INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(96925, 2015, 'bmc', 'Understanding and predicting binding between human leukocyte antigens (HLAs) and peptides by network analysis', 'Luo, Heng|Ye, Hao|Ng, Hui|Shi, Leming|Tong, Weida|Mattes, William|Mendrick, Donna|Hong, Huixiao', 'Research Division, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama, 335-8505, Japan.|Research Division, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama, 335-8505, Japan.|Research Division, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama, 335-8505, Japan.|Research Division, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama, 335-8505, Japan.|Research Division, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama, 335-8505, Japan.|Research Division, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama, 335-8505, Japan.|Research Division, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama, 335-8505, Japan.|Research Division, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama, 335-8505, Japan.', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan', 0, 'HLA Antigens|Histocompatibility Antigens Class II|Humans|Peptides', 'Journal Article', '10.1186/1471-2105-16-S13-S9', '26424483', 'As the major histocompatibility complex (MHC), human leukocyte antigens (HLAs) are one of the most polymorphic genes in humans. Patients carrying certain HLA alleles may develop adverse drug reactions (ADRs) after taking specific drugs. Peptides play an important role in HLA related ADRs as they are the necessary co-binders of HLAs with drugs. Many experimental data have been generated for understanding HLA-peptide binding. However, efficiently utilizing the data for understanding and accurately predicting HLA-peptide binding is challenging. Therefore, we developed a network analysis based method to understand and predict HLA-peptide binding.'),
(96926, 2015, 'bmc', 'Proceedings of the 2015 MidSouth Computational Biology and Bioinformatics Society (MCBIOS) Conference', 'Wren, Jonathan D|Thakkar, Shraddha|Homayouni, Ramin|Johann, Donald J|Dozmorov, Mikhail G', 'Research Division, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama, 335-8505, Japan.|Research Division, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama, 335-8505, Japan.|Research Division, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama, 335-8505, Japan.|Research Division, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama, 335-8505, Japan.|Research Division, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama, 335-8505, Japan.', 'Japan|Japan|Japan|Japan|Japan', 0, '-', 'Journal Article', '10.1186/1471-2105-16-S13-S1', '26424691', '-'),
(96927, 2015, 'bmc', 'Mass-Up: an all-in-one open software application for MALDI-TOF mass spectrometry knowledge discovery', 'López-Fernández, H|Santos, H M|Capelo, J L|Fdez-Riverola, F|Glez-Peña, D|Reboiro-Jato, M', 'Informatics Department, Universidad de Vigo, Campus Universitario As Lagoas s/n, 32004, Ourense, Spain. hlfernandez@uvigo.es.|BIOSCOPE Research Group, UCIBIO-REQUIMTE, Department of Chemistry, Faculty of Science and Technology, Universidade NOVA de Lisboa, Caparica, Setubal, Portugal. hms14862@fct.unl.pt.|BIOSCOPE Research Group, UCIBIO-REQUIMTE, Department of Chemistry, Faculty of Science and Technology, Universidade NOVA de Lisboa, Caparica, Setubal, Portugal. jlcm@fct.unl.pt.|Informatics Department, Universidad de Vigo, Campus Universitario As Lagoas s/n, 32004, Ourense, Spain. riverola@uvigo.es.|Informatics Department, Universidad de Vigo, Campus Universitario As Lagoas s/n, 32004, Ourense, Spain. dgpena@uvigo.es.|Informatics Department, Universidad de Vigo, Campus Universitario As Lagoas s/n, 32004, Ourense, Spain. mrjato@uvigo.es.', 'Spain|Portugal|Portugal|Spain|Spain|Spain', 0, 'Biomarkers|Cluster Analysis|Databases, Factual|Internet|Peptides|Principal Component Analysis|Proteins|Proteomics|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization|User-Computer Interface', 'Journal Article', '10.1186/s12859-015-0752-4', '26437641', 'Mass spectrometry is one of the most important techniques in the field of proteomics. MALDI-TOF mass spectrometry has become popular during the last decade due to its high speed and sensitivity for detecting proteins and peptides. MALDI-TOF-MS can be also used in combination with Machine Learning techniques and statistical methods for knowledge discovery. Although there are many software libraries and tools that can be combined for these kind of analysis, there is still a need for all-in-one solutions with graphical user-friendly interfaces and avoiding the need of programming skills.'),
