INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(68400, 2007, 'bib', 'Manuscript Central interactive online system and issue summary', 'Bishop, M J', 'Department of Biomolecular Sciences and Biotechnology, University of Milan, via Celoria 26, 20133 Milano, Italy. David.horner@unimi.it', 'Italy', 0, 'Chromosome Mapping|Genetic Techniques|Medical Informatics|Models, Molecular', 'Editorial', '10.1093/bib/bbm054', '18024470', '-'),
(68401, 2008, 'bib', 'Current trends in the bioinformatic sequence analysis of metabolic pathways in prokaryotes', 'Brilli, Matteo|Fani, Renato|Liò, Pietro', 'Department of Animal Biology and Genetics, University of Florence, Via Romana 17/19, 50125 Firenze, Italy. matteo.brilli@dbag.unifi.it|Department of Animal Biology and Genetics, University of Florence, Via Romana 17/19, 50125 Firenze, Italy. matteo.brilli@dbag.unifi.it|Department of Animal Biology and Genetics, University of Florence, Via Romana 17/19, 50125 Firenze, Italy. matteo.brilli@dbag.unifi.it', 'Italy|Italy|Italy', 3, 'Bacterial Physiological Phenomena|Bacterial Proteins|Computational Biology|Computer Simulation|Forecasting|Models, Biological|Protein Interaction Mapping|Sequence Analysis|Signal Transduction', 'Journal Article', '10.1093/bib/bbm051', '18024984', 'The study of metabolic pathways is becoming increasingly important to exploit an integrated, systems-level approach for optimizing a desired cellular property or phenotype. In this context, the integration of genomics data with genetic, metabolic and regulatory models is essential because the systematic design of artificial, biological systems requires the identification of robust building blocks like gene promoters, metabolic pathways or genetic circuits taken from natural organisms, and manipulated to develop ad hoc features. Computational tools allowing precise descriptions of natural pathways might thus allow improving the performance of artificial routes. In this review, we introduce the most recent bioinformatics tools enabling detailed characterizations of metabolic pathways in bacteria from different perspectives.'),
(68402, 2008, 'bib', 'Automation of in-silico data analysis processes through workflow management systems', 'Romano, Paolo', 'Bioinformatics, Istituto Nazionale per la Ricerca sul Cancro, Largo Rosanna Benzi 10, I-16132, (IST), Genova, Italy. paolo.romano@istge.it', 'Italy', 45, 'Algorithms|Computational Biology|Database Management Systems|Databases, Factual|Information Storage and Retrieval|Internet|Software|Systems Integration', 'Journal Article', '10.1093/bib/bbm056', '18056132', 'Data integration is needed in order to cope with the huge amounts of biological information now available and to perform data mining effectively. Current data integration systems have strict limitations, mainly due to the number of resources, their size and frequency of updates, their heterogeneity and distribution on the Internet. Integration must therefore be achieved by accessing network services through flexible and extensible data integration and analysis network tools. EXtensible Markup Language (XML), Web Services and Workflow Management Systems (WMS) can support the creation and deployment of such systems. Many XML languages and Web Services for bioinformatics have already been designed and implemented and some WMS have been proposed. In this article, we review a methodology for data integration in biomedical research that is based on these technologies. We also briefly describe some of the available WMS and discuss the current limitations of this methodology and the ways in which they can be overcome.'),
(68403, 2008, 'bib', 'Extensible open source content management systems and frameworks: a solution for many needs of a bioinformatics group', 'Mooney, Sean D|Baenziger, Peter H', 'Center for Computational Biology and Bioinformatics, Department of Medical and Molecular Genetics, Indiana University School of Medicine, 410 W 10th St, Suite 5000, Indianapolis, IN 46202, USA. sdmooney@iupui.edu|Center for Computational Biology and Bioinformatics, Department of Medical and Molecular Genetics, Indiana University School of Medicine, 410 W 10th St, Suite 5000, Indianapolis, IN 46202, USA. sdmooney@iupui.edu', 'India|India', 3, 'Algorithms|Computational Biology|Database Management Systems|Databases, Factual|Information Dissemination|Information Storage and Retrieval|Internet|Software|Systems Integration', 'Journal Article', '10.1093/bib/bbm057', '18057072', 'A common challenge for bioinformaticians, in either academic or industry laboratory environments, is providing informatic solutions via the Internet or through a web browser. Recently, the open source community began developing tools for building and maintaining web applications for many disciplines. These content management systems (CMS) provide many of the basic needs of an informatics group, whether in a small company, a group within a larger organisation or an academic laboratory. These tools aid in managing software development, website development, document development, course development, datasets, collaborations and customers. Since many of these tools are extensible, they can be developed to support other research-specific activities, such as handling large biomedical datasets or deploying bioanalytic tools. In this review of open source website management tools, the basic features of content management systems are discussed along with commonly used open source software. Additionally, some examples of their use in biomedical research are given.'),
(68404, 2008, 'bib', 'An overview of image-processing methods for Affymetrix GeneChips', 'Arteaga-Salas, Jose M|Zuzan, Harry|Langdon, William B|Upton, Graham J G|Harrison, Andrew P', 'Department of Mathematical Sciences, University of Essex, Wivenhoe Park, Colchester, UK.|Department of Mathematical Sciences, University of Essex, Wivenhoe Park, Colchester, UK.|Department of Mathematical Sciences, University of Essex, Wivenhoe Park, Colchester, UK.|Department of Mathematical Sciences, University of Essex, Wivenhoe Park, Colchester, UK.|Department of Mathematical Sciences, University of Essex, Wivenhoe Park, Colchester, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 27, 'Algorithms|Gene Expression Profiling|Image Enhancement|Image Interpretation, Computer-Assisted|In Situ Hybridization, Fluorescence|Microscopy, Fluorescence, Multiphoton|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bib/bbm055', '18057073', 'We present an overview of image-processing methods for Affymetrix GeneChips. All GeneChips are affected to some extent by spatially coherent defects and image processing has a number of potential impacts on the downstream analysis of GeneChip data. Fortunately, there are now a number of robust and accurate algorithms, which identify the most disabling defects. One group of algorithms concentrate on the transformation from the original hybridisation DAT image to the representative CEL file. Another set uses dedicated pattern recognition routines to detect different types of hybridisation defect in replicates. A third type exploits the information provided by public repositories of GeneChips (such as GEO). The use of these algorithms improves the sensitivity of GeneChips, and should be a prerequisite for studies in which there are only few probes per relevant biological signal, such as exon arrays and SNP chips.'),
(68405, 2008, 'bib', 'The HUPO proteomics standards initiative--easing communication and minimizing data loss in a changing world', 'Orchard, Sandra|Hermjakob, Henning', 'EMBL - European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK. orchard@ebi.ac.uk|EMBL - European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK. orchard@ebi.ac.uk', 'United Kingdom|United Kingdom', 26, 'Database Management Systems|Databases, Protein|Humans|Information Storage and Retrieval|Mass Spectrometry|Proteins|Proteomics|Publishing', 'Journal Article', '10.1093/bib/bbm061', '18065433', 'The amount of data currently being generated by proteomics laboratories around the world is increasing exponentially, making it ever more critical that scientists are able to exchange, compare and retrieve datasets when re-evaluation of their original conclusions becomes important. Only a fraction of this data is published in the literature and important information is being lost every day as data formats become obsolete. The Human Proteome Organisation Proteomics Standards Initiative (HUPO-PSI) was tasked with the creation of data standards and interchange formats to allow both the exchange and storage of such data irrespective of the hardware and software from which it was generated. This article will provide an update on the work of this group, the creation and implementation of these standards and the standards-compliant data repositories being established as result of their efforts.'),
(68406, 2008, 'bib', 'Biomedical ontologies: a functional perspective', 'Rubin, Daniel L|Shah, Nigam H|Noy, Natalya F', 'Stanford Center for Biomedical Informatics Research, Stanford, CA, USA. rubin@med.stanford.edu|Stanford Center for Biomedical Informatics Research, Stanford, CA, USA. rubin@med.stanford.edu|Stanford Center for Biomedical Informatics Research, Stanford, CA, USA. rubin@med.stanford.edu', 'United States|United States|United States', 78, 'Computational Biology|Databases, Factual|Documentation|Information Storage and Retrieval|Models, Biological|Natural Language Processing|Systems Integration|Vocabulary, Controlled', 'Journal Article', '10.1093/bib/bbm059', '18077472', 'The information explosion in biology makes it difficult for researchers to stay abreast of current biomedical knowledge and to make sense of the massive amounts of online information. Ontologies--specifications of the entities, their attributes and relationships among the entities in a domain of discourse--are increasingly enabling biomedical researchers to accomplish these tasks. In fact, bio-ontologies are beginning to proliferate in step with accruing biological data. The myriad of ontologies being created enables researchers not only to solve some of the problems in handling the data explosion but also introduces new challenges. One of the key difficulties in realizing the full potential of ontologies in biomedical research is the isolation of various communities involved: some workers spend their career developing ontologies and ontology-related tools, while few researchers (biologists and physicians) know how ontologies can accelerate their research. The objective of this review is to give an overview of biomedical ontology in practical terms by providing a functional perspective--describing how bio-ontologies can and are being used. As biomedical scientists begin to recognize the many different ways ontologies enable biomedical research, they will drive the emergence of new computer applications that will help them exploit the wealth of research data now at their fingertips.'),
(68407, 2008, 'bib', 'Three lectures on case-control genetic association analysis', 'Li, Wentian', 'The Robert S. Boas Center for Genomics and Human Genetics, The Feinstein Institute for Medical Research, North Shore LIJ Health System, Manhasset, NY 11030, USA. wli@nslij-genetics.org', 'United States', 3, 'Case-Control Studies|Controlled Clinical Trials as Topic|Data Interpretation, Statistical|Genetic Predisposition to Disease|Genetic Testing|Humans|Models, Genetic|Phenotype|Sample Size', 'Journal Article', '10.1093/bib/bbm058', '18083722', 'The purpose of this review is to focus on the three most important themes in genetic association studies using randomly selected patients (case, affected) and normal samples (control, unaffected), so that students and researchers alike who are new to this field may quickly grasp the key issues and command basic analysis methods. These three themes are: elementary categorical analysis, disease mutation as an unobserved entity, and the importance of homogeneity in genetic association analysis.'),
(68408, 2008, 'bib', 'ROC analysis: applications to the classification of biological sequences and 3D structures', 'Sonego, Paolo|Kocsor, András|Pongor, Sándor', 'Protein Structure and Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology, 34012 Trieste, Italy.|Protein Structure and Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology, 34012 Trieste, Italy.|Protein Structure and Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology, 34012 Trieste, Italy.', 'Italy|Italy|Italy', 34, 'Algorithms|Models, Chemical|Models, Molecular|Molecular Conformation|ROC Curve|Sequence Alignment|Sequence Analysis|Software', 'Journal Article', '10.1093/bib/bbm064', '18192302', 'ROC (receiver operator characteristics) analysis is a visual as well as numerical method used for assessing the performance of classification algorithms, such as those used for predicting structures and functions from sequence data. This review summarizes the fundamental concepts of ROC analysis and the interpretation of results using examples of sequence and structure comparison. We overview the available programs and provide evaluation guidelines for genomic/proteomic data, with particular regard to applications to large and heterogeneous databases used in bioinformatics.'),
(68409, 2008, 'bib', 'Two interactive Bioinformatics courses at the Bielefeld University Bioinformatics Server', 'Sczyrba, Alexander|Konermann, Susanne|Giegerich, Robert', 'Faculty of Technology, Bielefeld University, D-33594 Bielefeld, Germany. asczyrba@TechFak.Uni-Bielefeld.DE|Faculty of Technology, Bielefeld University, D-33594 Bielefeld, Germany. asczyrba@TechFak.Uni-Bielefeld.DE|Faculty of Technology, Bielefeld University, D-33594 Bielefeld, Germany. asczyrba@TechFak.Uni-Bielefeld.DE', 'Germany|Germany|Germany', 5, 'Computational Biology|Computer-Assisted Instruction|Curriculum|Education, Professional|Genomics|Germany|Internet|Teaching|User-Computer Interface', 'Journal Article', '10.1093/bib/bbm063', '18199576', 'Conferences in computational biology continue to provide tutorials on classical and new methods in the field. This can be taken as an indicator that education is still a bottleneck in our fields process of becoming an established scientific discipline. Bielefeld University has been one of the early providers of bioinformatics education, both locally and via the internet. The Bielefeld Bioinformatics Server (BiBiServ) offers a variety of older and new materials. Here, we report on two online courses made available recently, one introductory and one on the advanced level: (i) SADR: Sequence Analysis with Distributed Resources (http://bibiserv.techfak.uni-bielefeld.de/sadr/) and (ii) ADP: Algebraic Dynamic Programming in Bioinformatics (http://bibiserv.techfak.uni-bielefeld.de/dpcourse/).'),
(68410, 2008, 'bib', 'Gene-set approach for expression pattern analysis', 'Nam, Dougu|Kim, Seon-Young', 'Functional Genomics Research Center, KRIBB, 111 Gwahangno, Yuseong-gu, Daejeon 305-806, Korea.|Functional Genomics Research Center, KRIBB, 111 Gwahangno, Yuseong-gu, Daejeon 305-806, Korea.', '-|-', 253, 'Algorithms|Data Interpretation, Statistical|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1093/bib/bbn001', '18202032', 'Recently developed gene set analysis methods evaluate differential expression patterns of gene groups instead of those of individual genes. This approach especially targets gene groups whose constituents show subtle but coordinated expression changes, which might not be detected by the usual individual gene analysis. The approach has been quite successful in deriving new information from expression data, and a number of methods and tools have been developed intensively in recent years. We review those methods and currently available tools, classify them according to the statistical methods employed, and discuss their pros and cons. We also discuss several interesting extensions to the methods.'),
(68411, 2008, 'bib', 'The BREW workshop series: a stimulating experience in PhD education', 'Giegerich, Robert|Brazma, Alvis|Jonassen, Inge|Ukkonen, Esko|Vingron, Martin', 'Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de', 'Germany|Germany|Germany|Germany|Germany', 0, 'Computational Biology|Curriculum|Education, Graduate|Education, Professional|Europe|Genomics|Teaching', 'Journal Article', '10.1093/bib/bbn002', '18216087', 'Over recent years, five European PhD programmes have organized a series of Bioinformatics Research and Education Workshops. These workshops address the needs of first-year PhD students and have been designed to combine a maximum of educational impact and scientific stimulation with a minimum of financial and administrative effort. We describe the BREW experience and argue that this type of event constitutes an attractive component of PhD education in computational biology and beyond.'),
(68412, 2008, 'bib', 'Interoperability with Moby 10--its better than sharing your toothbrush!', 'BioMoby Consortium|Wilkinson, Mark D|Senger, Martin|Kawas, Edward|Bruskiewich, Richard|Gouzy, Jerome|Noirot, Celine|Bardou, Philippe|Ng, Ambrose|Haase, Dirk|Saiz, Enrique de Andres|Wang, Dennis|Gibbons, Frank|Gordon, Paul M K|Sensen, Christoph W|Carrasco, Jose Manuel Rodriguez|Fernández, José M|Shen, Lixin|Links, Matthew|Ng, Michael|Opushneva, Nina|Neerincx, Pieter B T|Leunissen, Jack A M|Ernst, Rebecca|Twigger, Simon|Usadel, Bjorn|Good, Benjamin|Wong, Yan|Stein, Lincoln|Crosby, William|Karlsson, Johan|Royo, Romina|Párraga, Iván|Ramírez, Sergio|Gelpi, Josep Lluis|Trelles, Oswaldo|Pisano, David G|Jimenez, Natalia|Kerhornou, Arnaud|Rosset, Roman|Zamacola, Leire|Tarraga, Joaquin|Huerta-Cepas, Jaime|Carazo, Jose María|Dopazo, Joaquin|Guigo, Roderic|Navarro, Arcadi|Orozco, Modesto|Valencia, Alfonso|Claros, M Gonzalo|Pérez, Antonio J|Aldana, Jose|Rojano, M  Mar|Fernandez-Santa Cruz, Raul|Navas, Ismael|Schiltz, Gary|Farmer, Andrew|Gessler, Damian|Schoof, Heiko|Groscurth, Andreas', 'Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, D-33501 Bielefeld, Germany. robert@techfak.uni-bielefeld.de', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germ', 73, 'Computational Biology|Database Management Systems|Databases, Factual|Information Storage and Retrieval|Internet|Programming Languages|Systems Integration', 'Journal Article', '10.1093/bib/bbn003', '18238804', 'The BioMoby project was initiated in 2001 from within the model organism database community. It aimed to standardize methodologies to facilitate information exchange and access to analytical resources, using a consensus driven approach. Six years later, the BioMoby development community is pleased to announce the release of the 1.0 version of the interoperability framework, registry Application Programming Interface and supporting Perl and Java code-bases. Together, these provide interoperable access to over 1400 bioinformatics resources worldwide through the BioMoby platform, and this number continues to grow. Here we highlight and discuss the features of BioMoby that make it distinct from other Semantic Web Service and interoperability initiatives, and that have been instrumental to its deployment and use by a wide community of bioinformatics service providers. The standard, client software, and supporting code libraries are all freely available at http://www.biomoby.org/.'),
(68413, 2008, 'bib', 'Information quality in proteomics', 'Stead, David A|Paton, Norman W|Missier, Paolo|Embury, Suzanne M|Hedeler, Cornelia|Jin, Binling|Brown, Alistair J P|Preece, Alun', 'School of Medical Sciences, University of Aberdeen, Institute of Medical Sciences, Foresterhill, Aberdeen AB25 2ZD, UK. d.stead@abdn.ac.uk|School of Medical Sciences, University of Aberdeen, Institute of Medical Sciences, Foresterhill, Aberdeen AB25 2ZD, UK. d.stead@abdn.ac.uk|School of Medical Sciences, University of Aberdeen, Institute of Medical Sciences, Foresterhill, Aberdeen AB25 2ZD, UK. d.stead@abdn.ac.uk|School of Medical Sciences, University of Aberdeen, Institute of Medical Sciences, Foresterhill, Aberdeen AB25 2ZD, UK. d.stead@abdn.ac.uk|School of Medical Sciences, University of Aberdeen, Institute of Medical Sciences, Foresterhill, Aberdeen AB25 2ZD, UK. d.stead@abdn.ac.uk|School of Medical Sciences, University of Aberdeen, Institute of Medical Sciences, Foresterhill, Aberdeen AB25 2ZD, UK. d.stead@abdn.ac.uk|School of Medical Sciences, University of Aberdeen, Institute of Medical Sciences, Foresterhill, Aberdeen AB25 2ZD, UK. d.stead@abdn.ac.uk|School of Medical Sciences, University of Aberdeen, Institute of Medical Sciences, Foresterhill, Aberdeen AB25 2ZD, UK. d.stead@abdn.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 22, 'Database Management Systems|Electrophoresis, Gel, Two-Dimensional|Information Storage and Retrieval|Mass Spectrometry|Proteins|Proteomics|Quality Control|Software', 'Journal Article', '10.1093/bib/bbn004', '18281347', 'Proteomics, the study of the protein complement of a biological system, is generating increasing quantities of data from rapidly developing technologies employed in a variety of different experimental workflows. Experimental processes, e.g. for comparative 2D gel studies or LC-MS/MS analyses of complex protein mixtures, involve a number of steps: from experimental design, through wet and dry lab operations, to publication of data in repositories and finally to data annotation and maintenance. The presence of inaccuracies throughout the processing pipeline, however, results in data that can be untrustworthy, thus offsetting the benefits of high-throughput technology. While researchers and practitioners are generally aware of some of the information quality issues associated with public proteomics data, there are few accepted criteria and guidelines for dealing with them. In this article, we highlight factors that impact on the quality of experimental data and review current approaches to information quality management in proteomics. Data quality issues are considered throughout the lifecycle of a proteomics experiment, from experiment design and technique selection, through data analysis, to archiving and sharing.'),
(68414, 2008, 'bib', 'Computational proteomics: management and analysis of proteomics data', 'Cannataro, Mario', 'School of Medical Sciences, University of Aberdeen, Institute of Medical Sciences, Foresterhill, Aberdeen AB25 2ZD, UK. d.stead@abdn.ac.uk', 'United Kingdom', 11, 'Algorithms|Biomarkers|Computational Biology|Computer Systems|Humans|Mass Spectrometry|Proteomics', 'Editorial', '10.1093/bib/bbn011', '18310104', '-'),
(68415, 2008, 'bib', 'Machine learning methods for predictive proteomics', 'Barla, Annalisa|Jurman, Giuseppe|Riccadonna, Samantha|Merler, Stefano|Chierici, Marco|Furlanello, Cesare', 'FBK, via Sommarive 18, I-38100 Povo (Trento), Italy.|FBK, via Sommarive 18, I-38100 Povo (Trento), Italy.|FBK, via Sommarive 18, I-38100 Povo (Trento), Italy.|FBK, via Sommarive 18, I-38100 Povo (Trento), Italy.|FBK, via Sommarive 18, I-38100 Povo (Trento), Italy.|FBK, via Sommarive 18, I-38100 Povo (Trento), Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy', 48, 'Algorithms|Animals|Area Under Curve|Artificial Intelligence|Biomarkers|Gene Expression Profiling|Humans|Mass Spectrometry|Microarray Analysis|Pattern Recognition, Automated|Proteomics|Signal Processing, Computer-Assisted', 'Journal Article', '10.1093/bib/bbn008', '18310105', 'The search for predictive biomarkers of disease from high-throughput mass spectrometry (MS) data requires a complex analysis path. Preprocessing and machine-learning modules are pipelined, starting from raw spectra, to set up a predictive classifier based on a shortlist of candidate features. As a machine-learning problem, proteomic profiling on MS data needs caution like the microarray case. The risk of overfitting and of selection bias effects is pervasive: not only potential features easily outnumber samples by 10(3) times, but it is easy to neglect information-leakage effects during preprocessing from spectra to peaks. The aim of this review is to explain how to build a general purpose design analysis protocol (DAP) for predictive proteomic profiling: we show how to limit leakage due to parameter tuning and how to organize classification and ranking on large numbers of replicate versions of the original data to avoid selection bias. The DAP can be used with alternative components, i.e. with different preprocessing methods (peak clustering or wavelet based), classifiers e.g. Support Vector Machine (SVM) or feature ranking methods (recursive feature elimination or I-Relief). A procedure for assessing stability and predictive value of the resulting biomarkers list is also provided. The approach is exemplified with experiments on synthetic datasets (from the Cromwell MS simulator) and with publicly available datasets from cancer studies.'),
(68416, 2008, 'bib', 'Approaches to dimensionality reduction in proteomic biomarker studies', 'Hilario, Melanie|Kalousis, Alexandros', 'Computer Science Department, University of Geneva, Battelle Bât. A, 7 route de Drize, CH-1227 Carouge, Switzerland. Melanie.Hilario@cui.unige.ch|Computer Science Department, University of Geneva, Battelle Bât. A, 7 route de Drize, CH-1227 Carouge, Switzerland. Melanie.Hilario@cui.unige.ch', 'Switzerland|Switzerland', 91, 'Algorithms|Biomarkers|Electronic Data Processing|Mass Spectrometry|Proteome|Proteomics|Research|Research Design', 'Journal Article', '10.1093/bib/bbn005', '18310106', 'Mass-spectra based proteomic profiles have received widespread attention as potential tools for biomarker discovery and early disease diagnosis. A major data-analytical problem involved is the extremely high dimensionality (i.e. number of features or variables) of proteomic data, in particular when the sample size is small. This article reviews dimensionality reduction methods that have been used in proteomic biomarker studies. It then focuses on the problem of selecting the most appropriate method for a specific task or dataset, and proposes method combination as a potential alternative to single-method selection. Finally, it points out the potential of novel dimension reduction techniques, in particular those that incorporate domain knowledge through the use of informative priors or causal inference.'),
(68417, 2008, 'bib', 'A review of bioinformatics education in Germany', 'Koch, Ina|Fuellen, Georg', 'Technical University of Applied Sciences Berlin, FB VI, Bioinformatics, Seestr. 64, 13347 Berlin. ina.koch@molgen.mpg.de|Technical University of Applied Sciences Berlin, FB VI, Bioinformatics, Seestr. 64, 13347 Berlin. ina.koch@molgen.mpg.de', '-|-', 20, 'Computational Biology|Curriculum|Education, Professional|Genomics|Germany', 'Journal Article', '10.1093/bib/bbn006', '18310676', 'We describe the establishment of bioinformatics in Germany and give an overview of current bioinformatics education in this country, from the perspective of the practitioner. The aim of this study is to demonstrate development of a strong bioinformatics education at German universities and research institutes during the last years. Beginning with a definition of the multi-disciplinary field bioinformatics, we give a survey of government initiatives in Germany in support of this field, which resulted in a wide spectrum of courses. To the best of our knowledge, we compile all ongoing courses at universities and research institutes. Five case studies featuring university courses with different educational focus illustrate the variety of efforts. In this context we also discuss the main components of German bioinformatics curricula. These components can be considered as the basic knowledge of German bioinformaticians. We conclude by giving perspectives for further development of bioinformatics education.'),
(68418, 2008, 'bib', 'Classification of mass-spectrometric data in clinical proteomics using learning vector quantization methods', 'Villmann, Thomas|Schleif, Frank-Michael|Kostrzewa, Markus|Walch, Axel|Hammer, Barbara', 'Medical Department, University Leipzig Germany. thomas.villmann@medizin.uni-leipzig.de|Medical Department, University Leipzig Germany. thomas.villmann@medizin.uni-leipzig.de|Medical Department, University Leipzig Germany. thomas.villmann@medizin.uni-leipzig.de|Medical Department, University Leipzig Germany. thomas.villmann@medizin.uni-leipzig.de|Medical Department, University Leipzig Germany. thomas.villmann@medizin.uni-leipzig.de', 'Germany|Germany|Germany|Germany|Germany', 40, 'Algorithms|Bacteria|Breast|Computing Methodologies|Data Interpretation, Statistical|Female|Fuzzy Logic|Humans|Mass Spectrometry|Mathematics|Models, Statistical|Neoplasms|Proteomics', 'Journal Article', '10.1093/bib/bbn009', '18334515', 'In the present contribution we propose two recently developed classification algorithms for the analysis of mass-spectrometric data-the supervised neural gas and the fuzzy-labeled self-organizing map. The algorithms are inherently regularizing, which is recommended, for these spectral data because of its high dimensionality and the sparseness for specific problems. The algorithms are both prototype-based such that the principle of characteristic representants is realized. This leads to an easy interpretation of the generated classifcation model. Further, the fuzzy-labeled self-organizing map is able to process uncertainty in data, and classification results can be obtained as fuzzy decisions. Moreover, this fuzzy classification together with the property of topographic mapping offers the possibility of class similarity detection, which can be used for class visualization. We demonstrate the power of both methods for two exemplary examples: the classification of bacteria (listeria types) and neoplastic and non-neoplastic cell populations in breast cancer tissue sections.'),
(68419, 2008, 'bib', 'Pfam 10 years on: 10,000 families and still growing', 'Sammut, Stephen John|Finn, Robert D|Bateman, Alex', 'Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton Hall, Hinxton, Cambridgeshire, CB10 1SA, UK.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton Hall, Hinxton, Cambridgeshire, CB10 1SA, UK.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton Hall, Hinxton, Cambridgeshire, CB10 1SA, UK.', 'United Kingdom|United Kingdom|United Kingdom', 87, 'Database Management Systems|Databases, Protein|Information Storage and Retrieval|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bib/bbn010', '18344544', 'Classifications of proteins into groups of related sequences are in some respects like a periodic table for biology, allowing us to understand the underlying molecular biology of any organism. Pfam is a large collection of protein domains and families. Its scientific goal is to provide a complete and accurate classification of protein families and domains. The next release of the database will contain over 10,000 entries, which leads us to reflect on how far we are from completing this work. Currently Pfam matches 72% of known protein sequences, but for proteins with known structure Pfam matches 95%, which we believe represents the likely upper bound. Based on our analysis a further 28,000 families would be required to achieve this level of coverage for the current sequence database. We also show that as more sequences are added to the sequence databases the fraction of sequences that Pfam matches is reduced, suggesting that continued addition of new families is essential to maintain its relevance.'),
(68420, 2008, 'bib', 'Algorithms and tools for analysis and management of mass spectrometry data', 'Veltri, Pierangelo', 'Dipartimento Medicina Sperimentale e Clinica, Università Magna Graecià - Catanzaro, Italy. veltri@unicz.it', 'Italy', 14, 'Algorithms|Amino Acid Sequence|Computational Biology|Databases, Protein|Information Storage and Retrieval|Mass Spectrometry|Molecular Sequence Data|Proteins|Software', 'Journal Article', '10.1093/bib/bbn007', '18356204', 'Mass spectrometry (MS) is a technique that is used for biological studies. It consists in associating a spectrum to a biological sample. A spectrum consists of couples of values (intensity, m/z), where intensity measures the abundance of biomolecules (as proteins) with a mass-to-charge ratio (m/z) present in the originating sample. In proteomics experiments, MS spectra are used to identify pattern expressions in clinical samples that may be responsible of diseases. Recently, to improve the identification of peptides/proteins related to patterns, MS/MS process is used, consisting in performing cascade of mass spectrometric analysis on selected peaks. Latter technique has been demonstrated to improve the identification and quantification of proteins/peptide in samples. Nevertheless, MS analysis deals with a huge amount of data, often affected by noises, thus requiring automatic data management systems. Tools have been developed and most of the time furnished with the instruments allowing: (i) spectra analysis and visualization, (ii) pattern recognition, (iii) protein databases querying, (iv) peptides/proteins quantification and identification. Currently most of the tools supporting such phases need to be optimized to improve the protein (and their functionalities) identification processes. In this article we survey on applications supporting spectrometrists and biologists in obtaining information from biological samples, analyzing available software for different phases. We consider different mass spectrometry techniques, and thus different requirements. We focus on tools for (i) data preprocessing, allowing to prepare results obtained from spectrometers to be analyzed, (ii) spectra analysis, representation and mining, aimed to identify common and/or hidden patterns in spectra sets or in classifying data, (iii) databases querying to identify peptides, and (iv) improving and boosting the identification and quantification of selected peaks. We trace some open problems and report on requirements that represent new challenges for bioinformatics.'),
(68421, 2008, 'bib', 'Recent developments in the MAFFT multiple sequence alignment program', 'Katoh, Kazutaka|Toh, Hiroyuki', 'Digital Medicine Initiative, Kyushu University, Fukuoka 812-8582, Japan. katoh@bioreg.kyushu-u.ac.jp|Digital Medicine Initiative, Kyushu University, Fukuoka 812-8582, Japan. katoh@bioreg.kyushu-u.ac.jp', 'Japan|Japan', 891, 'Algorithms|Artificial Intelligence|Pattern Recognition, Automated|Sequence Alignment|Sequence Analysis|Software', 'Journal Article', '10.1093/bib/bbn013', '18372315', 'The accuracy and scalability of multiple sequence alignment (MSA) of DNAs and proteins have long been and are still important issues in bioinformatics. To rapidly construct a reasonable MSA, we developed the initial version of the MAFFT program in 2002. MSA software is now facing greater challenges in both scalability and accuracy than those of 5 years ago. As increasing amounts of sequence data are being generated by large-scale sequencing projects, scalability is now critical in many situations. The requirement of accuracy has also entered a new stage since the discovery of functional noncoding RNAs (ncRNAs), the secondary structure should be considered for constructing a high-quality alignment of distantly related ncRNAs. To deal with these problems, in 2007, we updated MAFFT to Version 6 with two new techniques: the PartTree algorithm and the Four-way consistency objective function. The former improved the scalability of progressive alignment and the latter improved the accuracy of ncRNA alignment. We review these and other techniques that MAFFT uses and suggest possible future directions of MSA software as a basis of comparative analyses. MAFFT is available at http://align.bmr.kyushu-u.ac.jp/mafft/software/.'),
(68422, 2008, 'bib', 'MEGA: a biologist-centric software for evolutionary analysis of DNA and protein sequences', 'Kumar, Sudhir|Nei, Masatoshi|Dudley, Joel|Tamura, Koichiro', 'Biodesign Institute, A240, Arizona State University, Tempe, AZ 85287-5301, USA. s.kumar@asu.edu|Biodesign Institute, A240, Arizona State University, Tempe, AZ 85287-5301, USA. s.kumar@asu.edu|Biodesign Institute, A240, Arizona State University, Tempe, AZ 85287-5301, USA. s.kumar@asu.edu|Biodesign Institute, A240, Arizona State University, Tempe, AZ 85287-5301, USA. s.kumar@asu.edu', 'United States|United States|United States|United States', 2576, 'Algorithms|Base Sequence|Biology|Chromosome Mapping|DNA|DNA Mutational Analysis|Evolution, Molecular|Genetic Variation|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bib/bbn017', '18417537', 'The Molecular Evolutionary Genetics Analysis (MEGA) software is a desktop application designed for comparative analysis of homologous gene sequences either from multigene families or from different species with a special emphasis on inferring evolutionary relationships and patterns of DNA and protein evolution. In addition to the tools for statistical analysis of data, MEGA provides many convenient facilities for the assembly of sequence data sets from files or web-based repositories, and it includes tools for visual presentation of the results obtained in the form of interactive phylogenetic trees and evolutionary distance matrices. Here we discuss the motivation, design principles and priorities that have shaped the development of MEGA. We also discuss how MEGA might evolve in the future to assist researchers in their growing need to analyze large data set using new computational methods.'),
(68423, 2008, 'bib', 'The relative value of operon predictions', 'Brouwer, Rutger W W|Kuipers, Oscar P|van Hijum, Sacha A F T', 'Molecular Genetics Department of University of Groningen.|Molecular Genetics Department of University of Groningen.|Molecular Genetics Department of University of Groningen.', '-|-|-', 48, 'Algorithms|Base Sequence|Chromosome Mapping|Computer Simulation|Models, Genetic|Molecular Sequence Data|Operon|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bib/bbn019', '18420711', 'For most organisms, computational operon predictions are the only source of genome-wide operon information. Operon prediction methods described in literature are based on (a combination of) the following five criteria: (i) intergenic distance, (ii) conserved gene clusters, (iii) functional relation, (iv) sequence elements and (v) experimental evidence. The performance estimates of operon predictions reported in literature cannot directly be compared due to differences in methods and data used in these studies. Here, we survey the current status of operon prediction methods. Based on a comparison of the performance of operon predictions on Escherichia coli and Bacillus subtilis we conclude that there is still room for improvement. We expect that existing and newly generated genomics and transcriptomics data will further improve accuracy of operon prediction methods.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(68424, 2008, 'bib', 'IMGT, a system and an ontology that bridge biological and computational spheres in bioinformatics', 'Lefranc, Marie-Paule|Giudicelli, Véronique|Regnier, Laetitia|Duroux, Patrice', 'IMGT, Laboratoire dImmunoGénétique Moléculaire, LIGM, UPR CNRS 1142, IGH, 141 rue de la Cardonille, 34396 Montpellier Cedex 5, France. Marie-Paule.Lefranc@igh.cnrs.fr|IMGT, Laboratoire dImmunoGénétique Moléculaire, LIGM, UPR CNRS 1142, IGH, 141 rue de la Cardonille, 34396 Montpellier Cedex 5, France. Marie-Paule.Lefranc@igh.cnrs.fr|IMGT, Laboratoire dImmunoGénétique Moléculaire, LIGM, UPR CNRS 1142, IGH, 141 rue de la Cardonille, 34396 Montpellier Cedex 5, France. Marie-Paule.Lefranc@igh.cnrs.fr|IMGT, Laboratoire dImmunoGénétique Moléculaire, LIGM, UPR CNRS 1142, IGH, 141 rue de la Cardonille, 34396 Montpellier Cedex 5, France. Marie-Paule.Lefranc@igh.cnrs.fr', 'France|France|France|France', 39, 'Computational Biology|Database Management Systems|Databases, Genetic|Immunogenetics|Information Storage and Retrieval|Internationality|Internet|Systems Biology|Systems Integration', 'Journal Article', '10.1093/bib/bbn014', '18424816', 'IMGT, the international ImMunoGeneTics information system (http://imgt.cines.fr), is the reference in immunogenetics and immunoinformatics. IMGT standardizes and manages the complex immunogenetic data that include the immunoglobulins (IG) or antibodies, the T cell receptors (TR), the major histocompatibility complex (MHC) and the related proteins of the immune system (RPI), which belong to the immunoglobulin superfamily (IgSF) and the MHC superfamily (MhcSF). The accuracy and consistency of IMGT data and the coherence between the different IMGT components (databases, tools and Web resources) are based on IMGT-ONTOLOGY, the first ontology for immunogenetics and immunoinformatics. IMGT-ONTOLOGY manages the immunogenetics knowledge through diverse facets relying on seven axioms, IDENTIFICATION, DESCRIPTION, CLASSIFICATION, NUMEROTATION, LOCALIZATION, ORIENTATION and OBTENTION, that postulate that objects, processes and relations have to be identified, described, classified, numerotated, localized, orientated, and that the way they are obtained has to be determined. These axioms constitute the Formal IMGT-ONTOLOGY, also designated as IMGT-Kaleidoscope. These axioms have been essential for the conceptualization of the molecular immunogenetics knowledge and for the creation of IMGT. Indeed all the components of the IMGT integrated system have been developed, based on standardized concepts and relations, thus allowing IMGT to bridge biological and computational spheres in bioinformatics. The same axioms can be used to generate concepts for multi-scale level approaches at the molecule, cell, tissue, organ, organism or population level, emphasizing the generalization of the application domain. In that way the Formal IMGT-ONTOLOGY represents a paradigm for the elaboration of ontologies in system biology.'),
(68425, 2008, 'bib', 'Protein structure databases with new web services for structural biology and biomedical research', 'Standley, Daron M|Kinjo, Akira R|Kinoshita, Kengo|Nakamura, Haruki', 'Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan.|Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan.|Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan.|Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan.', 'Japan|Japan|Japan|Japan', 36, 'Biology|Computational Biology|Computer Graphics|Computer Simulation|Database Management Systems|Databases, Protein|Information Storage and Retrieval|Internet|Models, Chemical|Models, Molecular|Proteins|Research Design|User-Computer Interface', 'Journal Article', '10.1093/bib/bbn015', '18430752', 'The Protein Data Bank Japan (PDBj) curates, edits and distributes protein structural data as a member of the worldwide Protein Data Bank (wwPDB) and currently processes approximately 25-30% of all deposited data in the world. Structural information is enhanced by the addition of biological and biochemical functional data as well as experimental details extracted from the literature and other databases. Several applications have been developed at PDBj for structural biology and biomedical studies: (i) a Java-based molecular graphics viewer, jV, (ii) display of electron density maps for the evaluation of structure quality, (iii) an extensive database of molecular surfaces for functional sites, eF-site, as well as a search service for similar molecular surfaces, eF-seek, (iv) identification of sequence and structural neighbors, (v) a graphical user interface to all known protein folds with links to the above applications, Protein Globe. Recent examples are shown that highlight the utility of these tools in recognizing remote homologies between pairs of protein structures and in assigning putative biochemical functions to newly determined targets from structural genomics projects.'),
(68426, 2008, 'bib', 'A critical examination of stoichiometric and path-finding approaches to metabolic pathways', 'Planes, Francisco J|Beasley, John E', 'Mathematical Sciences, Brunel University, Uxbridge, UB8 3PH, UK.|Mathematical Sciences, Brunel University, Uxbridge, UB8 3PH, UK.', 'United Kingdom|United Kingdom', 43, 'Algorithms|Computer Simulation|Models, Biological|Programming Languages|Proteome|Signal Transduction|Software', 'Journal Article', '10.1093/bib/bbn018', '18436574', 'Advances in the field of genomics have enabled computational analysis of metabolic pathways at the genome scale. Singular attention has been devoted in the literature to stoichiometric approaches, and path-finding approaches, to metabolic pathways. Stoichiometric approaches make use of reaction stoichiometry when trying to determine metabolic pathways. Stoichiometric approaches involve elementary flux modes and extreme pathways. In contrast, path-finding approaches propose an alternative view based on graph theory in which reaction stoichiometry is not considered. Path-finding approaches use shortest path and k-shortest path concepts. In this article we give a critical overview of the theory, applications and key research challenges of stoichiometric and path-finding approaches to metabolic pathways.'),
(68427, 2008, 'bib', 'The TRANSFAC project as an example of framework technology that supports the analysis of genomic regulation', 'Wingender, Edgar', 'Department of Bioinformatics, Medical School, University of Göttingen, Goldschmidtstr. 1, D-37077 Göttingen, Germany. e.wingender@med.uni-goettingen.de', 'Germany', 155, 'Biotechnology|Chromosome Mapping|Computer Simulation|Gene Expression Regulation|Models, Biological|Proteome|Signal Transduction|Systems Biology|Systems Integration|Transcription Factors', 'Journal Article', '10.1093/bib/bbn016', '18436575', 'Since its beginning as a data collection more than 20 years ago, the TRANSFAC project underwent an evolution to become the basis for a complex platform for the description and analysis of gene regulatory events and networks. In the following, I describe what the original concepts were, what their present status is and how they may be expected to contribute to future system biology approaches.'),
(68428, 2008, 'bib', 'Biodiversity informatics: the challenge of linking data and the role of shared identifiers', 'Page, Roderic D M', 'Division of Environmental and Evolutional Biology, Institute of Biomedical and Life Sciences, University of Glasgow, Glasgow G12 8QQ, UK. r.page@bio.gla.ac.uk', 'United Kingdom', 35, 'Computational Biology|Database Management Systems|Databases, Factual|Documentation|Information Storage and Retrieval|Natural Language Processing|Terminology as Topic', 'Journal Article', '10.1093/bib/bbn022', '18445641', 'A major challenge facing biodiversity informatics is integrating data stored in widely distributed databases. Initial efforts have relied on taxonomic names as the shared identifier linking records in different databases. However, taxonomic names have limitations as identifiers, being neither stable nor globally unique, and the pace of molecular taxonomic and phylogenetic research means that a lot of information in public sequence databases is not linked to formal taxonomic names. This review explores the use of other identifiers, such as specimen codes and GenBank accession numbers, to link otherwise disconnected facts in different databases. The structure of these links can also be exploited using the PageRank algorithm to rank the results of searches on biodiversity databases. The key to rich integration is a commitment to deploy and reuse globally unique, shared identifiers [such as Digital Object Identifiers (DOIs) and Life Science Identifiers (LSIDs)], and the implementation of services that link those identifiers.'),
(68429, 2008, 'bib', 'Computational intelligence approaches for pattern discovery in biological systems', 'Fogel, Gary B', 'Natural Selection, Inc., 9330 Scranton Rd., Suite 150, San Diego, CA 92121, USA. gfogel@natural-selection.com', 'United States', 7, 'Artificial Intelligence|Computational Biology|Computer Simulation|Models, Biological|Pattern Recognition, Automated|Systems Biology', 'Journal Article', '10.1093/bib/bbn021', '18460474', 'Biology, chemistry and medicine are faced by tremendous challenges caused by an overwhelming amount of data and the need for rapid interpretation. Computational intelligence (CI) approaches such as artificial neural networks, fuzzy systems and evolutionary computation are being used with increasing frequency to contend with this problem, in light of noise, non-linearity and temporal dynamics in the data. Such methods can be used to develop robust models of processes either on their own or in combination with standard statistical approaches. This is especially true for database mining, where modeling is a key component of scientific understanding. This review provides an introduction to current CI methods, their application to biological problems, and concludes with a commentary about the anticipated impact of these approaches in bioinformatics.'),
(68430, 2008, 'bib', 'The Beta Workbench: a computational tool to study the dynamics of biological systems', 'Dematté, Lorenzo|Priami, Corrado|Romanel, Alessandro', 'ICT International Doctorate School at University of Trento and Microsoft Research, University of Trento, Centre for Computational and Systems Biology, Povo, TN, Italy.|ICT International Doctorate School at University of Trento and Microsoft Research, University of Trento, Centre for Computational and Systems Biology, Povo, TN, Italy.|ICT International Doctorate School at University of Trento and Microsoft Research, University of Trento, Centre for Computational and Systems Biology, Povo, TN, Italy.', 'Italy|Italy|Italy', 55, 'Algorithms|Computer Simulation|Models, Biological|Programming Languages|Proteome|Signal Transduction|Software|Software Design|Systems Biology', 'Journal Article', '10.1093/bib/bbn023', '18463130', 'We introduce the Beta Workbench (BWB), a scalable tool built on top of the newly defined BlenX language to model, simulate and analyse biological systems. We show the features and the incremental modelling process supported by the BWB on a running example based on the mitogen-activated kinase pathway. Finally, we provide a comparison with related approaches and some hints for future extensions.'),
(68431, 2008, 'bib', 'VisANT: an integrative framework for networks in systems biology', 'Hu, Zhenjun|Snitkin, Evan S|DeLisi, Charles', 'Bioinformatics Program, Boston University, 24 Cummington Street, Boston, MA 02115, USA.|Bioinformatics Program, Boston University, 24 Cummington Street, Boston, MA 02115, USA.|Bioinformatics Program, Boston University, 24 Cummington Street, Boston, MA 02115, USA.', 'United States|United States|United States', 83, 'Computer Graphics|Computer Simulation|Models, Biological|Proteome|Signal Transduction|Software|Systems Biology|Systems Integration|User-Computer Interface', 'Journal Article', '10.1093/bib/bbn020', '18463131', 'The essence of a living cell is adaptation to a changing environment, and a central goal of modern cell biology is to understand adaptive change under normal and pathological conditions. Because the number of components is large, and processes and conditions are many, visual tools are useful in providing an overview of relations that would otherwise be far more difficult to assimilate. Historically, representations were static pictures, with genes and proteins represented as nodes, and known or inferred correlations between them (links) represented by various kinds of lines. The modern challenge is to capture functional hierarchies and adaptation to environmental change, and to discover pathways and processes embedded in known data, but not currently recognizable. Among the tools being developed to meet this challenge is VisANT (freely available at http://visant.bu.edu) which integrates, mines and displays hierarchical information. Challenges to integrating modeling (discrete or continuous) and simulation capabilities into such visual mining software are briefly discussed.'),
