013, 'bib', 'BamView: visualizing and interpretation of next-generation sequencing read alignments', 'Carver, Tim|Harris, Simon R|Otto, Thomas D|Berriman, Matthew|Parkhill, Julian|McQuillan, Jacqueline A', 'Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK. artemis@sanger.ac.uk|Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK. artemis@sanger.ac.uk|Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK. artemis@sanger.ac.uk|Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK. artemis@sanger.ac.uk|Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK. artemis@sanger.ac.uk|Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK. artemis@sanger.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 42, 'Animals|Computational Biology|Computer Graphics|Data Display|Data Interpretation, Statistical|Databases, Genetic|High-Throughput Nucleotide Sequencing|Humans|Sequence Alignment|Software', 'Journal Article', '10.1093/bib/bbr073', '22253280', 'So-called next-generation sequencing (NGS) has provided the ability to sequence on a massive scale at low cost, enabling biologists to perform powerful experiments and gain insight into biological processes. BamView has been developed to visualize and analyse sequence reads from NGS platforms, which have been aligned to a reference sequence. It is a desktop application for browsing the aligned or mapped reads [Ruffalo, M, LaFramboise, T, Koyutürk, M. Comparative analysis of algorithms for next-generation sequencing read alignment. Bioinformatics 2011,27:2790-6] at different levels of magnification, from nucleotide level, where the base qualities can be seen, to genome or chromosome level where overall coverage is shown. To enable in-depth investigation of NGS data, various views are provided that can be configured to highlight interesting aspects of the data. Multiple read alignment files can be overlaid to compare results from different experiments, and filters can be applied to facilitate the interpretation of the aligned reads. As well as being a standalone application it can be used as an integrated part of the Artemis genome browser, BamView allows the user to study NGS data in the context of the sequence and annotation of the reference genome. Single nucleotide polymorphism (SNP) density and candidate SNP sites can be highlighted and investigated, and read-pair information can be used to discover large structural insertions and deletions. The application will also calculate simple analyses of the read mapping, including reporting the read counts and reads per kilobase per million mapped reads (RPKM) for genes selected by the user.'),
(68665, 2012, 'bib', 'Identification of aberrant pathways and network activities from high-throughput data', 'Wang, Jinlian|Zhang, Yuji|Marian, Catalin|Ressom, Habtom W', 'Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, USA.|Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, USA.|Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, USA.|Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, USA.', 'United States|United States|United States|United States', 17, 'Databases, Factual|Gene Regulatory Networks|High-Throughput Screening Assays|Systems Biology', 'Journal Article', '10.1093/bib/bbs001', '22287794', 'Many complex diseases such as cancer are associated with changes in biological pathways and molecular networks rather than being caused by single gene alterations. A major challenge in the diagnosis and treatment of such diseases is to identify characteristic aberrancies in the biological pathways and molecular network activities and elucidate their relationship to the disease. This review presents recent progress in using high-throughput biological assays to decipher aberrant pathways and network activities. In particular, this review provides specific examples in which high-throughput data have been applied to identify relationships between diseases and aberrant pathways and network activities. The achievements in this field have been remarkable, but many challenges have yet to be addressed.'),
(68666, 2012, 'bib', 'Measuring the microbiome: perspectives on advances in DNA-based techniques for exploring microbial life', 'Foster, James A|Bunge, John|Gilbert, Jack A|Moore, Jason H', 'Department of Biological Sciences, Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, ID 83844-3051, USA. foster@uidaho.edu|Department of Biological Sciences, Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, ID 83844-3051, USA. foster@uidaho.edu|Department of Biological Sciences, Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, ID 83844-3051, USA. foster@uidaho.edu|Department of Biological Sciences, Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, ID 83844-3051, USA. foster@uidaho.edu', 'United States|United States|United States|United States', 9, 'DNA|Ecosystem|Genomics|Metagenome', 'Journal Article', '10.1093/bib/bbr080', '22308073', 'This article reviews recent advances in microbiome studies: molecular, statistical and graphical techniques to explore and quantify how microbial organisms affect our environments and ourselves given recent increases in sequencing technology. Microbiome studies are moving beyond mere inventories of specific ecosystems to quantifications of community diversity and descriptions of their ecological function. We review the last 24 months of progress in this sort of research, and anticipate where the next 2 years will take us. We hope that bioinformaticians will find this a helpful springboard for new collaborations with microbiologists.'),
(68667, 2012, 'bib', 'Bayesian inference for genomic imprinting underlying developmental characteristics', 'Yang, Runqing|Wang, Xin|Cui, Yuehua', 'School of Agriculture and Biology, Shanghai Jiaotong University, China.|School of Agriculture and Biology, Shanghai Jiaotong University, China.|School of Agriculture and Biology, Shanghai Jiaotong University, China.', 'China|China|China', 0, 'Algorithms|Animals|Bayes Theorem|Body Weight|Genome|Genomic Imprinting|Mice|Mice, Inbred Strains|Models, Genetic|Monte Carlo Method|Quantitative Trait Loci', 'Journal Article', '10.1093/bib/bbr079', '22321607', 'The identification of imprinted genes is becoming a standard procedure in searching for quantitative trait loci (QTL) underlying complex traits. When a developmental characteristic such as growth or drug response is observed at multiple time points, understanding the dynamics of gene function governing the underlying feature should provide more biological information regarding the genetic control of an organism. Recognizing that differential imprinting can be development-specific, mapping imprinted genes considering the dynamic imprinting effect can provide additional biological insights into the epigenetic control of a complex trait. In this study, we proposed a Bayesian imprinted QTL (iQTL) mapping framework considering the dynamics of imprinting effects and model multiple iQTLs with an efficient Bayesian model selection procedure. The method overcomes the limitation of likelihood-based mapping procedure, and can simultaneously identify multiple iQTLs with different gene action modes across the whole genome with high computational efficiency. An inference procedure using Bayes factors to distinguish different imprinting patterns of iQTL was proposed. Monte Carlo simulations were conducted to evaluate the performance of the method. The utility of the approach was illustrated through an analysis of a body weight growth data set in an F(2) family derived from LG/J and SM/J mouse stains. The proposed Bayesian mapping method provides an efficient and computationally feasible framework for genome-wide multiple iQTL inference with complex developmental traits.'),
(68668, 2012, 'bib', 'Protein inference: a review', 'Huang, Ting|Wang, Jingjing|Yu, Weichuan|He, Zengyou', 'Dalian University of Technology, China.|Dalian University of Technology, China.|Dalian University of Technology, China.|Dalian University of Technology, China.', 'China|China|China|China', 57, 'Algorithms|Databases, Protein|Proteins|Proteomics', 'Journal Article', '10.1093/bib/bbs004', '22373723', 'Assembling peptides identified from tandem mass spectra into a list of proteins, referred to as protein inference, is a critical step in proteomics research. Due to the existence of degenerate peptides and one-hit wonders, it is very difficult to determine which proteins are present in the sample. In this paper, we review existing protein inference methods and classify them according to the source of peptide identifications and the principle of algorithms. It is hoped that the readers will gain a good understanding of the current development in this field after reading this review and come up with new protein inference algorithms.'),
(68669, 2012, 'bib', 'Network biology methods integrating biological data for translational science', 'Bebek, Gurkan|Koyutürk, Mehmet|Price, Nathan D|Chance, Mark R', 'Center for Proteomics and Bioinformatics, Case Western Reserve University, Cleveland, OH 44106, USA.|Center for Proteomics and Bioinformatics, Case Western Reserve University, Cleveland, OH 44106, USA.|Center for Proteomics and Bioinformatics, Case Western Reserve University, Cleveland, OH 44106, USA.|Center for Proteomics and Bioinformatics, Case Western Reserve University, Cleveland, OH 44106, USA.', 'United States|United States|United States|United States', 19, 'Databases, Factual|Genome|Genomics|Phenotype|Proteome|Proteomics', 'Journal Article', '10.1093/bib/bbr075', '22390873', 'The explosion of biomedical data, both on the genomic and proteomic side as well as clinical data, will require complex integration and analysis to provide new molecular variables to better understand the molecular basis of phenotype. Currently, much data exist in silos and is not analyzed in frameworks where all data are brought to bear in the development of biomarkers and novel functional targets. This is beginning to change. Network biology approaches, which emphasize the interactions between genes, proteins and metabolites provide a framework for data integration such that genome, proteome, metabolome and other -omics data can be jointly analyzed to understand and predict disease phenotypes. In this review, recent advances in network biology approaches and results are identified. A common theme is the potential for network analysis to provide multiplexed and functionally connected biomarkers for analyzing the molecular basis of disease, thus changing our approaches to analyzing and modeling genome- and proteome-wide data.'),
(68670, 2013, 'bib', 'A dynamic framework for quantifying the genetic architecture of phenotypic plasticity', 'Wang, Zhong|Pang, Xiaoming|Lv, Yafei|Xu, Fang|Zhou, Tao|Li, Xin|Feng, Sisi|Li, Jiahan|Li, Zhikang|Wu, Rongling', 'Center for Computational Biology, Beijing Forestry University, Beijing 100083, China.|Center for Computational Biology, Beijing Forestry University, Beijing 100083, China.|Center for Computational Biology, Beijing Forestry University, Beijing 100083, China.|Center for Computational Biology, Beijing Forestry University, Beijing 100083, China.|Center for Computational Biology, Beijing Forestry University, Beijing 100083, China.|Center for Computational Biology, Beijing Forestry University, Beijing 100083, China.|Center for Computational Biology, Beijing Forestry University, Beijing 100083, China.|Center for Computational Biology, Beijing Forestry University, Beijing 100083, China.|Center for Computational Biology, Beijing Forestry University, Beijing 100083, China.|Center for Computational Biology, Beijing Forestry University, Beijing 100083, China.', 'China|China|China|China|China|China|China|China|China|China', 14, 'Analysis of Variance|Computational Biology|Gene-Environment Interaction|Genetic Association Studies|Likelihood Functions|Models, Statistical|Oryza|Quantitative Trait Loci', 'Journal Article', '10.1093/bib/bbs009', '22396460', 'Despite its central role in the adaptation and microevolution of traits, the genetic architecture of phenotypic plasticity, i.e. multiple phenotypes produced by a single genotype in changing environments, remains elusive. We know little about the genes that underlie the plastic response of traits to the environment, their number, chromosomal locations and genetic interactions as well as environment impact on their effects. Here we review key statistical approaches for analyzing the genetic variation of phenotypic plasticity due to genotype-environment interactions and describe the implementation of a dynamic model to map specific quantitative trait loci (QTLs) that affect the gradient expression of a quantitative trait across a range of environments. This dynamic model is distinct by incorporating mathematical aspects of phenotypic plasticity into a QTL mapping framework, thereby better unraveling the quantitative attribute of trait response to the environment. By testing the curve parameters that specify environment-dependent trajectories of the trait, the model allows a series of fundamental hypotheses to be tested in a quantitative way about the interplay between gene action/interaction and environmental sensitivity. The model can also make the dynamic prediction of genetic control over phenotypic plasticity within the context of changing environments. We demonstrate the usefulness of the model by reanalyzing a QTL data set for rice, gleaning new insights into the genetic basis for phenotypic plasticity in plant height growth.'),
(68671, 2012, 'bib', 'Adjusting confounders in ranking biomarkers: a model-based ROC approach', 'Yu, Tao|Li, Jialiang|Ma, Shuangge', 'University of Wisconsin, Madison, USA.|University of Wisconsin, Madison, USA.|University of Wisconsin, Madison, USA.', 'United States|United States|United States', 13, 'Biomarkers|Gene Expression Profiling|Humans|Neoplasms|ROC Curve|Regression Analysis', 'Journal Article', '10.1093/bib/bbs008', '22396461', 'High-throughput studies have been extensively conducted in the research of complex human diseases. As a representative example, consider gene-expression studies where thousands of genes are profiled at the same time. An important objective of such studies is to rank the diagnostic accuracy of biomarkers (e.g. gene expressions) for predicting outcome variables while properly adjusting for confounding effects from low-dimensional clinical risk factors and environmental exposures. Existing approaches are often fully based on parametric or semi-parametric models and target evaluating estimation significance as opposed to diagnostic accuracy. Receiver operating characteristic (ROC) approaches can be employed to tackle this problem. However, existing ROC ranking methods focus on biomarkers only and ignore effects of confounders. In this article, we propose a model-based approach which ranks the diagnostic accuracy of biomarkers using ROC measures with a proper adjustment of confounding effects. To this end, three different methods for constructing the underlying regression models are investigated. Simulation study shows that the proposed methods can accurately identify biomarkers with additional diagnostic power beyond confounders. Analysis of two cancer gene-expression studies demonstrates that adjusting for confounders can lead to substantially different rankings of genes.'),
(68672, 2012, 'bib', 'Bioinformatics tools and database resources for systems genetics analysis in mice--a short review and an evaluation of future needs', 'Durrant, Caroline|Swertz, Morris A|Alberts, Rudi|Arends, Danny|Möller, Steffen|Mott, Richard|Prins, Pjotr|van der Velde, K Joeri|Jansen, Ritsert C|Schughart, Klaus', 'University of Oxford.|University of Oxford.|University of Oxford.|University of Oxford.|University of Oxford.|University of Oxford.|University of Oxford.|University of Oxford.|University of Oxford.|University of Oxford.', '-|-|-|-|-|-|-|-|-|-', 7, 'Algorithms|Animals|Computational Biology|Databases, Factual|Gene Regulatory Networks|Mice|Quantitative Trait Loci|Software', 'Journal Article', '10.1093/bib/bbr026', '22396485', 'During a meeting of the SYSGENET working group Bioinformatics, currently available software tools and databases for systems genetics in mice were reviewed and the needs for future developments discussed. The group evaluated interoperability and performed initial feasibility studies. To aid future compatibility of software and exchange of already developed software modules, a strong recommendation was made by the group to integrate HAPPY and R/qtl analysis toolboxes, GeneNetwork and XGAP database platforms, and TIQS and xQTL processing platforms. R should be used as the principal computer language for QTL data analysis in all platforms and a cloud should be used for software dissemination to the community. Furthermore, the working group recommended that all data models and software source code should be made visible in public repositories to allow a coordinated effort on the use of common data structures and file formats.'),
(68673, 2012, 'bib', 'LEPSCAN--a web server for searching latent periodicity in DNA sequences', 'Shelenkov, Andrew|Korotkov, Eugene', 'Bioinformatics Department, Bioengineering Centre, Russian Academy of Sciences, Moscow, Russia. fallandar@gmail.com|Bioinformatics Department, Bioengineering Centre, Russian Academy of Sciences, Moscow, Russia. fallandar@gmail.com', '-|-', 1, 'Base Sequence|Internet|Molecular Sequence Data|Sequence Alignment|Software|Tandem Repeat Sequences', 'Journal Article', '10.1093/bib/bbr044', '22396486', 'A web server for searching latent periodicity based on the method of modified profile analysis has been developed. This method allows searching latent periodicity in presence of insertions and deletions. During searching process, the periodicity classes are used which were found by us earlier for various groups of organisms. Period length belongs to the range 2-20 nt, not including the triplet periodicity. The results obtained are subjected to various filtration steps to ensure their statistical significance.'),
(68674, 2012, 'bib', 'Biological network motif detection: principles and practice', 'Wong, Elisabeth|Baur, Brittany|Quader, Saad|Huang, Chun-Hsi', 'Bowdoin College, Brunswick, Maine, USA.|Bowdoin College, Brunswick, Maine, USA.|Bowdoin College, Brunswick, Maine, USA.|Bowdoin College, Brunswick, Maine, USA.', 'United States|United States|United States|United States', 82, 'Algorithms|Computational Biology|Gene Regulatory Networks|Models, Biological|Protein Interaction Mapping', 'Journal Article', '10.1093/bib/bbr033', '22396487', 'Network motifs are statistically overrepresented sub-structures (sub-graphs) in a network, and have been recognized as the simple building blocks of complex networks. Study of biological network motifs may reveal answers to many important biological questions. The main difficulty in detecting larger network motifs in biological networks lies in the facts that the number of possible sub-graphs increases exponentially with the network or motif size (node counts, in general), and that no known polynomial-time algorithm exists in deciding if two graphs are topologically equivalent. This article discusses the biological significance of network motifs, the motivation behind solving the motif-finding problem, and strategies to solve the various aspects of this problem. A simple classification scheme is designed to analyze the strengths and weaknesses of several existing algorithms. Experimental results derived from a few comparative studies in the literature are discussed, with conclusions that lead to future research directions.'),
(68675, 2013, 'bib', 'Class-imbalanced classifiers for high-dimensional data', 'Lin, Wei-Jiun|Chen, James J', 'Department of Applied Mathematics, Feng Chia University, Taiwan.|Department of Applied Mathematics, Feng Chia University, Taiwan.', '-|-', 35, 'Algorithms|Computational Biology|Databases, Genetic|Discriminant Analysis|Female|Humans|Support Vector Machine', 'Journal Article', '10.1093/bib/bbs006', '22408190', 'A class-imbalanced classifier is a decision rule to predict the class membership of new samples from an available data set where the class sizes differ considerably. When the class sizes are very different, most standard classification algorithms may favor the larger (majority) class resulting in poor accuracy in the minority class prediction. A class-imbalanced classifier typically modifies a standard classifier by a correction strategy or by incorporating a new strategy in the training phase to account for differential class sizes. This article reviews and evaluates some most important methods for class prediction of high-dimensional imbalanced data. The evaluation addresses the fundamental issues of the class-imbalanced classification problem: imbalance ratio, small disjuncts and overlap complexity, lack of data and feature selection. Four class-imbalanced classifiers are considered. The four classifiers include three standard classification algorithms each coupled with an ensemble correction strategy and one support vector machines (SVM)-based correction classifier. The three algorithms are (i) diagonal linear discriminant analysis (DLDA), (ii) random forests (RFs) and (ii) SVMs. The SVM-based correction classifier is SVM threshold adjustment (SVM-THR). A Monte-Carlo simulation and five genomic data sets were used to illustrate the analysis and address the issues. The SVM-ensemble classifier appears to perform the best when the class imbalance is not too severe. The SVM-THR performs well if the imbalance is severe and predictors are highly correlated. The DLDA with a feature selection can perform well without using the ensemble correction.'),
(68676, 2013, 'bib', 'The automatic annotation of bacterial genomes', 'Richardson, Emily J|Watson, Mick', 'The Roslin Institute, University of Edinburgh, Easter Bush, EH25 9RG, UK.|The Roslin Institute, University of Edinburgh, Easter Bush, EH25 9RG, UK.', 'United Kingdom|United Kingdom', 44, 'Computational Biology|Databases, Genetic|Escherichia coli|Genome, Bacterial|Molecular Sequence Annotation|Salmonella|Software', 'Journal Article', '10.1093/bib/bbs007', '22408191', 'With the development of ultra-high-throughput technologies, the cost of sequencing bacterial genomes has been vastly reduced. As more genomes are sequenced, less time can be spent manually annotating those genomes, resulting in an increased reliance on automatic annotation pipelines. However, automatic pipelines can produce inaccurate genome annotation and their results often require manual curation. Here, we discuss the automatic and manual annotation of bacterial genomes, identify common problems introduced by the current genome annotation process and suggests potential solutions.'),
(68677, 2013, 'bib', 'Visualizing next-generation sequencing data with JBrowse', 'Westesson, Oscar|Skinner, Mitchell|Holmes, Ian', 'Department of Bioengineering, University of California, Berkeley, CA, USA.|Department of Bioengineering, University of California, Berkeley, CA, USA.|Department of Bioengineering, University of California, Berkeley, CA, USA.', 'United States|United States|United States', 50, 'Computational Biology|Data Display|Databases, Genetic|Genomics|High-Throughput Nucleotide Sequencing|Humans|Information Storage and Retrieval|Internet|Software|User-Computer Interface', 'Journal Article', '10.1093/bib/bbr078', '22411711', 'JBrowse is a web-based genome browser, allowing many sources of data to be visualized, interpreted and navigated in a coherent visual framework. JBrowse uses efficient data structures, pre-generation of image tiles and client-side rendering to provide a fast, interactive browsing experience. Many of JBrowses design features make it well suited for visualizing high-volume data, such as aligned next-generation sequencing reads.'),
(68678, 2013, 'bib', 'Cancer gene prioritization by integrative analysis of mRNA expression and DNA copy number data: a comparative review', 'Lahti, Leo|Schäfer, Martin|Klein, Hans-Ulrich|Bicciato, Silvio|Dugas, Martin', 'Wageningen University, Laboratory of Microbiology, 6703HB Wageningen, Netherlands. leo.lahti@iki.fi|Wageningen University, Laboratory of Microbiology, 6703HB Wageningen, Netherlands. leo.lahti@iki.fi|Wageningen University, Laboratory of Microbiology, 6703HB Wageningen, Netherlands. leo.lahti@iki.fi|Wageningen University, Laboratory of Microbiology, 6703HB Wageningen, Netherlands. leo.lahti@iki.fi|Wageningen University, Laboratory of Microbiology, 6703HB Wageningen, Netherlands. leo.lahti@iki.fi', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 16, 'Algorithms|Computational Biology|DNA Copy Number Variations|DNA, Neoplasm|Female|Gene Expression Regulation, Neoplastic|Genomics|Humans|Neoplasms|Oncogenes|RNA, Messenger|RNA, Neoplasm', 'Comparative Study', '10.1093/bib/bbs005', '22441573', 'A variety of genome-wide profiling techniques are available to investigate complementary aspects of genome structure and function. Integrative analysis of heterogeneous data sources can reveal higher level interactions that cannot be detected based on individual observations. A standard integration task in cancer studies is to identify altered genomic regions that induce changes in the expression of the associated genes based on joint analysis of genome-wide gene expression and copy number profiling measurements. In this review, we highlight common approaches to genomic data integration and provide a transparent benchmarking procedure to quantitatively compare method performances in cancer gene prioritization. Algorithms, data sets and benchmarking results are available at http://intcomp.r-forge.r-project.org.'),
(68679, 2013, 'bib', 'Using Tablet for visual exploration of second-generation sequencing data', 'Milne, Iain|Stephen, Gordon|Bayer, Micha|Cock, Peter J A|Pritchard, Leighton|Cardle, Linda|Shaw, Paul D|Marshall, David', 'Information & Computational Sciences, The James Hutton Institute JHI, Dundee DD2 5DA, Scotland, UK. iain.milne@hutton.ac.uk|Information & Computational Sciences, The James Hutton Institute JHI, Dundee DD2 5DA, Scotland, UK. iain.milne@hutton.ac.uk|Information & Computational Sciences, The James Hutton Institute JHI, Dundee DD2 5DA, Scotland, UK. iain.milne@hutton.ac.uk|Information & Computational Sciences, The James Hutton Institute JHI, Dundee DD2 5DA, Scotland, UK. iain.milne@hutton.ac.uk|Information & Computational Sciences, The James Hutton Institute JHI, Dundee DD2 5DA, Scotland, UK. iain.milne@hutton.ac.uk|Information & Computational Sciences, The James Hutton Institute JHI, Dundee DD2 5DA, Scotland, UK. iain.milne@hutton.ac.uk|Information & Computational Sciences, The James Hutton Institute JHI, Dundee DD2 5DA, Scotland, UK. iain.milne@hutton.ac.uk|Information & Computational Sciences, The James Hutton Institute JHI, Dundee DD2 5DA, Scotland, UK. iain.milne@hutton.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 451, 'Animals|Computational Biology|Computer Graphics|Data Display|Databases, Genetic|Genomics|High-Throughput Nucleotide Sequencing|Humans|Internet|Sequence Analysis|Software', 'Journal Article', '10.1093/bib/bbs012', '22445902', 'The advent of second-generation sequencing (2GS) has provided a range of significant new challenges for the visualization of sequence assemblies. These include the large volume of data being generated, short-read lengths and different data types and data formats associated with the diversity of new sequencing technologies. This article illustrates how Tablet-a high-performance graphical viewer for visualization of 2GS assemblies and read mappings-plays an important role in the analysis of these data. We present Tablet, and through a selection of use cases, demonstrate its value in quality assurance and scientific discovery, through features such as whole-reference coverage overviews, variant highlighting, paired-end read mark-up, GFF3-based feature tracks and protein translations. We discuss the computing and visualization techniques utilized to provide a rich and responsive graphical environment that enables users to view a range of file formats with ease. Tablet installers can be freely downloaded from http://bioinf.hutton.ac.uk/tablet in 32 or 64-bit versions for Windows, OS X, Linux or Solaris. For further details on the Tablet, contact tablet@hutton.ac.uk.'),
(68680, 2012, 'bib', 'Data mining the human gut microbiota for therapeutic targets', 'Collison, Matthew|Hirt, Robert P|Wipat, Anil|Nakjang, Sirintra|Sanseau, Philippe|Brown, James R', 'Biopharmaceutical Process Development Centre, School of Chemical Engineering and Advanced Materials, Newcastle University, UK.|Biopharmaceutical Process Development Centre, School of Chemical Engineering and Advanced Materials, Newcastle University, UK.|Biopharmaceutical Process Development Centre, School of Chemical Engineering and Advanced Materials, Newcastle University, UK.|Biopharmaceutical Process Development Centre, School of Chemical Engineering and Advanced Materials, Newcastle University, UK.|Biopharmaceutical Process Development Centre, School of Chemical Engineering and Advanced Materials, Newcastle University, UK.|Biopharmaceutical Process Development Centre, School of Chemical Engineering and Advanced Materials, Newcastle University, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 14, 'Data Mining|Gastrointestinal Tract|Humans|Metagenome|Probiotics|RNA, Ribosomal, 16S', 'Journal Article', '10.1093/bib/bbs002', '22445903', 'It is well known that microbes have an intricate role in human health and disease. However, targeted strategies for modulating human health through the modification of either human-associated microbial communities or associated human-host targets have yet to be realized. New knowledge about the role of microbial communities in the microbiota of the gastrointestinal tract (GIT) and their collective genomes, the GIT microbiome, in chronic diseases opens new opportunities for therapeutic interventions. GIT microbiota participation in drug metabolism is a further pharmaceutical consideration. In this review, we discuss how computational methods could lead to a systems-level understanding of the global physiology of the host-microbiota superorganism in health and disease. Such knowledge will provide a platform for the identification and development of new therapeutic strategies for chronic diseases possibly involving microbial as well as human-host targets that improve upon existing probiotics, prebiotics or antibiotics. In addition, integrative bioinformatics analysis will further our understanding of the microbial biotransformation of exogenous compounds or xenobiotics, which could lead to safer and more efficacious drugs.'),
(68681, 2013, 'bib', 'Semantic Web meets Integrative Biology: a survey', 'Chen, Huajun|Yu, Tong|Chen, Jake Y', 'College of Computer Science, Zhejiang University, Hangzhou, 310027, P.R. China. huajunsir@zju.edu.cn|College of Computer Science, Zhejiang University, Hangzhou, 310027, P.R. China. huajunsir@zju.edu.cn|College of Computer Science, Zhejiang University, Hangzhou, 310027, P.R. China. huajunsir@zju.edu.cn', 'China|China|China', 19, 'Biopharmaceutics|Computational Biology|Humans|Internet|Neurosciences|Semantics|Systems Biology|Systems Integration|Translational Medical Research', 'Journal Article', '10.1093/bib/bbs014', '22492191', 'Integrative Biology (IB) uses experimental or computational quantitative technologies to characterize biological systems at the molecular, cellular, tissue and population levels. IB typically involves the integration of the data, knowledge and capabilities across disciplinary boundaries in order to solve complex problems. We identify a series of bioinformatics problems posed by interdisciplinary integration: (i) data integration that interconnects structured data across related biomedical domains, (ii) ontology integration that brings jargons, terminologies and taxonomies from various disciplines into a unified network of ontologies, (iii) knowledge integration that integrates disparate knowledge elements from multiple sources, (iv) service integration that build applications out of services provided by different vendors. We argue that IB can benefit significantly from the integration solutions enabled by Semantic Web (SW) technologies. The SW enables scientists to share content beyond the boundaries of applications and websites, resulting into a web of data that is meaningful and understandable to any computers. In this review, we provide insight into how SW technologies can be used to build open, standardized and interoperable solutions for interdisciplinary integration on a global basis. We present a rich set of case studies in system biology, integrative neuroscience, bio-pharmaceutics and translational medicine, to highlight the technical features and benefits of SW applications in IB.'),
(68682, 2013, 'bib', 'A survey of error-correction methods for next-generation sequencing', 'Yang, Xiao|Chockalingam, Sriram P|Aluru, Srinivas', 'The Broad institute, 7 Cambridge Center, Cambridge, MA 02142, USA. xiaoyang@broadinstitute.org|The Broad institute, 7 Cambridge Center, Cambridge, MA 02142, USA. xiaoyang@broadinstitute.org|The Broad institute, 7 Cambridge Center, Cambridge, MA 02142, USA. xiaoyang@broadinstitute.org', 'United States|United States|United States', 67, 'Algorithms|Animals|Chromosome Mapping|Computational Biology|Databases, Genetic|Forecasting|Humans|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bib/bbs015', '22492192', 'Error Correction is important for most next-generation sequencing applications because highly accurate sequenced reads will likely lead to higher quality results. Many techniques for error correction of sequencing data from next-gen platforms have been developed in the recent years. However, compared with the fast development of sequencing technologies, there is a lack of standardized evaluation procedure for different error-correction methods, making it difficult to assess their relative merits and demerits. In this article, we provide a comprehensive review of many error-correction methods, and establish a common set of benchmark data and evaluation criteria to provide a comparative assessment. We present experimental results on quality, run-time, memory usage and scalability of several error-correction methods. Apart from providing explicit recommendations useful to practitioners, the review serves to identify the current state of the art and promising directions for future research.'),
(68683, 2013, 'bib', 'A unifying framework for bivalent multilocus linkage analysis of allotetraploids', 'Yang, Xiaoxia|Lv, Yafei|Pang, Xiaoming|Tong, Chunfa|Wang, Zhong|Li, Xin|Feng, Sisi|Tobias, Christian M|Wu, Rongling', 'Center for Computational Biology, Beijing Forestry University, Beijing 100083, China.|Center for Computational Biology, Beijing Forestry University, Beijing 100083, China.|Center for Computational Biology, Beijing Forestry University, Beijing 100083, China.|Center for Computational Biology, Beijing Forestry University, Beijing 100083, China.|Center for Computational Biology, Beijing Forestry University, Beijing 100083, China.|Center for Computational Biology, Beijing Forestry University, Beijing 100083, China.|Center for Computational Biology, Beijing Forestry University, Beijing 100083, China.|Center for Computational Biology, Beijing Forestry University, Beijing 100083, China.|Center for Computational Biology, Beijing Forestry University, Beijing 100083, China.', 'China|China|China|China|China|China|China|China|China', 8, 'Chromosome Mapping|Chromosome Pairing|Chromosome Segregation|Computational Biology|Computer Simulation|Genetic Linkage|Likelihood Functions|Meiosis|Models, Genetic|Plants|Tetraploidy', 'Journal Article', '10.1093/bib/bbs011', '22508791', 'An allotetraploid has four paired sets of chromosomes derived from different diploid species, whose meiotic behavior is qualitatively different from the underlying diploids. According to a traditional view, meiotic pairing occurs only between homologous chromosomes, but new evidence indicates that homoeologous chromosomes may also pair to a lesser extent compared with homolog pairing. Here, we describe and assess a unifying analytical framework that incorporates differential chromosomal pairing into a multilocus linkage model. The preferential pairing factor is used to quantify the probability difference of pairing occurring between homologous chromosomes and homoeologous chromosomes. The unifying framework allows simultaneous estimation of the linkage, genetic interference and preferential pairing factor using commonly existing multiplex markers. We compared the unifying approach and traditional approaches assuming random chromosomal pairing by analyzing marker data collected in a full-sib family of tetraploid switchgrass, a bioenergy species whose diploid origins are undefined, but with subgenomes that are genetically well differentiated. The unifying framework provides a better tool for estimating the meiotic linkage and constructing a genetic map for allotetraploids.'),
(68684, 2013, 'bib', 'Motif discovery and transcription factor binding sites before and after the next-generation sequencing era', 'Zambelli, Federico|Pesole, Graziano|Pavesi, Giulio', 'University of Milan.|University of Milan.|University of Milan.', '-|-|-', 31, 'Algorithms|Animals|Binding Sites|Chromatin Immunoprecipitation|Computational Biology|Consensus Sequence|DNA|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Humans|Regulatory Elements, Transcriptional|Transcription Factors', 'Journal Article', '10.1093/bib/bbs016', '22517426', 'Motif discovery has been one of the most widely studied problems in bioinformatics ever since genomic and protein sequences have been available. In particular, its application to the de novo prediction of putative over-represented transcription factor binding sites in nucleotide sequences has been, and still is, one of the most challenging flavors of the problem. Recently, novel experimental techniques like chromatin immunoprecipitation (ChIP) have been introduced, permitting the genome-wide identification of protein-DNA interactions. ChIP, applied to transcription factors and coupled with genome tiling arrays (ChIP on Chip) or next-generation sequencing technologies (ChIP-Seq) has opened new avenues in research, as well as posed new challenges to bioinformaticians developing algorithms and methods for motif discovery.'),
(68685, 2013, 'bib', 'Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration', 'Thorvaldsdóttir, Helga|Robinson, James T|Mesirov, Jill P', 'Broad Institute, 7 Cambridge Center 301B-5057, Cambridge, MA 02142, USA.|Broad Institute, 7 Cambridge Center 301B-5057, Cambridge, MA 02142, USA.|Broad Institute, 7 Cambridge Center 301B-5057, Cambridge, MA 02142, USA.', 'United States|United States|United States', 3379, 'Computational Biology|Computer Graphics|Data Display|Data Mining|Databases, Genetic|Genomics|High-Throughput Nucleotide Sequencing|Humans|Information Storage and Retrieval|Sequence Alignment|Software|User-Computer Interface', 'Journal Article', '10.1093/bib/bbs017', '22517427', 'Data visualization is an essential component of genomic data analysis. However, the size and diversity of the data sets produced by todays sequencing and array-based profiling methods present major challenges to visualization tools. The Integrative Genomics Viewer (IGV) is a high-performance viewer that efficiently handles large heterogeneous data sets, while providing a smooth and intuitive user experience at all levels of genome resolution. A key characteristic of IGV is its focus on the integrative nature of genomic studies, with support for both array-based and next-generation sequencing data, and the integration of clinical and phenotypic data. Although IGV is often used to view genomic data from public sources, its primary emphasis is to support researchers who wish to visualize and explore their own data sets or those from colleagues. To that end, IGV supports flexible loading of local and remote data sets, and is optimized to provide high-performance data visualization and exploration on standard desktop systems. IGV is freely available for download from http://www.broadinstitute.org/igv, under a GNU LGPL open-source license.'),
(68686, 2012, 'bib', 'Gut microbiota: methodological aspects to describe taxonomy and functionality', 'Ngom-Bru, Catherine|Barretto, Caroline', 'Nestlé Research Center, Lausanne, Switzerland.|Nestlé Research Center, Lausanne, Switzerland.', 'Switzerland|Switzerland', 3, 'Diet|Gastrointestinal Tract|Humans|Metagenome|Metagenomics', 'Journal Article', '10.1093/bib/bbs019', '22543244', 'The human gut is populated by a rich and diverse microbiota that has been described as a human metabolic organ. Its composition has an impact on the health status of the host and could be the target or effector of dietary health effects. The advent of high-throughput sequencing technologies and their integration with advanced analysis methods enables the development of new approaches to characterize the gut microbiota composition and initiates the understanding of its functionality. These technological aspects are not necessarily the limiting factor to successfully identify biological correlations between gut microbiota, diet and health status, as there are other very important aspects. The aim of this article is to address the gut microbiota characterization methodologies, highlight some advantages and challenges, and give our opinion on how critical the sampling and the study design are.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(68687, 2013, 'bib', 'Bioinformatics opportunities for identification and study of medicinal plants', 'Sharma, Vivekanand|Sarkar, Indra Neil', 'Department of Microbiology and Molecular Genetics, University of Vermont, 89 Beaumont Avenue, Given Courtyard N309, Burlington, VT 05405, USA.|Department of Microbiology and Molecular Genetics, University of Vermont, 89 Beaumont Avenue, Given Courtyard N309, Burlington, VT 05405, USA.', 'United States|United States', 36, 'Computational Biology|DNA Barcoding, Taxonomic|Databases, Genetic|Drug Discovery|High-Throughput Nucleotide Sequencing|Humans|Knowledge Bases|Phytotherapy|Plants, Medicinal', 'Journal Article', '10.1093/bib/bbs021', '22589384', 'Plants have been used as a source of medicine since historic times and several commercially important drugs are of plant-based origin. The traditional approach towards discovery of plant-based drugs often times involves significant amount of time and expenditure. These labor-intensive approaches have struggled to keep pace with the rapid development of high-throughput technologies. In the era of high volume, high-throughput data generation across the biosciences, bioinformatics plays a crucial role. This has generally been the case in the context of drug designing and discovery. However, there has been limited attention to date to the potential application of bioinformatics approaches that can leverage plant-based knowledge. Here, we review bioinformatics studies that have contributed to medicinal plants research. In particular, we highlight areas in medicinal plant research where the application of bioinformatics methodologies may result in quicker and potentially cost-effective leads toward finding plant-based remedies.'),
(68688, 2012, 'bib', 'Computational systems biology and in silico modeling of the human microbiome', 'Borenstein, Elhanan', 'University of Washington, Seattle, 98195-5065, USA. elbo@uw.edu', 'United States', 33, 'Computer Simulation|Humans|Metagenome|Metagenomics|Systems Biology', 'Journal Article', '10.1093/bib/bbs022', '22589385', 'The human microbiome is a complex biological system with numerous interacting components across multiple organizational levels. The assembly, ecology and dynamics of the microbiome and its contribution to the development, physiology and nutrition of the host are clearly affected not only by the set of genes or species in the microbiome but also by the way these genes are linked across numerous pathways and by the interactions between the various species. To date, however, most studies of the human microbiome have focused on characterizing the composition of the microbiome and on comparative analyses, whereas significantly less effort has been directed at elucidating, characterizing and modeling these interactions and on studying the microbiome as a complex, interconnected and cohesive system. Here, specifically, I highlight the pressing need for the development of predictive system-level models and for a system-level understanding of the microbiome, and discuss potential computational frameworks for metagenomic-based modeling of the microbiome at the cellular, ecological and supra-organismal level. I review some preliminary attempts at constructing such models and examine the challenges and hurdles that such modeling efforts face. I also discuss possible future applications and research avenues that such metagenomic systems biology and predictive system-level models may facilitate.'),