(96928, 2015, 'bmc', 'Interlog protein network: an evolutionary benchmark of protein interaction networks for the evaluation of clustering algorithms', 'Jafari, Mohieddin|Mirzaie, Mehdi|Sadeghi, Mehdi', 'Drug Design and Bioinformatics Unit, Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran, 69 Pasteur St, PO Box 13164, Tehran, Iran. mjafari@ipm.ir.|Department of Computational Biology, Faculty of High Technologies, Tarbiat Modares University, Jalal Ale Ahmad Highway, PO Box 14115-111, Tehran, Iran. mirzaie@ipm.ir.|National Institute of Genetic Engineering and Biotechnology (NIGEB), Pajoohesh Blvd, 17 Km Tehran-Karaj Highway, PO Box 161-14965, Tehran, Iran. sadeghi@nigeb.ac.ir.', '-|-|-', 0, 'Algorithms|Animals|Benchmarking|Biological Evolution|Cluster Analysis|Humans|Markov Chains|Mitochondria|Protein Interaction Maps|Proteins', 'Evaluation Studies', '10.1186/s12859-015-0755-1', '26437714', 'In the field of network science, exploring principal and crucial modules or communities is critical in the deduction of relationships and organization of complex networks. This approach expands an arena, and thus allows further study of biological functions in the field of network biology. As the clustering algorithms that are currently employed in finding modules have innate uncertainties, external and internal validations are necessary.'),
(96929, 2015, 'bmc', 'SCExV: a webtool for the analysis and visualisation of single cell qRT-PCR data', 'Lang, Stefan|Ugale, Amol|Erlandsson, Eva|Karlsson, Göran|Bryder, David|Soneji, Shamit', 'Division of Molecular Hematology, BMC B12, Lund University, Sölvegatan 19, Lund, 22184, Sweden. stefan.lang@med.lu.se.|Division of Molecular Hematology, BMC B12, Lund University, Sölvegatan 19, Lund, 22184, Sweden. amol.ugale@med.lu.se.|Division of Molecular Hematology, BMC B12, Lund University, Sölvegatan 19, Lund, 22184, Sweden. eva.erlandsson@med.lu.se.|Division of Molecular Hematology, BMC B12, Lund University, Sölvegatan 19, Lund, 22184, Sweden. goran.karlsson@med.lu.se.|Division of Molecular Hematology, BMC B12, Lund University, Sölvegatan 19, Lund, 22184, Sweden. david.bryder@med.lu.se.|Division of Molecular Hematology, BMC B12, Lund University, Sölvegatan 19, Lund, 22184, Sweden. shamit.soneji@med.lu.se.', 'Sweden|Sweden|Sweden|Sweden|Sweden|Sweden', 0, 'Animals|DNA|Internet|Mice|Real-Time Polymerase Chain Reaction|Stem Cells|User-Computer Interface', 'Journal Article', '10.1186/s12859-015-0757-z', '26437766', 'Single cell gene expression assays have become a powerful tool with which to dissect heterogeneous populations. While methods and software exist to interrogate such data, what has been lacking is a unified solution combining analysis and visualisation which is also accessible and intuitive for use by non-bioinformaticians, as well as bioinformaticians.'),
(96930, 2015, 'bmc', 'A computer simulation model of Wolbachia invasion for disease vector population modification', 'Guevara-Souza, Mauricio|Vallejo, Edgar E', 'Department of Computer Science, Tecnologico de Monterrey, Carretera a lago de Guadalupe km 3.5, Col. Margarita Maza de Juarez, Atizapan de Zaragoza, Adolfo Lopez Mateos, 52926, Mexico. guevara_mauricio@hotmail.com.|Department of Computer Science, Tecnologico de Monterrey, Carretera a lago de Guadalupe km 3.5, Col. Margarita Maza de Juarez, Atizapan de Zaragoza, Adolfo Lopez Mateos, 52926, Mexico. vallejo@itesm.mx.', 'Mexico|Mexico', 5, 'Animals|Culicidae|Insect Vectors|Models, Theoretical|Population Dynamics|Reproduction|Wolbachia', 'Journal Article', '10.1186/s12859-015-0746-2', '26438427', 'Wolbachia invasion has been proved to be a promising alternative for controlling vector-borne diseases, particularly Dengue fever. Creating computer models that can provide insight into how vector population modification can be achieved under different conditions would be most valuable for assessing the efficacy of control strategies for this disease.'),