(68432, 2008, 'bib', 'Bioinformatics, multiscale modeling and the IUPS Physiome Project', 'Hunter, Peter J|Crampin, Edmund J|Nielsen, Poul M F', 'Auckland Bioengineering Institute, The University of Auckland, 70 Symonds St, Auckland, New Zealand. p.hunter@auckland.ac.nz|Auckland Bioengineering Institute, The University of Auckland, 70 Symonds St, Auckland, New Zealand. p.hunter@auckland.ac.nz|Auckland Bioengineering Institute, The University of Auckland, 70 Symonds St, Auckland, New Zealand. p.hunter@auckland.ac.nz', 'New Zealand|New Zealand|New Zealand', 75, 'Computational Biology|Computer Simulation|Gene Expression Profiling|Models, Biological|Protein Interaction Mapping|Proteome|Research Design|Signal Transduction', 'Journal Article', '10.1093/bib/bbn024', '18477639', 'Multiscale modeling is required for linking physiological processes operating at the organ and tissue levels to signal transduction networks and other subcellular processes. Several XML markup languages, including CellML, have been developed to encode models and to facilitate the building of model repositories and general purpose software tools. Progress in this area is described and illustrated with reference to the heart Physiome Project which aims to understand cardiac arrhythmias in terms of structure-function relations from proteins up to cells, tissues and organs.'),
(68433, 2008, 'bib', 'Critical technologies for bioinformatics', 'Brusic, Vladimir|Ranganathan, Shoba', 'Auckland Bioengineering Institute, The University of Auckland, 70 Symonds St, Auckland, New Zealand. p.hunter@auckland.ac.nz|Auckland Bioengineering Institute, The University of Auckland, 70 Symonds St, Auckland, New Zealand. p.hunter@auckland.ac.nz', 'New Zealand|New Zealand', 2, 'Biotechnology|Chromosome Mapping|Computational Biology|Database Management Systems|Databases, Genetic|Genomics|Sequence Analysis', 'Editorial', '10.1093/bib/bbn025', '18499720', '-'),
(68434, 2008, 'bib', 'Penalized feature selection and classification in bioinformatics', 'Ma, Shuangge|Huang, Jian', 'Department of Epidemiology and Public Health, Yale University, USA. shuangge.ma@yale.edu|Department of Epidemiology and Public Health, Yale University, USA. shuangge.ma@yale.edu', 'United States|United States', 36, 'Algorithms|Artificial Intelligence|Cluster Analysis|Computational Biology|Computer Simulation|Models, Biological|Pattern Recognition, Automated|Software', 'Journal Article', '10.1093/bib/bbn027', '18562478', 'In bioinformatics studies, supervised classification with high-dimensional input variables is frequently encountered. Examples routinely arise in genomic, epigenetic and proteomic studies. Feature selection can be employed along with classifier construction to avoid over-fitting, to generate more reliable classifier and to provide more insights into the underlying causal relationships. In this article, we provide a review of several recently developed penalized feature selection and classification techniques--which belong to the family of embedded feature selection methods--for bioinformatics studies with high-dimensional input. Classification objective functions, penalty functions and computational algorithms are discussed. Our goal is to make interested researchers aware of these feature selection and classification methods that are applicable to high-dimensional bioinformatics data.'),
(68435, 2008, 'bib', 'A structured approach for the engineering of biochemical network models, illustrated for signalling pathways', 'Breitling, Rainer|Gilbert, David|Heiner, Monika|Orton, Richard', 'Groningen Bioinformatics Centre at University of Groningen, The Netherlands.|Groningen Bioinformatics Centre at University of Groningen, The Netherlands.|Groningen Bioinformatics Centre at University of Groningen, The Netherlands.|Groningen Bioinformatics Centre at University of Groningen, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands', 12, 'Algorithms|Biochemistry|Biomedical Engineering|Computer Simulation|Models, Biological|Programming Languages|Proteome|Signal Transduction|Software', 'Journal Article', '10.1093/bib/bbn026', '18573813', 'Quantitative models of biochemical networks (signal transduction cascades, metabolic pathways, gene regulatory circuits) are a central component of modern systems biology. Building and managing these complex models is a major challenge that can benefit from the application of formal methods adopted from theoretical computing science. Here we provide a general introduction to the field of formal modelling, which emphasizes the intuitive biochemical basis of the modelling process, but is also accessible for an audience with a background in computing science and/or model engineering. We show how signal transduction cascades can be modelled in a modular fashion, using both a qualitative approach--qualitative Petri nets, and quantitative approaches--continuous Petri nets and ordinary differential equations (ODEs). We review the major elementary building blocks of a cellular signalling model, discuss which critical design decisions have to be made during model building, and present a number of novel computational tools that can help to explore alternative modular models in an easy and intuitive manner. These tools, which are based on Petri net theory, offer convenient ways of composing hierarchical ODE models, and permit a qualitative analysis of their behaviour. We illustrate the central concepts using signal transduction as our main example. The ultimate aim is to introduce a general approach that provides the foundations for a structured formal engineering of large-scale models of biochemical networks.'),
(68436, 2008, 'bib', 'Detecting short tandem repeats from genome data: opening the software black box', 'Merkel, Angelika|Gemmell, Neil', 'School of Biological Sciences, University of Canterbury, Private Bag 4800, Christchurch 8041, New Zealand. ame52@student.canterbury.ac.nz|School of Biological Sciences, University of Canterbury, Private Bag 4800, Christchurch 8041, New Zealand. ame52@student.canterbury.ac.nz', 'New Zealand|New Zealand', 50, 'Algorithms|Base Sequence|Chromosome Mapping|Microsatellite Repeats|Molecular Sequence Data|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bib/bbn028', '18621747', 'Short tandem repeats, specifically microsatellites, are widely used genetic markers, associated with human genetic diseases, and play an important role in various regulatory mechanisms and evolution. Despite their importance, much is yet unknown about their mutational dynamics. The increasing availability of genome data has led to several in silico studies of microsatellite evolution which have produced a vast range of algorithms and software for tandem repeat detection. Documentation of these tools is often sparse, or provided in a format that is impenetrable to most biologists without informatics background. This article introduces the major concepts behind repeat detecting software essential for informed tool selection. We reflect on issues such as parameter settings and program bias, as well as redundancy filtering and efficiency using examples from the currently available range of programs, to provide an integrated comparison and practical guide to microsatellite detecting programs.'),
(68437, 2008, 'bib', 'Experience using web services for biological sequence analysis', 'Stockinger, Heinz|Attwood, Teresa|Chohan, Shahid Nadeem|Côté, Richard|Cudré-Mauroux, Philippe|Falquet, Laurent|Fernandes, Pedro|Finn, Robert D|Hupponen, Taavi|Korpelainen, Eija|Labarga, Alberto|Laugraud, Aurelie|Lima, Tania|Pafilis, Evangelos|Pagni, Marco|Pettifer, Steve|Phan, Isabelle|Rahman, Nazim', 'Swiss Institute of Bioinformatics, Vital-IT Group, Lausanne, Switzerland. Heinz.Stockinger@isb-sib.ch.|Swiss Institute of Bioinformatics, Vital-IT Group, Lausanne, Switzerland. Heinz.Stockinger@isb-sib.ch.|Swiss Institute of Bioinformatics, Vital-IT Group, Lausanne, Switzerland. Heinz.Stockinger@isb-sib.ch.|Swiss Institute of Bioinformatics, Vital-IT Group, Lausanne, Switzerland. Heinz.Stockinger@isb-sib.ch.|Swiss Institute of Bioinformatics, Vital-IT Group, Lausanne, Switzerland. Heinz.Stockinger@isb-sib.ch.|Swiss Institute of Bioinformatics, Vital-IT Group, Lausanne, Switzerland. Heinz.Stockinger@isb-sib.ch.|Swiss Institute of Bioinformatics, Vital-IT Group, Lausanne, Switzerland. Heinz.Stockinger@isb-sib.ch.|Swiss Institute of Bioinformatics, Vital-IT Group, Lausanne, Switzerland. Heinz.Stockinger@isb-sib.ch.|Swiss Institute of Bioinformatics, Vital-IT Group, Lausanne, Switzerland. Heinz.Stockinger@isb-sib.ch.|Swiss Institute of Bioinformatics, Vital-IT Group, Lausanne, Switzerland. Heinz.Stockinger@isb-sib.ch.|Swiss Institute of Bioinformatics, Vital-IT Group, Lausanne, Switzerland. Heinz.Stockinger@isb-sib.ch.|Swiss Institute of Bioinformatics, Vital-IT Group, Lausanne, Switzerland. Heinz.Stockinger@isb-sib.ch.|Swiss Institute of Bioinformatics, Vital-IT Group, Lausanne, Switzerland. Heinz.Stockinger@isb-sib.ch.|Swiss Institute of Bioinformatics, Vital-IT Group, Lausanne, Switzerland. Heinz.Stockinger@isb-sib.ch.|Swiss Institute of Bioinformatics, Vital-IT Group, Lausanne, Switzerland. Heinz.Stockinger@isb-sib.ch.|Swiss Institute of Bioinformatics, Vital-IT Group, Lausanne, Switzerland. Heinz.Stockinger@isb-sib.ch.|Swiss Institute of Bioinformatics, Vital-IT Group, Lausanne, Switzerland. Heinz.Stockinger@isb-sib.ch.|Swiss Institute of Bioinformatics, Vital-IT Group, Lausanne, Switzerland. Heinz.Stockinger@isb-sib.ch.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Swit', 12, 'Computational Biology|Database Management Systems|Databases, Genetic|Humans|Information Storage and Retrieval|Internet|Sequence Analysis|Systems Integration|User-Computer Interface', 'Journal Article', '10.1093/bib/bbn029', '18621748', 'Programmatic access to data and tools through the web using so-called web services has an important role to play in bioinformatics. In this article, we discuss the most popular approaches based on SOAP/WS-I and REST and describe our, a cross section of the community, experiences with providing and using web services in the context of biological sequence analysis. We briefly review main technological approaches as well as best practice hints that are useful for both users and developers. Finally, syntactic and semantic data integration issues with multiple web services are discussed.'),
(68438, 2008, 'bib', 'Categorization of services for seeking information in biomedical literature: a typology for improvement of practice', 'Kim, Jung-Jae|Rebholz-Schuhmann, Dietrich', 'European Bioinformatics Institute, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. kim@ebi.ac.uk.|European Bioinformatics Institute, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. kim@ebi.ac.uk.', 'United Kingdom|United Kingdom', 21, 'Behavior|Computational Biology|Databases, Bibliographic|Humans|Information Services|Information Storage and Retrieval|Information Systems|Reproducibility of Results|User-Computer Interface', 'Journal Article', '10.1093/bib/bbn032', '18660511', 'Biomedical researchers have to efficiently explore the scientific literature, keeping the focus on their research. This goal can only be achieved if the available means for accessing the literature meet the researchers retrieval needs and if they understand how the tools filter the perpetually increasing number of documents. We have examined existing web-based services for information retrieval in order to give users guidance to improve their everyday practice of literature analysis. We propose two dimensions along which the services may be categorized: categories of input and output formats, and categories of behavioural usage. The categorization would be helpful for biologists to understand the differences in the input and output formats and the tasks they fulfil in information-retrieval activities. Also, they may inspire future bioinformaticians to further innovative development in this field.'),
(68439, 2008, 'bib', 'Identification of replication origins in prokaryotic genomes', 'Sernova, Natalia V|Gelfand, Mikhail S', 'Institute for Information Transmission Problems (Kharkevich Institute), Russian Academy of Sciences, Bolshoi Karetny pereulok, 19, Moscow, 127994, Russia.|Institute for Information Transmission Problems (Kharkevich Institute), Russian Academy of Sciences, Bolshoi Karetny pereulok, 19, Moscow, 127994, Russia.', '-|-', 38, 'Algorithms|Base Sequence|Chromosome Mapping|Chromosomes, Bacterial|Molecular Sequence Data|Replication Origin|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bib/bbn031', '18660512', 'The availability of hundreds of complete bacterial genomes has created new challenges and simultaneously opportunities for bioinformatics. In the area of statistical analysis of genomic sequences, the studies of nucleotide compositional bias and gene bias between strands and replichores paved way to the development of tools for prediction of bacterial replication origins. Only a few (about 20) origin regions for eubacteria and archaea have been proven experimentally. One reason for that may be that this is now considered as an essentially bioinformatics problem, where predictions are sufficiently reliable not to run labor-intensive experiments, unless specifically needed. Here we describe the main existing approaches to the identification of replication origin (oriC) and termination (terC) loci in prokaryotic chromosomes and characterize a number of computational tools based on various skew types and other types of evidence. We also classify the eubacterial and archaeal chromosomes by predictability of their replication origins using skew plots. Finally, we discuss possible combined approaches to the identification of the oriC sites that may be used to improve the prediction tools, in particular, the analysis of DnaA binding sites using the comparative genomic methods.'),
(68440, 2009, 'bib', 'Web-based applications for building, managing and analysing kinetic models of biological systems', 'Lee, Dong-Yup|Saha, Rajib|Yusufi, Faraaz Noor Khan|Park, Wonjun|Karimi, Iftekhar A', 'Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117576. cheld@nus.edu.sg|Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117576. cheld@nus.edu.sg|Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117576. cheld@nus.edu.sg|Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117576. cheld@nus.edu.sg|Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117576. cheld@nus.edu.sg', 'Singapore|Singapore|Singapore|Singapore|Singapore', 3, 'Computational Biology|Computer Simulation|Databases, Factual|Humans|Internet|Mathematics|Models, Biological|Software|User-Computer Interface', 'Journal Article', '10.1093/bib/bbn039', '18805901', 'Mathematical modelling and computational analysis play an essential role in improving our capability to elucidate the functions and characteristics of complex biological systems such as metabolic, regulatory and cell signalling pathways. The modelling and concomitant simulation render it possible to predict the cellular behaviour of systems under various genetically and/or environmentally perturbed conditions. This motivates systems biologists/bioengineers/bioinformaticians to develop new tools and applications, allowing non-experts to easily conduct such modelling and analysis. However, among a multitude of systems biology tools developed to date, only a handful of projects have adopted a web-based approach to kinetic modelling. In this report, we evaluate the capabilities and characteristics of current web-based tools in systems biology and identify desirable features, limitations and bottlenecks for further improvements in terms of usability and functionality. A short discussion on software architecture issues involved in web-based applications and the approaches taken by existing tools is included for those interested in developing their own simulation applications.'),
(68441, 2008, 'bib', 'Literature mining in support of drug discovery', 'Agarwal, Pankaj|Searls, David B', 'GlaxoSmithKline R&D, 709 Swedeland Road, UW2230, King of Prussia, PA 19406. Pankaj.Agarwal@gsk.com|GlaxoSmithKline R&D, 709 Swedeland Road, UW2230, King of Prussia, PA 19406. Pankaj.Agarwal@gsk.com', '-|-', 52, 'Animals|Artificial Intelligence|Databases, Bibliographic|Drug Discovery|Humans|Information Storage and Retrieval|Internet|Medical Subject Headings|Proteins|PubMed', 'Journal Article', '10.1093/bib/bbn035', '18820304', 'The drug discovery enterprise provides strong drivers for data integration. While attention in this arena has tended to focus on integration of primary data from omics and other large platform technologies contributing to drug discovery and development, the scientific literature remains a major source of information valuable to pharmaceutical enterprises, and therefore tools for mining such data and integrating it with other sources are of vital interest and economic impact. This review provides a brief overview of approaches to literature mining as they relate to drug discovery, and offers an illustrative case study of a lightweight approach we have implemented within an industrial context.'),
(68442, 2009, 'bib', 'Gene-set analysis and reduction', 'Dinu, Irina|Potter, John D|Mueller, Thomas|Liu, Qi|Adewale, Adeniyi J|Jhangri, Gian S|Einecke, Gunilla|Famulski, Konrad S|Halloran, Philip|Yasui, Yutaka', 'PhD, School of Public Health, University of Alberta, 13-106J Clinical Sciences Building, Edmonton, Alberta T6G2G3, Canada. idinu@ualberta.ca|PhD, School of Public Health, University of Alberta, 13-106J Clinical Sciences Building, Edmonton, Alberta T6G2G3, Canada. idinu@ualberta.ca|PhD, School of Public Health, University of Alberta, 13-106J Clinical Sciences Building, Edmonton, Alberta T6G2G3, Canada. idinu@ualberta.ca|PhD, School of Public Health, University of Alberta, 13-106J Clinical Sciences Building, Edmonton, Alberta T6G2G3, Canada. idinu@ualberta.ca|PhD, School of Public Health, University of Alberta, 13-106J Clinical Sciences Building, Edmonton, Alberta T6G2G3, Canada. idinu@ualberta.ca|PhD, School of Public Health, University of Alberta, 13-106J Clinical Sciences Building, Edmonton, Alberta T6G2G3, Canada. idinu@ualberta.ca|PhD, School of Public Health, University of Alberta, 13-106J Clinical Sciences Building, Edmonton, Alberta T6G2G3, Canada. idinu@ualberta.ca|PhD, School of Public Health, University of Alberta, 13-106J Clinical Sciences Building, Edmonton, Alberta T6G2G3, Canada. idinu@ualberta.ca|PhD, School of Public Health, University of Alberta, 13-106J Clinical Sciences Building, Edmonton, Alberta T6G2G3, Canada. idinu@ualberta.ca|PhD, School of Public Health, University of Alberta, 13-106J Clinical Sciences Building, Edmonton, Alberta T6G2G3, Canada. idinu@ualberta.ca', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada', 48, 'Algorithms|Cell Line, Tumor|Gene Expression Profiling|Humans|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Phenotype|Tumor Suppressor Protein p53', 'Journal Article', '10.1093/bib/bbn042', '18836208', 'Gene-set analysis aims to identify differentially expressed gene sets (pathways) by a phenotype in DNA microarray studies. We review here important methodological aspects of gene-set analysis and illustrate them with varying performance of several methods proposed in the literature. We emphasize the importance of distinguishing between self-contained versus competitive methods, following Goeman and Bühlmann. We also discuss reducing a gene set to its subset, consisting of core members that chiefly contribute to the statistical significance of the differential expression of the initial gene set by phenotype. Significance analysis of microarray for gene-set reduction (SAM-GSR) can be used for an analytical reduction of gene sets to their core subsets. We apply SAM-GSR on a microarray dataset for identifying biological gene sets (pathways) whose gene expressions are associated with p53 mutation in cancer cell lines. Codes to implement SAM-GSR in the statistical package R can be downloaded from http://www.ualberta.ca/~yyasui/homepage.html.'),
(68443, 2009, 'bib', 'Bringing Web 20 to bioinformatics', 'Zhang, Zhang|Cheung, Kei-Hoi|Townsend, Jeffrey P', 'Department of Ecology and Evolutionary Biology, Yale University, New Haven, Connecticut 06520, USA.|Department of Ecology and Evolutionary Biology, Yale University, New Haven, Connecticut 06520, USA.|Department of Ecology and Evolutionary Biology, Yale University, New Haven, Connecticut 06520, USA.', 'United States|United States|United States', 35, 'Computational Biology|Computer-Assisted Instruction|Cooperative Behavior|Database Management Systems|Databases, Factual|Humans|Information Dissemination|Internet|Research|User-Computer Interface', 'Journal Article', '10.1093/bib/bbn041', '18842678', 'Enabling deft data integration from numerous, voluminous and heterogeneous data sources is a major bioinformatic challenge. Several approaches have been proposed to address this challenge, including data warehousing and federated databasing. Yet despite the rise of these approaches, integration of data from multiple sources remains problematic and toilsome. These two approaches follow a user-to-computer communication model for data exchange, and do not facilitate a broader concept of data sharing or collaboration among users. In this report, we discuss the potential of Web 2.0 technologies to transcend this model and enhance bioinformatics research. We propose a Web 2.0-based Scientific Social Community (SSC) model for the implementation of these technologies. By establishing a social, collective and collaborative platform for data creation, sharing and integration, we promote a web services-based pipeline featuring web services for computer-to-computer data exchange as users add value. This pipeline aims to simplify data integration and creation, to realize automatic analysis, and to facilitate reuse and sharing of data. SSC can foster collaboration and harness collective intelligence to create and discover new knowledge. In addition to its research potential, we also describe its potential role as an e-learning platform in education. We discuss lessons from information technology, predict the next generation of Web (Web 3.0), and describe its potential impact on the future of bioinformatics studies.'),
(68444, 2009, 'bib', 'Models of coding sequence evolution', 'Delport, Wayne|Scheffler, Konrad|Seoighe, Cathal', 'University of Cape Town, Observatory, 7925, Cape Town, South Africa.|University of Cape Town, Observatory, 7925, Cape Town, South Africa.|University of Cape Town, Observatory, 7925, Cape Town, South Africa.', 'South Africa|South Africa|South Africa', 51, 'Base Sequence|Codon|Evolution, Molecular|Markov Chains|Models, Genetic|Models, Statistical|Molecular Sequence Data|Phylogeny|Software', 'Journal Article', '10.1093/bib/bbn049', '18971241', 'Probabilistic models of sequence evolution are in widespread use in phylogenetics and molecular sequence evolution. These models have become increasingly sophisticated and combined with statistical model comparison techniques have helped to shed light on how genes and proteins evolve. Models of codon evolution have been particularly useful, because, in addition to providing a significant improvement in model realism for protein-coding sequences, codon models can also be designed to test hypotheses about the selective pressures that shape the evolution of the sequences. Such models typically assume a phylogeny and can be used to identify sites or lineages that have evolved adaptively. Recently some of the key assumptions that underlie phylogenetic tests of selection have been questioned, such as the assumption that the rate of synonymous changes is constant across sites or that a single phylogenetic tree can be assumed at all sites for recombining sequences. While some of these issues have been addressed through the development of novel methods, others remain as caveats that need to be considered on a case-by-case basis. Here, we outline the theory of codon models and their application to the detection of positive selection. We review some of the more recent developments that have improved their power and utility, laying a foundation for further advances in the modeling of coding sequence evolution.'),
(68445, 2009, 'bib', 'Knowledge-based expert systems and a proof-of-concept case study for multiple sequence alignment construction and analysis', 'Aniba, Mohamed Radhouene|Siguenza, Sophie|Friedrich, Anne|Plewniak, Frédéric|Poch, Olivier|Marchler-Bauer, Aron|Thompson, Julie Dawn', 'Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), F-67400 Illkirch, France.|Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), F-67400 Illkirch, France.|Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), F-67400 Illkirch, France.|Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), F-67400 Illkirch, France.|Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), F-67400 Illkirch, France.|Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), F-67400 Illkirch, France.|Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), F-67400 Illkirch, France.', 'France|France|France|France|France|France|France', 2, 'Algorithms|Amino Acid Sequence|Expert Systems|Humans|Information Storage and Retrieval|Knowledge|Molecular Sequence Data|Sequence Alignment|Software', 'Journal Article', '10.1093/bib/bbn045', '18971242', 'The traditional approach to bioinformatics analyses relies on independent task-specific services and applications, using different input and output formats, often idiosyncratic, and frequently not designed to inter-operate. In general, such analyses were performed by experts who manually verified the results obtained at each step in the process. Today, the amount of bioinformatics information continuously being produced means that handling the various applications used to study this information presents a major data management and analysis challenge to researchers. It is now impossible to manually analyse all this information and new approaches are needed that are capable of processing the large-scale heterogeneous data in order to extract the pertinent information. We review the recent use of integrated expert systems aimed at providing more efficient knowledge extraction for bioinformatics research. A general methodology for building knowledge-based expert systems is described, focusing on the unstructured information management architecture, UIMA, which provides facilities for both data and process management. A case study involving a multiple alignment expert system prototype called AlexSys is also presented.'),
(68446, 2008, 'bib', 'Data and knowledge integration in the life sciences', 'Philippi, Stephan', 'Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), F-67400 Illkirch, France.', 'France', 6, 'Animals|Biological Science Disciplines|Computational Biology|Humans|Information Storage and Retrieval|Internet|Knowledge|Systems Integration', 'Editorial', '10.1093/bib/bbn046', '18980960', '-'),
(68447, 2009, 'bib', 'Building biomedical web communities using a semantically aware content management system', 'Das, Sudeshna|Girard, Lisa|Green, Tom|Weitzman, Louis|Lewis-Bowen, Alister|Clark, Tim', 'Initiative in Innovative Computing, Harvard University, 60 Oxford Street, Cambridge, MA, USA. sudeshna_das@harvard.edu|Initiative in Innovative Computing, Harvard University, 60 Oxford Street, Cambridge, MA, USA. sudeshna_das@harvard.edu|Initiative in Innovative Computing, Harvard University, 60 Oxford Street, Cambridge, MA, USA. sudeshna_das@harvard.edu|Initiative in Innovative Computing, Harvard University, 60 Oxford Street, Cambridge, MA, USA. sudeshna_das@harvard.edu|Initiative in Innovative Computing, Harvard University, 60 Oxford Street, Cambridge, MA, USA. sudeshna_das@harvard.edu|Initiative in Innovative Computing, Harvard University, 60 Oxford Street, Cambridge, MA, USA. sudeshna_das@harvard.edu', 'United States|United States|United States|United States|United States|United States', 5, 'Biomedical Research|Database Management Systems|Databases, Factual|Humans|Information Dissemination|Information Storage and Retrieval|Internet|Semantics|Software', 'Journal Article', '10.1093/bib/bbn052', '19060302', 'Web-based biomedical communities are becoming an increasingly popular vehicle for sharing information amongst researchers and are fast gaining an online presence. However, information organization and exchange in such communities is usually unstructured, rendering interoperability between communities difficult. Furthermore, specialized software to create such communities at low cost-targeted at the specific common information requirements of biomedical researchers-has been largely lacking. At the same time, a growing number of biological knowledge bases and biomedical resources are being structured for the Semantic Web. Several groups are creating reference ontologies for the biomedical domain, actively publishing controlled vocabularies and making data available in Resource Description Framework (RDF) language. We have developed the Science Collaboration Framework (SCF) as a reusable platform for advanced structured online collaboration in biomedical research that leverages these ontologies and RDF resources. SCF supports structured Web 2.0 style community discourse amongst researchers, makes heterogeneous data resources available to the collaborating scientist, captures the semantics of the relationship among the resources and structures discourse around the resources. The first instance of the SCF framework is being used to create an open-access online community for stem cell research-StemBook (http://www.stembook.org). We believe that such a framework is required to achieve optimal productivity and leveraging of resources in interdisciplinary scientific research. We expect it to be particularly beneficial in highly interdisciplinary areas, such as neurodegenerative disease and neurorepair research, as well as having broad utility across the natural sciences.'),
(68448, 2008, 'bib', 'Facts from text: can text mining help to scale-up high-quality manual curation of gene products with ontologies?', 'Winnenburg, Rainer|Wächter, Thomas|Plake, Conrad|Doms, Andreas|Schroeder, Michael', 'Biotechnology Center, Technische Universität Dresden, Tatzberg 47-49, 01307 Dresden, Germany.|Biotechnology Center, Technische Universität Dresden, Tatzberg 47-49, 01307 Dresden, Germany.|Biotechnology Center, Technische Universität Dresden, Tatzberg 47-49, 01307 Dresden, Germany.|Biotechnology Center, Technische Universität Dresden, Tatzberg 47-49, 01307 Dresden, Germany.|Biotechnology Center, Technische Universität Dresden, Tatzberg 47-49, 01307 Dresden, Germany.', 'Germany|Germany|Germany|Germany|Germany', 60, 'Abstracting and Indexing as Topic|Animals|Computational Biology|Databases, Bibliographic|Databases, Genetic|Genes|Humans|Information Storage and Retrieval|Knowledge|Semantics', 'Journal Article', '10.1093/bib/bbn043', '19060303', 'The biomedical literature can be seen as a large integrated, but unstructured data repository. Extracting facts from literature and making them accessible is approached from two directions: manual curation efforts develop ontologies and vocabularies to annotate gene products based on statements in papers. Text mining aims to automatically identify entities and their relationships in text using information retrieval and natural language processing techniques. Manual curation is highly accurate but time consuming, and does not scale with the ever increasing growth of literature. Text mining as a high-throughput computational technique scales well, but is error-prone due to the complexity of natural language. How can both be married to combine scalability and accuracy? Here, we review the state-of-the-art text mining approaches that are relevant to annotation and discuss available online services analysing biomedical literature by means of text mining techniques, which could also be utilised by annotation projects. We then examine how far text mining has already been utilised in existing annotation projects and conclude how these techniques could be tightly integrated into the manual annotation process through novel authoring systems to scale-up high-quality manual curation.'),
(68449, 2008, 'bib', 'Data curation + process curation=data integration + science', 'Goble, Carole|Stevens, Robert|Hull, Duncan|Wolstencroft, Katy|Lopez, Rodrigo', 'School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK.|School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK.|School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK.|School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK.|School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 43, 'Computational Biology|Database Management Systems|Humans|Information Storage and Retrieval|Programming Languages|Reproducibility of Results|Semantics|Software|Systems Integration|User-Computer Interface', 'Journal Article', '10.1093/bib/bbn034', '19060304', 'In bioinformatics, we are familiar with the idea of curated data as a prerequisite for data integration. We neglect, often to our cost, the curation and cataloguing of the processes that we use to integrate and analyse our data. Programmatic access to services, for data and processes, means that compositions of services can be made that represent the in silico experiments or processes that bioinformaticians perform. Data integration through workflows depends on being able to know what services exist and where to find those services. The large number of services and the operations they perform, their arbitrary naming and lack of documentation, however, mean that they can be difficult to use. The workflows themselves are composite processes that could be pooled and reused but only if they too can be found and understood. Thus appropriate curation, including semantic mark-up, would enable processes to be found, maintained and consequently used more easily. This broader view on semantic annotation is vital for full data integration that is necessary for the modern scientific analyses in biology. This article will brief the community on the current state of the art and the current challenges for process curation, both within and without the Life Sciences.'),
(68450, 2009, 'bib', 'A pitfall of wiki solution for biological databases', 'Arita, Masanori', 'Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwanoha 5-1-5 GSFS CB05, 277-8561 Kashiwa, Japan. arita@k.u-tokyo.ac.jp', 'Japan', 7, 'Algorithms|Computational Biology|Database Management Systems|Databases, Factual|Humans|Information Dissemination|Information Storage and Retrieval|Internet|Software|User-Computer Interface', 'Journal Article', '10.1093/bib/bbn053', '19060305', 'Not a few biologists tend to consider wiki as a solution to manage and reorganize data by a community. However, in its basic functionality, wiki lacks a measure to check data consistency and is not suitable for a database. To circumvent this pitfall, installation of page dependency through in-line page searches is necessary. We also introduce two existing approaches that support in-line queries.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(68451, 2009, 'bib', 'Linked data and provenance in biological data webs', 'Zhao, Jun|Miles, Alistair|Klyne, Graham|Shotton, David', 'Image Bioinformatics Research Group, Department of Zoology, University of Oxford, The Tinbergen Building, South Parks Road, Oxford, OX1 3PS, UK. jun.zhao@zoo.ox.ac.uk|Image Bioinformatics Research Group, Department of Zoology, University of Oxford, The Tinbergen Building, South Parks Road, Oxford, OX1 3PS, UK. jun.zhao@zoo.ox.ac.uk|Image Bioinformatics Research Group, Department of Zoology, University of Oxford, The Tinbergen Building, South Parks Road, Oxford, OX1 3PS, UK. jun.zhao@zoo.ox.ac.uk|Image Bioinformatics Research Group, Department of Zoology, University of Oxford, The Tinbergen Building, South Parks Road, Oxford, OX1 3PS, UK. jun.zhao@zoo.ox.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 25, 'Algorithms|Animals|Biology|Data Collection|Database Management Systems|Databases, Factual|Humans|Information Dissemination|Information Storage and Retrieval|Internet|Knowledge Bases|Semantics|Software|User-Computer Interface|Vocabulary, Controlled', 'Journal Article', '10.1093/bib/bbn044', '19060306', 'The Web is now being used as a platform for publishing and linking life science data. The Webs linking architecture can be exploited to join heterogeneous data from multiple sources. However, as data are frequently being updated in a decentralized environment, provenance information becomes critical to providing reliable and trustworthy services to scientists. This article presents design patterns for representing and querying provenance information relating to mapping links between heterogeneous data from sources in the domain of functional genomics. We illustrate the use of named resource description framework (RDF) graphs at different levels of granularity to make provenance assertions about linked data, and demonstrate that these assertions are sufficient to support requirements including data currency, integrity, evidential support and historical queries.'),
(68452, 2008, 'bib', 'Building a knowledge base for systems pathology', 'Michael, Holger|Hogan, Jennifer|Kel, Alexander|Kel-Margoulis, Olga|Schacherer, Frank|Voss, Nico|Wingender, Edgar', 'Department of Bioinformatics, Goldschmidtstr. 1, D-37077 Göttingen, Germany.|Department of Bioinformatics, Goldschmidtstr. 1, D-37077 Göttingen, Germany.|Department of Bioinformatics, Goldschmidtstr. 1, D-37077 Göttingen, Germany.|Department of Bioinformatics, Goldschmidtstr. 1, D-37077 Göttingen, Germany.|Department of Bioinformatics, Goldschmidtstr. 1, D-37077 Göttingen, Germany.|Department of Bioinformatics, Goldschmidtstr. 1, D-37077 Göttingen, Germany.|Department of Bioinformatics, Goldschmidtstr. 1, D-37077 Göttingen, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 7, 'Database Management Systems|Databases, Factual|Diabetes Mellitus|Gene Regulatory Networks|Humans|Information Storage and Retrieval|Information Systems|Knowledge Bases|Semantics|Signal Transduction|User-Computer Interface', 'Journal Article', '10.1093/bib/bbn038', '19073714', 'Translating the exponentially growing amount of omics data into knowledge usable for a personalized medicine approach poses a formidable challenge. In this article-taking diabetes as a use case-we present strategies for developing data repositories into computer-accessible knowledge sources that can be used for a systemic view on the molecular causes of diseases, thus laying the foundation for systems pathology.'),
(68453, 2009, 'bib', 'Towards the integrated analysis, visualization and reconstruction of microbial gene regulatory networks', 'Baumbach, Jan|Tauch, Andreas|Rahmann, Sven', 'International Computer Science Institute, Berkeley, USA. jbaumbac@icsi.berkeley.edu|International Computer Science Institute, Berkeley, USA. jbaumbac@icsi.berkeley.edu|International Computer Science Institute, Berkeley, USA. jbaumbac@icsi.berkeley.edu', 'United States|United States|United States', 5, 'Acetates|Bacterial Proteins|Computational Biology|Corynebacterium|Database Management Systems|Databases, Genetic|Gene Expression Regulation, Bacterial|Gene Regulatory Networks|Genome, Bacterial|Glucose|Software|User-Computer Interface', 'Journal Article', '10.1093/bib/bbn055', '19074493', 'To handle changing environmental surroundings and to manage unfavorable conditions, microbial organisms have evolved complex transcriptional regulatory networks. To comprehensively analyze these gene regulatory networks, several online available databases and analysis platforms have been implemented and established. In this article, we address the typical cycle of scientific knowledge exploration and integration in the area of procaryotic transcriptional gene regulation. We briefly review five popular, publicly available systems that support (i) the integration of existing knowledge, (ii) visualization capabilities and (iii) computer analysis to predict promising wet lab targets. We exemplify the benefits of such integrated data analysis platforms by means of four application cases exemplarily performed with the corynebacterial reference database CoryneRegNet.'),
(68454, 2009, 'bib', 'Simulation of DNA sequence evolution under models of recent directional selection', 'Kim, Yuseob|Wiehe, Thomas', 'Universität zu Köln, Institut für Genetik, Zülpicher Strasse 47, 50674 Köln, Germany.|Universität zu Köln, Institut für Genetik, Zülpicher Strasse 47, 50674 Köln, Germany.', 'Germany|Germany', 10, 'Algorithms|Animals|Base Sequence|Computer Simulation|Evolution, Molecular|Genetic Variation|Models, Genetic|Selection, Genetic|Software', 'Journal Article', '10.1093/bib/bbn048', '19109303', 'Computer simulation is an essential tool in the analysis of DNA sequence variation for mapping events of recent adaptive evolution in the genome. Various simulation methods are employed to predict the signature of selection in sequence variation. The most informative and efficient method currently in use is coalescent simulation. However, this method is limited to simple models of directional selection. Whole-population forward-in-time simulations are the alternative to coalescent simulations for more complex models. The notorious problem of excessive computational cost in forward-in-time simulations can be overcome by various simplifying amendments. Overall, the success of simulations depends on the creative application of some population genetic theory to the simulation algorithm.'),
(68455, 2008, 'bib', 'Solutions for data integration in functional genomics: a critical assessment and case study', 'Smedley, Damian|Swertz, Morris A|Wolstencroft, Katy|Proctor, Glenn|Zouberakis, Michael|Bard, Jonathan|Hancock, John M|Schofield, Paul', 'European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. damian@ebi.ac.uk.|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. damian@ebi.ac.uk.|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. damian@ebi.ac.uk.|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. damian@ebi.ac.uk.|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. damian@ebi.ac.uk.|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. damian@ebi.ac.uk.|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. damian@ebi.ac.uk.|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. damian@ebi.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 13, 'Animals|Computational Biology|Database Management Systems|Databases, Genetic|Genomics|Humans|Information Storage and Retrieval|Mice|Software|User-Computer Interface', 'Journal Article', '10.1093/bib/bbn040', '19112082', 'The torrent of data emerging from the application of new technologies to functional genomics and systems biology can no longer be contained within the traditional modes of data sharing and publication with the consequence that data is being deposited in, distributed across and disseminated through an increasing number of databases. The resulting fragmentation poses serious problems for the model organism community which increasingly rely on data mining and computational approaches that require gathering of data from a range of sources. In the light of these problems, the European Commission has funded a coordination action, CASIMIR (coordination and sustainability of international mouse informatics resources), with a remit to assess the technical and social aspects of database interoperability that currently prevent the full realization of the potential of data integration in mouse functional genomics. In this article, we assess the current problems with interoperability, with particular reference to mouse functional genomics, and critically review the technologies that can be deployed to overcome them. We describe a typical use-case where an investigator wishes to gather data on variation, genomic context and metabolic pathway involvement for genes discovered in a genome-wide screen. We go on to develop an automated approach involving an in silico experimental workflow tool, Taverna, using web services, BioMart and MOLGENIS technologies for data retrieval. Finally, we focus on the current impediments to adopting such an approach in a wider context, and strategies to overcome them.'),
(68456, 2009, 'bib', 'Biochemical simulations: stochastic, approximate stochastic and hybrid approaches', 'Pahle, Jürgen', 'Bioquant/Institute of Zoology, University of Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany. juergen.pahle@bioquant.uni-heidelberg.de', 'Germany', 28, 'Algorithms|Computer Simulation|Models, Biological|Monte Carlo Method|Software|Stochastic Processes', 'Journal Article', '10.1093/bib/bbn050', '19151097', 'Computer simulations have become an invaluable tool to study the sometimes counterintuitive temporal dynamics of (bio-)chemical systems. In particular, stochastic simulation methods have attracted increasing interest recently. In contrast to the well-known deterministic approach based on ordinary differential equations, they can capture effects that occur due to the underlying discreteness of the systems and random fluctuations in molecular numbers. Numerous stochastic, approximate stochastic and hybrid simulation methods have been proposed in the literature. In this article, they are systematically reviewed in order to guide the researcher and help her find the appropriate method for a specific problem.'),
(68457, 2009, 'bib', 'Domain mobility in proteins: functional and evolutionary implications', 'Basu, Malay Kumar|Poliakov, Eugenia|Rogozin, Igor B', 'J. Craig Venter Institute, 9704 Medical Center Drive, Rockville, MD 20850, USA. malaykbasu@gmail.com|J. Craig Venter Institute, 9704 Medical Center Drive, Rockville, MD 20850, USA. malaykbasu@gmail.com|J. Craig Venter Institute, 9704 Medical Center Drive, Rockville, MD 20850, USA. malaykbasu@gmail.com', 'United States|United States|United States', 42, 'Animals|Eukaryotic Cells|Evolution, Molecular|Genome, Human|Humans|Protein Conformation|Protein Interaction Mapping|Protein Structure, Tertiary|Proteins|Signal Transduction', 'Journal Article', '10.1093/bib/bbn057', '19151098', 'A substantial fraction of eukaryotic proteins contains multiple domains, some of which show a tendency to occur in diverse domain architectures and can be considered mobile (or promiscuous). These promiscuous domains are typically involved in protein-protein interactions and play crucial roles in interaction networks, particularly those contributing to signal transduction. They also play a major role in creating diversity of protein domain architecture in the proteome. It is now apparent that promiscuity is a volatile and relatively fast-changing feature in evolution, and that only a few domains retain their promiscuity status throughout evolution. Many such domains attained their promiscuity status independently in different lineages. Only recently, we have begun to understand the diversity of protein domain architectures and the role the promiscuous domains play in evolution of this diversity. However, many of the biological mechanisms of protein domain mobility remain shrouded in mystery. In this review, we discuss our present understanding of protein domain promiscuity, its evolution and its role in cellular function.'),
(68458, 2009, 'bib', 'Moby and Moby 2: creatures of the deep (web)', 'Vandervalk, Ben P|McCarthy, E Luke|Wilkinson, Mark D', 'Department of Medical Genetics, The Providence Heart + Lung Research Institute at St Pauls Hospital, University of British Columbia, Vancouver, British Columbia V6Z 1Y6, Canada.|Department of Medical Genetics, The Providence Heart + Lung Research Institute at St Pauls Hospital, University of British Columbia, Vancouver, British Columbia V6Z 1Y6, Canada.|Department of Medical Genetics, The Providence Heart + Lung Research Institute at St Pauls Hospital, University of British Columbia, Vancouver, British Columbia V6Z 1Y6, Canada.', 'Canada|Canada|Canada', 9, 'Algorithms|Computational Biology|Databases, Factual|Humans|Internet|Semantics|Software|User-Computer Interface|Vocabulary, Controlled', 'Journal Article', '10.1093/bib/bbn051', '19151099', 'Facile and meaningful integration of data from disparate resources is the holy grail of bioinformatics. Some resources have begun to address this problem by providing their data using Semantic Web standards, specifically the Resource Description Framework (RDF) and the Web Ontology Language (OWL). Unfortunately, adoption of Semantic Web standards has been slow overall, and even in cases where the standards are being utilized, interconnectivity between resources is rare. In response, we have seen the emergence of centralized semantic warehouses that collect public data from third parties, integrate it, translate it into OWL/RDF and provide it to the community as a unified and queryable resource. One limitation of the warehouse approach is that queries are confined to the resources that have been selected for inclusion. A related problem, perhaps of greater concern, is that the majority of bioinformatics data exists in the Deep Web-that is, the data does not exist until an application or analytical tool is invoked, and therefore does not have a predictable Web address. The inability to utilize Uniform Resource Identifiers (URIs) to address this data is a barrier to its accessibility via URI-centric Semantic Web technologies. Here we examine The State of the Union for the adoption of Semantic Web standards in the health care and life sciences domain by key bioinformatics resources, explore the nature and connectivity of several community-driven semantic warehousing projects, and report on our own progress with the CardioSHARE/Moby-2 project, which aims to make the resources of the Deep Web transparently accessible through SPARQL queries.'),
(68459, 2009, 'bib', 'Next generation tools for the annotation of human SNPs', 'Karchin, Rachel', 'Biomedical Engineering Department and Institute for Computational Medicine, Johns Hopkins University, 3400 N. Charles St, Baltimore, MD 212218, USA. karchin@jhu.edu', 'United States', 58, 'Computational Biology|Databases, Genetic|Genome, Human|Genotype|Humans|Internet|Polymorphism, Single Nucleotide|Schizophrenia|Sequence Analysis, DNA|User-Computer Interface', 'Journal Article', '10.1093/bib/bbn047', '19181721', 'Computational biology has the opportunity to play an important role in the identification of functional single nucleotide polymorphisms (SNPs) discovered in large-scale genotyping studies, ultimately yielding new drug targets and biomarkers. The medical genetics and molecular biology communities are increasingly turning to computational biology methods to prioritize interesting SNPs found in linkage and association studies. Many such methods are now available through web interfaces, but the interested user is confronted with an array of predictive results that are often in disagreement with each other. Many tools today produce results that are difficult to understand without bioinformatics expertise, are biased towards non-synonymous SNPs, and do not necessarily reflect up-to-date versions of their source bioinformatics resources, such as public SNP repositories. Here, I assess the utility of the current generation of webservers, and suggest improvements for the next generation of webservers to better deliver value to medical geneticists and molecular biologists.'),
(68460, 2009, 'bib', 'A survey of available tools and web servers for analysis of protein-protein interactions and interfaces', 'Tuncbag, Nurcan|Kar, Gozde|Keskin, Ozlem|Gursoy, Attila|Nussinov, Ruth', 'Computational Sciences and Engineering Program at Koc University, Istanbul, Turkey.|Computational Sciences and Engineering Program at Koc University, Istanbul, Turkey.|Computational Sciences and Engineering Program at Koc University, Istanbul, Turkey.|Computational Sciences and Engineering Program at Koc University, Istanbul, Turkey.|Computational Sciences and Engineering Program at Koc University, Istanbul, Turkey.', 'Turkey|Turkey|Turkey|Turkey|Turkey', 41, 'Animals|Binding Sites|Databases, Protein|Humans|Internet|Models, Molecular|Protein Conformation|Protein Interaction Mapping|Proteins|Proto-Oncogene Proteins p21(ras)|Signal Transduction|Tumor Suppressor Protein p53', 'Journal Article', '10.1093/bib/bbp001', '19240123', 'The unanimous agreement that cellular processes are (largely) governed by interactions between proteins has led to enormous community efforts culminating in overwhelming information relating to these proteins, to the regulation of their interactions, to the way in which they interact and to the function which is determined by these interactions. These data have been organized in databases and servers. However, to make these really useful, it is essential not only to be aware of these, but in particular to have a working knowledge of which tools to use for a given problem, what are the tool advantages and drawbacks, and no less important how to combine these for a particular goal since usually it is not one tool, but some combination of tool-modules that is needed. This is the goal of this review.'),