(68689, 2013, 'bib', 'Machine learning approaches for the discovery of gene-gene interactions in disease data', 'Upstill-Goddard, Rosanna|Eccles, Diana|Fliege, Joerg|Collins, Andrew', 'University of Southampton, UK.|University of Southampton, UK.|University of Southampton, UK.|University of Southampton, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 18, 'Artificial Intelligence|Computational Biology|Data Mining|Epistasis, Genetic|Genetic Predisposition to Disease|Genome-Wide Association Study|Humans|Multifactorial Inheritance|Neural Networks (Computer)|Polymorphism, Single Nucleotide|Support Vector Machine', 'Journal Article', '10.1093/bib/bbs024', '22611119', 'Because of the complexity of gene-phenotype relationships machine learning approaches have considerable appeal as a strategy for modelling interactions. A number of such methods have been developed and applied in recent years with some modest success. Progress is hampered by the challenges presented by the complexity of the disease genetic data, including phenotypic and genetic heterogeneity, polygenic forms of inheritance and variable penetrance, combined with the analytical and computational issues arising from the enormous number of potential interactions. We review here recent and current approaches focusing, wherever possible, on applications to real data (particularly in the context of genome-wide association studies) and looking ahead to the further challenges posed by next generation sequencing data.'),
(68690, 2013, 'bib', 'Review of tandem repeat search tools: a systematic approach to evaluating algorithmic performance', 'Lim, Kian Guan|Kwoh, Chee Keong|Hsu, Li Yang|Wirawan, Adrianto', 'Division of Software and Information Systems, School of Computer Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798. kglim1@e.ntu.edu.sg|Division of Software and Information Systems, School of Computer Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798. kglim1@e.ntu.edu.sg|Division of Software and Information Systems, School of Computer Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798. kglim1@e.ntu.edu.sg|Division of Software and Information Systems, School of Computer Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798. kglim1@e.ntu.edu.sg', 'Singapore|Singapore|Singapore|Singapore', 53, 'Algorithms|Animals|Computational Biology|Databases, Nucleic Acid|Humans|Search Engine|Sequence Analysis, DNA|Tandem Repeat Sequences', 'Journal Article', '10.1093/bib/bbs023', '22648964', 'The prevalence of tandem repeats in eukaryotic genomes and their association with a number of genetic diseases has raised considerable interest in locating these repeats. Over the last 10-15 years, numerous tools have been developed for searching tandem repeats, but differences in the search algorithms adopted and difficulties with parameter settings have confounded many users resulting in widely varying results. In this review, we have systematically separated the algorithmic aspect of the search tools from the influence of the parameter settings. We hope that this will give a better understanding of how the tools differ in algorithmic performance, their inherent constraints and how one should approach in evaluating and selecting them.'),
(68691, 2013, 'bib', 'Joint analysis of miRNA and mRNA expression data', 'Muniategui, Ander|Pey, Jon|Planes, Francisco J|Rubio, Angel', 'CEIT, University of Navarra, San Sebastian, Spain.|CEIT, University of Navarra, San Sebastian, Spain.|CEIT, University of Navarra, San Sebastian, Spain.|CEIT, University of Navarra, San Sebastian, Spain.', 'Spain|Spain|Spain|Spain', 76, 'Bayes Theorem|Databases, Genetic|Gene Expression Regulation|Least-Squares Analysis|Linear Models|MicroRNAs|Models, Theoretical|RNA, Messenger', 'Journal Article', '10.1093/bib/bbs028', '22692086', 'miRNAs are small RNA molecules (22xe2x80x89nt) that interact with their target mRNAs inhibiting translation or/and cleavaging the target mRNA. This interaction is guided by sequence complentarity and results in the reduction of mRNA and/or protein levels. miRNAs are involved in key biological processes and different diseases. Therefore, deciphering miRNA targets is crucial for diagnostics and therapeutics. However, miRNA regulatory mechanisms are complex and there is still no high-throughput and low-cost miRNA target screening technique. In recent years, several computational methods based on sequence complementarity of the miRNA and the mRNAs have been developed. However, the predicted interactions using these computational methods are inconsistent and the expected false positive rates are still large. Recently, it has been proposed to use the expression values of miRNAs and mRNAs (and/or proteins) to refine the results of sequence-based putative targets for a particular experiment. These methods have shown to be effective identifying the most prominent interactions from the databases of putative targets. Here, we review these methods that combine both expression and sequence-based putative targets to predict miRNA targets.'),
(68692, 2013, 'bib', 'Analysis of insertion-deletion from deep-sequencing data: software evaluation for optimal detection', 'Neuman, Joseph A|Isakov, Ofer|Shomron, Noam', 'Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.|Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.|Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.', 'Israel|Israel|Israel', 53, 'Computational Biology|Computer Simulation|DNA Mutational Analysis|Humans|INDEL Mutation|Mutagenesis, Insertional|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Sequence Deletion|Software', 'Evaluation Studies', '10.1093/bib/bbs013', '22707752', 'Insertion and deletion (indel) mutations, the most common type of structural variance in the human genome, affect a multitude of human traits and diseases. New sequencing technologies, such as deep sequencing, allow massive throughput of sequence data and greatly contribute to the field of disease causing mutation detection, in general, and indel detection, specifically. In order to infer indel presence (indel calling), the deep-sequencing data have to undergo comprehensive computational analysis. Selecting which indel calling software to use can often skew the results and inherent tool limitations may affect downstream analysis. In order to better understand these inter-software differences, we evaluated the performance of several indel calling software for short indel (1-10xe2x80x89nt) detection. We compared the softwares sensitivity and predictive values in the presence of varying parameters such as read depth (coverage), read length, indel size and frequency. We pinpoint several key features that assist successful experimental design and appropriate tool selection. Our study may also serve as a basis for future evaluation of additional indel calling methods.'),
(68693, 2013, 'bib', 'A statistical procedure to map high-order epistasis for complex traits', 'Pang, Xiaoming|Wang, Zhong|Yap, John S|Wang, Jianxin|Zhu, Junjia|Bo, Wenhao|Lv, Yafei|Xu, Fang|Zhou, Tao|Peng, Shaofeng|Shen, Dengfeng|Wu, Rongling', 'Beijing Forestry University, Beijing 100083, China.|Beijing Forestry University, Beijing 100083, China.|Beijing Forestry University, Beijing 100083, China.|Beijing Forestry University, Beijing 100083, China.|Beijing Forestry University, Beijing 100083, China.|Beijing Forestry University, Beijing 100083, China.|Beijing Forestry University, Beijing 100083, China.|Beijing Forestry University, Beijing 100083, China.|Beijing Forestry University, Beijing 100083, China.|Beijing Forestry University, Beijing 100083, China.|Beijing Forestry University, Beijing 100083, China.|Beijing Forestry University, Beijing 100083, China.', 'China|China|China|China|China|China|China|China|China|China|China|China', 3, 'Computer Simulation|Epistasis, Genetic|Models, Genetic|Quantitative Trait Loci', 'Journal Article', '10.1093/bib/bbs027', '22723459', 'Genetic interactions or epistasis have been thought to play a pivotal role in shaping the formation, development and evolution of life. Previous work focused on lower-order interactions between a pair of genes, but it is obviously inadequate to explain a complex network of genetic interactions and pathways. We review and assess a statistical model for characterizing high-order epistasis among more than two genes or quantitative trait loci (QTLs) that control a complex trait. The model includes a series of start-of-the-art standard procedures for estimating and testing the nature and magnitude of QTL interactions. Results from simulation studies and real data analysis warrant the statistical properties of the model and its usefulness in practice. High-order epistatic mapping will provide a routine procedure for charting a detailed picture of the genetic regulation mechanisms underlying the phenotypic variation of complex traits.'),
(68694, 2013, 'bib', 'Bioinformatics tools and challenges in structural analysis of lipidomics MS/MS data', 'Hartler, Jürgen|Tharakan, Ravi|Köfeler, Harald C|Graham, David R|Thallinger, Gerhard G', 'Austrian Centre of Industrial Biotechnology.|Austrian Centre of Industrial Biotechnology.|Austrian Centre of Industrial Biotechnology.|Austrian Centre of Industrial Biotechnology.|Austrian Centre of Industrial Biotechnology.', 'Austria|Austria|Austria|Austria|Austria', 8, 'Computational Biology|High-Throughput Screening Assays|Lipids|Molecular Structure|Tandem Mass Spectrometry', 'Journal Article', '10.1093/bib/bbs030', '22764120', 'Lipidomics, the systematic study of the lipid composition of a cell or tissue, is an invaluable complement to knowledge gained by genomics and proteomics research. Mass spectrometry provides a means to detect hundreds of lipids in parallel, and this includes low abundance species of lipids. Nevertheless, frequently occurring isobaric and isomeric lipid species complicate lipidomics analyses from an analytical and bioinformatics perspective. Various MS/MS strategies have evolved to resolve ambiguous identifications of lipid species, and these strategies have been supported by corresponding bioinformatics analysis tools. This review intends to familiarize readers with available bioinformatics MS/MS analysis tools and databases, the structural information obtainable from these, and their applicability to different MS/MS strategies. Finally, future challenges in detecting double bond positions are investigated from a bioinformatics perspective.'),
(68695, 2013, 'bib', 'A brief introduction to web-based genome browsers', 'Wang, Jun|Kong, Lei|Gao, Ge|Luo, Jingchu', 'College of Life Sciences, Peking University, Beijing 100871, P.R. China.|College of Life Sciences, Peking University, Beijing 100871, P.R. China.|College of Life Sciences, Peking University, Beijing 100871, P.R. China.|College of Life Sciences, Peking University, Beijing 100871, P.R. China.', 'China|China|China|China', 15, 'Animals|Computational Biology|Computer Graphics|Data Display|Databases, Genetic|Genomics|Humans|Information Storage and Retrieval|Internet|User-Computer Interface', 'Journal Article', '10.1093/bib/bbs029', '22764121', 'Genome browser provides a graphical interface for users to browse, search, retrieve and analyze genomic sequence and annotation data. Web-based genome browsers can be classified into general genome browsers with multiple species and species-specific genome browsers. In this review, we attempt to give an overview for the main functions and features of web-based genome browsers, covering data visualization, retrieval, analysis and customization. To give a brief introduction to the multiple-species genome browser, we describe the user interface and main functions of the Ensembl and UCSC genome browsers using the human alpha-globin gene cluster as an example. We further use the MSU and the Rice-Map genome browsers to show some special features of species-specific genome browser, taking a rice transcription factor gene OsSPL14 as an example.'),
(68696, 2012, 'bib', 'Functional assignment of metagenomic data: challenges and applications', 'Prakash, Tulika|Taylor, Todd D', 'Laboratory for MetaSystems Research at RIKEN, Japan.|Laboratory for MetaSystems Research at RIKEN, Japan.', 'Japan|Japan', 87, 'Algorithms|Metagenome|Metagenomics|Sequence Analysis, DNA', 'Journal Article', '10.1093/bib/bbs033', '22772835', 'Metagenomic sequencing provides a unique opportunity to explore earths limitless environments harboring scores of yet unknown and mostly unculturable microbes and other organisms. Functional analysis of the metagenomic data plays a central role in projects aiming to explore the most essential questions in microbiology, namely In a given environment, among the microbes present, what are they doing, and how are they doing it? Toward this goal, several large-scale metagenomic projects have recently been conducted or are currently underway. Functional analysis of metagenomic data mainly suffers from the vast amount of data generated in these projects. The shear amount of data requires much computational time and storage space. These problems are compounded by other factors potentially affecting the functional analysis, including, sample preparation, sequencing method and average genome size of the metagenomic samples. In addition, the read-lengths generated during sequencing influence sequence assembly, gene prediction and subsequently the functional analysis. The level of confidence for functional predictions increases with increasing read-length. Usually, the most reliable functional annotations for metagenomic sequences are achieved using homology-based approaches against publicly available reference sequence databases. Here, we present an overview of the current state of functional analysis of metagenomic sequence data, bottlenecks frequently encountered and possible solutions in light of currently available resources and tools. Finally, we provide some examples of applications from recent metagenomic studies which have been successfully conducted in spite of the known difficulties.'),
(68697, 2012, 'bib', 'Ultrafast clustering algorithms for metagenomic sequence analysis', 'Li, Weizhong|Fu, Limin|Niu, Beifang|Wu, Sitao|Wooley, John', 'Center for Research in Biological Systems, University of California San Diego, USA. liwz@sdsc.edu|Center for Research in Biological Systems, University of California San Diego, USA. liwz@sdsc.edu|Center for Research in Biological Systems, University of California San Diego, USA. liwz@sdsc.edu|Center for Research in Biological Systems, University of California San Diego, USA. liwz@sdsc.edu|Center for Research in Biological Systems, University of California San Diego, USA. liwz@sdsc.edu', 'United States|United States|United States|United States|United States', 198, 'Algorithms|Cluster Analysis|Metagenome|Metagenomics|Sequence Analysis, DNA', 'Journal Article', '10.1093/bib/bbs035', '22772836', 'The rapid advances of high-throughput sequencing technologies dramatically prompted metagenomic studies of microbial communities that exist at various environments. Fundamental questions in metagenomics include the identities, composition and dynamics of microbial populations and their functions and interactions. However, the massive quantity and the comprehensive complexity of these sequence data pose tremendous challenges in data analysis. These challenges include but are not limited to ever-increasing computational demand, biased sequence sampling, sequence errors, sequence artifacts and novel sequences. Sequence clustering methods can directly answer many of the fundamental questions by grouping similar sequences into families. In addition, clustering analysis also addresses the challenges in metagenomics. Thus, a large redundant data set can be represented with a small non-redundant set, where each cluster can be represented by a single entry or a consensus. Artifacts can be rapidly detected through clustering. Errors can be identified, filtered or corrected by using consensus from sequences within clusters.'),
(68698, 2013, 'bib', 'A comparative analysis of biclustering algorithms for gene expression data', 'Eren, Kemal|Deveci, Mehmet|Küçüktunç, Onur|Çatalyürek, Ümit V', 'Department of Computer Science and Engineering, The Ohio State University, 3165 Graves Hall 333 West 10th Avenue. Columbus, OH 43210, USA.|Department of Computer Science and Engineering, The Ohio State University, 3165 Graves Hall 333 West 10th Avenue. Columbus, OH 43210, USA.|Department of Computer Science and Engineering, The Ohio State University, 3165 Graves Hall 333 West 10th Avenue. Columbus, OH 43210, USA.|Department of Computer Science and Engineering, The Ohio State University, 3165 Graves Hall 333 West 10th Avenue. Columbus, OH 43210, USA.', 'United States|United States|United States|United States', 38, 'Algorithms|Animals|Cluster Analysis|Factor Analysis, Statistical|Gene Expression|Humans|Models, Theoretical|Oligonucleotide Array Sequence Analysis', 'Comparative Study', '10.1093/bib/bbs032', '22772837', 'The need to analyze high-dimension biological data is driving the development of new data mining methods. Biclustering algorithms have been successfully applied to gene expression data to discover local patterns, in which a subset of genes exhibit similar expression levels over a subset of conditions. However, it is not clear which algorithms are best suited for this task. Many algorithms have been published in the past decade, most of which have been compared only to a small number of algorithms. Surveys and comparisons exist in the literature, but because of the large number and variety of biclustering algorithms, they are quickly outdated. In this article we partially address this problem of evaluating the strengths and weaknesses of existing biclustering methods. We used the BiBench package to compare 12 algorithms, many of which were recently published or have not been extensively studied. The algorithms were tested on a suite of synthetic data sets to measure their performance on data with varying conditions, such as different bicluster models, varying noise, varying numbers of biclusters and overlapping biclusters. The algorithms were also tested on eight large gene expression data sets obtained from the Gene Expression Omnibus. Gene Ontology enrichment analysis was performed on the resulting biclusters, and the best enrichment terms are reported. Our analyses show that the biclustering method and its parameters should be selected based on the desired model, whether that model allows overlapping biclusters, and its robustness to noise. In addition, we observe that the biclustering algorithms capable of finding more than one model are more successful at capturing biologically relevant clusters.'),
(68699, 2012, 'bib', 'Reference databases for taxonomic assignment in metagenomics', 'Santamaria, Monica|Fosso, Bruno|Consiglio, Arianna|De Caro, Giorgio|Grillo, Giorgio|Licciulli, Flavio|Liuni, Sabino|Marzano, Marinella|Alonso-Alemany, Daniel|Valiente, Gabriel|Pesole, Graziano', 'Institute of Biomembranes and Bioenergetics, National Research Council, Bari, Italy.|Institute of Biomembranes and Bioenergetics, National Research Council, Bari, Italy.|Institute of Biomembranes and Bioenergetics, National Research Council, Bari, Italy.|Institute of Biomembranes and Bioenergetics, National Research Council, Bari, Italy.|Institute of Biomembranes and Bioenergetics, National Research Council, Bari, Italy.|Institute of Biomembranes and Bioenergetics, National Research Council, Bari, Italy.|Institute of Biomembranes and Bioenergetics, National Research Council, Bari, Italy.|Institute of Biomembranes and Bioenergetics, National Research Council, Bari, Italy.|Institute of Biomembranes and Bioenergetics, National Research Council, Bari, Italy.|Institute of Biomembranes and Bioenergetics, National Research Council, Bari, Italy.|Institute of Biomembranes and Bioenergetics, National Research Council, Bari, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 31, 'Algorithms|Databases, Genetic|Fungi|Genes, rRNA|Metagenomics|RNA, Ribosomal, 16S', 'Journal Article', '10.1093/bib/bbs036', '22786784', 'Metagenomics is providing an unprecedented access to the environmental microbial diversity. The amplicon-based metagenomics approach involves the PCR-targeted sequencing of a genetic locus fitting different features. Namely, it must be ubiquitous in the taxonomic range of interest, variable enough to discriminate between different species but flanked by highly conserved sequences, and of suitable size to be sequenced through next-generation platforms. The internal transcribed spacers 1 and 2 (ITS1 and ITS2) of the ribosomal DNA operon and one or more hyper-variable regions of 16S ribosomal RNA gene are typically used to identify fungal and bacterial species, respectively. In this context, reliable reference databases and taxonomies are crucial to assign amplicon sequence reads to the correct phylogenetic ranks. Several resources provide consistent phylogenetic classification of publicly available 16S ribosomal DNA sequences, whereas the state of ribosomal internal transcribed spacers reference databases is notably less advanced. In this review, we aim to give an overview of existing reference resources for both types of markers, highlighting strengths and possible shortcomings of their use for metagenomics purposes. Moreover, we present a new database, ITSoneDB, of well annotated and phylogenetically classified ITS1 sequences to be used as a reference collection in metagenomic studies of environmental fungal communities. ITSoneDB is available for download and browsing at http://itsonedb.ba.itb.cnr.it/.'),
(68700, 2013, 'bib', 'Data mining in the Life Sciences with Random Forest: a walk in the park or lost in the jungle?', 'Touw, Wouter G|Bayjanov, Jumamurat R|Overmars, Lex|Backus, Lennart|Boekhorst, Jos|Wels, Michiel|van Hijum, Sacha A F T', 'Radboud University of Nijmegen, the Netherlands.|Radboud University of Nijmegen, the Netherlands.|Radboud University of Nijmegen, the Netherlands.|Radboud University of Nijmegen, the Netherlands.|Radboud University of Nijmegen, the Netherlands.|Radboud University of Nijmegen, the Netherlands.|Radboud University of Nijmegen, the Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 159, 'Algorithms|Biological Science Disciplines|Data Mining|Humans|Neoplasms|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bib/bbs034', '22786785', 'In the Life Sciences omics data is increasingly generated by different high-throughput technologies. Often only the integration of these data allows uncovering biological insights that can be experimentally validated or mechanistically modelled, i.e. sophisticated computational approaches are required to extract the complex non-linear trends present in omics data. Classification techniques allow training a model based on variables (e.g. SNPs in genetic association studies) to separate different classes (e.g. healthy subjects versus patients). Random Forest (RF) is a versatile classification algorithm suited for the analysis of these large data sets. In the Life Sciences, RF is popular because RF classification models have a high-prediction accuracy and provide information on importance of variables for classification. For omics data, variables or conditional relations between variables are typically important for a subset of samples of the same class. For example: within a class of cancer patients certain SNP combinations may be important for a subset of patients that have a specific subtype of cancer, but not important for a different subset of patients. These conditional relationships can in principle be uncovered from the data with RF as these are implicitly taken into account by the algorithm during the creation of the classification model. This review details some of the to the best of our knowledge rarely or never used RF properties that allow maximizing the biological insights that can be extracted from complex omics data sets using RF.'),
(68701, 2013, 'bib', 'A reversed framework for the identification of microRNA-target pairs in plants', 'Shao, Chaogang|Chen, Ming|Meng, Yijun', 'College of Life Sciences, Huzhou Teachers College.|College of Life Sciences, Huzhou Teachers College.|College of Life Sciences, Huzhou Teachers College.', '-|-|-', 7, 'Algorithms|High-Throughput Nucleotide Sequencing|MicroRNAs|Plants|RNA, Plant', 'Journal Article', '10.1093/bib/bbs040', '22811545', 'Most plant microRNAs (miRNAs) perform their repressive regulation through target cleavages. The resulting slicing sites on the target transcripts could be mapped by sequencing of the 3-cleavage remnants, called degradome sequencing. The high sequence complementarity between miRNAs and their targets has greatly facilitated the development of the target prediction tools for plant miRNAs. The prediction results were then subjected to degradome sequencing data-based validation, through which numerous miRNA-target interactions have been extracted. However, some drawbacks are unavoidable when using this forward approach. Essentially, a known list of plant miRNAs should be obtained in advance of target prediction and validation. This becomes an obstacle to discover novel miRNAs and their targets. Here, after reviewing the current available algorithms for reverse identification of miRNA-target pairs in plants, a case study was performed by using a newly established framework with adjustable parameters. In this workflow, integration of degradome and ARGONAUTE 1-enriched small RNA sequencing data was recommended to do a relatively comprehensive and reliable search. Besides, several computational algorithms such as BLAST, target plots and RNA secondary structure prediction were used. The results demonstrated the prevalent utility of the reversed approach for uncovering miRNA-target interactions in plants.'),
(68702, 2012, 'bib', 'Editorial: current progress in Bioinformatics 2012', 'Altman, Russ B', 'College of Life Sciences, Huzhou Teachers College.', '-', 0, 'Computational Biology', 'Editorial', '10.1093/bib/bbs042', '22833494', '-'),
(68703, 2012, 'bib', 'Reverse engineering biomolecular systems using -omic data: challenges, progress and opportunities', 'Quo, Chang F|Kaddi, Chanchala|Phan, John H|Zollanvari, Amin|Xu, Mingqing|Wang, May D|Alterovitz, Gil', 'Georgia Institute of Technology, Atlanta, GA 30332, USA.|Georgia Institute of Technology, Atlanta, GA 30332, USA.|Georgia Institute of Technology, Atlanta, GA 30332, USA.|Georgia Institute of Technology, Atlanta, GA 30332, USA.|Georgia Institute of Technology, Atlanta, GA 30332, USA.|Georgia Institute of Technology, Atlanta, GA 30332, USA.|Georgia Institute of Technology, Atlanta, GA 30332, USA.', 'Georgia|Georgia|Georgia|Georgia|Georgia|Georgia|Georgia', 11, 'Bioengineering|Biotechnology|Data Mining|Systems Biology', 'Journal Article', '10.1093/bib/bbs026', '22833495', 'Recent advances in high-throughput biotechnologies have led to the rapid growing research interest in reverse engineering of biomolecular systems (REBMS). Data-driven approaches, i.e. data mining, can be used to extract patterns from large volumes of biochemical data at molecular-level resolution while design-driven approaches, i.e. systems modeling, can be used to simulate emergent system properties. Consequently, both data- and design-driven approaches applied to -omic data may lead to novel insights in reverse engineering biological systems that could not be expected before using low-throughput platforms. However, there exist several challenges in this fast growing field of reverse engineering biomolecular systems: (i) to integrate heterogeneous biochemical data for data mining, (ii) to combine top-down and bottom-up approaches for systems modeling and (iii) to validate system models experimentally. In addition to reviewing progress made by the community and opportunities encountered in addressing these challenges, we explore the emerging field of synthetic biology, which is an exciting approach to validate and analyze theoretical system models directly through experimental synthesis, i.e. analysis-by-synthesis. The ultimate goal is to address the present and future challenges in reverse engineering biomolecular systems (REBMS) using integrated workflow of data mining, systems modeling and synthetic biology.'),
(68704, 2012, 'bib', 'Mining the pharmacogenomics literature--a survey of the state of the art', 'Hahn, Udo|Cohen, K Bretonnel|Garten, Yael|Shah, Nigam H', 'Jena University Language and Information Engineering (JULIE) Lab, Friedrich-Schiller-Universität Jena, Jena, Germany. udo.hahn@uni-jena.de|Jena University Language and Information Engineering (JULIE) Lab, Friedrich-Schiller-Universität Jena, Jena, Germany. udo.hahn@uni-jena.de|Jena University Language and Information Engineering (JULIE) Lab, Friedrich-Schiller-Universität Jena, Jena, Germany. udo.hahn@uni-jena.de|Jena University Language and Information Engineering (JULIE) Lab, Friedrich-Schiller-Universität Jena, Jena, Germany. udo.hahn@uni-jena.de', 'Germany|Germany|Germany|Germany', 15, 'Data Collection|Data Mining|Information Storage and Retrieval|Natural Language Processing|Pharmacogenetics|Publications|Semantics', 'Journal Article', '10.1093/bib/bbs018', '22833496', 'This article surveys efforts on text mining of the pharmacogenomics literature, mainly from the period 2008 to 2011. Pharmacogenomics (or pharmacogenetics) is the field that studies how human genetic variation impacts drug response. Therefore, publications span the intersection of research in genotypes, phenotypes and pharmacology, a topic that has increasingly become a focus of active research in recent years. This survey covers efforts dealing with the automatic recognition of relevant named entities (e.g. genes, gene variants and proteins, diseases and other pathological phenomena, drugs and other chemicals relevant for medical treatment), as well as various forms of relations between them. A wide range of text genres is considered, such as scientific publications (abstracts, as well as full texts), patent texts and clinical narratives. We also discuss infrastructure and resources needed for advanced text analytics, e.g. document corpora annotated with corresponding semantic metadata (gold standards and training data), biomedical terminologies and ontologies providing domain-specific background knowledge at different levels of formality and specificity, software architectures for building complex and scalable text analytics pipelines and Web services grounded to them, as well as comprehensive ways to disseminate and interact with the typically huge amounts of semiformal knowledge structures extracted by text mining tools. Finally, we consider some of the novel applications that have already been developed in the field of pharmacogenomic text mining and point out perspectives for future research.'),
(68705, 2013, 'bib', 'Automated glycopeptide analysis--review of current state and future directions', 'Dallas, David C|Martin, William F|Hua, Serenus|German, J Bruce', 'University of California, Davis, Davis, CA 95616, USA.|University of California, Davis, Davis, CA 95616, USA.|University of California, Davis, Davis, CA 95616, USA.|University of California, Davis, Davis, CA 95616, USA.', 'United States|United States|United States|United States', 63, 'Automation|Chromatography, Liquid|Glycopeptides|High-Throughput Screening Assays|Proteomics|Software|Tandem Mass Spectrometry', 'Journal Article', '10.1093/bib/bbs045', '22843980', 'Glycosylation of proteins is involved in immune defense, cell-cell adhesion, cellular recognition and pathogen binding and is one of the most common and complex post-translational modifications. Science is still struggling to assign detailed mechanisms and functions to this form of conjugation. Even the structural analysis of glycoproteins-glycoproteomics-remains in its infancy due to the scarcity of high-throughput analytical platforms capable of determining glycopeptide composition and structure, especially platforms for complex biological mixtures. Glycopeptide composition and structure can be determined with high mass-accuracy mass spectrometry, particularly when combined with chromatographic separation, but the sheer volume of generated data necessitates computational software for interpretation. This review discusses the current state of glycopeptide assignment software-advances made to date and issues that remain to be addressed. The various software and algorithms developed so far provide important insights into glycoproteomics. However, there is currently no freely available software that can analyze spectral data in batch and unambiguously determine glycopeptide compositions for N- and O-linked glycopeptides from relevant biological sources such as human milk and serum. Few programs are capable of aiding in structural determination of the glycan component. To significantly advance the field of glycoproteomics, analytical software and algorithms are required that: (i) solve for both N- and O-linked glycopeptide compositions, structures and glycosites in biological mixtures, (ii) are high-throughput and process data in batches, (iii) can interpret mass spectral data from a variety of sources and (iv) are open source and freely available.'),
(68706, 2013, 'bib', 'Batch effect removal methods for microarray gene expression data integration: a survey', 'Lazar, Cosmin|Meganck, Stijn|Taminau, Jonatan|Steenhoff, David|Coletta, Alain|Molter, Colin|Weiss-Solís, David Y|Duque, Robin|Bersini, Hugues|Nowé, Ann', 'Como, Vrije Universiteit Brussel, Pleinlaanz, 1050 Brussels, Belgium. vlazar@vub.ac.be|Como, Vrije Universiteit Brussel, Pleinlaanz, 1050 Brussels, Belgium. vlazar@vub.ac.be|Como, Vrije Universiteit Brussel, Pleinlaanz, 1050 Brussels, Belgium. vlazar@vub.ac.be|Como, Vrije Universiteit Brussel, Pleinlaanz, 1050 Brussels, Belgium. vlazar@vub.ac.be|Como, Vrije Universiteit Brussel, Pleinlaanz, 1050 Brussels, Belgium. vlazar@vub.ac.be|Como, Vrije Universiteit Brussel, Pleinlaanz, 1050 Brussels, Belgium. vlazar@vub.ac.be|Como, Vrije Universiteit Brussel, Pleinlaanz, 1050 Brussels, Belgium. vlazar@vub.ac.be|Como, Vrije Universiteit Brussel, Pleinlaanz, 1050 Brussels, Belgium. vlazar@vub.ac.be|Como, Vrije Universiteit Brussel, Pleinlaanz, 1050 Brussels, Belgium. vlazar@vub.ac.be|Como, Vrije Universiteit Brussel, Pleinlaanz, 1050 Brussels, Belgium. vlazar@vub.ac.be', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 70, 'Computer Simulation|Databases, Genetic|Gene Expression|Genetic Variation|Genome|Genomics|Oligonucleotide Array Sequence Analysis|Transcriptome', 'Journal Article', '10.1093/bib/bbs037', '22851511', 'Genomic data integration is a key goal to be achieved towards large-scale genomic data analysis. This process is very challenging due to the diverse sources of information resulting from genomics experiments. In this work, we review methods designed to combine genomic data recorded from microarray gene expression (MAGE) experiments. It has been acknowledged that the main source of variation between different MAGE datasets is due to the so-called batch effects. The methods reviewed here perform data integration by removing (or more precisely attempting to remove) the unwanted variation associated with batch effects. They are presented in a unified framework together with a wide range of evaluation tools, which are mandatory in assessing the efficiency and the quality of the data integration process. We provide a systematic description of the MAGE data integration methodology together with some basic recommendation to help the users in choosing the appropriate tools to integrate MAGE data for large-scale analysis, and also how to evaluate them from different perspectives in order to quantify their efficiency. All genomic data used in this study for illustration purposes were retrieved from InSilicoDB http://insilico.ulb.ac.be. '),
(68707, 2013, 'bib', 'Systematic analysis of the Plk-mediated phosphoregulation in eukaryotes', 'Liu, Zexian|Ren, Jian|Cao, Jun|He, Jiang|Yao, Xuebiao|Jin, Changjiang|Xue, Yu', 'Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.|Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.|Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.|Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.|Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.|Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.|Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.', 'China|China|China|China|China|China|China', 17, 'Algorithms|Cell Cycle Proteins|Chromosomal Proteins, Non-Histone|Eukaryotic Cells|Humans|Models, Biological|Phosphorylation|Protein Binding|Protein-Serine-Threonine Kinases|Proto-Oncogene Proteins', 'Journal Article', '10.1093/bib/bbs041', '22851512', 'Substantial evidence has confirmed that Polo-like kinases (Plks) play a crucial role in a variety of cellular processes via phosphorylation-mediated signaling transduction. Identification of Plk phospho-binding proteins and phosphorylation substrates is fundamental for elucidating the molecular mechanisms of Plks. Here, we present an integrative approach for the analysis of Plk-specific phospho-binding and phosphorylation sites (p-sites) in proteins. From the currently available phosphoproteomic data, we predicted tens of thousands of potential Plk phospho-binding and phosphorylation sites in eukaryotes, respectively. Furthermore, statistical analysis suggested that Plk phospho-binding proteins are more closely implicated in mitosis than their phosphorylation substrates. Additional computational analysis together with in vitro and in vivo experimental assays demonstrated that human Mis18B is a novel interacting partner of Plk1, while pT14 and pS48 of Mis18B were identified as phospho-binding sites. Taken together, this systematic analysis provides a global landscape of the complexity and diversity of potential Plk-mediated phosphoregulation, and the prediction results can be helpful for further experimental investigation.'),
(68708, 2012, 'bib', 'Taxonomic binning of metagenome samples generated by next-generation sequencing technologies', 'Dröge, Johannes|McHardy, Alice C', 'Heinrich Heine University Düsseldorf, Germany.|Heinrich Heine University Düsseldorf, Germany.', 'Germany|Germany', 66, 'Binding Sites|Cluster Analysis|Metagenome|Metagenomics|Sequence Analysis, DNA', 'Journal Article', '10.1093/bib/bbs031', '22851513', 'Metagenome research uses random shotgun sequencing of microbial community DNA to study the genetic sequences of its members without cultivation. This development has been strongly supported by improvements in sequencing technologies, which have rendered sequencing cheaper than before. As a consequence, downstream computational analysis of metagenome sequence samples is now faced with large amounts of complex data. One of the essential steps in metagenome analysis is reconstruction of draft genomes for populations of a community or of draft pan-genomes for higher level clades. Taxonomic binning corresponds to the process of assigning a taxonomic identifier to sequence fragments, based on information such as sequence similarity, sequence composition or read coverage. This is used for draft genome reconstruction, if sequencing coverage is insufficient for reconstruction based on assembly information alone. Subsequent functional and metabolic annotation of draft genomes allows a genome-level analysis of novel uncultured microbial species and even inference of their cultivation requirements.'),
(68709, 2013, 'bib', 'Network-based drug discovery by integrating systems biology and computational technologies', 'Leung, Elaine L|Cao, Zhi-Wei|Jiang, Zhi-Hong|Zhou, Hua|Liu, Liang', 'State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau, China.|State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau, China.|State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau, China.|State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau, China.|State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau, China.', 'China|China|China|China|China', 35, 'Computational Biology|Databases, Factual|Drug Discovery|Proteomics|Software|Systems Biology', 'Journal Article', '10.1093/bib/bbs043', '22877768', 'Network-based intervention has been a trend of curing systemic diseases, but it relies on regimen optimization and valid multi-target actions of the drugs. The complex multi-component nature of medicinal herbs may serve as valuable resources for network-based multi-target drug discovery due to its potential treatment effects by synergy. Recently, robustness of multiple systems biology platforms shows powerful to uncover molecular mechanisms and connections between the drugs and their targeting dynamic network. However, optimization methods of drug combination are insufficient, owning to lacking of tighter integration across multiple -omics databases. The newly developed algorithm- or network-based computational models can tightly integrate -omics databases and optimize combinational regimens of drug development, which encourage using medicinal herbs to develop into new wave of network-based multi-target drugs. However, challenges on further integration across the databases of medicinal herbs with multiple system biology platforms for multi-target drug optimization remain to the uncertain reliability of individual data sets, width and depth and degree of standardization of herbal medicine. Standardization of the methodology and terminology of multiple system biology and herbal database would facilitate the integration. Enhance public accessible databases and the number of research using system biology platform on herbal medicine would be helpful. Further integration across various -omics platforms and computational tools would accelerate development of network-based drug discovery and network medicine. '),
(68710, 2013, 'bib', 'Application of next generation sequencing to human gene fusion detection: computational tools, features and perspectives', 'Wang, Qingguo|Xia, Junfeng|Jia, Peilin|Pao, William|Zhao, Zhongming', 'Vanderbilt University, Nashville, TN 37235, USA.|Vanderbilt University, Nashville, TN 37235, USA.|Vanderbilt University, Nashville, TN 37235, USA.|Vanderbilt University, Nashville, TN 37235, USA.|Vanderbilt University, Nashville, TN 37235, USA.', 'United States|United States|United States|United States|United States', 38, 'Gene Fusion|Genomics|High-Throughput Nucleotide Sequencing|Humans|Neoplasms', 'Journal Article', '10.1093/bib/bbs044', '22877769', 'Gene fusions are important genomic events in human cancer because their fusion gene products can drive the development of cancer and thus are potential prognostic tools or therapeutic targets in anti-cancer treatment. Major advancements have been made in computational approaches for fusion gene discovery over the past 3 years due to improvements and widespread applications of high-throughput next generation sequencing (NGS) technologies. To identify fusions from NGS data, existing methods typically leverage the strengths of both sequencing technologies and computational strategies. In this article, we review the NGS and computational features of existing methods for fusion gene detection and suggest directions for future development. '),