(68461, 2009, 'bib', 'A roadmap of clustering algorithms: finding a match for a biomedical application', 'Andreopoulos, Bill|An, Aijun|Wang, Xiaogang|Schroeder, Michael', 'Biotechnological Centre, Technische Universität Dresden, Germany. williama@biotec.tu-dresden.de|Biotechnological Centre, Technische Universität Dresden, Germany. williama@biotec.tu-dresden.de|Biotechnological Centre, Technische Universität Dresden, Germany. williama@biotec.tu-dresden.de|Biotechnological Centre, Technische Universität Dresden, Germany. williama@biotec.tu-dresden.de', 'Germany|Germany|Germany|Germany', 33, 'Algorithms|Cluster Analysis|Computational Biology|Gene Expression Profiling|Information Storage and Retrieval|Models, Statistical|Protein Interaction Mapping', 'Journal Article', '10.1093/bib/bbn058', '19240124', 'Clustering is ubiquitously applied in bioinformatics with hierarchical clustering and k-means partitioning being the most popular methods. Numerous improvements of these two clustering methods have been introduced, as well as completely different approaches such as grid-based, density-based and model-based clustering. For improved bioinformatics analysis of data, it is important to match clusterings to the requirements of a biomedical application. In this article, we present a set of desirable clustering features that are used as evaluation criteria for clustering algorithms. We review 40 different clustering algorithms of all approaches and datatypes. We compare algorithms on the basis of desirable clustering features, and outline algorithms benefits and drawbacks as a basis for matching them to biomedical applications.'),
(68462, 2009, 'bib', 'Towards pharmacogenomics knowledge discovery with the semantic web', 'Dumontier, Michel|Villanueva-Rosales, Natalia', 'Department of Biology, Carleton University, 1125 Colonel By Drive, Ottawa, Ontario, Canada K1S 5B6. michel_dumontier@carleton.ca|Department of Biology, Carleton University, 1125 Colonel By Drive, Ottawa, Ontario, Canada K1S 5B6. michel_dumontier@carleton.ca', 'Canada|Canada', 44, 'Databases, Factual|Depression|Drug Design|Humans|Information Dissemination|Information Storage and Retrieval|Internet|Knowledge Bases|Pharmacogenetics|Semantics|Systems Integration|Terminology as Topic|Vocabulary, Controlled', 'Journal Article', '10.1093/bib/bbn056', '19240125', 'Pharmacogenomics aims to understand pharmacological response with respect to genetic variation. Essential to the delivery of better health care is the use of pharmacogenomics knowledge to answer questions about therapeutic, pharmacological or genetic aspects. Several XML markup languages have been developed to capture pharmacogenomic and related information so as to facilitate data sharing. However, recent advances in semantic web technologies have presented exciting new opportunities for pharmacogenomics knowledge discovery by representing the information with machine understandable semantics. Progress in this area is illustrated with reference to the personalized medicine project that aims to facilitate pharmacogenomics knowledge discovery through intuitive knowledge capture and sophisticated question answering using automated reasoning over expressive ontologies.'),
(68463, 2009, 'bib', 'Computational systems biology of the cell cycle', 'Csikász-Nagy, Attila', 'The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Piazza Manci 17, Povo-Trento I-38100, Italy. csikasz@cosbi.eu', 'Italy', 19, 'Animals|Cell Cycle|Cell Cycle Proteins|Computational Biology|History, 20th Century|Models, Biological|Signal Transduction|Systems Biology', 'Historical Article', '10.1093/bib/bbp005', '19270018', 'One of the early success stories of computational systems biology was the work done on cell-cycle regulation. The earliest mathematical descriptions of cell-cycle control evolved into very complex, detailed computational models that describe the regulation of cell division in many different cell types. On the way these models predicted several dynamical properties and unknown components of the system that were later experimentally verified/identified. Still, research on this field is far from over. We need to understand how the core cell-cycle machinery is controlled by internal and external signals, also in yeast cells and in the more complex regulatory networks of higher eukaryotes. Furthermore, there are many computational challenges what we face as new types of data appear thanks to continuing advances in experimental techniques. We have to deal with cell-to-cell variations, revealed by single cell measurements, as well as the tremendous amount of data flowing from high throughput machines. We need new computational concepts and tools to handle these data and develop more detailed, more precise models of cell-cycle regulation in various organisms. Here we review past and present of computational modeling of cell-cycle regulation, and discuss possible future directions of the field.'),
(68464, 2009, 'bib', 'Computational biology for cardiovascular biomarker discovery', 'Azuaje, Francisco|Devaux, Yvan|Wagner, Daniel', 'Laboratory of Cardiovascular Research, Centre de Recherche Public - Santé, Luxembourg. francisco.azuaje@crp-sante.lu|Laboratory of Cardiovascular Research, Centre de Recherche Public - Santé, Luxembourg. francisco.azuaje@crp-sante.lu|Laboratory of Cardiovascular Research, Centre de Recherche Public - Santé, Luxembourg. francisco.azuaje@crp-sante.lu', 'Luxembourg|Luxembourg|Luxembourg', 30, 'Biomarkers|Cardiovascular Diseases|Computational Biology|Genomics|Models, Biological|Prognosis|Proteomics', 'Journal Article', '10.1093/bib/bbp008', '19276200', 'Computational biology is essential in the process of translating biological knowledge into clinical practice, as well as in the understanding of biological phenomena based on the resources and technologies originating from the clinical environment. One such key contribution of computational biology is the discovery of biomarkers for predicting clinical outcomes using omic information. This process involves the predictive modelling and integration of different types of data and knowledge for screening, diagnostic or prognostic purposes. Moreover, this requires the design and combination of different methodologies based on statistical analysis and machine learning. This article introduces key computational approaches and applications to biomarker discovery based on different types of omic data. Although we emphasize applications in cardiovascular research, the computational requirements and advances discussed here are also relevant to other domains. We will start by introducing some of the contributions of computational biology to translational research, followed by an overview of methods and technologies used for the identification of biomarkers with predictive or classification value. The main types of omic approaches to biomarker discovery will be presented with specific examples from cardiovascular research. This will include a review of computational methodologies for single-source and integrative data applications. Major computational methods for model evaluation will be described together with recommendations for reporting models and results. We will present recent advances in cardiovascular biomarker discovery based on the combination of gene expression and functional network analyses. The review will conclude with a discussion of key challenges for computational biology, including perspectives from the biosciences and clinical areas.'),
(68465, 2009, 'bib', 'Exploring autonomy through computational biomodelling', 'Palfreyman, Niall', 'Department of Biotechnology and Bioinformatics, Weihenstephan University of Applied Sciences, 85350 Freising, Germany. niall.palfreyman@fh-weihenstephan.de', 'Germany', 0, 'Animals|Biological Evolution|Computational Biology|Humans|Models, Biological|Philosophy', 'Journal Article', '10.1093/bib/bbp003', '19276201', 'The question of whether living organisms possess autonomy of action is tied up with the nature of causal efficacy. Yet the nature of organisms is such that they frequently defy conventional causal language. Did the fig wasp select the fig, or vice versa? Is this an epithelial cell because of its genetic structure, or because it develops within the epithelium? The intimate coupling of biological levels of organisation leads developmental systems theory to deconstruct the biological organism into a life-cycle process which constitutes itself from the resources available within a complete developmental system. This radical proposal necessarily raises questions regarding the ontological status of organisms: Does an organism possess existence distinguishable from its molecular composition and social environment? The ambiguity of biological causality makes such questions difficult to answer or even formulate, and computational biology has an important role to play in operationalising the language in which they are framed. In this article we review the role played by computational biomodels in shedding light on the ontological status of organisms. These models are drawn from backgrounds ranging from molecular kinetics to niche construction, and all attempt to trace biological processes to a causal, and therefore existent, source. We conclude that computational biomodelling plays a fertile role in furnishing a proof of concept for conjectures in the philosophy of biology, and suggests the need for a process-based ontology of biological systems.'),
(68466, 2009, 'bib', 'Potential bias in GO::TermFinder', 'Flight, Robert M|Wentzell, Peter D', 'Department of Chemistry, Dalhousie University, Halifax, Nova Scotia B3H 4J3, Canada.|Department of Chemistry, Dalhousie University, Halifax, Nova Scotia B3H 4J3, Canada.', 'Canada|Canada', 8, 'Algorithms|Computational Biology|Databases, Genetic|Gene Expression Profiling|Information Storage and Retrieval|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1093/bib/bbn054', '19279157', 'The increased need for multiple statistical comparisons under conditions of non-independence in bioinformatics applications, such as DNA microarray data analysis, has led to the development of alternatives to the conventional Bonferroni correction for adjusting P-values. The use of the false discovery rate (FDR), in particular, has grown considerably. However, the calculation of the FDR frequently depends on drawing random samples from a population, and inappropriate sampling will result in a bias in the calculated FDR. In this work, we demonstrate a bias due to incorrect random sampling in the widely used GO::TermFinder package. Both T(2) and permutation tests are used to confirm the bias for a test set of data, which leads to an overestimation of the FDR of about 10%. A simple fix to the random sampling method is proposed to remove the bias.'),
(68467, 2009, 'bib', 'Life sciences on the Semantic Web: the Neurocommons and beyond', 'Ruttenberg, Alan|Rees, Jonathan A|Samwald, Matthias|Marshall, M Scott', 'Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands.|Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands.|Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands.|Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands', 80, 'Animals|Biological Science Disciplines|Computational Biology|Database Management Systems|Databases, Factual|Humans|Information Dissemination|Information Storage and Retrieval|Internet|Knowledge Bases|Semantics', 'Journal Article', '10.1093/bib/bbp004', '19282504', 'Translational research, the effort to couple the results of basic research to clinical applications, depends on the ability to effectively answer questions using information that spans multiple disciplines. The Semantic Web, with its emphasis on combining information using standard representation languages, access to that information via standard web protocols, and technologies to leverage computation, such as in the form of inference and distributable query, offers a social and technological basis for assembling, integrating and making available biomedical knowledge at Web scale. In this article, we discuss the use of Semantic Web technology for assembling and querying biomedical knowledge from multiple sources and disciplines. We present the Neurocommons prototype knowledge base, a demonstration intended to show the feasibility and benefits of using these technologies. The prototype knowledge base can be used to experiment with and assess the scalability of current tools and methods for creating such a resource, and to elicit issues that will need to be addressed in order to expand the scope and use of it. We demonstrate the utility of the knowledge base by reviewing a few example queries that provide answers to precise questions relevant to the understanding of disease. All components of the knowledge base are freely available at http://neurocommons.org/, enabling readers to reconstruct the knowledge base and experiment with this new technology.'),
(68468, 2009, 'bib', 'Flux balance analysis of biological systems: applications and challenges', 'Raman, Karthik|Chandra, Nagasuma', 'Department of Biochemistry, University of Zurich.|Department of Biochemistry, University of Zurich.', '-|-', 210, 'Computational Biology|Computer Simulation|Energy Metabolism|Humans|Metabolic Networks and Pathways|Models, Biological|Signal Transduction|Software|Thermodynamics', 'Journal Article', '10.1093/bib/bbp011', '19287049', 'Systems level modelling and simulations of biological processes are proving to be invaluable in obtaining a quantitative and dynamic perspective of various aspects of cellular function. In particular, constraint-based analyses of metabolic networks have gained considerable popularity for simulating cellular metabolism, of which flux balance analysis (FBA), is most widely used. Unlike mechanistic simulations that depend on accurate kinetic data, which are scarcely available, FBA is based on the principle of conservation of mass in a network, which utilizes the stoichiometric matrix and a biologically relevant objective function to identify optimal reaction flux distributions. FBA has been used to analyse genome-scale reconstructions of several organisms, it has also been used to analyse the effect of perturbations, such as gene deletions or drug inhibitions in silico. This article reviews the usefulness of FBA as a tool for gaining biological insights, advances in methodology enabling integration of regulatory information and thermodynamic constraints, and finally addresses the challenges that lie ahead. Various use scenarios and biological insights obtained from FBA, and applications in fields such metabolic engineering and drug target identification, are also discussed. Genome-scale constraint-based models have an immense potential for building and testing hypotheses, as well as to guide experimentation.'),
(68469, 2009, 'bib', 'The virtual cell--a candidate co-ordinator for middle-out modelling of biological systems', 'Walker, Dawn C|Southgate, Jennifer', 'Department of Computer Science at the University of Sheffield.|Department of Computer Science at the University of Sheffield.', '-|-', 69, 'Cellular Structures|Computer Simulation|Models, Biological|Software|Systems Biology|Time Factors', 'Journal Article', '10.1093/bib/bbp010', '19293250', 'Understanding the functioning of biological systems depends on tackling complexity spanning spatial scales from genome to organ to whole organism. The basic unit of life, the cell, acts to co-ordinate information received across these scales and processes the myriad of signals to produce an integrated cellular response. Cells interact with and respond to other cells through direct or indirect contact, resulting in emergent structure and function of tissues and organs. Systems biology has traditionally used either a top-down or bottom-up approach. However, neither approach takes account of heterogeneity or noise, which is an inherent feature of cellular behaviour and may have significant impact on system level behaviour. We review existing approaches to modelling that use cellular automata or agent-based methodologies, where individual cells are represented as equivalent virtual entities governed by simple rules. These paradigms allow a direct one-to-one mapping between real and virtual cells that can be exploited in terms of acquiring parameters from experimental systems, or for model validation. Such models are inherently extensible and can be integrated with other modelling modalities (e.g. partial or ordinary differential equations) to model multi-scale phenomena. Alternatively, hierarchical agent models may be used to explore the functions of biological systems across temporal and spatial scales. This review examines individual-based models and the application of the paradigm to explore multi-scale phenomena in biology. In so doing, it demonstrates how cellular-based models have begun to play an important role in the development of middle-out models, but with considerable potential for future development.'),
(68470, 2009, 'bib', 'Semantic Web for Health Care and Life Sciences: a review of the state of the art', 'Cheung, Kei-Hoi|Prudhommeaux, Eric|Wang, Yimin|Stephens, Susie', 'Department of Computer Science at the University of Sheffield.|Department of Computer Science at the University of Sheffield.|Department of Computer Science at the University of Sheffield.|Department of Computer Science at the University of Sheffield.', '-|-|-|-', 37, 'Biological Science Disciplines|Delivery of Health Care|Education|Humans|Internet|Semantics', 'Editorial', '10.1093/bib/bbp015', '19304871', '-'),
(68471, 2009, 'bib', 'Scaling the walls of discovery: using semantic metadata for integrative problem solving', 'Manning, Maurice|Aggarwal, Amit|Gao, Kevin|Tucker-Kellogg, Greg', 'Lilly Singapore Centre for Drug Discovery, 8A Biomedical Grove #02-05, Immunos, Biopolis, 138648, Singapore.|Lilly Singapore Centre for Drug Discovery, 8A Biomedical Grove #02-05, Immunos, Biopolis, 138648, Singapore.|Lilly Singapore Centre for Drug Discovery, 8A Biomedical Grove #02-05, Immunos, Biopolis, 138648, Singapore.|Lilly Singapore Centre for Drug Discovery, 8A Biomedical Grove #02-05, Immunos, Biopolis, 138648, Singapore.', 'Singapore|Singapore|Singapore|Singapore', 11, 'Computational Biology|Database Management Systems|Databases, Factual|Decision Support Techniques|Humans|Information Dissemination|Information Storage and Retrieval|Knowledge Bases|Semantics|Systems Integration|User-Computer Interface', 'Journal Article', '10.1093/bib/bbp007', '19304872', 'Current data integration approaches by bioinformaticians frequently involve extracting data from a wide variety of public and private data repositories, each with a unique vocabulary and schema, via scripts. These separate data sets must then be normalized through the tedious and lengthy process of resolving naming differences and collecting information into a single view. Attempts to consolidate such diverse data using data warehouses or federated queries add significant complexity and have shown limitations in flexibility. The alternative of complete semantic integration of data requires a massive, sustained effort in mapping data types and maintaining ontologies. We focused instead on creating a data architecture that leverages semantic mapping of experimental metadata, to support the rapid prototyping of scientific discovery applications with the twin goals of reducing architectural complexity while still leveraging semantic technologies to provide flexibility, efficiency and more fully characterized data relationships. A metadata ontology was developed to describe our discovery process. A metadata repository was then created by mapping metadata from existing data sources into this ontology, generating RDF triples to describe the entities. Finally an interface to the repository was designed which provided not only search and browse capabilities but complex query templates that aggregate data from both RDF and RDBMS sources. We describe how this approach (i) allows scientists to discover and link relevant data across diverse data sources and (ii) provides a platform for development of integrative informatics applications.'),
(68472, 2009, 'bib', 'Semantic web for integrated network analysis in biomedicine', 'Chen, Huajun|Ding, Li|Wu, Zhaohui|Yu, Tong|Dhanapalan, Lavanya|Chen, Jake Y', 'School of Computer Science, Zhejiang University. huajunsir@gmail.com|School of Computer Science, Zhejiang University. huajunsir@gmail.com|School of Computer Science, Zhejiang University. huajunsir@gmail.com|School of Computer Science, Zhejiang University. huajunsir@gmail.com|School of Computer Science, Zhejiang University. huajunsir@gmail.com|School of Computer Science, Zhejiang University. huajunsir@gmail.com', '-|-|-|-|-|-', 39, 'Biomedical Research|Database Management Systems|Databases, Factual|Humans|Information Dissemination|Information Storage and Retrieval|Internet|Medical Informatics|Semantics|Systems Integration', 'Journal Article', '10.1093/bib/bbp002', '19304873', 'The Semantic Web technology enables integration of heterogeneous data on the World Wide Web by making the semantics of data explicit through formal ontologies. In this article, we survey the feasibility and state of the art of utilizing the Semantic Web technology to represent, integrate and analyze the knowledge in various biomedical networks. We introduce a new conceptual framework, semantic graph mining, to enable researchers to integrate graph mining with ontology reasoning in network data analysis. Through four case studies, we demonstrate how semantic graph mining can be applied to the analysis of disease-causal genes, Gene Ontology category cross-talks, drug efficacy analysis and herb-drug interactions analysis.'),
(68473, 2009, 'bib', 'An introduction to artificial neural networks in bioinformatics--application to complex microarray and mass spectrometry datasets in cancer studies', 'Lancashire, Lee J|Lemetre, Christophe|Ball, Graham R', 'Clinical and Experimental Pharmacology, Paterson Institute for Cancer Research, University of Manchester, Manchester M20 4BX, UK. llancashire@picr.man.ac.uk|Clinical and Experimental Pharmacology, Paterson Institute for Cancer Research, University of Manchester, Manchester M20 4BX, UK. llancashire@picr.man.ac.uk|Clinical and Experimental Pharmacology, Paterson Institute for Cancer Research, University of Manchester, Manchester M20 4BX, UK. llancashire@picr.man.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 94, 'Artificial Intelligence|Bayes Theorem|Computational Biology|Databases, Genetic|Databases, Protein|Genomics|Humans|Mass Spectrometry|Microarray Analysis|Neoplasms|Neural Networks (Computer)|Proteomics|Reproducibility of Results', 'Journal Article', '10.1093/bib/bbp012', '19307287', 'Applications of genomic and proteomic technologies have seen a major increase, resulting in an explosion in the amount of highly dimensional and complex data being generated. Subsequently this has increased the effort by the bioinformatics community to develop novel computational approaches that allow for meaningful information to be extracted. This information must be of biological relevance and thus correlate to disease phenotypes of interest. Artificial neural networks are a form of machine learning from the field of artificial intelligence with proven pattern recognition capabilities and have been utilized in many areas of bioinformatics. This is due to their ability to cope with highly dimensional complex datasets such as those developed by protein mass spectrometry and DNA microarray experiments. As such, neural networks have been applied to problems such as disease classification and identification of biomarkers. This review introduces and describes the concepts related to neural networks, the advantages and caveats to their use, examples of their applications in mass spectrometry and microarray research (with a particular focus on cancer studies), and illustrations from recent literature showing where neural networks have performed well in comparison to other machine learning methods. This should form the necessary background knowledge and information enabling researchers with an interest in these methodologies, but not necessarily from a machine learning background, to apply the concepts to their own datasets, thus maximizing the information gain from these complex biological systems.'),
(68474, 2009, 'bib', 'FINDSITE: a combined evolution/structure-based approach to protein function prediction', 'Skolnick, Jeffrey|Brylinski, Michal', 'Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology 250 14th St NW, Atlanta, GA 30318, USA. skolnick@gatech.edu|Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology 250 14th St NW, Atlanta, GA 30318, USA. skolnick@gatech.edu', 'Georgia|Georgia', 75, 'Algorithms|Binding Sites|Ligands|Models, Molecular|Molecular Sequence Data|Protein Conformation|Protein Folding|Proteins|Sequence Analysis, Protein|Software|Structure-Activity Relationship', 'Journal Article', '10.1093/bib/bbp017', '19324930', 'A key challenge of the post-genomic era is the identification of the function(s) of all the molecules in a given organism. Here, we review the status of sequence and structure-based approaches to protein function inference and ligand screening that can provide functional insights for a significant fraction of the approximately 50% of ORFs of unassigned function in an average proteome. We then describe FINDSITE, a recently developed algorithm for ligand binding site prediction, ligand screening and molecular function prediction, which is based on binding site conservation across evolutionary distant proteins identified by threading. Importantly, FINDSITE gives comparable results when high-resolution experimental structures as well as predicted protein models are used.'),
(68475, 2009, 'bib', 'Expression profiling of microRNAs by deep sequencing', 'Creighton, Chad J|Reid, Jeffrey G|Gunaratne, Preethi H', 'Dan L. Duncan Cancer Center Division of Biostatistics, Baylor College of Medicine, Houston, TX 77030, USA. creighto@bcm.edu|Dan L. Duncan Cancer Center Division of Biostatistics, Baylor College of Medicine, Houston, TX 77030, USA. creighto@bcm.edu|Dan L. Duncan Cancer Center Division of Biostatistics, Baylor College of Medicine, Houston, TX 77030, USA. creighto@bcm.edu', 'United States|United States|United States', 223, 'Animals|Gene Expression Profiling|Humans|MicroRNAs|Nucleic Acid Conformation|Oligonucleotide Array Sequence Analysis|Sequence Analysis, RNA', 'Journal Article', '10.1093/bib/bbp019', '19332473', 'MicroRNAs are short non-coding RNAs that regulate the stability and translation of mRNAs. Profiling experiments, using microarray or deep sequencing technology, have identified microRNAs that are preferentially expressed in certain tissues, specific stages of development, or disease states such as cancer. Deep sequencing utilizes massively parallel sequencing, generating millions of small RNA sequence reads from a given sample. Profiling of microRNAs by deep sequencing measures absolute abundance and allows for the discovery of novel microRNAs that have eluded previous cloning and standard sequencing efforts. Public databases provide in silico predictions of microRNA gene targets by various algorithms. To better determine which of these predictions represent true positives, microRNA expression data can be integrated with gene expression data to identify putative microRNA:mRNA functional pairs. Here we discuss tools and methodologies for the analysis of microRNA expression data from deep sequencing.'),
(68476, 2009, 'bib', '2D molecular graphics: a flattened world of chemistry and biology', 'Zhou, Peng|Shang, Zhicai', 'Department of Chemistry, Zhejiang University, Hangzhou 310027, China.|Department of Chemistry, Zhejiang University, Hangzhou 310027, China.', 'China|China', 10, 'Algorithms|Computer Graphics|Computer Simulation|Models, Biological|Models, Chemical|Models, Molecular|Molecular Structure|Proteins|RNA|Software|User-Computer Interface', 'Journal Article', '10.1093/bib/bbp013', '19332474', 'Molecular graphics provides an intuitive way for representation, modeling and analysis of complex chemical and biological systems. It is now widely used in the theoretical chemistry, structural biology, molecular modeling and drug design communities. Traditional molecular graphics techniques mainly dedicate to showing molecular architectures at three-dimensional (3D) level. However, in some occasions the two-dimensional (2D) representation of molecular configurations, profiles, behaviors and interactions may be more readily acceptable for audiences, especially when we need to describe abstract information in a straightforward way or to present numerous data in schematic diagrams. In recent years, 2D representation methods/tools have been developed rapidly for various purposes, ranging from the aesthetic depiction of atomic arrangement for small organic molecules to schematic layout of complicated nonbonding network across the biomolecular binding interfaces, and have received considerable interest in the fields of chemistry, biology and medicine. In this article we first propose the term of 2D molecular graphics to cover the spectrum of 2D representing chemical and biological systems, we also give a comprehensive review on the methods, tools and applications of 2D molecular graphics.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(68477, 2009, 'bib', 'Taming the complexity of biological pathways through parallel computing', 'Ballarini, Paolo|Guido, Rosita|Mazza, Tommaso|Prandi, Davide', 'The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Piazza Manci 17 38100 Povo, Trento, Italy. ballarini@cosbi.eu|The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Piazza Manci 17 38100 Povo, Trento, Italy. ballarini@cosbi.eu|The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Piazza Manci 17 38100 Povo, Trento, Italy. ballarini@cosbi.eu|The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Piazza Manci 17 38100 Povo, Trento, Italy. ballarini@cosbi.eu', 'Italy|Italy|Italy|Italy', 26, 'Algorithms|Computational Biology|Computer Simulation|Models, Biological|Software|Stochastic Processes|Systems Integration', 'Journal Article', '10.1093/bib/bbp020', '19339382', 'Biological systems are characterised by a large number of interacting entities whose dynamics is described by a number of reaction equations. Mathematical methods for modelling biological systems are mostly based on a centralised solution approach: the modelled system is described as a whole and the solution technique, normally the integration of a system of ordinary differential equations (ODEs) or the simulation of a stochastic model, is commonly computed in a centralised fashion. In recent times, research efforts moved towards the definition of parallel/distributed algorithms as a means to tackle the complexity of biological models analysis. In this article, we present a survey on the progresses of such parallelisation efforts describing the most promising results so far obtained.'),
(68478, 2009, 'bib', 'Development of biomarker classifiers from high-dimensional data', 'Baek, Songjoon|Tsai, Chen-An|Chen, James J', 'National Center for Toxicological Research, U.S. Food and Drug Administration, USA. jamesj.chen@fda.hhs.gov|National Center for Toxicological Research, U.S. Food and Drug Administration, USA. jamesj.chen@fda.hhs.gov|National Center for Toxicological Research, U.S. Food and Drug Administration, USA. jamesj.chen@fda.hhs.gov', 'United States|United States|United States', 46, 'Algorithms|Biomarkers|Computational Biology|Databases, Genetic|Mathematics|Models, Biological|Reproducibility of Results', 'Journal Article', '10.1093/bib/bbp016', '19346320', 'Recent development of high-throughput technology has accelerated interest in the development of molecular biomarker classifiers for safety assessment, disease diagnostics and prognostics, and prediction of response for patient assignment. This article reviews and evaluates some important aspects and key issues in the development of biomarker classifiers. Development of a biomarker classifier for high-throughput data involves two components: (i) model building and (ii) performance assessment. This article focuses on feature selection in model building and cross validation for performance assessment. A frequency approach to feature selection is presented and compared to the conventional approach in terms of the predictive accuracy and stability of the selected feature set. The two approaches are compared based on four biomarker classifiers, each with a different feature selection method and well-known classification algorithm. In each of the four classifiers the feature predictor set selected by the frequency approach is more stable than the feature set selected by the conventional approach.'),
(68479, 2009, 'bib', 'Progress and challenges in predicting protein-protein interaction sites', 'Ezkurdia, Iakes|Bartoli, Lisa|Fariselli, Piero|Casadio, Rita|Valencia, Alfonso|Tress, Michael L', 'Centro Nacional de Biotechnolgia, Spanish National Cancer Research Centre (CNIO), Madrid, Spain.|Centro Nacional de Biotechnolgia, Spanish National Cancer Research Centre (CNIO), Madrid, Spain.|Centro Nacional de Biotechnolgia, Spanish National Cancer Research Centre (CNIO), Madrid, Spain.|Centro Nacional de Biotechnolgia, Spanish National Cancer Research Centre (CNIO), Madrid, Spain.|Centro Nacional de Biotechnolgia, Spanish National Cancer Research Centre (CNIO), Madrid, Spain.|Centro Nacional de Biotechnolgia, Spanish National Cancer Research Centre (CNIO), Madrid, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain', 123, 'Algorithms|Binding Sites|Databases, Protein|Multiprotein Complexes|Protein Conformation|Protein Interaction Mapping|Proteins|Static Electricity|Surface Properties', 'Journal Article', '10.1093/bib/bbp021', '19346321', 'The identification of protein-protein interaction sites is an essential intermediate step for mutant design and the prediction of protein networks. In recent years a significant number of methods have been developed to predict these interface residues and here we review the current status of the field. Progress in this area requires a clear view of the methodology applied, the data sets used for training and testing the systems, and the evaluation procedures. We have analysed the impact of a representative set of features and algorithms and highlighted the problems inherent in generating reliable protein data sets and in the posterior analysis of the results. Although it is clear that there have been some improvements in methods for predicting interacting sites, several major bottlenecks remain. Proteins in complexes are still under-represented in the structural databases and in particular many proteins involved in transient complexes are still to be crystallized. We provide suggestions for effective feature selection, and make it clear that community standards for testing, training and performance measures are necessary for progress in the field.'),
(68480, 2009, 'bib', 'Probes containing runs of guanines provide insights into the biophysics and bioinformatics of Affymetrix GeneChips', 'Langdon, William B|Upton, Graham J G|Harrison, Andrew P', 'Department of Mathematical Sciences and Department of Biological Sciences, University of Essex, Wivenhoe Park, Colchester, Essex CO4 3SQ, UK.|Department of Mathematical Sciences and Department of Biological Sciences, University of Essex, Wivenhoe Park, Colchester, Essex CO4 3SQ, UK.|Department of Mathematical Sciences and Department of Biological Sciences, University of Essex, Wivenhoe Park, Colchester, Essex CO4 3SQ, UK.', 'United Kingdom|United Kingdom|United Kingdom', 21, 'Algorithms|Animals|Base Sequence|Biophysics|Computational Biology|DNA Probes|Databases, Genetic|Gene Expression Profiling|Guanine|Humans|Molecular Sequence Data|Molecular Structure|Nucleic Acid Conformation|Nucleic Acid Hybridization|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bib/bbp018', '19359259', 'The reliable interpretation of Affymetrix GeneChip data is a multi-faceted problem. The interplay between biophysics, bioinformatics and mining of GeneChip surveys is leading to new insights into how best to analyse the data. Many of the molecular processes occurring on the surfaces of GeneChips result from the high surface density of probes. Interactions between neighbouring adjacent probes affect their rate and strength of hybridization to targets. Competing targets may hybridize to the same probe, and targets may partially bind to more than one probe. The formation of these partial hybrids results in a number of probes not reaching thermodynamic equilibrium during hybridization. Moreover, some targets fold up, or cross-hybridize to other targets. Furthermore, probes may fold and can undergo chemical saturation. There are also sequence-dependent differences in the rates of target desorption during the washing stage. Improvements in the mappings between probe sequence and biological databases are leading to more accurate gene expression profiles. Moreover, algorithms that combine the intensities of multiple probes into single measures of expression are increasingly dependent upon models of the hybridization processes occurring on GeneChips. The large repositories of GeneChip data can be searched for systematic effects across many experiments. This data mining has led to the discovery of a family of thousands of probes, which show correlated expression across thousands of GeneChip experiments. These probes contain runs of guanines, suggesting that G-quadruplexes are able to form on GeneChips. We discuss the impact of these structures on the interpretation of data from GeneChip experiments.'),
(68481, 2009, 'bib', 'An Ariadnes thread to the identification and annotation of noncoding RNAs in eukaryotes', 'Soldà, Giulia|Makunin, Igor V|Sezerman, Osman U|Corradin, Alberto|Corti, Giorgio|Guffanti, Alessandro', 'Department of Biology and Genetics for Medical Sciences, University of Milano, 20133 Milan, Italy. giulia.solda@unimi.it|Department of Biology and Genetics for Medical Sciences, University of Milano, 20133 Milan, Italy. giulia.solda@unimi.it|Department of Biology and Genetics for Medical Sciences, University of Milano, 20133 Milan, Italy. giulia.solda@unimi.it|Department of Biology and Genetics for Medical Sciences, University of Milano, 20133 Milan, Italy. giulia.solda@unimi.it|Department of Biology and Genetics for Medical Sciences, University of Milano, 20133 Milan, Italy. giulia.solda@unimi.it|Department of Biology and Genetics for Medical Sciences, University of Milano, 20133 Milan, Italy. giulia.solda@unimi.it', 'Italy|Italy|Italy|Italy|Italy|Italy', 16, 'Animals|Base Sequence|Computational Biology|Databases, Genetic|Eukaryotic Cells|Genomics|Nucleic Acid Conformation|RNA, Untranslated', 'Journal Article', '10.1093/bib/bbp022', '19383843', 'Non-protein coding RNAs (ncRNAs) have emerged as a vast and heterogeneous portion of eukaryotic transcriptomes. Several ncRNA families, either short (<200 nucleotides, nt) or long (>200 nt), have been described and implicated in a variety of biological processes, from translation to gene expression regulation and nuclear trafficking. Most probably, other families are still to be discovered. Computational methods for ncRNA research require different approaches from the ones normally used in the prediction of protein-coding genes. Indeed, primary sequence alone is often insufficient to infer ncRNA functionality, whereas secondary structure and local conservation of portions of the transcript could provide useful information for both the prediction and the functional annotation of ncRNAs. Here we present an overview of computational methods and bioinformatics resources currently available for studying ncRNA genes, introducing the common themes as well as the different approaches required for long and short ncRNA identification and annotation.'),
(68482, 2009, 'bib', 'ImmunoGrid, an integrative environment for large-scale simulation of the immune system for vaccine discovery, design and optimization', 'Pappalardo, Francesco|Halling-Brown, Mark D|Rapin, Nicolas|Zhang, Ping|Alemani, Davide|Emerson, Andrew|Paci, Paola|Duroux, Patrice|Pennisi, Marzio|Palladini, Arianna|Miotto, Olivo|Churchill, Daniel|Rossi, Elda|Shepherd, Adrian J|Moss, David S|Castiglione, Filippo|Bernaschi, Massimo|Lefranc, Marie-Paule|Brunak, Søren|Motta, Santo|Lollini, Pier-Luigi|Basford, Kaye E|Brusic, Vladimir', 'Cancer Vaccine Center, Dana-Farber Cancer Institute, 77 Avenue Louis Pasteur, HIM 401, Boston, MA 02115, USA.|Cancer Vaccine Center, Dana-Farber Cancer Institute, 77 Avenue Louis Pasteur, HIM 401, Boston, MA 02115, USA.|Cancer Vaccine Center, Dana-Farber Cancer Institute, 77 Avenue Louis Pasteur, HIM 401, Boston, MA 02115, USA.|Cancer Vaccine Center, Dana-Farber Cancer Institute, 77 Avenue Louis Pasteur, HIM 401, Boston, MA 02115, USA.|Cancer Vaccine Center, Dana-Farber Cancer Institute, 77 Avenue Louis Pasteur, HIM 401, Boston, MA 02115, USA.|Cancer Vaccine Center, Dana-Farber Cancer Institute, 77 Avenue Louis Pasteur, HIM 401, Boston, MA 02115, USA.|Cancer Vaccine Center, Dana-Farber Cancer Institute, 77 Avenue Louis Pasteur, HIM 401, Boston, MA 02115, USA.|Cancer Vaccine Center, Dana-Farber Cancer Institute, 77 Avenue Louis Pasteur, HIM 401, Boston, MA 02115, USA.|Cancer Vaccine Center, Dana-Farber Cancer Institute, 77 Avenue Louis Pasteur, HIM 401, Boston, MA 02115, USA.|Cancer Vaccine Center, Dana-Farber Cancer Institute, 77 Avenue Louis Pasteur, HIM 401, Boston, MA 02115, USA.|Cancer Vaccine Center, Dana-Farber Cancer Institute, 77 Avenue Louis Pasteur, HIM 401, Boston, MA 02115, USA.|Cancer Vaccine Center, Dana-Farber Cancer Institute, 77 Avenue Louis Pasteur, HIM 401, Boston, MA 02115, USA.|Cancer Vaccine Center, Dana-Farber Cancer Institute, 77 Avenue Louis Pasteur, HIM 401, Boston, MA 02115, USA.|Cancer Vaccine Center, Dana-Farber Cancer Institute, 77 Avenue Louis Pasteur, HIM 401, Boston, MA 02115, USA.|Cancer Vaccine Center, Dana-Farber Cancer Institute, 77 Avenue Louis Pasteur, HIM 401, Boston, MA 02115, USA.|Cancer Vaccine Center, Dana-Farber Cancer Institute, 77 Avenue Louis Pasteur, HIM 401, Boston, MA 02115, USA.|Cancer Vaccine Center, Dana-Farber Cancer Institute, 77 Avenue Louis Pasteur, HIM 401, Boston, MA 02115, USA.|Cancer Vaccine Center, Dana-Farber Cancer Institute, 77 Avenue Louis Pasteur, HIM 401, Boston, MA 02115, USA.|Cancer Vaccine Center, Dana-Farber Cancer Institute, 77 Avenue Louis Pasteur, HIM 401, Boston, MA 02115, USA.|Cancer Vaccine Center, Dana-Farber Cancer Institute, 77 Avenue Louis Pasteur, HIM 401, Boston, MA 02115, USA.|Cancer Vaccine Center, Dana-Farber Cancer Institute, 77 Avenue Louis Pasteur, HIM 401, Boston, MA 02115, USA.|Cancer Vaccine Center, Dana-Farber Cancer Institute, 77 Avenue Louis Pasteur, HIM 401, Boston, MA 02115, USA.|Cancer Vaccine Center, Dana-Farber Cancer Institute, 77 Avenue Louis Pasteur, HIM 401, Boston, MA 02115, USA.', '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|United States|United States|United States|United States|United States|Uni', 35, 'Computational Biology|Computer Systems|Database Management Systems|Databases, Factual|Drug Design|Humans|Immune System|Major Histocompatibility Complex|Models, Biological|Systems Integration|Vaccines', 'Journal Article', '10.1093/bib/bbp014', '19383844', 'Vaccine research is a combinatorial science requiring computational analysis of vaccine components, formulations and optimization. We have developed a framework that combines computational tools for the study of immune function and vaccine development. This framework, named ImmunoGrid combines conceptual models of the immune system, models of antigen processing and presentation, system-level models of the immune system, Grid computing, and database technology to facilitate discovery, formulation and optimization of vaccines. ImmunoGrid modules share common conceptual models and ontologies. The ImmunoGrid portal offers access to educational simulators where previously defined cases can be displayed, and to research simulators that allow the development of new, or tuning of existing, computational models. The portal is accessible at <igrid-ext.cryst.bbk.ac.uk/immunogrid>.'),
(68483, 2009, 'bib', 'Recent advances in computer-aided drug design', 'Song, Chun Meng|Lim, Shen Jean|Tong, Joo Chuan', 'Institute for Infocomm Research, Connexis South Tower, Singapore 138632.|Institute for Infocomm Research, Connexis South Tower, Singapore 138632.|Institute for Infocomm Research, Connexis South Tower, Singapore 138632.', 'Singapore|Singapore|Singapore', 120, 'Computational Biology|Computer-Aided Design|Databases, Factual|Drug Design|Humans|Models, Molecular|Small Molecule Libraries|Software|Technology, Pharmaceutical', 'Journal Article', '10.1093/bib/bbp023', '19433475', 'Modern drug discovery is characterized by the production of vast quantities of compounds and the need to examine these huge libraries in short periods of time. The need to store, manage and analyze these rapidly increasing resources has given rise to the field known as computer-aided drug design (CADD). CADD represents computational methods and resources that are used to facilitate the design and discovery of new therapeutic solutions. Digital repositories, containing detailed information on drugs and other useful compounds, are goldmines for the study of chemical reactions capabilities. Design libraries, with the potential to generate molecular variants in their entirety, allow the selection and sampling of chemical compounds with diverse characteristics. Fold recognition, for studying sequence-structure homology between protein sequences and structures, are helpful for inferring binding sites and molecular functions. Virtual screening, the in silico analog of high-throughput screening, offers great promise for systematic evaluation of huge chemical libraries to identify potential lead candidates that can be synthesized and tested. In this article, we present an overview of the most important data sources and computational methods for the discovery of new molecular entities. The workflow of the entire virtual screening campaign is discussed, from data collection through to post-screening analysis.'),
(68484, 2009, 'bib', 'Biological knowledge management: the emerging role of the Semantic Web technologies', 'Antezana, Erick|Kuiper, Martin|Mironov, Vladimir', 'Department of Biology at the Norwegian University of Science and Technology.|Department of Biology at the Norwegian University of Science and Technology.|Department of Biology at the Norwegian University of Science and Technology.', '-|-|-', 109, 'Computational Biology|Database Management Systems|Humans|Information Dissemination|Information Storage and Retrieval|Internet|Semantics|Systems Biology|User-Computer Interface', 'Journal Article', '10.1093/bib/bbp024', '19457869', 'New knowledge is produced at a continuously increasing speed, and the list of papers, databases and other knowledge sources that a researcher in the life sciences needs to cope with is actually turning into a problem rather than an asset. The adequate management of knowledge is therefore becoming fundamentally important for life scientists, especially if they work with approaches that thoroughly depend on knowledge integration, such as systems biology. Several initiatives to organize biological knowledge sources into a readily exploitable resourceome are presently being carried out. Ontologies and Semantic Web technologies revolutionize these efforts. Here, we review the benefits, trends, current possibilities, and the potential this holds for the biosciences.'),
(68485, 2009, 'bib', 'Genome assembly reborn: recent computational challenges', 'Pop, Mihai', 'Department of Computer Science and the Center for Bioinformatics and Computational Biology at the University of Maryland, College Park, MD 20742, USA. mpop@umd.edu', 'United States', 205, 'Algorithms|Computational Biology|Databases, Genetic|Genome|Genomics|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bib/bbp026', '19482960', 'Research into genome assembly algorithms has experienced a resurgence due to new challenges created by the development of next generation sequencing technologies. Several genome assemblers have been published in recent years specifically targeted at the new sequence data, however, the ever-changing technological landscape leads to the need for continued research. In addition, the low cost of next generation sequencing data has led to an increased use of sequencing in new settings. For example, the new field of metagenomics relies on large-scale sequencing of entire microbial communities instead of isolate genomes, leading to new computational challenges. In this article, we outline the major algorithmic approaches for genome assembly and describe recent developments in this domain.'),
(68486, 2009, 'bib', 'Computational methods for the detection of cis-regulatory modules', 'Van Loo, Peter|Marynen, Peter', 'Department of Human Genetics, VIB and University of Leuven, B-3000 Leuven, Belgium. peter.vanloo@med.kuleuven.be|Department of Human Genetics, VIB and University of Leuven, B-3000 Leuven, Belgium. peter.vanloo@med.kuleuven.be', 'Belgium|Belgium', 47, 'Algorithms|Animals|Computational Biology|Gene Expression Regulation|Humans|Regulatory Sequences, Nucleic Acid|Sequence Alignment|Sequence Analysis, DNA|Transcription, Genetic', 'Journal Article', '10.1093/bib/bbp025', '19498042', 'Metazoan transcription regulation occurs through the concerted action of multiple transcription factors that bind co-operatively to cis-regulatory modules (CRMs). The annotation of these key regulators of transcription is lagging far behind the annotation of the transcriptome itself. Here, we give an overview of existing computational methods to detect these CRMs in metazoan genomes. We subdivide these methods into three classes: CRM scanners screen sequences for CRMs based on predefined models that often consist of multiple position weight matrices (PWMs). CRM builders construct models of similar CRMs controlling a set of co-regulated or co-expressed genes. CRM genome screeners screen sequences or complete genomes for CRMs as homotypic or heterotypic clusters of binding sites for any combination of transcription factors. We believe that CRM scanners are currently the most advanced methods, although their applicability is limited. Finally, we argue that CRM builders that make use of PWM libraries will benefit greatly from future advances and will prove to be most instrumental for the annotation of regulatory regions in metazoan genomes.'),
(68487, 2009, 'bib', 'Briefings in bioinformatics', 'Dubitzky, Werner', 'Department of Human Genetics, VIB and University of Leuven, B-3000 Leuven, Belgium. peter.vanloo@med.kuleuven.be', 'Belgium', 4, 'Computational Biology|Periodicals as Topic', 'Editorial', '10.1093/bib/bbp031', '19505887', '-'),
(68488, 2009, 'bib', 'Approaches to neuroscience data integration', 'Cheung, Kei-Hoi|Lim, Ernest|Samwald, Matthias|Chen, Huajun|Marenco, Luis|Holford, Matthew E|Morse, Thomas M|Mutalik, Pradeep|Shepherd, Gordon M|Miller, Perry L', 'Center for Medical Informatics, Yale University School of Medicine, New Haven, CT 06511, USA. kei.cheung@yale.edu|Center for Medical Informatics, Yale University School of Medicine, New Haven, CT 06511, USA. kei.cheung@yale.edu|Center for Medical Informatics, Yale University School of Medicine, New Haven, CT 06511, USA. kei.cheung@yale.edu|Center for Medical Informatics, Yale University School of Medicine, New Haven, CT 06511, USA. kei.cheung@yale.edu|Center for Medical Informatics, Yale University School of Medicine, New Haven, CT 06511, USA. kei.cheung@yale.edu|Center for Medical Informatics, Yale University School of Medicine, New Haven, CT 06511, USA. kei.cheung@yale.edu|Center for Medical Informatics, Yale University School of Medicine, New Haven, CT 06511, USA. kei.cheung@yale.edu|Center for Medical Informatics, Yale University School of Medicine, New Haven, CT 06511, USA. kei.cheung@yale.edu|Center for Medical Informatics, Yale University School of Medicine, New Haven, CT 06511, USA. kei.cheung@yale.edu|Center for Medical Informatics, Yale University School of Medicine, New Haven, CT 06511, USA. kei.cheung@yale.edu', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 11, 'Database Management Systems|Databases, Factual|Information Storage and Retrieval|Internet|Neurosciences|Software|User-Computer Interface|Vocabulary, Controlled', 'Journal Article', '10.1093/bib/bbp029', '19505888', 'As the number of neuroscience databases increases, the need for neuroscience data integration grows. This paper reviews and compares several approaches, including the Neuroscience Database Gateway (NDG), Neuroscience Information Framework (NIF) and Entrez Neuron, which enable neuroscience database annotation and integration. These approaches cover a range of activities spanning from registry, discovery and integration of a wide variety of neuroscience data sources. They also provide different user interfaces for browsing, querying and displaying query results. In Entrez Neuron, for example, four different facets or tree views (neuron, neuronal property, gene and drug) are used to hierarchically organize concepts that can be used for querying a collection of ontologies. The facets are also used to define the structure of the query results.'),