(68711, 2013, 'bib', 'A solid quality-control analysis of AB SOLiD short-read sequencing data', 'Castellana, Stefano|Romani, Marta|Valente, Enza Maria|Mazza, Tommaso', 'IRCCS Casa Sollievo della Sofferenza, Mendel. Tel.: +39 0644160526, Fax: +39 0644160538, t.mazza@css-mendel.it.|IRCCS Casa Sollievo della Sofferenza, Mendel. Tel.: +39 0644160526, Fax: +39 0644160538, t.mazza@css-mendel.it.|IRCCS Casa Sollievo della Sofferenza, Mendel. Tel.: +39 0644160526, Fax: +39 0644160538, t.mazza@css-mendel.it.|IRCCS Casa Sollievo della Sofferenza, Mendel. Tel.: +39 0644160526, Fax: +39 0644160538, t.mazza@css-mendel.it.', '-|-|-|-', 3, 'High-Throughput Nucleotide Sequencing|Humans|Quality Control', 'Journal Article', '10.1093/bib/bbs048', '22877770', 'Next generation sequencers have greatly improved our ability to mine polymorphisms and mutations out of entire (or portions of) genomes. The reliability of their outputs, though, showed to be very related to the sequencing chemistry and to deeply affect the quality of the downstream analyses. We focus here on the two-base color code chemistry of AB SOLiD sequencers and propose a comprehensive quality control methodological and software pipeline. We used existing and custom tools to detect and purge short-reads of some common flaws due to sequencing errors and chemical hitches. We apply them to a cohort of SOLiD 4 runs and measure their joint efficacy in terms of the resulting ability to detect the greatest possible number of true variants. ');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(68712, 2013, 'bib', 'Towards a bioinformatics analysis of anti-Alzheimers herbal medicines from a target network perspective', 'Sun, Yi|Zhu, Ruixin|Ye, Hao|Tang, Kailin|Zhao, Jing|Chen, Yujia|Liu, Qi|Cao, Zhiwei', 'School of Life Sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 200092, China.|School of Life Sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 200092, China.|School of Life Sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 200092, China.|School of Life Sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 200092, China.|School of Life Sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 200092, China.|School of Life Sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 200092, China.|School of Life Sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 200092, China.|School of Life Sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 200092, China.', 'China|China|China|China|China|China|China|China', 13, 'Alzheimer Disease|Computational Biology|Herbal Medicine|Humans|Phytotherapy|United States|United States Food and Drug Administration', 'Journal Article', '10.1093/bib/bbs025', '22887889', 'With the growth of aging population all over the world, a rising incidence of Alzheimers disease (AD) has been recently observed. In contrast to FDA-approved western drugs, herbal medicines, featured as abundant ingredients and multi-targeting, have been acknowledged with notable anti-AD effects although the mechanism of action (MOA) is unknown. Investigating the possible MOA for these herbs can not only refresh but also extend the current knowledge of AD pathogenesis. In this study, clinically tested anti-AD herbs, their ingredients as well as their corresponding target proteins were systematically reviewed together with applicable bioinformatics resources and methodologies. Based on above information and resources, we present a systematically target network analysis framework to explore the mechanism of anti-AD herb ingredients. Our results indicated that, in addition to the binding of those symptom-relieving targets as the FDA-approved drugs usually do, ingredients of anti-AD herbs also interact closely with a variety of successful therapeutic targets related to other diseases, such as inflammation, cancer and diabetes, suggesting the possible cross-talks between these complicated diseases. Furthermore, pathways of Ca(2+) equilibrium maintaining upstream of cell proliferation and inflammation were densely targeted by the anti-AD herbal ingredients with rigorous statistic evaluation. In addition to the holistic understanding of the pathogenesis of AD, the integrated network analysis on the MOA of herbal ingredients may also suggest new clues for the future disease modifying strategies.'),
(68713, 2013, 'bib', 'The UCSC genome browser and associated tools', 'Kuhn, Robert M|Haussler, David|Kent, W James', 'Center for Biomolecular Science and Engineering, University of California, Santa Cruz, 1156 High Street, Santa Cruz, CA 95064, USA. kuhn@soe.ucsc.edu|Center for Biomolecular Science and Engineering, University of California, Santa Cruz, 1156 High Street, Santa Cruz, CA 95064, USA. kuhn@soe.ucsc.edu|Center for Biomolecular Science and Engineering, University of California, Santa Cruz, 1156 High Street, Santa Cruz, CA 95064, USA. kuhn@soe.ucsc.edu', 'United States|United States|United States', 244, 'Animals|Computational Biology|Computer Graphics|Data Display|Data Mining|Databases, Genetic|Genomics|Humans|Information Storage and Retrieval|Internet|Search Engine|Sequence Alignment|Software', 'Journal Article', '10.1093/bib/bbs038', '22908213', 'The UCSC Genome Browser (http://genome.ucsc.edu) is a graphical viewer for genomic data now in its 13th year. Since the early days of the Human Genome Project, it has presented an integrated view of genomic data of many kinds. Now home to assemblies for 58 organisms, the Browser presents visualization of annotations mapped to genomic coordinates. The ability to juxtapose annotations of many types facilitates inquiry-driven data mining. Gene predictions, mRNA alignments, epigenomic data from the ENCODE project, conservation scores from vertebrate whole-genome alignments and variation data may be viewed at any scale from a single base to an entire chromosome. The Browser also includes many other widely used tools, including BLAT, which is useful for alignments from high-throughput sequencing experiments. Private data uploaded as Custom Tracks and Data Hubs in many formats may be displayed alongside the rich compendium of precomputed data in the UCSC database. The Table Browser is a full-featured graphical interface, which allows querying, filtering and intersection of data tables. The Saved Session feature allows users to store and share customized views, enhancing the utility of the system for organizing multiple trains of thought. Binary Alignment/Map (BAM), Variant Call Format and the Personal Genome Single Nucleotide Polymorphisms (SNPs) data formats are useful for visualizing a large sequencing experiment (whole-genome or whole-exome), where the differences between the data set and the reference assembly may be displayed graphically. Support for high-throughput sequencing extends to compact, indexed data formats, such as BAM, bigBed and bigWig, allowing rapid visualization of large datasets from RNA-seq and ChIP-seq experiments via local hosting.'),
(68714, 2014, 'bib', 'The impact of HGT on phylogenomic reconstruction methods', 'Lapierre, Pascal|Lasek-Nesselquist, Erica|Gogarten, Johann Peter', 'Biotechnology/Bioservices Center, University of Connecticut, Storrs, CT, USA. pascal.lap@gmail.com.|Biotechnology/Bioservices Center, University of Connecticut, Storrs, CT, USA. pascal.lap@gmail.com.|Biotechnology/Bioservices Center, University of Connecticut, Storrs, CT, USA. pascal.lap@gmail.com.', 'United States|United States|United States', 11, 'Computational Biology|Computer Simulation|Evolution, Molecular|Gene Transfer, Horizontal|Genomics|Models, Genetic|Phylogeny|Sequence Alignment', 'Journal Article', '10.1093/bib/bbs050', '22908214', 'Supermatrix and supertree analyses are frequently used to more accurately recover vertical evolutionary history but debate still exists over which method provides greater reliability. Traditional methods that resolve relationships among organisms from single genes are often unreliable because of the frequent lack of strong phylogenetic signal and the presence of systematic artifacts. Methods developed to reconstruct organismal history from multiple genes can be divided into supermatrix and supertree approaches. A supermatrix analysis consists of the concatenation of multiple genes into a single, possibly partitioned alignment, from which phylogenies are reconstructed using a variety of approaches. Supertrees build consensus trees from the topological information contained within individual gene trees. Both methods are now widely used and have been demonstrated to solve previously ambiguous or unresolved phylogenies with high statistical support. However, the amount of misleading signal needed to induce erroneous phylogenies for both strategies is still unknown. Using genome simulations, we test the accuracy of supertree and supermatrix approaches in recovering the true organismal phylogeny under increased amounts of horizontally transferred genes and changes in substitution rates. Our results show that overall, supermatrix approaches are preferable when a low amount of gene transfer is suspected to be present in the dataset, while supertrees have greater reliability in the presence of a moderate amount of misleading gene transfers. In the face of very high or very low substitution rates without horizontal gene transfers, supermatrix approaches outperform supertrees as individual gene trees remain unresolved and additional sequences contribute to a congruent phylogenetic signal. '),
(68715, 2013, 'bib', 'Assessing protein conformational sampling methods based on bivariate lag-distributions of backbone angles', 'Maadooliat, Mehdi|Gao, Xin|Huang, Jianhua Z', 'Mathematical and Computer Sciences and Engineering Division, 4700 King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia, xin.gao@kaust.edu.sa. Jianhua Z. Huang, Department of Statistics, 447 Blocker Building, Texas A&M University, 3143 TAMU, College Station, TX 77843-3143 (USA), E-mail: jianhua@stat.tamu.edu.|Mathematical and Computer Sciences and Engineering Division, 4700 King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia, xin.gao@kaust.edu.sa. Jianhua Z. Huang, Department of Statistics, 447 Blocker Building, Texas A&M University, 3143 TAMU, College Station, TX 77843-3143 (USA), E-mail: jianhua@stat.tamu.edu.|Mathematical and Computer Sciences and Engineering Division, 4700 King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia, xin.gao@kaust.edu.sa. Jianhua Z. Huang, Department of Statistics, 447 Blocker Building, Texas A&M University, 3143 TAMU, College Station, TX 77843-3143 (USA), E-mail: jianhua@stat.tamu.edu.', 'Saudi Arabia|Saudi Arabia|Saudi Arabia', 3, 'Markov Chains|Protein Conformation|Proteins', 'Journal Article', '10.1093/bib/bbs052', '22926831', 'Despite considerable progress in the past decades, protein structure prediction remains one of the major unsolved problems in computational biology. Angular-sampling-based methods have been extensively studied recently due to their ability to capture the continuous conformational space of protein structures. The literature has focused on using a variety of parametric models of the sequential dependencies between angle pairs along the protein chains. In this article, we present a thorough review of angular-sampling-based methods by assessing three main questions: What is the best distribution type to model the protein angles? What is a reasonable number of components in a mixture model that should be considered to accurately parameterize the joint distribution of the angles? and What is the order of the local sequence-structure dependency that should be considered by a prediction method? We assess the model fits for different methods using bivariate lag-distributions of the dihedral/planar angles. Moreover, the main information across the lags can be extracted using a technique called Lag singular value decomposition (LagSVD), which considers the joint distribution of the dihedral/planar angles over different lags using a nonparametric approach and monitors the behavior of the lag-distribution of the angles using singular value decomposition. As a result, we developed graphical tools and numerical measurements to compare and evaluate the performance of different model fits. Furthermore, we developed a web-tool (http://www.stat.tamu.edu/xe2x88xbcmadoliat/LagSVD) that can be used to produce informative animations. '),
(68716, 2014, 'bib', 'Towards a comprehensive picture of the genetic landscape of complex traits', 'Wang, Zhong|Wang, Yaqun|Wang, Ningtao|Wang, Jianxin|Wang, Zuoheng|Vallejos, C Eduardo|Wu, Rongling', 'Center for Statistical Genetics, The Pennsylvania State University, Hershey, PA 17033, USA. rwu@phs.psu.edu.|Center for Statistical Genetics, The Pennsylvania State University, Hershey, PA 17033, USA. rwu@phs.psu.edu.|Center for Statistical Genetics, The Pennsylvania State University, Hershey, PA 17033, USA. rwu@phs.psu.edu.|Center for Statistical Genetics, The Pennsylvania State University, Hershey, PA 17033, USA. rwu@phs.psu.edu.|Center for Statistical Genetics, The Pennsylvania State University, Hershey, PA 17033, USA. rwu@phs.psu.edu.|Center for Statistical Genetics, The Pennsylvania State University, Hershey, PA 17033, USA. rwu@phs.psu.edu.|Center for Statistical Genetics, The Pennsylvania State University, Hershey, PA 17033, USA. rwu@phs.psu.edu.', 'United States|United States|United States|United States|United States|United States|United States', 8, 'Animals|Chromosome Mapping|Computational Biology|Epistasis, Genetic|Gene Expression Regulation, Developmental|Gene Regulatory Networks|Genetic Association Studies|Humans|Models, Genetic|Quantitative Trait Loci|Systems Biology', 'Journal Article', '10.1093/bib/bbs049', '22930650', 'The formation of phenotypic traits, such as biomass production, tumor volume and viral abundance, undergoes a complex process in which interactions between genes and developmental stimuli take place at each level of biological organization from cells to organisms. Traditional studies emphasize the impact of genes by directly linking DNA-based markers with static phenotypic values. Functional mapping, derived to detect genes that control developmental processes using growth equations, has proven powerful for addressing questions about the roles of genes in development. By treating phenotypic formation as a cohesive system using differential equations, a different approach-systems mapping-dissects the system into interconnected elements and then map genes that determine a web of interactions among these elements, facilitating our understanding of the genetic machineries for phenotypic development. Here, we argue that genetic mapping can play a more important role in studying the genotype-phenotype relationship by filling the gaps in the biochemical and regulatory process from DNA to end-point phenotype. We describe a new framework, named network mapping, to study the genetic architecture of complex traits by integrating the regulatory networks that cause a high-order phenotype. Network mapping makes use of a system of differential equations to quantify the rule by which transcriptional, proteomic and metabolomic components interact with each other to organize into a functional whole. The synthesis of functional mapping, systems mapping and network mapping provides a novel avenue to decipher a comprehensive picture of the genetic landscape of complex phenotypes that underlie economically and biomedically important traits. '),
(68717, 2012, 'bib', 'Interfaces to PeptideAtlas: a case study of standard data access systems', 'Killcoyne, Sarah|Handcock, Jeremy|Robinson, Thomas|Deutsch, Eric W|Boyle, John', 'Institute for Systems Biology, Seattle, WA 98109, USA.|Institute for Systems Biology, Seattle, WA 98109, USA.|Institute for Systems Biology, Seattle, WA 98109, USA.|Institute for Systems Biology, Seattle, WA 98109, USA.|Institute for Systems Biology, Seattle, WA 98109, USA.', 'United States|United States|United States|United States|United States', 1, 'Databases, Genetic|Genome|Genomics|Information Storage and Retrieval|Internet|Peptides|Software', 'Journal Article', '10.1093/bib/bbr067', '22941959', 'Access to public data sets is important to the scientific community as a resource to develop new experiments or validate new data. Projects such as the PeptideAtlas, Ensembl and The Cancer Genome Atlas (TCGA) offer both access to public data and a repository to share their own data. Access to these data sets is often provided through a web page form and a web service API. Access technologies based on web protocols (e.g. http) have been in use for over a decade and are widely adopted across the industry for a variety of functions (e.g. search, commercial transactions, and social media). Each architecture adapts these technologies to provide users with tools to access and share data. Both commonly used web service technologies (e.g. REST and SOAP), and custom-built solutions over HTTP are utilized in providing access to research data. Providing multiple access points ensures that the community can access the data in the simplest and most effective manner for their particular needs. This article examines three common access mechanisms for web accessible data: BioMart, caBIG, and Google Data Sources. These are illustrated by implementing each over the PeptideAtlas repository and reviewed for their suitability based on specific usages common to research. BioMart, Google Data Sources, and caBIG are each suitable for certain uses. The tradeoffs made in the development of the technology are dependent on the uses each was designed for (e.g. security versus speed). This means that an understanding of specific requirements and tradeoffs is necessary before selecting the access technology.'),
(68718, 2013, 'bib', 'A quantitative model of transcriptional differentiation driving host-pathogen interactions', 'Zhu, Sheng|Wang, Zhong|Wang, Jianxin|Wang, Yaqun|Wang, Ningtao|Wang, Zuoheng|Xu, Meng|Su, Xiaohua|Wang, Mingxiu|Zhang, Shougong|Huang, Minren|Wu, Rongling', 'Center for Computational Biology, Beijing Forestry University, Beijing 100083, China. rwu@bjfu.edu.cn, Center for Statistical Genetics, Pennsylvania State University, Hershey, PA 17033, USA. Tel: +001 717 531 2037, Fax: +001 717 531 0480, E-mail: rwu@phs.psu.edu.|Center for Computational Biology, Beijing Forestry University, Beijing 100083, China. rwu@bjfu.edu.cn, Center for Statistical Genetics, Pennsylvania State University, Hershey, PA 17033, USA. Tel: +001 717 531 2037, Fax: +001 717 531 0480, E-mail: rwu@phs.psu.edu.|Center for Computational Biology, Beijing Forestry University, Beijing 100083, China. rwu@bjfu.edu.cn, Center for Statistical Genetics, Pennsylvania State University, Hershey, PA 17033, USA. Tel: +001 717 531 2037, Fax: +001 717 531 0480, E-mail: rwu@phs.psu.edu.|Center for Computational Biology, Beijing Forestry University, Beijing 100083, China. rwu@bjfu.edu.cn, Center for Statistical Genetics, Pennsylvania State University, Hershey, PA 17033, USA. Tel: +001 717 531 2037, Fax: +001 717 531 0480, E-mail: rwu@phs.psu.edu.|Center for Computational Biology, Beijing Forestry University, Beijing 100083, China. rwu@bjfu.edu.cn, Center for Statistical Genetics, Pennsylvania State University, Hershey, PA 17033, USA. Tel: +001 717 531 2037, Fax: +001 717 531 0480, E-mail: rwu@phs.psu.edu.|Center for Computational Biology, Beijing Forestry University, Beijing 100083, China. rwu@bjfu.edu.cn, Center for Statistical Genetics, Pennsylvania State University, Hershey, PA 17033, USA. Tel: +001 717 531 2037, Fax: +001 717 531 0480, E-mail: rwu@phs.psu.edu.|Center for Computational Biology, Beijing Forestry University, Beijing 100083, China. rwu@bjfu.edu.cn, Center for Statistical Genetics, Pennsylvania State University, Hershey, PA 17033, USA. Tel: +001 717 531 2037, Fax: +001 717 531 0480, E-mail: rwu@phs.psu.edu.|Center for Computational Biology, Beijing Forestry University, Beijing 100083, China. rwu@bjfu.edu.cn, Center for Statistical Genetics, Pennsylvania State University, Hershey, PA 17033, USA. Tel: +001 717 531 2037, Fax: +001 717 531 0480, E-mail: rwu@phs.psu.edu.|Center for Computational Biology, Beijing Forestry University, Beijing 100083, China. rwu@bjfu.edu.cn, Center for Statistical Genetics, Pennsylvania State University, Hershey, PA 17033, USA. Tel: +001 717 531 2037, Fax: +001 717 531 0480, E-mail: rwu@phs.psu.edu.|Center for Computational Biology, Beijing Forestry University, Beijing 100083, China. rwu@bjfu.edu.cn, Center for Statistical Genetics, Pennsylvania State University, Hershey, PA 17033, USA. Tel: +001 717 531 2037, Fax: +001 717 531 0480, E-mail: rwu@phs.psu.edu.|Center for Computational Biology, Beijing Forestry University, Beijing 100083, China. rwu@bjfu.edu.cn, Center for Statistical Genetics, Pennsylvania State University, Hershey, PA 17033, USA. Tel: +001 717 531 2037, Fax: +001 717 531 0480, E-mail: rwu@phs.psu.edu.|Center for Computational Biology, Beijing Forestry University, Beijing 100083, China. rwu@bjfu.edu.cn, Center for Statistical Genetics, Pennsylvania State University, Hershey, PA 17033, USA. Tel: +001 717 531 2037, Fax: +001 717 531 0480, E-mail: rwu@phs.psu.edu.', 'China|China|China|China|China|China|China|China|China|China|China|China', 5, 'Ascomycota|Host-Pathogen Interactions|Likelihood Functions|Populus|Transcription, Genetic', 'Journal Article', '10.1093/bib/bbs047', '22962337', 'Despite our expanding knowledge about the biochemistry of gene regulation involved in host-pathogen interactions, a quantitative understanding of this process at a transcriptional level is still limited. We devise and assess a computational framework that can address this question. This framework is founded on a mixture model-based likelihood, equipped with functionality to cluster genes per dynamic and functional changes of gene expression within an interconnected system composed of the host and pathogen. If genes from the host and pathogen are clustered in the same group due to a similar pattern of dynamic profiles, they are likely to be reciprocally co-evolving. If genes from the two organisms are clustered in different groups, this means that they experience strong host-pathogen interactions. The framework can test the rates of change for individual gene clusters during pathogenic infection and quantify their impacts on host-pathogen interactions. The framework was validated by a pathological study of poplar leaves infected by fungal Marssonina brunnea in which co-evolving and interactive genes that determine poplar-fungus interactions are identified. The new framework should find its wide application to studying host-pathogen interactions for any other interconnected systems. '),
(68719, 2012, 'bib', 'Classification of metagenomic sequences: methods and challenges', 'Mande, Sharmila S|Mohammed, Monzoorul Haque|Ghosh, Tarini Shankar', 'Bio-Sciences Division, TCS Innovation Labs, India. sharmila@atc.tcs.com|Bio-Sciences Division, TCS Innovation Labs, India. sharmila@atc.tcs.com|Bio-Sciences Division, TCS Innovation Labs, India. sharmila@atc.tcs.com', 'India|India|India', 108, 'Algorithms|Databases, Genetic|Metagenome|Metagenomics|Sequence Analysis, DNA', 'Journal Article', '10.1093/bib/bbs054', '22962338', 'Characterizing the taxonomic diversity of microbial communities is one of the primary objectives of metagenomic studies. Taxonomic analysis of microbial communities, a process referred to as binning, is challenging for the following reasons. Primarily, query sequences originating from the genomes of most microbes in an environmental sample lack taxonomically related sequences in existing reference databases. This absence of a taxonomic context makes binning a very challenging task. Limitations of current sequencing platforms, with respect to short read lengths and sequencing errors/artifacts, are also key factors that determine the overall binning efficiency. Furthermore, the sheer volume of metagenomic datasets also demands highly efficient algorithms that can operate within reasonable requirements of compute power. This review discusses the premise, methodologies, advantages, limitations and challenges of various methods available for binning of metagenomic datasets obtained using the shotgun sequencing approach. Various parameters as well as strategies used for evaluating binning efficiency are then reviewed.'),
(68720, 2012, 'bib', 'Metagenomic analysis: the challenge of the data bonanza', 'Hunter, Chris I|Mitchell, Alex|Jones, Philip|McAnulla, Craig|Pesseat, Sebastien|Scheremetjew, Maxim|Hunter, Sarah', 'European Bioinformatics Institute, Cambridge, UK. chrish@ebi.ac.uk|European Bioinformatics Institute, Cambridge, UK. chrish@ebi.ac.uk|European Bioinformatics Institute, Cambridge, UK. chrish@ebi.ac.uk|European Bioinformatics Institute, Cambridge, UK. chrish@ebi.ac.uk|European Bioinformatics Institute, Cambridge, UK. chrish@ebi.ac.uk|European Bioinformatics Institute, Cambridge, UK. chrish@ebi.ac.uk|European Bioinformatics Institute, Cambridge, UK. chrish@ebi.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 7, 'Databases, Factual|Metagenome|Metagenomics|Sequence Analysis, DNA', 'Journal Article', '10.1093/bib/bbs020', '22962339', 'Several thousand metagenomes have already been sequenced, and this number is set to grow rapidly in the forthcoming years as the uptake of high-throughput sequencing technologies continues. Hand-in-hand with this data bonanza comes the computationally overwhelming task of analysis. Herein, we describe some of the bioinformatic approaches currently used by metagenomics researchers to analyze their data, the issues they face and the steps that could be taken to help overcome these challenges.'),
(68721, 2013, 'bib', 'Evaluation of research in biomedical ontologies', 'Hoehndorf, Robert|Dumontier, Michel|Gkoutos, Georgios V', 'Department of Computer Science, Aberystwyth University, Aberystwyth, Ceredigion, SY23 3DB, UK. leechuck@leechuck.de.|Department of Computer Science, Aberystwyth University, Aberystwyth, Ceredigion, SY23 3DB, UK. leechuck@leechuck.de.|Department of Computer Science, Aberystwyth University, Aberystwyth, Ceredigion, SY23 3DB, UK. leechuck@leechuck.de.', 'United Kingdom|United Kingdom|United Kingdom', 44, 'Biological Ontologies|Biomedical Research', 'Evaluation Studies', '10.1093/bib/bbs053', '22962340', 'Ontologies are now pervasive in biomedicine, where they serve as a means to standardize terminology, to enable access to domain knowledge, to verify data consistency and to facilitate integrative analyses over heterogeneous biomedical data. For this purpose, research on biomedical ontologies applies theories and methods from diverse disciplines such as information management, knowledge representation, cognitive science, linguistics and philosophy. Depending on the desired applications in which ontologies are being applied, the evaluation of research in biomedical ontologies must follow different strategies. Here, we provide a classification of research problems in which ontologies are being applied, focusing on the use of ontologies in basic and translational research, and we demonstrate how research results in biomedical ontologies can be evaluated. The evaluation strategies depend on the desired application and measure the success of using an ontology for a particular biomedical problem. For many applications, the success can be quantified, thereby facilitating the objective evaluation and comparison of research in biomedical ontology. The objective, quantifiable comparison of research results based on scientific applications opens up the possibility for systematically improving the utility of ontologies in biomedical research. '),
(68722, 2012, 'bib', 'Current opportunities and challenges in microbial metagenome analysis--a bioinformatic perspective', 'Teeling, Hanno|Glöckner, Frank Oliver', 'Max Planck Institute for Marine Microbiology, Bremen, Germany.|Max Planck Institute for Marine Microbiology, Bremen, Germany.', 'Germany|Germany', 106, 'Biodiversity|Computational Biology|Genome, Archaeal|Genome, Bacterial|Metagenome|Metagenomics', 'Journal Article', '10.1093/bib/bbs039', '22966151', 'Metagenomics has become an indispensable tool for studying the diversity and metabolic potential of environmental microbes, whose bulk is as yet non-cultivable. Continual progress in next-generation sequencing allows for generating increasingly large metagenomes and studying multiple metagenomes over time or space. Recently, a new type of holistic ecosystem study has emerged that seeks to combine metagenomics with biodiversity, meta-expression and contextual data. Such ecosystems biology approaches bear the potential to not only advance our understanding of environmental microbes to a new level but also impose challenges due to increasing data complexities, in particular with respect to bioinformatic post-processing. This mini review aims to address selected opportunities and challenges of modern metagenomics from a bioinformatics perspective and hopefully will serve as a useful resource for microbial ecologists and bioinformaticians alike.'),
(68723, 2013, 'bib', 'A multivalent three-point linkage analysis model of autotetraploids', 'Lu, Yafei|Yang, Xiaoxia|Tong, Chunfa|Li, Xin|Feng, Sisi|Wang, Zhong|Pang, Xiaoming|Wang, Yaqun|Wang, Ningtao|Tobias, Christian M|Wu, Rongling', 'Center for Computational Biology, Beijing Forestry University, Beijing, China.|Center for Computational Biology, Beijing Forestry University, Beijing, China.|Center for Computational Biology, Beijing Forestry University, Beijing, China.|Center for Computational Biology, Beijing Forestry University, Beijing, China.|Center for Computational Biology, Beijing Forestry University, Beijing, China.|Center for Computational Biology, Beijing Forestry University, Beijing, China.|Center for Computational Biology, Beijing Forestry University, Beijing, China.|Center for Computational Biology, Beijing Forestry University, Beijing, China.|Center for Computational Biology, Beijing Forestry University, Beijing, China.|Center for Computational Biology, Beijing Forestry University, Beijing, China.|Center for Computational Biology, Beijing Forestry University, Beijing, China.', 'China|China|China|China|China|China|China|China|China|China|China', 2, 'Chromosome Mapping|Genetic Linkage|Genetic Markers|Models, Genetic|Models, Statistical|Polyploidy', 'Journal Article', '10.1093/bib/bbs051', '22988254', 'Because of its widespread occurrence and role in shaping evolutionary processes in the biological kingdom, especially in plants, polyploidy has been increasingly studied from cytological to molecular levels. By inferring gene order, gene distances and gene homology, linkage mapping with molecular markers has proven powerful for investigating genome structure and organization. Here we review and assess a general statistical model for three-point linkage analysis in autotetraploids by integrating double reduction, a phenomenon that commonly occurs in autopolyploids whose chromosomes are derived from a single ancestral species. This model does not require any assumption on the distribution of the occurrence of double reduction and can handle the complexity of multilocus linkage in terms of crossover interference. Implemented with the expectation-maximization (EM) algorithms, the model can estimate and test the recombination fractions between less informative dominant markers, thus facilitating its practical implications for any autopolyploids in most of which inexpensive dominant markers are still used for their genetic and evolutionary studies. The model was applied to reanalyze a published data in tetraploid switchgrass, validating its practical usefulness and utilization. '),
(68724, 2013, 'bib', 'Developing an online programme in computational biology', 'Vincent, Heather M|Page, Christopher', 'Faculty of Life Sciences, University of Manchester, Manchester M13 9PT, UK. heather.vincent@manchester.ac.uk.|Faculty of Life Sciences, University of Manchester, Manchester M13 9PT, UK. heather.vincent@manchester.ac.uk.', 'United Kingdom|United Kingdom', 2, 'Computational Biology|Curriculum|Internet|Program Development', 'Journal Article', '10.1093/bib/bbs059', '22988255', 'Much has been written about the need for continuing education and training to enable life scientists and computer scientists to manage and exploit the different types of biological data now becoming available. Here we describe the development of an online programme that combines short training courses, so that those who require an educational programme can progress to complete a formal qualification. Although this flexible approach fits the needs of course participants, it does not fit easily within the organizational structures of a campus-based university. '),
(68725, 2013, 'bib', 'A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis', 'Dillies, Marie-Agnès|Rau, Andrea|Aubert, Julie|Hennequet-Antier, Christelle|Jeanmougin, Marine|Servant, Nicolas|Keime, Céline|Marot, Guillemette|Castel, David|Estelle, Jordi|Guernec, Gregory|Jagla, Bernd|Jouneau, Luc|Laloë, Denis|Le Gall, Caroline|Schaëffer, Brigitte|Le Crom, Stéphane|Guedj, Mickaël|Jaffrézic, Florence|French StatOmique Consortium', 'Institut Pasteur, PF2 Plate-forme Transcriptome et Epigénome, 28 rue du Dr Roux, Paris CEDEX 15, F-75724 France. Tel.: +33 (0) 145688651, Fax: +33 (0) 145688406, marie-agnes.dillies@pasteur.fr.|Institut Pasteur, PF2 Plate-forme Transcriptome et Epigénome, 28 rue du Dr Roux, Paris CEDEX 15, F-75724 France. Tel.: +33 (0) 145688651, Fax: +33 (0) 145688406, marie-agnes.dillies@pasteur.fr.|Institut Pasteur, PF2 Plate-forme Transcriptome et Epigénome, 28 rue du Dr Roux, Paris CEDEX 15, F-75724 France. Tel.: +33 (0) 145688651, Fax: +33 (0) 145688406, marie-agnes.dillies@pasteur.fr.|Institut Pasteur, PF2 Plate-forme Transcriptome et Epigénome, 28 rue du Dr Roux, Paris CEDEX 15, F-75724 France. Tel.: +33 (0) 145688651, Fax: +33 (0) 145688406, marie-agnes.dillies@pasteur.fr.|Institut Pasteur, PF2 Plate-forme Transcriptome et Epigénome, 28 rue du Dr Roux, Paris CEDEX 15, F-75724 France. Tel.: +33 (0) 145688651, Fax: +33 (0) 145688406, marie-agnes.dillies@pasteur.fr.|Institut Pasteur, PF2 Plate-forme Transcriptome et Epigénome, 28 rue du Dr Roux, Paris CEDEX 15, F-75724 France. Tel.: +33 (0) 145688651, Fax: +33 (0) 145688406, marie-agnes.dillies@pasteur.fr.|Institut Pasteur, PF2 Plate-forme Transcriptome et Epigénome, 28 rue du Dr Roux, Paris CEDEX 15, F-75724 France. Tel.: +33 (0) 145688651, Fax: +33 (0) 145688406, marie-agnes.dillies@pasteur.fr.|Institut Pasteur, PF2 Plate-forme Transcriptome et Epigénome, 28 rue du Dr Roux, Paris CEDEX 15, F-75724 France. Tel.: +33 (0) 145688651, Fax: +33 (0) 145688406, marie-agnes.dillies@pasteur.fr.|Institut Pasteur, PF2 Plate-forme Transcriptome et Epigénome, 28 rue du Dr Roux, Paris CEDEX 15, F-75724 France. Tel.: +33 (0) 145688651, Fax: +33 (0) 145688406, marie-agnes.dillies@pasteur.fr.|Institut Pasteur, PF2 Plate-forme Transcriptome et Epigénome, 28 rue du Dr Roux, Paris CEDEX 15, F-75724 France. Tel.: +33 (0) 145688651, Fax: +33 (0) 145688406, marie-agnes.dillies@pasteur.fr.|Institut Pasteur, PF2 Plate-forme Transcriptome et Epigénome, 28 rue du Dr Roux, Paris CEDEX 15, F-75724 France. Tel.: +33 (0) 145688651, Fax: +33 (0) 145688406, marie-agnes.dillies@pasteur.fr.|Institut Pasteur, PF2 Plate-forme Transcriptome et Epigénome, 28 rue du Dr Roux, Paris CEDEX 15, F-75724 France. Tel.: +33 (0) 145688651, Fax: +33 (0) 145688406, marie-agnes.dillies@pasteur.fr.|Institut Pasteur, PF2 Plate-forme Transcriptome et Epigénome, 28 rue du Dr Roux, Paris CEDEX 15, F-75724 France. Tel.: +33 (0) 145688651, Fax: +33 (0) 145688406, marie-agnes.dillies@pasteur.fr.|Institut Pasteur, PF2 Plate-forme Transcriptome et Epigénome, 28 rue du Dr Roux, Paris CEDEX 15, F-75724 France. Tel.: +33 (0) 145688651, Fax: +33 (0) 145688406, marie-agnes.dillies@pasteur.fr.|Institut Pasteur, PF2 Plate-forme Transcriptome et Epigénome, 28 rue du Dr Roux, Paris CEDEX 15, F-75724 France. Tel.: +33 (0) 145688651, Fax: +33 (0) 145688406, marie-agnes.dillies@pasteur.fr.|Institut Pasteur, PF2 Plate-forme Transcriptome et Epigénome, 28 rue du Dr Roux, Paris CEDEX 15, F-75724 France. Tel.: +33 (0) 145688651, Fax: +33 (0) 145688406, marie-agnes.dillies@pasteur.fr.|Institut Pasteur, PF2 Plate-forme Transcriptome et Epigénome, 28 rue du Dr Roux, Paris CEDEX 15, F-75724 France. Tel.: +33 (0) 145688651, Fax: +33 (0) 145688406, marie-agnes.dillies@pasteur.fr.|Institut Pasteur, PF2 Plate-forme Transcriptome et Epigénome, 28 rue du Dr Roux, Paris CEDEX 15, F-75724 France. Tel.: +33 (0) 145688651, Fax: +33 (0) 145688406, marie-agnes.dillies@pasteur.fr.|Institut Pasteur, PF2 Plate-forme Transcriptome et Epigénome, 28 rue du Dr Roux, Paris CEDEX 15, F-75724 France. Tel.: +33 (0) 145688651, Fax: +33 (0) 145688406, marie-agnes.dillies@pasteur.fr.|Institut Pasteur, PF2 Plate-forme Transcriptome et Epigénome, 28 rue du Dr Roux, Paris CEDEX 15, F-75724 France. Tel.: +33 (0) 145688651, Fax: +33 (0) 145688406, marie-agnes.dillies@pasteur.fr.', 'France|France|France|France|France|France|France|France|France|France|France|France|France|France|Fr', 557, 'High-Throughput Nucleotide Sequencing|Sequence Analysis, RNA', 'Journal Article', '10.1093/bib/bbs046', '22988256', 'During the last 3 years, a number of approaches for the normalization of RNA sequencing data have emerged in the literature, differing both in the type of bias adjustment and in the statistical strategy adopted. However, as data continue to accumulate, there has been no clear consensus on the appropriate normalization method to be used or the impact of a chosen method on the downstream analysis. In this work, we focus on a comprehensive comparison of seven recently proposed normalization methods for the differential analysis of RNA-seq data, with an emphasis on the use of varied real and simulated datasets involving different species and experimental designs to represent data characteristics commonly observed in practice. Based on this comparison study, we propose practical recommendations on the appropriate normalization method to be used and its impact on the differential analysis of RNA-seq data. '),
(68726, 2013, 'bib', 'On the classification of microarray gene-expression data', 'Basford, Kaye E|McLachlan, Geoffrey J|Rathnayake, Suren I', 'Department of Mathematics, University of Queensland, St Lucia, QLD 4072, Australia.|Department of Mathematics, University of Queensland, St Lucia, QLD 4072, Australia.|Department of Mathematics, University of Queensland, St Lucia, QLD 4072, Australia.', 'Australia|Australia|Australia', 10, 'Child|Cluster Analysis|Databases, Genetic|Gene Expression|Genomics|Humans|Oligonucleotide Array Sequence Analysis|Organ Specificity|Precursor Cell Lymphoblastic Leukemia-Lymphoma|Transcriptome', 'Journal Article', '10.1093/bib/bbs056', '22988257', 'We consider the classification of microarray gene-expression data. First, attention is given to the supervised case, where the tissue samples are classified with respect to a number of predefined classes and the intent is to assign a new unclassified tissue to one of these classes. The problems of forming a classifier and estimating its error rate are addressed in the context of there being a relatively small number of observations (tissue samples) compared to the number of variables (that is, the genes, which can number in the tens of thousands). We then proceed to the unsupervised case and consider the clustering of the tissue samples and also the clustering of the gene profiles. Both problems can be viewed as being non-standard ones in statistics and we address some of the key issues involved. The focus is on the use of mixture models to effect the clustering for both problems. '),
(68727, 2014, 'bib', 'Iteratively reweighted LASSO for mapping multiple quantitative trait loci', 'Liu, Yongxin|Yang, Tianfu|Li, Hongwang|Yang, Runqing', 'Beidaihe Central Experiment Station, Chinese Academy of Fishery Sciences, Qinhuangdao 066100, Peoples Republic of China. runqingyang@sjtu.edu.cn.|Beidaihe Central Experiment Station, Chinese Academy of Fishery Sciences, Qinhuangdao 066100, Peoples Republic of China. runqingyang@sjtu.edu.cn.|Beidaihe Central Experiment Station, Chinese Academy of Fishery Sciences, Qinhuangdao 066100, Peoples Republic of China. runqingyang@sjtu.edu.cn.|Beidaihe Central Experiment Station, Chinese Academy of Fishery Sciences, Qinhuangdao 066100, Peoples Republic of China. runqingyang@sjtu.edu.cn.', 'China|China|China|China', 2, 'Bayes Theorem|Chromosome Mapping|Computational Biology|Computer Simulation|Databases, Genetic|Genome, Plant|Hordeum|Least-Squares Analysis|Likelihood Functions|Models, Genetic|Quantitative Trait Loci', 'Journal Article', '10.1093/bib/bbs062', '23023740', 'The iteratively reweighted least square (IRLS) method is mostly identical to maximum likelihood (ML) method in terms of parameter estimation and power of quantitative trait locus (QTL) detection. But the IRLS is greatly superior to ML in terms of computing speed and the robustness of parameter estimation. In conjunction with the priors of parameters, ML can analyze multiple QTL model based on Bayesian theory, whereas under a single QTL model, IRLS has very limited statistical power to detect multiple QTLs. In this study, we proposed the iteratively reweighted least absolute shrinkage and selection operator (IRLASSO) for extending IRLS to simultaneously map multiple QTLs. The LASSO with coordinate descent step is employed to efficiently estimate non-zero genetic effect of each locus scanned over entire genome. Simulations demonstrate that IRLASSO has a higher precision of parameter estimation and power to detect QTL than IRLS, and is able to estimate residual variance more accurately than the unweighted LASSO based on LS. Especially, IRLASSO is very fast, usually taking less than five iterations to converge. The barley dataset from the North American Barley Genome Mapping Project is reanalyzed by our proposed method. '),
(68728, 2014, 'bib', 'Data management strategies for multinational large-scale systems biology projects', 'Wruck, Wasco|Peuker, Martin|Regenbrecht, Christian R A', 'Institute of Pathology, Charite - Universitaetsmedizin Berlin, Chariteplatz 1, 10117 Berlin. Tel.: +49 30 2093 8951, Fax: +49 30 450 536 909, wasco.wruck@charite.de.|Institute of Pathology, Charite - Universitaetsmedizin Berlin, Chariteplatz 1, 10117 Berlin. Tel.: +49 30 2093 8951, Fax: +49 30 450 536 909, wasco.wruck@charite.de.|Institute of Pathology, Charite - Universitaetsmedizin Berlin, Chariteplatz 1, 10117 Berlin. Tel.: +49 30 2093 8951, Fax: +49 30 450 536 909, wasco.wruck@charite.de.', '-|-|-', 18, 'Access to Information|Computational Biology|Data Collection|Database Management Systems|Information Dissemination|Internationality|Software|Systems Biology', 'Comparative Study', '10.1093/bib/bbs064', '23047157', 'Good accessibility of publicly funded research data is essential to secure an open scientific system and eventually becomes mandatory [Wellcome Trust will Penalise Scientists Who Dont Embrace Open Access. The Guardian 2012]. By the use of high-throughput methods in many research areas from physics to systems biology, large data collections are increasingly important as raw material for research. Here, we present strategies worked out by international and national institutions targeting open access to publicly funded research data via incentives or obligations to share data. Funding organizations such as the British Wellcome Trust therefore have developed data sharing policies and request commitment to data management and sharing in grant applications. Increased citation rates are a profound argument for sharing publication data. Pre-publication sharing might be rewarded by a data citation credit system via digital object identifiers (DOIs) which have initially been in use for data objects. Besides policies and incentives, good practice in data management is indispensable. However, appropriate systems for data management of large-scale projects for example in systems biology are hard to find. Here, we give an overview of a selection of open-source data management systems proved to be employed successfully in large-scale projects. '),
(68729, 2013, 'bib', 'Mathematical modeling of biological systems', 'Motta, Santo|Pappalardo, Francesco', 'Department of Mathematics and Computer Science, University of Catania, V.le A. Doria, 6, 95125 Catania, Italy. motta@dmi.unict.it|Department of Mathematics and Computer Science, University of Catania, V.le A. Doria, 6, 95125 Catania, Italy. motta@dmi.unict.it', 'Italy|Italy', 43, 'Biomedical Research|Computer Simulation|Databases, Factual|Genomics|Models, Biological|Proteomics', 'Journal Article', '10.1093/bib/bbs061', '23063928', 'Mathematical and computational models are increasingly used to help interpret biomedical data produced by high-throughput genomics and proteomics projects. The application of advanced computer models enabling the simulation of complex biological processes generates hypotheses and suggests experiments. Appropriately interfaced with biomedical databases, models are necessary for rapid access to, and sharing of knowledge through data mining and knowledge discovery approaches. '),
(68730, 2013, 'bib', 'Accounting for noise when clustering biological data', 'Sloutsky, Roman|Jimenez, Nicolas|Swamidass, S Joshua|Naegle, Kristen M', 'Division of Biology and Biomedical Sciences, Washington University in St. Louis, One Brookings Drive, Brauer 2004, St. Louis, MO 63130, USA.|Division of Biology and Biomedical Sciences, Washington University in St. Louis, One Brookings Drive, Brauer 2004, St. Louis, MO 63130, USA.|Division of Biology and Biomedical Sciences, Washington University in St. Louis, One Brookings Drive, Brauer 2004, St. Louis, MO 63130, USA.|Division of Biology and Biomedical Sciences, Washington University in St. Louis, One Brookings Drive, Brauer 2004, St. Louis, MO 63130, USA.', 'United States|United States|United States|United States', 14, 'Algorithms|Cluster Analysis|Gene Expression|Phosphorylation|Proteins|Transcriptome', 'Journal Article', '10.1093/bib/bbs057', '23063929', 'Clustering is a powerful and commonly used technique that organizes and elucidates the structure of biological data. Clustering data from gene expression, metabolomics and proteomics experiments has proven to be useful at deriving a variety of insights, such as the shared regulation or function of biochemical components within networks. However, experimental measurements of biological processes are subject to substantial noise-stemming from both technical and biological variability-and most clustering algorithms are sensitive to this noise. In this article, we explore several methods of accounting for noise when analyzing biological data sets through clustering. Using a toy data set and two different case studies-gene expression and protein phosphorylation-we demonstrate the sensitivity of clustering algorithms to noise. Several methods of accounting for this noise can be used to establish when clustering results can be trusted. These methods span a range of assumptions about the statistical properties of the noise and can therefore be applied to virtually any biological data source. '),