(68489, 2009, 'bib', 'Computational methods for discovering gene networks from expression data', 'Lee, Wei-Po|Tzou, Wen-Shyong', 'Department of Information Management, National Sun Yat-sen University, Kaohsiung, Taiwan. wplee@mail.nsysu.edu.tw|Department of Information Management, National Sun Yat-sen University, Kaohsiung, Taiwan. wplee@mail.nsysu.edu.tw', '-|-', 52, 'Algorithms|Bayes Theorem|Computational Biology|Databases, Genetic|Gene Expression Profiling|Gene Regulatory Networks|Models, Genetic|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1093/bib/bbp028', '19505889', 'Designing and conducting experiments are routine practices for modern biologists. The real challenge, especially in the post-genome era, usually comes not from acquiring data, but from subsequent activities such as data processing, analysis, knowledge generation and gaining insight into the research question of interest. The approach of inferring gene regulatory networks (GRNs) has been flourishing for many years, and new methods from mathematics, information science, engineering and social sciences have been applied. We review different kinds of computational methods biologists use to infer networks of varying levels of accuracy and complexity. The primary concern of biologists is how to translate the inferred network into hypotheses that can be tested with real-life experiments. Taking the biologists viewpoint, we scrutinized several methods for predicting GRNs in mammalian cells, and more importantly show how the power of different knowledge databases of different types can be used to identify modules and subnetworks, thereby reducing complexity and facilitating the generation of testable hypotheses.'),
(68490, 2009, 'bib', 'Towards accurate human promoter recognition: a review of currently used sequence features and classification methods', 'Zeng, Jia|Zhu, Shanfeng|Yan, Hong', 'Department of Computer Science, Hong Kong Baptist University, Kowloon, Hong Kong. j.zeng@ieee.org|Department of Computer Science, Hong Kong Baptist University, Kowloon, Hong Kong. j.zeng@ieee.org|Department of Computer Science, Hong Kong Baptist University, Kowloon, Hong Kong. j.zeng@ieee.org', 'Hong Kong|Hong Kong|Hong Kong', 36, 'Gene Expression Regulation|Genome, Human|Humans|Models, Genetic|Promoter Regions, Genetic|PubMed|Sequence Alignment|Sequence Analysis, DNA|Transcription Initiation Site', 'Journal Article', '10.1093/bib/bbp027', '19531545', 'This review describes important advances that have been made during the past decade for genome-wide human promoter recognition. Interest in promoter recognition algorithms on a genome-wide scale is worldwide and touches on a number of practical systems that are important in analysis of gene regulation and in genome annotation without experimental support of ESTs, cDNAs or mRNAs. The main focus of this review is on feature extraction and model selection for accurate human promoter recognition, with descriptions of what they are, what has been accomplished, and what remains to be done.'),
(68491, 2009, 'bib', 'Architecture, function and prediction of long signal peptides', 'Hiss, Jan A|Schneider, Gisbert', 'Johann Wolfgang Goethe-University, D-60323 Frankfurt am Main, Germany.|Johann Wolfgang Goethe-University, D-60323 Frankfurt am Main, Germany.', 'Germany|Germany', 7, 'Amino Acid Sequence|Animals|Computational Biology|Endoplasmic Reticulum|Humans|Molecular Sequence Data|Protein Sorting Signals|Protein Structure, Secondary|Sequence Analysis, Protein', 'Journal Article', '10.1093/bib/bbp030', '19535397', 'Protein targeting in eukaryotic cells is vital for cell survival and development. N-terminal signal peptides guide proteins to the membrane of the endoplasmic reticulum (ER) and initiate translocation into the ER lumen. Here, we review the status of signal peptide architecture and prediction with an emphasis on exceptionally long signal peptides, which often escape the notion of the currently available prediction methods. We benchmark publicly available prediction methods for their ability to correctly identify exceptionally long signal peptides. A set of 136 annotated eukaryotic signals served as reference data. The best prediction tool detected only 63%. A potential reason for the poor performance is the domain architecture of long signal peptides, whose structural peculiarities are insufficiently considered by current prediction algorithms. To overcome this limitation, we motivate a general domain view of long signal peptides, which becomes detectable when both the overall length and secondary structure of long signal peptides are taken into consideration. This concept provides a structural framework for identifying and understanding multiple targeting and post-targeting functions.'),
(68492, 2009, 'bib', 'Optimized detection of differential expression in global profiling experiments: case studies in clinical transcriptomic and quantitative proteomic datasets', 'Elo, Laura L|Hiissa, Jukka|Tuimala, Jarno|Kallio, Aleksi|Korpelainen, Eija|Aittokallio, Tero', 'Department of Mathematics, University of Turku, FI-20014 Turku, Finland. laliel@utu.fi|Department of Mathematics, University of Turku, FI-20014 Turku, Finland. laliel@utu.fi|Department of Mathematics, University of Turku, FI-20014 Turku, Finland. laliel@utu.fi|Department of Mathematics, University of Turku, FI-20014 Turku, Finland. laliel@utu.fi|Department of Mathematics, University of Turku, FI-20014 Turku, Finland. laliel@utu.fi|Department of Mathematics, University of Turku, FI-20014 Turku, Finland. laliel@utu.fi', 'Finland|Finland|Finland|Finland|Finland|Finland', 20, 'Biomarkers|Gene Expression Profiling|Humans|Models, Statistical|Oligonucleotide Array Sequence Analysis|Proteomics|Reproducibility of Results|Sensitivity and Specificity|Software', 'Journal Article', '10.1093/bib/bbp033', '19549804', 'Identification of reliable molecular markers that show differential expression between distinct groups of samples has remained a fundamental research problem in many large-scale profiling studies, such as those based on DNA microarray or mass-spectrometry technologies. Despite the availability of a wide spectrum of statistical procedures, the users of the high-throughput platforms are still facing the crucial challenge of deciding which test statistic is best adapted to the intrinsic properties of their own datasets. To meet this challenge, we recently introduced an adaptive procedure, named ROTS (Reproducibility-Optimized Test Statistic), which learns an optimal statistic directly from the given data, and whose relative benefits have previously been shown in comparison with state-of-the-art procedures for detecting differential expression. Using gene expression microarray and mass-spectrometry (MS)-based protein expression datasets as case studies, we illustrate here the practical usage and advantages of ROTS toward detecting reliable marker lists in clinical transcriptomic and proteomic studies. In a public leukemia microarray dataset, the procedure could improve the sensitivity of the gene marker lists detected with high specificity. When applied to a recent LC-MS dataset, involving plasma samples from severe burn patients, the procedure could identify several peptide markers that remained undetected in the conventional analysis, thus demonstrating the effectiveness of ROTS also for global quantitative proteomic studies. To promote its widespread usage, we have made freely available efficient implementations of ROTS, which are easily accessible either as a stand-alone R-package or as integrated in the open-source data analysis software Chipster.'),
(68493, 2009, 'bib', 'Finding sequence motifs in prokaryotic genomes--a brief practical guide for a microbiologist', 'Mrázek, Jan', 'Department of Microbiology and Institute of Bioinformatics, University of Georgia, Athens, GA 30602-2605, USA. mrazek@uga.edu', 'Georgia', 7, 'Algorithms|Base Sequence|Binding Sites|Databases, Genetic|Genome|Models, Statistical|Molecular Sequence Data|Phylogeny|Prokaryotic Cells|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bib/bbp032', '19553402', 'Finding significant nucleotide sequence motifs in prokaryotic genomes can be divided into three types of tasks: (1) supervised motif finding, where a sample of motif sequences is used to find other similar sequences in genomes, (2) unsupervised motif finding, which typically relates to the task of finding regulatory motifs and protein binding sites and (3) exploratory motif finding, which aims to identify potential functionally significant sequence motifs as those that are unusual in some statistical sense. This article provides a conceptual overview for each type of task, a brief description of basic algorithms used in their solution, and a review of selected relevant software available online.'),
(68494, 2009, 'bib', 'Stability and aggregation of ranked gene lists', 'Boulesteix, Anne-Laure|Slawski, Martin', 'Department of Medical Informatics, Biometry and Epidemiology (IBE), Marchioninistr. 15, 81377 Munich, Germany. boulesteix@ibe.med.uni-muenchen.de|Department of Medical Informatics, Biometry and Epidemiology (IBE), Marchioninistr. 15, 81377 Munich, Germany. boulesteix@ibe.med.uni-muenchen.de', 'Germany|Germany', 116, 'Algorithms|Databases, Genetic|Gene Expression Profiling|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1093/bib/bbp034', '19679825', 'Ranked gene lists are highly instable in the sense that similar measures of differential gene expression may yield very different rankings, and that a small change of the data set usually affects the obtained gene list considerably. Stability issues have long been under-considered in the literature, but they have grown to a hot topic in the last few years, perhaps as a consequence of the increasing skepticism on the reproducibility and clinical applicability of molecular research findings. In this article, we review existing approaches for the assessment of stability of ranked gene lists and the related problem of aggregation, give some practical recommendations, and warn against potential misuse of these methods. This overview is illustrated through an application to a recent leukemia data set using the freely available Bioconductor package GeneSelector.'),
(68495, 2009, 'bib', 'Improved criteria and comparative genomics tool provide new insights into grass paleogenomics', 'Salse, Jerome|Abrouk, Michael|Murat, Florent|Quraishi, Umar Masood|Feuillet, Catherine', 'INRA/Université Blaise Pascal UMR 1095, Génétique, Diversité et Ecophysiologie des Céréales, Domaine de Crouelle, 234 avenue du Brézet, 63100 Clermont-Ferrand, France. jsalse@clermont.inra.fr|INRA/Université Blaise Pascal UMR 1095, Génétique, Diversité et Ecophysiologie des Céréales, Domaine de Crouelle, 234 avenue du Brézet, 63100 Clermont-Ferrand, France. jsalse@clermont.inra.fr|INRA/Université Blaise Pascal UMR 1095, Génétique, Diversité et Ecophysiologie des Céréales, Domaine de Crouelle, 234 avenue du Brézet, 63100 Clermont-Ferrand, France. jsalse@clermont.inra.fr|INRA/Université Blaise Pascal UMR 1095, Génétique, Diversité et Ecophysiologie des Céréales, Domaine de Crouelle, 234 avenue du Brézet, 63100 Clermont-Ferrand, France. jsalse@clermont.inra.fr|INRA/Université Blaise Pascal UMR 1095, Génétique, Diversité et Ecophysiologie des Céréales, Domaine de Crouelle, 234 avenue du Brézet, 63100 Clermont-Ferrand, France. jsalse@clermont.inra.fr', 'France|France|France|France|France', 20, 'Biological Evolution|Chromosome Mapping|Evolution, Molecular|Genome, Plant|Genomics|Paleontology|Poaceae|Quantitative Trait Loci', 'Journal Article', '10.1093/bib/bbp037', '19720678', 'In the past decade, a number of bioinformatics tools have been developed to perform comparative genomics studies in plants and animals. However, most of the publicly available and user friendly tools lack common standards for the identification of robust orthologous relationships between genomes leading non-specialists to often over interpret the results of large scale comparative sequence analyses. Recently, we have established a number of improved parameters and tools to define significant relationships between genomes as a basis to develop paleogenomics studies in grasses. Here, we describe our approaches and propose the development of community-based standards that can be used in comparative genomic studies to (i) identify robust sets of orthologous gene pairs, (ii) derive complete sets of chromosome to chromosome relationships within and between genomes and (iii) model common paleo-ancestor genome structures. The rice and sorghum genome sequences are used to exemplify step-by-step a methodology that should allow users to perform accurate comparative genome analyses in their favourite species. Finally, we describe two applications for accurate gene annotation and synteny-based cloning of agronomically important traits.'),
(68496, 2009, 'bib', 'Bioinformatics in the orphan crops', 'Armstead, Ian|Huang, Lin|Ravagnani, Adriana|Robson, Paul|Ougham, Helen', 'Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Plas Gogerddan, Aberystwyth, Ceredigion SY23 3EB, Wales, UK.|Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Plas Gogerddan, Aberystwyth, Ceredigion SY23 3EB, Wales, UK.|Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Plas Gogerddan, Aberystwyth, Ceredigion SY23 3EB, Wales, UK.|Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Plas Gogerddan, Aberystwyth, Ceredigion SY23 3EB, Wales, UK.|Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Plas Gogerddan, Aberystwyth, Ceredigion SY23 3EB, Wales, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 22, 'Computational Biology|Crops, Agricultural|DNA, Plant|Genome, Plant|Plants, Genetically Modified', 'Journal Article', '10.1093/bib/bbp036', '19734255', 'Orphan crops are those which are grown as food, animal feed or other crops of some importance in agriculture, but which have not yet received the investment of research effort or funding required to develop significant public bioinformatics resources. Where an orphan crop is related to a well-characterised model plant species, comparative genomics and bioinformatics can often, though not always, be exploited to assist research and crop improvement. This review addresses some challenges and opportunities presented by bioinformatics in the orphan crops, using three examples: forage grasses from the genera Lolium and Festuca, forage legumes and the second generation energy crop Miscanthus.'),
(68497, 2010, 'bib', 'Advances in metaheuristics for gene selection and classification of microarray data', 'Duval, Béatrice|Hao, Jin-Kao', 'University of Angers, 2 Boulevard Lavoisier, 49045 Angers Cedex 01, France.|University of Angers, 2 Boulevard Lavoisier, 49045 Angers Cedex 01, France.', 'France|France', 15, 'Algorithms|Genes|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bib/bbp035', '19789265', 'Gene selection aims at identifying a (small) subset of informative genes from the initial data in order to obtain high predictive accuracy for classification. Gene selection can be considered as a combinatorial search problem and thus be conveniently handled with optimization methods. In this article, we summarize some recent developments of using metaheuristic-based methods within an embedded approach for gene selection. In particular, we put forward the importance and usefulness of integrating problem-specific knowledge into the search operators of such a method. To illustrate the point, we explain how ranking coefficients of a linear classifier such as support vector machine (SVM) can be profitably used to reinforce the search efficiency of Local Search and Evolutionary Search metaheuristic algorithms for gene selection and classification.'),
(68498, 2010, 'bib', 'Gene association analysis: a survey of frequent pattern mining from gene expression data', 'Alves, Ronnie|Rodriguez-Baena, Domingo S|Aguilar-Ruiz, Jesus S', 'Institute of Developmental Biology and Cancer, Centre de Biochimie, Faculte des Sciences, the University of Nice, 06108 Nice cedex 2. alves@unice.fr|Institute of Developmental Biology and Cancer, Centre de Biochimie, Faculte des Sciences, the University of Nice, 06108 Nice cedex 2. alves@unice.fr|Institute of Developmental Biology and Cancer, Centre de Biochimie, Faculte des Sciences, the University of Nice, 06108 Nice cedex 2. alves@unice.fr', '-|-|-', 56, 'Algorithms|Computational Biology|Gene Expression|Gene Expression Profiling|Gene Regulatory Networks|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Sequence Analysis, DNA', 'Journal Article', '10.1093/bib/bbp042', '19815645', 'Establishing an association between variables is always of interest in genomic studies. Generation of DNA microarray gene expression data introduces a variety of data analysis issues not encountered in traditional molecular biology or medicine. Frequent pattern mining (FPM) has been applied successfully in business and scientific data for discovering interesting association patterns, and is becoming a promising strategy in microarray gene expression analysis. We review the most relevant FPM strategies, as well as surrounding main issues when devising efficient and practical methods for gene association analysis (GAA). We observed that, so far, scalability achieved by efficient methods does not imply biological soundness of the discovered association patterns, and vice versa. Ideally, GAA should employ a balanced mining model taking into account best practices employed by methods reviewed in this survey. Integrative approaches, in which biological knowledge plays an important role within the mining process, are becoming more reliable.'),
(68499, 2010, 'bib', 'Knowledge-based data analysis comes of age', 'Ochs, Michael F', 'Division of Oncology Biostatistics and Bioinformatics, 550 North Broadway, Suite 1103, Johns Hopkins University, Baltimore, MD 21205, USA. mfo@jhu.edu', 'United States', 6, 'Bayes Theorem|Genome-Wide Association Study|Oligonucleotide Array Sequence Analysis|Quantitative Trait Loci|Systems Biology', 'Journal Article', '10.1093/bib/bbp044', '19854753', 'The emergence of high-throughput technologies for measuring biological systems has introduced problems for data interpretation that must be addressed for proper inference. First, analysis techniques need to be matched to the biological system, reflecting in their mathematical structure the underlying behavior being studied. When this is not done, mathematical techniques will generate answers, but the values and reliability estimates may not accurately reflect the biology. Second, analysis approaches must address the vast excess in variables measured (e.g. transcript levels of genes) over the number of samples (e.g. tumors, time points), known as the large-p, small-n problem. In large-p, small-n paradigms, standard statistical techniques generally fail, and computational learning algorithms are prone to overfit the data. Here we review the emergence of techniques that match mathematical structure to the biology, the use of integrated data and prior knowledge to guide statistical analysis, and the recent emergence of analysis approaches utilizing simple biological models. We show that novel biological insights have been gained using these techniques.'),
(68500, 2010, 'bib', 'Bioinformatics approaches for genomics and post genomics applications of next-generation sequencing', 'Horner, David Stephen|Pavesi, Giulio|Castrignanò, Tiziana|De Meo, Paolo DOnorio|Liuni, Sabino|Sammeth, Michael|Picardi, Ernesto|Pesole, Graziano', 'University of Milan (Italy).|University of Milan (Italy).|University of Milan (Italy).|University of Milan (Italy).|University of Milan (Italy).|University of Milan (Italy).|University of Milan (Italy).|University of Milan (Italy).', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 33, 'Base Sequence|Chromosome Mapping|Computational Biology|Gene Expression Profiling|Genomics|Humans|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide|RNA|Sequence Analysis, DNA', 'Journal Article', '10.1093/bib/bbp046', '19864250', 'Technical advances such as the development of molecular cloning, Sanger sequencing, PCR and oligonucleotide microarrays are key to our current capacity to sequence, annotate and study complete organismal genomes. Recent years have seen the development of a variety of so-called next-generation sequencing platforms, with several others anticipated to become available shortly. The previously unimaginable scale and economy of these methods, coupled with their enthusiastic uptake by the scientific community and the potential for further improvements in accuracy and read length, suggest that these technologies are destined to make a huge and ongoing impact upon genomic and post-genomic biology. However, like the analysis of microarray data and the assembly and annotation of complete genome sequences from conventional sequencing data, the management and analysis of next-generation sequencing data requires (and indeed has already driven) the development of informatics tools able to assemble, map, and interpret huge quantities of relatively or extremely short nucleotide sequence data. Here we provide a broad overview of bioinformatics approaches that have been introduced for several genomics and functional genomics applications of next-generation sequencing.'),
(68501, 2010, 'bib', 'The challenges of informatics in synthetic biology: from biomolecular networks to artificial organisms', 'Alterovitz, Gil|Muso, Taro|Ramoni, Marco F', 'Childrens Hospital Informatics Program, Harvard/MITDivision of Health Sciences and Technology, USA.|Childrens Hospital Informatics Program, Harvard/MITDivision of Health Sciences and Technology, USA.|Childrens Hospital Informatics Program, Harvard/MITDivision of Health Sciences and Technology, USA.', 'United States|United States|United States', 30, 'Algorithms|Base Sequence|DNA|Molecular Sequence Data|Sequence Homology, Nucleic Acid|Software|Systems Biology', 'Journal Article', '10.1093/bib/bbp054', '19906839', 'The field of synthetic biology holds an inspiring vision for the future, it integrates computational analysis, biological data and the systems engineering paradigm in the design of new biological machines and systems. These biological machines are built from basic biomolecular components analogous to electrical devices, and the information flow among these components requires the augmentation of biological insight with the power of a formal approach to information management. Here we review the informatics challenges in synthetic biology along three dimensions: in silico, in vitro and in vivo. First, we describe state of the art of the in silico support of synthetic biology, from the specific data exchange formats, to the most popular software platforms and algorithms. Next, we cast in vitro synthetic biology in terms of information flow, and discuss genetic fidelity in DNA manipulation, development strategies of biological parts and the regulation of biomolecular networks. Finally, we explore how the engineering chassis can manipulate biological circuitries in vivo to give rise to future artificial organisms.'),
(68502, 2009, 'bib', 'Editorial Plant bioinformatics', 'Dicks, Jo', 'Childrens Hospital Informatics Program, Harvard/MITDivision of Health Sciences and Technology, USA.', 'United States', 0, 'Computational Biology|Genome, Plant|Plants|Plants, Genetically Modified', 'Editorial', '10.1093/bib/bbp052', '19933207', '-');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(68503, 2009, 'bib', 'Computational approaches and software tools for genetic linkage map estimation in plants', 'Cheema, Jitender|Dicks, Jo', 'John Innes Centre, Norwich Research Park, Colney, Norwich, NR4 7UH, UK.|John Innes Centre, Norwich Research Park, Colney, Norwich, NR4 7UH, UK.', 'United Kingdom|United Kingdom', 66, 'Algorithms|Chromosome Mapping|DNA, Plant|Genetic Linkage|Genome, Plant|Plants|Software', 'Journal Article', '10.1093/bib/bbp045', '19933208', 'Genetic maps are an important component within the plant biologists toolkit, underpinning crop plant improvement programs. The estimation of plant genetic maps is a conceptually simple yet computationally complex problem, growing ever more so with the development of inexpensive, high-throughput DNA markers. The challenge for bioinformaticians is to develop analytical methods and accompanying software tools that can cope with datasets of differing sizes, from tens to thousands of markers, that can incorporate the expert knowledge that plant biologists typically use when developing their maps, and that facilitate user-friendly approaches to achieving these goals. Here, we aim to give a flavour of computational approaches for genetic map estimation, discussing briefly many of the key concepts involved, and describing a selection of software tools that employ them. This review is intended both for plant geneticists as an introduction to software tools with which to estimate genetic maps, and for bioinformaticians as an introduction to the underlying computational approaches.'),
(68504, 2009, 'bib', 'De novo sequencing of plant genomes using second-generation technologies', 'Imelfort, Michael|Edwards, David', 'Australian Centre for Plant Functional Genomics, Institute for Molecular Biosciences and School of Land, Crop and Food Sciences, University of Queensland, Brisbane, QLD 4072, Australia.|Australian Centre for Plant Functional Genomics, Institute for Molecular Biosciences and School of Land, Crop and Food Sciences, University of Queensland, Brisbane, QLD 4072, Australia.', 'Australia|Australia', 86, 'Algorithms|Base Sequence|Chromosome Mapping|DNA, Plant|Genome, Plant|Molecular Sequence Data|Plants|Sequence Analysis, DNA', 'Journal Article', '10.1093/bib/bbp039', '19933209', 'The ability to sequence the DNA of an organism has become one of the most important tools in modern biological research. Until recently, the sequencing of even small model genomes required substantial funds and international collaboration. The development of second-generation sequencing technology has increased the throughput and reduced the cost of sequence generation by several orders of magnitude. These new methods produce vast numbers of relatively short reads, usually at the expense of read accuracy. Since the first commercial second-generation sequencing system was produced by 454 Technologies and commercialised by Roche, several other companies including Illumina, Applied Biosystems, Helicos Biosciences and Pacific Biosciences have joined the competition. Because of the relatively high error rate and lack of assembly tools, short-read sequence technology has mainly been applied to the re-sequencing of genomes. However, some recent applications have focused on the de novo assembly of these data. De novo assembly remains the greatest challenge for DNA sequencing and there are specific problems for second generation sequencing which produces short reads with a high error rate. However, a number of different approaches for short-read assembly have been proposed and some have been implemented in working software. In this review, we compare the current approaches for second-generation genome sequencing, explore the future direction of this technology and the implications for plant genome research.'),
(68505, 2009, 'bib', 'Common introns within orthologous genes: software and application to plants', 'Wilkerson, Matthew D|Ru, Yuanbin|Brendel, Volker P', 'University of North Carolina, Chapel Hill, NC, USA.|University of North Carolina, Chapel Hill, NC, USA.|University of North Carolina, Chapel Hill, NC, USA.', 'United States|United States|United States', 24, 'Algorithms|Chromosome Mapping|DNA, Plant|Genome, Plant|Internet|Introns|Software', 'Journal Article', '10.1093/bib/bbp051', '19933210', 'The residence of spliceosomal introns within protein-coding genes can fluctuate over time, with genes gaining, losing or conserving introns in a complex process that is not entirely understood. One approach for studying intron evolution is to compare introns with respect to position and type within closely related genes. Here, we describe new, freely available software called Common Introns Within Orthologous Genes (CIWOG), available at http://ciwog.gdcb.iastate.edu/, which detects common introns in protein-coding genes based on position and sequence conservation in the corresponding protein alignments. CIWOG provides dynamic web displays that facilitate detailed intron studies within orthologous genes. User-supplied options control how introns are clustered into sets of common introns. CIWOG also identifies special classes of introns, in particular those with GC- or U12-type donor sites, which enables analyses of these introns in relation to their counterparts in the other genes in orthologous groups. The software is demonstrated with application to a comprehensive study of eight plant transcriptomes. Three specific examples are discussed: intron class conversion from GT- to GC-donor-type introns in monocots, plant U12-type intron conservation and a global analysis of intron evolution across the eight plant species.'),
(68506, 2009, 'bib', 'Computational techniques for elucidating plant-pathogen interactions from large-scale experiments on fungi and oomycetes', 'Soderlund, Carol', 'BIO5 Institute, 1657 Helen Street, University of Arizona, Tucson AZ 85721, USA. cari@agcol.arizona.edu', 'United States', 3, 'Chromosome Mapping|Eukaryota|Fungi|Host-Parasite Interactions|Plants|Software', 'Journal Article', '10.1093/bib/bbp053', '19933211', 'Eukaryotic plant pathogens are responsible for the destruction of billions of dollars worth of crops each year. With large-scale genomics of both pathogens and hosts and the corresponding computational analysis, biologists are now able to gain knowledge about many pathogenic and defense genes concurrently. To study the interactions between these two organism groups, it is necessary to design experiments to elucidate the genes being expressed during the invasion of the pathogen into the host. For the most part, this does not require new software development, though it does require the use of existing software in novel ways. This article provides a broad overview of several key and illustrative experiments and the corresponding computational analyses, outlining the knowledge gained in each. It goes on to describe databases for plant-pathogen data and important initiatives such as Plant-Associated Microbe Gene Ontology. It discusses how various emerging approaches will increase the power of computers in host-pathogen interaction studies.'),
(68507, 2009, 'bib', 'Software engineering the mixed model for genome-wide association studies on large samples', 'Zhang, Zhiwu|Buckler, Edward S|Casstevens, Terry M|Bradbury, Peter J', 'Institute for Genomic Diversity, Cornell University, Ithaca, New York, USA. zz19@cornell.edu|Institute for Genomic Diversity, Cornell University, Ithaca, New York, USA. zz19@cornell.edu|Institute for Genomic Diversity, Cornell University, Ithaca, New York, USA. zz19@cornell.edu|Institute for Genomic Diversity, Cornell University, Ithaca, New York, USA. zz19@cornell.edu', 'United States|United States|United States|United States', 40, 'Algorithms|Chromosome Mapping|Computer Simulation|Genetic Linkage|Genetics, Population|Genome-Wide Association Study|Models, Genetic|Software', 'Journal Article', '10.1093/bib/bbp050', '19933212', 'Mixed models improve the ability to detect phenotype-genotype associations in the presence of population stratification and multiple levels of relatedness in genome-wide association studies (GWAS), but for large data sets the resource consumption becomes impractical. At the same time, the sample size and number of markers used for GWAS is increasing dramatically, resulting in greater statistical power to detect those associations. The use of mixed models with increasingly large data sets depends on the availability of software for analyzing those models. While multiple software packages implement the mixed model method, no single package provides the best combination of fast computation, ability to handle large samples, flexible modeling and ease of use. Key elements of association analysis with mixed models are reviewed, including modeling phenotype-genotype associations using mixed models, population stratification, kinship and its estimation, variance component estimation, use of best linear unbiased predictors or residuals in place of raw phenotype, improving efficiency and software-user interaction. The available software packages are evaluated, and suggestions made for future software development.'),
(68508, 2009, 'bib', 'Data integration for plant genomics--exemplars from the integration of Arabidopsis thaliana databases', 'Lysenko, Artem|Lysenko, Atem|Hindle, Matthew Morritt|Taubert, Jan|Saqi, Mansoor|Rawlings, Christopher John', 'Centre for Mathematical and Computational Biology, Rothamsted Research, Harpenden AL5 2JQ, UK.|Centre for Mathematical and Computational Biology, Rothamsted Research, Harpenden AL5 2JQ, UK.|Centre for Mathematical and Computational Biology, Rothamsted Research, Harpenden AL5 2JQ, UK.|Centre for Mathematical and Computational Biology, Rothamsted Research, Harpenden AL5 2JQ, UK.|Centre for Mathematical and Computational Biology, Rothamsted Research, Harpenden AL5 2JQ, UK.|Centre for Mathematical and Computational Biology, Rothamsted Research, Harpenden AL5 2JQ, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 18, 'Arabidopsis|Arabidopsis Proteins|Chromosome Mapping|Database Management Systems|Databases, Genetic|Genome, Plant|Information Storage and Retrieval|Protein Interaction Mapping|Systems Integration', 'Journal Article', '10.1093/bib/bbp047', '19933213', 'The development of a systems based approach to problems in plant sciences requires integration of existing information resources. However, the available information is currently often incomplete and dispersed across many sources and the syntactic and semantic heterogeneity of the data is a challenge for integration. In this article, we discuss strategies for data integration and we use a graph based integration method (Ondex) to illustrate some of these challenges with reference to two example problems concerning integration of (i) metabolic pathway and (ii) protein interaction data for Arabidopsis thaliana. We quantify the degree of overlap for three commonly used pathway and protein interaction information sources. For pathways, we find that the AraCyc database contains the widest coverage of enzyme reactions and for protein interactions we find that the IntAct database provides the largest unique contribution to the integrated dataset. For both examples, however, we observe a relatively small amount of data common to all three sources. Analysis and visual exploration of the integrated networks was used to identify a number of practical issues relating to the interpretation of these datasets. We demonstrate the utility of these approaches to the analysis of groups of coexpressed genes from an individual microarray experiment, in the context of pathway information and for the combination of coexpression data with an integrated protein interaction network.'),
(68509, 2010, 'bib', 'BioModelsnet Web Services, a free and integrated toolkit for computational modelling software', 'Li, Chen|Courtot, Mélanie|Le Novère, Nicolas|Laibe, Camille', 'Computational Neurobiology Group, at the European Bioinformatics Institute, Hinxton, UK.|Computational Neurobiology Group, at the European Bioinformatics Institute, Hinxton, UK.|Computational Neurobiology Group, at the European Bioinformatics Institute, Hinxton, UK.|Computational Neurobiology Group, at the European Bioinformatics Institute, Hinxton, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 35, 'Algorithms|Computer Simulation|Data Mining|Database Management Systems|Databases, Factual|Internet|Models, Biological|Programming Languages|Software|Software Design|Systems Integration', 'Journal Article', '10.1093/bib/bbp056', '19939940', 'Exchanging and sharing scientific results are essential for researchers in the field of computational modelling. BioModels.net defines agreed-upon standards for model curation. A fundamental one, MIRIAM (Minimum Information Requested in the Annotation of Models), standardises the annotation and curation process of quantitative models in biology. To support this standard, MIRIAM Resources maintains a set of standard data types for annotating models, and provides services for manipulating these annotations. Furthermore, BioModels.net creates controlled vocabularies, such as SBO (Systems Biology Ontology) which strictly indexes, defines and links terms used in Systems Biology. Finally, BioModels Database provides a free, centralised, publicly accessible database for storing, searching and retrieving curated and annotated computational models. Each resource provides a web interface to submit, search, retrieve and display its data. In addition, the BioModels.net team provides a set of Web Services which allows the community to programmatically access the resources. A user is then able to perform remote queries, such as retrieving a model and resolving all its MIRIAM Annotations, as well as getting the details about the associated SBO terms. These web services use established standards. Communications rely on SOAP (Simple Object Access Protocol) messages and the available queries are described in a WSDL (Web Services Description Language) file. Several libraries are provided in order to simplify the development of client software. BioModels.net Web Services make one step further for the researchers to simulate and understand the entirety of a biological system, by allowing them to retrieve biological models in their own tool, combine queries in workflows and efficiently analyse models.'),
(68510, 2010, 'bib', 'Detection call algorithms for high-throughput gene expression microarray data', 'Archer, Kellie J|Reese, Sarah E', 'Department of Biostatistics, Virginia Commonwealth University, Richmond, VA 23298-0032, USA. kjarcher@vcu.edu|Department of Biostatistics, Virginia Commonwealth University, Richmond, VA 23298-0032, USA. kjarcher@vcu.edu', 'United States|United States', 24, 'Algorithms|Databases, Genetic|Gene Expression Profiling|High-Throughput Screening Assays|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA', 'Journal Article', '10.1093/bib/bbp055', '19939941', 'Extensive methodological research has been conducted to improve gene expression summary methods. However, in addition to quantitative gene expression summaries, most platforms, including all those examined in the MicroArray Quality Control project, provide a qualitative detection call result for each gene on the platform. These detection call algorithms are intended to render an assessment of whether or not each transcript is reliably measured. In this paper, we review uses of these qualitative detection call results in the analysis of microarray data. We also review the detection call algorithms for two widely used gene expression microarray platforms, Affymetrix GeneChips and Illumina BeadArrays, and more clearly formalize the mathematical notation for the Illumina BeadArray detection call algorithm. Both algorithms result in a P-value which is then used for determining the qualitative detection calls. We examined the performance of these detection call algorithms and default parameters by applying the methods to two spike-in datasets. We show that the default parameters for qualitative detection calls yield few absent calls for high spike-in concentrations. When genes of interest are expected to be present at very low concentrations, spike-in datasets can be useful for appropriately adjusting the tuning parameters for qualitative detection calls.'),
(68511, 2010, 'bib', 'Detection of human interchromosomal trans-splicing in sequence databanks', 'Herai, Roberto Hirochi|Yamagishi, Michel E Beleza', 'Genetics and Molecular Biology Department, Biology Institute, State University of Campinas, 13083-862 Campinas, SP, Brazil. rherai@cnptia.embrapa.br|Genetics and Molecular Biology Department, Biology Institute, State University of Campinas, 13083-862 Campinas, SP, Brazil. rherai@cnptia.embrapa.br', 'Brazil|Brazil', 26, 'Animals|Base Sequence|Databases, Nucleic Acid|Humans|Molecular Sequence Data|RNA, Messenger|Sequence Alignment|Sequence Analysis, DNA|Spliceosomes|Trans-Splicing', 'Journal Article', '10.1093/bib/bbp041', '19955235', 'Trans-splicing is a common phenomenon in nematodes and kinetoplastids, and it has also been reported in other organisms, including humans. Up to now, all in silico strategies to find evidence of trans-splicing in humans have required that the candidate sequences follow the consensus splicing site rules (spliceosome-mediated mechanism). However, this criterion is not supported by the best human experimental evidence, which, except in a single case, do not follow canonical splicing sites. Moreover, recent findings describe a novel alternative tRNA mediated trans-splicing mechanism, which prescinds the spliceosome machinery. In order to answer the question, Are there hybrid mRNAs in sequence databanks, whose characteristics resemble those of the best human experimental evidence?, we have developed a methodology that successfully identified 16 hybrid mRNAs which might be instances of interchromosomal trans-splicing. Each hybrid mRNA is formed by a trans-spliced region (TSR), which was successfully mapped either onto known genes or onto a human endogenous retrovirus (HERV-K) transcript which supports their transcription. The existence of these hybrid mRNAs indicates that trans-splicing may be more widespread than believed. Furthermore, non-canonical splice site patterns suggest that infrequent splicing sites may occur under special conditions, or that an alternative trans-splicing mechanism is involved. Finally, our candidates are supposedly from normal tissue, and a recent study has reported that trans-splicing may occur not only in malignant tissues, but in normal tissues as well. Our methodology can be applied to 5-UTR, coding sequences and 3-UTR in order to find new candidates for a posteriori experimental confirmation.'),
(68512, 2010, 'bib', 'Current progress in patient-specific modeling', 'Neal, Maxwell Lewis|Kerckhoffs, Roy', 'Division of Biomedical and Health Informatics, University of Washington, USA.|Division of Biomedical and Health Informatics, University of Washington, USA.', 'United States|United States', 24, 'Computer Simulation|Decision Support Systems, Clinical|Humans|Image Processing, Computer-Assisted|Patient Simulation', 'Journal Article', '10.1093/bib/bbp049', '19955236', 'We present a survey of recent advancements in the emerging field of patient-specific modeling (PSM). Researchers in this field are currently simulating a wide variety of tissue and organ dynamics to address challenges in various clinical domains. The majority of this research employs three-dimensional, image-based modeling techniques. Recent PSM publications mostly represent feasibility or preliminary validation studies on modeling technologies, and these systems will require further clinical validation and usability testing before they can become a standard of care. We anticipate that with further testing and research, PSM-derived technologies will eventually become valuable, versatile clinical tools.'),
(68513, 2010, 'bib', 'Pathway Tools version 130: integrated software for pathway/genome informatics and systems biology', 'Karp, Peter D|Paley, Suzanne M|Krummenacker, Markus|Latendresse, Mario|Dale, Joseph M|Lee, Thomas J|Kaipa, Pallavi|Gilham, Fred|Spaulding, Aaron|Popescu, Liviu|Altman, Tomer|Paulsen, Ian|Keseler, Ingrid M|Caspi, Ron', 'Artificial Intelligence Center, SRI International, 333 Ravenswood Ave, AE206, Menlo Park, CA 94025, USA. pkarp@ai.sri.com|Artificial Intelligence Center, SRI International, 333 Ravenswood Ave, AE206, Menlo Park, CA 94025, USA. pkarp@ai.sri.com|Artificial Intelligence Center, SRI International, 333 Ravenswood Ave, AE206, Menlo Park, CA 94025, USA. pkarp@ai.sri.com|Artificial Intelligence Center, SRI International, 333 Ravenswood Ave, AE206, Menlo Park, CA 94025, USA. pkarp@ai.sri.com|Artificial Intelligence Center, SRI International, 333 Ravenswood Ave, AE206, Menlo Park, CA 94025, USA. pkarp@ai.sri.com|Artificial Intelligence Center, SRI International, 333 Ravenswood Ave, AE206, Menlo Park, CA 94025, USA. pkarp@ai.sri.com|Artificial Intelligence Center, SRI International, 333 Ravenswood Ave, AE206, Menlo Park, CA 94025, USA. pkarp@ai.sri.com|Artificial Intelligence Center, SRI International, 333 Ravenswood Ave, AE206, Menlo Park, CA 94025, USA. pkarp@ai.sri.com|Artificial Intelligence Center, SRI International, 333 Ravenswood Ave, AE206, Menlo Park, CA 94025, USA. pkarp@ai.sri.com|Artificial Intelligence Center, SRI International, 333 Ravenswood Ave, AE206, Menlo Park, CA 94025, USA. pkarp@ai.sri.com|Artificial Intelligence Center, SRI International, 333 Ravenswood Ave, AE206, Menlo Park, CA 94025, USA. pkarp@ai.sri.com|Artificial Intelligence Center, SRI International, 333 Ravenswood Ave, AE206, Menlo Park, CA 94025, USA. pkarp@ai.sri.com|Artificial Intelligence Center, SRI International, 333 Ravenswood Ave, AE206, Menlo Park, CA 94025, USA. pkarp@ai.sri.com|Artificial Intelligence Center, SRI International, 333 Ravenswood Ave, AE206, Menlo Park, CA 94025, USA. pkarp@ai.sri.com', '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|United States', 328, 'Computational Biology|Genome|Internet|Software|Systems Biology', 'Journal Article', '10.1093/bib/bbp043', '19955237', 'Pathway Tools is a production-quality software environment for creating a type of model-organism database called a Pathway/Genome Database (PGDB). A PGDB such as EcoCyc integrates the evolving understanding of the genes, proteins, metabolic network and regulatory network of an organism. This article provides an overview of Pathway Tools capabilities. The software performs multiple computational inferences including prediction of metabolic pathways, prediction of metabolic pathway hole fillers and prediction of operons. It enables interactive editing of PGDBs by DB curators. It supports web publishing of PGDBs, and provides a large number of query and visualization tools. The software also supports comparative analyses of PGDBs, and provides several systems biology analyses of PGDBs including reachability analysis of metabolic networks, and interactive tracing of metabolites through a metabolic network. More than 800 PGDBs have been created using Pathway Tools by scientists around the world, many of which are curated DBs for important model organisms. Those PGDBs can be exchanged using a peer-to-peer DB sharing system called the PGDB Registry.'),
(68514, 2010, 'bib', 'Dealing with missing values in large-scale studies: microarray data imputation and beyond', 'Aittokallio, Tero', 'Biomathematics Research Group, Department of Mathematics, FI-20014 University of Turku, Finland. tero.aittokallio@utu.fi', 'Finland', 89, 'Algorithms|Gene Expression Profiling|High-Throughput Screening Assays|Humans|Microarray Analysis|Models, Statistical|Research Design|Software', 'Journal Article', '10.1093/bib/bbp059', '19965979', 'High-throughput biotechnologies, such as gene expression microarrays or mass-spectrometry-based proteomic assays, suffer from frequent missing values due to various experimental reasons. Since the missing data points can hinder downstream analyses, there exists a wide variety of ways in which to deal with missing values in large-scale data sets. Nowadays, it has become routine to estimate (or impute) the missing values prior to the actual data analysis. After nearly a decade since the publication of the first missing value imputation methods for gene expression microarray data, new imputation approaches are still being developed at an increasing rate. However, what is lagging behind is a systematic and objective evaluation of the strengths and weaknesses of the different approaches when faced with different types of data sets and experimental questions. In this review, the present strategies for missing value imputation and the measures for evaluating their performance are described. The imputation methods are first reviewed in the context of gene expression microarray data, since most of the methods have been developed for estimating gene expression levels, then, we turn to other large-scale data sets that also suffer from the problems posed by missing values, together with pointers to possible imputation approaches in these settings. Along with a description of the basic principles behind the different imputation approaches, the review tries to provide practical guidance for the users of high-throughput technologies on how to choose the imputation tool for their data and questions, and some additional research directions for the developers of imputation methodologies.'),
(68515, 2010, 'bib', 'Advances in translational bioinformatics: computational approaches for the hunting of disease genes', 'Kann, Maricel G', 'University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA. mkann@umbc.edu', 'United States', 21, 'Computational Biology|Genetic Diseases, Inborn|Genetic Predisposition to Disease|Humans', 'Journal Article', '10.1093/bib/bbp048', '20007728', 'Over a 100 years ago, William Bateson provided, through his observations of the transmission of alkaptonuria in first cousin offspring, evidence of the application of Mendelian genetics to certain human traits and diseases. His work was corroborated by Archibald Garrod (Archibald AE. The incidence of alkaptonuria: a study in chemical individuality. Lancert 1902,ii:1616-20) and William Farabee (Farabee WC. Inheritance of digital malformations in man. In: Papers of the Peabody Museum of American Archaeology and Ethnology. Cambridge, Mass: Harvard University, 1905, 65-78), who recorded the familial tendencies of inheritance of malformations of human hands and feet. These were the pioneers of the hunt for disease genes that would continue through the century and result in the discovery of hundreds of genes that can be associated with different diseases. Despite many ground-breaking discoveries during the last century, we are far from having a complete understanding of the intricate network of molecular processes involved in diseases, and we are still searching for the cures for most complex diseases. In the last few years, new genome sequencing and other high-throughput experimental techniques have generated vast amounts of molecular and clinical data that contain crucial information with the potential of leading to the next major biomedical discoveries. The need to mine, visualize and integrate these data has motivated the development of several informatics approaches that can broadly be grouped in the research area of translational bioinformatics. This review highlights the latest advances in the field of translational bioinformatics, focusing on the advances of computational techniques to search for and classify disease genes.'),