(68731, 2013, 'bib', 'Tools for the functional interpretation of metabolomic experiments', 'Chagoyen, Monica|Pazos, Florencio', 'National Center for Biotechnology (CNB-CSIC), Darwin 3. 28049 Madrid, Spain. pazos@cnb.csic.es.|National Center for Biotechnology (CNB-CSIC), Darwin 3. 28049 Madrid, Spain. pazos@cnb.csic.es.', 'Spain|Spain', 38, 'Magnetic Resonance Spectroscopy|Mass Spectrometry|Metabolomics', 'Journal Article', '10.1093/bib/bbs055', '23063930', 'The so-called omics approaches used in modern biology aim at massively characterizing the molecular repertories of living systems at different levels. Metabolomics is one of the last additions to the omics family and it deals with the characterization of the set of metabolites in a given biological system. As metabolomic techniques become more massive and allow characterizing larger sets of metabolites, automatic methods for analyzing these sets in order to obtain meaningful biological information are required. Only recently the first tools specifically designed for this task in metabolomics appeared. They are based on approaches previously used in transcriptomics and other omics, such as annotation enrichment analysis. These, together with generic tools for metabolic analysis and visualization not specifically designed for metabolomics will for sure be in the toolbox of the researches doing metabolomic experiments in the near future. '),
(68732, 2013, 'bib', 'A guide to in silico vaccine discovery for eukaryotic pathogens', 'Goodswen, Stephen J|Kennedy, Paul J|Ellis, John T', 'School of Medical and Molecular Sciences, Ithree Institute, University of Technology Sydney. Tel.: +61 2 9514 4161, John.Ellis@uts.edu.au .|School of Medical and Molecular Sciences, Ithree Institute, University of Technology Sydney. Tel.: +61 2 9514 4161, John.Ellis@uts.edu.au .|School of Medical and Molecular Sciences, Ithree Institute, University of Technology Sydney. Tel.: +61 2 9514 4161, John.Ellis@uts.edu.au .', '-|-|-', 16, 'Computer Simulation|Eukaryotic Cells|Vaccines', 'Journal Article', '10.1093/bib/bbs066', '23097412', 'In this article, a framework for an in silico pipeline is presented as a guide to high-throughput vaccine candidate discovery for eukaryotic pathogens, such as helminths and protozoa. Eukaryotic pathogens are mostly parasitic and cause some of the most damaging and difficult to treat diseases in humans and livestock. Consequently, these parasitic pathogens have a significant impact on economy and human health. The pipeline is based on the principle of reverse vaccinology and is constructed from freely available bioinformatics programs. There are several successful applications of reverse vaccinology to the discovery of subunit vaccines against prokaryotic pathogens but not yet against eukaryotic pathogens. The overriding aim of the pipeline, which focuses on eukaryotic pathogens, is to generate through computational processes of elimination and evidence gathering a ranked list of proteins based on a scoring system. These proteins are either surface components of the target pathogen or are secreted by the pathogen and are of a type known to be antigenic. No perfect predictive method is yet available, therefore, the highest-scoring proteins from the list require laboratory validation. ');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(68733, 2014, 'bib', 'Structural mapping: how to study the genetic architecture of a phenotypic trait through its formation mechanism', 'Tong, Chunfa|Shen, Lianying|Lv, Yafei|Wang, Zhong|Wang, Xiaoling|Feng, Sisi|Li, Xin|Sui, Yihan|Pang, Xiaoming|Wu, Rongling', '*These authors contributed equally to this work.|*These authors contributed equally to this work.|*These authors contributed equally to this work.|*These authors contributed equally to this work.|*These authors contributed equally to this work.|*These authors contributed equally to this work.|*These authors contributed equally to this work.|*These authors contributed equally to this work.|*These authors contributed equally to this work.|*These authors contributed equally to this work.', '-|-|-|-|-|-|-|-|-|-', 3, 'Acclimatization|Algorithms|Chromosome Mapping|Computational Biology|Computer Simulation|Crosses, Genetic|Dielectric Spectroscopy|Genetic Association Studies|Genome, Plant|Likelihood Functions|Models, Genetic|Quantitative Trait Loci|Regression Analysis|Ziziphus', 'Journal Article', '10.1093/bib/bbs067', '23104859', 'Traditional approaches for genetic mapping are to simply associate the genotypes of a quantitative trait locus (QTL) with the phenotypic variation of a complex trait. A more mechanistic strategy has emerged to dissect the trait phenotype into its structural components and map specific QTLs that control the mechanistic and structural formation of a complex trait. We describe and assess such a strategy, called structural mapping, by integrating the internal structural basis of trait formation into a QTL mapping framework. Electrical impedance spectroscopy (EIS) has been instrumental for describing the structural components of a phenotypic trait and their interactions. By building robust mathematical models on circuit EIS data and embedding these models within a mixture model-based likelihood for QTL mapping, structural mapping implements the EM algorithm to obtain maximum likelihood estimates of QTL genotype-specific EIS parameters. The uniqueness of structural mapping is to make it possible to test a number of hypotheses about the pattern of the genetic control of structural components. We validated structural mapping by analyzing an EIS data collected for QTL mapping of frost hardiness in a controlled cross of jujube trees. The statistical properties of parameter estimates were examined by simulation studies. Structural mapping can be a powerful alternative for genetic mapping of complex traits by taking account into the biological and physical mechanisms underlying their formation. '),
(68734, 2014, 'bib', 'Software applications for flux balance analysis', 'Lakshmanan, Meiyappan|Koh, Geoffrey|Chung, Bevan K S|Lee, Dong-Yup', 'Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117576, Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01, Centros, Singapore 138668. Tel.: +65-6516-6907, Fax: +65-6779-1936, cheld@nus.edu.sg.|Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117576, Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01, Centros, Singapore 138668. Tel.: +65-6516-6907, Fax: +65-6779-1936, cheld@nus.edu.sg.|Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117576, Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01, Centros, Singapore 138668. Tel.: +65-6516-6907, Fax: +65-6779-1936, cheld@nus.edu.sg.|Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117576, Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01, Centros, Singapore 138668. Tel.: +65-6516-6907, Fax: +65-6779-1936, cheld@nus.edu.sg.', 'Singapore|Singapore|Singapore|Singapore', 55, 'Algorithms|Computational Biology|Computer Simulation|Genomics|Metabolic Flux Analysis|Models, Biological|Models, Genetic|Phenotype|Software|User-Computer Interface', 'Journal Article', '10.1093/bib/bbs069', '23131418', 'Flux balance analysis (FBA) is a widely used computational method for characterizing and engineering intrinsic cellular metabolism. The increasing number of its successful applications and growing popularity are possibly attributable to the availability of specific software tools for FBA. Each tool has its unique features and limitations with respect to operational environment, user-interface and supported analysis algorithms. Presented herein is an in-depth evaluation of currently available FBA applications, focusing mainly on usability, functionality, graphical representation and inter-operability. Overall, most of the applications are able to perform basic features of model creation and FBA simulation. COBRA toolbox, OptFlux and FASIMU are versatile to support advanced in silico algorithms to identify environmental and genetic targets for strain design. SurreyFBA, WEbcoli, Acorn, FAME, GEMSiRV and MetaFluxNet are the distinct tools which provide the user friendly interfaces in model handling. In terms of software architecture, FBA-SimVis and OptFlux have the flexible environments as they enable the plug-in/add-on feature to aid prospective functional extensions. Notably, an increasing trend towards the implementation of more tailored e-services such as central model repository and assistance to collaborative efforts was observed among the web-based applications with the help of advanced web-technologies. Furthermore, most recent applications such as the Model SEED, FAME, MetaFlux and MicrobesFlux have even included several routines to facilitate the reconstruction of genome-scale metabolic models. Finally, a brief discussion on the future directions of FBA applications was made for the benefit of potential tool developers. '),
(68735, 2014, 'bib', 'Software applications toward quantitative metabolic flux analysis and modeling', 'Dandekar, Thomas|Fieselmann, Astrid|Majeed, Saman|Ahmed, Zeeshan', 'Department of Bioinformatics, Biocenter, University of Wüerzburg, Am Hubland, 97074 Wuerzburg, Germany. Tel.: +49-931-318-4551, Fax: +49-931-318-4552, dandekar@biozentrum.uni-wuerzburg.de.|Department of Bioinformatics, Biocenter, University of Wüerzburg, Am Hubland, 97074 Wuerzburg, Germany. Tel.: +49-931-318-4551, Fax: +49-931-318-4552, dandekar@biozentrum.uni-wuerzburg.de.|Department of Bioinformatics, Biocenter, University of Wüerzburg, Am Hubland, 97074 Wuerzburg, Germany. Tel.: +49-931-318-4551, Fax: +49-931-318-4552, dandekar@biozentrum.uni-wuerzburg.de.|Department of Bioinformatics, Biocenter, University of Wüerzburg, Am Hubland, 97074 Wuerzburg, Germany. Tel.: +49-931-318-4551, Fax: +49-931-318-4552, dandekar@biozentrum.uni-wuerzburg.de.', 'Germany|Germany|Germany|Germany', 28, 'Algorithms|Computational Biology|Computer Simulation|Kinetics|Metabolic Flux Analysis|Metabolic Networks and Pathways|Models, Biological|Software', 'Journal Article', '10.1093/bib/bbs065', '23142828', 'Metabolites and their pathways are central for adaptation and survival. Metabolic modeling elucidates in silico all the possible flux pathways (flux balance analysis, FBA) and predicts the actual fluxes under a given situation, further refinement of these models is possible by including experimental isotopologue data. In this review, we initially introduce the key theoretical concepts and different analysis steps in the modeling process before comparing flux calculation and metabolite analysis programs such as C13, BioOpt, COBRA toolbox, Metatool, efmtool, FiatFlux, ReMatch, VANTED, iMAT and YANA. Their respective strengths and limitations are discussed and compared to alternative software. While data analysis of metabolites, calculation of metabolic fluxes, pathways and their condition-specific changes are all possible, we highlight the considerations that need to be taken into account before deciding on a specific software. Current challenges in the field include the computation of large-scale networks (in elementary mode analysis), regulatory interactions and detailed kinetics, and these are discussed in the light of powerful new approaches. '),
(68736, 2013, 'bib', 'State-of-the-art technology in modern computer-aided drug design', 'Dalkas, Georgios A|Vlachakis, Dimitrios|Tsagkrasoulis, Dimosthenis|Kastania, Anastasia|Kossida, Sophia', '*These authors contributed equally to this work.|*These authors contributed equally to this work.|*These authors contributed equally to this work.|*These authors contributed equally to this work.|*These authors contributed equally to this work.', '-|-|-|-|-', 23, 'Computer-Aided Design|Drug Design', 'Journal Article', '10.1093/bib/bbs063', '23148324', 'The quest for small drug-like compounds that selectively inhibit the function of biological targets has always been a major focus in the pharmaceutical industry and in academia as well. High-throughput screening of compound libraries requires time, cost and resources. Therefore, the use of alternative methods is necessary for facilitating lead discovery. Computational techniques that dock small molecules into macromolecular targets and predict the affinity and activity of the small molecule are widely used in drug design and discovery, and have become an integral part of the industrial and academic research. In this review, we present an overview of some state-of-the-art technologies in modern drug design that have been developed for expediting the search for novel drug candidates. '),
(68737, 2014, 'bib', 'Reconciliation of metabolites and biochemical reactions for metabolic networks', 'Bernard, Thomas|Bridge, Alan|Morgat, Anne|Moretti, Sébastien|Xenarios, Ioannis|Pagni, Marco', 'Corresponding author. Marco Pagni.|Corresponding author. Marco Pagni.|Corresponding author. Marco Pagni.|Corresponding author. Marco Pagni.|Corresponding author. Marco Pagni.|Corresponding author. Marco Pagni.', '-|-|-|-|-|-', 36, 'Computational Biology|Computer Simulation|Databases, Factual|Genomics|Metabolic Networks and Pathways|Models, Biological|Molecular Structure|Software', 'Journal Article', '10.1093/bib/bbs058', '23172809', 'Genome-scale metabolic network reconstructions are now routinely used in the study of metabolic pathways, their evolution and design. The development of such reconstructions involves the integration of information on reactions and metabolites from the scientific literature as well as public databases and existing genome-scale metabolic models. The reconciliation of discrepancies between data from these sources generally requires significant manual curation, which constitutes a major obstacle in efforts to develop and apply genome-scale metabolic network reconstructions. In this work, we discuss some of the major difficulties encountered in the mapping and reconciliation of metabolic resources and review three recent initiatives that aim to accelerate this process, namely BKM-react, MetRxn and MNXref (presented in this article). Each of these resources provides a pre-compiled reconciliation of many of the most commonly used metabolic resources. By reducing the time required for manual curation of metabolite and reaction discrepancies, these resources aim to accelerate the development and application of high-quality genome-scale metabolic network reconstructions and models. '),
(68738, 2014, 'bib', 'Identifying miRNAs, targets and functions', 'Liu, Bing|Li, Jiuyong|Cairns, Murray J', 'School of Biomedical Sciences and Pharmacy, The University of Newcastle, University Drive, Callaghan NSW 2308, Australia. murray.cairns@newcastle.edu.au.|School of Biomedical Sciences and Pharmacy, The University of Newcastle, University Drive, Callaghan NSW 2308, Australia. murray.cairns@newcastle.edu.au.|School of Biomedical Sciences and Pharmacy, The University of Newcastle, University Drive, Callaghan NSW 2308, Australia. murray.cairns@newcastle.edu.au.', 'Australia|Australia|Australia', 55, '3 Untranslated Regions|Algorithms|Animals|Binding Sites|Computational Biology|Databases, Nucleic Acid|Gene Expression Regulation|Humans|MicroRNAs|Models, Genetic|Molecular Sequence Annotation|RNA Interference', 'Journal Article', '10.1093/bib/bbs075', '23175680', 'microRNAs (miRNAs) are small endogenous non-coding RNAs that function as the universal specificity factors in post-transcriptional gene silencing. Discovering miRNAs, identifying their targets and further inferring miRNA functions have been a critical strategy for understanding normal biological processes of miRNAs and their roles in the development of disease. In this review, we focus on computational methods of inferring miRNA functions, including miRNA functional annotation and inferring miRNA regulatory modules, by integrating heterogeneous data sources. We also briefly introduce the research in miRNA discovery and miRNA-target identification with an emphasis on the challenges to computational biology. '),
(68739, 2012, 'bib', 'Bioinformatics approaches and tools for metagenomic analysis Editorial', 'Valiente, Gabriel|Pesole, Graziano', 'School of Biomedical Sciences and Pharmacy, The University of Newcastle, University Drive, Callaghan NSW 2308, Australia. murray.cairns@newcastle.edu.au.|School of Biomedical Sciences and Pharmacy, The University of Newcastle, University Drive, Callaghan NSW 2308, Australia. murray.cairns@newcastle.edu.au.', 'Australia|Australia', 0, 'Computational Biology|Metagenome|Metagenomics', 'Editorial', '10.1093/bib/bbs079', '23175747', '-'),
(68740, 2012, 'bib', 'Bioinformatic approaches for functional annotation and pathway inference in metagenomics data', 'De Filippo, Carlotta|Ramazzotti, Matteo|Fontana, Paolo|Cavalieri, Duccio', 'CRI-FEM, Trento, Italy.|CRI-FEM, Trento, Italy.|CRI-FEM, Trento, Italy.|CRI-FEM, Trento, Italy.', 'Italy|Italy|Italy|Italy', 46, 'Metagenome|Metagenomics|Molecular Sequence Annotation|Phenotype', 'Journal Article', '10.1093/bib/bbs070', '23175748', 'Metagenomic approaches are increasingly recognized as a baseline for understanding the ecology and evolution of microbial ecosystems. The development of methods for pathway inference from metagenomics data is of paramount importance to link a phenotype to a cascade of events stemming from a series of connected sets of genes or proteins. Biochemical and regulatory pathways have until recently been thought and modelled within one cell type, one organism, one species. This vision is being dramatically changed by the advent of whole microbiome sequencing studies, revealing the role of symbiotic microbial populations in fundamental biochemical functions. The new landscape we face requires a clear picture of the potentialities of existing tools and development of new tools to characterize, reconstruct and model biochemical and regulatory pathways as the result of integration of function in complex symbiotic interactions of ontologically and evolutionary distinct cell types.'),
(68741, 2014, 'bib', 'Alpha shape and Delaunay triangulation in studies of protein-related interactions', 'Zhou, Weiqiang|Yan, Hong', 'Department of Electronic Engineering, City University of Hong Kong, Tat Chee Avenue 83, Hong Kong. kenandzhou@hotmail.com.|Department of Electronic Engineering, City University of Hong Kong, Tat Chee Avenue 83, Hong Kong. kenandzhou@hotmail.com.', 'Hong Kong|Hong Kong', 12, 'Algorithms|Binding Sites|Computational Biology|DNA|Ligands|Models, Molecular|Protein Binding|Protein Conformation|Protein Folding|Protein Interaction Domains and Motifs|Proteins|Structural Homology, Protein', 'Journal Article', '10.1093/bib/bbs077', '23193202', 'In recent years, more 3D protein structures have become available, which has made the analysis of large molecular structures much easier. There is a strong demand for geometric models for the study of protein-related interactions. Alpha shape and Delaunay triangulation are powerful tools to represent protein structures and have advantages in characterizing the surface curvature and atom contacts. This review presents state-of-the-art applications of alpha shape and Delaunay triangulation in the studies on protein-DNA, protein-protein, protein-ligand interactions and protein structure analysis. '),
(68742, 2013, 'bib', 'Comparability and reproducibility of biomedical data', 'Huang, Yunda|Gottardo, Raphael', 'Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, Mailstop M2-C200, Seattle, WA 98109-1024, USA.|Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, Mailstop M2-C200, Seattle, WA 98109-1024, USA.', 'United States|United States', 18, 'Biomedical Research|Computational Biology|Meta-Analysis as Topic|Peer Review, Research|Reproducibility of Results', 'Comparative Study', '10.1093/bib/bbs078', '23193203', 'With the development of novel assay technologies, biomedical experiments and analyses have gone through substantial evolution. Today, a typical experiment can simultaneously measure hundreds to thousands of individual features (e.g. genes) in dozens of biological conditions, resulting in gigabytes of data that need to be processed and analyzed. Because of the multiple steps involved in the data generation and analysis and the lack of details provided, it can be difficult for independent researchers to try to reproduce a published study. With the recent outrage following the halt of a cancer clinical trial due to the lack of reproducibility of the published study, researchers are now facing heavy pressure to ensure that their results are reproducible. Despite the global demand, too many published studies remain non-reproducible mainly due to the lack of availability of experimental protocol, data and/or computer code. Scientific discovery is an iterative process, where a published study generates new knowledge and data, resulting in new follow-up studies or clinical trials based on these results. As such, it is important for the results of a study to be quickly confirmed or discarded to avoid wasting time and money on novel projects. The availability of high-quality, reproducible data will also lead to more powerful analyses (or meta-analyses) where multiple data sets are combined to generate new knowledge. In this article, we review some of the recent developments regarding biomedical reproducibility and comparability and discuss some of the areas where the overall field could be improved. '),
(68743, 2014, 'bib', 'Knowledge representation in metabolic pathway databases', 'Stobbe, Miranda D|Jansen, Gerbert A|Moerland, Perry D|van Kampen, Antoine H C', 'Bioinformatics Laboratory, Academic Medical Center, PO Box 22700, 1100 DE Amsterdam, the Netherlands. Tel.: ++31 20 5667096, a.h.vankampen@amc.uva.nl.|Bioinformatics Laboratory, Academic Medical Center, PO Box 22700, 1100 DE Amsterdam, the Netherlands. Tel.: ++31 20 5667096, a.h.vankampen@amc.uva.nl.|Bioinformatics Laboratory, Academic Medical Center, PO Box 22700, 1100 DE Amsterdam, the Netherlands. Tel.: ++31 20 5667096, a.h.vankampen@amc.uva.nl.|Bioinformatics Laboratory, Academic Medical Center, PO Box 22700, 1100 DE Amsterdam, the Netherlands. Tel.: ++31 20 5667096, a.h.vankampen@amc.uva.nl.', 'Netherlands|Netherlands|Netherlands|Netherlands', 10, 'Artificial Intelligence|Computational Biology|Computer Simulation|Databases, Factual|Enzymes|Fatty Acids|Humans|Metabolic Networks and Pathways', 'Journal Article', '10.1093/bib/bbs060', '23202525', 'The accurate representation of all aspects of a metabolic network in a structured format, such that it can be used for a wide variety of computational analyses, is a challenge faced by a growing number of researchers. Analysis of five major metabolic pathway databases reveals that each database has made widely different choices to address this challenge, including how to deal with knowledge that is uncertain or missing. In concise overviews, we show how concepts such as compartments, enzymatic complexes and the direction of reactions are represented in each database. Importantly, also concepts which a database does not represent are described. Which aspects of the metabolic network need to be available in a structured format and to what detail differs per application. For example, for in silico phenotype prediction, a detailed representation of gene-protein-reaction relations and the compartmentalization of the network is essential. Our analysis also shows that current databases are still limited in capturing all details of the biology of the metabolic network, further illustrated with a detailed analysis of three metabolic processes. Finally, we conclude that the conceptual differences between the databases, which make knowledge exchange and integration a challenge, have not been resolved, so far, by the exchange formats in which knowledge representation is standardized. '),
(68744, 2014, 'bib', 'Experimental evidence validating the computational inference of functional associations from gene fusion events: a critical survey', 'Promponas, Vasilis J|Ouzounis, Christos A|Iliopoulos, Ioannis', 'Institute of Agrobiotechnology, Centre for Research & Technology Hellas (CERTH), 57001 Thessaloniki, Greece. ouzounis@certh.gr.|Institute of Agrobiotechnology, Centre for Research & Technology Hellas (CERTH), 57001 Thessaloniki, Greece. ouzounis@certh.gr.|Institute of Agrobiotechnology, Centre for Research & Technology Hellas (CERTH), 57001 Thessaloniki, Greece. ouzounis@certh.gr.', 'Greece|Greece|Greece', 10, 'Animals|Computational Biology|Gene Fusion|Genes, Fungal|Genome, Human|Genomics|Humans|Phylogeny|Protein Interaction Mapping|Proteomics|Saccharomyces cerevisiae', 'Journal Article', '10.1093/bib/bbs072', '23220349', 'More than a decade ago, a number of methods were proposed for the inference of protein interactions, using whole-genome information from gene clusters, gene fusions and phylogenetic profiles. This structural and evolutionary view of entire genomes has provided a valuable approach for the functional characterization of proteins, especially those without sequence similarity to proteins of known function. Furthermore, this view has raised the real possibility to detect functional associations of genes and their corresponding proteins for any entire genome sequence. Yet, despite these exciting developments, there have been relatively few cases of real use of these methods outside the computational biology field, as reflected from citation analysis. These methods have the potential to be used in high-throughput experimental settings in functional genomics and proteomics to validate results with very high accuracy and good coverage. In this critical survey, we provide a comprehensive overview of 30 most prominent examples of single pairwise protein interaction cases in small-scale studies, where protein interactions have either been detected by gene fusion or yielded additional, corroborating evidence from biochemical observations. Our conclusion is that with the derivation of a validated gold-standard corpus and better data integration with big experiments, gene fusion detection can truly become a valuable tool for large-scale experimental biology. '),
(68745, 2014, 'bib', 'Architecture for interoperable software in biology', 'Bare, James Christopher|Baliga, Nitin S', 'Institute of Agrobiotechnology, Centre for Research & Technology Hellas (CERTH), 57001 Thessaloniki, Greece. ouzounis@certh.gr.|Institute of Agrobiotechnology, Centre for Research & Technology Hellas (CERTH), 57001 Thessaloniki, Greece. ouzounis@certh.gr.', 'Greece|Greece', 2, 'Biology|Software|Systems Biology', 'Journal Article', '10.1093/bib/bbs074', '23235920', 'Understanding biological complexity demands a combination of high-throughput data and interdisciplinary skills. One way to bring to bear the necessary combination of data types and expertise is by encapsulating domain knowledge in software and composing that software to create a customized data analysis environment. To this end, simple flexible strategies are needed for interconnecting heterogeneous software tools and enabling data exchange between them. Drawing on our own work and that of others, we present several strategies for interoperability and their consequences, in particular, a set of simple data structures--list, matrix, network, table and tuple--that have proven sufficient to achieve a high degree of interoperability. We provide a few guidelines for the development of future software that will function as part of an interoperable community of software tools for biological data analysis and visualization.'),
(68746, 2014, 'bib', 'Network information improves cancer outcome prediction', 'Roy, Janine|Winter, Christof|Isik, Zerrin|Schroeder, Michael', 'Institute of Agrobiotechnology, Centre for Research & Technology Hellas (CERTH), 57001 Thessaloniki, Greece. ouzounis@certh.gr.|Institute of Agrobiotechnology, Centre for Research & Technology Hellas (CERTH), 57001 Thessaloniki, Greece. ouzounis@certh.gr.|Institute of Agrobiotechnology, Centre for Research & Technology Hellas (CERTH), 57001 Thessaloniki, Greece. ouzounis@certh.gr.|Institute of Agrobiotechnology, Centre for Research & Technology Hellas (CERTH), 57001 Thessaloniki, Greece. ouzounis@certh.gr.', 'Greece|Greece|Greece|Greece', 16, 'Algorithms|Biomarkers, Tumor|Disease Progression|Humans|Neoplasms', 'Journal Article', '10.1093/bib/bbs083', '23255167', 'Disease progression in cancer can vary substantially between patients. Yet, patients often receive the same treatment. Recently, there has been much work on predicting disease progression and patient outcome variables from gene expression in order to personalize treatment options. Despite first diagnostic kits in the market, there are open problems such as the choice of random gene signatures or noisy expression data. One approach to deal with these two problems employs protein-protein interaction networks and ranks genes using the random surfer model of Googles PageRank algorithm. In this work, we created a benchmark dataset collection comprising 25 cancer outcome prediction datasets from literature and systematically evaluated the use of networks and a PageRank derivative, NetRank, for signature identification. We show that the NetRank performs significantly better than classical methods such as fold change or t-test. Despite an order of magnitude difference in network size, a regulatory and protein-protein interaction network perform equally well. Experimental evaluation on cancer outcome prediction in all of the 25 underlying datasets suggests that the network-based methodology identifies highly overlapping signatures over all cancer types, in contrast to classical methods that fail to identify highly common gene sets across the same cancer types. Integration of network information into gene expression analysis allows the identification of more reliable and accurate biomarkers and provides a deeper understanding of processes occurring in cancer development and progression. '),
(68747, 2014, 'bib', 'A survey on annotation tools for the biomedical literature', 'Neves, Mariana|Leser, Ulf', 'Department of Computer Science, Humboldt-Universität zu Berlin, Rudower Chaussee 25, 12489 Berlin, Germany. neves@informatik.hu-berlin.de.|Department of Computer Science, Humboldt-Universität zu Berlin, Rudower Chaussee 25, 12489 Berlin, Germany. neves@informatik.hu-berlin.de.', 'Germany|Germany', 19, 'Algorithms|Artificial Intelligence|Computational Biology|Data Mining|Humans|Natural Language Processing|Publications|Software', 'Journal Article', '10.1093/bib/bbs084', '23255168', 'New approaches to biomedical text mining crucially depend on the existence of comprehensive annotated corpora. Such corpora, commonly called gold standards, are important for learning patterns or models during the training phase, for evaluating and comparing the performance of algorithms and also for better understanding the information sought for by means of examples. Gold standards depend on human understanding and manual annotation of natural language text. This process is very time-consuming and expensive because it requires high intellectual effort from domain experts. Accordingly, the lack of gold standards is considered as one of the main bottlenecks for developing novel text mining methods. This situation led the development of tools that support humans in annotating texts. Such tools should be intuitive to use, should support a range of different input formats, should include visualization of annotated texts and should generate an easy-to-parse output format. Today, a range of tools which implement some of these functionalities are available. In this survey, we present a comprehensive survey of tools for supporting annotation of biomedical texts. Altogether, we considered almost 30 tools, 13 of which were selected for an in-depth comparison. The comparison was performed using predefined criteria and was accompanied by hands-on experiences whenever possible. Our survey shows that current tools can support many of the tasks in biomedical text annotation in a satisfying manner, but also that no tool can be considered as a true comprehensive solution. '),
(68748, 2014, 'bib', 'Benchmarking of viral haplotype reconstruction programmes: an overview of the capacities and limitations of currently available programmes', 'Schirmer, Melanie|Sloan, William T|Quince, Christopher', 'University of Glasgow, Rankine Building, Oakfield Avenue, Glasgow G12 8LT, UK. Tel.: +44-141-330-6311. m.schirmer.1@research.gla.ac.uk.|University of Glasgow, Rankine Building, Oakfield Avenue, Glasgow G12 8LT, UK. Tel.: +44-141-330-6311. m.schirmer.1@research.gla.ac.uk.|University of Glasgow, Rankine Building, Oakfield Avenue, Glasgow G12 8LT, UK. Tel.: +44-141-330-6311. m.schirmer.1@research.gla.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom', 39, 'Animals|Computational Biology|Computer Simulation|Databases, Nucleic Acid|Evolution, Molecular|Foot-and-Mouth Disease Virus|HIV-1|Haplotypes|High-Throughput Nucleotide Sequencing|Humans|Polymorphism, Single Nucleotide|RNA Viruses|Software', 'Journal Article', '10.1093/bib/bbs081', '23257116', 'Viral haplotype reconstruction from a set of observed reads is one of the most challenging problems in bioinformatics today. Next-generation sequencing technologies enable us to detect single-nucleotide polymorphisms (SNPs) of haplotypes-even if the haplotypes appear at low frequencies. However, there are two major problems. First, we need to distinguish real SNPs from sequencing errors. Second, we need to determine which SNPs occur on the same haplotype, which cannot be inferred from the reads if the distance between SNPs on a haplotype exceeds the read length. We conducted an independent benchmarking study that directly compares the currently available viral haplotype reconstruction programmes. We also present nine in silico data sets that we generated to reflect biologically plausible populations. For these data sets, we simulated 454 and Illumina reads and applied the programmes to test their capacity to reconstruct whole genomes and individual genes. We developed a novel statistical framework to demonstrate the strengths and limitations of the programmes. Our benchmarking demonstrated that all the programmes we tested performed poorly when sequence divergence was low and failed to recover haplotype populations with rare haplotypes. '),
(68749, 2014, 'bib', 'A statistical design for testing apomictic diversification through linkage analysis', 'Zeng, Yanru|Hou, Wei|Song, Shuang|Feng, Sisi|Shen, Lin|Xia, Guohua|Wu, Rongling', 'The Nurturing Station for the State Key Laboratory of Subtropical Silviculture, Zhejiang Agricultural and Forestry University, Linan, Zhejiang 311300, China, Center for Statistical Genetics, The Pennsylvania State University, Hershey, PA 17033, USA. Tel.: +1 717 531 2037, Fax: +1 717 531 0480, rwu@phs.psu.edu.|The Nurturing Station for the State Key Laboratory of Subtropical Silviculture, Zhejiang Agricultural and Forestry University, Linan, Zhejiang 311300, China, Center for Statistical Genetics, The Pennsylvania State University, Hershey, PA 17033, USA. Tel.: +1 717 531 2037, Fax: +1 717 531 0480, rwu@phs.psu.edu.|The Nurturing Station for the State Key Laboratory of Subtropical Silviculture, Zhejiang Agricultural and Forestry University, Linan, Zhejiang 311300, China, Center for Statistical Genetics, The Pennsylvania State University, Hershey, PA 17033, USA. Tel.: +1 717 531 2037, Fax: +1 717 531 0480, rwu@phs.psu.edu.|The Nurturing Station for the State Key Laboratory of Subtropical Silviculture, Zhejiang Agricultural and Forestry University, Linan, Zhejiang 311300, China, Center for Statistical Genetics, The Pennsylvania State University, Hershey, PA 17033, USA. Tel.: +1 717 531 2037, Fax: +1 717 531 0480, rwu@phs.psu.edu.|The Nurturing Station for the State Key Laboratory of Subtropical Silviculture, Zhejiang Agricultural and Forestry University, Linan, Zhejiang 311300, China, Center for Statistical Genetics, The Pennsylvania State University, Hershey, PA 17033, USA. Tel.: +1 717 531 2037, Fax: +1 717 531 0480, rwu@phs.psu.edu.|The Nurturing Station for the State Key Laboratory of Subtropical Silviculture, Zhejiang Agricultural and Forestry University, Linan, Zhejiang 311300, China, Center for Statistical Genetics, The Pennsylvania State University, Hershey, PA 17033, USA. Tel.: +1 717 531 2037, Fax: +1 717 531 0480, rwu@phs.psu.edu.|The Nurturing Station for the State Key Laboratory of Subtropical Silviculture, Zhejiang Agricultural and Forestry University, Linan, Zhejiang 311300, China, Center for Statistical Genetics, The Pennsylvania State University, Hershey, PA 17033, USA. Tel.: +1 717 531 2037, Fax: +1 717 531 0480, rwu@phs.psu.edu.', 'China|China|China|China|China|China|China', 4, 'Algorithms|Apomixis|Breeding|Carya|Computational Biology|Genetic Linkage|Genetic Variation|Models, Genetic|Models, Statistical|Plant Physiological Phenomena|Plants', 'Journal Article', '10.1093/bib/bbs080', '23271157', 'The capacity of apomixis to generate maternal clones through seed reproduction has made it a useful characteristic for the fixation of heterosis in plant breeding. It has been observed that apomixis displays pronounced intra- and interspecific diversification, but the genetic mechanisms underlying this diversification remains elusive, obstructing the exploitation of this phenomenon in practical breeding programs. By capitalizing on molecular information in mapping populations, we describe and assess a statistical design that deploys linkage analysis to estimate and test the pattern and extent of apomictic differences at various levels from genotypes to species. The design is based on two reciprocal crosses between two individuals each chosen from a hermaphrodite or monoecious species. A multinomial distribution likelihood is constructed by combining marker information from two crosses. The EM algorithm is implemented to estimate the rate of apomixis and test its difference between two plant populations or species as the parents. The design is validated by computer simulation. A real data analysis of two reciprocal crosses between hickory (Carya cathayensis) and pecan (C. illinoensis) demonstrates the utilization and usefulness of the design in practice. The design provides a tool to address fundamental and applied questions related to the evolution and breeding of apomixis. '),
(68750, 2014, 'bib', 'Reconsideration of in silico siRNA design from a perspective of heterogeneous data integration: problems and solutions', 'Liu, Qi|Zhou, Han|Zhu, Ruixin|Xu, Ying|Cao, Zhiwei', 'Department of Biochemistry and Molecular Biology A110, Life Science Building, 120 Green Street, University of Georgia, Athens, GA 30602-7229, USA. Tel.: +706-542-9779, Fax: +706-542-9751/7782, xyn@bmb.uga.edu, Zhiwei Cao, School of Life Sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 200092, China. Tel.: +86-21-54065003, Fax: +86-21-65980296, E-mail: zwcao@tongji.edu.cn.|Department of Biochemistry and Molecular Biology A110, Life Science Building, 120 Green Street, University of Georgia, Athens, GA 30602-7229, USA. Tel.: +706-542-9779, Fax: +706-542-9751/7782, xyn@bmb.uga.edu, Zhiwei Cao, School of Life Sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 200092, China. Tel.: +86-21-54065003, Fax: +86-21-65980296, E-mail: zwcao@tongji.edu.cn.|Department of Biochemistry and Molecular Biology A110, Life Science Building, 120 Green Street, University of Georgia, Athens, GA 30602-7229, USA. Tel.: +706-542-9779, Fax: +706-542-9751/7782, xyn@bmb.uga.edu, Zhiwei Cao, School of Life Sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 200092, China. Tel.: +86-21-54065003, Fax: +86-21-65980296, E-mail: zwcao@tongji.edu.cn.|Department of Biochemistry and Molecular Biology A110, Life Science Building, 120 Green Street, University of Georgia, Athens, GA 30602-7229, USA. Tel.: +706-542-9779, Fax: +706-542-9751/7782, xyn@bmb.uga.edu, Zhiwei Cao, School of Life Sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 200092, China. Tel.: +86-21-54065003, Fax: +86-21-65980296, E-mail: zwcao@tongji.edu.cn.|Department of Biochemistry and Molecular Biology A110, Life Science Building, 120 Green Street, University of Georgia, Athens, GA 30602-7229, USA. Tel.: +706-542-9779, Fax: +706-542-9751/7782, xyn@bmb.uga.edu, Zhiwei Cao, School of Life Sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 200092, China. Tel.: +86-21-54065003, Fax: +86-21-65980296, E-mail: zwcao@tongji.edu.cn.', 'China|China|China|China|China', 6, 'Artificial Intelligence|Computational Biology|Computer Simulation|Computer-Aided Design|Genetic Techniques|Humans|RNA Interference|RNA, Small Interfering', 'Journal Article', '10.1093/bib/bbs073', '23275634', 'The success of RNA interference (RNAi) depends on the interaction between short interference RNAs (siRNAs) and mRNAs. Design of highly efficient and specific siRNAs has become a challenging issue in applications of RNAi. Here, we present a detailed survey on the state-of-the-art siRNAs design, focusing on several key issues with the current in silico RNAi studies, including: (i) inconsistencies among the proposed guidelines for siRNAs design and the incomplete list of siRNAs features, (ii) improper integration of the heterogeneous cross-platform siRNAs data, (iii) inadequate consideration of the binding specificity of the target mRNAs and (iv) reduction in the off-target effect in siRNAs design. With these considerations, the popular in silico siRNAs design rules are reexamined and several inconsistent viewpoints toward siRNAs feature identifications are clarified. In addition, novel computational models for siRNAs design using state-of-art machine learning techniques are discussed, which focus on heterogeneous data integration, joint feature selection and customized siRNAs screening toward highly specific targets. We believe that addressing such issues in siRNA study will provide new clues for further improved design of more efficient and specific siRNAs in RNAi. '),
(68751, 2014, 'bib', 'Boosting signals in gene-based association studies via efficient SNP selection', 'Wu, Cen|Cui, Yuehua', 'Department of Statistics and Probability, Michigan State University, 619 Red Cedar Road, Rm C432, East Lansing, MI 48824, USA. Tel.: +1-517-432-7098, Fax: +1-517-432-1405, cui@stt.msu.edu.|Department of Statistics and Probability, Michigan State University, 619 Red Cedar Road, Rm C432, East Lansing, MI 48824, USA. Tel.: +1-517-432-7098, Fax: +1-517-432-1405, cui@stt.msu.edu.', 'United States|United States', 10, 'Collagen Type I|Computational Biology|Computer Simulation|Disease|Genome-Wide Association Study|Humans|Infant, Newborn|Infant, Small for Gestational Age|Information Theory|Linkage Disequilibrium|Models, Genetic|Models, Statistical|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bib/bbs087', '23325548', 'Set-based association studies based on genes or pathways have shown great promise in interpreting association signals associated with complex diseases. These approaches are particularly useful when variants in a set have moderate effects and are difficult to be detected with single marker analysis, especially when variants function jointly in a complicated manner. The set-based analyses use a summary statistic such as the maximum or average of individual signal (e.g. a chi-square statistic) over all variants in a set, or consider their joint distribution to assess the significance of the set. The signal obtained with this treatment, however, could be potentially diluted when noisy variants are not taken good care of, leading to either inflated false negatives or false positives. Thus, the selection of disease informative single-nucleotide polymorphism (diSNPs) plays a crucial role in improving the power of the set-based association study. In this work, we propose an efficient diSNP selection method based on the information theory. We select diSNP variants by considering their relative information contribution to a disease status, which is different from the usual tag SNP selection. The relative merit of pre-selecting diSNPs in a set-based association analysis is demonstrated through extensive simulation studies and real data analysis. '),