(68516, 2010, 'bib', 'Bioinformatical approaches to characterize intrinsically disordered/unstructured proteins', 'Dosztányi, Zsuzsanna|Mészáros, Bálint|Simon, István', 'Institute of Enzymology, H-1518 Budapest, Hungary.|Institute of Enzymology, H-1518 Budapest, Hungary.|Institute of Enzymology, H-1518 Budapest, Hungary.', 'Hungary|Hungary|Hungary', 38, 'Algorithms|Amino Acid Sequence|Computational Biology|Databases, Protein|Evolution, Molecular|Humans|Molecular Sequence Data|Protein Structure, Secondary|Protein Structure, Tertiary|Proteins', 'Journal Article', '10.1093/bib/bbp061', '20007729', 'Intrinsically disordered/unstructured proteins exist without a stable three-dimensional (3D) structure as highly flexible conformational ensembles. The available genome sequences revealed that these proteins are surprisingly common and their frequency reaches high proportions in eukaryotes. Due to their vital role in various biological processes including signaling and regulation and their involvement in various diseases, disordered proteins and protein segments are the focus of many biochemical, molecular biological, pathological and pharmaceutical studies. These proteins are difficult to study experimentally because of the lack of unique structure in the isolated form. Their amino acid sequence, however, is available, and can be used for their identification and characterization by bioinformatic tools, analogously to globular proteins. In this review, we first present a small survey of current methods to identify disordered proteins or protein segments, focusing on those that are publicly available as web servers. In more detail we also discuss approaches that predict disordered regions and specific regions involved in protein binding by modeling the physical background of protein disorder. In our review we argue that the heterogeneity of disordered segments needs to be taken into account for a better understanding of protein disorder.'),
(68517, 2010, 'bib', 'Multi-scale modelling in computational biomedicine', 'Sloot, Peter M A|Hoekstra, Alfons G', 'Computational Science, Faculty of Science, University of Amsterdam, Kruislaan 403, 1098 SJ Amsterdam, The Netherlands. p.m.a.sloot@uva.nl|Computational Science, Faculty of Science, University of Amsterdam, Kruislaan 403, 1098 SJ Amsterdam, The Netherlands. p.m.a.sloot@uva.nl', 'Netherlands|Netherlands', 62, 'Computational Biology|Coronary Artery Disease|HIV Infections|Humans|Models, Theoretical|Recurrence|Stents', 'Journal Article', '10.1093/bib/bbp038', '20028713', 'The inherent complexity of biomedical systems is well recognized, they are multi-scale, multi-science systems, bridging a wide range of temporal and spatial scales. This article reviews the currently emerging field of multi-scale modelling in computational biomedicine. Many exciting multi-scale models exist or are under development. However, an underpinning multi-scale modelling methodology seems to be missing. We propose a direction that complements the classic dynamical systems approach and introduce two distinct case studies, transmission of resistance in human immunodeficiency virus spreading and in-stent restenosis in coronary artery disease.'),
(68518, 2010, 'bib', 'Molecular networks for the study of TCM pharmacology', 'Zhao, Jing|Jiang, Peng|Zhang, Weidong', 'Department of Natural Medicinal Chemistry, Second Military Medical University, PR China.|Department of Natural Medicinal Chemistry, Second Military Medical University, PR China.|Department of Natural Medicinal Chemistry, Second Military Medical University, PR China.', 'China|China|China', 166, 'Medicine, Chinese Traditional|Pharmacology', 'Journal Article', '10.1093/bib/bbp063', '20038567', 'To target complex, multi-factorial diseases more effectively, there has been an emerging trend of multi-target drug development based on network biology, as well as an increasing interest in traditional Chinese medicine (TCM) that applies a more holistic treatment to diseases. Thousands of years clinic practices in TCM have accumulated a considerable number of formulae that exhibit reliable in vivo efficacy and safety. However, the molecular mechanisms responsible for their therapeutic effectiveness are still unclear. The development of network-based systems biology has provided considerable support for the understanding of the holistic, complementary and synergic essence of TCM in the context of molecular networks. This review introduces available sources and methods that could be utilized for the network-based study of TCM pharmacology, proposes a workflow for network-based TCM pharmacology study, and presents two case studies on applying these sources and methods to understand the mode of action of TCM recipes.'),
(68519, 2010, 'bib', 'Simulation of P systems with active membranes on CUDA', 'Cecilia, José M|García, José M|Guerrero, Ginés D|Martínez-del-Amor, Miguel A|Pérez-Hurtado, Ignacio|Pérez-Jiménez, Mario J', 'DITEC, Facultad de Informática, Universidad de Murcia, Campus de Espinardo 30100, Murcia, Spain. chema@ditec.um.es.|DITEC, Facultad de Informática, Universidad de Murcia, Campus de Espinardo 30100, Murcia, Spain. chema@ditec.um.es.|DITEC, Facultad de Informática, Universidad de Murcia, Campus de Espinardo 30100, Murcia, Spain. chema@ditec.um.es.|DITEC, Facultad de Informática, Universidad de Murcia, Campus de Espinardo 30100, Murcia, Spain. chema@ditec.um.es.|DITEC, Facultad de Informática, Universidad de Murcia, Campus de Espinardo 30100, Murcia, Spain. chema@ditec.um.es.|DITEC, Facultad de Informática, Universidad de Murcia, Campus de Espinardo 30100, Murcia, Spain. chema@ditec.um.es.', 'Spain|Spain|Spain|Spain|Spain|Spain', 48, 'Algorithms|Biology|Biomimetics|Computer Simulation|Models, Biological|Programming Languages|Software|Software Design|Systems Integration', 'Journal Article', '10.1093/bib/bbp064', '20038568', 'P systems or Membrane Systems provide a high-level computational modelling framework that combines the structure and dynamic aspects of biological systems in a relevant and understandable way. They are inherently parallel and non-deterministic computing devices. In this article, we discuss the motivation, design principles and key of the implementation of a simulator for the class of recognizer P systems with active membranes running on a (GPU). We compare our parallel simulator for GPUs to the simulator developed for a single central processing unit (CPU), showing that GPUs are better suited than CPUs to simulate P systems due to their highly parallel nature.'),
(68520, 2010, 'bib', 'Real-time classification of datasets with hardware embedded neuromorphic neural networks', 'Bako, Laszlo', 'Petru MaiorUniversity, Tirgu-Mures, Romania. lbako@ms.sapientia.ro', 'Romania', 14, 'Biomimetic Materials|Databases, Factual|Information Storage and Retrieval|Nerve Net|Neural Networks (Computer)|Pattern Recognition, Automated', 'Journal Article', '10.1093/bib/bbp066', '20053732', 'Neuromorphic artificial neural networks attempt to understand the essential computations that take place in the dense networks of interconnected neurons making up the central nervous systems in living creatures. This article demonstrates that artificial spiking neural networks--built to resemble the biological model--encoding information in the timing of single spikes, are capable of computing and learning clusters from realistic data. It shows how a spiking neural network based on spike-time coding can successfully perform unsupervised and supervised clustering on real-world data. A temporal encoding procedure of continuously valued data is developed, together with a hardware implementation oriented new learning rule set. Solutions that make use of embedded soft-core microcontrollers are investigated, to implement some of the most resource-consuming components of the artificial neural network. Details of the implementations are given, with benchmark application evaluation and test bench description. Measurement results are presented, showing real-time and adaptive data processing capabilities, comparing these to related findings in the specific literature.'),
(68521, 2010, 'bib', 'Genome variation discovery with high-throughput sequencing data', 'Dalca, Adrian V|Brudno, Michael', 'Computer Science, MIT, USA.|Computer Science, MIT, USA.', 'United States|United States', 29, 'Algorithms|Genome|Sequence Analysis', 'Journal Article', '10.1093/bib/bbp058', '20053733', 'The advent of high-throughput sequencing (HTS) technologies is enabling sequencing of human genomes at a significantly lower cost. The availability of these genomes is hoped to enable novel medical diagnostics and treatment, specific to the individual, thus launching the era of personalized medicine. The data currently generated by HTS machines require extensive computational analysis in order to identify genomic variants present in the sequenced individual. In this paper, we overview HTS technologies and discuss several of the plethora of algorithms and tools designed to analyze HTS data, including algorithms for read mapping, as well as methods for identification of single-nucleotide polymorphisms, insertions/deletions and large-scale structural variants and copy-number variants from these mappings.'),
(68522, 2010, 'bib', 'Developing computational model-based diagnostics to analyse clinical chemistry data', 'van Schalkwijk, Daniël B|van Bochove, Kees|van Ommen, Ben|Freidig, Andreas P|van Someren, Eugene P|van der Greef, Jan|de Graaf, Albert A', 'U TNO Quality of Life Research Institute and at Leiden Amsterdam Centre for Drug Research at Leiden University. daan.vanschalkwijk@tno.nl|U TNO Quality of Life Research Institute and at Leiden Amsterdam Centre for Drug Research at Leiden University. daan.vanschalkwijk@tno.nl|U TNO Quality of Life Research Institute and at Leiden Amsterdam Centre for Drug Research at Leiden University. daan.vanschalkwijk@tno.nl|U TNO Quality of Life Research Institute and at Leiden Amsterdam Centre for Drug Research at Leiden University. daan.vanschalkwijk@tno.nl|U TNO Quality of Life Research Institute and at Leiden Amsterdam Centre for Drug Research at Leiden University. daan.vanschalkwijk@tno.nl|U TNO Quality of Life Research Institute and at Leiden Amsterdam Centre for Drug Research at Leiden University. daan.vanschalkwijk@tno.nl|U TNO Quality of Life Research Institute and at Leiden Amsterdam Centre for Drug Research at Leiden University. daan.vanschalkwijk@tno.nl', '-|-|-|-|-|-|-', 5, 'Computer Simulation|Diagnosis|Humans', 'Journal Article', '10.1093/bib/bbp071', '20056728', 'This article provides methodological and technical considerations to researchers starting to develop computational model-based diagnostics using clinical chemistry data. These models are of increasing importance, since novel metabolomics and proteomics measuring technologies are able to produce large amounts of data that are difficult to interpret at first sight, but have high diagnostic potential. Computational models aid interpretation and make the data accessible for clinical diagnosis. We discuss the issues that a modeller has to take into account during the design, construction and evaluation phases of model development. We use the example of Particle Profiler development, a model-based diagnostic tool for lipoprotein disorders, as a case study, to illustrate our considerations. The case study also offers techniques for efficient model formulation, model calculation, workflow structuring and quality control.'),
(68523, 2010, 'bib', 'Online tools for understanding rat physiology', 'Dwinell, Melinda R', 'Human & Molecular Genetics Center at Medical College of Wisconsin, USA. mrdwinel@mcw.edu', 'United States', 9, 'Animals|Internet|Rats|Species Specificity', 'Journal Article', '10.1093/bib/bbp069', '20056729', 'Rat models have been used to investigate physiological and pathophysiological mechanisms for decades. With the availability of the rat genome and other online resources, tools to identify rat models that mimic human disease are an important step in translational research. Despite the large number of papers published each year using rat models, integrating this information remains a problem. Resources for the rat genome are continuing to grow rapidly, while resources providing access to rat phenotype data are just emerging. An overview of rat models of disease, tools to characterize strain by phenotype and genotype, and steps being taken to integrate rat physiological data is presented in this article. Integrating functional and physiological data with the rat genome will build a solid research platform to facilitate innovative studies to unravel the mechanisms resulting in disease.'),
(68524, 2011, 'bib', 'Comparative study of computational methods to detect the correlated reaction sets in biochemical networks', 'Xi, Yanping|Chen, Yi-Ping Phoebe|Qian, Chen|Wang, Fei', 'Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China.|Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China.|Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China.|Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China.', 'China|China|China|China', 8, 'Algorithms|Computational Biology|Escherichia coli|Metabolic Networks and Pathways|Monte Carlo Method|Signal Transduction', 'Comparative Study', '10.1093/bib/bbp068', '20056730', 'Correlated reaction sets (Co-Sets) are mathematically defined modules in biochemical reaction networks which facilitate the study of biological processes by decomposing complex reaction networks into conceptually simple units. According to the degree of association, Co-Sets can be classified into three types: perfect, partial and directional. Five approaches have been developed to calculate Co-Sets, including network-based pathway analysis, Monte Carlo sampling, linear optimization, enzyme subsets and hard-coupled reaction sets. However, differences in design and implementation of these methods lead to discrepancies in the resulted Co-Sets as well as in their use in biotechnology which need careful interpretation. In this paper, we provide a comparative study of the methods for Co-Sets computing in detail from four aspects: (i) sensitivity, (ii) completeness and soundness, (iii) flexibility and (iv) scalability. By applying them to Escherichia coli core metabolic network, the differences and relationships among these methods are clearly articulated which may be useful for potential users.'),
(68525, 2010, 'bib', 'CaliBayes and BASIS: integrated tools for the calibration, simulation and storage of biological simulation models', 'Chen, Yuhui|Lawless, Conor|Gillespie, Colin S|Wu, Jake|Boys, Richard J|Wilkinson, Darren J', 'Institute for Ageing and Health, Centre for Integrated Systems Biology of Ageing and Nutrition at Newcastle, UK.|Institute for Ageing and Health, Centre for Integrated Systems Biology of Ageing and Nutrition at Newcastle, UK.|Institute for Ageing and Health, Centre for Integrated Systems Biology of Ageing and Nutrition at Newcastle, UK.|Institute for Ageing and Health, Centre for Integrated Systems Biology of Ageing and Nutrition at Newcastle, UK.|Institute for Ageing and Health, Centre for Integrated Systems Biology of Ageing and Nutrition at Newcastle, UK.|Institute for Ageing and Health, Centre for Integrated Systems Biology of Ageing and Nutrition at Newcastle, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 5, 'Algorithms|Biology|Computer Simulation|Models, Biological|Programming Languages|Software|Software Design|Systems Integration', 'Journal Article', '10.1093/bib/bbp072', '20056731', 'Dynamic simulation modelling of complex biological processes forms the backbone of systems biology. Discrete stochastic models are particularly appropriate for describing sub-cellular molecular interactions, especially when critical molecular species are thought to be present at low copy-numbers. For example, these stochastic effects play an important role in models of human ageing, where ageing results from the long-term accumulation of random damage at various biological scales. Unfortunately, realistic stochastic simulation of discrete biological processes is highly computationally intensive, requiring specialist hardware, and can benefit greatly from parallel and distributed approaches to computation and analysis. For these reasons, we have developed the BASIS system for the simulation and storage of stochastic SBML models together with associated simulation results. This system is exposed as a set of web services to allow users to incorporate its simulation tools into their workflows. Parameter inference for stochastic models is also difficult and computationally expensive. The CaliBayes system provides a set of web services (together with an R package for consuming these and formatting data) which addresses this problem for SBML models. It uses a sequential Bayesian MCMC method, which is powerful and flexible, providing very rich information. However this approach is exceptionally computationally intensive and requires the use of a carefully designed architecture. Again, these tools are exposed as web services to allow users to take advantage of this system. In this article, we describe these two systems and demonstrate their integrated use with an example workflow to estimate the parameters of a simple model of Saccharomyces cerevisiae growth on agar plates.'),
(68526, 2010, 'bib', 'Toward the dynamic interactome: its about time', 'Przytycka, Teresa M|Singh, Mona|Slonim, Donna K', 'National Center of Biotechnology Information, NLM, NIH, 8000 Rockville Pike, Bethesda MD 20814, USA. przytyck@ncbi.nlm.nih.gov|National Center of Biotechnology Information, NLM, NIH, 8000 Rockville Pike, Bethesda MD 20814, USA. przytyck@ncbi.nlm.nih.gov|National Center of Biotechnology Information, NLM, NIH, 8000 Rockville Pike, Bethesda MD 20814, USA. przytyck@ncbi.nlm.nih.gov', 'United States|United States|United States', 68, 'Proteins|Systems Biology', 'Journal Article', '10.1093/bib/bbp057', '20061351', 'Dynamic molecular interactions play a central role in regulating the functioning of cells and organisms. The availability of experimentally determined large-scale cellular networks, along with other high-throughput experimental data sets that provide snapshots of biological systems at different times and conditions, is increasingly helpful in elucidating interaction dynamics. Here we review the beginnings of a new subfield within computational biology, one focused on the global inference and analysis of the dynamic interactome. This burgeoning research area, which entails a shift from static to dynamic network analysis, promises to be a major step forward in our ability to model and reason about cellular function and behavior.'),
(68527, 2010, 'bib', 'Estimating the divisibility of complex biological networks by sparseness indices', 'Mazza, Tommaso|Romanel, Alessandro|Jordán, Ferenc', 'The Microsoft Research, University of Trento Centre for Computational and Systems Biology, Piazza Manci 17, 38123 Povo, Trento, Italy. mazza@cosbi.eu|The Microsoft Research, University of Trento Centre for Computational and Systems Biology, Piazza Manci 17, 38123 Povo, Trento, Italy. mazza@cosbi.eu|The Microsoft Research, University of Trento Centre for Computational and Systems Biology, Piazza Manci 17, 38123 Povo, Trento, Italy. mazza@cosbi.eu', 'Italy|Italy|Italy', 8, 'Algorithms|Animals|Computer Simulation|Humans|Models, Biological|Proteome|Signal Transduction', 'Journal Article', '10.1093/bib/bbp060', '20064873', 'In order to understand the complex relationships among the components of biological systems, network models have been used for a long time. Although they have been extensively used for visualization, data storage, structural analysis and simulation, some computational processes are still very inefficient when applied on complex networks. In particular, any parallel simulation technique requires a network previously divided into a number of clusters in numbers equal to that of the available processors. At the same time, let maximally disconnected clusters be chosen in order to minimize extra-communication overhead and to optimize the overall computational efficiency. Obtaining such a disconnection becomes a computationally hard problem when disconnection conditions are complex in themselves, like in the case of parallel simulation. Before applying any clustering method, topological indices might contribute to give an a priori insight about the divisibility of a network. Here we present a class of them, the sparseness indices. As particular topological indices provide either local or global quantification of network structure, they can help in identifying locally dense, but globally sparsely connected subgraphs.'),
(68528, 2010, 'bib', 'Editorial: Current progress in Bioinformatics 2010', 'Altman, Russ B', 'The Microsoft Research, University of Trento Centre for Computational and Systems Biology, Piazza Manci 17, 38123 Povo, Trento, Italy. mazza@cosbi.eu', 'Italy', 1, 'Computational Biology|Publishing', 'Editorial', '10.1093/bib/bbq001', '20097719', '-');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(68529, 2010, 'bib', 'Exploration of cellular reaction systems', 'Kirkilionis, Markus', 'Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK. mak@maths.warwick.ac.uk', 'United Kingdom', 1, 'Models, Theoretical|Systems Biology', 'Journal Article', '10.1093/bib/bbp062', '20097720', 'We discuss and review different ways to map cellular components and their temporal interaction with other such components to different non-spatially explicit mathematical models. The essential choices made in the literature are between discrete and continuous state spaces, between rule and event-based state updates and between deterministic and stochastic series of such updates. The temporal modelling of cellular regulatory networks (dynamic network theory) is compared with static network approaches in two first introductory sections on general network modelling. We concentrate next on deterministic rate-based dynamic regulatory networks and their derivation. In the derivation, we include methods from multiscale analysis and also look at structured large particles, here called macromolecular machines. It is clear that mass-action systems and their derivatives, i.e. networks based on enzyme kinetics, play the most dominant role in the literature. The tools to analyse cellular reaction networks are without doubt most complete for mass-action systems. We devote a long section at the end of the review to make a comprehensive review of related tools and mathematical methods. The emphasis is to show how cellular reaction networks can be analysed with the help of different associated graphs and the dissection into modules, i.e. sub-networks.'),
(68530, 2010, 'bib', 'Flexible experimentation in the modeling and simulation framework JAMES II--implications for computational systems biology', 'Ewald, Roland|Himmelspach, Jan|Jeschke, Matthias|Leye, Stefan|Uhrmacher, Adelinde M', 'Institute of Computer Science, University of Rostock, Rostock, Germany.|Institute of Computer Science, University of Rostock, Rostock, Germany.|Institute of Computer Science, University of Rostock, Rostock, Germany.|Institute of Computer Science, University of Rostock, Rostock, Germany.|Institute of Computer Science, University of Rostock, Rostock, Germany.', 'Germany|Germany|Germany|Germany|Germany', 31, 'Algorithms|Biology|Computer Simulation|Models, Biological|Programming Languages|Software|Software Design|Systems Integration', 'Journal Article', '10.1093/bib/bbp067', '20118105', 'Dry-lab experimentation is being increasingly used to complement wet-lab experimentation. However, conducting dry-lab experiments is a challenging endeavor that requires the combination of diverse techniques. JAMES II, a plug-in-based open source modeling and simulation framework, facilitates the exploitation and configuration of these techniques. The different aspects that form an experiment are made explicit to facilitate repeatability and reuse. Each of those influences the performance and the quality of the simulation experiment. Common experimentation pitfalls and current challenges are discussed along the way.'),
(68531, 2010, 'bib', 'High performance cellular level agent-based simulation with FLAME for the GPU', 'Richmond, Paul|Walker, Dawn|Coakley, Simon|Romano, Daniela', 'Department of Computer Science, University of Sheffield, Regent Court, 211 Portobello, Sheffield S1 4DP, UK. p.richmond@sheffield.ac.uk|Department of Computer Science, University of Sheffield, Regent Court, 211 Portobello, Sheffield S1 4DP, UK. p.richmond@sheffield.ac.uk|Department of Computer Science, University of Sheffield, Regent Court, 211 Portobello, Sheffield S1 4DP, UK. p.richmond@sheffield.ac.uk|Department of Computer Science, University of Sheffield, Regent Court, 211 Portobello, Sheffield S1 4DP, UK. p.richmond@sheffield.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 107, 'Algorithms|Cell Physiological Phenomena|Computer Graphics|Computer Simulation|Models, Biological|Software|Systems Integration|User-Computer Interface', 'Journal Article', '10.1093/bib/bbp073', '20123941', 'Driven by the availability of experimental data and ability to simulate a biological scale which is of immediate interest, the cellular scale is fast emerging as an ideal candidate for middle-out modelling. As with bottom-up simulation approaches, cellular level simulations demand a high degree of computational power, which in large-scale simulations can only be achieved through parallel computing. The flexible large-scale agent modelling environment (FLAME) is a template driven framework for agent-based modelling (ABM) on parallel architectures ideally suited to the simulation of cellular systems. It is available for both high performance computing clusters (www.flame.ac.uk) and GPU hardware (www.flamegpu.com) and uses a formal specification technique that acts as a universal modelling format. This not only creates an abstraction from the underlying hardware architectures, but avoids the steep learning curve associated with programming them. In benchmarking tests and simulations of advanced cellular systems, FLAME GPU has reported massive improvement in performance over more traditional ABM frameworks. This allows the time spent in the development and testing stages of modelling to be drastically reduced and creates the possibility of real-time visualisation for simple visual face-validation.'),
(68532, 2010, 'bib', 'Semiparametric prognosis models in genomic studies', 'Ma, Shuangge|Huang, Jian|Shi, Mingyu|Li, Yang|Shia, Ben-Chang', 'School of Public Health, Yale University, USA. shuangge.ma@yale.edu|School of Public Health, Yale University, USA. shuangge.ma@yale.edu|School of Public Health, Yale University, USA. shuangge.ma@yale.edu|School of Public Health, Yale University, USA. shuangge.ma@yale.edu|School of Public Health, Yale University, USA. shuangge.ma@yale.edu', 'United States|United States|United States|United States|United States', 5, 'Genomics|Humans|Models, Theoretical|Prognosis', 'Journal Article', '10.1093/bib/bbp070', '20123942', 'Development of high-throughput technologies makes it possible to survey the whole genome. Genomic studies have been extensively conducted, searching for markers with predictive power for prognosis of complex diseases such as cancer, diabetes and obesity. Most existing statistical analyses are focused on developing marker selection techniques, while little attention is paid to the underlying prognosis models. In this article, we review three commonly used prognosis models, namely the Cox, additive risk and accelerated failure time models. We conduct simulation and show that gene identification can be unsatisfactory under model misspecification. We analyze three cancer prognosis studies under the three models, and show that the gene identification results, prediction performance of all identified genes combined, and reproducibility of each identified gene are model-dependent. We suggest that in practical data analysis, more attention should be paid to the model assumption, and multiple models may need to be considered.'),
(68533, 2010, 'bib', 'Fast and efficient searching of biological data resources--using EB-eye', 'Valentin, Franck|Squizzato, Silvano|Goujon, Mickael|McWilliam, Hamish|Paern, Juri|Lopez, Rodrigo', 'Computer Science from University of Rennes, France.|Computer Science from University of Rennes, France.|Computer Science from University of Rennes, France.|Computer Science from University of Rennes, France.|Computer Science from University of Rennes, France.|Computer Science from University of Rennes, France.', 'France|France|France|France|France|France', 23, 'Animals|Databases, Factual|Humans|Information Storage and Retrieval', 'Journal Article', '10.1093/bib/bbp065', '20150321', 'The EB-eye is a fast and efficient search engine that provides easy and uniform access to the biological data resources hosted at the EMBL-EBI. Currently, users can access information from more than 62 distinct datasets covering some 400 million entries. The data resources represented in the EB-eye include: nucleotide and protein sequences at both the genomic and proteomic levels, structures ranging from chemicals to macro-molecular complexes, gene-expression experiments, binary level molecular interactions as well as reaction maps and pathway models, functional classifications, biological ontologies, and comprehensive literature libraries covering the biomedical sciences and related intellectual property. The EB-eye can be accessed over the web or programmatically using a SOAP Web Services interface. This allows its search and retrieval capabilities to be exploited in workflows and analytical pipe-lines. The EB-eye is a novel alternative to existing biological search and retrieval engines. In this article we describe in detail how to exploit its powerful capabilities.'),
(68534, 2010, 'bib', 'DNA barcoding: a six-question tour to improve users awareness about the method', 'Casiraghi, Maurizio|Labra, Massimo|Ferri, Emanuele|Galimberti, Andrea|De Mattia, Fabrizio', 'ZooPlantLab, Dipartimento di Biotecnologie e Bioscienze, Università degli Studi di Milano Bicocca, Piazza della Scienza 2 - 20126, Milan, Italy. maurizio.casiraghi@unimib.it|ZooPlantLab, Dipartimento di Biotecnologie e Bioscienze, Università degli Studi di Milano Bicocca, Piazza della Scienza 2 - 20126, Milan, Italy. maurizio.casiraghi@unimib.it|ZooPlantLab, Dipartimento di Biotecnologie e Bioscienze, Università degli Studi di Milano Bicocca, Piazza della Scienza 2 - 20126, Milan, Italy. maurizio.casiraghi@unimib.it|ZooPlantLab, Dipartimento di Biotecnologie e Bioscienze, Università degli Studi di Milano Bicocca, Piazza della Scienza 2 - 20126, Milan, Italy. maurizio.casiraghi@unimib.it|ZooPlantLab, Dipartimento di Biotecnologie e Bioscienze, Università degli Studi di Milano Bicocca, Piazza della Scienza 2 - 20126, Milan, Italy. maurizio.casiraghi@unimib.it', 'Italy|Italy|Italy|Italy|Italy', 101, 'Awareness|DNA|Electronic Data Processing|Humans', 'Journal Article', '10.1093/bib/bbq003', '20156987', 'DNA barcoding is a recent and widely used molecular-based identification system that aims to identify biological specimens, and to assign them to a given species. However, DNA barcoding is even more than this, and besides many practical uses, it can be considered the core of an integrated taxonomic system, where bioinformatics plays a key role. DNA barcoding data could be interpreted in different ways depending on the examined taxa but the technique relies on standardized approaches, methods and analyses. The existing reference towards a common way to treat DNA barcoding data, analyses and results is the Barcode of Life Data Systems. However, the scientific community has produced in the recent years a number of alternative methods to manage barcoding data. The present work starts from this point, because users should be aware of the consequences their choices produce on the results. Despite the fact that a strict standardization is the essence of DNA barcoding, we propose a tour of six questions to improve the users awareness about the method, the correct use of concepts and alternative tools provided by scientific community.'),
(68535, 2011, 'bib', 'Preprocessing and downstream analysis of microarray DNA copy number profiles', 'van de Wiel, Mark A|Picard, Franck|van Wieringen, Wessel N|Ylstra, Bauke', 'Department of Epidemiology & Biostatistics, VU University Medical Center, Amsterdam, The Netherlands. mark.vdwiel@vumc.nl|Department of Epidemiology & Biostatistics, VU University Medical Center, Amsterdam, The Netherlands. mark.vdwiel@vumc.nl|Department of Epidemiology & Biostatistics, VU University Medical Center, Amsterdam, The Netherlands. mark.vdwiel@vumc.nl|Department of Epidemiology & Biostatistics, VU University Medical Center, Amsterdam, The Netherlands. mark.vdwiel@vumc.nl', 'Netherlands|Netherlands|Netherlands|Netherlands', 22, 'Comparative Genomic Hybridization|Computational Biology|DNA Copy Number Variations|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bib/bbq004', '20172948', 'Analysis of DNA copy number profiles requires methods tailored to the specific nature of these data. The number of available data analysis methods has grown enormously in the last 5 years. We discuss the typical characteristics of DNA copy number data, as measured by microarray technology and review the extensive literature on preprocessing methods such as segmentation and calling. Subsequently, the focus narrows to applications of DNA copy number in cancer, in particular, several downstream analyses of multi-sample data sets such as testing, clustering and classification. Finally, we look ahead: what should we prepare for and which methodology-related topics may deserve attention in the near future?'),
(68536, 2011, 'bib', 'Ensemble learning algorithms for classification of mtDNA into haplogroups', 'Wong, Carol|Li, Yuran|Lee, Chih|Huang, Chun-Hsi', 'Department of Bioengineering, University of Pennsylvania, USA.|Department of Bioengineering, University of Pennsylvania, USA.|Department of Bioengineering, University of Pennsylvania, USA.|Department of Bioengineering, University of Pennsylvania, USA.', 'United States|United States|United States|United States', 8, 'Algorithms|DNA, Mitochondrial|Databases, Factual|Evolution, Molecular|Haplotypes|Humans|Pattern Recognition, Automated', 'Journal Article', '10.1093/bib/bbq008', '20203074', 'Classification of mitochondrial DNA (mtDNA) into their respective haplogroups allows the addressing of various anthropologic and forensic issues. Unique to mtDNA is its abundance and non-recombining uni-parental mode of inheritance, consequently, mutations are the only changes observed in the genetic material. These individual mutations are classified into their cladistic haplogroups allowing the tracing of different genetic branch points in human (and other organisms) evolution. Due to the large number of samples, it becomes necessary to automate the classification process. Using 5-fold cross-validation, we investigated two classification techniques on the consented database of 21 141 samples published by the Genographic project. The support vector machines (SVM) algorithm achieved a macro-accuracy of 88.06% and micro-accuracy of 96.59%, while the random forest (RF) algorithm achieved a macro-accuracy of 87.35% and micro-accuracy of 96.19%. In addition to being faster and more memory-economic in making predictions, SVM and RF are better than or comparable to the nearest-neighbor method employed by the Genographic project in terms of prediction accuracy.'),
(68537, 2010, 'bib', 'GPU computing for systems biology', 'Dematté, Lorenzo|Prandi, Davide', 'CoSBi Centre, Italy.|CoSBi Centre, Italy.', 'Italy|Italy', 25, 'Algorithms|Computer Graphics|Computer Simulation|Equipment Design|Models, Biological|Software|Systems Biology|Systems Integration|User-Computer Interface', 'Journal Article', '10.1093/bib/bbq006', '20211843', 'The development of detailed, coherent, models of complex biological systems is recognized as a key requirement for integrating the increasing amount of experimental data. In addition, in-silico simulation of bio-chemical models provides an easy way to test different experimental conditions, helping in the discovery of the dynamics that regulate biological systems. However, the computational power required by these simulations often exceeds that available on common desktop computers and thus expensive high performance computing solutions are required. An emerging alternative is represented by general-purpose scientific computing on graphics processing units (GPGPU), which offers the power of a small computer cluster at a cost of approximately $400. Computing with a GPU requires the development of specific algorithms, since the programming paradigm substantially differs from traditional CPU-based computing. In this paper, we review some recent efforts in exploiting the processing power of GPUs for the simulation of biological systems.'),
(68538, 2011, 'bib', 'Online resources of cancer data: barriers, benefits and lessons', 'Gadaleta, Emanuela|Lemoine, Nicholas R|Chelala, Claude', 'Barts & The London School of Medicine, QMUL, London, UK.|Barts & The London School of Medicine, QMUL, London, UK.|Barts & The London School of Medicine, QMUL, London, UK.', 'United Kingdom|United Kingdom|United Kingdom', 16, 'Computational Biology|Data Mining|Databases, Factual|Internet|Neoplasms', 'Journal Article', '10.1093/bib/bbq010', '20348133', 'With advances in high-throughput techniques, the volume of data generated has resulted in the creation of a plethora of resources for the cancer research community. However, a key factor in the utility, sustainability and future use of a novel resource lies in its ability to allow for data sharing and to be interoperable with major international cancer research efforts. This article will introduce some of these efforts, the interoperable cancer data-mining resources and repositories, from a user-perspective. Some of the considerations to be addressed when building interoperable, sustainable cancer resources will be discussed with case studies-hoping this will prove useful for researchers designing their own cancer databases.'),
(68539, 2011, 'bib', 'Targeted metabolic reconstruction: a novel approach for the characterization of plant-pathogen interactions', 'Pinzón, Andrés|Rodriguez-R, Luis M|González, Andrés|Bernal, Adriana|Restrepo, Silvia', 'Laboratorio de Micología y Fitopatología, Universidad de los Andes, Bogotá, Colombia. am.pinzon196@uniandes.edu.co|Laboratorio de Micología y Fitopatología, Universidad de los Andes, Bogotá, Colombia. am.pinzon196@uniandes.edu.co|Laboratorio de Micología y Fitopatología, Universidad de los Andes, Bogotá, Colombia. am.pinzon196@uniandes.edu.co|Laboratorio de Micología y Fitopatología, Universidad de los Andes, Bogotá, Colombia. am.pinzon196@uniandes.edu.co|Laboratorio de Micología y Fitopatología, Universidad de los Andes, Bogotá, Colombia. am.pinzon196@uniandes.edu.co', 'Colombia|Colombia|Colombia|Colombia|Colombia', 10, 'Computational Biology|Genome|Host-Parasite Interactions|Metabolic Networks and Pathways|Phytophthora infestans|Plant Diseases|Solanum tuberosum', 'Journal Article', '10.1093/bib/bbq009', '20353961', 'Genome-scale metabolic reconstruction (GEMR), along with flux balance analysis, has been widely used to study complex metabolic networks in several microbial organisms. This approach is of particular applicability in biological systems where the lack of kinetics data is typical. This is the case of plant-pathogen interactions, where these methods open the possibility of studying host metabolic network phenotype during the interaction with pathogens. Since GEMRs are based on sequenced genomes, its applicability to organisms where genomic information is lacking is limited. Here we describe an alternative approach to GEMR: targeted metabolic reconstruction, where network reconstruction is guided by transcriptomic data instead of genomic information. This approach is being applied successfully in our laboratory for the Phytophthora infestans--Solanum tuberosum pathosystem.'),
(68540, 2011, 'bib', 'Letter to the editor: Stability of Random Forest importance measures', 'Calle, M Luz|Urrea, Víctor', 'Laboratorio de Micología y Fitopatología, Universidad de los Andes, Bogotá, Colombia. am.pinzon196@uniandes.edu.co|Laboratorio de Micología y Fitopatología, Universidad de los Andes, Bogotá, Colombia. am.pinzon196@uniandes.edu.co', 'Colombia|Colombia', 32, 'Algorithms|Artificial Intelligence|Computer Simulation|Databases, Factual|Gene Expression Profiling', 'Comment', '10.1093/bib/bbq011', '20360022', 'The goal of this article (letter to the editor) is to emphasize the value of exploring ranking stability when using the importance measures, mean decrease accuracy (MDA) and mean decrease Gini (MDG), provided by Random Forest. We illustrate with a real and a simulated example that ranks based on the MDA are unstable to small perturbations of the dataset and ranks based on the MDG provide more robust results.'),
(68541, 2010, 'bib', 'Application of second-generation sequencing to cancer genomics', 'Robison, Keith', 'Infinity Pharmaceuticals Inc., Cambridge, MA 02139, USA. keith.robison@infi.com', 'United States', 37, 'Base Sequence|Genetic Variation|Genomics|Humans|Mutation|Neoplasms|Sequence Analysis, DNA', 'Journal Article', '10.1093/bib/bbq013', '20427421', 'New generations of DNA sequencing technologies are enabling the systematic study of genetic derangement in cancers. Sequencing of cancer exomes or transcriptomes or even entire cancer genomes are now possible, though technical and economic challenges remain. Cancer samples are inherently heterogeneous and are often contaminated with normal DNA, placing additional demands on informatics tools for detecting genetic variation. However, even low coverage sequencing data can provide valuable information on genetic rearrangements, amplifications and losses in tumor genomes. Novel recurrent oncogenic mutations and fusion transcripts have been discovered with these technologies. In some sequenced cancer genomes, tens of thousands of genetic alterations have been discovered. While this enables the detailed dissection of mutation classes, it also presents a formidable informatics problem of sorting active driver mutations from inactive passenger mutations in order to prioritize these for further experimental characterization.'),
(68542, 2010, 'bib', 'Introduction into the analysis of high-throughput-sequencing based epigenome data', 'Huss, Mikael', 'Genome Institute of Singapore. mikael.huss@scilifelab.se', 'Singapore', 26, 'Base Sequence|Chromatin|DNA Methylation|Epigenesis, Genetic|Epigenomics|Genome|High-Throughput Nucleotide Sequencing|Histones|Humans|RNA, Untranslated', 'Journal Article', '10.1093/bib/bbq014', '20457755', 'Sequencing-based approaches now allow high-resolution, genome-scale investigation of cellular epigenetic landscapes. For example, mapping of open chromatin regions, post-translational histone modifications and DNA methylation across a whole genome is now feasible, and new non-coding regulatory RNAs can be sensitively identified via RNA sequencing. The resulting large-scale data sets promise to contribute towards a more precise and complete understanding of gene regulation and to yield insights into the interplay between genomes and the environment. In this article, I review some of the conceptual issues and currently available software tools for the analysis of sequencing-based whole-genome epigenetics data.'),
(68543, 2010, 'bib', 'A survey of sequence alignment algorithms for next-generation sequencing', 'Li, Heng|Homer, Nils', 'Broad Institute, Cambridge, MA 02142, USA. hengli@broadinstitute.org|Broad Institute, Cambridge, MA 02142, USA. hengli@broadinstitute.org', 'United States|United States', 216, 'Algorithms|Base Sequence|Genome, Human|Humans|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bib/bbq015', '20460430', 'Rapidly evolving sequencing technologies produce data on an unparalleled scale. A central challenge to the analysis of this data is sequence alignment, whereby sequence reads must be compared to a reference. A wide variety of alignment algorithms and software have been subsequently developed over the past two years. In this article, we will systematically review the current development of these algorithms and introduce their practical applications on different types of experimental data. We come to the conclusion that short-read alignment is no longer the bottleneck of data analyses. We also consider future development of alignment algorithms with respect to emerging long sequence reads and the prospect of cloud computing.'),
(68544, 2010, 'bib', 'Editorial: Accelerating systems biology', 'Mazza, Tommaso', 'Broad Institute, Cambridge, MA 02142, USA. hengli@broadinstitute.org', 'United States', 1, 'Computer Simulation|Forecasting|Genomics|Models, Biological|Systems Biology', 'Editorial', '10.1093/bib/bbq012', '20478854', '-'),
(68545, 2010, 'bib', 'High-performance analysis of biological systems dynamics with the DiVinE model checker', 'Barnat, Jirí|Brim, Lubos|Safránek, Dravid', 'Masaryk University, Botanická 68a, 602 00 Brno, Czech Republic.|Masaryk University, Botanická 68a, 602 00 Brno, Czech Republic.|Masaryk University, Botanická 68a, 602 00 Brno, Czech Republic.', '-|-|-', 18, 'Algorithms|Biology|Computer Simulation|Models, Biological|Programming Languages|Software|Software Design|Systems Integration', 'Journal Article', '10.1093/bib/bbp074', '20478855', 'The current interest in systems biology is to gain a better understanding of how the complex dynamic behaviour of the cell emerges from mutual interactions of molecular species. When solving such a nontrivial goal, biological data have to be necessarily integrated with mathematical modelling and computer analysis. Since the key aspect of biological modelling is based on unifying several kinds of data captured in terms of large-scale biological networks, scalable and automatized methods are necessary to obtain novel predictions and understanding. In this review, we provide a brief description of the tool DiVinE adapted for automatized analysis of biological systems dynamics. The tool employs high-performance computing techniques to enable analysis of large models.'),
(68546, 2010, 'bib', 'Next generation sequencing in functional genomics', 'Werner, Thomas', 'Genomatix Software GmbH, D-80335 München, Germany. werner@genomatix.de', 'Germany', 27, 'Base Sequence|Binding Sites|Gene Expression Regulation|Genome|Genomics|Humans|Precision Medicine|Sequence Analysis, DNA|Systems Biology|Transcription Factors', 'Journal Article', '10.1093/bib/bbq018', '20501549', 'Genome-wide sequencing has enabled modern biomedical research to relate more and more events in healthy as well as disease-affected cells and tissues to the genomic sequence. Now next generation sequencing (NGS) extends that reach into multiple almost complete genomes of the same species, revealing more and more details about how individual genomes as well as individual aspects of their regulation differ from each other. The inclusion of NGS-based transcriptome sequencing, chromatin-immunoprecipitation (ChIP) of transcription factor binding and epigenetic analyses (usually based on DNA methylation or histone modification ChIP) completes the picture with unprecedented resolution enabling the detection of even subtle differences such as alternative splicing of individual exons. Functional genomics aims at the elucidation of the molecular basis of biological functions and requires analyses that go far beyond the primary analysis of the reads such as mapping to a genome template sequence. The various and complex interactions between the genome, gene products and metabolites define biological function, which necessitates inclusion of results other than sequence tags in quite elaborative approaches. However, the extra efforts pay off in revealing mechanisms as well as providing the foundation for new strategies in systems biology and personalized medicine. This review emphasizes the particular contribution NGS-based technologies make to functional genomics research with a special focus on gene regulation by transcription factor binding sites.'),
(68547, 2011, 'bib', 'Computational discrete models of tissue growth and regeneration', 'Azuaje, Francisco', 'Centre de Recherche Public, Santé, Luxembourg. francisco.azuaje@crp-sante.lu', 'Luxembourg', 19, 'Algorithms|Blood Vessels|Computational Biology|Regeneration|Tissue Engineering', 'Journal Article', '10.1093/bib/bbq017', '20513669', 'Tissue growth and regeneration are fundamental processes underpinning crucial physiological and pathological conditions: ranging from normal blood vessel network development, response to stem cells therapy and cancers. Modelling of such biological phenomena has been addressed through mathematical and algorithmic approaches. The former implements continuous representations based on differential equations. The latter exploit operational descriptions in the form of computing programs to represent and execute the models. Within this area, models that define the cell as the fundamental unit of model development, as well as discrete representations of different model entities, are important to plan in vitro experiments and to generate new testable hypotheses. This article reviews the application of algorithmic discrete models, with a focus on tissue growth and regeneration phenomena in the context of health and disease. The review begins with an overview of basic concepts, problems and approaches of computational discrete models. This will include a discussion of basic assumptions and design principles. An overview of key cell-driven approaches and examples of applications in tissue growth and regeneration is provided. The specification, implementation and analysis of a model are illustrated with a hypothetical example, which mimics the branching and sprouting patterns observed in blood vessel network development. The article concludes with a discussion of current challenges and recommendations.'),
(68548, 2010, 'bib', 'Challenges of sequencing human genomes', 'Koboldt, Daniel C|Ding, Li|Mardis, Elaine R|Wilson, Richard K', 'The Genome Center at Washington University, St. Louis, Missouri 63108, USA. dkoboldt@genome.wustl.edu|The Genome Center at Washington University, St. Louis, Missouri 63108, USA. dkoboldt@genome.wustl.edu|The Genome Center at Washington University, St. Louis, Missouri 63108, USA. dkoboldt@genome.wustl.edu|The Genome Center at Washington University, St. Louis, Missouri 63108, USA. dkoboldt@genome.wustl.edu', 'United States|United States|United States|United States', 101, 'Base Sequence|Genetic Variation|Genome, Human|Humans|Neoplasms|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bib/bbq016', '20519329', 'Massively parallel sequencing technologies continue to alter the study of human genetics. As the cost of sequencing declines, next-generation sequencing (NGS) instruments and datasets will become increasingly accessible to the wider research community. Investigators are understandably eager to harness the power of these new technologies. Sequencing human genomes on these platforms, however, presents numerous production and bioinformatics challenges. Production issues like sample contamination, library chimaeras and variable run quality have become increasingly problematic in the transition from technology development lab to production floor. Analysis of NGS data, too, remains challenging, particularly given the short-read lengths (35-250 bp) and sheer volume of data. The development of streamlined, highly automated pipelines for data analysis is critical for transition from technology adoption to accelerated research and publication. This review aims to describe the state of current NGS technologies, as well as the strategies that enable NGS users to characterize the full spectrum of DNA sequence variation in humans.'),