(68752, 2014, 'bib', 'A case-control design for testing and estimating epigenetic effects on complex diseases', 'Sui, Yihan|Wu, Weimiao|Wang, Zhong|Wang, Jianxin|Wang, Zuoheng|Wu, Rongling', 'Center for Computational Biology, College of Biological Science and Technology, Beijing Forestry University, Beijing 100083, China. Tel.: +86-10-6233-6283, Fax: +86-10-6233-6164, rwu@bjfu.edu.cn, Center for Statistical Genetics, The Pennsylvania State University, Hershey, PA 17033, USA. Tel.: +1-717-531-2037, Fax: +1-717-531-0480, E-mail: rwu@phs.psu.edu.|Center for Computational Biology, College of Biological Science and Technology, Beijing Forestry University, Beijing 100083, China. Tel.: +86-10-6233-6283, Fax: +86-10-6233-6164, rwu@bjfu.edu.cn, Center for Statistical Genetics, The Pennsylvania State University, Hershey, PA 17033, USA. Tel.: +1-717-531-2037, Fax: +1-717-531-0480, E-mail: rwu@phs.psu.edu.|Center for Computational Biology, College of Biological Science and Technology, Beijing Forestry University, Beijing 100083, China. Tel.: +86-10-6233-6283, Fax: +86-10-6233-6164, rwu@bjfu.edu.cn, Center for Statistical Genetics, The Pennsylvania State University, Hershey, PA 17033, USA. Tel.: +1-717-531-2037, Fax: +1-717-531-0480, E-mail: rwu@phs.psu.edu.|Center for Computational Biology, College of Biological Science and Technology, Beijing Forestry University, Beijing 100083, China. Tel.: +86-10-6233-6283, Fax: +86-10-6233-6164, rwu@bjfu.edu.cn, Center for Statistical Genetics, The Pennsylvania State University, Hershey, PA 17033, USA. Tel.: +1-717-531-2037, Fax: +1-717-531-0480, E-mail: rwu@phs.psu.edu.|Center for Computational Biology, College of Biological Science and Technology, Beijing Forestry University, Beijing 100083, China. Tel.: +86-10-6233-6283, Fax: +86-10-6233-6164, rwu@bjfu.edu.cn, Center for Statistical Genetics, The Pennsylvania State University, Hershey, PA 17033, USA. Tel.: +1-717-531-2037, Fax: +1-717-531-0480, E-mail: rwu@phs.psu.edu.|Center for Computational Biology, College of Biological Science and Technology, Beijing Forestry University, Beijing 100083, China. Tel.: +86-10-6233-6283, Fax: +86-10-6233-6164, rwu@bjfu.edu.cn, Center for Statistical Genetics, The Pennsylvania State University, Hershey, PA 17033, USA. Tel.: +1-717-531-2037, Fax: +1-717-531-0480, E-mail: rwu@phs.psu.edu.', 'China|China|China|China|China|China', 5, 'Case-Control Studies|Chi-Square Distribution|Computer Simulation|Disease|Epigenesis, Genetic|Gene-Environment Interaction|Genome-Wide Association Study|Humans|Models, Genetic|Stochastic Processes', 'Journal Article', '10.1093/bib/bbs085', '23334587', 'Epigenetic modifications may play an important role in the formation and progression of complex diseases through the regulation of gene expression. The systematic identification of epigenetic variants that contribute to human diseases can be made possible using genome-wide association studies (GWAS), although epigenetic effects are currently not included in commonly used case-control designs for GWAS. Here, we show that epigenetic modifications can be integrated into a case-control setting by dissolving the overall genetic effect into its different components, additive, dominant and epigenetic. We describe a general procedure for testing and estimating the significance of each component based on a conventional chi-squared test approach. Simulation studies were performed to investigate the power and false-positive rate of this procedure, providing recommendations for its practical use. The integration of epigenetic variants into GWAS can potentially improve our understanding of how genetic, environmental and stochastic factors interact with epialleles to construct the genetic architecture of complex diseases. '),
(68753, 2013, 'bib', 'Detecting miRNAs in deep-sequencing data: a software performance comparison and evaluation', 'Williamson, Vernell|Kim, Albert|Xie, Bin|McMichael, G Omari|Gao, Yuan|Vladimirov, Vladimir', 'Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Medical College of Virginia of Virginia Commonwealth University, Richmond, VA, USA. vswilliamson@vcu.edu|Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Medical College of Virginia of Virginia Commonwealth University, Richmond, VA, USA. vswilliamson@vcu.edu|Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Medical College of Virginia of Virginia Commonwealth University, Richmond, VA, USA. vswilliamson@vcu.edu|Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Medical College of Virginia of Virginia Commonwealth University, Richmond, VA, USA. vswilliamson@vcu.edu|Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Medical College of Virginia of Virginia Commonwealth University, Richmond, VA, USA. vswilliamson@vcu.edu|Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Medical College of Virginia of Virginia Commonwealth University, Richmond, VA, USA. vswilliamson@vcu.edu', 'United States|United States|United States|United States|United States|United States', 31, 'Algorithms|Animals|Cell Line, Tumor|Computational Biology|Databases, Nucleic Acid|Humans|MicroRNAs|Sequence Analysis, RNA|Software', 'Comparative Study', '10.1093/bib/bbs010', '23334922', 'Deep sequencing has become a popular tool for novel miRNA detection but its data must be viewed carefully as the state of the field is still undeveloped. Using three different programs, miRDeep (v1, 2), miRanalyzer and DSAP, we have analyzed seven data sets (six biological and one simulated) to provide a critical evaluation of the programs performance. We selected these software based on their popularity and overall approach toward the detection of novel and known miRNAs using deep-sequencing data. The program comparisons suggest that, despite differing stringency levels they all identify a similar set of known and novel predictions. Comparisons between the first and second version of miRDeep suggest that the stringency level of each of these programs may, in fact, be a result of the algorithm used to map the reads to the target. Different stringency levels are likely to affect the number of possible novel candidates for functional verification, causing undue strain on resources and time. With that in mind, we propose that an intersection across multiple programs be taken, especially if considering novel candidates that will be targeted for additional analysis. Using this approach, we identify and performed initial validation of 12 novel predictions in our in-house data with real-time PCR, six of which have been previously unreported.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(68754, 2014, 'bib', 'A survey of tools for variant analysis of next-generation genome sequencing data', 'Pabinger, Stephan|Dander, Andreas|Fischer, Maria|Snajder, Rene|Sperk, Michael|Efremova, Mirjana|Krabichler, Birgit|Speicher, Michael R|Zschocke, Johannes|Trajanoski, Zlatko', 'Division for Bioinformatics, Innsbruck Medical University, Innrain 80, 6020 Innsbruck, Austria. Tel.: +43-512-9003-71401, Fax: +43-512-9003-73100, zlatko.trajanoski@i-med.ac.at.|Division for Bioinformatics, Innsbruck Medical University, Innrain 80, 6020 Innsbruck, Austria. Tel.: +43-512-9003-71401, Fax: +43-512-9003-73100, zlatko.trajanoski@i-med.ac.at.|Division for Bioinformatics, Innsbruck Medical University, Innrain 80, 6020 Innsbruck, Austria. Tel.: +43-512-9003-71401, Fax: +43-512-9003-73100, zlatko.trajanoski@i-med.ac.at.|Division for Bioinformatics, Innsbruck Medical University, Innrain 80, 6020 Innsbruck, Austria. Tel.: +43-512-9003-71401, Fax: +43-512-9003-73100, zlatko.trajanoski@i-med.ac.at.|Division for Bioinformatics, Innsbruck Medical University, Innrain 80, 6020 Innsbruck, Austria. Tel.: +43-512-9003-71401, Fax: +43-512-9003-73100, zlatko.trajanoski@i-med.ac.at.|Division for Bioinformatics, Innsbruck Medical University, Innrain 80, 6020 Innsbruck, Austria. Tel.: +43-512-9003-71401, Fax: +43-512-9003-73100, zlatko.trajanoski@i-med.ac.at.|Division for Bioinformatics, Innsbruck Medical University, Innrain 80, 6020 Innsbruck, Austria. Tel.: +43-512-9003-71401, Fax: +43-512-9003-73100, zlatko.trajanoski@i-med.ac.at.|Division for Bioinformatics, Innsbruck Medical University, Innrain 80, 6020 Innsbruck, Austria. Tel.: +43-512-9003-71401, Fax: +43-512-9003-73100, zlatko.trajanoski@i-med.ac.at.|Division for Bioinformatics, Innsbruck Medical University, Innrain 80, 6020 Innsbruck, Austria. Tel.: +43-512-9003-71401, Fax: +43-512-9003-73100, zlatko.trajanoski@i-med.ac.at.|Division for Bioinformatics, Innsbruck Medical University, Innrain 80, 6020 Innsbruck, Austria. Tel.: +43-512-9003-71401, Fax: +43-512-9003-73100, zlatko.trajanoski@i-med.ac.at.', 'Austria|Austria|Austria|Austria|Austria|Austria|Austria|Austria|Austria|Austria', 324, 'Computational Biology|DNA Copy Number Variations|Disease|Exome|Genetic Variation|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Molecular Sequence Annotation|Mutation|Sequence Alignment|Sequence Analysis, DNA|Software', 'Evaluation Studies', '10.1093/bib/bbs086', '23341494', 'Recent advances in genome sequencing technologies provide unprecedented opportunities to characterize individual genomic landscapes and identify mutations relevant for diagnosis and therapy. Specifically, whole-exome sequencing using next-generation sequencing (NGS) technologies is gaining popularity in the human genetics community due to the moderate costs, manageable data amounts and straightforward interpretation of analysis results. While whole-exome and, in the near future, whole-genome sequencing are becoming commodities, data analysis still poses significant challenges and led to the development of a plethora of tools supporting specific parts of the analysis workflow or providing a complete solution. Here, we surveyed 205 tools for whole-genome/whole-exome sequencing data analysis supporting five distinct analytical steps: quality assessment, alignment, variant identification, variant annotation and visualization. We report an overview of the functionality, features and specific requirements of the individual tools. We then selected 32 programs for variant identification, variant annotation and visualization, which were subjected to hands-on evaluation using four data sets: one set of exome data from two patients with a rare disease for testing identification of germline mutations, two cancer data sets for testing variant callers for somatic mutations, copy number variations and structural variations, and one semi-synthetic data set for testing identification of copy number variations. Our comprehensive survey and evaluation of NGS tools provides a valuable guideline for human geneticists working on Mendelian disorders, complex diseases and cancers. '),
(68755, 2014, 'bib', 'Pharmaco-miR: linking microRNAs and drug effects', 'Rukov, Jakob Lewin|Wilentzik, Roni|Jaffe, Ishai|Vinther, Jeppe|Shomron, Noam', 'Division for Bioinformatics, Innsbruck Medical University, Innrain 80, 6020 Innsbruck, Austria. Tel.: +43-512-9003-71401, Fax: +43-512-9003-73100, zlatko.trajanoski@i-med.ac.at.|Division for Bioinformatics, Innsbruck Medical University, Innrain 80, 6020 Innsbruck, Austria. Tel.: +43-512-9003-71401, Fax: +43-512-9003-73100, zlatko.trajanoski@i-med.ac.at.|Division for Bioinformatics, Innsbruck Medical University, Innrain 80, 6020 Innsbruck, Austria. Tel.: +43-512-9003-71401, Fax: +43-512-9003-73100, zlatko.trajanoski@i-med.ac.at.|Division for Bioinformatics, Innsbruck Medical University, Innrain 80, 6020 Innsbruck, Austria. Tel.: +43-512-9003-71401, Fax: +43-512-9003-73100, zlatko.trajanoski@i-med.ac.at.|Division for Bioinformatics, Innsbruck Medical University, Innrain 80, 6020 Innsbruck, Austria. Tel.: +43-512-9003-71401, Fax: +43-512-9003-73100, zlatko.trajanoski@i-med.ac.at.', 'Austria|Austria|Austria|Austria|Austria', 29, 'Databases, Genetic|Down-Regulation|MicroRNAs|Pharmacogenetics', 'Journal Article', '10.1093/bib/bbs082', '23376192', 'MicroRNAs (miRNAs) are short regulatory RNAs that down-regulate gene expression. They are essential for cell homeostasis and active in many disease states. A major discovery is the ability of miRNAs to determine the efficacy of drugs, which has given rise to the field of miRNA pharmacogenomics through Pharmaco-miRs. miRNAs play a significant role in pharmacogenomics by down-regulating genes that are important for drug function. These interactions can be described as triplet sets consisting of a miRNA, a target gene and a drug associated with the gene. We have developed a web server which links miRNA expression and drug function by combining data on miRNA targeting and protein-drug interactions. miRNA targeting information derive from both experimental data and computational predictions, and protein-drug interactions are annotated by the Pharmacogenomics Knowledge base (PharmGKB). Pharmaco-miRs input consists of miRNAs, genes and/or drug names and the output consists of miRNA pharmacogenomic sets or a list of unique associated miRNAs, genes and drugs. We have furthermore built a database, named Pharmaco-miR Verified Sets (VerSe), which contains miRNA pharmacogenomic data manually curated from the literature, can be searched and downloaded via Pharmaco-miR and informs on trends and generalities published in the field. Overall, we present examples of how Pharmaco-miR provides possible explanations for previously published observations, including how the cisplatin and 5-fluorouracil resistance induced by miR-148a may be caused by miR-148a targeting of the gene KIT. The information is available at www.Pharmaco-miR.org. '),
(68756, 2013, 'bib', 'Using GBrowse 20 to visualize and share next-generation sequence data', 'Stein, Lincoln D', 'Ontario Institute for Cancer Research, 101 College Street, Suite 800, Toronto, Ontario, Canada. lincoln.stein@gmail.com', 'Canada', 53, 'Animals|Computational Biology|Computer Graphics|Computer Storage Devices|Data Display|Databases, Genetic|High-Throughput Nucleotide Sequencing|Humans|Information Storage and Retrieval|Internet|Sequence Alignment|Software', 'Journal Article', '10.1093/bib/bbt001', '23376193', 'GBrowse is a mature web-based genome browser that is suitable for deployment on both public and private web sites. It supports most of genome browser features, including qualitative and quantitative (wiggle) tracks, track uploading, track sharing, interactive track configuration, semantic zooming and limited smooth track panning. As of version 2.0, GBrowse supports next-generation sequencing (NGS) data by providing for the direct display of SAM and BAM sequence alignment files. SAM/BAM tracks provide semantic zooming and support both local and remote data sources. This article provides step-by-step instructions for configuring GBrowse to display NGS data.'),
(68757, 2013, 'bib', 'A practical guide for the functional annotation of genetic variations using SNPnexus', 'Dayem Ullah, Abu Z|Lemoine, Nicholas R|Chelala, Claude', 'Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK.|Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK.|Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK.', 'United Kingdom|United Kingdom|United Kingdom', 51, 'Databases, Genetic|Genetic Variation|Genomics|HapMap Project|Molecular Sequence Annotation|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bib/bbt004', '23395730', 'Broader functional annotation of known as well as putative genetic variations is a valuable mean for prioritizing targets in disease studies and large-scale genotyping projects. In this article, we present a practical guide to SNPnexus, a web-based tool that provides an aggregate set of functional annotations for genomic variation data by characterizing related consequences at the transcriptome/proteome levels with in-depth analysis of potential deleterious effects, inferring physical and cytogenetic mapping, reporting related HapMap data, finding overlaps with potential regulatory, structural as well as conserved elements and retrieving links with previously reported genetic disease studies. We focus on the SNPnexus query system, its annotation categories and the biological interpretation of results. '),
(68758, 2014, 'bib', 'Survey of MapReduce frame operation in bioinformatics', 'Zou, Quan|Li, Xu-Bin|Jiang, Wen-Rui|Lin, Zi-Yu|Li, Gui-Lin|Chen, Ke', 'Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK.|Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK.|Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK.|Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK.|Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK.|Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 55, 'Computational Biology|Data Collection|Programming Languages', 'Journal Article', '10.1093/bib/bbs088', '23396756', 'Bioinformatics is challenged by the fact that traditional analysis tools have difficulty in processing large-scale data from high-throughput sequencing. The open source Apache Hadoop project, which adopts the MapReduce framework and a distributed file system, has recently given bioinformatics researchers an opportunity to achieve scalable, efficient and reliable computing performance on Linux clusters and on cloud computing services. In this article, we present MapReduce frame-based applications that can be employed in the next-generation sequencing and other biological domains. In addition, we discuss the challenges faced by this field as well as the future works on parallel computing in bioinformatics. '),
(68759, 2014, 'bib', 'Gene set analysis methods: statistical models and methodological differences', 'Maciejewski, Henryk', 'Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK.', 'United Kingdom', 40, 'Genome-Wide Association Study|Models, Statistical', 'Journal Article', '10.1093/bib/bbt002', '23413432', 'Many methods of gene set analysis developed in recent years have been compared empirically in a number of comprehensive review articles. Although it is recognized that different methods tend to identify different gene sets as significant, no consensus has been worked out as to which method is preferable, as the recommendations are often contradictory. In this article, we want to group and compare different methods in terms of the methodological assumptions pertaining to definition of a sample and formulation of the actual null hypothesis. We discuss four models of statistical experiment explicitly or implicitly assumed by most if not all currently available methods of gene set analysis. We analyse validity of the models in the context of the actual biological experiment. Based on this, we recommend a group of methods that provide biologically interpretable results in statistically sound way. Finally, we demonstrate how correlated or low signal-to-noise data affects performance of different methods, observed in terms of the false-positive rate and power.'),
(68760, 2014, 'bib', 'Understanding and identifying amino acid repeats', 'Luo, Hong|Nijveen, Harm', 'Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK.|Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK.', 'United Kingdom|United Kingdom', 30, 'Algorithms|DNA|Gene Duplication|Proteome|Repetitive Sequences, Amino Acid', 'Journal Article', '10.1093/bib/bbt003', '23418055', 'Amino acid repeats (AARs) are abundant in protein sequences. They have particular roles in protein function and evolution. Simple repeat patterns generated by DNA slippage tend to introduce length variations and point mutations in repeat regions. Loss of normal and gain of abnormal function owing to their variable length are potential risks leading to diseases. Repeats with complex patterns mostly refer to the functional domain repeats, such as the well-known leucine-rich repeat and WD repeat, which are frequently involved in protein–protein interaction. They are mainly derived from internal gene duplication events and stabilized by gate-keeper residues, which play crucial roles in preventing inter-domain aggregation. AARs are widely distributed in different proteomes across a variety of taxonomic ranges, and especially abundant in eukaryotic proteins. However, their specific evolutionary and functional scenarios are still poorly understood. Identifying AARs in protein sequences is the first step for the further investigation of their biological function and evolutionary mechanism. In principle, this is an NP-hard problem, as most of the repeat fragments are shaped by a series of sophisticated evolutionary events and become latent periodical patterns. It is not possible to define a uniform criterion for detecting and verifying various repeat patterns. Instead, different algorithms based on different strategies have been developed to cope with different repeat patterns. In this review, we attempt to describe the amino acid repeat-detection algorithms currently available and compare their strategies based on an in-depth analysis of the biological significance of protein repeats.'),
(68761, 2014, 'bib', 'Genome-scale bacterial transcriptional regulatory networks: reconstruction and integrated analysis with metabolic models', 'Faria, José P|Overbeek, Ross|Xia, Fangfang|Rocha, Miguel|Rocha, Isabel|Henry, Christopher S', 'Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK.|Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK.|Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK.|Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK.|Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK.|Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 11, 'Bacteria|Databases, Genetic|Gene Regulatory Networks|Genome, Bacterial|Metabolism|Models, Biological|Phylogeny|Transcription, Genetic', 'Journal Article', '10.1093/bib/bbs071', '23422247', 'Advances in sequencing technology are resulting in the rapid emergence of large numbers of complete genome sequences. High-throughput annotation and metabolic modeling of these genomes is now a reality. The high-throughput reconstruction and analysis of genome-scale transcriptional regulatory networks represent the next frontier in microbial bioinformatics. The fruition of this next frontier will depend on the integration of numerous data sources relating to mechanisms, components and behavior of the transcriptional regulatory machinery, as well as the integration of the regulatory machinery into genome-scale cellular models. Here, we review existing repositories for different types of transcriptional regulatory data, including expression data, transcription factor data and binding site locations and we explore how these data are being used for the reconstruction of new regulatory networks. From template network-based methods to de novo reverse engineering from expression data, we discuss how regulatory networks can be reconstructed and integrated with metabolic models to improve model predictions and performance. We also explore the impact these integrated models can have in simulating phenotypes, optimizing the production of compounds of interest or paving the way to a whole-cell model.'),
(68762, 2014, 'bib', 'Systems mapping: how to map genes for biomass allocation toward an ideotype', 'Bo, Wenhao|Fu, Guifang|Wang, Zhong|Xu, Fang|Shen, Yong|Xu, Jichen|Huang, Zhongwen|Gai, Junyi|Vallejos, C Eduardo|Wu, Rongling', 'Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK.|Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK.|Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK.|Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK.|Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK.|Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK.|Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK.|Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK.|Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK.|Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 2, 'Biomass|Chromosome Mapping|Quantitative Trait Loci|Systems Biology', 'Journal Article', '10.1093/bib/bbs089', '23428353', 'The recent availability of high-throughput genetic and genomic data allows the genetic architecture of complex traits to be systematically mapped. The application of these genetic results to design and breed new crop types can be made possible through systems mapping. Systems mapping is a computational model that dissects a complex phenotype into its underlying components, coordinates different components in terms of biological laws through mathematical equations and maps specific genes that mediate each component and its connection with other components. Here, we present a new direction of systems mapping by integrating this tool with carbon economy. With an optimal spatial distribution of carbon fluxes between sources and sinks, plants tend to maximize whole-plant growth and competitive ability under limited availability of resources. We argue that such an economical strategy for plant growth and development, once integrated with systems mapping, will not only provide mechanistic insights into plant biology, but also help to spark a renaissance of interest in ideotype breeding in crops and trees. '),
(68763, 2014, 'bib', 'Biological network extraction from scientific literature: state of the art and challenges', 'Li, Chen|Liakata, Maria|Rebholz-Schuhmann, Dietrich', 'Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK.|Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK.|Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK.', 'United Kingdom|United Kingdom|United Kingdom', 20, 'Data Mining|Linguistics|Signal Transduction', 'Journal Article', '10.1093/bib/bbt006', '23434632', 'Networks of molecular interactions explain complex biological processes, and all known information on molecular events is contained in a number of public repositories including the scientific literature. Metabolic and signalling pathways are often viewed separately, even though both types are composed of interactions involving proteins and other chemical entities. It is necessary to be able to combine data from all available resources to judge the functionality, complexity and completeness of any given network overall, but especially the full integration of relevant information from the scientific literature is still an ongoing and complex task. Currently, the text-mining research community is steadily moving towards processing the full body of the scientific literature by making use of rich linguistic features such as full text parsing, to extract biological interactions. The next step will be to combine these with information from scientific databases to support hypothesis generation for the discovery of new knowledge and the extension of biological networks. The generation of comprehensive networks requires technologies such as entity grounding, coordination resolution and co-reference resolution, which are not fully solved and are required to further improve the quality of results. Here, we analyse the state of the art for the extraction of network information from the scientific literature and the evaluation of extraction methods against reference corpora, discuss challenges involved and identify directions for future research.'),
(68764, 2013, 'bib', 'The Rat Genome Database 2013--data, tools and users', 'Laulederkind, Stanley J F|Hayman, G Thomas|Wang, Shur-Jen|Smith, Jennifer R|Lowry, Timothy F|Nigam, Rajni|Petri, Victoria|de Pons, Jeff|Dwinell, Melinda R|Shimoyama, Mary|Munzenmaier, Diane H|Worthey, Elizabeth A|Jacob, Howard J', 'Human and Molecular Genetics Center, Medical College of Wisconsin, 8701 Watertown Plank Rd, Milwaukee, WI 53226-3548, USA. slaulederkind@mcw.edu|Human and Molecular Genetics Center, Medical College of Wisconsin, 8701 Watertown Plank Rd, Milwaukee, WI 53226-3548, USA. slaulederkind@mcw.edu|Human and Molecular Genetics Center, Medical College of Wisconsin, 8701 Watertown Plank Rd, Milwaukee, WI 53226-3548, USA. slaulederkind@mcw.edu|Human and Molecular Genetics Center, Medical College of Wisconsin, 8701 Watertown Plank Rd, Milwaukee, WI 53226-3548, USA. slaulederkind@mcw.edu|Human and Molecular Genetics Center, Medical College of Wisconsin, 8701 Watertown Plank Rd, Milwaukee, WI 53226-3548, USA. slaulederkind@mcw.edu|Human and Molecular Genetics Center, Medical College of Wisconsin, 8701 Watertown Plank Rd, Milwaukee, WI 53226-3548, USA. slaulederkind@mcw.edu|Human and Molecular Genetics Center, Medical College of Wisconsin, 8701 Watertown Plank Rd, Milwaukee, WI 53226-3548, USA. slaulederkind@mcw.edu|Human and Molecular Genetics Center, Medical College of Wisconsin, 8701 Watertown Plank Rd, Milwaukee, WI 53226-3548, USA. slaulederkind@mcw.edu|Human and Molecular Genetics Center, Medical College of Wisconsin, 8701 Watertown Plank Rd, Milwaukee, WI 53226-3548, USA. slaulederkind@mcw.edu|Human and Molecular Genetics Center, Medical College of Wisconsin, 8701 Watertown Plank Rd, Milwaukee, WI 53226-3548, USA. slaulederkind@mcw.edu|Human and Molecular Genetics Center, Medical College of Wisconsin, 8701 Watertown Plank Rd, Milwaukee, WI 53226-3548, USA. slaulederkind@mcw.edu|Human and Molecular Genetics Center, Medical College of Wisconsin, 8701 Watertown Plank Rd, Milwaukee, WI 53226-3548, USA. slaulederkind@mcw.edu|Human and Molecular Genetics Center, Medical College of Wisconsin, 8701 Watertown Plank Rd, Milwaukee, WI 53226-3548, USA. slaulederkind@mcw.edu', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 41, 'Animals|Databases, Genetic|Genome|Humans|Mice|Phenotype|Rats', 'Journal Article', '10.1093/bib/bbt007', '23434633', 'The Rat Genome Database (RGD) was started >10 years ago to provide a core genomic resource for rat researchers. Currently, RGD combines genetic, genomic, pathway, phenotype and strain information with a focus on disease. RGD users are provided with access to structured and curated data from the molecular level through the organismal level. Those users access RGD from all over the world. End users are not only rat researchers but also researchers working with mouse and human data. Translational research is supported by RGDs comparative genetics/genomics data in disease portals, in GBrowse, in VCMap and on gene report pages. The impact of RGD also goes beyond the traditional biomedical researcher, as the influence of RGD reaches bioinformaticians, tool developers and curators. Import of RGD data into other publicly available databases expands the influence of RGD to a larger set of end users than those who avail themselves of the RGD website. The value of RGD continues to grow as more types of data and more tools are added, while reaching more types of end users. '),
(68765, 2013, 'bib', 'A first course in computing with applications to biology', 'Libeskind-Hadas, Ran|Bush, Eliot', 'Department of Computer Science, Harvey Mudd College, Claremont, CA 91711, USA. Tel.: +909-621-8976, Fax: +909-621-8465, hadas@cs.hmc.edu.|Department of Computer Science, Harvey Mudd College, Claremont, CA 91711, USA. Tel.: +909-621-8976, Fax: +909-621-8465, hadas@cs.hmc.edu.', 'United States|United States', 9, 'Algorithms|California|Computational Biology|Curriculum|Female|Humans|Learning|Male|Software|Universities', 'Journal Article', '10.1093/bib/bbt005', '23449003', 'We believe that undergraduate biology students must acquire a foundational background in computing including how to formulate a computational problem, develop an algorithmic solution, implement their solution in software and then test, document and use their code to explore biological phenomena. Moreover, by learning these skills in the first year, students acquire a powerful tool set that they can use and build on throughout their studies. To address this need, we have developed a first-year undergraduate course that teaches students the foundations of computational thinking and programming in the context of problems in biology. This article describes the structure and content of the course and summarizes assessment data on both affective and learning outcomes. '),
(68766, 2014, 'bib', 'Shape mapping: genetic mapping meets geometric morphometrics', 'Bo, Wenhao|Wang, Zhong|Xu, Fang|Fu, Guifang|Sui, Yihan|Wu, Weimiao|Zhu, Xuli|Yin, Danni|Yan, Qin|Wu, Rongling', 'Department of Computer Science, Harvey Mudd College, Claremont, CA 91711, USA. Tel.: +909-621-8976, Fax: +909-621-8465, hadas@cs.hmc.edu.|Department of Computer Science, Harvey Mudd College, Claremont, CA 91711, USA. Tel.: +909-621-8976, Fax: +909-621-8465, hadas@cs.hmc.edu.|Department of Computer Science, Harvey Mudd College, Claremont, CA 91711, USA. Tel.: +909-621-8976, Fax: +909-621-8465, hadas@cs.hmc.edu.|Department of Computer Science, Harvey Mudd College, Claremont, CA 91711, USA. Tel.: +909-621-8976, Fax: +909-621-8465, hadas@cs.hmc.edu.|Department of Computer Science, Harvey Mudd College, Claremont, CA 91711, USA. Tel.: +909-621-8976, Fax: +909-621-8465, hadas@cs.hmc.edu.|Department of Computer Science, Harvey Mudd College, Claremont, CA 91711, USA. Tel.: +909-621-8976, Fax: +909-621-8465, hadas@cs.hmc.edu.|Department of Computer Science, Harvey Mudd College, Claremont, CA 91711, USA. Tel.: +909-621-8976, Fax: +909-621-8465, hadas@cs.hmc.edu.|Department of Computer Science, Harvey Mudd College, Claremont, CA 91711, USA. Tel.: +909-621-8976, Fax: +909-621-8465, hadas@cs.hmc.edu.|Department of Computer Science, Harvey Mudd College, Claremont, CA 91711, USA. Tel.: +909-621-8976, Fax: +909-621-8465, hadas@cs.hmc.edu.|Department of Computer Science, Harvey Mudd College, Claremont, CA 91711, USA. Tel.: +909-621-8976, Fax: +909-621-8465, hadas@cs.hmc.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 8, 'Algorithms|Models, Statistical|Quantitative Trait Loci', 'Journal Article', '10.1093/bib/bbt008', '23460593', 'Knowledge about biological shape has important implications in biology and biomedicine, but the underlying genetic mechanisms for shape variation have not been well studied. Statistical models play a pivotal role in mapping specific quantitative trait loci (QTLs) that contribute to biological shape and its developmental trajectories. We describe and assess a statistical framework for shape gene identification that incorporates shape and image analysis into a mixture-model framework for QTL mapping. Statistical parameters that define genotype-specific differences in biological shape are estimated by implementing statistical and computational algorithms. A state-of-the-art procedure is described to examine the control patterns of specific QTLs on the origin, properties and functions of biological shape. The statistical framework described will help to address many integrative biological and genetic questions and challenges in shape variation faced by the life sciences community.'),
(68767, 2013, 'bib', 'Congruency in the prediction of pathogenic missense mutations: state-of-the-art web-based tools', 'Castellana, Stefano|Mazza, Tommaso', 'University Aldo Moro, Bari, Italy.|University Aldo Moro, Bari, Italy.', 'Italy|Italy', 62, 'Disease|Exome|Genomics|Humans|Internet|Mutation, Missense|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bib/bbt013', '23505257', 'A remarkable degree of genetic variation has been found in the protein-encoding regions of DNA through deep sequencing of samples obtained from thousands of subjects from several populations. Approximately half of the 20 000 single nucleotide polymorphisms present, even in normal healthy subjects, are nonsynonymous amino acid substitutions that could potentially affect protein function. The greatest challenges currently facing investigators are data interpretation and the development of strategies to identify the few gene-coding variants that actually cause or confer susceptibility to disease. A confusing array of options is available to address this problem. Unfortunately, the overall accuracy of these tools at ultraconserved positions is low, and predictions generated by current computational tools may mislead researchers involved in downstream experimental and clinical studies. First, we have presented an updated review of these tools and their primary functionalities, focusing on those that are naturally prone to analyze massive variant sets, to infer some interesting similarities among their results. Additionally, we have evaluated the prediction congruency for real whole-exome sequencing data in a proof-of-concept study on some of these web-based tools. '),
(68768, 2014, 'bib', 'Choosing appropriate models for protein-protein interaction networks: a comparison study', 'Shao, Mingyu|Yang, Yi|Guan, Jihong|Zhou, Shuigeng', 'University Aldo Moro, Bari, Italy.|University Aldo Moro, Bari, Italy.|University Aldo Moro, Bari, Italy.|University Aldo Moro, Bari, Italy.', 'Italy|Italy|Italy|Italy', 2, 'Animals|Models, Chemical|Protein Binding|Proteins', 'Comparative Study', '10.1093/bib/bbt014', '23515467', 'With the increase of available protein-protein interaction (PPI) data, more and more efforts have been put to PPI network modeling, and a number of models of PPI networks have been proposed. Roughly speaking, good models of PPI networks should be able to accurately describe PPI mechanisms, and thus reproduce the structures of PPI networks. With such models, theoretical and/or computational biologists can efficiently explore the evolution and dynamics of PPI networks. However, a theoretical and/or computational biologist may feel confused when she/he has to choose a proper PPI model for her/his research work from a dozen of candidate models, while there is no guideline available to help her/him. To tackle this problem, in this article, we carry out a comprehensive performance comparison study on 12 existing models over PPI datasets of four species (yeast, mouse, fruit fly and nematode), by comparing the global and local statistical properties of the original PPI networks and the model-reproduced ones. To draw more convincing conclusions, we use the mean reciprocal rank to combine the ranks of a certain model on all statistical properties. Our experimental results indicate that the PS_Seed model [Solé and Pastor-Satorras (PS) model with seed] the STICKY model and the DD_Seed model (Duplication-Divergence model with seed) fit best with the test PPI datasets. By analyzing the underlying mechanisms of the models with better fitting ability, our analysis shows that the evolutionary mechanism of node duplication and link dynamics and the mechanisms with degree-weighted behaviors seem to be able to describe the PPI networks better.'),
(68769, 2013, 'bib', 'Navigating the changing learning landscape: perspective from bioinformaticsca', 'Brazas, Michelle D|Ouellette, B F Francis', 'Informatics and Bio-computing Program, Ontario Institute for Cancer Research, 101 College, Toronto, Ontario M5G 1L7, Canada. Tel/Fax: +416-673-8502. michelle.brazas@oicr.on.ca.|Informatics and Bio-computing Program, Ontario Institute for Cancer Research, 101 College, Toronto, Ontario M5G 1L7, Canada. Tel/Fax: +416-673-8502. michelle.brazas@oicr.on.ca.', 'Canada|Canada', 9, 'Canada|Computational Biology|Computer-Assisted Instruction|Education, Continuing|Internet|Learning|Teaching', 'Journal Article', '10.1093/bib/bbt016', '23515468', 'With the advent of YouTube channels in bioinformatics, open platforms for problem solving in bioinformatics, active web forums in computing analyses and online resources for learning to code or use a bioinformatics tool, the more traditional continuing education bioinformatics training programs have had to adapt. Bioinformatics training programs that solely rely on traditional didactic methods are being superseded by these newer resources. Yet such face-to-face instruction is still invaluable in the learning continuum. Bioinformatics.ca, which hosts the Canadian Bioinformatics Workshops, has blended more traditional learning styles with current online and social learning styles. Here we share our growing experiences over the past 12 years and look toward what the future holds for bioinformatics training programs. '),
(68770, 2013, 'bib', 'Visualization is important', 'Markel, Scott|Carter, Richard', 'Informatics and Bio-computing Program, Ontario Institute for Cancer Research, 101 College, Toronto, Ontario M5G 1L7, Canada. Tel/Fax: +416-673-8502. michelle.brazas@oicr.on.ca.|Informatics and Bio-computing Program, Ontario Institute for Cancer Research, 101 College, Toronto, Ontario M5G 1L7, Canada. Tel/Fax: +416-673-8502. michelle.brazas@oicr.on.ca.', 'Canada|Canada', 0, 'Computational Biology|Data Display|Genomics|High-Throughput Nucleotide Sequencing|Humans|Software', 'Editorial', '10.1093/bib/bbt017', '23515523', '-'),
(68771, 2014, 'bib', 'Bioinformatics approaches for functional annotation of membrane proteins', 'Gromiha, M Michael|Ou, Yu-Yen', 'Department of Biotechnology, Indian Institute of Technology Madras, Chennai 600036, Tamilnadu, India. Tel.: +91-44-2257-4138, Fax: +91-44-2257-4102, gromiha@iitm.ac.in.|Department of Biotechnology, Indian Institute of Technology Madras, Chennai 600036, Tamilnadu, India. Tel.: +91-44-2257-4138, Fax: +91-44-2257-4102, gromiha@iitm.ac.in.', 'India|India', 3, 'Algorithms|Computational Biology|Databases, Protein|Humans|Ion Channels|Membrane Proteins|Membrane Transport Proteins|Molecular Sequence Annotation|Protein Processing, Post-Translational|Receptors, Cell Surface', 'Journal Article', '10.1093/bib/bbt015', '23524979', 'Membrane proteins perform diverse functions in living organisms such as transporters, receptors and channels. The functions of membrane proteins have been investigated with several computational approaches, such as developing databases, analyzing the structure-function relationship and establishing algorithms to discriminate different type of membrane proteins. However, compilation of bioinformatics resources for the functions of membrane proteins is not well documented compared with their structural aspects. In this comprehensive review, we elaborately focus on three aspects of membrane protein functions: (i) databases for different types of membrane proteins based on their functions including transporters, receptors and ion channels, annotated functional data for genomes, as well as functionally important amino acid residues in membrane proteins obtained from experimental data, (ii) analysis of membrane protein functions based on their structures, motifs, amino acid properties and other features and (iii) algorithms for discriminating different types of membrane proteins and annotating them in genomic sequences. In addition, we provide a list of online resources for the databases and web servers for functional annotation of membrane proteins. '),
(68772, 2014, 'bib', 'An allometric model for mapping seed development in plants', 'Huang, Zhongwen|Tong, Chunfa|Bo, Wenhao|Pang, Xiaoming|Wang, Zhong|Xu, Jichen|Gai, Junyi|Wu, Rongling', 'Department of Biotechnology, Indian Institute of Technology Madras, Chennai 600036, Tamilnadu, India. Tel.: +91-44-2257-4138, Fax: +91-44-2257-4102, gromiha@iitm.ac.in.|Department of Biotechnology, Indian Institute of Technology Madras, Chennai 600036, Tamilnadu, India. Tel.: +91-44-2257-4138, Fax: +91-44-2257-4102, gromiha@iitm.ac.in.|Department of Biotechnology, Indian Institute of Technology Madras, Chennai 600036, Tamilnadu, India. Tel.: +91-44-2257-4138, Fax: +91-44-2257-4102, gromiha@iitm.ac.in.|Department of Biotechnology, Indian Institute of Technology Madras, Chennai 600036, Tamilnadu, India. Tel.: +91-44-2257-4138, Fax: +91-44-2257-4102, gromiha@iitm.ac.in.|Department of Biotechnology, Indian Institute of Technology Madras, Chennai 600036, Tamilnadu, India. Tel.: +91-44-2257-4138, Fax: +91-44-2257-4102, gromiha@iitm.ac.in.|Department of Biotechnology, Indian Institute of Technology Madras, Chennai 600036, Tamilnadu, India. Tel.: +91-44-2257-4138, Fax: +91-44-2257-4102, gromiha@iitm.ac.in.|Department of Biotechnology, Indian Institute of Technology Madras, Chennai 600036, Tamilnadu, India. Tel.: +91-44-2257-4138, Fax: +91-44-2257-4102, gromiha@iitm.ac.in.|Department of Biotechnology, Indian Institute of Technology Madras, Chennai 600036, Tamilnadu, India. Tel.: +91-44-2257-4138, Fax: +91-44-2257-4102, gromiha@iitm.ac.in.', 'India|India|India|India|India|India|India|India', 7, 'Models, Biological|Plants|Quantitative Trait Loci|Seeds', 'Journal Article', '10.1093/bib/bbt019', '23543351', 'Despite a tremendous effort to map quantitative trait loci (QTLs) responsible for agriculturally and biologically important traits in plants, our understanding of how a QTL governs the developmental process of plant seeds remains elusive. In this article, we address this issue by describing a model for functional mapping of seed development through the incorporation of the relationship between vegetative and reproductive growth. The time difference of reproductive from vegetative growth is described by Reeve and Huxleys allometric equation. Thus, the implementation of this equation into the framework of functional mapping allows dynamic QTLs for seed development to be identified more precisely. By estimating and testing mathematical parameters that define Reeve and Huxleys allometric equations of seed growth, the dynamic pattern of the genetic effects of the QTLs identified can be analyzed. We used the model to analyze a soybean data, leading to the detection of QTLs that control the growth of seed dry weight. Three dynamic QTLs, located in two different linkage groups, were detected to affect growth curves of seed dry weight. The QTLs detected may be used to improve seed yield with marker-assisted selection by altering the pattern of seed development in a hope to achieve a maximum size of seeds at a harvest time.'),