(68549, 2011, 'bib', 'Protein mass spectra data analysis for clinical biomarker discovery: a global review', 'Roy, Pascal|Truntzer, Caroline|Maucort-Boulch, Delphine|Jouve, Thomas|Molinari, Nicolas', 'Hospices Civils de Lyon, Service de Biostatistique, Lyon, F-69003, France.|Hospices Civils de Lyon, Service de Biostatistique, Lyon, F-69003, France.|Hospices Civils de Lyon, Service de Biostatistique, Lyon, F-69003, France.|Hospices Civils de Lyon, Service de Biostatistique, Lyon, F-69003, France.|Hospices Civils de Lyon, Service de Biostatistique, Lyon, F-69003, France.', 'France|France|France|France|France', 3, 'Biomarkers|Mass Spectrometry|Proteins|Proteomics', 'Journal Article', '10.1093/bib/bbq019', '20534688', 'The identification of new diagnostic or prognostic biomarkers is one of the main aims of clinical cancer research. In recent years there has been a growing interest in using high throughput technologies for the detection of such biomarkers. In particular, mass spectrometry appears as an exciting tool with great potential. However, to extract any benefit from the massive potential of clinical proteomic studies, appropriate methods, improvement and validation are required. To better understand the key statistical points involved with such studies, this review presents the main data analysis steps of protein mass spectra data analysis, from the pre-processing of the data to the identification and validation of biomarkers.'),
(68550, 2010, 'bib', 'Bioinformatics training: a review of challenges, actions and support requirements', 'Schneider, Maria Victoria|Watson, James|Attwood, Teresa|Rother, Kristian|Budd, Aidan|McDowall, Jennifer|Via, Allegra|Fernandes, Pedro|Nyronen, Tommy|Blicher, Thomas|Jones, Phil|Blatter, Marie-Claude|De Las Rivas, Javier|Judge, David Phillip|van der Gool, Wouter|Brooksbank, Cath', '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', 17, 'Computational Biology|Research|Teaching', 'Journal Article', '10.1093/bib/bbq021', '20562256', 'As bioinformatics becomes increasingly central to research in the molecular life sciences, the need to train non-bioinformaticians to make the most of bioinformatics resources is growing. Here, we review the key challenges and pitfalls to providing effective training for users of bioinformatics services, and discuss successful training strategies shared by a diverse set of bioinformatics trainers. We also identify steps that trainers in bioinformatics could take together to advance the state of the art in current training practices. The ideas presented in this article derive from the first Trainer Networking Session held under the auspices of the EU-funded SLING Integrating Activity, which took place in November 2009.'),
(68551, 2010, 'bib', 'Education resources of the National Center for Biotechnology Information', 'Cooper, Peter S|Lipshultz, Dawn|Matten, Wayne T|McGinnis, Scott D|Pechous, Steven|Romiti, Monica L|Tao, Tao|Valjavec-Gratian, Majda|Sayers, Eric W', 'NCBI/NLM/NIH, 45 Center Drive, Bethesda, MD 20892, USA. cooper@ncbi.nlm.nih.gov|NCBI/NLM/NIH, 45 Center Drive, Bethesda, MD 20892, USA. cooper@ncbi.nlm.nih.gov|NCBI/NLM/NIH, 45 Center Drive, Bethesda, MD 20892, USA. cooper@ncbi.nlm.nih.gov|NCBI/NLM/NIH, 45 Center Drive, Bethesda, MD 20892, USA. cooper@ncbi.nlm.nih.gov|NCBI/NLM/NIH, 45 Center Drive, Bethesda, MD 20892, USA. cooper@ncbi.nlm.nih.gov|NCBI/NLM/NIH, 45 Center Drive, Bethesda, MD 20892, USA. cooper@ncbi.nlm.nih.gov|NCBI/NLM/NIH, 45 Center Drive, Bethesda, MD 20892, USA. cooper@ncbi.nlm.nih.gov|NCBI/NLM/NIH, 45 Center Drive, Bethesda, MD 20892, USA. cooper@ncbi.nlm.nih.gov|NCBI/NLM/NIH, 45 Center Drive, Bethesda, MD 20892, USA. cooper@ncbi.nlm.nih.gov', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 21, 'Computational Biology|Databases, Factual|Internet|National Institutes of Health (U.S.)|National Library of Medicine (U.S.)|PubMed|Teaching|United States', 'Journal Article', '10.1093/bib/bbq022', '20570844', 'The National Center for Biotechnology Information (NCBI) hosts 39 literature and molecular biology databases containing almost half a billion records. As the complexity of these data and associated resources and tools continues to expand, so does the need for educational resources to help investigators, clinicians, information specialists and the general public make use of the wealth of public data available at the NCBI. This review describes the educational resources available at NCBI via the NCBI Education page (www.ncbi.nlm.nih.gov/Education/). These resources include materials designed for new users, such as About NCBI and the NCBI Guide, as well as documentation, Frequently Asked Questions (FAQs) and writings on the NCBI Bookshelf such as the NCBI Help Manual and the NCBI Handbook. NCBI also provides teaching materials such as tutorials, problem sets and educational tools such as the Amino Acid Explorer, PSSM Viewer and Ebot. NCBI also offers training programs including the Discovery Workshops, webinars and tutorials at conferences. To help users keep up-to-date, NCBI produces the online NCBI News and offers RSS feeds and mailing lists, along with a presence on Facebook, Twitter and YouTube.'),
(68552, 2010, 'bib', 'Bioinformatics training: selecting an appropriate learning content management system--an example from the European Bioinformatics Institute', 'Wright, Victoria Ann|Vaughan, Brendan W|Laurent, Thomas|Lopez, Rodrigo|Brooksbank, Cath|Schneider, Maria Victoria', 'EMBL Outstation, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. elearning@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. elearning@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. elearning@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. elearning@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. elearning@ebi.ac.uk|EMBL Outstation, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. elearning@ebi.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 6, 'Computational Biology|Curriculum|Databases, Nucleic Acid|Databases, Protein|Internet', 'Journal Article', '10.1093/bib/bbq023', '20601435', 'Todays molecular life scientists are well educated in the emerging experimental tools of their trade, but when it comes to training on the myriad of resources and tools for dealing with biological data, a less ideal situation emerges. Often bioinformatics users receive no formal training on how to make the most of the bioinformatics resources and tools available in the public domain. The European Bioinformatics Institute, which is part of the European Molecular Biology Laboratory (EMBL-EBI), holds the worlds most comprehensive collection of molecular data, and training the research community to exploit this information is embedded in the EBIs mission. We have evaluated eLearning, in parallel with face-to-face courses, as a means of training users of our data resources and tools. We anticipate that eLearning will become an increasingly important vehicle for delivering training to our growing user base, so we have undertaken an extensive review of Learning Content Management Systems (LCMSs). Here, we describe the process that we used, which considered the requirements of trainees, trainers and systems administrators, as well as taking into account our organizational values and needs. This review describes the literature survey, user discussions and scripted platform testing that we performed to narrow down our choice of platform from 36 to a single platform. We hope that it will serve as guidance for others who are seeking to incorporate eLearning into their bioinformatics training programmes.'),
(68553, 2010, 'bib', 'SynBioSS designer: a web-based tool for the automated generation of kinetic models for synthetic biological constructs', 'Weeding, Emma|Houle, Jason|Kaznessis, Yiannis N', 'Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455-0132, USA.|Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455-0132, USA.|Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455-0132, USA.', 'United States|United States|United States', 34, 'Automation|DNA|Internet|Kinetics|Models, Biological', 'Journal Article', '10.1093/bib/bbq002', '20639523', 'Modeling tools can play an important role in synthetic biology the same way modeling helps in other engineering disciplines: simulations can quickly probe mechanisms and provide a clear picture of how different components influence the behavior of the whole. We present a brief review of available tools and present SynBioSS Designer. The Synthetic Biology Software Suite (SynBioSS) is used for the generation, storing, retrieval and quantitative simulation of synthetic biological networks. SynBioSS consists of three distinct components: the Desktop Simulator, the Wiki, and the Designer. SynBioSS Designer takes as input molecular parts involved in gene expression and regulation (e.g. promoters, transcription factors, ribosome binding sites, etc.), and automatically generates complete networks of reactions that represent transcription, translation, regulation, induction and degradation of those parts. Effectively, Designer uses DNA sequences as input and generates networks of biomolecular reactions as output. In this paper we describe how Designer uses universal principles of molecular biology to generate models of any arbitrary synthetic biological system. These models are useful as they explain biological phenotypic complexity in mechanistic terms. In turn, such mechanistic explanations can assist in designing synthetic biological systems. We also discuss, giving practical guidance to users, how Designer interfaces with the Registry of Standard Biological Parts, the de facto compendium of parts used in synthetic biology applications.'),
(68554, 2010, 'bib', 'Multi-stage learning aids applied to hands-on software training', 'Rother, Kristian|Rother, Magdalena|Pleus, Alexandra|Upmeier zu Belzen, Annette', 'Laboratory of Structural Bioinformatics, Biological Faculty, Adam Mickiewicz University, ul. Umultowska 89, 61-614 Poznan, Poland. krother@genesilico.pl|Laboratory of Structural Bioinformatics, Biological Faculty, Adam Mickiewicz University, ul. Umultowska 89, 61-614 Poznan, Poland. krother@genesilico.pl|Laboratory of Structural Bioinformatics, Biological Faculty, Adam Mickiewicz University, ul. Umultowska 89, 61-614 Poznan, Poland. krother@genesilico.pl|Laboratory of Structural Bioinformatics, Biological Faculty, Adam Mickiewicz University, ul. Umultowska 89, 61-614 Poznan, Poland. krother@genesilico.pl', 'Poland|Poland|Poland|Poland', 0, 'Computational Biology|Education|Internet|Software|Teaching|Technology', 'Journal Article', '10.1093/bib/bbq024', '20656749', 'Delivering hands-on tutorials on bioinformatics software and web applications is a challenging didactic scenario. The main reason is that trainees have heterogeneous backgrounds, different previous knowledge and vary in learning speed. In this article, we demonstrate how multi-stage learning aids can be used to allow all trainees to progress at a similar speed. In this technique, the trainees can utilize cards with hints and answers to guide themselves self-dependently through a complex task. We have successfully conducted a tutorial for the molecular viewer PyMOL using two sets of learning aid cards. The trainees responded positively, were able to complete the task, and the trainer had spare time to respond to individual questions. This encourages us to conclude that multi-stage learning aids overcome many disadvantages of established forms of hands-on software training.'),
(68555, 2010, 'bib', 'A model for Bioinformatics training: the marine biological laboratory', 'Yamashita, Grant|Miller, Holly|Goddard, Anthony|Norton, Cathy', 'Arizona State University, PO Box 873301, Tempe, AZ 85287-3301, USA. grant.yamashita@asu.edu.|Arizona State University, PO Box 873301, Tempe, AZ 85287-3301, USA. grant.yamashita@asu.edu.|Arizona State University, PO Box 873301, Tempe, AZ 85287-3301, USA. grant.yamashita@asu.edu.|Arizona State University, PO Box 873301, Tempe, AZ 85287-3301, USA. grant.yamashita@asu.edu.', 'United States|United States|United States|United States', 1, 'Computational Biology|Marine Biology|Medical Informatics|Models, Educational', 'Journal Article', '10.1093/bib/bbq029', '20660497', 'Many areas of science such as biology, medicine and oceanography are becoming increasingly data-rich and most programs that train scientists do not address informatics techniques or technologies that are necessary for managing and analysing large amounts of data. Educational resources for scientists in informatics are scarce, yet scientists need the skills and knowledge to work with informaticians and manage graduate students and post-docs in informatics projects. The Marine Biological Laboratory houses a world-renowned library and is involved in a number of informatics projects in the sciences. The MBL has been home to the National Library of Medicines BioMedical Informatics Course for nearly two decades and is committed to educating scientists and other scholars in informatics. In an innovative, immersive learning experience, G.Y., a biologist and post-doc at Arizona State University, visited the Science Informatics Group at the MBL to learn first hand how informatics is done and how informatics teams work. Hands-on work with developers, systems administrators, librarians and other scientists provided an invaluable education in informatics and is a model for future science informatics training.'),
(68556, 2011, 'bib', 'Evolution of gene regulation--on the road towards computational inferences', 'Fuellen, Georg', 'Institute for Biostatistics and Informatics in Medicine and Ageing Research-IBIMA, University of Rostock, Medical Faculty, Ernst-Heydemann-Str. 8, 18057 Rostock, Germany. fuellen@uni-rostock.de', 'Germany', 5, 'Binding Sites|Evolution, Molecular|Gene Expression Regulation|Gene Regulatory Networks|Genome|Genomics|Transcription Factors', 'Journal Article', '10.1093/bib/bbq060', '20702596', 'If fragments of DNA are transcribed (expressed), they deserve to be called (parts of) a gene. Whether transcription takes place depends on the gene regulatory network. This network is defined as the complex interplay of the sequence, biochemical modifications and structure of the chromosomal DNA with the regulatory proteins/RNA (transcription factors, co-factors, regulating RNA and the transcriptional apparatus itself). Gene regulatory networks play a role in various stages of development as well as in the maintenance of the organism, in this review we will concentrate on the former. Their evolutionary reconstruction is daunting (to say the least), and bioinformatics tools are in their infancy. However, gain of understanding offers a reward beyond itself, since evolutionary considerations can enable discoveries in the first place, e.g. the computational identification of conserved transcription factor binding sites. We discuss the evolution of gene regulation in the context of the Genetic Theory of Morphological Evolution as described by Carroll, identifying those parts of the theory that are relevant for bioinformatics, and their implications. We discuss the important question of how bioinformatics analysis results on the evolution of gene regulation may be validated. Finally, we briefly exemplify use of the UCSC genome browser, exploiting its pre-computed alignments to describe the evolution of gene regulation.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(68557, 2010, 'bib', 'Bioinformatics education in India', 'Kulkarni-Kale, Urmila|Sawant, Sangeeta|Chavan, Vishwas', 'Bioinformatics Centre, University of Pune.|Bioinformatics Centre, University of Pune.|Bioinformatics Centre, University of Pune.', '-|-|-', 10, 'Biotechnology|Computational Biology|Curriculum|Education, Professional|Educational Status|Humans|India', 'Journal Article', '10.1093/bib/bbq027', '20705754', 'An account of bioinformatics education in India is presented along with future prospects. Establishment of BTIS network by Department of Biotechnology (DBT), Government of India in the 1980s had been a systematic effort in the development of bioinformatics infrastructure in India to provide services to scientific community. Advances in the field of bioinformatics underpinned the need for well-trained professionals with skills in information technology and biotechnology. As a result, programmes for capacity building in terms of human resource development were initiated. Educational programmes gradually evolved from the organisation of short-term workshops to the institution of formal diploma/degree programmes. A case study of the Masters degree course offered at the Bioinformatics Centre, University of Pune is discussed. Currently, many universities and institutes are offering bioinformatics courses at different levels with variations in the course contents and degree of detailing. BioInformatics National Certification (BINC) examination initiated in 2005 by DBT provides a common yardstick to assess the knowledge and skill sets of students passing out of various institutions. The potential for broadening the scope of bioinformatics to transform it into a data intensive discovery discipline is discussed. This necessitates introduction of amendments in the existing curricula to accommodate the upcoming developments.'),
(68558, 2010, 'bib', 'Perspectives on presentation and pedagogy in aid of bioinformatics education', 'Buttigieg, Pier Luigi', 'Max Planck Institute for Marine Microbiology, D-28359 Bremen, Germany. pbuttigi@mpi-bremen.de', 'Germany', 5, 'Communication|Computational Biology|Curriculum|Educational Status|Multimedia|Teaching', 'Journal Article', '10.1093/bib/bbq062', '20724457', 'Using live presentation to communicate the interdisciplinary and abstract content of bioinformatics to its educationally diverse studentship is a sizeable challenge. This review collects a number of perspectives on multimedia presentation, visual communication and pedagogy. The aim is to encourage educators to reflect on the great potential of live presentation in facilitating bioinformatics education.'),
(68559, 2010, 'bib', 'De novo assembly of short sequence reads', 'Paszkiewicz, Konrad|Studholme, David J', 'Imperial College, London.|Imperial College, London.', '-|-', 120, 'Algorithms|Animals|Base Sequence|Computational Biology|Databases, Genetic|Genome|Humans|Molecular Sequence Data|Sequence Analysis, DNA', 'Journal Article', '10.1093/bib/bbq020', '20724458', 'A new generation of sequencing technologies is revolutionizing molecular biology. Illuminas Solexa and Applied Biosystems SOLiD generate gigabases of nucleotide sequence per week. However, a perceived limitation of these ultra-high-throughput technologies is their short read-lengths. De novo assembly of sequence reads generated by classical Sanger capillary sequencing is a mature field of research. Unfortunately, the existing sequence assembly programs were not effective for short sequence reads generated by Illumina and SOLiD platforms. Early studies suggested that, in principle, sequence reads as short as 20-30 nucleotides could be used to generate useful assemblies of both prokaryotic and eukaryotic genome sequences, albeit containing many gaps. The early feasibility studies and proofs of principle inspired several bioinformatics research groups to implement new algorithms as freely available software tools specifically aimed at assembling reads of 30-50 nucleotides in length. This has led to the generation of several draft genome sequences based exclusively on short sequence Illumina sequence reads, recently culminating in the assembly of the 2.25-Gb genome of the giant panda from Illumina sequence reads with an average length of just 52 nucleotides. As well as reviewing recent developments in the field, we discuss some practical aspects such as data filtering and submission of assembly data to public repositories.'),
(68560, 2010, 'bib', 'OpenHelix: bioinformatics education outside of a different box', 'Williams, Jennifer M|Mangan, Mary E|Perreault-Micale, Cynthia|Lathe, Scott|Sirohi, Neeraj|Lathe, Warren C', 'OpenHelix LLC, 12600 SE 38th Street, Suite 230 Bellevue, WA 98006, USA. jwilliams@openhelix.com|OpenHelix LLC, 12600 SE 38th Street, Suite 230 Bellevue, WA 98006, USA. jwilliams@openhelix.com|OpenHelix LLC, 12600 SE 38th Street, Suite 230 Bellevue, WA 98006, USA. jwilliams@openhelix.com|OpenHelix LLC, 12600 SE 38th Street, Suite 230 Bellevue, WA 98006, USA. jwilliams@openhelix.com|OpenHelix LLC, 12600 SE 38th Street, Suite 230 Bellevue, WA 98006, USA. jwilliams@openhelix.com|OpenHelix LLC, 12600 SE 38th Street, Suite 230 Bellevue, WA 98006, USA. jwilliams@openhelix.com', 'United States|United States|United States|United States|United States|United States', 7, 'Computational Biology|Databases, Factual|Software|Teaching', 'Journal Article', '10.1093/bib/bbq026', '20798181', 'The amount of biological data is increasing rapidly, and will continue to increase as new rapid technologies are developed. Professionals in every area of bioscience will have data management needs that require publicly available bioinformatics resources. Not all scientists desire a formal bioinformatics education but would benefit from more informal educational sources of learning. Effective bioinformatics education formats will address a broad range of scientific needs, will be aimed at a variety of user skill levels, and will be delivered in a number of different formats to address different learning styles. Informal sources of bioinformatics education that are effective are available, and will be explored in this review.'),
(68561, 2010, 'bib', 'Broader incorporation of bioinformatics in education: opportunities and challenges', 'Cummings, Michael P|Temple, Glena G', 'Center for Bioinformatics and Computational Biology, Biomolecular Sciences Building, University of Maryland, College Park, MD 20742, USA. mike@umiacs.umd.edu|Center for Bioinformatics and Computational Biology, Biomolecular Sciences Building, University of Maryland, College Park, MD 20742, USA. mike@umiacs.umd.edu', 'United States|United States', 23, 'Computational Biology|Curriculum|Educational Status|Humans|Learning|Students|Teaching', 'Journal Article', '10.1093/bib/bbq058', '20798182', 'The major opportunities for broader incorporation of bioinformatics in education can be placed into three general categories: general applicability of bioinformatics in life science and related curricula, inherent fit of bioinformatics for promoting student learning in most biology programs, and the general experience and associated comfort students have with computers and technology. Conversely, the major challenges for broader incorporation of bioinformatics in education can be placed into three general categories: required infrastructure and logistics, instructor knowledge of bioinformatics and continuing education, and the breadth of bioinformatics, and the diversity of students and educational objectives. Broader incorporation of bioinformatics at all education levels requires overcoming the challenges to using transformative computer-requiring learning activities, assisting faculty in collecting assessment data on mastery of student learning outcomes, as well as creating more faculty development opportunities that span diverse skill levels, with an emphasis placed on providing resource materials that are kept up-to-date as the field and tools change.'),
(68562, 2011, 'bib', 'Protein-protein interaction and pathway databases, a graphical review', 'Klingström, Tomas|Plewczynski, Dariusz', 'Uppsala University.|Uppsala University.', '-|-', 46, 'Binding Sites|Computational Biology|Databases, Factual|Databases, Protein|Protein Interaction Mapping|Proteins|Proteomics|User-Computer Interface', 'Journal Article', '10.1093/bib/bbq064', '20851835', 'The amount of information regarding protein-protein interactions (PPI) at a proteomic scale is constantly increasing. This is paralleled with an increase of databases making information available. Consequently there are diverse ways of delivering information about not only PPIs but also regarding the databases themselves. This creates a time consuming obstacle for many researchers working in the field. Our survey provides a valuable tool for researchers to reduce the time necessary to gain a broad overview of PPI-databases and is supported by a graphical representation of data exchange. The graphical representation is made available in cooperation with the team maintaining www.pathguide.org and can be accessed at http://www.pathguide.org/interactions.php in a new Cytoscape web implementation. The local copy of Cytoscape cys file can be downloaded from http://bio.icm.edu.pl/~darman/ppi web page.'),
(68563, 2011, 'bib', 'Computational analysis of miRNA targets in plants: current status and challenges', 'Dai, Xinbin|Zhuang, Zhaohong|Zhao, Patrick Xuechun', 'Plant Biology Division, The Samuel Roberts Noble Foundation, Ardmore, OK 73401, USA.|Plant Biology Division, The Samuel Roberts Noble Foundation, Ardmore, OK 73401, USA.|Plant Biology Division, The Samuel Roberts Noble Foundation, Ardmore, OK 73401, USA.', 'United States|United States|United States', 83, 'Algorithms|Base Sequence|Computational Biology|Gene Expression Regulation, Plant|MicroRNAs|Molecular Sequence Data|Plants|RNA, Plant', 'Journal Article', '10.1093/bib/bbq065', '20858738', 'Plant microRNAs (miRNA) target recognition mechanism was once thought to be simple and straightforward, i.e. through perfect reverse complementary matching, therefore, very few target prediction tools and algorithms were developed for plants as compared to those for animals. However, the discovery of transcription suppression and the more recent observation of widespread translational regulation by miRNAs highlight the enormous diversity and complexity of gene regulation in plant systems. This, in turn, necessitates the need for advanced computational tools/algorithms for comprehensive miRNA target analysis to help understand miRNA regulatory mechanisms. Yet, advanced/comprehensive plant miRNA target analysis tools are still lacking despite the desirability and importance of such tools, especially the ability of predicting translational inhibition and integrating transcriptome data. This review focuses on recent progress in plant miRNA target recognition mechanism, principles of target prediction based on these understandings, comparison of current prediction tools and algorithms for plant miRNA target analysis and the outlook for future directions in the development of plant miRNA target tools and algorithms.'),
(68564, 2010, 'bib', 'Editorial: Second-generation sequencing', 'Robison, Keith', 'Plant Biology Division, The Samuel Roberts Noble Foundation, Ardmore, OK 73401, USA.', 'United States', 1, 'Genome|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA', 'Editorial', '10.1093/bib/bbq066', '20861023', '-'),
(68565, 2011, 'bib', 'Social pathway annotation: extensions of the systems biology metabolic modelling assistant', 'Navas-Delgado, Ismael|Real-Chicharro, Alejandro|Medina, Miguel Ángel|Sánchez-Jiménez, Francisca|Aldana-Montes, José F', 'Boulevard Louis Pasteur n°35. E.T.S.I. Informática, University of Malaga, 29071, Málaga, Spain.|Boulevard Louis Pasteur n°35. E.T.S.I. Informática, University of Malaga, 29071, Málaga, Spain.|Boulevard Louis Pasteur n°35. E.T.S.I. Informática, University of Malaga, 29071, Málaga, Spain.|Boulevard Louis Pasteur n°35. E.T.S.I. Informática, University of Malaga, 29071, Málaga, Spain.|Boulevard Louis Pasteur n°35. E.T.S.I. Informática, University of Malaga, 29071, Málaga, Spain.', 'Spain|Spain|Spain|Spain|Spain', 2, 'Computational Biology|Databases, Factual|Internet|Metabolic Networks and Pathways|Software|Systems Biology|User-Computer Interface', 'Journal Article', '10.1093/bib/bbq061', '20965999', 'High-throughput experiments have produced large amounts of heterogeneous data in the life sciences. These data are usually represented in different formats (and sometimes in technical documents) on the Web. Inevitably, life science researchers have to deal with all these data and different formats to perform their daily research, but it is simply not possible for a single human mind to analyse all these data. The integration of data in the life sciences is a key component in the analysis of biological processes. These data may contain errors, but the curation of the vast amount of data generated in the omic era cannot be done by individual researchers. To address this problem, community-driven tools could be used to assist with data analysis. In this article, we focus on a tool with social networking capabilities built on top of the SBMM (Systems Biology Metabolic Modelling) Assistant to enable the collaborative improvement of metabolic pathway models (the application is freely available at http://sbmm.uma.es/SPA).'),
(68566, 2010, 'bib', 'The GTPB training programme in Portugal', 'Fernandes, Pedro L', 'Instituto Gulbenkian de Ciência, R. da Quinta Grande 6, 2780-901 Oeiras, Portugal. pfern@igc.gulbenkian.pt', 'Portugal', 7, 'Computational Biology|Portugal', 'Journal Article', '10.1093/bib/bbq063', '20966000', 'The Gulbenkian Training Programme in Bioinformatics has been offering hands-on training courses in Oeiras, PT for more than a decade. This article is a review of its functional organization and evolution. We aim to share our experience with people considering setting-up similar training facilities elsewhere. More than 1600 course attendees, so far, have attended our courses. Their experiences have helped us to know how to satisfy their training requirements and what is feasible with very limited resources.'),
(68567, 2010, 'bib', 'Bioinformatics education dissemination with an evolutionary problem solving perspective', 'Jungck, John R|Donovan, Samuel S|Weisstein, Anton E|Khiripet, Noppadon|Everse, Stephen J', 'Department of Biology, Beloit College, 700 College St, Beloit, WI 53511, USA. jungck@beloit.edu|Department of Biology, Beloit College, 700 College St, Beloit, WI 53511, USA. jungck@beloit.edu|Department of Biology, Beloit College, 700 College St, Beloit, WI 53511, USA. jungck@beloit.edu|Department of Biology, Beloit College, 700 College St, Beloit, WI 53511, USA. jungck@beloit.edu|Department of Biology, Beloit College, 700 College St, Beloit, WI 53511, USA. jungck@beloit.edu', 'United States|United States|United States|United States|United States', 9, 'Computational Biology|Curriculum|Educational Status|Humans|Problem Solving|Software|Teaching', 'Journal Article', '10.1093/bib/bbq028', '21036947', 'Bioinformatics is central to biology education in the 21st century. With the generation of terabytes of data per day, the application of computer-based tools to stored and distributed data is fundamentally changing research and its application to problems in medicine, agriculture, conservation and forensics. In light of this information revolution, undergraduate biology curricula must be redesigned to prepare the next generation of informed citizens as well as those who will pursue careers in the life sciences. The BEDROCK initiative (Bioinformatics Education Dissemination: Reaching Out, Connecting and Knitting together) has fostered an international community of bioinformatics educators. The initiatives goals are to: (i) Identify and support faculty who can take leadership roles in bioinformatics education, (ii) Highlight and distribute innovative approaches to incorporating evolutionary bioinformatics data and techniques throughout undergraduate education, (iii) Establish mechanisms for the broad dissemination of bioinformatics resource materials and teaching models, (iv) Emphasize phylogenetic thinking and problem solving, and (v) Develop and publish new software tools to help students develop and test evolutionary hypotheses. Since 2002, BEDROCK has offered more than 50 faculty workshops around the world, published many resources and supported an environment for developing and sharing bioinformatics education approaches. The BEDROCK initiative builds on the established pedagogical philosophy and academic community of the BioQUEST Curriculum Consortium to assemble the diverse intellectual and human resources required to sustain an international reform effort in undergraduate bioinformatics education.'),
(68568, 2011, 'bib', 'Rapid innovation in ChIP-seq peak-calling algorithms is outdistancing benchmarking efforts', 'Szalkowski, Adam M|Schmid, Christoph D', 'ETH Zurich, Universitätstrasse 6, 8092 Zürich, Switzerland. adam.szalkowski@inf.ethz.ch|ETH Zurich, Universitätstrasse 6, 8092 Zürich, Switzerland. adam.szalkowski@inf.ethz.ch', 'Switzerland|Switzerland', 24, 'Algorithms|Benchmarking|Binding Sites|Chromatin Immunoprecipitation|DNA-Binding Proteins|Genome|Genomics|Software', 'Journal Article', '10.1093/bib/bbq068', '21059603', 'The current understanding of the regulation of transcription does not keep the pace with the spectacular advances in the determination of genomic sequences. Chromatin immunoprecipitation followed by massively parallel sequencing (ChIP-seq) promises to give better insight into transcription regulation by locating sites of protein-DNA interactions. Such loci of putative interactions can be inferred from the genome-wide distributions of ChIP-seq data by peak-calling software. The analysis of ChIP-seq data critically depends on this step and a multitude of these peak-callers have been deployed in the recent years. A recent study reported severe variation among peak-calling results. Yet, peak-calling still lacks systematic quantitative benchmarking. Here, we summarize benchmarking efforts and explain potential drawbacks of each benchmarking method.'),
(68569, 2011, 'bib', 'Challenges in integrating Escherichia coli molecular biology data', 'Lourenço, Anália|Carneiro, Sónia|Rocha, Miguel|Ferreira, Eugénio C|Rocha, Isabel', 'Institute for Biotechnology and Bioengineering, Center of Biological Engineering, University of Minho. analia@deb.uminho.pt|Institute for Biotechnology and Bioengineering, Center of Biological Engineering, University of Minho. analia@deb.uminho.pt|Institute for Biotechnology and Bioengineering, Center of Biological Engineering, University of Minho. analia@deb.uminho.pt|Institute for Biotechnology and Bioengineering, Center of Biological Engineering, University of Minho. analia@deb.uminho.pt|Institute for Biotechnology and Bioengineering, Center of Biological Engineering, University of Minho. analia@deb.uminho.pt', '-|-|-|-|-', 4, 'Computational Biology|Escherichia coli K12', 'Journal Article', '10.1093/bib/bbq067', '21059604', 'One key challenge in Systems Biology is to provide mechanisms to collect and integrate the necessary data to be able to meet multiple analysis requirements. Typically, biological contents are scattered over multiple data sources and there is no easy way of comparing heterogeneous data contents. This work discusses ongoing standardisation and interoperability efforts and exposes integration challenges for the model organism Escherichia coli K-12. The goal is to analyse the major obstacles faced by integration processes, suggest ways to systematically identify them, and whenever possible, propose solutions or means to assist manual curation. Integration of gene, protein and compound data was evaluated by performing comparisons over EcoCyc, KEGG, BRENDA, ChEBI, Entrez Gene and UniProt contents. Cross-links, a number of standard nomenclatures and name information supported the comparisons. Except for the gene integration scenario, in no other scenario an element of integration performed well enough to support the process by itself. Indeed, both the integration of enzyme and compound records imply considerable curation. Results evidenced that, even for a well-studied model organism, source contents are still far from being as standardized as it would be desired and metadata varies considerably from source to source. Before designing any data integration pipeline, researchers should decide on the sources that best fit the purpose of analysis and be aware of existing conflicts/inconsistencies to be able to intervene in their resolution. Moreover, they should be aware of the limits of automatic integration such that they can define the extent of necessary manual curation for each application.'),
(68570, 2011, 'bib', 'Ranking prognosis markers in cancer genomic studies', 'Ma, Shuangge|Song, Xiao', 'Yale University, New Haven, CT 06520, USA. shuangge.ma@yale.edu|Yale University, New Haven, CT 06520, USA. shuangge.ma@yale.edu', 'United States|United States', 9, 'Gene Expression Profiling|Genome|Neoplasms|Oligonucleotide Array Sequence Analysis|Prognosis|ROC Curve', 'Journal Article', '10.1093/bib/bbq069', '21087949', 'In cancer research, high-throughput genomic studies have been extensively conducted, searching for markers associated with cancer diagnosis, prognosis and variation in response to treatment. In this article, we analyze cancer prognosis studies and investigate ranking markers based on their marginal prognosis power. To avoid ambiguity, we focus on microarray gene expression studies where genes are the markers, but note that the methodology and results are applicable to other high-throughput studies. The objectives of this study are 2-fold. First, we investigate ranking markers under three commonly adopted semiparametric models, namely the Cox, accelerated failure time and additive risk models. Data analysis shows that the ranking may vary significantly under different models. Second, we describe a nonparametric concordance measure, which has roots in the time-dependent ROC (receiver operating characteristic) framework and relies on much weaker assumptions than the semiparametric models. In simulation, it is shown that ranking using the concordance measure is not sensitive to model specification whereas ranking under the semiparametric models is. In data analysis, the concordance measure generates rankings significantly different from those under the semiparametric models.'),
(68571, 2010, 'bib', 'Editorial: Bioinformatics education in the 21st century', 'Neil Sarkar, Indra', 'Yale University, New Haven, CT 06520, USA. shuangge.ma@yale.edu', 'United States', 5, 'Computational Biology|History, 21st Century', 'Editorial', '10.1093/bib/bbq071', '21087950', '-'),
(68572, 2011, 'bib', 'Missing value imputation for gene expression data: computational techniques to recover missing data from available information', 'Liew, Alan Wee-Chung|Law, Ngai-Fong|Yan, Hong', 'School of Information and Communication Technology, Gold Coast Campus, Griffith University, QLD4222, Australia. a.liew@griffith.edu.au|School of Information and Communication Technology, Gold Coast Campus, Griffith University, QLD4222, Australia. a.liew@griffith.edu.au|School of Information and Communication Technology, Gold Coast Campus, Griffith University, QLD4222, Australia. a.liew@griffith.edu.au', 'Australia|Australia|Australia', 117, 'Algorithms|Animals|Databases, Factual|Gene Expression|Gene Expression Profiling|Humans|Microarray Analysis', 'Journal Article', '10.1093/bib/bbq080', '21156727', 'Microarray gene expression data generally suffers from missing value problem due to a variety of experimental reasons. Since the missing data points can adversely affect downstream analysis, many algorithms have been proposed to impute missing values. In this survey, we provide a comprehensive review of existing missing value imputation algorithms, focusing on their underlying algorithmic techniques and how they utilize local or global information from within the data, or their use of domain knowledge during imputation. In addition, we describe how the imputation results can be validated and the different ways to assess the performance of different imputation algorithms, as well as a discussion on some possible future research directions. It is hoped that this review will give the readers a good understanding of the current development in this field and inspire them to come up with the next generation of imputation algorithms.'),
(68573, 2011, 'bib', 'Validation of gene regulatory networks: scientific and inferential', 'Dougherty, Edward R', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, USA. edward@mail.ece.tamu.edu', 'United States', 11, 'Algorithms|Computational Biology|Gene Regulatory Networks|Systems Biology', 'Journal Article', '10.1093/bib/bbq078', '21183477', 'Gene regulatory network models are a major area of study in systems and computational biology and the construction of network models is among the most important problems in these disciplines. The critical epistemological issue concerns validation. Validity can be approached from two different perspectives (i) given a hypothesized network model, its scientific validity relates to the ability to make predictions from the model that can be checked against experimental observations, and (ii) the validity of a network inference procedure must be evaluated relative to its ability to infer a network from sample points generated by the network. This article examines both perspectives in the framework of a distance function between two networks. It considers some of the obstacles to validation and provides examples of both validation paradigms.'),
(68574, 2011, 'bib', 'Literature-aided interpretation of gene expression data with the weighted global test', 'Jelier, Rob|Goeman, Jelle J|Hettne, Kristina M|Schuemie, Martijn J|den Dunnen, Johan T|t Hoen, Peter A C', 'Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands.|Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands.|Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands.|Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands.|Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands.|Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 19, 'Data Mining|Databases, Factual|Databases, Genetic|Gene Expression|Gene Expression Profiling|Genomics', 'Journal Article', '10.1093/bib/bbq082', '21183478', 'Most methods for the interpretation of gene expression profiling experiments rely on the categorization of genes, as provided by the Gene Ontology (GO) and pathway databases. Due to the manual curation process, such databases are never up-to-date and tend to be limited in focus and coverage. Automated literature mining tools provide an attractive, alternative approach. We review how they can be employed for the interpretation of gene expression profiling experiments. We illustrate that their comprehensive scope aids the interpretation of data from domains poorly covered by GO or alternative databases, and allows for the linking of gene expression with diseases, drugs, tissues and other types of concepts. A framework for proper statistical evaluation of the associations between gene expression values and literature concepts was lacking and is now implemented in a weighted extension of global test. The weights are the literature association scores and reflect the importance of a gene for the concept of interest. In a direct comparison with classical GO-based gene sets, we show that use of literature-based associations results in the identification of much more specific GO categories. We demonstrate the possibilities for linking of gene expression data to patient survival in breast cancer and the action and metabolism of drugs. Coupling with online literature mining tools ensures transparency and allows further study of the identified associations. Literature mining tools are therefore powerful additions to the toolbox for the interpretation of high-throughput genomics data.'),
(68575, 2011, 'bib', 'Principal component analysis based methods in bioinformatics studies', 'Ma, Shuangge|Dai, Ying', '60 College ST, LEPH 209, School of Public Health, Yale University, New Haven, CT 06520, USA. shuangge.ma@yale.edu|60 College ST, LEPH 209, School of Public Health, Yale University, New Haven, CT 06520, USA. shuangge.ma@yale.edu', 'United States|United States', 34, 'Computational Biology|Gene Expression Profiling|Genomics|Polymorphism, Single Nucleotide|Principal Component Analysis', 'Journal Article', '10.1093/bib/bbq090', '21242203', 'In analysis of bioinformatics data, a unique challenge arises from the high dimensionality of measurements. Without loss of generality, we use genomic study with gene expression measurements as a representative example but note that analysis techniques discussed in this article are also applicable to other types of bioinformatics studies. Principal component analysis (PCA) is a classic dimension reduction approach. It constructs linear combinations of gene expressions, called principal components (PCs). The PCs are orthogonal to each other, can effectively explain variation of gene expressions, and may have a much lower dimensionality. PCA is computationally simple and can be realized using many existing software packages. This article consists of the following parts. First, we review the standard PCA technique and their applications in bioinformatics data analysis. Second, we describe recent non-standard applications of PCA, including accommodating interactions among genes, pathways and network modules and conducting PCA with estimating equations as opposed to gene expressions. Third, we introduce several recently proposed PCA-based techniques, including the supervised PCA, sparse PCA and functional PCA. The supervised PCA and sparse PCA have been shown to have better empirical performance than the standard PCA. The functional PCA can analyze time-course gene expression data. Last, we raise the awareness of several critical but unsolved problems related to PCA. The goal of this article is to make bioinformatics researchers aware of the PCA technique and more importantly its most recent development, so that this simple yet effective dimension reduction technique can be better employed in bioinformatics data analysis.'),
(68576, 2011, 'bib', 'Added predictive value of high-throughput molecular data to clinical data and its validation', 'Boulesteix, Anne-Laure|Sauerbrei, Willi', 'University of Munich, Germany. boulesteix@ibe.med.uni-muenchen.de|University of Munich, Germany. boulesteix@ibe.med.uni-muenchen.de', 'Germany|Germany', 14, 'Algorithms|Computational Biology|Databases, Factual|High-Throughput Screening Assays|Survival Analysis', 'Journal Article', '10.1093/bib/bbq085', '21245078', 'Hundreds of molecular signatures have been proposed in the literature to predict patient outcome in clinical settings from high-dimensional data, many of which eventually failed to get validated. Validation of such molecular research findings is thus becoming an increasingly important branch of clinical bioinformatics. Moreover, in practice well-known clinical predictors are often already available. From a statistical and bioinformatics point of view, poor attention has been given to the evaluation of the added predictive value of a molecular signature given that clinical predictors or an established index are available. This article reviews procedures that assess and validate the added predictive value of high-dimensional molecular data. It critically surveys various approaches for the construction of combined prediction models using both clinical and molecular data, for validating added predictive value based on independent data, and for assessing added predictive value using a single data set.'),
(68577, 2011, 'bib', 'Base-calling for next-generation sequencing platforms', 'Ledergerber, Christian|Dessimoz, Christophe', 'ETH Zurich, Switzerland.|ETH Zurich, Switzerland.', 'Switzerland|Switzerland', 82, 'Animals|Base Sequence|Humans|Sequence Analysis, DNA', 'Comparative Study', '10.1093/bib/bbq077', '21245079', 'Next-generation sequencing platforms are dramatically reducing the cost of DNA sequencing. With these technologies, bases are inferred from light intensity signals, a process commonly referred to as base-calling. Thus, understanding and improving the quality of sequence data generated using these approaches are of high interest. Recently, a number of papers have characterized the biases associated with base-calling and proposed methodological improvements. In this review, we summarize recent development of base-calling approaches for the Illumina and Roche 454 sequencing platforms.'),
(68578, 2011, 'bib', 'Critical assessment of high-throughput standalone methods for secondary structure prediction', 'Zhang, Hua|Zhang, Tuo|Chen, Ke|Kedarisetti, Kanaka Durga|Mizianty, Marcin J|Bao, Qingbo|Stach, Wojciech|Kurgan, Lukasz', 'Zhejiang Gongshang University, Hangzhou, Zhejiang, P.R. China.|Zhejiang Gongshang University, Hangzhou, Zhejiang, P.R. China.|Zhejiang Gongshang University, Hangzhou, Zhejiang, P.R. China.|Zhejiang Gongshang University, Hangzhou, Zhejiang, P.R. China.|Zhejiang Gongshang University, Hangzhou, Zhejiang, P.R. China.|Zhejiang Gongshang University, Hangzhou, Zhejiang, P.R. China.|Zhejiang Gongshang University, Hangzhou, Zhejiang, P.R. China.|Zhejiang Gongshang University, Hangzhou, Zhejiang, P.R. China.', 'China|China|China|China|China|China|China|China', 11, 'Algorithms|Databases, Protein|Models, Molecular|Protein Structure, Secondary|Proteins|Solvents', 'Journal Article', '10.1093/bib/bbq088', '21252072', 'Sequence-based prediction of protein secondary structure (SS) enjoys wide-spread and increasing use for the analysis and prediction of numerous structural and functional characteristics of proteins. The lack of a recent comprehensive and large-scale comparison of the numerous prediction methods results in an often arbitrary selection of a SS predictor. To address this void, we compare and analyze 12 popular, standalone and high-throughput predictors on a large set of 1975 proteins to provide in-depth, novel and practical insights. We show that there is no universally best predictor and thus detailed comparative studies are needed to support informed selection of SS predictors for a given application. Our study shows that the three-state accuracy (Q3) and segment overlap (SOV3) of the SS prediction currently reach 82% and 81%, respectively. We demonstrate that carefully designed consensus-based predictors improve the Q3 by additional 2% and that homology modeling-based methods are significantly better by 1.5% Q3 than ab initio approaches. Our empirical analysis reveals that solvent exposed and flexible coils are predicted with a higher quality than the buried and rigid coils, while inverse is true for the strands and helices. We also show that longer helices are easier to predict, which is in contrast to longer strands that are harder to find. The current methods confuse 1-6% of strand residues with helical residues and vice versa and they perform poorly for residues in the xcexb2- bridge and 3(10)-helix conformations. Finally, we compare predictions of the standalone implementations of four well-performing methods with their corresponding web servers.'),
(68579, 2011, 'bib', 'Result verification, code verification and computation of support values in phylogenetics', 'Stamatakis, Alexandros|Izquierdo-Carrasco, Fernando', 'Heidelberg Institute for Theoretical Studies, Germany. alexandros.stamatakis@h-its.org|Heidelberg Institute for Theoretical Studies, Germany. alexandros.stamatakis@h-its.org', 'Germany|Germany', 0, 'Computer Simulation|Databases, Genetic|Evolution, Molecular|Phylogeny', 'Journal Article', '10.1093/bib/bbq079', '21257650', 'Verification in phylogenetics represents an extremely difficult subject. Phylogenetic analysis deals with the reconstruction of evolutionary histories of species, and as long as mankind is not able to travel in time, it will not be possible to verify deep evolutionary histories reconstructed with modern computational methods. Here, we focus on two more tangible issues that are related to verification in phylogenetics (i) the inference of support values on trees that provide some notion about the correctness of the tree within narrow limits and, more importantly, (ii) issues pertaining to program verification, especially with respect to codes that rely heavily on floating-point arithmetics. Program verification represents a largely underestimated problem in computational science that can have fatal effects on scientific conclusions.'),