(68773, 2013, 'bib', 'Next-generation sequencing: a challenge to meet the increasing demand for training workshops in Australia', 'Watson-Haigh, Nathan S|Shang, Catherine A|Haimel, Matthias|Kostadima, Myrto|Loos, Remco|Deshpande, Nandan|Duesing, Konsta|Li, Xi|McGrath, Annette|McWilliam, Sean|Michnowicz, Simon|Moolhuijzen, Paula|Quenette, Steve|Revote, Jerico Nico De Leon|Tyagi, Sonika|Schneider, Maria V', 'The Australian Centre for Plant Functional Genomics, University of Adelaide, SA 5064, Australia. nathan.haigh@acpfg.com.au.|The Australian Centre for Plant Functional Genomics, University of Adelaide, SA 5064, Australia. nathan.haigh@acpfg.com.au.|The Australian Centre for Plant Functional Genomics, University of Adelaide, SA 5064, Australia. nathan.haigh@acpfg.com.au.|The Australian Centre for Plant Functional Genomics, University of Adelaide, SA 5064, Australia. nathan.haigh@acpfg.com.au.|The Australian Centre for Plant Functional Genomics, University of Adelaide, SA 5064, Australia. nathan.haigh@acpfg.com.au.|The Australian Centre for Plant Functional Genomics, University of Adelaide, SA 5064, Australia. nathan.haigh@acpfg.com.au.|The Australian Centre for Plant Functional Genomics, University of Adelaide, SA 5064, Australia. nathan.haigh@acpfg.com.au.|The Australian Centre for Plant Functional Genomics, University of Adelaide, SA 5064, Australia. nathan.haigh@acpfg.com.au.|The Australian Centre for Plant Functional Genomics, University of Adelaide, SA 5064, Australia. nathan.haigh@acpfg.com.au.|The Australian Centre for Plant Functional Genomics, University of Adelaide, SA 5064, Australia. nathan.haigh@acpfg.com.au.|The Australian Centre for Plant Functional Genomics, University of Adelaide, SA 5064, Australia. nathan.haigh@acpfg.com.au.|The Australian Centre for Plant Functional Genomics, University of Adelaide, SA 5064, Australia. nathan.haigh@acpfg.com.au.|The Australian Centre for Plant Functional Genomics, University of Adelaide, SA 5064, Australia. nathan.haigh@acpfg.com.au.|The Australian Centre for Plant Functional Genomics, University of Adelaide, SA 5064, Australia. nathan.haigh@acpfg.com.au.|The Australian Centre for Plant Functional Genomics, University of Adelaide, SA 5064, Australia. nathan.haigh@acpfg.com.au.|The Australian Centre for Plant Functional Genomics, University of Adelaide, SA 5064, Australia. nathan.haigh@acpfg.com.au.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|', 13, 'Australia|Computational Biology|Computer-Assisted Instruction|Cooperative Behavior|Curriculum|High-Throughput Nucleotide Sequencing|Teaching', 'Journal Article', '10.1093/bib/bbt022', '23543352', 'The widespread adoption of high-throughput next-generation sequencing (NGS) technology among the Australian life science research community is highlighting an urgent need to up-skill biologists in tools required for handling and analysing their NGS data. There is currently a shortage of cutting-edge bioinformatics training courses in Australia as a consequence of a scarcity of skilled trainers with time and funding to develop and deliver training courses. To address this, a consortium of Australian research organizations, including Bioplatforms Australia, the Commonwealth Scientific and Industrial Research Organisation and the Australian Bioinformatics Network, have been collaborating with EMBL-EBI training team. A group of Australian bioinformaticians attended the train-the-trainer workshop to improve training skills in developing and delivering bioinformatics workshop curriculum. A 2-day NGS workshop was jointly developed to provide hands-on knowledge and understanding of typical NGS data analysis workflows. The road show-style workshop was successfully delivered at five geographically distant venues in Australia using the newly established Australian NeCTAR Research Cloud. We highlight the challenges we had to overcome at different stages from design to delivery, including the establishment of an Australian bioinformatics training network and the computing infrastructure and resource development. A virtual machine image, workshop materials and scripts for configuring a machine with workshop contents have all been made available under a Creative Commons Attribution 3.0 Unported License. This means participants continue to have convenient access to an environment they had become familiar and bioinformatics trainers are able to access and reuse these resources. '),
(68774, 2013, 'bib', 'The challenges of delivering bioinformatics training in the analysis of high-throughput data', 'Carvalho, Benilton S|Rustici, Gabriella', '*Functional Genomics Group, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton CB10 1SD, UK. Tel.: +44-1223-492539, Fax: +44-1223-494468, gabry@ebi.ac.uk.|*Functional Genomics Group, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton CB10 1SD, UK. Tel.: +44-1223-492539, Fax: +44-1223-494468, gabry@ebi.ac.uk.', 'United Kingdom|United Kingdom', 7, 'Academies and Institutes|Computational Biology|Curriculum|Data Interpretation, Statistical|Europe|Faculty|Genomics|High-Throughput Nucleotide Sequencing|Humans|Software|Teaching', 'Journal Article', '10.1093/bib/bbt018', '23543353', 'High-throughput technologies are widely used in the field of functional genomics and used in an increasing number of applications. For many wet lab scientists, the analysis of the large amount of data generated by such technologies is a major bottleneck that can only be overcome through very specialized training in advanced data analysis methodologies and the use of dedicated bioinformatics software tools. In this article, we wish to discuss the challenges related to delivering training in the analysis of high-throughput sequencing data and how we addressed these challenges in the hands-on training courses that we have developed at the European Bioinformatics Institute. '),
(68775, 2014, 'bib', 'Data construction for phosphorylation site prediction', 'Gong, Haipeng|Liu, Xiaoqing|Wu, Jun|He, Zengyou', '*Functional Genomics Group, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton CB10 1SD, UK. Tel.: +44-1223-492539, Fax: +44-1223-494468, gabry@ebi.ac.uk.|*Functional Genomics Group, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton CB10 1SD, UK. Tel.: +44-1223-492539, Fax: +44-1223-494468, gabry@ebi.ac.uk.|*Functional Genomics Group, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton CB10 1SD, UK. Tel.: +44-1223-492539, Fax: +44-1223-494468, gabry@ebi.ac.uk.|*Functional Genomics Group, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton CB10 1SD, UK. Tel.: +44-1223-492539, Fax: +44-1223-494468, gabry@ebi.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 2, 'Algorithms|Phosphorylation|Proteins', 'Journal Article', '10.1093/bib/bbt012', '23543354', 'Protein phosphorylation is one of the most pervasive post-translational modifications, regulating diverse cellular processes in various organisms. As mass spectrometry-based experimental approaches for identifying phosphorylation events are resource-intensive, many computational methods have been proposed, in which phosphorylation site prediction is formulated as a classification problem. They differ in several ways, and one crucial issue is the construction of training data and test data for unbiased performance evaluation. In this article, we categorize the existing data construction methods and try to answer three questions: (i) Is it equivalent to use different data construction methods in the assessment of phosphorylation site prediction algorithms? (ii) What kind of test data set is unbiased for assessing the prediction performance of a trained algorithm in different real world scenarios? (iii) Among the summarized training data construction methods, which one(s) has better generalization performance for most scenarios? To answer these questions, we conduct comprehensive experimental studies for both non-kinase-specific and kinase-specific prediction tasks. The experimental results show that: (i) different data construction methods can lead to significantly different prediction performance, (ii) there can be different test data construction methods that are unbiased with respect to different real world scenarios, and (iii) different data construction methods have different generalization performance in different real world scenarios. Therefore, when developing new algorithms in future research, people should concentrate on what kind of scenario their algorithm will work for, what the corresponding unbiased test data are and which training data construction method can generate best generalization performance.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(68776, 2013, 'bib', 'Pattern recognition in bioinformatics', 'de Ridder, Dick|de Ridder, Jeroen|Reinders, Marcel J T', 'Delft Bioinformatics Lab, Department of Intelligent Systems, Faculty of Electrical Engineering, Mathematics & Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands. Tel.: +31 15 2785114, Fax: +31 15 2787022, d.deridder@tudelft.nl.|Delft Bioinformatics Lab, Department of Intelligent Systems, Faculty of Electrical Engineering, Mathematics & Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands. Tel.: +31 15 2785114, Fax: +31 15 2787022, d.deridder@tudelft.nl.|Delft Bioinformatics Lab, Department of Intelligent Systems, Faculty of Electrical Engineering, Mathematics & Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands. Tel.: +31 15 2785114, Fax: +31 15 2787022, d.deridder@tudelft.nl.', 'Netherlands|Netherlands|Netherlands', 37, 'Algorithms|Artificial Intelligence|Cluster Analysis|Computational Biology|Curriculum|High-Throughput Nucleotide Sequencing|Pattern Recognition, Automated', 'Journal Article', '10.1093/bib/bbt020', '23559637', 'Pattern recognition is concerned with the development of systems that learn to solve a given problem using a set of example instances, each represented by a number of features. These problems include clustering, the grouping of similar instances, classification, the task of assigning a discrete label to a given instance, and dimensionality reduction, combining or selecting features to arrive at a more useful representation. The use of statistical pattern recognition algorithms in bioinformatics is pervasive. Classification and clustering are often applied to high-throughput measurement data arising from microarray, mass spectrometry and next-generation sequencing experiments for selecting markers, predicting phenotype and grouping objects or genes. Less explicitly, classification is at the core of a wide range of tools such as predictors of genes, protein function, functional or genetic interactions, etc., and used extensively in systems biology. A course on pattern recognition (or machine learning) should therefore be at the core of any bioinformatics education program. In this review, we discuss the main elements of a pattern recognition course, based on material developed for courses taught at the BSc, MSc and PhD levels to an audience of bioinformaticians, computer scientists and life scientists. We pay attention to common problems and pitfalls encountered in applications and in interpretation of the results obtained. '),
(68777, 2014, 'bib', 'Visualizing time-related data in biology, a review', 'Secrier, Maria|Schneider, Reinhard', 'Delft Bioinformatics Lab, Department of Intelligent Systems, Faculty of Electrical Engineering, Mathematics & Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands. Tel.: +31 15 2785114, Fax: +31 15 2787022, d.deridder@tudelft.nl.|Delft Bioinformatics Lab, Department of Intelligent Systems, Faculty of Electrical Engineering, Mathematics & Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands. Tel.: +31 15 2785114, Fax: +31 15 2787022, d.deridder@tudelft.nl.', 'Netherlands|Netherlands', 23, 'Biology|Software|Time', 'Journal Article', '10.1093/bib/bbt021', '23585583', 'Time is of the essence in biology as in so much else. For example, monitoring disease progression or the timing of developmental defects is important for the processes of drug discovery and therapy trials. Furthermore, an understanding of the basic dynamics of biological phenomena that are often strictly time regulated (e.g. circadian rhythms) is needed to make accurate inferences about the evolution of biological processes. Recent advances in technologies have enabled us to measure timing effects more accurately and in more detail. This has driven related advances in visualization and analysis tools that try to effectively exploit this data. Beyond timeline plots, notable attempts at more involved temporal interpretation have been made in recent years, but awareness of the available resources is still limited within the scientific community. Here, we review some advances in biological visualization of time-driven processes and consider how they aid data analysis and interpretation.'),
(68778, 2014, 'bib', 'Reproducible probe-level analysis of the Affymetrix Exon 10 ST array with R/Bioconductor', 'Rodrigo-Domingo, Maria|Waagepetersen, Rasmus|Bødker, Julie Støve|Falgreen, Steffen|Kjeldsen, Malene Krag|Johnsen, Hans Erik|Dybkær, Karen|Bøgsted, Martin', 'Delft Bioinformatics Lab, Department of Intelligent Systems, Faculty of Electrical Engineering, Mathematics & Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands. Tel.: +31 15 2785114, Fax: +31 15 2787022, d.deridder@tudelft.nl.|Delft Bioinformatics Lab, Department of Intelligent Systems, Faculty of Electrical Engineering, Mathematics & Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands. Tel.: +31 15 2785114, Fax: +31 15 2787022, d.deridder@tudelft.nl.|Delft Bioinformatics Lab, Department of Intelligent Systems, Faculty of Electrical Engineering, Mathematics & Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands. Tel.: +31 15 2785114, Fax: +31 15 2787022, d.deridder@tudelft.nl.|Delft Bioinformatics Lab, Department of Intelligent Systems, Faculty of Electrical Engineering, Mathematics & Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands. Tel.: +31 15 2785114, Fax: +31 15 2787022, d.deridder@tudelft.nl.|Delft Bioinformatics Lab, Department of Intelligent Systems, Faculty of Electrical Engineering, Mathematics & Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands. Tel.: +31 15 2785114, Fax: +31 15 2787022, d.deridder@tudelft.nl.|Delft Bioinformatics Lab, Department of Intelligent Systems, Faculty of Electrical Engineering, Mathematics & Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands. Tel.: +31 15 2785114, Fax: +31 15 2787022, d.deridder@tudelft.nl.|Delft Bioinformatics Lab, Department of Intelligent Systems, Faculty of Electrical Engineering, Mathematics & Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands. Tel.: +31 15 2785114, Fax: +31 15 2787022, d.deridder@tudelft.nl.|Delft Bioinformatics Lab, Department of Intelligent Systems, Faculty of Electrical Engineering, Mathematics & Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands. Tel.: +31 15 2785114, Fax: +31 15 2787022, d.deridder@tudelft.nl.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 6, 'Alternative Splicing|Exons|Humans|Reproducibility of Results', 'Journal Article', '10.1093/bib/bbt011', '23603090', 'The presence of different transcripts of a gene across samples can be analysed by whole-transcriptome microarrays. Reproducing results from published microarray data represents a challenge owing to the vast amounts of data and the large variety of preprocessing and filtering steps used before the actual analysis is carried out. To guarantee a firm basis for methodological development where results with new methods are compared with previous results, it is crucial to ensure that all analyses are completely reproducible for other researchers. We here give a detailed workflow on how to perform reproducible analysis of the GeneChip®Human Exon 1.0 ST Array at probe and probeset level solely in R/Bioconductor, choosing packages based on their simplicity of use. To exemplify the use of the proposed workflow, we analyse differential splicing and differential gene expression in a publicly available dataset using various statistical methods. We believe this study will provide other researchers with an easy way of accessing gene expression data at different annotation levels and with the sufficient details needed for developing their own tools for reproducible analysis of the GeneChip®Human Exon 1.0 ST Array.'),
(68779, 2014, 'bib', 'BLAST output visualization in the new sequencing era', 'Neumann, Ralf Stefan|Kumar, Surendra|Shalchian-Tabrizi, Kamran', 'Delft Bioinformatics Lab, Department of Intelligent Systems, Faculty of Electrical Engineering, Mathematics & Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands. Tel.: +31 15 2785114, Fax: +31 15 2787022, d.deridder@tudelft.nl.|Delft Bioinformatics Lab, Department of Intelligent Systems, Faculty of Electrical Engineering, Mathematics & Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands. Tel.: +31 15 2785114, Fax: +31 15 2787022, d.deridder@tudelft.nl.|Delft Bioinformatics Lab, Department of Intelligent Systems, Faculty of Electrical Engineering, Mathematics & Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands. Tel.: +31 15 2785114, Fax: +31 15 2787022, d.deridder@tudelft.nl.', 'Netherlands|Netherlands|Netherlands', 12, 'Algorithms|Computational Biology|Sequence Alignment|Sequence Analysis', 'Journal Article', '10.1093/bib/bbt009', '23603091', 'The Basic Local Alignment Search Tool (BLAST) algorithm remains one of the most widely used bioinformatic programs. For many projects, new sequencing technologies and increased database sizes will increase the BLAST output significantly. Frequently, this output is so large that it is no longer able to be processed manually. As BLAST users are increasingly recruited from mainstream biology without any bioinformatic background, user-friendly programs capable of BLAST output visualization, analysis and post-processing are in demand. In this review, freely available BLAST output processing programs are categorized as BLAST output interpreters, BLAST environments, BLAST output parsers or specialized tools. They are evaluated according to their user-friendliness, analysis features and high-throughput data processing capabilities.'),
(68780, 2013, 'bib', 'Bioinformatics and systems biology: bridging the gap between heterogeneous student backgrounds', 'Abeln, Sanne|Molenaar, Douwe|Feenstra, K Anton|Hoefsloot, Huub C J|Teusink, Bas|Heringa, Jaap', 'Centre for Integrative Bioinformatics VU (IBIVU), VU University Amsterdam, Amsterdam, The Netherlands. Tel.: +31 20 59 87816, Fax: +31 20 59 87653, s.abeln@vu.nl.|Centre for Integrative Bioinformatics VU (IBIVU), VU University Amsterdam, Amsterdam, The Netherlands. Tel.: +31 20 59 87816, Fax: +31 20 59 87653, s.abeln@vu.nl.|Centre for Integrative Bioinformatics VU (IBIVU), VU University Amsterdam, Amsterdam, The Netherlands. Tel.: +31 20 59 87816, Fax: +31 20 59 87653, s.abeln@vu.nl.|Centre for Integrative Bioinformatics VU (IBIVU), VU University Amsterdam, Amsterdam, The Netherlands. Tel.: +31 20 59 87816, Fax: +31 20 59 87653, s.abeln@vu.nl.|Centre for Integrative Bioinformatics VU (IBIVU), VU University Amsterdam, Amsterdam, The Netherlands. Tel.: +31 20 59 87816, Fax: +31 20 59 87653, s.abeln@vu.nl.|Centre for Integrative Bioinformatics VU (IBIVU), VU University Amsterdam, Amsterdam, The Netherlands. Tel.: +31 20 59 87816, Fax: +31 20 59 87653, s.abeln@vu.nl.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 5, 'Computational Biology|Curriculum|Education, Graduate|Humans|Netherlands|Students|Systems Biology', 'Journal Article', '10.1093/bib/bbt023', '23603092', 'Teaching students with very diverse backgrounds can be extremely challenging. This article uses the Bioinformatics and Systems Biology MSc in Amsterdam as a case study to describe how the knowledge gap for students with heterogeneous backgrounds can be bridged. We show that a mix in backgrounds can be turned into an advantage by creating a stimulating learning environment for the students. In the MSc Programme, conversion classes help to bridge differences between students, by mending initial knowledge and skill gaps. Mixing students from different backgrounds in a group to solve a complex task creates an opportunity for the students to reflect on their own abilities. We explain how a truly interdisciplinary approach to teaching helps students of all backgrounds to achieve the MSc end terms. Moreover, transferable skills obtained by the students in such a mixed study environment are invaluable for their later careers. '),
(68781, 2014, 'bib', 'Detecting tissue-specific early warning signals for complex diseases based on dynamical network biomarkers: study of type 2 diabetes by cross-tissue analysis', 'Li, Meiyi|Zeng, Tao|Liu, Rui|Chen, Luonan', 'Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yue-Yang Road, Shanghai 200031, China. Tel.: +86 21-5492-0100, Fax: +86 21-5497-2551, lnchen@sibs.ac.cn.|Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yue-Yang Road, Shanghai 200031, China. Tel.: +86 21-5492-0100, Fax: +86 21-5497-2551, lnchen@sibs.ac.cn.|Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yue-Yang Road, Shanghai 200031, China. Tel.: +86 21-5492-0100, Fax: +86 21-5497-2551, lnchen@sibs.ac.cn.|Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yue-Yang Road, Shanghai 200031, China. Tel.: +86 21-5492-0100, Fax: +86 21-5497-2551, lnchen@sibs.ac.cn.', 'China|China|China|China', 67, 'Algorithms|Animals|Biomarkers|Computational Biology|Diabetes Mellitus, Type 2|Disease Progression|Early Diagnosis|Genetic Markers|High-Throughput Screening Assays|Humans|Models, Biological|Rats|Signal Transduction|Steroids|Systems Biology|Tissue Distribution', 'Journal Article', '10.1093/bib/bbt027', '23620135', 'Identifying early warning signals of critical transitions during disease progression is a key to achieving early diagnosis of complex diseases. By exploiting rich information of high-throughput data, a novel model-free method has been developed to detect early warning signals of diseases. Its theoretical foundation is based on dynamical network biomarker (DNB), which is also called as the driver (or leading) network of the disease because components or molecules in DNB actually drive the whole system from one state (e.g. normal state) to another (e.g. disease state). In this article, we first reviewed the concept and main results of DNB theory, and then applied the new method to the analysis of type 2 diabetes mellitus (T2DM). Specifically, based on the temporal-spatial gene expression data of T2DM, we identified tissue-specific DNBs corresponding to the critical transitions occurring in liver, adipose and muscle during T2DM development and progression. Actually, we found that there are two different critical states during T2DM development characterized as responses to insulin resistance and serious inflammation, respectively. Interestingly, a new T2DM-associated function, i.e. steroid hormone biosynthesis, was discovered, and those related genes were significantly dysregulated in liver and adipose at the first critical transition during T2DM deterioration. Moreover, the dysfunction of genes related to responding hormone was also detected in muscle at the similar period. Based on the functional and network analysis on pathogenic molecular mechanism of T2DM, we showed that most of DNB genes, in particular the core ones, tended to be located at the upstream of biological pathways, which implied that DNB genes act as the causal factors rather than the consequence to drive the downstream molecules to change their transcriptional activities. This also validated our theoretical prediction of DNB as the driver network. As shown in this study, DNB can not only signal the emergence of the critical transitions for early diagnosis of diseases, but can also provide the causal network of the transitions for revealing molecular mechanisms of disease initiation and progression at a network level. '),
(68782, 2014, 'bib', 'Web scraping technologies in an API world', 'Glez-Peña, Daniel|Lourenço, Anália|López-Fernández, Hugo|Reboiro-Jato, Miguel|Fdez-Riverola, Florentino', 'Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yue-Yang Road, Shanghai 200031, China. Tel.: +86 21-5492-0100, Fax: +86 21-5497-2551, lnchen@sibs.ac.cn.|Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yue-Yang Road, Shanghai 200031, China. Tel.: +86 21-5492-0100, Fax: +86 21-5497-2551, lnchen@sibs.ac.cn.|Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yue-Yang Road, Shanghai 200031, China. Tel.: +86 21-5492-0100, Fax: +86 21-5497-2551, lnchen@sibs.ac.cn.|Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yue-Yang Road, Shanghai 200031, China. Tel.: +86 21-5492-0100, Fax: +86 21-5497-2551, lnchen@sibs.ac.cn.|Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yue-Yang Road, Shanghai 200031, China. Tel.: +86 21-5492-0100, Fax: +86 21-5497-2551, lnchen@sibs.ac.cn.', 'China|China|China|China|China', 33, 'Database Management Systems|Internet|User-Computer Interface', 'Journal Article', '10.1093/bib/bbt026', '23632294', 'Web services are the de facto standard in biomedical data integration. However, there are data integration scenarios that cannot be fully covered by Web services. A number of Web databases and tools do not support Web services, and existing Web services do not cover for all possible user data demands. As a consequence, Web data scraping, one of the oldest techniques for extracting Web contents, is still in position to offer a valid and valuable service to a wide range of bioinformatics applications, ranging from simple extraction robots to online meta-servers. This article reviews existing scraping frameworks and tools, identifying their strengths and limitations in terms of extraction capabilities. The main focus is set on showing how straightforward it is today to set up a data scraping pipeline, with minimal programming effort, and answer a number of practical needs. For exemplification purposes, we introduce a biomedical data extraction scenario where the desired data sources, well-known in clinical microbiology and similar domains, do not offer programmatic interfaces yet. Moreover, we describe the operation of WhichGenes and PathJam, two bioinformatics meta-servers that use scraping as means to cope with gene set enrichment analysis.'),
(68783, 2014, 'bib', 'Are all of the human exons alternatively spliced?', 'Chen, Feng-Chi', 'Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yue-Yang Road, Shanghai 200031, China. Tel.: +86 21-5492-0100, Fax: +86 21-5497-2551, lnchen@sibs.ac.cn.', 'China', 6, 'Alternative Splicing|Exons|High-Throughput Nucleotide Sequencing|Humans', 'Journal Article', '10.1093/bib/bbt025', '23640569', 'Alternative mRNA splicing (AS) is a major mechanism for increasing regulatory complexity. A key concept in AS is the distinction between alternatively and constitutively spliced exons (ASEs and CSEs, respectively). ASEs and CSEs have been reported to be differentially regulated, and to have distinct biological properties. However, the recent flood of RNA-sequencing data has obscured the boundary between ASEs and CSEs. Researchers are beginning to question whether authentic CSEs do exist, and whether the ASE/CSE distinction is biologically invalid. Here, I examine the influences of increasing transcriptome data on the human ASE/CSE classification and our past understanding of the properties of these two types of exons. Interestingly, although the percentage of human ASEs has increased dramatically in recent years, the overall distinction between ASEs and CSEs remain valid. For example, CSEs are longer, evolve more slowly, and less frequently correspond to intrinsically disordered protein regions than ASEs. In addition, only a relatively small number of human genes have their transcripts composed entirely of ASEs despite the large amount of high-throughput transcriptome information. Therefore, the backbone concept of AS, in which CSEs constitute the invariant part and ASEs the flexible part of the transcript, appears to be generally true despite the increasing percentage of ASEs in the human exome.'),
(68784, 2013, 'bib', 'Teaching the bioinformatics of signaling networks: an integrated approach to facilitate multi-disciplinary learning', 'Korcsmaros, Tamas|Dunai, Zsuzsanna A|Vellai, Tibor|Csermely, Peter', 'Department of Genetics, Eotvos Lorand University, H-1117 Budapest, Pázmány s. 1/C, Hungary. Tel.: +36302686590, korcsmaros@gmail.com.|Department of Genetics, Eotvos Lorand University, H-1117 Budapest, Pázmány s. 1/C, Hungary. Tel.: +36302686590, korcsmaros@gmail.com.|Department of Genetics, Eotvos Lorand University, H-1117 Budapest, Pázmány s. 1/C, Hungary. Tel.: +36302686590, korcsmaros@gmail.com.|Department of Genetics, Eotvos Lorand University, H-1117 Budapest, Pázmány s. 1/C, Hungary. Tel.: +36302686590, korcsmaros@gmail.com.', 'Hungary|Hungary|Hungary|Hungary', 5, 'Cell Biology|Computational Biology|Curriculum|Interdisciplinary Communication|Learning|Molecular Biology|Signal Transduction|Systems Biology|Universities', 'Journal Article', '10.1093/bib/bbt024', '23640570', 'The number of bioinformatics tools and resources that support molecular and cell biology approaches is continuously expanding. Moreover, systems and network biology analyses are accompanied more and more by integrated bioinformatics methods. Traditional information-centered university teaching methods often fail, as (1) it is impossible to cover all existing approaches in the frame of a single course, and (2) a large segment of the current bioinformation can become obsolete in a few years. Signaling network offers an excellent example for teaching bioinformatics resources and tools, as it is both focused and complex at the same time. Here, we present an outline of a university bioinformatics course with four sample practices to demonstrate how signaling network studies can integrate biochemistry, genetics, cell biology and network sciences. We show that several bioinformatics resources and tools, as well as important concepts and current trends, can also be integrated to signaling network studies. The research-type hands-on experiences we show enable the students to improve key competences such as teamworking, creative and critical thinking and problem solving. Our classroom course curriculum can be re-formulated as an e-learning material or applied as a part of a specific training course. The multi-disciplinary approach and the mosaic setup of the course have the additional benefit to support the advanced teaching of talented students. '),
(68785, 2014, 'bib', 'A bi-Poisson model for clustering gene expression profiles by RNA-seq', 'Wang, Ningtao|Wang, Yaqun|Hao, Han|Wang, Luojun|Wang, Zhong|Wang, Jianxin|Wu, Rongling', 'Department of Genetics, Eotvos Lorand University, H-1117 Budapest, Pázmány s. 1/C, Hungary. Tel.: +36302686590, korcsmaros@gmail.com.|Department of Genetics, Eotvos Lorand University, H-1117 Budapest, Pázmány s. 1/C, Hungary. Tel.: +36302686590, korcsmaros@gmail.com.|Department of Genetics, Eotvos Lorand University, H-1117 Budapest, Pázmány s. 1/C, Hungary. Tel.: +36302686590, korcsmaros@gmail.com.|Department of Genetics, Eotvos Lorand University, H-1117 Budapest, Pázmány s. 1/C, Hungary. Tel.: +36302686590, korcsmaros@gmail.com.|Department of Genetics, Eotvos Lorand University, H-1117 Budapest, Pázmány s. 1/C, Hungary. Tel.: +36302686590, korcsmaros@gmail.com.|Department of Genetics, Eotvos Lorand University, H-1117 Budapest, Pázmány s. 1/C, Hungary. Tel.: +36302686590, korcsmaros@gmail.com.|Department of Genetics, Eotvos Lorand University, H-1117 Budapest, Pázmány s. 1/C, Hungary. Tel.: +36302686590, korcsmaros@gmail.com.', 'Hungary|Hungary|Hungary|Hungary|Hungary|Hungary|Hungary', 4, 'Algorithms|Computer Simulation|Gene Expression Profiling|Models, Statistical|Poisson Distribution|Sequence Analysis, RNA', 'Journal Article', '10.1093/bib/bbt029', '23665510', 'With the availability of gene expression data by RNA-seq, powerful statistical approaches for grouping similar gene expression profiles across different environments have become increasingly important. We describe and assess a computational model for clustering genes into distinct groups based on the pattern of gene expression in response to changing environment. The model capitalizes on the Poisson distribution to capture the count property of RNA-seq data. A two-stage hierarchical expectation–maximization (EM) algorithm is implemented to estimate an optimal number of groups and mean expression amounts of each group across two environments. A procedure is formulated to test whether and how a given group shows a plastic response to environmental changes. The impact of gene–environment interactions on the phenotypic plasticity of the organism can also be visualized and characterized. The model was used to analyse an RNA-seq dataset measured from two cell lines of breast cancer that respond differently to an anti-cancer drug, from which genes associated with the resistance and sensitivity of the cell lines are identified. We performed simulation studies to validate the statistical behaviour of the model. The model provides a useful tool for clustering gene expression data by RNA-seq, facilitating our understanding of gene functions and networks.'),
(68786, 2013, 'bib', 'Integrating bioinformatics into senior high school: design principles and implications', 'Machluf, Yossy|Yarden, Anat', 'Department of Science Teaching, Weizmann Institute of Science, P.O. Box 26, Rehovot 76100, Israel. Tel.: +972-8-9342273, Fax: +972-8-9342279, Machluf.yossy@weizmann.ac.il .|Department of Science Teaching, Weizmann Institute of Science, P.O. Box 26, Rehovot 76100, Israel. Tel.: +972-8-9342273, Fax: +972-8-9342279, Machluf.yossy@weizmann.ac.il .', 'Israel|Israel', 15, 'Adolescent|Computational Biology|Computer-Assisted Instruction|Curriculum|Humans|Knowledge|Learning|Students|Teaching', 'Journal Article', '10.1093/bib/bbt030', '23665511', 'Bioinformatics is an integral part of modern life sciences. It has revolutionized and redefined how research is carried out and has had an enormous impact on biotechnology, medicine, agriculture and related areas. Yet, it is only rarely integrated into high school teaching and learning programs, playing almost no role in preparing the next generation of information-oriented citizens. Here, we describe the design principles of bioinformatics learning environments, including our own, that are aimed at introducing bioinformatics into senior high school curricula through engaging learners in scientifically authentic inquiry activities. We discuss the bioinformatics-related benefits and challenges that high school teachers and students face in the course of the implementation process, in light of previous studies and our own experience. Based on these lessons, we present a new approach for characterizing the questions embedded in bioinformatics teaching and learning units, based on three criteria: the type of domain-specific knowledge required to answer each question (declarative knowledge, procedural knowledge, strategic knowledge, situational knowledge), the scientific approach from which each question stems (biological, bioinformatics, a combination of the two) and the associated cognitive process dimension (remember, understand, apply, analyze, evaluate, create). We demonstrate the feasibility of this approach using a learning environment, which we developed for the high school level, and suggest some of its implications. This review sheds light on unique and critical characteristics related to broader integration of bioinformatics in secondary education, which are also relevant to the undergraduate level, and especially on curriculum design, development of suitable learning environments and teaching and learning processes. '),
(68787, 2014, 'bib', 'A review of genomic data warehousing systems', 'Triplet, Thomas|Butler, Gregory', 'Department of Science Teaching, Weizmann Institute of Science, P.O. Box 26, Rehovot 76100, Israel. Tel.: +972-8-9342273, Fax: +972-8-9342279, Machluf.yossy@weizmann.ac.il .|Department of Science Teaching, Weizmann Institute of Science, P.O. Box 26, Rehovot 76100, Israel. Tel.: +972-8-9342273, Fax: +972-8-9342279, Machluf.yossy@weizmann.ac.il .', 'Israel|Israel', 10, 'Genomics|Information Storage and Retrieval', 'Journal Article', '10.1093/bib/bbt031', '23673292', 'To facilitate the integration and querying of genomics data, a number of generic data warehousing frameworks have been developed. They differ in their design and capabilities, as well as their intended audience. We provide a comprehensive and quantitative review of those genomic data warehousing frameworks in the context of large-scale systems biology. We reviewed in detail four genomic data warehouses (BioMart, BioXRT, InterMine and PathwayTools) freely available to the academic community. We quantified 20 aspects of the warehouses, covering the accuracy of their responses, their computational requirements and development efforts. Performance of the warehouses was evaluated under various hardware configurations to help laboratories optimize hardware expenses. Each aspect of the benchmark may be dynamically weighted by scientists using our online tool BenchDW (http://warehousebenchmark.fungalgenomics.ca/benchmark/) to build custom warehouse profiles and tailor our results to their specific needs.'),
(68788, 2013, 'bib', 'Environmental bio-monitoring with high-throughput sequencing', 'Wang, Jing|McLenachan, Patricia A|Biggs, Patrick J|Winder, Linton H|Schoenfeld, Barbara I K|Narayan, Vinay V|Phiri, Bernard J|Lockhart, Peter J', 'Institute of Fundamental Sciences, Massey University, Palmerston North, New Zealand. P.J.Lockhart@massey.ac.nz.|Institute of Fundamental Sciences, Massey University, Palmerston North, New Zealand. P.J.Lockhart@massey.ac.nz.|Institute of Fundamental Sciences, Massey University, Palmerston North, New Zealand. P.J.Lockhart@massey.ac.nz.|Institute of Fundamental Sciences, Massey University, Palmerston North, New Zealand. P.J.Lockhart@massey.ac.nz.|Institute of Fundamental Sciences, Massey University, Palmerston North, New Zealand. P.J.Lockhart@massey.ac.nz.|Institute of Fundamental Sciences, Massey University, Palmerston North, New Zealand. P.J.Lockhart@massey.ac.nz.|Institute of Fundamental Sciences, Massey University, Palmerston North, New Zealand. P.J.Lockhart@massey.ac.nz.|Institute of Fundamental Sciences, Massey University, Palmerston North, New Zealand. P.J.Lockhart@massey.ac.nz.', 'New Zealand|New Zealand|New Zealand|New Zealand|New Zealand|New Zealand|New Zealand|New Zealand', 22, 'Algorithms|Animals|Computational Biology|Databases, Genetic|Ecosystem|Environmental Monitoring|High-Throughput Nucleotide Sequencing|Metagenomics|Software', 'Journal Article', '10.1093/bib/bbt032', '23677899', 'There is much interest in using high-throughput DNA sequencing methodology to monitor microorganisms, complex plant and animal communities. However, there are experimental and analytical issues to consider before applying a sequencing technology, which was originally developed for genome projects, to ecological projects. Many of these issues have been highlighted by recent microbial studies. Understanding how high-throughput sequencing is best implemented is important for the interpretation of recent results and the success of future applications. Addressing complex biological questions with metagenomics requires the interaction of researchers who bring different skill sets to problem solving. Educators can help by nurturing a collaborative interdisciplinary approach to genome science, which is essential for effective problem solving. Educators are in a position to help students, teachers, the public and policy makers interpret the new knowledge that metagenomics brings. To do this, they need to understand, not only the excitement of the science but also the pitfalls and shortcomings of methodology and research designs. We review these issues and some of the research directions that are helping to move the field forward. '),
(68789, 2013, 'bib', 'Obituary: In memory of Jack Leunissen', 'Luo, Jingchu|Nijveen, Harm|Attwood, Teresa|Judge, David|Pongor, Sandor|Landsman, David|Bishop, Martin', 'Institute of Fundamental Sciences, Massey University, Palmerston North, New Zealand. P.J.Lockhart@massey.ac.nz.|Institute of Fundamental Sciences, Massey University, Palmerston North, New Zealand. P.J.Lockhart@massey.ac.nz.|Institute of Fundamental Sciences, Massey University, Palmerston North, New Zealand. P.J.Lockhart@massey.ac.nz.|Institute of Fundamental Sciences, Massey University, Palmerston North, New Zealand. P.J.Lockhart@massey.ac.nz.|Institute of Fundamental Sciences, Massey University, Palmerston North, New Zealand. P.J.Lockhart@massey.ac.nz.|Institute of Fundamental Sciences, Massey University, Palmerston North, New Zealand. P.J.Lockhart@massey.ac.nz.|Institute of Fundamental Sciences, Massey University, Palmerston North, New Zealand. P.J.Lockhart@massey.ac.nz.', 'New Zealand|New Zealand|New Zealand|New Zealand|New Zealand|New Zealand|New Zealand', 0, 'Computational Biology|History, 20th Century|History, 21st Century|Netherlands', 'Biography', '10.1093/bib/bbt036', '23696635', '-'),
(68790, 2014, 'bib', 'Supervised, semi-supervised and unsupervised inference of gene regulatory networks', 'Maetschke, Stefan R|Madhamshettiwar, Piyush B|Davis, Melissa J|Ragan, Mark A', 'Institute for Molecular Bioscience and ARC Centre of Excellence in Bioinformatics, Brisbane, QLD 4072, Australia, Tel.: 61 7 3346 2616, Fax: 61 7 3346 2101, m.ragan@uq.edu.au.|Institute for Molecular Bioscience and ARC Centre of Excellence in Bioinformatics, Brisbane, QLD 4072, Australia, Tel.: 61 7 3346 2616, Fax: 61 7 3346 2101, m.ragan@uq.edu.au.|Institute for Molecular Bioscience and ARC Centre of Excellence in Bioinformatics, Brisbane, QLD 4072, Australia, Tel.: 61 7 3346 2616, Fax: 61 7 3346 2101, m.ragan@uq.edu.au.|Institute for Molecular Bioscience and ARC Centre of Excellence in Bioinformatics, Brisbane, QLD 4072, Australia, Tel.: 61 7 3346 2616, Fax: 61 7 3346 2101, m.ragan@uq.edu.au.', 'Australia|Australia|Australia|Australia', 31, 'Algorithms|Artificial Intelligence|Computational Biology|Computer Simulation|Databases, Genetic|Escherichia coli|Gene Expression Profiling|Gene Regulatory Networks|Genes, Bacterial|Genes, Fungal|Saccharomyces cerevisiae|Software|Support Vector Machine|Systems Biology', 'Evaluation Studies', '10.1093/bib/bbt034', '23698722', 'Inference of gene regulatory network from expression data is a challenging task. Many methods have been developed to this purpose but a comprehensive evaluation that covers unsupervised, semi-supervised and supervised methods, and provides guidelines for their practical application, is lacking. We performed an extensive evaluation of inference methods on simulated and experimental expression data. The results reveal low prediction accuracies for unsupervised techniques with the notable exception of the Z-SCORE method on knockout data. In all other cases, the supervised approach achieved the highest accuracies and even in a semi-supervised setting with small numbers of only positive samples, outperformed the unsupervised techniques. '),
(68791, 2014, 'bib', 'DRISEE overestimates errors in metagenomic sequencing data', 'Eren, A Murat|Morrison, Hilary G|Huse, Susan M|Sogin, Mitchell L', 'Institute for Molecular Bioscience and ARC Centre of Excellence in Bioinformatics, Brisbane, QLD 4072, Australia, Tel.: 61 7 3346 2616, Fax: 61 7 3346 2101, m.ragan@uq.edu.au.|Institute for Molecular Bioscience and ARC Centre of Excellence in Bioinformatics, Brisbane, QLD 4072, Australia, Tel.: 61 7 3346 2616, Fax: 61 7 3346 2101, m.ragan@uq.edu.au.|Institute for Molecular Bioscience and ARC Centre of Excellence in Bioinformatics, Brisbane, QLD 4072, Australia, Tel.: 61 7 3346 2616, Fax: 61 7 3346 2101, m.ragan@uq.edu.au.|Institute for Molecular Bioscience and ARC Centre of Excellence in Bioinformatics, Brisbane, QLD 4072, Australia, Tel.: 61 7 3346 2616, Fax: 61 7 3346 2101, m.ragan@uq.edu.au.', 'Australia|Australia|Australia|Australia', 2, 'Base Sequence|DNA|Metagenomics|Polymerase Chain Reaction|Sequence Analysis, DNA', 'Journal Article', '10.1093/bib/bbt010', '23698723', 'The extremely high error rates reported by Keegan et al. in A platform-independent method for detecting errors in metagenomic sequencing data: DRISEE (PLoS Comput Biol 2012, 8: :e1002541) for many next-generation sequencing datasets prompted us to re-examine their results. Our analysis reveals that the presence of conserved artificial sequences, e.g. Illumina adapters, and other naturally occurring sequence motifs accounts for most of the reported errors. We conclude that DRISEE reports inflated levels of sequencing error, particularly for Illumina data. Tools offered for evaluating large datasets need scrupulous review before they are implemented.'),
(68792, 2014, 'bib', 'Inference of dynamic networks using time-course data', 'Kim, Yongsoo|Han, Seungmin|Choi, Seungjin|Hwang, Daehee', 'POSTECH, Pohang, 790-784, Republic of Korea. Tel.: 82-54-279-2393, Fax: 82-54-279-8409, dhhwang@postech.ac.kr.|POSTECH, Pohang, 790-784, Republic of Korea. Tel.: 82-54-279-2393, Fax: 82-54-279-8409, dhhwang@postech.ac.kr.|POSTECH, Pohang, 790-784, Republic of Korea. Tel.: 82-54-279-2393, Fax: 82-54-279-8409, dhhwang@postech.ac.kr.|POSTECH, Pohang, 790-784, Republic of Korea. Tel.: 82-54-279-2393, Fax: 82-54-279-8409, dhhwang@postech.ac.kr.', '-|-|-|-', 33, 'Algorithms|Bayes Theorem|Computational Biology|Databases, Genetic|Gene Ontology|Gene Regulatory Networks|Genomics|Humans|Proteomics|Software|Systems Biology', 'Journal Article', '10.1093/bib/bbt028', '23698724', 'Cells execute their functions through dynamic operations of biological networks. Dynamic networks delineate the operation of biological networks in terms of temporal changes of abundances or activities of nodes (proteins and RNAs), as well as formation of new edges and disappearance of existing edges over time. Global genomic and proteomic technologies can be used to decode dynamic networks. However, using these experimental methods, it is still challenging to identify temporal transition of nodes and edges. Thus, several computational methods for estimating dynamic topological and functional characteristics of networks have been introduced. In this review, we summarize concepts and applications of these computational methods for inferring dynamic networks and further summarize methods for estimating spatial transition of biological networks. '),
(68793, 2013, 'bib', 'A review of bioinformatics training applied to research in molecular medicine, agriculture and biodiversity in Costa Rica and Central America', 'Orozco, Allan|Morera, Jessica|Jiménez, Sergio|Boza, Ricardo', 'School of Medicine (Bioinformatics Program), University of Costa Rica, San Pedro de Montes de Oca, 2060 San José, Costa Rica. Tel.: +506-215448, Fax: +506-25114570, bioinformatica.em@ucr.ac.cr.|School of Medicine (Bioinformatics Program), University of Costa Rica, San Pedro de Montes de Oca, 2060 San José, Costa Rica. Tel.: +506-215448, Fax: +506-25114570, bioinformatica.em@ucr.ac.cr.|School of Medicine (Bioinformatics Program), University of Costa Rica, San Pedro de Montes de Oca, 2060 San José, Costa Rica. Tel.: +506-215448, Fax: +506-25114570, bioinformatica.em@ucr.ac.cr.|School of Medicine (Bioinformatics Program), University of Costa Rica, San Pedro de Montes de Oca, 2060 San José, Costa Rica. Tel.: +506-215448, Fax: +506-25114570, bioinformatica.em@ucr.ac.cr.', 'Costa Rica|Costa Rica|Costa Rica|Costa Rica', 1, 'Agriculture|Animals|Biodiversity|Central America|Computational Biology|Costa Rica|Education, Graduate|Humans|Internet|Molecular Medicine|Societies, Scientific', 'Journal Article', '10.1093/bib/bbt033', '23723382', 'Today, Bioinformatics has become a scientific discipline with great relevance for the Molecular Biosciences and for the Omics sciences in general. Although developed countries have progressed with large strides in Bioinformatics education and research, in other regions, such as Central America, the advances have occurred in a gradual way and with little support from the Academia, either at the undergraduate or graduate level. To address this problem, the University of Costa Ricas Medical School, a regional leader in Bioinformatics in Central America, has been conducting a series of Bioinformatics workshops, seminars and courses, leading to the creation of the regions first Bioinformatics Masters Degree. The recent creation of the Central American Bioinformatics Network (BioCANET), associated to the deployment of a supporting computational infrastructure (HPC Cluster) devoted to provide computing support for Molecular Biology in the region, is providing a foundational stone for the development of Bioinformatics in the area. Central American bioinformaticians have participated in the creation of as well as co-founded the Iberoamerican Bioinformatics Society (SOIBIO). In this article, we review the most recent activities in education and research in Bioinformatics from several regional institutions. These activities have resulted in further advances for Molecular Medicine, Agriculture and Biodiversity research in Costa Rica and the rest of the Central American countries. Finally, we provide summary information on the first Central America Bioinformatics International Congress, as well as the creation of the first Bioinformatics company (Indromics Bioinformatics), spin-off the Academy in Central America and the Caribbean. '),
(68794, 2014, 'bib', 'Systems pharmacology in drug discovery and therapeutic insight for herbal medicines', 'Huang, Chao|Zheng, Chunli|Li, Yan|Wang, Yonghua|Lu, Aiping|Yang, Ling', 'School of Medicine (Bioinformatics Program), University of Costa Rica, San Pedro de Montes de Oca, 2060 San José, Costa Rica. Tel.: +506-215448, Fax: +506-25114570, bioinformatica.em@ucr.ac.cr.|School of Medicine (Bioinformatics Program), University of Costa Rica, San Pedro de Montes de Oca, 2060 San José, Costa Rica. Tel.: +506-215448, Fax: +506-25114570, bioinformatica.em@ucr.ac.cr.|School of Medicine (Bioinformatics Program), University of Costa Rica, San Pedro de Montes de Oca, 2060 San José, Costa Rica. Tel.: +506-215448, Fax: +506-25114570, bioinformatica.em@ucr.ac.cr.|School of Medicine (Bioinformatics Program), University of Costa Rica, San Pedro de Montes de Oca, 2060 San José, Costa Rica. Tel.: +506-215448, Fax: +506-25114570, bioinformatica.em@ucr.ac.cr.|School of Medicine (Bioinformatics Program), University of Costa Rica, San Pedro de Montes de Oca, 2060 San José, Costa Rica. Tel.: +506-215448, Fax: +506-25114570, bioinformatica.em@ucr.ac.cr.|School of Medicine (Bioinformatics Program), University of Costa Rica, San Pedro de Montes de Oca, 2060 San José, Costa Rica. Tel.: +506-215448, Fax: +506-25114570, bioinformatica.em@ucr.ac.cr.', 'Costa Rica|Costa Rica|Costa Rica|Costa Rica|Costa Rica|Costa Rica', 39, 'Drug Discovery|Herbal Medicine|Pharmacokinetics|Pharmacology', 'Journal Article', '10.1093/bib/bbt035', '23736100', 'Systems pharmacology is an emerging field that integrates systems biology and pharmacology to advance the process of drug discovery, development and the understanding of therapeutic mechanisms. The aim of the present work is to highlight the role that the systems pharmacology plays across the traditional herbal medicines discipline, which is exemplified by a case study of botanical drugs applied in the treatment of depression. First, based on critically examined pharmacology and clinical knowledge, we propose a large-scale statistical analysis to evaluate the efficiency of herbs used in traditional medicines. Second, we focus on the exploration of the active ingredients and targets by carrying out complex structure-, omics- and network-based systematic investigations. Third, specific informatics methods are developed to infer drug-disease connections, with purpose to understand how drugs work on the specific targets and pathways. Finally, we propose a new systems pharmacology method, which is further applied to an integrated platform (Herbal medicine Systems Pharmacology) of blended herbal medicine and omics data sets, allowing for the systematization of current and traditional knowledge of herbal medicines and, importantly, for the application of this emerging body of knowledge to the development of new drugs for complex human diseases.'),
(68795, 2014, 'bib', 'Forward LASSO analysis for high-order interactions in genome-wide association study', 'Gao, Huijiang|Wu, Yang|Li, Jiahan|Li, Hongwang|Li, Junya|Yang, Runqing', 'School of Medicine (Bioinformatics Program), University of Costa Rica, San Pedro de Montes de Oca, 2060 San José, Costa Rica. Tel.: +506-215448, Fax: +506-25114570, bioinformatica.em@ucr.ac.cr.|School of Medicine (Bioinformatics Program), University of Costa Rica, San Pedro de Montes de Oca, 2060 San José, Costa Rica. Tel.: +506-215448, Fax: +506-25114570, bioinformatica.em@ucr.ac.cr.|School of Medicine (Bioinformatics Program), University of Costa Rica, San Pedro de Montes de Oca, 2060 San José, Costa Rica. Tel.: +506-215448, Fax: +506-25114570, bioinformatica.em@ucr.ac.cr.|School of Medicine (Bioinformatics Program), University of Costa Rica, San Pedro de Montes de Oca, 2060 San José, Costa Rica. Tel.: +506-215448, Fax: +506-25114570, bioinformatica.em@ucr.ac.cr.|School of Medicine (Bioinformatics Program), University of Costa Rica, San Pedro de Montes de Oca, 2060 San José, Costa Rica. Tel.: +506-215448, Fax: +506-25114570, bioinformatica.em@ucr.ac.cr.|School of Medicine (Bioinformatics Program), University of Costa Rica, San Pedro de Montes de Oca, 2060 San José, Costa Rica. Tel.: +506-215448, Fax: +506-25114570, bioinformatica.em@ucr.ac.cr.', 'Costa Rica|Costa Rica|Costa Rica|Costa Rica|Costa Rica|Costa Rica', 8, 'Genome-Wide Association Study|Humans|Linear Models|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bib/bbt037', '23775311', 'Previous genome-wide association study (GWAS) focused on low-order interactions between pairwise single-nucleotide polymorphisms (SNPs) with significant main effects. Little is known how high-order interactions effect, especially one among the SNPs without main effects regulates quantitative traits. Within the frameworks of linear model and generalized linear model, the LASSO with coordinate descent step can be used to simultaneously analyze thousands and thousands of SNPs for normal and discrete traits. With consideration of high-order interactions among SNPs, a huge number of genetic effects make the LASSO failing to work under the presented condition of computation. Forward LASSO analysis is, therefore, proposed to shrink most of genetic effects to be zeros stage by stage. Simulation demonstrates that our proposed method could be used instead of the LASSO method for full model in mapping high-order interactions. Application of forward LASSO method is provided to GWAS for carcass traits and meat quality traits in beef cattle.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(68796, 2014, 'bib', 'Identifying protein complexes and functional modules--from static PPI networks to dynamic PPI networks', 'Chen, Bolin|Fan, Weiwei|Liu, Juan|Wu, Fang-Xiang', 'School of Computer, Wuhan University, Wuhan 430072, China. Tel.: +86-27-6877-5711, Fax: +86-27-6877-5711, liujuan@whu.edu.cn, Fang-Xiang Wu, College of Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK S7N 5A9, Canada. Tel.: +1-306-966-5280, Fax: +1-306-966-5427, E-mail: faw341@mail.usask.ca.|School of Computer, Wuhan University, Wuhan 430072, China. Tel.: +86-27-6877-5711, Fax: +86-27-6877-5711, liujuan@whu.edu.cn, Fang-Xiang Wu, College of Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK S7N 5A9, Canada. Tel.: +1-306-966-5280, Fax: +1-306-966-5427, E-mail: faw341@mail.usask.ca.|School of Computer, Wuhan University, Wuhan 430072, China. Tel.: +86-27-6877-5711, Fax: +86-27-6877-5711, liujuan@whu.edu.cn, Fang-Xiang Wu, College of Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK S7N 5A9, Canada. Tel.: +1-306-966-5280, Fax: +1-306-966-5427, E-mail: faw341@mail.usask.ca.|School of Computer, Wuhan University, Wuhan 430072, China. Tel.: +86-27-6877-5711, Fax: +86-27-6877-5711, liujuan@whu.edu.cn, Fang-Xiang Wu, College of Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK S7N 5A9, Canada. Tel.: +1-306-966-5280, Fax: +1-306-966-5427, E-mail: faw341@mail.usask.ca.', 'Canada|Canada|Canada|Canada', 86, 'Algorithms|Computational Biology|Databases, Protein|Models, Molecular|Multiprotein Complexes|Protein Interaction Mapping|Protein Interaction Maps', 'Journal Article', '10.1093/bib/bbt039', '23780996', 'Cellular processes are typically carried out by protein complexes and functional modules. Identifying them plays an important role for our attempt to reveal principles of cellular organizations and functions. In this article, we review computational algorithms for identifying protein complexes and/or functional modules from protein-protein interaction (PPI) networks. We first describe issues and pitfalls when interpreting PPI networks. Then based on types of data used and main ideas involved, we briefly describe protein complex and/or functional module identification algorithms in four categories: (i) those based on topological structures of unweighted PPI networks, (ii) those based on characters of weighted PPI networks, (iii) those based on multiple data integrations, and (iv) those based on dynamic PPI networks. The PPI networks are modelled increasingly precise when integrating more types of data, and the study of protein complexes would benefit by shifting from static to dynamic PPI networks. '),
(68797, 2014, 'bib', 'Parallel stochastic systems biology in the cloud', 'Aldinucci, Marco|Torquati, Massimo|Spampinato, Concetto|Drocco, Maurizio|Misale, Claudia|Calcagno, Cristina|Coppo, Mario', 'School of Computer, Wuhan University, Wuhan 430072, China. Tel.: +86-27-6877-5711, Fax: +86-27-6877-5711, liujuan@whu.edu.cn, Fang-Xiang Wu, College of Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK S7N 5A9, Canada. Tel.: +1-306-966-5280, Fax: +1-306-966-5427, E-mail: faw341@mail.usask.ca.|School of Computer, Wuhan University, Wuhan 430072, China. Tel.: +86-27-6877-5711, Fax: +86-27-6877-5711, liujuan@whu.edu.cn, Fang-Xiang Wu, College of Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK S7N 5A9, Canada. Tel.: +1-306-966-5280, Fax: +1-306-966-5427, E-mail: faw341@mail.usask.ca.|School of Computer, Wuhan University, Wuhan 430072, China. Tel.: +86-27-6877-5711, Fax: +86-27-6877-5711, liujuan@whu.edu.cn, Fang-Xiang Wu, College of Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK S7N 5A9, Canada. Tel.: +1-306-966-5280, Fax: +1-306-966-5427, E-mail: faw341@mail.usask.ca.|School of Computer, Wuhan University, Wuhan 430072, China. Tel.: +86-27-6877-5711, Fax: +86-27-6877-5711, liujuan@whu.edu.cn, Fang-Xiang Wu, College of Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK S7N 5A9, Canada. Tel.: +1-306-966-5280, Fax: +1-306-966-5427, E-mail: faw341@mail.usask.ca.|School of Computer, Wuhan University, Wuhan 430072, China. Tel.: +86-27-6877-5711, Fax: +86-27-6877-5711, liujuan@whu.edu.cn, Fang-Xiang Wu, College of Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK S7N 5A9, Canada. Tel.: +1-306-966-5280, Fax: +1-306-966-5427, E-mail: faw341@mail.usask.ca.|School of Computer, Wuhan University, Wuhan 430072, China. Tel.: +86-27-6877-5711, Fax: +86-27-6877-5711, liujuan@whu.edu.cn, Fang-Xiang Wu, College of Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK S7N 5A9, Canada. Tel.: +1-306-966-5280, Fax: +1-306-966-5427, E-mail: faw341@mail.usask.ca.|School of Computer, Wuhan University, Wuhan 430072, China. Tel.: +86-27-6877-5711, Fax: +86-27-6877-5711, liujuan@whu.edu.cn, Fang-Xiang Wu, College of Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK S7N 5A9, Canada. Tel.: +1-306-966-5280, Fax: +1-306-966-5427, E-mail: faw341@mail.usask.ca.', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada', 3, 'Computational Biology|Computer Simulation|Information Storage and Retrieval|Stochastic Processes|Systems Biology', 'Journal Article', '10.1093/bib/bbt040', '23780997', 'The stochastic modelling of biological systems, coupled with Monte Carlo simulation of models, is an increasingly popular technique in bioinformatics. The simulation-analysis workflow may result computationally expensive reducing the interactivity required in the model tuning. In this work, we advocate the high-level software design as a vehicle for building efficient and portable parallel simulators for the cloud. In particular, the Calculus of Wrapped Components (CWC) simulator for systems biology, which is designed according to the FastFlow pattern-based approach, is presented and discussed. Thanks to the FastFlow framework, the CWC simulator is designed as a high-level workflow that can simulate CWC models, merge simulation results and statistically analyse them in a single parallel workflow in the cloud. To improve interactivity, successive phases are pipelined in such a way that the workflow begins to output a stream of analysis results immediately after simulation is started. Performance and effectiveness of the CWC simulator are validated on the Amazon Elastic Compute Cloud.'),
(68798, 2014, 'bib', 'Similarity of markers identified from cancer gene expression studies: observations from GEO', 'Shi, Xingjie|Shen, Shihao|Liu, Jin|Huang, Jian|Zhou, Yong|Ma, Shuangge', 'School of Computer, Wuhan University, Wuhan 430072, China. Tel.: +86-27-6877-5711, Fax: +86-27-6877-5711, liujuan@whu.edu.cn, Fang-Xiang Wu, College of Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK S7N 5A9, Canada. Tel.: +1-306-966-5280, Fax: +1-306-966-5427, E-mail: faw341@mail.usask.ca.|School of Computer, Wuhan University, Wuhan 430072, China. Tel.: +86-27-6877-5711, Fax: +86-27-6877-5711, liujuan@whu.edu.cn, Fang-Xiang Wu, College of Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK S7N 5A9, Canada. Tel.: +1-306-966-5280, Fax: +1-306-966-5427, E-mail: faw341@mail.usask.ca.|School of Computer, Wuhan University, Wuhan 430072, China. Tel.: +86-27-6877-5711, Fax: +86-27-6877-5711, liujuan@whu.edu.cn, Fang-Xiang Wu, College of Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK S7N 5A9, Canada. Tel.: +1-306-966-5280, Fax: +1-306-966-5427, E-mail: faw341@mail.usask.ca.|School of Computer, Wuhan University, Wuhan 430072, China. Tel.: +86-27-6877-5711, Fax: +86-27-6877-5711, liujuan@whu.edu.cn, Fang-Xiang Wu, College of Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK S7N 5A9, Canada. Tel.: +1-306-966-5280, Fax: +1-306-966-5427, E-mail: faw341@mail.usask.ca.|School of Computer, Wuhan University, Wuhan 430072, China. Tel.: +86-27-6877-5711, Fax: +86-27-6877-5711, liujuan@whu.edu.cn, Fang-Xiang Wu, College of Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK S7N 5A9, Canada. Tel.: +1-306-966-5280, Fax: +1-306-966-5427, E-mail: faw341@mail.usask.ca.|School of Computer, Wuhan University, Wuhan 430072, China. Tel.: +86-27-6877-5711, Fax: +86-27-6877-5711, liujuan@whu.edu.cn, Fang-Xiang Wu, College of Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK S7N 5A9, Canada. Tel.: +1-306-966-5280, Fax: +1-306-966-5427, E-mail: faw341@mail.usask.ca.', 'Canada|Canada|Canada|Canada|Canada|Canada', 6, 'Biomarkers, Tumor|Gene Expression Profiling|Humans|Models, Theoretical|Neoplasms', 'Journal Article', '10.1093/bib/bbt044', '23788798', 'Gene expression profiling has been extensively conducted in cancer research. The analysis of multiple independent cancer gene expression datasets may provide additional information and complement single-dataset analysis. In this study, we conduct multi-dataset analysis and are interested in evaluating the similarity of cancer-associated genes identified from different datasets. The first objective of this study is to briefly review some statistical methods that can be used for such evaluation. Both marginal analysis and joint analysis methods are reviewed. The second objective is to apply those methods to 26 Gene Expression Omnibus (GEO) datasets on five types of cancers. Our analysis suggests that for the same cancer, the marker identification results may vary significantly across datasets, and different datasets share few common genes. In addition, datasets on different cancers share few common genes. The shared genetic basis of datasets on the same or different cancers, which has been suggested in the literature, is not observed in the analysis of GEO data.'),
(68799, 2014, 'bib', 'Integrative approaches for predicting protein function and prioritizing genes for complex phenotypes using protein interaction networks', 'Ma, Xiaotu|Chen, Ting|Sun, Fengzhu', 'School of Computer, Wuhan University, Wuhan 430072, China. Tel.: +86-27-6877-5711, Fax: +86-27-6877-5711, liujuan@whu.edu.cn, Fang-Xiang Wu, College of Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK S7N 5A9, Canada. Tel.: +1-306-966-5280, Fax: +1-306-966-5427, E-mail: faw341@mail.usask.ca.|School of Computer, Wuhan University, Wuhan 430072, China. Tel.: +86-27-6877-5711, Fax: +86-27-6877-5711, liujuan@whu.edu.cn, Fang-Xiang Wu, College of Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK S7N 5A9, Canada. Tel.: +1-306-966-5280, Fax: +1-306-966-5427, E-mail: faw341@mail.usask.ca.|School of Computer, Wuhan University, Wuhan 430072, China. Tel.: +86-27-6877-5711, Fax: +86-27-6877-5711, liujuan@whu.edu.cn, Fang-Xiang Wu, College of Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK S7N 5A9, Canada. Tel.: +1-306-966-5280, Fax: +1-306-966-5427, E-mail: faw341@mail.usask.ca.', 'Canada|Canada|Canada', 8, 'Gene Expression Profiling|Models, Theoretical|Phenotype|Protein Binding|Proteins|RNA Interference', 'Journal Article', '10.1093/bib/bbt041', '23788799', 'With the rapid development of biotechnologies, many types of biological data including molecular networks are now available. However, to obtain a more complete understanding of a biological system, the integration of molecular networks with other data, such as molecular sequences, protein domains and gene expression profiles, is needed. A key to the use of networks in biological studies is the definition of similarity among proteins over the networks. Here, we review applications of similarity measures over networks with a special focus on the following four problems: (i) predicting protein functions, (ii) prioritizing genes related to a phenotype given a set of seed genes that have been shown to be related to the phenotype, (iii) prioritizing genes related to a phenotype by integrating gene expression profiles and networks and (iv) identification of false positives and false negatives from RNAi experiments. Diffusion kernels are demonstrated to give superior performance in all these tasks, leading to the suggestion that diffusion kernels should be the primary choice for a network similarity metric over other similarity measures such as direct neighbors and shortest path distance.'),
(68800, 2013, 'bib', 'The NGS WikiBook: a dynamic collaborative online training effort with long-term sustainability', 'Li, Jing-Woei|Bolser, Dan|Manske, Magnus|Giorgi, Federico Manuel|Vyahhi, Nikolay|Usadel, Björn|Clavijo, Bernardo J|Chan, Ting-Fung|Wong, Nathalie|Zerbino, Daniel|Schneider, Maria Victoria', 'School of Life Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR. Tel.: +852-39431302, marcowanger@gmail.com.|School of Life Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR. Tel.: +852-39431302, marcowanger@gmail.com.|School of Life Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR. Tel.: +852-39431302, marcowanger@gmail.com.|School of Life Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR. Tel.: +852-39431302, marcowanger@gmail.com.|School of Life Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR. Tel.: +852-39431302, marcowanger@gmail.com.|School of Life Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR. Tel.: +852-39431302, marcowanger@gmail.com.|School of Life Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR. Tel.: +852-39431302, marcowanger@gmail.com.|School of Life Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR. Tel.: +852-39431302, marcowanger@gmail.com.|School of Life Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR. Tel.: +852-39431302, marcowanger@gmail.com.|School of Life Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR. Tel.: +852-39431302, marcowanger@gmail.com.|School of Life Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR. Tel.: +852-39431302, marcowanger@gmail.com.', 'Hong Kong|Hong Kong|Hong Kong|Hong Kong|Hong Kong|Hong Kong|Hong Kong|Hong Kong|Hong Kong|Hong Kong|', 5, 'Computational Biology|Computer-Assisted Instruction|Cooperative Behavior|High-Throughput Nucleotide Sequencing|Internet|Teaching', 'Journal Article', '10.1093/bib/bbt045', '23793381', 'Next-generation sequencing (NGS) is increasingly being adopted as the backbone of biomedical research. With the commercialization of various affordable desktop sequencers, NGS will be reached by increasing numbers of cellular and molecular biologists, necessitating community consensus on bioinformatics protocols to tackle the exponential increase in quantity of sequence data. The current resources for NGS informatics are extremely fragmented. Finding a centralized synthesis is difficult. A multitude of tools exist for NGS data analysis, however, none of these satisfies all possible uses and needs. This gap in functionality could be filled by integrating different methods in customized pipelines, an approach helped by the open-source nature of many NGS programmes. Drawing from community spirit and with the use of the Wikipedia framework, we have initiated a collaborative NGS resource: The NGS WikiBook. We have collected a sufficient amount of text to incentivize a broader community to contribute to it. Users can search, browse, edit and create new content, so as to facilitate self-learning and feedback to the community. The overall structure and style for this dynamic material is designed for the bench biologists and non-bioinformaticians. The flexibility of online material allows the readers to ignore details in a first read, yet have immediate access to the information they need. Each chapter comes with practical exercises so readers may familiarize themselves with each step. The NGS WikiBook aims to create a collective laboratory book and protocol that explains the key concepts and describes best practices in this fast-evolving field. '),
(68801, 2014, 'bib', 'Fighting against uncertainty: an essential issue in bioinformatics', 'Hamada, Michiaki', 'School of Life Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR. Tel.: +852-39431302, marcowanger@gmail.com.', 'Hong Kong', 4, 'Base Sequence|Computational Biology|Likelihood Functions|Molecular Sequence Data|RNA|Uncertainty', 'Journal Article', '10.1093/bib/bbt038', '23803300', 'Many bioinformatics problems, such as sequence alignment, gene prediction, phylogenetic tree estimation and RNA secondary structure prediction, are often affected by the uncertainty of a solution, that is, the probability of the solution is extremely small. This situation arises for estimation problems on high-dimensional discrete spaces in which the number of possible discrete solutions is immense. In the analysis of biological data or the development of prediction algorithms, this uncertainty should be handled carefully and appropriately. In this review, I will explain several methods to combat this uncertainty, presenting a number of examples in bioinformatics. The methods include (i) avoiding point estimation, (ii) maximum expected accuracy (MEA) estimations and (iii) several strategies to design a pipeline involving several prediction methods. I believe that the basic concepts and ideas described in this review will be generally useful for estimation problems in various areas of bioinformatics.'),
(68802, 2013, 'bib', 'Best practices in bioinformatics training for life scientists', 'Via, Allegra|Blicher, Thomas|Bongcam-Rudloff, Erik|Brazas, Michelle D|Brooksbank, Cath|Budd, Aidan|De Las Rivas, Javier|Dreyer, Jacqueline|Fernandes, Pedro L|van Gelder, Celia|Jacob, Joachim|Jimenez, Rafael C|Loveland, Jane|Moran, Federico|Mulder, Nicola|Nyrönen, Tommi|Rother, Kristian|Schneider, Maria Victoria|Attwood, Teresa K', 'Department of Physics, Sapienza University, P.le Aldo Moro 5, 00185 Rome, Italy. allegra.via@uniroma1.it.|Department of Physics, Sapienza University, P.le Aldo Moro 5, 00185 Rome, Italy. allegra.via@uniroma1.it.|Department of Physics, Sapienza University, P.le Aldo Moro 5, 00185 Rome, Italy. allegra.via@uniroma1.it.|Department of Physics, Sapienza University, P.le Aldo Moro 5, 00185 Rome, Italy. allegra.via@uniroma1.it.|Department of Physics, Sapienza University, P.le Aldo Moro 5, 00185 Rome, Italy. allegra.via@uniroma1.it.|Department of Physics, Sapienza University, P.le Aldo Moro 5, 00185 Rome, Italy. allegra.via@uniroma1.it.|Department of Physics, Sapienza University, P.le Aldo Moro 5, 00185 Rome, Italy. allegra.via@uniroma1.it.|Department of Physics, Sapienza University, P.le Aldo Moro 5, 00185 Rome, Italy. allegra.via@uniroma1.it.|Department of Physics, Sapienza University, P.le Aldo Moro 5, 00185 Rome, Italy. allegra.via@uniroma1.it.|Department of Physics, Sapienza University, P.le Aldo Moro 5, 00185 Rome, Italy. allegra.via@uniroma1.it.|Department of Physics, Sapienza University, P.le Aldo Moro 5, 00185 Rome, Italy. allegra.via@uniroma1.it.|Department of Physics, Sapienza University, P.le Aldo Moro 5, 00185 Rome, Italy. allegra.via@uniroma1.it.|Department of Physics, Sapienza University, P.le Aldo Moro 5, 00185 Rome, Italy. allegra.via@uniroma1.it.|Department of Physics, Sapienza University, P.le Aldo Moro 5, 00185 Rome, Italy. allegra.via@uniroma1.it.|Department of Physics, Sapienza University, P.le Aldo Moro 5, 00185 Rome, Italy. allegra.via@uniroma1.it.|Department of Physics, Sapienza University, P.le Aldo Moro 5, 00185 Rome, Italy. allegra.via@uniroma1.it.|Department of Physics, Sapienza University, P.le Aldo Moro 5, 00185 Rome, Italy. allegra.via@uniroma1.it.|Department of Physics, Sapienza University, P.le Aldo Moro 5, 00185 Rome, Italy. allegra.via@uniroma1.it.|Department of Physics, Sapienza University, P.le Aldo Moro 5, 00185 Rome, Italy. allegra.via@uniroma1.it.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Ital', 19, 'Biological Science Disciplines|Computational Biology|Curriculum|Data Mining|Database Management Systems|Programming Languages|Software Design|Teaching', 'Journal Article', '10.1093/bib/bbt043', '23803301', 'The mountains of data thrusting from the new landscape of modern high-throughput biology are irrevocably changing biomedical research and creating a near-insatiable demand for training in data management and manipulation and data mining and analysis. Among life scientists, from clinicians to environmental researchers, a common theme is the need not just to use, and gain familiarity with, bioinformatics tools and resources but also to understand their underlying fundamental theoretical and practical concepts. Providing bioinformatics training to empower life scientists to handle and analyse their data efficiently, and progress their research, is a challenge across the globe. Delivering good training goes beyond traditional lectures and resource-centric demos, using interactivity, problem-solving exercises and cooperative learning to substantially enhance training quality and learning outcomes. In this context, this article discusses various pragmatic criteria for identifying training needs and learning objectives, for selecting suitable trainees and trainers, for developing and maintaining training skills and evaluating training quality. Adherence to these criteria may help not only to guide course organizers and trainers on the path towards bioinformatics training excellence but, importantly, also to improve the training experience for life scientists. '),
(68803, 2014, 'bib', 'Evaluating template-based and template-free protein-protein complex structure prediction', 'Vreven, Thom|Hwang, Howook|Pierce, Brian G|Weng, Zhiping', 'Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, ASC-5th floor room 1069, 368 Plantation St., Worcester, MA 01605, USA. Zhiping.Weng@umassmed.edu.|Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, ASC-5th floor room 1069, 368 Plantation St., Worcester, MA 01605, USA. Zhiping.Weng@umassmed.edu.|Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, ASC-5th floor room 1069, 368 Plantation St., Worcester, MA 01605, USA. Zhiping.Weng@umassmed.edu.|Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, ASC-5th floor room 1069, 368 Plantation St., Worcester, MA 01605, USA. Zhiping.Weng@umassmed.edu.', 'United States|United States|United States|United States', 11, 'Algorithms|Computational Biology|Databases, Protein|Protein Conformation|Protein Interaction Domains and Motifs|Protein Interaction Mapping|Sequence Alignment|Software|Structural Homology, Protein', 'Comparative Study', '10.1093/bib/bbt047', '23818491', 'We compared the performance of template-free (docking) and template-based methods for the prediction of protein-protein complex structures. We found similar performance for a template-based method based on threading (COTH) and another template-based method based on structural alignment (PRISM). The template-based methods showed similar performance to a docking method (ZDOCK) when the latter was allowed one prediction for each complex, but when the same number of predictions was allowed for each method, the docking approach outperformed template-based approaches. We identified strengths and weaknesses in each method. Template-based approaches were better able to handle complexes that involved conformational changes upon binding. Furthermore, the threading-based and docking methods were better than the structural-alignment-based method for enzyme-inhibitor complex prediction. Finally, we show that the near-native (correct) predictions were generally not shared by the various approaches, suggesting that integrating their results could be the superior strategy. '),
(68804, 2014, 'bib', 'Identifying driver mutations from sequencing data of heterogeneous tumors in the era of personalized genome sequencing', 'Zhang, Jing|Liu, Jie|Sun, Jianbo|Chen, Chen|Foltz, Gregory|Lin, Biaoyang', 'Hangzhou, 310003, China, biaoylin@gmail.com.|Hangzhou, 310003, China, biaoylin@gmail.com.|Hangzhou, 310003, China, biaoylin@gmail.com.|Hangzhou, 310003, China, biaoylin@gmail.com.|Hangzhou, 310003, China, biaoylin@gmail.com.|Hangzhou, 310003, China, biaoylin@gmail.com.', 'China|China|China|China|China|China', 28, 'Algorithms|Artificial Intelligence|Computational Biology|DNA Mutational Analysis|High-Throughput Nucleotide Sequencing|Humans|Likelihood Functions|Mutation|Neoplasms|Polymorphism, Single Nucleotide|Precision Medicine|Software', 'Journal Article', '10.1093/bib/bbt042', '23818492', 'Distinguishing driver mutations from passenger mutations is critical to the understanding of the molecular mechanisms of carcinogenesis and for identifying prognostic and diagnostic markers as well as therapeutic targets. We reviewed the current approaches and software for identifying driver mutations from passenger mutations including both biology-based approaches and machine-learning-based approaches. We also reviewed approaches to identify driver mutations in the context of pathways or gene sets. Finally, we discussed the challenges of predicting driver mutations considering the complexities of inter- and intra-tumor heterogeneity as well as the evolution and progression of tumors. '),
(68805, 2013, 'bib', 'Mathematics and evolutionary biology make bioinformatics education comprehensible', 'Jungck, John R|Weisstein, Anton E', 'Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.', 'United States|United States', 1, 'Algorithms|Biological Evolution|Biology|Computational Biology|Cooperative Behavior|Curriculum|Mathematics|Phylogeny|Sequence Alignment|Software', 'Journal Article', '10.1093/bib/bbt046', '23821621', 'The patterns of variation within a molecular sequence data set result from the interplay between population genetic, molecular evolutionary and macroevolutionary processes-the standard purview of evolutionary biologists. Elucidating these patterns, particularly for large data sets, requires an understanding of the structure, assumptions and limitations of the algorithms used by bioinformatics software-the domain of mathematicians and computer scientists. As a result, bioinformatics often suffers a two-culture problem because of the lack of broad overlapping expertise between these two groups. Collaboration among specialists in different fields has greatly mitigated this problem among active bioinformaticians. However, science education researchers report that much of bioinformatics education does little to bridge the cultural divide, the curriculum too focused on solving narrow problems (e.g. interpreting pre-built phylogenetic trees) rather than on exploring broader ones (e.g. exploring alternative phylogenetic strategies for different kinds of data sets). Herein, we present an introduction to the mathematics of tree enumeration, tree construction, split decomposition and sequence alignment. We also introduce off-line downloadable software tools developed by the BioQUEST Curriculum Consortium to help students learn how to interpret and critically evaluate the results of standard bioinformatics analyses. '),
(68806, 2014, 'bib', 'A model for family-based case-control studies of genetic imprinting and epistasis', 'Li, Xin|Sui, Yihan|Liu, Tian|Wang, Jianxin|Li, Yongci|Lin, Zhenwu|Hegarty, John|Koltun, Walter A|Wang, Zuoheng|Wu, Rongling', 'Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 10, 'Case-Control Studies|Computational Biology|Computer Simulation|Databases, Genetic|Epistasis, Genetic|Female|Genomic Imprinting|Humans|Inflammatory Bowel Diseases|Male|Models, Genetic|Models, Statistical|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bib/bbt050', '23887693', 'Genetic imprinting, or called the parent-of-origin effect, has been recognized to play an important role in the formation and pathogenesis of human diseases. Although the epigenetic mechanisms that establish genetic imprinting have been a focus of many genetic studies, our knowledge about the number of imprinting genes and their chromosomal locations and interactions with other genes is still scarce, limiting precise inference of the genetic architecture of complex diseases. In this article, we present a statistical model for testing and estimating the effects of genetic imprinting on complex diseases using a commonly used case-control design with family structure. For each subject sampled from a case and control population, we not only genotype its own single nucleotide polymorphisms (SNPs) but also collect its parents genotypes. By tracing the transmission pattern of SNP alleles from parental to offspring generation, the model allows the characterization of genetic imprinting effects based on Pearson tests of a 2 × 2 contingency table. The model is expanded to test the interactions between imprinting effects and additive, dominant and epistatic effects in a complex web of genetic interactions. Statistical properties of the model are investigated, and its practical usefulness is validated by a real data analysis. The model will provide a useful tool for genome-wide association studies aimed to elucidate the picture of genetic control over complex human diseases. '),
(68807, 2014, 'bib', 'BioFNet: biological functional network database for analysis and synthesis of biological systems', 'Kurata, Hiroyuki|Maeda, Kazuhiro|Onaka, Toshikazu|Takata, Takenori', 'Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.', 'United States|United States|United States|United States', 3, 'Database Management Systems|Models, Theoretical', 'Journal Article', '10.1093/bib/bbt048', '23894104', 'In synthetic biology and systems biology, a bottom-up approach can be used to construct a complex, modular, hierarchical structure of biological networks. To analyze or design such networks, it is critical to understand the relationship between network structure and function, the mechanism through which biological parts or biomolecules are assembled into building blocks or functional networks. A functional network is defined as a subnetwork of biomolecules that performs a particular function. Understanding the mechanism of building functional networks would help develop a methodology for analyzing the structure of large-scale networks and design a robust biological circuit to perform a target function. We propose a biological functional network database, named BioFNet, which can cover the whole cell at the level of molecular interactions. The BioFNet takes an advantage in implementing the simulation program for the mathematical models of the functional networks, visualizing the simulated results. It presents a sound basis for rational design of biochemical networks and for understanding how functional networks are assembled to create complex high-level functions, which would reveal design principles underlying molecular architectures.'),
(68808, 2014, 'bib', 'Controlling for confounding variables in MS-omics protocol: why modularity matters', 'Smith, Rob|Ventura, Dan|Prince, John T', 'Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.', 'United States|United States|United States', 11, 'Biomarkers, Tumor|Humans|Lipids|Mass Spectrometry|Metabolomics|Neoplasm Proteins|Neoplasms|Proteomics', 'Journal Article', '10.1093/bib/bbt049', '23894105', 'As the field of bioinformatics research continues to grow, more and more novel techniques are proposed to meet new challenges and improvements upon solutions to long-standing problems. These include data processing techniques and wet lab protocol techniques. Although the literature is consistently thorough in experimental detail and variable-controlling rigor for wet lab protocol techniques, bioinformatics techniques tend to be less described and less controlled. As the validation or rejection of hypotheses rests on the experiments ability to isolate and measure a variable of interest, we urge the importance of reducing confounding variables in bioinformatics techniques during mass spectrometry experimentation.'),
(68809, 2014, 'bib', 'Alignment-free genetic sequence comparisons: a review of recent approaches by word analysis', 'Bonham-Carter, Oliver|Steele, Joe|Bastola, Dhundy', 'Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.', 'United States|United States|United States', 76, 'Algorithms|Animals|Computational Biology|Information Theory|Phylogeography|Sequence Alignment|Sequence Analysis, DNA', 'Comparative Study', '10.1093/bib/bbt052', '23904502', 'Modern sequencing and genome assembly technologies have provided a wealth of data, which will soon require an analysis by comparison for discovery. Sequence alignment, a fundamental task in bioinformatics research, may be used but with some caveats. Seminal techniques and methods from dynamic programming are proving ineffective for this work owing to their inherent computational expense when processing large amounts of sequence data. These methods are prone to giving misleading information because of genetic recombination, genetic shuffling and other inherent biological events. New approaches from information theory, frequency analysis and data compression are available and provide powerful alternatives to dynamic programming. These new methods are often preferred, as their algorithms are simpler and are not affected by synteny-related problems. In this review, we provide a detailed discussion of computational tools, which stem from alignment-free methods based on statistical analysis from word frequencies. We provide several clear examples to demonstrate applications and the interpretations over several different areas of alignment-free analysis such as base-base correlations, feature frequency profiles, compositional vectors, an improved string composition and the D2 statistic metric. Additionally, we provide detailed discussion and an example of analysis by Lempel-Ziv techniques from data compression. '),
(68810, 2014, 'bib', 'Facilitating the use of large-scale biological data and tools in the era of translational bioinformatics', 'Kouskoumvekaki, Irene|Shublaq, Nour|Brunak, Søren', 'Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.', 'United States|United States|United States', 6, 'Biological Ontologies|Computational Biology|Data Interpretation, Statistical|Database Management Systems|Female|High-Throughput Nucleotide Sequencing|Humans|Male|Software|Translational Medical Research', 'Journal Article', '10.1093/bib/bbt055', '23908249', 'As both the amount of generated biological data and the processing compute power increase, computational experimentation is no longer the exclusivity of bioinformaticians, but it is moving across all biomedical domains. For bioinformatics to realize its translational potential, domain experts need access to user-friendly solutions to navigate, integrate and extract information out of biological databases, as well as to combine tools and data resources in bioinformatics workflows. In this review, we present services that assist biomedical scientists in incorporating bioinformatics tools into their research. We review recent applications of Cytoscape, BioGPS and DAVID for data visualization, integration and functional enrichment. Moreover, we illustrate the use of Taverna, Kepler, GenePattern, and Galaxy as open-access workbenches for bioinformatics workflows. Finally, we mention services that facilitate the integration of biomedical ontologies and bioinformatics tools in computational workflows. '),