(68580, 2011, 'bib', 'Toward microRNA-mediated gene regulatory networks in plants', 'Meng, Yijun|Shao, Chaogang|Chen, Ming', 'Department of Bioinformatics and State Key Laboratory of Plant Physiology and Biochemistry, College of Life Sciences, Zhejiang University, Zijingang Campus, Yu Hang Tang Road 388#, Hangzhou 310058, P. R. China.|Department of Bioinformatics and State Key Laboratory of Plant Physiology and Biochemistry, College of Life Sciences, Zhejiang University, Zijingang Campus, Yu Hang Tang Road 388#, Hangzhou 310058, P. R. China.|Department of Bioinformatics and State Key Laboratory of Plant Physiology and Biochemistry, College of Life Sciences, Zhejiang University, Zijingang Campus, Yu Hang Tang Road 388#, Hangzhou 310058, P. R. China.', 'China|China|China', 22, 'Computational Biology|Gene Regulatory Networks|MicroRNAs|Plants|Transcription Factors', 'Journal Article', '10.1093/bib/bbq091', '21262742', 'Current achievements in plant microRNA (miRNA) research area are inspiring. Molecular cloning and functional elucidation have greatly advanced our understanding of this small RNA species. As one of the ultimate goals, many research efforts devoted to draw a comprehensive view of miRNA-mediated gene regulatory networks in plants. Numerous bioinformatics tools competent for network analysis have been available. However, the most important point for network construction is to obtain reliable analytical results based on sufficient experimental data. Here, we introduced a general workflow to retrieve and analyze the desired data sets that serve as the cornerstones for network construction. For the upstream analyses of miRNA genes, the sequence feature of miRNA promoters should be characterized. And, regulatory relationships between transcription factors (TFs) and miRNA genes need to be investigated. For the downstream part, we emphasized that the high-throughput degradome sequencing data were especially useful for genuine miRNA-target pair identification. Functional characterization of the miRNA targets is essential to provide deep biological insights into certain miRNA-mediated pathways. For miRNAs themselves, studies on their organ- or tissue-specific expression patterns and the mechanism of self-regulation were discussed. Besides, exhaustive literature mining is required to further support or improve the established networks. It is desired that the introduced framework for miRNA-mediated network construction is timely and useful and could inspire more research efforts in the miRNA research area.'),
(68581, 2011, 'bib', 'Case studies in reproducibility', 'Hothorn, Torsten|Leisch, Friedrich', 'Ludwig-Maximilians-Universität München, Germany. torsten.hothorn@stat.uni-muenchen.de|Ludwig-Maximilians-Universität München, Germany. torsten.hothorn@stat.uni-muenchen.de', 'Germany|Germany', 42, 'Computational Biology|Models, Statistical|Reproducibility of Results|Research', 'Journal Article', '10.1093/bib/bbq084', '21278369', 'Reproducible research is a concept of providing access to data and software along with published scientific findings. By means of some case studies from different disciplines, we will illustrate reasons why readers should be given the possibility to look at the data and software independently from the authors of the original publication. We report results of a survey comprising 100 papers recently published in Bioinformatics. The main finding is that authors of this journal share a culture of making data available. However, the number of papers where source code for simulation studies or analyzes is available is still rather limited.'),
(68582, 2011, 'bib', 'A guide to web tools to prioritize candidate genes', 'Tranchevent, Léon-Charles|Capdevila, Francisco Bonachela|Nitsch, Daniela|De Moor, Bart|De Causmaecker, Patrick|Moreau, Yves', 'Katholieke Universiteit Leuven, Belgium.|Katholieke Universiteit Leuven, Belgium.|Katholieke Universiteit Leuven, Belgium.|Katholieke Universiteit Leuven, Belgium.|Katholieke Universiteit Leuven, Belgium.|Katholieke Universiteit Leuven, Belgium.', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 132, 'Computational Biology|Genes|Humans|Internet|Software', 'Journal Article', '10.1093/bib/bbq007', '21278374', 'Finding the most promising genes among large lists of candidate genes has been defined as the gene prioritization problem. It is a recurrent problem in genetics in which genetic conditions are reported to be associated with chromosomal regions. In the last decade, several different computational approaches have been developed to tackle this challenging task. In this study, we review 19 computational solutions for human gene prioritization that are freely accessible as web tools and illustrate their differences. We summarize the various biological problems to which they have been successfully applied. Ultimately, we describe several research directions that could increase the quality and applicability of the tools. In addition we developed a website (http://www.esat.kuleuven.be/gpp) containing detailed information about these and other tools, which is regularly updated. This review and the associated website constitute together a guide to help users select a gene prioritization strategy that suits best their needs.'),
(68583, 2011, 'bib', 'PHAST and RPHAST: phylogenetic analysis with space/time models', 'Hubisz, Melissa J|Pollard, Katherine S|Siepel, Adam', 'Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853, USA. acs4@cornell.edu|Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853, USA. acs4@cornell.edu|Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853, USA. acs4@cornell.edu', 'United States|United States|United States', 112, 'Databases, Genetic|Genome|Genomics|Information Storage and Retrieval|Internet|Phylogeny|Software', 'Journal Article', '10.1093/bib/bbq072', '21278375', 'The PHylogenetic Analysis with Space/Time models (PHAST) software package consists of a collection of command-line programs and supporting libraries for comparative genomics. PHAST is best known as the engine behind the Conservation tracks in the University of California, Santa Cruz (UCSC) Genome Browser. However, it also includes several other tools for phylogenetic modeling and functional element identification, as well as utilities for manipulating alignments, trees and genomic annotations. PHAST has been in development since 2002 and has now been downloaded more than 1000 times, but so far it has been released only as provisional (beta) software. Here, we describe the first official release (v1.0) of PHAST, with improved stability, portability and documentation and several new features. We outline the components of the package and detail recent improvements. In addition, we introduce a new interface to the PHAST libraries from the R statistical computing environment, called RPHAST, and illustrate its use in a series of vignettes. We demonstrate that RPHAST can be particularly useful in applications involving both large-scale phylogenomics and complex statistical analyses. The R interface also makes the PHAST libraries acccessible to non-C programmers, and is useful for rapid prototyping. PHAST v1.0 and RPHAST v1.0 are available for download at http://compgen.bscb.cornell.edu/phast, under the terms of an unrestrictive BSD-style license. RPHAST can also be obtained from the Comprehensive R Archive Network (CRAN, http://cran.r-project.org).'),
(68584, 2011, 'bib', 'An empirical assessment of validation practices for molecular classifiers', 'Castaldi, Peter J|Dahabreh, Issa J|Ioannidis, John P A', 'Institute for Clinical Research and Health Policy Studies at Tufts Medical Center, USA.|Institute for Clinical Research and Health Policy Studies at Tufts Medical Center, USA.|Institute for Clinical Research and Health Policy Studies at Tufts Medical Center, USA.', 'United States|United States|United States', 21, 'Classification|Computational Biology|Databases, Factual|Pattern Recognition, Automated|Proteomics|PubMed', 'Journal Article', '10.1093/bib/bbq073', '21300697', 'Proposed molecular classifiers may be overfit to idiosyncrasies of noisy genomic and proteomic data. Cross-validation methods are often used to obtain estimates of classification accuracy, but both simulations and case studies suggest that, when inappropriate methods are used, bias may ensue. Bias can be bypassed and generalizability can be tested by external (independent) validation. We evaluated 35 studies that have reported on external validation of a molecular classifier. We extracted information on study design and methodological features, and compared the performance of molecular classifiers in internal cross-validation versus external validation for 28 studies where both had been performed. We demonstrate that the majority of studies pursued cross-validation practices that are likely to overestimate classifier performance. Most studies were markedly underpowered to detect a 20% decrease in sensitivity or specificity between internal cross-validation and external validation [median power was 36% (IQR, 21-61%) and 29% (IQR, 15-65%), respectively]. The median reported classification performance for sensitivity and specificity was 94% and 98%, respectively, in cross-validation and 88% and 81% for independent validation. The relative diagnostic odds ratio was 3.26 (95% CI 2.04-5.21) for cross-validation versus independent validation. Finally, we reviewed all studies (n = 758) which cited those in our study sample, and identified only one instance of additional subsequent independent validation of these classifiers. In conclusion, these results document that many cross-validation practices employed in the literature are potentially biased and genuine progress in this field will require adoption of routine external validation of molecular classifiers, preferably in much larger studies than in current practice.'),
(68585, 2011, 'bib', 'GPU-powered tools boost molecular visualization', 'Chavent, Matthieu|Lévy, Bruno|Krone, Michael|Bidmon, Katrin|Nominé, Jean-Philippe|Ertl, Thomas|Baaden, Marc', 'Commissariat à lénergie atomique et aux énergies alternatives.|Commissariat à lénergie atomique et aux énergies alternatives.|Commissariat à lénergie atomique et aux énergies alternatives.|Commissariat à lénergie atomique et aux énergies alternatives.|Commissariat à lénergie atomique et aux énergies alternatives.|Commissariat à lénergie atomique et aux énergies alternatives.|Commissariat à lénergie atomique et aux énergies alternatives.', '-|-|-|-|-|-|-', 32, 'Computer Graphics|Macromolecular Substances|Microscopy, Electron|Models, Molecular|Protein Conformation|Proteins|Scattering, Small Angle|Software', 'Journal Article', '10.1093/bib/bbq089', '21310717', 'Recent advances in experimental structure determination provide a wealth of structural data on huge macromolecular assemblies such as the ribosome or viral capsids, available in public databases. Further structural models arise from reconstructions using symmetry orders or fitting crystal structures into low-resolution maps obtained by electron-microscopy or small angle X-ray scattering experiments. Visual inspection of these huge structures remains an important way of unravelling some of their secrets. However, such visualization cannot conveniently be carried out using conventional rendering approaches, either due to performance limitations or due to lack of realism. Recent developments, in particular drawing benefit from the capabilities of Graphics Processing Units (GPUs), herald the next generation of molecular visualization solutions addressing these issues. In this article, we present advances in computer science and visualization that help biologists visualize, understand and manipulate large and complex molecular systems, introducing concepts that remain little-known in the bioinformatics field. Furthermore, we compile currently available software and methods enhancing the shape perception of such macromolecular assemblies, for example based on surface simplification or lighting ameliorations.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(68586, 2011, 'bib', 'Using cross-validation to evaluate predictive accuracy of survival risk classifiers based on high-dimensional data', 'Simon, Richard M|Subramanian, Jyothi|Li, Ming-Chung|Menezes, Supriya', 'Biometric Research Branch, US National Cancer Institute, Bethesda, MD 20892-7434, USA. rsimon@nih.gov|Biometric Research Branch, US National Cancer Institute, Bethesda, MD 20892-7434, USA. rsimon@nih.gov|Biometric Research Branch, US National Cancer Institute, Bethesda, MD 20892-7434, USA. rsimon@nih.gov|Biometric Research Branch, US National Cancer Institute, Bethesda, MD 20892-7434, USA. rsimon@nih.gov', 'United States|United States|United States|United States', 50, 'Databases, Factual|Kaplan-Meier Estimate|Models, Statistical|ROC Curve|Research Design|Risk', 'Journal Article', '10.1093/bib/bbr001', '21324971', 'Developments in whole genome biotechnology have stimulated statistical focus on prediction methods. We review here methodology for classifying patients into survival risk groups and for using cross-validation to evaluate such classifications. Measures of discrimination for survival risk models include separation of survival curves, time-dependent ROC curves and Harrells concordance index. For high-dimensional data applications, however, computing these measures as re-substitution statistics on the same data used for model development results in highly biased estimates. Most developments in methodology for survival risk modeling with high-dimensional data have utilized separate test data sets for model evaluation. Cross-validation has sometimes been used for optimization of tuning parameters. In many applications, however, the data available are too limited for effective division into training and test sets and consequently authors have often either reported re-substitution statistics or analyzed their data using binary classification methods in order to utilize familiar cross-validation. In this article we have tried to indicate how to utilize cross-validation for the evaluation of survival risk models, specifically how to compute cross-validated estimates of survival distributions for predicted risk groups and how to compute cross-validated time-dependent ROC curves. We have also discussed evaluation of the statistical significance of a survival risk model and evaluation of whether high-dimensional genomic data adds predictive accuracy to a model based on standard covariates alone.'),
(68587, 2011, 'bib', 'The what, where, how and why of gene ontology--a primer for bioinformaticians', 'du Plessis, Louis|Skunca, Nives|Dessimoz, Christophe', 'ETH Zurich, Computer Science, Universitätstr. 6, 8092 Zurich, Switzerland.|ETH Zurich, Computer Science, Universitätstr. 6, 8092 Zurich, Switzerland.|ETH Zurich, Computer Science, Universitätstr. 6, 8092 Zurich, Switzerland.', 'Switzerland|Switzerland|Switzerland', 36, 'Computational Biology|Databases, Genetic|Genes|Molecular Sequence Annotation|Vocabulary, Controlled', 'Journal Article', '10.1093/bib/bbr002', '21330331', 'With high-throughput technologies providing vast amounts of data, it has become more important to provide systematic, quality annotations. The Gene Ontology (GO) project is the largest resource for cataloguing gene function. Nonetheless, its use is not yet ubiquitous and is still fraught with pitfalls. In this review, we provide a short primer to the GO for bioinformaticians. We summarize important aspects of the structure of the ontology, describe sources and types of functional annotations, survey measures of GO annotation similarity, review typical uses of GO and discuss other important considerations pertaining to the use of GO in bioinformatics applications.'),
(68588, 2011, 'bib', 'Database identifies FDA-approved drugs with potential to be repurposed for treatment of orphan diseases', 'Xu, Kui|Coté, Timothy R', 'Office of Orphan Products Development, US Food and Drug Administration, Silver Spring, MD 20993-0002, USA.|Office of Orphan Products Development, US Food and Drug Administration, Silver Spring, MD 20993-0002, USA.', 'United States|United States', 49, 'Databases, Factual|Drug Approval|Drug Repositioning|Humans|Pharmaceutical Preparations|Rare Diseases|United States|United States Food and Drug Administration', 'Journal Article', '10.1093/bib/bbr006', '21357612', 'Facing substantial obstacles to developing new therapies for rare diseases, some sponsors are looking to repurpose drugs already approved for other conditions and use those therapies to treat rare diseases. In an effort to facilitate such repurposing and speed the delivery of new therapies to people who need them, we have established a new resource, the Rare Disease Repurposing Database (RDRD). The advantages of repurposed compounds include their demonstrated efficacy (in some clinical contexts), their observed toxicity profiles and their clearly described manufacturing controls. To create the RDRD, we matched the US Food and Drug Administration (FDA) orphan designation database to FDA drug and biological product approval lists. The RDRD lists 236 products that have received orphan status designation--that is, were found to be promising for the treatment of a rare disease--and though not yet approved for marketing for that rare disease, they are already approved for marketing to treat some other disease or condition. The RDRD contains three tables: Orphan-designated products with at least one marketing approval for a common disease indication (N = 109), orphan-designated products with at least one marketing approval for a rare disease indication (N = 76), and orphan-designated products with marketing approvals for both common and rare disease indications (N = 51). While the data included in the database is a re-configuration/cross-indexing of information already released by the FDA, it offers sponsors a new tool for finding special opportunities to develop niche therapies for rare disease patients.'),
(68589, 2012, 'bib', 'Tools for managing and analyzing microarray data', 'Koschmieder, André|Zimmermann, Karin|Trissl, Silke|Stoltmann, Thomas|Leser, Ulf', 'Department of Computer Science, Humboldt-Universität zu Berlin, Rudower Chaussee 25, 12489 Berlin, Germany. koschmie@informatik.hu-berlin.de|Department of Computer Science, Humboldt-Universität zu Berlin, Rudower Chaussee 25, 12489 Berlin, Germany. koschmie@informatik.hu-berlin.de|Department of Computer Science, Humboldt-Universität zu Berlin, Rudower Chaussee 25, 12489 Berlin, Germany. koschmie@informatik.hu-berlin.de|Department of Computer Science, Humboldt-Universität zu Berlin, Rudower Chaussee 25, 12489 Berlin, Germany. koschmie@informatik.hu-berlin.de|Department of Computer Science, Humboldt-Universität zu Berlin, Rudower Chaussee 25, 12489 Berlin, Germany. koschmie@informatik.hu-berlin.de', 'Germany|Germany|Germany|Germany|Germany', 6, 'Computational Biology|Gene Expression|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|PubMed|Software', 'Journal Article', '10.1093/bib/bbr010', '21422065', 'The microarray-based analysis of gene expression has become a workhorse for biomedical research. Managing the amount and diversity of data that such experiments produce is a task that must be supported by appropriate software tools, which led to the creation of literally hundreds of systems. In consequence, choosing the right tool for a given project is difficult even for the expert. We report on the results of a survey encompassing 78 of such tools, of which 22 were inspected in detail and seven were tested hands-on. We report on our experiences with a focus on completeness of functionality, ease-of-use, and necessary effort for installation and maintenance. Thereby, our survey provides a valuable guideline for any project considering the use of a microarray data management system. It reveals which tasks are covered by mature tools and also shows that important requirements, especially in the area of integrated analysis of different experimental data, are not yet met satisfyingly by existing systems.'),
(68590, 2012, 'bib', 'Caveats and pitfalls of ROC analysis in clinical microarray research (and how to avoid them)', 'Berrar, Daniel|Flach, Peter', 'Tokyo Institute of Technology, Interdisciplinary Graduate School of Science and Engineering, Suzukakedai Campus, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan. berrar.d.aa@m.titech.ac.jp|Tokyo Institute of Technology, Interdisciplinary Graduate School of Science and Engineering, Suzukakedai Campus, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan. berrar.d.aa@m.titech.ac.jp', 'Japan|Japan', 15, 'Area Under Curve|Biomedical Research|Microarray Analysis|ROC Curve|Reproducibility of Results|Research Design', 'Journal Article', '10.1093/bib/bbr008', '21422066', 'The receiver operating characteristic (ROC) has emerged as the gold standard for assessing and comparing the performance of classifiers in a wide range of disciplines including the life sciences. ROC curves are frequently summarized in a single scalar, the area under the curve (AUC). This article discusses the caveats and pitfalls of ROC analysis in clinical microarray research, particularly in relation to (i) the interpretation of AUC (especially a value close to 0.5), (ii) model comparisons based on AUC, (iii) the differences between ranking and classification, (iv) effects due to multiple hypotheses testing, (v) the importance of confidence intervals for AUC, and (vi) the choice of the appropriate performance metric. With a discussion of illustrative examples and concrete real-world studies, this article highlights critical misconceptions that can profoundly impact the conclusions about the observed performance.'),
(68591, 2011, 'bib', 'Mutational analysis in RNAs: comparing programs for RNA deleterious mutation prediction', 'Barash, Danny|Churkin, Alexander', 'Department of Computer Science, Ben-Gurion University, 84105 Beer-Shera, Israel. dbarash@cs.bgu.ac.il|Department of Computer Science, Ben-Gurion University, 84105 Beer-Shera, Israel. dbarash@cs.bgu.ac.il', 'Israel|Israel', 5, 'Base Sequence|Genomics|Molecular Sequence Data|Mutation|Nucleic Acid Conformation|RNA|Sequence Analysis, RNA|Software', 'Comparative Study', '10.1093/bib/bbq059', '21422070', 'Programs for RNA mutational analysis that are structure-based and rely on secondary structure prediction have been developed and expanded in the past several years. They can be used for a variety of purposes, such as in suggesting point mutations that will alter RNA virus replication or translation initiation, investigating the effect of deleterious and compensatory mutations in allosteric ribozymes and riboswitches, computing an optimal path of mutations to get from one ribozyme fold to another, or analyzing regulatory RNA sequences by their mutational profile. This review describes three different freeware programs (RNAMute, RDMAS and RNAmutants) that have been developed for such purposes. RNAMute and RDMAS in principle perform energy minimization prediction by available software such as RNAfold from the Vienna RNA package or Zukers Mfold, while RNAmutants provides an efficient method using essential ingredients from energy minimization prediction. Both RNAMute in its extended version that uses RNAsubopt from the Vienna RNA package and the RNAmutants software are able to predict multiple-point mutations using developed methodologies, while RDMAS is currently restricted to single-point mutations. The strength of RNAMute in its extended version is the ability to predict a small number of point mutations in an accurate manner. RNAmutants is well fit for large scale simulations involving the calculation of all k-mutants, where k can be a large integer number, of a given RNA sequence.'),
(68592, 2011, 'bib', 'Critical assessment of structure-based sequence alignment methods at distant relationships', 'Kalaimathy, Singarevelu|Sowdhamini, Ramanathan|Kanagarajadurai, Karuppiah', 'National Centre for Biological Sciences (TIFR), GKVK Campus, Bellary Road, Bangalore 560065, India.|National Centre for Biological Sciences (TIFR), GKVK Campus, Bellary Road, Bangalore 560065, India.|National Centre for Biological Sciences (TIFR), GKVK Campus, Bellary Road, Bangalore 560065, India.', 'India|India|India', 7, 'Algorithms|Amino Acid Sequence|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bib/bbq025', '21422071', 'Accurate sequence alignments are crucial for modelling and to provide an evolutionary picture of related proteins. It is well-known that alignments are hard to obtain during distant relationships. Three thousand and fifty-two alignments of 218 pairs of protein domain structural entries, with <40% sequence identity, belonging to different structural classes, of diverse domain sizes and length-rigid/variable domains were performed using 12 programs. Structural parameters such as root mean square deviation, secondary-structural content and equivalences were considered for critical assessment. Methods that compare fragments and permit twists and translations align well during distant relationships and length variations.'),
(68593, 2012, 'bib', 'The impact of taxon sampling on phylogenetic inference: a review of two decades of controversy', 'Nabhan, Ahmed Ragab|Sarkar, Indra Neil', 'Center for Clinical and Translational Science, 89 Beaumont Avenue, Given Courtyard N309, Burlington, VT 05405, USA.|Center for Clinical and Translational Science, 89 Beaumont Avenue, Given Courtyard N309, Burlington, VT 05405, USA.', 'United States|United States', 85, 'Evolution, Molecular|Genome|Molecular Sequence Data|Phylogeny|Sequence Analysis, DNA', 'Journal Article', '10.1093/bib/bbr014', '21436145', 'Over the past two decades, there has been a long-standing debate about the impact of taxon sampling on phylogenetic inference. Studies have been based on both real and simulated data sets, within actual and theoretical contexts, and using different inference methods, to study the impact of taxon sampling. In some cases, conflicting conclusions have been drawn for the same data set. The main questions explored in studies to date have been about the effects of using sparse data, adding new taxa, including more characters from genome sequences and using different (or concatenated) locus regions. These questions can be reduced to more fundamental ones about the assessment of data quality and the design guidelines of taxon sampling in phylogenetic inference experiments. This review summarizes progress to date in understanding the impact of taxon sampling on the accuracy of phylogenetic analysis.'),
(68594, 2012, 'bib', 'Travelling the world of gene-gene interactions', 'Steen, Kristel Van', 'Department of Electrical Engineering and Computer Science (Montefiore Institute), Grande Traverse, Bioinformatique 4000 Liège 1, Belgium. Kristel.VanSteen@ulg.ac.be', 'Belgium', 125, 'Animals|Epistasis, Genetic|Genes|Genotype|Humans|Models, Genetic', 'Journal Article', '10.1093/bib/bbr012', '21441561', 'Over the last few years, main effect genetic association analysis has proven to be a successful tool to unravel genetic risk components to a variety of complex diseases. In the quest for disease susceptibility factors and the search for the missing heritability, supplementary and complementary efforts have been undertaken. These include the inclusion of several genetic inheritance assumptions in model development, the consideration of different sources of information, and the acknowledgement of disease underlying pathways of networks. The search for epistasis or gene-gene interaction effects on traits of interest is marked by an exponential growth, not only in terms of methodological development, but also in terms of practical applications, translation of statistical epistasis to biological epistasis and integration of omics information sources. The current popularity of the field, as well as its attraction to interdisciplinary teams, each making valuable contributions with sometimes rather unique viewpoints, renders it impossible to give an exhaustive review of to-date available approaches for epistasis screening. The purpose of this work is to give a perspective view on a selection of currently active analysis strategies and concerns in the context of epistasis detection, and to provide an eye to the future of gene-gene interaction analysis.'),
(68595, 2011, 'bib', 'Old friends in new guise: repositioning of known drugs with structural bioinformatics', 'Haupt, V Joachim|Schroeder, Michael', 'Bioinformatics Group, BIOTEC of TU Dresden, Germany.|Bioinformatics Group, BIOTEC of TU Dresden, Germany.', 'Germany|Germany', 109, 'Binding Sites|Computational Biology|Drug Design|Drug Repositioning|Humans|Models, Molecular|Molecular Structure|Pharmaceutical Preparations', 'Journal Article', '10.1093/bib/bbr011', '21441562', 'Developing a drug de novo is a laborious and costly endeavor. Thus, the repositioning of already approved drugs for the treatment of new diseases is promising and valuable. One computational approach to repositioning exploits the structural similarity of binding sites of known and new targets. Here, we review computational methods to represent and align binding sites. We review available tools, present success stories and discuss limits of the approach.'),
(68596, 2012, 'bib', 'Probabilistic graphical models for genetic association studies', 'Mourad, Raphaël|Sinoquet, Christine|Leray, Philippe', 'Ecole Polytechnique de lUniversité de Nantes, rue Christian Pauc, BP 50609, 44306 Nantes Cedex 3, France. raphael.mourad@univ-nantes.fr|Ecole Polytechnique de lUniversité de Nantes, rue Christian Pauc, BP 50609, 44306 Nantes Cedex 3, France. raphael.mourad@univ-nantes.fr|Ecole Polytechnique de lUniversité de Nantes, rue Christian Pauc, BP 50609, 44306 Nantes Cedex 3, France. raphael.mourad@univ-nantes.fr', 'France|France|France', 15, 'Animals|Computational Biology|Genetic Association Studies|Genetic Linkage|Genome|Humans|Linkage Disequilibrium|Models, Genetic|Models, Statistical', 'Journal Article', '10.1093/bib/bbr015', '21450805', 'Probabilistic graphical models have been widely recognized as a powerful formalism in the bioinformatics field, especially in gene expression studies and linkage analysis. Although less well known in association genetics, many successful methods have recently emerged to dissect the genetic architecture of complex diseases. In this review article, we cover the applications of these models to the population association studies context, such as linkage disequilibrium modeling, fine mapping and candidate gene studies, and genome-scale association studies. Significant breakthroughs of the corresponding methods are highlighted, but emphasis is also given to their current limitations, in particular, to the issue of scalability. Finally, we give promising directions for future research in this field.'),
(68597, 2011, 'bib', 'Biological feature validation of estimated gene interaction networks from microarray data: a case study on MYC in lymphomas', 'Mansmann, Ulrich|Jurinovic, Vindi', 'Department of Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-Universitaet, Muenchen, Germany. mansmann@ibe.med.uni-muenchen.de|Department of Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-Universitaet, Muenchen, Germany. mansmann@ibe.med.uni-muenchen.de', 'Germany|Germany', 3, 'Computational Biology|Databases, Genetic|Gene Expression Profiling|Gene Regulatory Networks|Genes, myc|Humans|Lymphoma|Oligonucleotide Array Sequence Analysis|Systems Biology', 'Journal Article', '10.1093/bib/bbr007', '21498549', 'Gene expression is a dynamic process where thousands of components interact dynamically in a complex way. A major goal in systems biology/medicine is to reconstruct the network of components from microarray data. Here, we address two key aspects of network reconstruction: (i) ergodicity supports the interpretation of the measured data as time averages and (ii) confounding is an important aspect of network reconstruction. To elucidate these aspects, we explore a data set of 214 lymphoma patients with translocated or normal MYC gene. MYC (c-Myc) translocations to immunoglobulin heavy-chain (IGH@) or light-chain (IGK@, IGL@) loci lead to c-Myc overexpression and are widely believed to be the crucial initiating oncogenic events. There is a rich body of knowledge on the biological implications of the different translocations. In the context of these data, the article reflects the relationship between the biological knowledge and the results of formal statistical estimates of gene interaction networks. The article identifies key steps to provide a trustworthy biological feature validation: (i) analysing a medium-sized network as a subnet of a more extensive environment to avoid bias by confounding, (ii) the use of external data to demonstrate the stability and reproducibility of the derived structures, (iii) a systematic literature review on the relevant issue, (iv) use of structured knowledge from databases to support the derived findings and (v) a strategy for biological experiments derived from the findings in steps (i-iv).'),
(68598, 2011, 'bib', 'Exon array data analysis using Affymetrix power tools and R statistical software', 'Lockstone, Helen E', 'Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford, OX3 7BN, UK. hel23@well.ox.ac.uk', 'United Kingdom', 69, 'Exons|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Software|Transcriptome', 'Journal Article', '10.1093/bib/bbq086', '21498550', 'The use of microarray technology to measure gene expression on a genome-wide scale has been well established for more than a decade. Methods to process and analyse the vast quantity of expression data generated by a typical microarray experiment are similarly well-established. The Affymetrix Exon 1.0 ST array is a relatively new type of array, which has the capability to assess expression at the individual exon level. This allows a more comprehensive analysis of the transcriptome, and in particular enables the study of alternative splicing, a gene regulation mechanism important in both normal conditions and in diseases. Some aspects of exon array data analysis are shared with those for standard gene expression data but others present new challenges that have required development of novel tools. Here, I will introduce the exon array and present a detailed example tutorial for analysis of data generated using this platform.'),
(68599, 2011, 'bib', 'Design and validation issues in RNA-seq experiments', 'Fang, Zhide|Cui, Xiangqin', 'Biostatistics Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, USA.|Biostatistics Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, USA.', 'United States|United States', 118, 'Base Sequence|Gene Expression Profiling|Genome|RNA|Reproducibility of Results|Sequence Analysis, RNA', 'Journal Article', '10.1093/bib/bbr004', '21498551', 'The next-generation sequencing technologies are being rapidly applied in biological research. Tens of millions of short sequences generated in a single experiment provide us enormous information on genome composition, genetic variants, gene expression levels and protein binding sites depending on the applications. Various methods are being developed for analyzing the data generated by these technologies. However, the relevant experimental design issues have rarely been discussed. In this review, we use RNA-seq as an example to bring this topic into focus and to discuss experimental design and validation issues pertaining to next-generation sequencing in the quantification of transcripts.'),
(68600, 2011, 'bib', 'Letter to the editor: on the stability and ranking of predictors from random forest variable importance measures', 'Nicodemus, Kristin K', 'Biostatistics Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, USA.', 'United States', 64, 'Artificial Intelligence', 'Letter', '10.1093/bib/bbr016', '21498552', 'A recent study examined the stability of rankings from random forests using two variable importance measures (mean decrease accuracy (MDA) and mean decrease Gini (MDG)) and concluded that rankings based on the MDG were more robust than MDA. However, studies examining data-specific characteristics on ranking stability have been few. Rankings based on the MDG measure showed sensitivity to within-predictor correlation and differences in category frequencies, even when the number of categories was held constant, and thus may produce spurious results. The MDA measure was robust to these data characteristics. Further, under strong within-predictor correlation, MDG rankings were less stable than those using MDA.'),
(68601, 2011, 'bib', 'Drug repositioning for orphan diseases', 'Sardana, Divya|Zhu, Cheng|Zhang, Minlu|Gudivada, Ranga C|Yang, Lun|Jegga, Anil G', 'Department of Computer Science, University of Cincinnati, OH, USA.|Department of Computer Science, University of Cincinnati, OH, USA.|Department of Computer Science, University of Cincinnati, OH, USA.|Department of Computer Science, University of Cincinnati, OH, USA.|Department of Computer Science, University of Cincinnati, OH, USA.|Department of Computer Science, University of Cincinnati, OH, USA.', 'United States|United States|United States|United States|United States|United States', 136, 'Drug Repositioning|Health Services Needs and Demand|Humans|Orphan Drug Production|Rare Diseases|United States|United States Food and Drug Administration', 'Journal Article', '10.1093/bib/bbr021', '21504985', 'The need and opportunity to discover therapeutics for rare or orphan diseases are enormous. Due to limited prevalence and/or commercial potential, of the approximately 6000 orphan diseases (defined by the FDA Orphan Drug Act as <200 000 US prevalence), only a small fraction (5%) is of interest to the biopharmaceutical industry. The fact that drug development is complicated, time-consuming and expensive with extremely low success rates only adds to the low rate of therapeutics available for orphan diseases. An alternative and efficient strategy to boost the discovery of orphan disease therapeutics is to find connections between an existing drug product and orphan disease. Drug Repositioning or Drug Repurposing--finding a new indication for a drug--is one way to maximize the potential of a drug. The advantages of this approach are manifold, but rational drug repositioning for orphan diseases is not trivial and poses several formidable challenges--pharmacologically and computationally. Most of the repositioned drugs currently in the market are the result of serendipity. One reason the connection between drug candidates and their potential new applications are not identified in an earlier or more systematic fashion is that the underlying mechanism connecting them is either very intricate and unknown or indirect or dispersed and buried in an ever-increasing sea of information, much of which is emerging only recently and therefore is not well organized. In this study, we will review some of these issues and the current methodologies adopted or proposed to overcome them and translate chemical and biological discoveries into safe and effective orphan disease therapeutics.'),
(68602, 2011, 'bib', 'Computational challenges of sequence classification in microbiomic data', 'Ribeca, Paolo|Valiente, Gabriel', 'Spanish National Center for Genomic Analysis (CNAG), Barcelona, Spain.|Spanish National Center for Genomic Analysis (CNAG), Barcelona, Spain.', 'Spain|Spain', 12, 'Archaea|Bacteria|DNA, Bacterial|Metagenome|Metagenomics|RNA, Ribosomal, 16S', 'Journal Article', '10.1093/bib/bbr019', '21504986', 'Next-generation sequencing technologies have opened up an unprecedented opportunity for microbiology by enabling the culture-independent genetic study of complex microbial communities, which were so far largely unknown. The analysis of metagenomic data is challenging: potentially, one is faced with a sample containing a mixture of many different bacterial species, whose genome has not necessarily been sequenced beforehand. In the simpler case of the analysis of 16S ribosomal RNA metagenomic data, for which databases of reference sequences are known, we survey the computational challenges to be solved in order to be able to characterize and quantify a sample. In particular, we examine two aspects: how the necessary adoption of new tools geared towards high-throughput analysis impacts the quality of the results, and how good is the performance of various established methods to assign sequence reads to microbial species, with and without taking taxonomic information into account.'),
(68603, 2011, 'bib', 'Evidence for short-time divergence and long-time conservation of tissue-specific expression after gene duplication', 'Huerta-Cepas, Jaime|Dopazo, Joaquín|Huynen, Martijn A|Gabaldón, Toni', 'Centre for Genomic Regulation, Barcelona, Spain.|Centre for Genomic Regulation, Barcelona, Spain.|Centre for Genomic Regulation, Barcelona, Spain.|Centre for Genomic Regulation, Barcelona, Spain.', 'Spain|Spain|Spain|Spain', 31, 'Animals|Conserved Sequence|Evolution, Molecular|Gene Duplication|Gene Expression|Genome|Humans|Mice|Organ Specificity', 'Journal Article', '10.1093/bib/bbr022', '21515902', 'Gene duplication is one of the main mechanisms by which genomes can acquire novel functions. It has been proposed that the retention of gene duplicates can be associated to processes of tissue expression divergence. These models predict that acquisition of divergent expression patterns should be acquired shortly after the duplication, and that larger divergence in tissue expression would be expected for paralogs, as compared to orthologs of a similar age. Many studies have shown that gene duplicates tend to have divergent expression patterns and that gene family expansions are associated with high levels of tissue specificity. However, the timeframe in which these processes occur have rarely been investigated in detail, particularly in vertebrates, and most analyses do not include direct comparisons of orthologs as a baseline for the expected levels of tissue specificity in absence of duplications. To assess the specific contribution of duplications to expression divergence, we combine here phylogenetic analyses and expression data from human and mouse. In particular, we study differences in spatial expression among human-mouse paralogs, specifically duplicated after the radiation of mammals, and compare them to pairs of orthologs in the same species. Our results show that gene duplication leads to increased levels of tissue specificity and that this tends to occur promptly after the duplication event.'),
(68604, 2012, 'bib', 'A large-scale benchmark study of existing algorithms for taxonomy-independent microbial community analysis', 'Sun, Yijun|Cai, Yunpeng|Huse, Susan M|Knight, Rob|Farmerie, William G|Wang, Xiaoyu|Mai, Volker', 'Interdisciplinary Center for Biotechnology Research, University of Florida, PO Box 103622, Gainesville, FL 32610-3622, USA. sunyijun@biotech.ufl.edu|Interdisciplinary Center for Biotechnology Research, University of Florida, PO Box 103622, Gainesville, FL 32610-3622, USA. sunyijun@biotech.ufl.edu|Interdisciplinary Center for Biotechnology Research, University of Florida, PO Box 103622, Gainesville, FL 32610-3622, USA. sunyijun@biotech.ufl.edu|Interdisciplinary Center for Biotechnology Research, University of Florida, PO Box 103622, Gainesville, FL 32610-3622, USA. sunyijun@biotech.ufl.edu|Interdisciplinary Center for Biotechnology Research, University of Florida, PO Box 103622, Gainesville, FL 32610-3622, USA. sunyijun@biotech.ufl.edu|Interdisciplinary Center for Biotechnology Research, University of Florida, PO Box 103622, Gainesville, FL 32610-3622, USA. sunyijun@biotech.ufl.edu|Interdisciplinary Center for Biotechnology Research, University of Florida, PO Box 103622, Gainesville, FL 32610-3622, USA. sunyijun@biotech.ufl.edu', 'United States|United States|United States|United States|United States|United States|United States', 105, 'Algorithms|Biota|Cluster Analysis|Computational Biology|Genome, Bacterial|RNA, Ribosomal, 16S|Sequence Alignment', 'Journal Article', '10.1093/bib/bbr009', '21525143', 'Recent advances in massively parallel sequencing technology have created new opportunities to probe the hidden world of microbes. Taxonomy-independent clustering of the 16S rRNA gene is usually the first step in analyzing microbial communities. Dozens of algorithms have been developed in the last decade, but a comprehensive benchmark study is lacking. Here, we survey algorithms currently used by microbiologists, and compare seven representative methods in a large-scale benchmark study that addresses several issues of concern. A new experimental protocol was developed that allows different algorithms to be compared using the same platform, and several criteria were introduced to facilitate a quantitative evaluation of the clustering performance of each algorithm. We found that existing methods vary widely in their outputs, and that inappropriate use of distance levels for taxonomic assignments likely resulted in substantial overestimates of biodiversity in many studies. The benchmark study identified our recently developed ESPRIT-Tree, a fast implementation of the average linkage-based hierarchical clustering algorithm, as one of the best algorithms available in terms of computational efficiency and clustering accuracy.'),
(68605, 2011, 'bib', 'Validation in bioinformatics and molecular medicine Editorial', 'Boulesteix, Anne-Laure', 'Interdisciplinary Center for Biotechnology Research, University of Florida, PO Box 103622, Gainesville, FL 32610-3622, USA. sunyijun@biotech.ufl.edu', 'United States', 1, 'Biomedical Research|Computational Biology|Gene Regulatory Networks|Genetic Association Studies|Reproducibility of Results', 'Editorial', '10.1093/bib/bbr027', '21546447', '-'),
(68606, 2011, 'bib', 'Validation in genetic association studies', 'König, Inke R', 'Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Germany. inke.koenig@imbs.uni-luebeck.de', 'Germany', 44, 'Computer Simulation|Databases, Genetic|Genetic Association Studies', 'Journal Article', '10.1093/bib/bbq074', '21546448', 'Validation of genetic associations is understood to be a cornerstone for the scientific credibility of the results. To approach this topic, the general concept of genetic association studies is introduced briefly, followed by how the term validation is used in the context of genetic association studies. As a central issue, reasons for the importance of validation and for failure of validation will be described.'),
(68607, 2011, 'bib', 'The meta-analysis of genome-wide association studies', 'Thompson, John R|Attia, John|Minelli, Cosetta', 'University of Leicester, UK. trj@leicester.ac.uk|University of Leicester, UK. trj@leicester.ac.uk|University of Leicester, UK. trj@leicester.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 43, 'Genetics, Population|Genome|Genome-Wide Association Study|Humans|Models, Genetic', 'Journal Article', '10.1093/bib/bbr020', '21546449', 'The pressure to publish novel genetic associations has meant that meta-analysis has been applied to genome-wide association studies without the time for a careful consideration of the methods that are used. This review distinguishes between the use of meta-analysis to validate previously reported genetic associations and its use for gene discovery, and advocates viewing gene discovery as an exploratory screen that requires independent replication instead of treating it as the application of hundreds of thousands of statistical tests. The review considers the use of fixed and random effects meta-analyses, the investigation of between-study heterogeneity, adjustment for confounding, assessing the combined evidence and genomic control, and comments on alternative approaches that have been used in the literature.'),
(68608, 2011, 'bib', 'OrthoDisease: tracking disease gene orthologs across 100 species', 'Forslund, Kristoffer|Schreiber, Fabian|Thanintorn, Nattaphon|Sonnhammer, Erik L L', 'Stockholm Bioinformatics Center, Department of Biochemistry and Biophysics, Stockholm University, Albanova, 10691 Stockholm, Sweden.|Stockholm Bioinformatics Center, Department of Biochemistry and Biophysics, Stockholm University, Albanova, 10691 Stockholm, Sweden.|Stockholm Bioinformatics Center, Department of Biochemistry and Biophysics, Stockholm University, Albanova, 10691 Stockholm, Sweden.|Stockholm Bioinformatics Center, Department of Biochemistry and Biophysics, Stockholm University, Albanova, 10691 Stockholm, Sweden.', 'Sweden|Sweden|Sweden|Sweden', 9, 'Animals|Databases, Genetic|Disease|Disease Models, Animal|Genetic Predisposition to Disease|Genome|Humans|Phylogeny|Software|Species Specificity', 'Journal Article', '10.1093/bib/bbr024', '21565935', 'Orthology is one of the most important tools available to modern biology, as it allows making inferences from easily studied model systems to much less tractable systems of interest, such as ourselves. This becomes important not least in the study of genetic diseases. We here review work on the orthology of disease-associated genes and also present an updated version of the InParanoid-based disease orthology database and web site OrthoDisease, with 14-fold increased species coverage since the previous version. Using this resource, we survey the taxonomic distribution of orthologs of human genes involved in different disease categories. The hypothesis that paralogs can mask the effect of deleterious mutations predicts that known heritable disease genes should have fewer close paralogs. We found large-scale support for this hypothesis as significantly fewer duplications were observed for disease genes in the OrthoDisease ortholog groups.'),
(68609, 2012, 'bib', 'A computational framework for the inheritance pattern of genomic imprinting for complex traits', 'Wang, Chenguang|Wang, Zhong|Prows, Daniel R|Wu, Rongling', 'Office of Surveillance and Biometrics, Center for Devices and Radiological Health, Food and Drug Administration, USA.|Office of Surveillance and Biometrics, Center for Devices and Radiological Health, Food and Drug Administration, USA.|Office of Surveillance and Biometrics, Center for Devices and Radiological Health, Food and Drug Administration, USA.|Office of Surveillance and Biometrics, Center for Devices and Radiological Health, Food and Drug Administration, USA.', 'United States|United States|United States|United States', 15, 'Animals|Breeding|Computer Simulation|Crosses, Genetic|Epigenesis, Genetic|Genomic Imprinting|Genotype|Inheritance Patterns|Mice|Quantitative Trait Loci', 'Journal Article', '10.1093/bib/bbr023', '21565936', 'Genetic imprinting, by which the expression of a gene depends on the parental origin of its alleles, may be subjected to reprogramming through each generation. Currently, such reprogramming is limited to qualitative description only, lacking more precise quantitative estimation for its extent, pattern and mechanism. Here, we present a computational framework for analyzing the magnitude of genetic imprinting and its transgenerational inheritance mode. This quantitative model is based on the breeding scheme of reciprocal backcrosses between reciprocal F(1) hybrids and original inbred parents, in which the transmission of genetic imprinting across generations can be tracked. We define a series of quantitative genetic parameters that describe the extent and transmission mode of genetic imprinting and further estimate and test these parameters within a genetic mapping framework using a new powerful computational algorithm. The model and algorithm described will enable geneticists to identify and map imprinted quantitative trait loci and dictate a comprehensive atlas of developmental and epigenetic mechanisms related to genetic imprinting. We illustrate the new discovery of the role of genetic imprinting in regulating hyperoxic acute lung injury survival time using a mouse reciprocal backcross design.'),
(68610, 2012, 'bib', 'Learning transcriptional regulation on a genome scale: a theoretical analysis based on gene expression data', 'Wu, Ming|Chan, Christina', 'Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA.|Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA.', 'United States|United States', 14, 'Gene Expression Profiling|Gene Expression Regulation|Genome, Fungal|Microarray Analysis|Saccharomyces cerevisiae|Transcription, Genetic|Transcriptome', 'Journal Article', '10.1093/bib/bbr029', '21622543', 'The recent advent of high-throughput microarray data has enabled the global analysis of the transcriptome, driving the development and application of computational approaches to study transcriptional regulation on the genome scale, by reconstructing in silico the regulatory interactions of the gene network. Although there are many in-depth reviews of such reverse-engineering methodologies, most have focused on the practical aspect of data mining, and few on the biological problem and the biological relevance of the methodology. Therefore, in this review, from a biological perspective, we used a set of yeast microarray data as a working example, to evaluate the fundamental assumptions implicit in associating transcription factor (TF)-target gene expression levels and estimating TFs activity, and further explore cooperative models. Finally we confirm that the detailed transcription mechanism is overly-complex for expression data alone to reveal, nevertheless, future network reconstruction studies could benefit from the incorporation of context-specific information, the modeling of multiple layers of regulation (e.g. micro-RNA), or the development of approaches for context-dependent analysis, to uncover the mechanisms of gene regulation.'),