(68811, 2014, 'bib', 'Bioinformatics approaches for improved recombinant protein production in Escherichia coli: protein solubility prediction', 'Chang, Catherine Ching Han|Song, Jiangning|Tey, Beng Ti|Ramanan, Ramakrishnan Nagasundara', 'Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.', 'United States|United States|United States|United States', 31, 'Algorithms|Artificial Intelligence|Computational Biology|Computer Simulation|Databases, Factual|Escherichia coli|Recombinant Proteins|Software|Solubility', 'Comparative Study', '10.1093/bib/bbt057', '23926206', 'The solubility of recombinant protein expressed in Escherichia coli often represents the production yield. However, up-to-date, instances of successful production of soluble recombinant proteins in E. coli expression system with high yield remain scarce. This is mainly due to the difficulties in improving the overall production capacity, as most of the well-established strategies usually involve a series of trial and error steps with unguaranteed success. One way to concurrently improve the production yield and minimize the production cost would be incorporating the potency of bioinformatics tools to conduct in silico studies, which forecasts the outcome before actual experimental work. In this article, we review and compare seven prediction tools available, which predict the solubility of protein expressed in E. coli, using the following criteria: prediction performance, usability, utility, prediction tool development and validation methodologies. This comprehensive review will be a valuable resource for researchers with limited prior experience in bioinformatics tools. As such, this will facilitate their choice of appropriate tools for studies related to enhancement of intracellular recombinant protein production in E. coli. '),
(68812, 2014, 'bib', 'Similarity-based machine learning methods for predicting drug-target interactions: a brief review', 'Ding, Hao|Takigawa, Ichigaku|Mamitsuka, Hiroshi|Zhu, Shanfeng', 'Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.', 'United States|United States|United States|United States', 170, 'Artificial Intelligence|Drug Interactions|Models, Theoretical', 'Journal Article', '10.1093/bib/bbt056', '23933754', 'Computationally predicting drug-target interactions is useful to select possible drug (or target) candidates for further biochemical verification. We focus on machine learning-based approaches, particularly similarity-based methods that use drug and target similarities, which show relationships among drugs and those among targets, respectively. These two similarities represent two emerging concepts, the chemical space and the genomic space. Typically, the methods combine these two types of similarities to generate models for predicting new drug-target interactions. This process is also closely related to a lot of work in pharmacogenomics or chemical biology that attempt to understand the relationships between the chemical and genomic spaces. This background makes the similarity-based approaches attractive and promising. This article reviews the similarity-based machine learning methods for predicting drug-target interactions, which are state-of-the-art and have aroused great interest in bioinformatics. We describe each of these methods briefly, and empirically compare these methods under a uniform experimental setting to explore their advantages and limitations.'),
(68813, 2014, 'bib', 'Extracting reaction networks from databases-opening Pandoras box', 'Fearnley, Liam G|Davis, Melissa J|Ragan, Mark A|Nielsen, Lars K', 'Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.', 'United States|United States|United States|United States', 3, 'Cell Communication|Computational Biology|Databases, Factual|Humans|Knowledge Bases|MAP Kinase Signaling System|Models, Biological|Signal Transduction|Systems Biology', 'Journal Article', '10.1093/bib/bbt058', '23946492', 'Large quantities of information describing the mechanisms of biological pathways continue to be collected in publicly available databases. At the same time, experiments have increased in scale, and biologists increasingly use pathways defined in online databases to interpret the results of experiments and generate hypotheses. Emerging computational techniques that exploit the rich biological information captured in reaction systems require formal standardized descriptions of pathways to extract these reaction networks and avoid the alternative: time-consuming and largely manual literature-based network reconstruction. Here, we systematically evaluate the effects of commonly used knowledge representations on the seemingly simple task of extracting a reaction network describing signal transduction from a pathway database. We show that this process is in fact surprisingly difficult, and the pathway representations adopted by various knowledge bases have dramatic consequences for reaction network extraction, connectivity, capture of pathway crosstalk and in the modelling of cell-cell interactions. Researchers constructing computational models built from automatically extracted reaction networks must therefore consider the issues we outline in this review to maximize the value of existing pathway knowledge. ');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(68814, 2014, 'bib', 'The dilemma of choosing the ideal permutation strategy while estimating statistical significance of genome-wide enrichment', 'De, Subhajyoti|Pedersen, Brent S|Kechris, Katerina', 'Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.', 'United States|United States|United States', 12, 'Binding Sites|CCCTC-Binding Factor|Computational Biology|DNA|Epigenomics|G-Quadruplexes|Gene Expression Profiling|Genome, Human|Genome-Wide Association Study|Genomics|High-Throughput Nucleotide Sequencing|Humans|Models, Genetic|Models, Statistical|Repressor Proteins|STAT2 Transcription Factor|Software', 'Journal Article', '10.1093/bib/bbt053', '23956260', 'Integrative analyses of genomic, epigenomic and transcriptomic features for human and various model organisms have revealed that many such features are nonrandomly distributed in the genome. Significant enrichment (or depletion) of genomic features is anticipated to be biologically important. Detection of genomic regions having enrichment of certain features and estimation of corresponding statistical significance rely on the expected null distribution generated by a permutation model. We discuss different genome-wide permutation approaches, present examples where the permutation strategy affects the null model and show that the confidence in estimating statistical significance of genome-wide enrichment might depend on the choice of the permutation approach. In those cases, where biologically relevant constraints are unclear, it is preferable to examine whether key conclusions are consistent, irrespective of the choice of the randomization strategy. '),
(68815, 2014, 'bib', 'A comparative study of statistical methods used to identify dependencies between gene expression signals', 'de Siqueira Santos, Suzana|Takahashi, Daniel Yasumasa|Nakata, Asuka|Fujita, André', 'Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.', 'United States|United States|United States|United States', 51, 'Computational Biology|Computer Simulation|DNA, Neoplasm|Databases, Genetic|Decision Trees|Gene Expression Profiling|Gene Regulatory Networks|Humans|Linear Models|Lung Neoplasms|Models, Genetic|Models, Statistical|Monte Carlo Method|Nonlinear Dynamics|ROC Curve', 'Comparative Study', '10.1093/bib/bbt051', '23962479', 'One major task in molecular biology is to understand the dependency among genes to model gene regulatory networks. Pearsons correlation is the most common method used to measure dependence between gene expression signals, but it works well only when data are linearly associated. For other types of association, such as non-linear or non-functional relationships, methods based on the concepts of rank correlation and information theory-based measures are more adequate than the Pearsons correlation, but are less used in applications, most probably because of a lack of clear guidelines for their use. This work seeks to summarize the main methods (Pearsons, Spearmans and Kendalls correlations, distance correlation, Hoeffdings D: measure, Heller-Heller-Gorfine measure, mutual information and maximal information coefficient) used to identify dependency between random variables, especially gene expression data, and also to evaluate the strengths and limitations of each method. Systematic Monte Carlo simulation analyses ranging from sample size, local dependence and linear/non-linear and also non-functional relationships are shown. Moreover, comparisons in actual gene expression data are carried out. Finally, we provide a suggestive list of methods that can be used for each type of data set. '),
(68816, 2014, 'bib', 'Systematic identification of Class I HDAC substrates', 'Li, Tingting|Song, Boyan|Wu, Zheng|Lu, Ming|Zhu, Wei-Guo', 'Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.', 'United States|United States|United States|United States|United States', 2, 'Acetylation|Amino Acid Sequence|Animals|Binding Sites|Computational Biology|HEK293 Cells|Hep G2 Cells|Histone Deacetylases|Histones|Humans|Internet|Lysine|Mice|Molecular Sequence Data|Recombinant Proteins|Sequence Homology, Amino Acid|Substrate Specificity', 'Evaluation Studies', '10.1093/bib/bbt060', '23975722', 'Lysine acetylation is a common post-translational modification of histone and non-histone proteins. This process has an important function in regulating transcriptional activities and other biological processes. Although several computer programs have been developed to predict protein acetylation sites, deacetylases responsible for known or predicted acetylation sites remain unknown. In this research, Class I histone deacetylases (HDACs) substrates were manually obtained, and sequence features of deacetylation sites were analyzed. We found that three members of Class I HDACs (HDAC1, HDAC2 and HDAC3) shared similar sequence features. Therefore, a method was proposed to identify the substrates of Class I HDACs. We evaluated the efficiency of the prediction based on P-value distribution analysis and leave-one-out test. To validate the result of the prediction, we overexpressed Class I HDACs in cells and detected the acetylation levels of potential substrates. In the experiment, five of the seven predicted proteins were deacetylated by Class I HDACs. These results suggested that our method could effectively predict protein deacetylation sites. The work has been integrated to the website ASEB, which was freely available at http://cmbi.bjmu.edu.cn/huac. '),
(68817, 2014, 'bib', 'A comprehensive overview of Infinium HumanMethylation450 data processing', 'Dedeurwaerder, Sarah|Defrance, Matthieu|Bizet, Martin|Calonne, Emilie|Bontempi, Gianluca|Fuks, François', 'Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.', 'United States|United States|United States|United States|United States|United States', 91, 'Computational Biology|CpG Islands|DNA Methylation|Data Interpretation, Statistical|Genome, Human|Humans|Oligonucleotide Array Sequence Analysis|Oligonucleotide Probes|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bib/bbt054', '23990268', 'Infinium HumanMethylation450 beadarray is a popular technology to explore DNA methylomes in health and disease, and there is a current explosion in the use of this technique. Despite experience acquired from gene expression microarrays, analyzing Infinium Methylation arrays appeared more complex than initially thought and several difficulties have been encountered, as those arrays display specific features that need to be taken into consideration during data processing. Here, we review several issues that have been highlighted by the scientific community, and we present an overview of the general data processing scheme and an evaluation of the different normalization methods available to date to guide the 450K users in their analysis and data interpretation. '),
(68818, 2014, 'bib', 'An efficient approach to large-scale genotype-phenotype association analyses', 'Yang, Runqing|Li, Hongwang|Fu, Lina|Liu, Yongxin', 'Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.', 'United States|United States|United States|United States', 1, 'DNA|Genome-Wide Association Study|Genotype|Humans|Models, Genetic|Phenotype|Quantitative Trait Loci', 'Journal Article', '10.1093/bib/bbt061', '23990269', 'Modern molecular biotechnology generates a great deal of intermediate information, such as transcriptional and metabolic products in bridging DNA and complex traits. In genome-wide linkage analysis and genome-wide association study, regression analysis for large-scale correlated phenotypes is applied to map genes for those by-products that are regarded as quantitative traits. For a single trait, least absolute shrinkage and selection operator with coordinate descent step can be employed to efficiently shrink sparse non-zero genetic effects of quantitative trait loci (QTLs). However, regression analyses in a trait-by-trait basis do not take account of the correlations among the analyzed traits. In this study, conditional phenotype of each trait is defined, given other traits. Large-scale genotype-phenotype association analyses are therefore transformed to separate genotype-conditional phenotype ones. Meanwhile, the correlation architecture between each trait and other traits can also be provided by shrinkage estimation for each conditional phenotype. Simulation demonstrates that the proposed conditional mapping method is generally identical to joint mapping method based on multivariate analysis in terms of statistical detection power and parameter estimation. Application of the method is provided to locate eQTL in yeast.'),
(68819, 2015, 'bib', 'RegScan: a GWAS tool for quick estimation of allele effects on continuous traits and their combinations', 'Haller, Toomas|Kals, Mart|Esko, Tõnu|Mägi, Reedik|Fischer, Krista', 'Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.', 'United States|United States|United States|United States|United States', 11, 'Computational Biology|Data Interpretation, Statistical|Gene Frequency|Genome-Wide Association Study|Humans|Linear Models', 'Journal Article', '10.1093/bib/bbt066', '24008273', 'Genome-wide association studies are becoming computationally more demanding with the growing amounts of data. Combinatorial traits can increase the data dimensions beyond the computational capabilities of the current tools. We addressed this issue by creating an application for quick association analysis that is ten to hundreds of times faster than the leading fast methods. Our tool (RegScan) is designed for performing basic linear regression analysis with continuous traits maximally fast on large data sets. RegScan specifically targets association analysis of combinatorial traits in metabolomics. It can both generate and analyze the combinatorial traits efficiently. RegScan is capable of analyzing any number of traits together without the need to specify each trait individually. The main goal of the article is to show that RegScan can be the preferred analytical tool when large amounts of data need to be analyzed quickly using the allele frequency test.'),
(68820, 2014, 'bib', 'Teaching the ABCs of bioinformatics: a brief introduction to the Applied Bioinformatics Course', 'Luo, Jingchu', 'Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.', 'United States', 3, 'Animals|China|Computational Biology|Curriculum|Databases, Factual|Education, Graduate|Geese|Hemoglobins|Humans|Plants|RNA, Messenger|RNA, Plant|Search Engine|Sequence Alignment|Sequence Analysis, RNA|Takifugu|Universities', 'Journal Article', '10.1093/bib/bbt065', '24008274', 'With the development of the Internet and the growth of online resources, bioinformatics training for wet-lab biologists became necessary as a part of their education. This article describes a one-semester course Applied Bioinformatics Course (ABC, http://abc.cbi.pku.edu.cn/) that the author has been teaching to biological graduate students at the Peking University and the Chinese Academy of Agricultural Sciences for the past 13 years. ABC is a hands-on practical course to teach students to use online bioinformatics resources to solve biological problems related to their ongoing research projects in molecular biology. With a brief introduction to the background of the course, detailed information about the teaching strategies of the course are outlined in the How to teach section. The contents of the course are briefly described in the What to teach section with some real examples. The author wishes to share his teaching experiences and the online teaching materials with colleagues working in bioinformatics education both in local and international universities. '),
(68821, 2014, 'bib', 'A comparative evaluation on prediction methods of nucleosome positioning', 'Liu, Hui|Zhang, Ruichang|Xiong, Wei|Guan, Jihong|Zhuang, Ziheng|Zhou, Shuigeng', 'Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.', 'United States|United States|United States|United States|United States|United States', 18, '5 Untranslated Regions|Animals|Base Composition|Base Sequence|Computational Biology|Databases, Genetic|Drosophila melanogaster|Exons|Humans|Mice|Models, Genetic|Nucleosomes|Promoter Regions, Genetic|Saccharomyces cerevisiae|Software Design|Thermodynamics', 'Comparative Study', '10.1093/bib/bbt062', '24023366', 'Nucleosome positioning plays an essential role in cellular processes by modulating accessibility of DNA to proteins. Many computational models have been developed to predict genome-wide nucleosome positions from DNA sequences. Comparative analysis of predicted and experimental nucleosome positioning maps facilitates understanding the regulatory mechanisms of transcription and DNA replication. Therefore, a comprehensive evaluation of existing computational methods is important and useful for biologists to choose appropriate ones in their research. In this article, we carried out a performance comparison among eight widely used computational methods on four species including yeast, fruitfly, mouse and human. In particular, we compared these methods on different regions of each species such as gene sequences, promoters and 5UTR exons. The experimental results show that the performances of the two latest versions of the thermodynamic model are relatively steadier than the other four methods. Moreover, these methods are workable on four species, but their performances decrease gradually from yeast to human, indicating that the fundamental mechanism of nucleosome positioning is conserved through the evolution process, but more and more factors participate in the determination of nucleosome positions, which leads to sophisticated regulation mechanisms. '),
(68822, 2013, 'bib', 'International, interdisciplinary, multi-level bioinformatics training and education', 'Schneider, Maria Victoria|Jungck, John R', 'Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.', 'United States|United States', 2, 'Computational Biology|Curriculum|Interdisciplinary Communication|Internationality', 'Editorial', '10.1093/bib/bbt064', '24030777', '-'),
(68823, 2015, 'bib', 'On the validity of time-dependent AUC estimators', 'Schmid, Matthias|Kestler, Hans A|Potapov, Sergej', 'Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.|Departments of Biological Sciences and Mathematics, Interdisciplinary Science and Engineering Laboratory, University of Delaware, Newark, DE 19716, USA. Tel.: +1-302-831-6400, Fax: +1-302-831-6477, jrjungck@gmail.com.', 'United States|United States|United States', 4, 'Area Under Curve|Biomarkers|Humans|Models, Statistical|Prognosis|Survival Analysis', 'Journal Article', '10.1093/bib/bbt059', '24036698', 'Recent developments in molecular biology have led to the massive discovery of new marker candidates for the prediction of patient survival. To evaluate the predictive value of these markers, statistical tools for measuring the performance of survival models are needed. We consider estimators of discrimination measures, which are a popular approach to evaluate survival predictions in biomarker studies. Estimators of discrimination measures are usually based on regularity assumptions such as the proportional hazards assumption. Based on two sets of molecular data and a simulation study, we show that violations of the regularity assumptions may lead to over-optimistic estimates of prediction accuracy and may therefore result in biased conclusions regarding the clinical utility of new biomarkers. In particular, we demonstrate that biased medical decision making is possible even if statistical checks indicate that all regularity assumptions are satisfied. '),
(68824, 2014, 'bib', 'Information theory applications for biological sequence analysis', 'Vinga, Susana', 'IDMEC, Instituto Superior Técnico - Universidade de Lisboa (IST-UL), Av. Rovisco Pais, 1049-001 Lisboa, Portugal. Tel.: +351-218419504, Fax: +351-218498097, svinga@dem.ist.utl.pt.', 'Portugal', 52, 'Binding Sites|Computational Biology|Genomics|Humans|Information Theory|Models, Statistical|Nonlinear Dynamics|Phylogeny|Saccharomyces cerevisiae|Sequence Alignment|Sequence Analysis|Software|Transcription Factors', 'Journal Article', '10.1093/bib/bbt068', '24058049', 'Information theory (IT) addresses the analysis of communication systems and has been widely applied in molecular biology. In particular, alignment-free sequence analysis and comparison greatly benefited from concepts derived from IT, such as entropy and mutual information. This review covers several aspects of IT applications, ranging from genome global analysis and comparison, including block-entropy estimation and resolution-free metrics based on iterative maps, to local analysis, comprising the classification of motifs, prediction of transcription factor binding sites and sequence characterization based on linguistic complexity and entropic profiles. IT has also been applied to high-level correlations that combine DNA, RNA or protein features with sequence-independent properties, such as gene mapping and phenotype analysis, and has also provided models based on communication systems theory to describe information transmission channels at the cell level and also during evolutionary processes. While not exhaustive, this review attempts to categorize existing methods and to indicate their relation with broader transversal topics such as genomic signatures, data compression and complexity, time series analysis and phylogenetic classification, providing a resource for future developments in this promising area. '),
(68825, 2014, 'bib', 'New developments of alignment-free sequence comparison: measures, statistics and next-generation sequencing', 'Song, Kai|Ren, Jie|Reinert, Gesine|Deng, Minghua|Waterman, Michael S|Sun, Fengzhu', 'Molecular and Computational Biology Program, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089, USA. fsun@usc.edu or msw@usc.edu.|Molecular and Computational Biology Program, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089, USA. fsun@usc.edu or msw@usc.edu.|Molecular and Computational Biology Program, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089, USA. fsun@usc.edu or msw@usc.edu.|Molecular and Computational Biology Program, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089, USA. fsun@usc.edu or msw@usc.edu.|Molecular and Computational Biology Program, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089, USA. fsun@usc.edu or msw@usc.edu.|Molecular and Computational Biology Program, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089, USA. fsun@usc.edu or msw@usc.edu.', 'United States|United States|United States|United States|United States|United States', 37, 'Algorithms|Computational Biology|Genomics|High-Throughput Nucleotide Sequencing|Markov Chains|Models, Statistical|Sequence Alignment|Sequence Analysis', 'Journal Article', '10.1093/bib/bbt067', '24064230', 'With the development of next-generation sequencing (NGS) technologies, a large amount of short read data has been generated. Assembly of these short reads can be challenging for genomes and metagenomes without template sequences, making alignment-based genome sequence comparison difficult. In addition, sequence reads from NGS can come from different regions of various genomes and they may not be alignable. Sequence signature-based methods for genome comparison based on the frequencies of word patterns in genomes and metagenomes can potentially be useful for the analysis of short reads data from NGS. Here we review the recent development of alignment-free genome and metagenome comparison based on the frequencies of word patterns with emphasis on the dissimilarity measures between sequences, the statistical power of these measures when two sequences are related and the applications of these measures to NGS data. '),
(68826, 2014, 'bib', 'Three-stage quality control strategies for DNA re-sequencing data', 'Guo, Yan|Ye, Fei|Sheng, Quanghu|Clark, Travis|Samuels, David C', 'Molecular and Computational Biology Program, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089, USA. fsun@usc.edu or msw@usc.edu.|Molecular and Computational Biology Program, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089, USA. fsun@usc.edu or msw@usc.edu.|Molecular and Computational Biology Program, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089, USA. fsun@usc.edu or msw@usc.edu.|Molecular and Computational Biology Program, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089, USA. fsun@usc.edu or msw@usc.edu.|Molecular and Computational Biology Program, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089, USA. fsun@usc.edu or msw@usc.edu.', 'United States|United States|United States|United States|United States', 80, 'Computational Biology|DNA|Gene Library|High-Throughput Nucleotide Sequencing|Humans|Neoplasms|Polymorphism, Single Nucleotide|Quality Control|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bib/bbt069', '24067931', 'Advances in next-generation sequencing (NGS) technologies have greatly improved our ability to detect genomic variants for biomedical research. In particular, NGS technologies have been recently applied with great success to the discovery of mutations associated with the growth of various tumours and in rare Mendelian diseases. The advance in NGS technologies has also created significant challenges in bioinformatics. One of the major challenges is quality control of the sequencing data. In this review, we discuss the proper quality control procedures and parameters for Illumina technology-based human DNA re-sequencing at three different stages of sequencing: raw data, alignment and variant calling. Monitoring quality control metrics at each of the three stages of NGS data provides unique and independent evaluations of data quality from differing perspectives. Properly conducting quality control protocols at all three stages and correctly interpreting the quality control results are crucial to ensure a successful and meaningful study. '),
(68827, 2014, 'bib', 'Modern bioinformatics meets traditional Chinese medicine', 'Gu, Peiqin|Chen, Huajun', 'Molecular and Computational Biology Program, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089, USA. fsun@usc.edu or msw@usc.edu.|Molecular and Computational Biology Program, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089, USA. fsun@usc.edu or msw@usc.edu.', 'United States|United States', 39, 'Biological Ontologies|Biomarkers|Computational Biology|Cross-Cultural Comparison|Databases, Genetic|Databases, Pharmaceutical|Drug Discovery|Drugs, Chinese Herbal|Evidence-Based Medicine|Genomics|Humans|Integrative Medicine|Medicine, Chinese Traditional|Metabolomics|Proteomics|Syndrome|Systems Biology', 'Journal Article', '10.1093/bib/bbt063', '24067932', 'Traditional Chinese medicine (TCM) is gaining increasing attention with the emergence of integrative medicine and personalized medicine, characterized by pattern differentiation on individual variance and treatments based on natural herbal synergism. Investigating the effectiveness and safety of the potential mechanisms of TCM and the combination principles of drug therapies will bridge the cultural gap with Western medicine and improve the development of integrative medicine. Dealing with rapidly growing amounts of biomedical data and their heterogeneous nature are two important tasks among modern biomedical communities. Bioinformatics, as an emerging interdisciplinary field of computer science and biology, has become a useful tool for easing the data deluge pressure by automating the computation processes with informatics methods. Using these methods to retrieve, store and analyze the biomedical data can effectively reveal the associated knowledge hidden in the data, and thus promote the discovery of integrated information. Recently, these techniques of bioinformatics have been used for facilitating the interactional effects of both Western medicine and TCM. The analysis of TCM data using computational technologies provides biological evidence for the basic understanding of TCM mechanisms, safety and efficacy of TCM treatments. At the same time, the carrier and targets associated with TCM remedies can inspire the rethinking of modern drug development. This review summarizes the significant achievements of applying bioinformatics techniques to many aspects of the research in TCM, such as analysis of TCM-related -omics data and techniques for analyzing biological processes and pharmaceutical mechanisms of TCM, which have shown certain potential of bringing new thoughts to both sides.'),
(68828, 2014, 'bib', 'Pattern recognition and probabilistic measures in alignment-free sequence analysis', 'Schwende, Isabel|Pham, Tuan D', 'PhD, Aizu Research Cluster for Medical Informatics and Engineering (ARC-Medical), Research Center for Advanced Information Science and Technology (CAIST), The University of Aizu, Aizuwakamatsu, Fukushima 965-8580, Japan. tdpham@u-aizu.ac.jp.|PhD, Aizu Research Cluster for Medical Informatics and Engineering (ARC-Medical), Research Center for Advanced Information Science and Technology (CAIST), The University of Aizu, Aizuwakamatsu, Fukushima 965-8580, Japan. tdpham@u-aizu.ac.jp.', 'Japan|Japan', 11, 'Computational Biology|Genomics|Markov Chains|Models, Statistical|Pattern Recognition, Automated|Phylogeny|Proteomics|Sequence Alignment|Sequence Analysis|Software', 'Journal Article', '10.1093/bib/bbt070', '24096012', 'With the massive production of genomic and proteomic data, the number of available biological sequences in databases has reached a level that is not feasible anymore for exact alignments even when just a fraction of all sequences is used. To overcome this inevitable time complexity, ultrafast alignment-free methods are studied. Within the past two decades, a broad variety of nonalignment methods have been proposed including dissimilarity measures on classical representations of sequences like k-words or Markov models. Furthermore, articles were published that describe distance measures on alternative representations such as compression complexity, spectral time series or chaos game representation. However, alignments are still the standard method for real world applications in biological sequence analysis, and the time efficient alignment-free approaches are usually applied in cases when the accustomed algorithms turn out to fail or be too inconvenient. '),
(68829, 2015, 'bib', 'Pathogenicity phenomena in three model systems: from network mining to emerging system-level properties', 'Castelhano Santos, Nadine|Pereira, Maria Olívia|Lourenço, Anália', 'PhD, Aizu Research Cluster for Medical Informatics and Engineering (ARC-Medical), Research Center for Advanced Information Science and Technology (CAIST), The University of Aizu, Aizuwakamatsu, Fukushima 965-8580, Japan. tdpham@u-aizu.ac.jp.|PhD, Aizu Research Cluster for Medical Informatics and Engineering (ARC-Medical), Research Center for Advanced Information Science and Technology (CAIST), The University of Aizu, Aizuwakamatsu, Fukushima 965-8580, Japan. tdpham@u-aizu.ac.jp.|PhD, Aizu Research Cluster for Medical Informatics and Engineering (ARC-Medical), Research Center for Advanced Information Science and Technology (CAIST), The University of Aizu, Aizuwakamatsu, Fukushima 965-8580, Japan. tdpham@u-aizu.ac.jp.', 'Japan|Japan|Japan', 0, 'Biofilms|Candida albicans|Computational Biology|Data Mining|Humans|Models, Biological|Pseudomonas aeruginosa|Staphylococcus aureus|Virulence', 'Journal Article', '10.1093/bib/bbt071', '24106130', 'Understanding the interconnections of microbial pathogenicity phenomena, such as biofilm formation, quorum sensing and antimicrobial resistance, is a tremendous open challenge for biomedical research. Progress made by wet-lab researchers and bioinformaticians in understanding the underlying regulatory phenomena has been significant, with converging evidence from multiple high-throughput technologies. Notably, network reconstructions are already of considerable size and quality, tackling both intracellular regulation and signal mediation in microbial infection. Therefore, it stands to reason that in silico investigations would play a more active part in this research. Drug target identification and drug repurposing could take much advantage of the ability to simulate pathogen regulatory systems, host-pathogen interactions and pathogen cross-talking. Here, we review the bioinformatics resources and tools available for the study of the gram-negative bacterium Pseudomonas aeruginosa, the gram-positive bacterium Staphylococcus aureus and the fungal species Candida albicans. The choice of these three microorganisms fits the rationale of the review converging into pathogens of great clinical importance, which thrive in biofilm consortia and manifest growing antimicrobial resistance. '),
(68830, 2014, 'bib', 'Sequence analysis by iterated maps, a review', 'Almeida, Jonas S', 'Division of Informatics, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, USA. jalmeida@uab.edu.', 'United States', 10, 'Computational Biology|Fractals|Models, Statistical|Nonlinear Dynamics|Sequence Alignment|Sequence Analysis', 'Journal Article', '10.1093/bib/bbt072', '24162172', 'Among alignment-free methods, Iterated Maps (IMs) are on a particular extreme: they are also scale free (order free). The use of IMs for sequence analysis is also distinct from other alignment-free methodologies in being rooted in statistical mechanics instead of computational linguistics. Both of these roots go back over two decades to the use of fractal geometry in the characterization of phase-space representations. The time series analysis origin of the field is betrayed by the title of the manuscript that started this alignment-free subdomain in 1990, Chaos Game Representation. The clash between the analysis of sequences as continuous series and the better established use of Markovian approaches to discrete series was almost immediate, with a defining critique published in same journal 2 years later. The rest of that decade would go by before the scale-free nature of the IM space was uncovered. The ensuing decade saw this scalability generalized for non-genomic alphabets as well as an interest in its use for graphic representation of biological sequences. Finally, in the past couple of years, in step with the emergence of BigData and MapReduce as a new computational paradigm, there is a surprising third act in the IM story. Multiple reports have described gains in computational efficiency of multiple orders of magnitude over more conventional sequence analysis methodologies. The stage appears to be now set for a recasting of IMs with a central role in processing nextgen sequencing results. '),
(68831, 2015, 'bib', 'A primer to frequent itemset mining for bioinformatics', 'Naulaerts, Stefan|Meysman, Pieter|Bittremieux, Wout|Vu, Trung Nghia|Vanden Berghe, Wim|Goethals, Bart|Laukens, Kris', 'Division of Informatics, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, USA. jalmeida@uab.edu.|Division of Informatics, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, USA. jalmeida@uab.edu.|Division of Informatics, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, USA. jalmeida@uab.edu.|Division of Informatics, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, USA. jalmeida@uab.edu.|Division of Informatics, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, USA. jalmeida@uab.edu.|Division of Informatics, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, USA. jalmeida@uab.edu.|Division of Informatics, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, USA. jalmeida@uab.edu.', 'United States|United States|United States|United States|United States|United States|United States', 44, 'Algorithms|Animals|Cluster Analysis|Computational Biology|Data Mining|Gene Expression Profiling|Gene Regulatory Networks|High-Throughput Nucleotide Sequencing|Humans|Pattern Recognition, Automated|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bib/bbt074', '24162173', 'Over the past two decades, pattern mining techniques have become an integral part of many bioinformatics solutions. Frequent itemset mining is a popular group of pattern mining techniques designed to identify elements that frequently co-occur. An archetypical example is the identification of products that often end up together in the same shopping basket in supermarket transactions. A number of algorithms have been developed to address variations of this computationally non-trivial problem. Frequent itemset mining techniques are able to efficiently capture the characteristics of (complex) data and succinctly summarize it. Owing to these and other interesting properties, these techniques have proven their value in biological data analysis. Nevertheless, information about the bioinformatics applications of these techniques remains scattered. In this primer, we introduce frequent itemset mining and their derived association rules for life scientists. We give an overview of various algorithms, and illustrate how they can be used in several real-life bioinformatics application domains. We end with a discussion of the future potential and open challenges for frequent itemset mining in the life sciences. '),
(68832, 2014, 'bib', 'Revealing the architecture of genetic and epigenetic regulation: a maximum likelihood model', 'Wang, Fang|Zhang, Shaojun|Wen, Yanhua|Wei, Yanjun|Yan, Haidan|Liu, Hongbo|Su, Jianzhong|Zhang, Yan|Che, Jianhua', 'Division of Informatics, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, USA. jalmeida@uab.edu.|Division of Informatics, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, USA. jalmeida@uab.edu.|Division of Informatics, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, USA. jalmeida@uab.edu.|Division of Informatics, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, USA. jalmeida@uab.edu.|Division of Informatics, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, USA. jalmeida@uab.edu.|Division of Informatics, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, USA. jalmeida@uab.edu.|Division of Informatics, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, USA. jalmeida@uab.edu.|Division of Informatics, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, USA. jalmeida@uab.edu.|Division of Informatics, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, USA. jalmeida@uab.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 2, 'Computational Biology|Computer Simulation|CpG Islands|DNA Methylation|Databases, Genetic|Epigenesis, Genetic|Gene Expression Regulation|Gene Ontology|Genome-Wide Association Study|Humans|Likelihood Functions|Models, Genetic|Polymorphism, Single Nucleotide|Quantitative Trait Loci', 'Journal Article', '10.1093/bib/bbt076', '24174551', 'Gene expression is modulated by multiple mechanisms, including genetic and/or epigenetic regulation, and associated with the processes of cellular differentiation and morphogenesis. Single nucleotide polymorphisms (SNPs) and DNA methylation play important roles in regulating gene expression. In this study, we focused on revealing the relationship between SNPs, DNA methylation and gene expression in two human populations genome-wide through proposing four regulation patterns and developed maximum likelihood estimate models. Using simulated data with different correlation coefficients between any two traits, the power of our approach showed a favourable performance and relative stability. In all, 6733 SNP-CpG-gene pairs including 957 genes were obtained in Northern European ancestry (CEU) population. As the results showed, SNPs and DNA methylation had approximately the same effect on expression regulation of 49% genes, which was termed cooperative/antagonistic regulation pattern. Less than 30% of genes are controlled only by one of the factors (SNP/DNA methylation). The others showed SNPs that affect methylation have no consequent effects or crosstalk regulation on gene expression. Similar result was shown in Yourba (YRI) population. Specific genes were inferred using the different mechanisms of gene regulation involved in complex diseases by combining literature. This approach provides a method to comprehensively assess regulation patterns of gene expression in the whole genome. '),
(68833, 2014, 'bib', 'A statistical model for QTL mapping in polysomic autotetraploids underlying double reduction', 'Xu, Fang|Lyu, Yafei|Tong, Chunfa|Wu, Weimiao|Zhu, Xuli|Yin, Danni|Yan, Qin|Zhang, Jian|Pang, Xiaoming|Tobias, Christian M|Wu, Rongling', 'Division of Informatics, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, USA. jalmeida@uab.edu.|Division of Informatics, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, USA. jalmeida@uab.edu.|Division of Informatics, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, USA. jalmeida@uab.edu.|Division of Informatics, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, USA. jalmeida@uab.edu.|Division of Informatics, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, USA. jalmeida@uab.edu.|Division of Informatics, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, USA. jalmeida@uab.edu.|Division of Informatics, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, USA. jalmeida@uab.edu.|Division of Informatics, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, USA. jalmeida@uab.edu.|Division of Informatics, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, USA. jalmeida@uab.edu.|Division of Informatics, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, USA. jalmeida@uab.edu.|Division of Informatics, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, USA. jalmeida@uab.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 6, 'Algorithms|Chromosome Mapping|Computational Biology|Computer Simulation|Likelihood Functions|Models, Genetic|Models, Statistical|Monte Carlo Method|Panicum|Plants|Polyribosomes|Quantitative Trait Loci|Tetraploidy', 'Journal Article', '10.1093/bib/bbt073', '24177379', 'As a group of economically important species, linkage mapping of polysomic autotetraploids, including potato, sugarcane and rose, is difficult to conduct due to their unique meiotic property of double reduction that allows sister chromatids to enter into the same gamete. We describe and assess a statistical model for mapping quantitative trait loci (QTLs) in polysomic autotetraploids. The model incorporates double reduction, built in the mixture model-based framework and implemented with the expectation-maximization algorithm. It allows the simultaneous estimation of QTL positions, QTL effects and the degree of double reduction as well as the assessment of the estimation precision of these parameters. We performed computer simulation to examine the statistical properties of the method and validate its use through analyzing real data in tetraploid switchgrass. '),
(68834, 2015, 'bib', 'Allotetraploid and autotetraploid models of linkage analysis', 'Xu, Fang|Tong, Chunfa|Lyu, Yafei|Bo, Wenhao|Pang, Xiaoming|Wu, Rongling', 'Division of Informatics, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, USA. jalmeida@uab.edu.|Division of Informatics, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, USA. jalmeida@uab.edu.|Division of Informatics, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, USA. jalmeida@uab.edu.|Division of Informatics, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, USA. jalmeida@uab.edu.|Division of Informatics, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, USA. jalmeida@uab.edu.|Division of Informatics, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, USA. jalmeida@uab.edu.', 'United States|United States|United States|United States|United States|United States', 4, 'Arabidopsis|Chromosome Mapping|Genes, Plant|Genetic Linkage|Models, Genetic|Recombination, Genetic|Tetraploidy', 'Journal Article', '10.1093/bib/bbt075', '24177380', 'As a group of important plant species in agriculture and biology, polyploids have been increasingly studied in terms of their genome structure and organization. There are two types of polyploids, allopolyploids and autopolyploids, each resulting from a different genetic origin, which undergo meiotic divisions of a distinct complexity. A set of statistical models has been developed for linkage analysis, respectively for each type, by taking into account their unique meiotic behavior, i.e. preferential pairing for allopolyploids and double reduction for autopolyploids. We synthesized these models and modified them to accommodate the linkage analysis of less informative dominant markers. By reanalysing a published data set of varying ploidy in Arabidopsis, we corrected the estimates of the meiotic recombination frequency aimed to study the significance of polyploidization. ');