(68611, 2011, 'bib', 'Letter to the editor: SeqXML and OrthoXML: standards for sequence and orthology information', 'Schmitt, Thomas|Messina, David N|Schreiber, Fabian|Sonnhammer, Erik L L', 'Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA.|Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA.|Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA.|Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA.', 'United States|United States|United States|United States', 33, 'Databases, Factual|Internet|Molecular Sequence Annotation|Proteome|Sequence Analysis|Software', 'Letter', '10.1093/bib/bbr025', '21666252', 'There is a great need for standards in the orthology field. Users must contend with different ortholog data representations from each provider, and the providers themselves must independently gather and parse the input sequence data. These burdensome and redundant procedures make data comparison and integration difficult. We have designed two XML-based formats, SeqXML and OrthoXML, to solve these problems. SeqXML is a lightweight format for sequence records-the input for orthology prediction. It stores the same sequence and metadata as typical FASTA format records, but overcomes common problems such as unstructured metadata in the header and erroneous sequence content. XML provides validation to prevent data integrity problems that are frequent in FASTA files. The range of applications for SeqXML is broad and not limited to ortholog prediction. We provide read/write functions for BioJava, BioPerl, and Biopython. OrthoXML was designed to represent ortholog assignments from any source in a consistent and structured way, yet cater to specific needs such as scoring schemes or meta-information. A unified format is particularly valuable for ortholog consumers that want to integrate data from numerous resources, e.g. for gene annotation projects. Reference proteomes for 61 organisms are already available in SeqXML, and 10 orthology databases have signed on to OrthoXML. Adoption by the entire field would substantially facilitate exchange and quality control of sequence and orthology information.'),
(68612, 2012, 'bib', 'Combining literature text mining with microarray data: advances for system biology modeling', 'Faro, Alberto|Giordano, Daniela|Spampinato, Concetto', 'Department of Informatics and Telecommunication Engineering-University of Catania, Catania, Italy.|Department of Informatics and Telecommunication Engineering-University of Catania, Catania, Italy.|Department of Informatics and Telecommunication Engineering-University of Catania, Catania, Italy.', 'Italy|Italy|Italy', 52, 'Computational Biology|Data Mining|Databases, Factual|Genomics|MEDLINE|Microarray Analysis|Systems Biology', 'Journal Article', '10.1093/bib/bbr018', '21677032', 'A huge amount of important biomedical information is hidden in the bulk of research articles in biomedical fields. At the same time, the publication of databases of biological information and of experimental datasets generated by high-throughput methods is in great expansion, and a wealth of annotated gene databases, chemical, genomic (including microarray datasets), clinical and other types of data repositories are now available on the Web. Thus a current challenge of bioinformatics is to develop targeted methods and tools that integrate scientific literature, biological databases and experimental data for reducing the time of database curation and for accessing evidence, either in the literature or in the datasets, useful for the analysis at hand. Under this scenario, this article reviews the knowledge discovery systems that fuse information from the literature, gathered by text mining, with microarray data for enriching the lists of down and upregulated genes with elements for biological understanding and for generating and validating new biological hypothesis. Finally, an easy to use and freely accessible tool, GeneWizard, that exploits text mining and microarray data fusion for supporting researchers in discovering gene-disease relationships is described.'),
(68613, 2011, 'bib', 'When orthologs diverge between human and mouse', 'Gharib, Walid H|Robinson-Rechavi, Marc', 'Department of Ecology and Evolution, Biophore, Swiss Institute of Bioinformatics, Lausanne University, CH-1015 Lausanne, Switzerland.|Department of Ecology and Evolution, Biophore, Swiss Institute of Bioinformatics, Lausanne University, CH-1015 Lausanne, Switzerland.', 'Switzerland|Switzerland', 44, 'Animals|Databases, Genetic|Evolution, Molecular|Gene Expression Profiling|Genome|Genomics|Humans|Mice|Species Specificity', 'Journal Article', '10.1093/bib/bbr031', '21677033', 'Despite the common assumption that orthologs usually share the same function, there have been various reports of divergence between orthologs, even among species as close as mammals. The comparison of mouse and human is of special interest, because mouse is often used as a model organism to understand human biology. We review the literature on evidence for divergence between human and mouse orthologous genes, and discuss it in the context of biomedical research.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(68614, 2011, 'bib', 'Computational methods for Gene Orthology inference', 'Kristensen, David M|Wolf, Yuri I|Mushegian, Arcady R|Koonin, Eugene V', 'National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.', 'United States|United States|United States|United States', 138, 'Algorithms|Animals|Computational Biology|Evolution, Molecular|Genome|Genomics|Humans|Phylogeny|Synteny', 'Journal Article', '10.1093/bib/bbr030', '21690100', 'Accurate inference of orthologous genes is a pre-requisite for most comparative genomics studies, and is also important for functional annotation of new genomes. Identification of orthologous gene sets typically involves phylogenetic tree analysis, heuristic algorithms based on sequence conservation, synteny analysis, or some combination of these approaches. The most direct tree-based methods typically rely on the comparison of an individual gene tree with a species tree. Once the two trees are accurately constructed, orthologs are straightforwardly identified by the definition of orthology as those homologs that are related by speciation, rather than gene duplication, at their most recent point of origin. Although ideal for the purpose of orthology identification in principle, phylogenetic trees are computationally expensive to construct for large numbers of genes and genomes, and they often contain errors, especially at large evolutionary distances. Moreover, in many organisms, in particular prokaryotes and viruses, evolution does not appear to have followed a simple tree-like mode, which makes conventional tree reconciliation inapplicable. Other, heuristic methods identify probable orthologs as the closest homologous pairs or groups of genes in a set of organisms. These approaches are faster and easier to automate than tree-based methods, with efficient implementations provided by graph-theoretical algorithms enabling comparisons of thousands of genomes. Comparisons of these two approaches show that, despite conceptual differences, they produce similar sets of orthologs, especially at short evolutionary distances. Synteny also can aid in identification of orthologs. Often, tree-based, sequence similarity- and synteny-based approaches can be combined into flexible hybrid methods.'),
(68615, 2011, 'bib', 'Exploiting drug-disease relationships for computational drug repositioning', 'Dudley, Joel T|Deshpande, Tarangini|Butte, Atul J', 'Stanford University, Stanford, CA, USA.|Stanford University, Stanford, CA, USA.|Stanford University, Stanford, CA, USA.', 'United States|United States|United States', 270, 'Algorithms|Animals|Computational Biology|Disease|Drug Design|Drug Industry|Drug Repositioning|Drug-Related Side Effects and Adverse Reactions|Humans|Pharmaceutical Preparations', 'Journal Article', '10.1093/bib/bbr013', '21690101', 'Finding new uses for existing drugs, or drug repositioning, has been used as a strategy for decades to get drugs to more patients. As the ability to measure molecules in high-throughput ways has improved over the past decade, it is logical that such data might be useful for enabling drug repositioning through computational methods. Many computational predictions for new indications have been borne out in cellular model systems, though extensive animal model and clinical trial-based validation are still pending. In this review, we show that computational methods for drug repositioning can be classified in two axes: drug based, where discovery initiates from the chemical perspective, or disease based, where discovery initiates from the clinical perspective of disease or its pathology. Newer algorithms for computational drug repositioning will likely span these two axes, will take advantage of newer types of molecular measurements, and will certainly play a role in reducing the global burden of disease.'),
(68616, 2012, 'bib', 'GO-function: deriving biologically relevant functions from statistically significant functions', 'Wang, Jing|Zhou, Xianxiao|Zhu, Jing|Gu, Yunyan|Zhao, Wenyuan|Zou, Jinfeng|Guo, Zheng', 'Bioinformatics Centre, Key Laboratory for NeuroInformation of Ministry of Education and School of Life Science and Technology, University of Electronic Science and Technology of China.|Bioinformatics Centre, Key Laboratory for NeuroInformation of Ministry of Education and School of Life Science and Technology, University of Electronic Science and Technology of China.|Bioinformatics Centre, Key Laboratory for NeuroInformation of Ministry of Education and School of Life Science and Technology, University of Electronic Science and Technology of China.|Bioinformatics Centre, Key Laboratory for NeuroInformation of Ministry of Education and School of Life Science and Technology, University of Electronic Science and Technology of China.|Bioinformatics Centre, Key Laboratory for NeuroInformation of Ministry of Education and School of Life Science and Technology, University of Electronic Science and Technology of China.|Bioinformatics Centre, Key Laboratory for NeuroInformation of Ministry of Education and School of Life Science and Technology, University of Electronic Science and Technology of China.|Bioinformatics Centre, Key Laboratory for NeuroInformation of Ministry of Education and School of Life Science and Technology, University of Electronic Science and Technology of China.', 'China|China|China|China|China|China|China', 55, 'Algorithms|Colorectal Neoplasms|Computational Biology|Databases, Genetic|Gene Expression Profiling', 'Journal Article', '10.1093/bib/bbr041', '21705405', 'In high-throughput studies of diseases, terms enriched with disease-related genes based on Gene Ontology (GO) are routinely found. However, most current algorithms used to find significant GO terms cannot handle the redundancy that results from the dependencies of GO terms. Simply based on some numerical considerations, current algorithms developed for reducing this redundancy may produce results that do not account for biologically interesting cases. In this article, we present several rules used to design a tool called GO-function for extracting biologically relevant terms from statistically significant GO terms for a disease. Using one gene expression profile for colorectal cancer, we compared GO-function with four algorithms designed to treat redundancy. Then, we validated results obtained in this data set by GO-function using another data set for colorectal cancer. Our analysis showed that GO-function can identify disease-related terms that are more statistically and biologically meaningful than those found by the other four algorithms.'),
(68617, 2011, 'bib', 'Positional orthology: putting genomic evolutionary relationships into context', 'Dewey, Colin N', 'Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, 5785 Medical Sciences Center, 1300 University Ave, Madison, WI 53706, USA. cdewey@biostat.wisc.edu', 'United States', 47, 'Animals|Evolution, Molecular|Gene Order|Genome|Genomics|Humans', 'Journal Article', '10.1093/bib/bbr040', '21705766', 'Orthology is a powerful refinement of homology that allows us to describe more precisely the evolution of genomes and understand the function of the genes they contain. However, because orthology is not concerned with genomic position, it is limited in its ability to describe genes that are likely to have equivalent roles in different genomes. Because of this limitation, the concept of positional orthology has emerged, which describes the relation between orthologous genes that retain their ancestral genomic positions. In this review, we formally define this concept, for which we introduce the shorter term toporthology, with respect to the evolutionary events experienced by a genes ancestors. Through a discussion of recent studies on the role of genomic context in gene evolution, we show that the distinction between orthology and toporthology is biologically significant. We then review a number of orthology prediction methods that take genomic context into account and thus that may be used to infer the important relation of toporthology.'),
(68618, 2011, 'bib', 'Comparative genomics approach to detecting split-coding regions in a low-coverage genome: lessons from the chimaera Callorhinchus milii (Holocephali, Chondrichthyes)', 'Dessimoz, Christophe|Zoller, Stefan|Manousaki, Tereza|Qiu, Huan|Meyer, Axel|Kuraku, Shigehiro', 'ETH Zurich, Computer Science, Universitätstrasse 6, 8092 Zurich, Switzerland. cdessimoz@inf.ethz.ch|ETH Zurich, Computer Science, Universitätstrasse 6, 8092 Zurich, Switzerland. cdessimoz@inf.ethz.ch|ETH Zurich, Computer Science, Universitätstrasse 6, 8092 Zurich, Switzerland. cdessimoz@inf.ethz.ch|ETH Zurich, Computer Science, Universitätstrasse 6, 8092 Zurich, Switzerland. cdessimoz@inf.ethz.ch|ETH Zurich, Computer Science, Universitätstrasse 6, 8092 Zurich, Switzerland. cdessimoz@inf.ethz.ch|ETH Zurich, Computer Science, Universitätstrasse 6, 8092 Zurich, Switzerland. cdessimoz@inf.ethz.ch', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 9, 'Algorithms|Animals|Chimera|Databases, Nucleic Acid|Genome|Genomics|Humans|Mice|Open Reading Frames|Phylogeny|Polymerase Chain Reaction|Sequence Analysis, DNA|Sharks', 'Journal Article', '10.1093/bib/bbr038', '21712341', 'Recent development of deep sequencing technologies has facilitated de novo genome sequencing projects, now conducted even by individual laboratories. However, this will yield more and more genome sequences that are not well assembled, and will hinder thorough annotation when no closely related reference genome is available. One of the challenging issues is the identification of protein-coding sequences split into multiple unassembled genomic segments, which can confound orthology assignment and various laboratory experiments requiring the identification of individual genes. In this study, using the genome of a cartilaginous fish, Callorhinchus milii, as test case, we performed gene prediction using a model specifically trained for this genome. We implemented an algorithm, designated ESPRIT, to identify possible linkages between multiple protein-coding portions derived from a single genomic locus split into multiple unassembled genomic segments. We developed a validation framework based on an artificially fragmented human genome, improvements between early and recent mouse genome assemblies, comparison with experimentally validated sequences from GenBank, and phylogenetic analyses. Our strategy provided insights into practical solutions for efficient annotation of only partially sequenced (low-coverage) genomes. To our knowledge, our study is the first formulation of a method to link unassembled genomic segments based on proteomes of relatively distantly related species as references.'),
(68619, 2011, 'bib', 'Literature mining, ontologies and information visualization for drug repurposing', 'Andronis, Christos|Sharma, Anuj|Virvilis, Vassilis|Deftereos, Spyros|Persidis, Aris', 'Research and Development, Biovista, Charlottesville, VA 22903, USA.|Research and Development, Biovista, Charlottesville, VA 22903, USA.|Research and Development, Biovista, Charlottesville, VA 22903, USA.|Research and Development, Biovista, Charlottesville, VA 22903, USA.|Research and Development, Biovista, Charlottesville, VA 22903, USA.', 'United States|United States|United States|United States|United States', 135, 'Computational Biology|Data Mining|Drug Discovery|Drug Repositioning|MEDLINE|Vocabulary, Controlled', 'Journal Article', '10.1093/bib/bbr005', '21712342', 'The immense growth of MEDLINE coupled with the realization that a vast amount of biomedical knowledge is recorded in free-text format, has led to the appearance of a large number of literature mining techniques aiming to extract biomedical terms and their inter-relations from the scientific literature. Ontologies have been extensively utilized in the biomedical domain either as controlled vocabularies or to provide the framework for mapping relations between concepts in biology and medicine. Literature-based approaches and ontologies have been used in the past for the purpose of hypothesis generation in connection with drug discovery. Here, we review the application of literature mining and ontology modeling and traversal to the area of drug repurposing (DR). In recent years, DR has emerged as a noteworthy alternative to the traditional drug development process, in response to the decreased productivity of the biopharmaceutical industry. Thus, systematic approaches to DR have been developed, involving a variety of in silico, genomic and high-throughput screening technologies. Attempts to integrate literature mining with other types of data arising from the use of these technologies as well as visualization tools assisting in the discovery of novel associations between existing drugs and new indications will also be presented.'),
(68620, 2011, 'bib', 'Ortholog identification in the presence of domain architecture rearrangement', 'Sjölander, Kimmen|Datta, Ruchira S|Shen, Yaoqing|Shoffner, Grant M', '308C Stanley Hall #1762, Department of Bioengineering, University of California, Berkeley, CA 94720, USA. kimmen@berkeley.edu|308C Stanley Hall #1762, Department of Bioengineering, University of California, Berkeley, CA 94720, USA. kimmen@berkeley.edu|308C Stanley Hall #1762, Department of Bioengineering, University of California, Berkeley, CA 94720, USA. kimmen@berkeley.edu|308C Stanley Hall #1762, Department of Bioengineering, University of California, Berkeley, CA 94720, USA. kimmen@berkeley.edu', 'United States|United States|United States|United States', 21, 'Animals|Computational Biology|Databases, Factual|Evolution, Molecular|Genome|Humans|Phylogeny|Proteins', 'Journal Article', '10.1093/bib/bbr036', '21712343', 'Ortholog identification is used in gene functional annotation, species phylogeny estimation, phylogenetic profile construction and many other analyses. Bioinformatics methods for ortholog identification are commonly based on pairwise protein sequence comparisons between whole genomes. Phylogenetic methods of ortholog identification have also been developed, these methods can be applied to protein data sets sharing a common domain architecture or which share a single functional domain but differ outside this region of homology. While promiscuous domains represent a challenge to all orthology prediction methods, overall structural similarity is highly correlated with proximity in a phylogenetic tree, conferring a degree of robustness to phylogenetic methods. In this article, we review the issues involved in orthology prediction when data sets include sequences with structurally heterogeneous domain architectures, with particular attention to automated methods designed for high-throughput application, and present a case study to illustrate the challenges in this area.'),
(68621, 2011, 'bib', 'Mining small-molecule screens to repurpose drugs', 'Swamidass, S Joshua', 'Division of Laboratory and Genomic Medicine, Department of Pathology and Immunology, Washington University School of Medicine, St Louis, MO, USA. swamidass@wustl.edu', 'United States', 56, 'Data Mining|Databases, Factual|Drug Design|Drug Discovery|Drug Repositioning|Humans', 'Journal Article', '10.1093/bib/bbr028', '21715466', 'Repurposing and repositioning drugs--discovering new uses for existing and experimental medicines-is an attractive strategy for rescuing stalled pharmaceutical projects, finding treatments for neglected diseases, and reducing the time, cost and risk of drug development. As this strategy emerged, academic researchers began performing high-throughput screens (HTS) of small molecules--the type of experiments once exclusively conducted in industry--and making the data from these screens available to all. Several methods can mine this data to inform repurposing and repositioning efforts. Despite these methods limitations, it is hopeful that they will accelerate the discovery of new uses for known drugs, but this hope has not yet been realized.'),
(68622, 2012, 'bib', 'Calculating transcription factor binding maps for chromatin', 'Teif, Vladimir B|Rippe, Karsten', 'BioQuant and German Cancer Research Center (DKFZ), Im Neuenheimer Feld 267, 69120 Heidelberg, Germany. vladimir.teif@bioquant.uni-heidelberg.de|BioQuant and German Cancer Research Center (DKFZ), Im Neuenheimer Feld 267, 69120 Heidelberg, Germany. vladimir.teif@bioquant.uni-heidelberg.de', 'Germany|Germany', 15, 'Algorithms|Binding Sites|Chromatin|DNA|Nucleic Acid Conformation|Nucleosomes|Transcription Factors', 'Journal Article', '10.1093/bib/bbr037', '21737419', 'Current high-throughput experiments already generate enough data for retrieving the DNA sequence-dependent binding affinities of transcription factors (TF) and other chromosomal proteins throughout the complete genome. However, the reverse task of calculating binding maps in a chromatin context for a given set of concentrations and TF affinities appears to be even more challenging and computationally demanding. The problem can be addressed by considering the DNA sequence as a one-dimensional lattice with units of one or more base pairs. To calculate protein occupancies in chromatin, one needs to consider the competition of TF and histone octamers for binding sites as well as the partial unwrapping of nucleosomal DNA. Here, we consider five different classes of algorithms to compute binding maps that include the binary variable, combinatorial, sequence generating function, transfer matrix and dynamic programming approaches. The calculation time of the binary variable algorithm scales exponentially with DNA length, which limits its use to the analysis of very small genomic regions. For regulatory regions with many overlapping binding sites, potentially applicable algorithms reduce either to the transfer matrix or dynamic programming approach. In addition to the recently proposed transfer matrix formalism for TF access to the nucleosomal organized DNA, we develop here a dynamic programming algorithm that accounts for this feature. In the absence of nucleosomes, dynamic programming outperforms the transfer matrix approach, but the latter is faster when nucleosome unwrapping has to be considered. Strategies are discussed that could further facilitate calculations to allow computing genome-wide TF binding maps.'),
(68623, 2011, 'bib', 'Conceptual framework and pilot study to benchmark phylogenomic databases based on reference gene trees', 'Boeckmann, Brigitte|Robinson-Rechavi, Marc|Xenarios, Ioannis|Dessimoz, Christophe', 'Swiss Institute of Bioinformatics, Centre Médical Universitaire, 1211 Geneva 4, Switzerland. brigitte.boeckmann@isb-sib.ch|Swiss Institute of Bioinformatics, Centre Médical Universitaire, 1211 Geneva 4, Switzerland. brigitte.boeckmann@isb-sib.ch|Swiss Institute of Bioinformatics, Centre Médical Universitaire, 1211 Geneva 4, Switzerland. brigitte.boeckmann@isb-sib.ch|Swiss Institute of Bioinformatics, Centre Médical Universitaire, 1211 Geneva 4, Switzerland. brigitte.boeckmann@isb-sib.ch', 'Switzerland|Switzerland|Switzerland|Switzerland', 24, 'Algorithms|Databases, Genetic|Evolution, Molecular|Genomics|Phylogeny|Pilot Projects', 'Journal Article', '10.1093/bib/bbr034', '21737420', 'Phylogenomic databases provide orthology predictions for species with fully sequenced genomes. Although the goal seems well-defined, the content of these databases differs greatly. Seven ortholog databases (Ensembl Compara, eggNOG, HOGENOM, InParanoid, OMA, OrthoDB, Panther) were compared on the basis of reference trees. For three well-conserved protein families, we observed a generally high specificity of orthology assignments for these databases. We show that differences in the completeness of predicted gene relationships and in the phylogenetic information are, for the great majority, not due to the methods used, but to differences in the underlying database concepts. According to our metrics, none of the databases provides a fully correct and comprehensive protein classification. Our results provide a framework for meaningful and systematic comparisons of phylogenomic databases. In the future, a sustainable set of Gold standard phylogenetic trees could provide a robust method for phylogenomic databases to assess their current quality status, measure changes following new database releases and diagnose improvements subsequent to an upgrade of the analysis procedure.'),
(68624, 2012, 'bib', 'How to cluster gene expression dynamics in response to environmental signals', 'Wang, Yaqun|Xu, Meng|Wang, Zhong|Tao, Ming|Zhu, Junjia|Wang, Li|Li, Runze|Berceli, Scott A|Wu, Rongling', 'Department of Statistics, Pennsylvania State University, Hershey, PA 17033, USA.|Department of Statistics, Pennsylvania State University, Hershey, PA 17033, USA.|Department of Statistics, Pennsylvania State University, Hershey, PA 17033, USA.|Department of Statistics, Pennsylvania State University, Hershey, PA 17033, USA.|Department of Statistics, Pennsylvania State University, Hershey, PA 17033, USA.|Department of Statistics, Pennsylvania State University, Hershey, PA 17033, USA.|Department of Statistics, Pennsylvania State University, Hershey, PA 17033, USA.|Department of Statistics, Pennsylvania State University, Hershey, PA 17033, USA.|Department of Statistics, Pennsylvania State University, Hershey, PA 17033, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 19, 'Algorithms|Cell Differentiation|Cluster Analysis|Computer Simulation|Databases, Genetic|Gene Expression|Gene Expression Profiling|Gene-Environment Interaction', 'Journal Article', '10.1093/bib/bbr032', '21746694', 'Organisms usually cope with change in the environment by altering the dynamic trajectory of gene expression to adjust the complement of active proteins. The identification of particular sets of genes whose expression is adaptive in response to environmental changes helps to understand the mechanistic base of gene-environment interactions essential for organismic development. We describe a computational framework for clustering the dynamics of gene expression in distinct environments through Gaussian mixture fitting to the expression data measured at a set of discrete time points. We outline a number of quantitative testable hypotheses about the patterns of dynamic gene expression in changing environments and gene-environment interactions causing developmental differentiation. The future directions of gene clustering in terms of incorporations of the latest biological discoveries and statistical innovations are discussed. We provide a set of computational tools that are applicable to modeling and analysis of dynamic gene expression data measured in multiple environments.'),
(68625, 2011, 'bib', 'Editorial: computational methods for drug repurposing', 'Sanseau, Philippe|Koehler, Jacob', 'Department of Statistics, Pennsylvania State University, Hershey, PA 17033, USA.|Department of Statistics, Pennsylvania State University, Hershey, PA 17033, USA.', 'United States|United States', 4, 'Computational Biology|Drug Discovery|Drug Repositioning|Pharmaceutical Preparations', 'Editorial', '10.1093/bib/bbr047', '21768130', '-'),
(68626, 2011, 'bib', 'Identify drug repurposing candidates by mining the protein data bank', 'Moriaud, Fabrice|Richard, Stéphane B|Adcock, Stewart A|Chanas-Martin, Laetitia|Surgand, Jean-Sébastien|Ben Jelloul, Marouane|Delfaud, François', 'MEDIT SA, 2 rue du Belvedere, 91120 Palaiseau, France. fmoriaud@medit.fr|MEDIT SA, 2 rue du Belvedere, 91120 Palaiseau, France. fmoriaud@medit.fr|MEDIT SA, 2 rue du Belvedere, 91120 Palaiseau, France. fmoriaud@medit.fr|MEDIT SA, 2 rue du Belvedere, 91120 Palaiseau, France. fmoriaud@medit.fr|MEDIT SA, 2 rue du Belvedere, 91120 Palaiseau, France. fmoriaud@medit.fr|MEDIT SA, 2 rue du Belvedere, 91120 Palaiseau, France. fmoriaud@medit.fr|MEDIT SA, 2 rue du Belvedere, 91120 Palaiseau, France. fmoriaud@medit.fr', 'France|France|France|France|France|France|France', 32, 'Binding Sites|Data Mining|Databases, Protein|Drug Discovery|Drug Repositioning|Pharmaceutical Preparations|Protein Conformation|Proteins|Structure-Activity Relationship', 'Journal Article', '10.1093/bib/bbr017', '21768131', 'Predicting off-targets by computational methods is gaining increasing interest in early-stage drug discovery. Here, we present a computational method based on full 3D comparisons of 3D structures. When a similar binding site is detected in the Protein Data Bank (PDB) (or any protein structure database), it is possible that the corresponding ligand also binds to that similar site. On one hand, this target hopping case is probably rare because it requires a high similarity between the binding sites. On the other hand, it could be a strong rational evidence to highlight possible off-target reactions and possibly a potential undesired side effect. This target-based drug repurposing can be extended a significant step further with the capability of searching the full surface of all proteins in the PDB, and therefore not relying on pocket detection. Using this approach, we describe how MED-SuMo reproduces the repurposing of tadalafil from PDE5A to PDE4A and a structure of PDE4A with tadalafil. Searching for local protein similarities generates more hits than for whole binding site similarities and therefore fragment repurposing is more likely to occur than for drug-sized compounds. In this work, we illustrate that by mining the PDB for proteins sharing similarities with the hinge region of protein kinases. The experimentally validated examples, biotin carboxylase and synapsin, are retrieved. Further to fragment repurposing, this approach can be applied to the detection of druggable sites from 3D structures. This is illustrated with detection of the protein kinase hinge motif in the HIV-RT non-nucleosidic allosteric site.'),
(68627, 2011, 'bib', 'Letter to the editor: Current progress in patient-specific modeling by Neal and Kerckhoffs (2010)', 'Wittek, Adam|Miller, Karol', 'MEDIT SA, 2 rue du Belvedere, 91120 Palaiseau, France. fmoriaud@medit.fr|MEDIT SA, 2 rue du Belvedere, 91120 Palaiseau, France. fmoriaud@medit.fr', 'France|France', 0, 'Computer Simulation|Decision Support Systems, Clinical|Humans|Patient Simulation', 'Letter', '10.1093/bib/bbr046', '21798956', 'A recent review article on Current progress in patient-specific modeling in Briefings in Bioinformatics contains the statement summarizing the results of our previous study On the unimportance of constitutive models in computing brain deformation for image-guided surgery published in Biomechanics and Modeling in Mechanobiology as confirmation of adequacy of linear elastic model for such computation. The purpose of this Letter to the Editor is to clarify this statement by informing the Readers of Briefings in Bioinformatics that our study indicates the following: (i) a simple linear elastic constitutive model for the brain tissue is sufficient when used with an appropriate finite deformation solution (i.e. geometrically non-linear analysis), and (ii) Linear analysis approach that assumes infinitesimally small brain deformations leads to unrealistic results.'),
(68628, 2012, 'bib', 'Tutorial videos of bioinformatics resources: online distribution trial in Japan named TogoTV', 'Kawano, Shin|Ono, Hiromasa|Takagi, Toshihisa|Bono, Hidemasa', 'Database Center for Life Science, Research Organization of Information and Systems, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032, Japan.|Database Center for Life Science, Research Organization of Information and Systems, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032, Japan.|Database Center for Life Science, Research Organization of Information and Systems, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032, Japan.|Database Center for Life Science, Research Organization of Information and Systems, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032, Japan.', 'Japan|Japan|Japan|Japan', 3, 'Computational Biology|Databases, Factual|Information Storage and Retrieval|Internet|Japan|Software|Videotape Recording', 'Journal Article', '10.1093/bib/bbr039', '21803786', 'In recent years, biological web resources such as databases and tools have become more complex because of the enormous amounts of data generated in the field of life sciences. Traditional methods of distributing tutorials include publishing textbooks and posting web documents, but these static contents cannot adequately describe recent dynamic web services. Due to improvements in computer technology, it is now possible to create dynamic content such as video with minimal effort and low cost on most modern computers. The ease of creating and distributing video tutorials instead of static content improves accessibility for researchers, annotators and curators. This article focuses on online video repositories for educational and tutorial videos provided by resource developers and users. It also describes a project in Japan named TogoTV (http://togotv.dbcls.jp/en/) and discusses the production and distribution of high-quality tutorial videos, which would be useful to viewer, with examples. This article intends to stimulate and encourage researchers who develop and use databases and tools to distribute how-to videos as a tool to enhance product usability.'),
(68629, 2012, 'bib', 'A toolbox for developing bioinformatics software', 'Rother, Kristian|Potrzebowski, Wojciech|Puton, Tomasz|Rother, Magdalena|Wywial, Ewa|Bujnicki, Janusz M', 'Laboratory of Structural Bioinformatics, Institute of Molecular Biology and Biotechnology, Collegium Biologicum, Adam Mickiewicz University, ul. Umultowska 89, 61-614 Poznan, Poland. krother@genesilico.pl|Laboratory of Structural Bioinformatics, Institute of Molecular Biology and Biotechnology, Collegium Biologicum, Adam Mickiewicz University, ul. Umultowska 89, 61-614 Poznan, Poland. krother@genesilico.pl|Laboratory of Structural Bioinformatics, Institute of Molecular Biology and Biotechnology, Collegium Biologicum, Adam Mickiewicz University, ul. Umultowska 89, 61-614 Poznan, Poland. krother@genesilico.pl|Laboratory of Structural Bioinformatics, Institute of Molecular Biology and Biotechnology, Collegium Biologicum, Adam Mickiewicz University, ul. Umultowska 89, 61-614 Poznan, Poland. krother@genesilico.pl|Laboratory of Structural Bioinformatics, Institute of Molecular Biology and Biotechnology, Collegium Biologicum, Adam Mickiewicz University, ul. Umultowska 89, 61-614 Poznan, Poland. krother@genesilico.pl|Laboratory of Structural Bioinformatics, Institute of Molecular Biology and Biotechnology, Collegium Biologicum, Adam Mickiewicz University, ul. Umultowska 89, 61-614 Poznan, Poland. krother@genesilico.pl', 'Poland|Poland|Poland|Poland|Poland|Poland', 16, 'Computational Biology|RNA|Software', 'Journal Article', '10.1093/bib/bbr035', '21803787', 'Creating useful software is a major activity of many scientists, including bioinformaticians. Nevertheless, software development in an academic setting is often unsystematic, which can lead to problems associated with maintenance and long-term availibility. Unfortunately, well-documented software development methodology is difficult to adopt, and technical measures that directly improve bioinformatic programming have not been described comprehensively. We have examined 22 software projects and have identified a set of practices for software development in an academic environment. We found them useful to plan a project, support the involvement of experts (e.g. experimentalists), and to promote higher quality and maintainability of the resulting programs. This article describes 12 techniques that facilitate a quick start into software engineering. We describe 3 of the 22 projects in detail and give many examples to illustrate the usage of particular techniques. We expect this toolbox to be useful for many bioinformatics programming projects and to the training of scientific programmers.'),
(68630, 2011, 'bib', 'Phylogenetic-based propagation of functional annotations within the Gene Ontology consortium', 'Gaudet, Pascale|Livstone, Michael S|Lewis, Suzanna E|Thomas, Paul D', 'Swiss Institute for Bioinformatics, CMU, 1 Rue Michel Servet, 1211 Geneva 4, Switzerland. pascale.gaudet@isb-sib.ch|Swiss Institute for Bioinformatics, CMU, 1 Rue Michel Servet, 1211 Geneva 4, Switzerland. pascale.gaudet@isb-sib.ch|Swiss Institute for Bioinformatics, CMU, 1 Rue Michel Servet, 1211 Geneva 4, Switzerland. pascale.gaudet@isb-sib.ch|Swiss Institute for Bioinformatics, CMU, 1 Rue Michel Servet, 1211 Geneva 4, Switzerland. pascale.gaudet@isb-sib.ch', 'Switzerland|Switzerland|Switzerland|Switzerland', 171, 'Databases, Genetic|Genome|Genomics|Molecular Sequence Annotation|Phylogeny|Proteins', 'Journal Article', '10.1093/bib/bbr042', '21873635', 'The goal of the Gene Ontology (GO) project is to provide a uniform way to describe the functions of gene products from organisms across all kingdoms of life and thereby enable analysis of genomic data. Protein annotations are either based on experiments or predicted from protein sequences. Since most sequences have not been experimentally characterized, most available annotations need to be based on predictions. To make as accurate inferences as possible, the GO Consortiums Reference Genome Project is using an explicit evolutionary framework to infer annotations of proteins from a broad set of genomes from experimental annotations in a semi-automated manner. Most components in the pipeline, such as selection of sequences, building multiple sequence alignments and phylogenetic trees, retrieving experimental annotations and depositing inferred annotations, are fully automated. However, the most crucial step in our pipeline relies on software-assisted curation by an expert biologist. This curation tool, Phylogenetic Annotation and INference Tool (PAINT) helps curators to infer annotations among members of a protein family. PAINT allows curators to make precise assertions as to when functions were gained and lost during evolution and record the evidence (e.g. experimentally supported GO annotations and phylogenetic information including orthology) for those assertions. In this article, we describe how we use PAINT to infer protein function in a phylogenetic context with emphasis on its strengths, limitations and guidelines. We also discuss specific examples showing how PAINT annotations compare with those generated by other highly used homology-based methods.'),
(68631, 2011, 'bib', 'RNA tertiary structure prediction with ModeRNA', 'Rother, Magdalena|Rother, Kristian|Puton, Tomasz|Bujnicki, Janusz M', 'Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, 02-109 Warsaw, Poland.|Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, 02-109 Warsaw, Poland.|Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, 02-109 Warsaw, Poland.|Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, 02-109 Warsaw, Poland.', 'Poland|Poland|Poland|Poland', 11, 'Amino Acyl-tRNA Synthetases|Base Sequence|Models, Molecular|Molecular Sequence Data|Nucleic Acid Conformation|Protein Conformation|RNA|RNA, Transfer|Sequence Alignment|Software', 'Journal Article', '10.1093/bib/bbr050', '21896613', 'Noncoding RNAs perform important roles in the cell. As their function is tightly connected with structure, and as experimental methods are time-consuming and expensive, the field of RNA structure prediction is developing rapidly. Here, we present a detailed study on using the ModeRNA software. The tool uses the comparative modeling approach and can be applied when a structural template is available and an alignment of reasonable quality can be performed. We guide the reader through the entire process of modeling Escherichia coli tRNA(Thr) in a conformation corresponding to the complex with an aminoacyl-tRNA synthetase (aaRS). We describe the choice of a template structure, preparation of input files, and explore three possible modeling strategies. In the end, we evaluate the resulting models using six alternative benchmarks. The ModeRNA software can be freely downloaded from http://iimcb.genesilico.pl/moderna/ under the conditions of the General Public License. It runs under LINUX, Windows and Mac OS. It is also available as a server at http://iimcb.genesilico.pl/modernaserver/. The models and the script to reproduce the study from this article are available at http://www.genesilico.pl/moderna/examples/.'),
(68632, 2012, 'bib', 'Gene set enrichment analysis: performance evaluation and usage guidelines', 'Hung, Jui-Hung|Yang, Tun-Hsiang|Hu, Zhenjun|Weng, Zhiping|DeLisi, Charles', 'Bioinformatics and Integrative Biology, University of Massachusetts Medical School, 364 Plantation Street, Worcester, MA 01605, USA.|Bioinformatics and Integrative Biology, University of Massachusetts Medical School, 364 Plantation Street, Worcester, MA 01605, USA.|Bioinformatics and Integrative Biology, University of Massachusetts Medical School, 364 Plantation Street, Worcester, MA 01605, USA.|Bioinformatics and Integrative Biology, University of Massachusetts Medical School, 364 Plantation Street, Worcester, MA 01605, USA.|Bioinformatics and Integrative Biology, University of Massachusetts Medical School, 364 Plantation Street, Worcester, MA 01605, USA.', 'United States|United States|United States|United States|United States', 106, 'Algorithms|Computational Biology|Databases, Genetic|Gene Expression|Guidelines as Topic|Humans', 'Evaluation Studies', '10.1093/bib/bbr049', '21900207', 'A central goal of biology is understanding and describing the molecular basis of plasticity: the sets of genes that are combinatorially selected by exogenous and endogenous environmental changes, and the relations among the genes. The most viable current approach to this problem consists of determining whether sets of genes are connected by some common theme, e.g. genes from the same pathway are overrepresented among those whose differential expression in response to a perturbation is most pronounced. There are many approaches to this problem, and the results they produce show a fair amount of dispersion, but they all fall within a common framework consisting of a few basic components. We critically review these components, suggest best practices for carrying out each step, and propose a voting method for meeting the challenge of assessing different methods on a large number of experimental data sets in the absence of a gold standard.'),
(68633, 2012, 'bib', 'Dissection of human MiRNA regulatory influence to subpathway', 'Li, Xia|Jiang, Wei|Li, Wei|Lian, Baofeng|Wang, Shuyuan|Liao, Mingzhi|Chen, Xiaowen|Wang, Yanqiu|Lv, Yingli|Wang, Shiyuan|Yang, Lei', 'College of Bioinformatics Science and Technology and Bio-pharmaceutical Key Laboratory of Heilongjiang Province, Harbin Medical University, Harbin 150081, P. R. China. lixia@hrbmu.edu.cn|College of Bioinformatics Science and Technology and Bio-pharmaceutical Key Laboratory of Heilongjiang Province, Harbin Medical University, Harbin 150081, P. R. China. lixia@hrbmu.edu.cn|College of Bioinformatics Science and Technology and Bio-pharmaceutical Key Laboratory of Heilongjiang Province, Harbin Medical University, Harbin 150081, P. R. China. lixia@hrbmu.edu.cn|College of Bioinformatics Science and Technology and Bio-pharmaceutical Key Laboratory of Heilongjiang Province, Harbin Medical University, Harbin 150081, P. R. China. lixia@hrbmu.edu.cn|College of Bioinformatics Science and Technology and Bio-pharmaceutical Key Laboratory of Heilongjiang Province, Harbin Medical University, Harbin 150081, P. R. China. lixia@hrbmu.edu.cn|College of Bioinformatics Science and Technology and Bio-pharmaceutical Key Laboratory of Heilongjiang Province, Harbin Medical University, Harbin 150081, P. R. China. lixia@hrbmu.edu.cn|College of Bioinformatics Science and Technology and Bio-pharmaceutical Key Laboratory of Heilongjiang Province, Harbin Medical University, Harbin 150081, P. R. China. lixia@hrbmu.edu.cn|College of Bioinformatics Science and Technology and Bio-pharmaceutical Key Laboratory of Heilongjiang Province, Harbin Medical University, Harbin 150081, P. R. China. lixia@hrbmu.edu.cn|College of Bioinformatics Science and Technology and Bio-pharmaceutical Key Laboratory of Heilongjiang Province, Harbin Medical University, Harbin 150081, P. R. China. lixia@hrbmu.edu.cn|College of Bioinformatics Science and Technology and Bio-pharmaceutical Key Laboratory of Heilongjiang Province, Harbin Medical University, Harbin 150081, P. R. China. lixia@hrbmu.edu.cn|College of Bioinformatics Science and Technology and Bio-pharmaceutical Key Laboratory of Heilongjiang Province, Harbin Medical University, Harbin 150081, P. R. China. lixia@hrbmu.edu.cn', 'China|China|China|China|China|China|China|China|China|China|China', 27, 'Algorithms|Databases, Genetic|Gene Regulatory Networks|Humans|MicroRNAs|Transcription Factors', 'Journal Article', '10.1093/bib/bbr043', '21908864', 'The global insight into the relationships between miRNAs and their regulatory influences remains poorly understood. And most of complex diseases may be attributed to certain local areas of pathway (subpathway) instead of the entire pathway. Here, we reviewed the studies on miRNA regulations to pathways and constructed a bipartite miRNAs and subpathways network for systematic analyzing the miRNA regulatory influences to subpathways. We found that a small fraction of miRNAs were global regulators, environmental information processing pathways were preferentially regulated by miRNAs, and miRNAs had synergistic effect on regulating group of subpathways with similar function. Integrating the disease states of miRNAs, we also found that disease miRNAs regulated more subpathways than nondisease miRNAs, and for all miRNAs, the number of regulated subpathways was not in proportion to the number of the related diseases. Therefore, the study not only provided a global view on the relationships among disease, miRNA and subpathway, but also uncovered the function aspects of miRNA regulations and potential pathogenesis of complex diseases. A web server to query, visualize and download for all the data can be freely accessed at http://bioinfo.hrbmu.edu.cn/miR2Subpath.'),
(68634, 2012, 'bib', 'Random forest Gini importance favours SNPs with large minor allele frequency: impact, sources and recommendations', 'Boulesteix, Anne-Laure|Bender, Andreas|Lorenzo Bermejo, Justo|Strobl, Carolin', 'Department of Medical Informatics, Biometry and Epidemiology, University of Munich, Marchioninistr. 15, 81377 Munich, Germany. boulesteix@ibe.med.uni-muenchen.de|Department of Medical Informatics, Biometry and Epidemiology, University of Munich, Marchioninistr. 15, 81377 Munich, Germany. boulesteix@ibe.med.uni-muenchen.de|Department of Medical Informatics, Biometry and Epidemiology, University of Munich, Marchioninistr. 15, 81377 Munich, Germany. boulesteix@ibe.med.uni-muenchen.de|Department of Medical Informatics, Biometry and Epidemiology, University of Munich, Marchioninistr. 15, 81377 Munich, Germany. boulesteix@ibe.med.uni-muenchen.de', 'Germany|Germany|Germany|Germany', 40, 'Algorithms|Gene Frequency|Genomics|Logistic Models|Models, Genetic|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bib/bbr053', '21908865', 'The use of random forests is increasingly common in genetic association studies. The variable importance measure (VIM) that is automatically calculated as a by-product of the algorithm is often used to rank polymorphisms with respect to their ability to predict the investigated phenotype. Here, we investigate a characteristic of this methodology that may be considered as an important pitfall, namely that common variants are systematically favoured by the widely used Gini VIM. As a consequence, researchers may overlook rare variants that contribute to the missing heritability. The goal of the present article is 3-fold: (i) to assess this effect quantitatively using simulation studies for different types of random forests (classical random forests and conditional inference forests, that employ unbiased variable selection criteria) as well as for different importance measures (Gini and permutation based), (ii) to explore the trees and to compare the behaviour of random forests and the standard logistic regression model in order to understand the statistical mechanisms behind the preference for common variants, and (iii) to summarize these results and previously investigated properties of random forest VIMs in the context of genetic association studies and to make practical recommendations regarding the choice of the random forest and variable importance type. All our analyses can be reproduced using R code available from the companion website: http://www.ibe.med.uni-muenchen.de/organisation/mitarbeiter/020_professuren/boulesteix/ginibias/.'),
(68635, 2012, 'bib', 'A practical guide for the computational selection of residues to be experimentally characterized in protein families', 'Benítez-Páez, Alfonso|Cárdenas-Brito, Sonia|Gutiérrez, Andrés J', 'Bioinformatic Analysis Group - GABi, Centro de Investigación y Desarrollo en Biotecnología, Bogotá DC, Colombia. abenitez@cidbio.org|Bioinformatic Analysis Group - GABi, Centro de Investigación y Desarrollo en Biotecnología, Bogotá DC, Colombia. abenitez@cidbio.org|Bioinformatic Analysis Group - GABi, Centro de Investigación y Desarrollo en Biotecnología, Bogotá DC, Colombia. abenitez@cidbio.org', 'Colombia|Colombia|Colombia', 8, 'Amino Acids|Computational Biology|Databases, Protein|Phylogeny|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bib/bbr052', '21930656', 'In recent years, numerous biocomputational tools have been designed to extract functional and evolutionary information from multiple sequence alignments (MSAs) of proteins and genes. Most biologists working actively on the characterization of proteins from a single or family perspective use the MSA analysis to retrieve valuable information about amino acid conservation and the functional role of residues in query protein(s). In MSAs, adjustment of alignment parameters is a key point to improve the quality of MSA output. However, this issue is frequently underestimated and/or misunderstood by scientists and there is no in-depth knowledge available in this field. This brief review focuses on biocomputational approaches complementary to MSA to help distinguish functional residues in protein families. These additional analyses involve issues ranging from phylogenetic to statistical, which address the detection of amino acids pivotal for protein function at any level. In recent years, a large number of tools has been designed for this very purpose. Using some of these relevant, useful tools, we have designed a practical pipeline to perform in silico studies with a view to improving the characterization of family proteins and their functional residues. This review-guide aims to present biologists a set of specially designed tools to study proteins. These tools are user-friendly as they use web servers or easy-to-handle applications. Such criteria are essential for this review as most of the biologists (experimentalists) working in this field are unfamiliar with these biocomputational analysis approaches.');