(96931, 2015, 'bmc', 'Accelerating the Smith-Waterman algorithm with interpair pruning and band optimization for the all-pairs comparison of base sequences', 'Okada, Daiki|Ino, Fumihiko|Hagihara, Kenichi', 'Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, 565-0871, Japan.|Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, 565-0871, Japan. ino@ist.osaka-u.ac.jp.|Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, 565-0871, Japan. hagihara@ist.osaka-u.ac.jp.', 'Japan|Japan|Japan', 7, 'Algorithms|Animals|Bacillus anthracis|Genome|Gorilla gorilla|Humans|Pan troglodytes|Phylogeny|Sequence Alignment', 'Journal Article', '10.1186/s12859-015-0744-4', '26445214', 'The Smith-Waterman algorithm is known to be a more sensitive approach than heuristic algorithms for local sequence alignment algorithms. Despite its sensitivity, a greater time complexity associated with the Smith-Waterman algorithm prevents its application to the all-pairs comparisons of base sequences, which aids in the construction of accurate phylogenetic trees. The aim of this study is to achieve greater acceleration using the Smith-Waterman algorithm (by realizing interpair block pruning and band optimization) compared with that achieved using a previous method that performs intrapair block pruning on graphics processing units (GPUs).'),
(96932, 2015, 'bmc', 'DECIPHER: harnessing local sequence context to improve protein multiple sequence alignment', 'Wright, Erik S', 'Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, 53715, USA. eswright@wisc.edu.', 'United States', 61, 'Algorithms|Amino Acid Sequence|Databases, Protein|Humans|Molecular Sequence Data|Programming Languages|Protein Structure, Secondary|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1186/s12859-015-0749-z', '26445311', 'Alignment of large and diverse sequence sets is a common task in biological investigations, yet there remains considerable room for improvement in alignment quality. Multiple sequence alignment programs tend to reach maximal accuracy when aligning only a few sequences, and then diminish steadily as more sequences are added. This drop in accuracy can be partly attributed to a build-up of error and ambiguity as more sequences are aligned. Most high-throughput sequence alignment algorithms do not use contextual information under the assumption that sites are independent. This study examines the extent to which local sequence context can be exploited to improve the quality of large multiple sequence alignments.'),
(96933, 2015, 'bmc', 'DectICO: an alignment-free supervised metagenomic classification method based on feature extraction and dynamic selection', 'Ding, Xiao|Cheng, Fudong|Cao, Changchang|Sun, Xiao', 'State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China. dx8715@163.com.|State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China. 1357043455@qq.com.|State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China. caochch@gmail.com.|State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China. xsun@seu.edu.cn.', 'China|China|China|China', 0, 'Algorithms|Bacteria|Least-Squares Analysis|Metagenome|Metagenomics|Support Vector Machine', 'Journal Article', '10.1186/s12859-015-0753-3', '26446672', 'Continual progress in next-generation sequencing allows for generating increasingly large metagenomes which are over time or space. Comparing and classifying the metagenomes with different microbial communities is critical. Alignment-free supervised classification is important for discriminating between the multifarious components of metagenomic samples, because it can be accomplished independently of known microbial genomes.'),
(96934, 2015, 'bmc', 'Sequence specificity between interacting and non-interacting homologs identifies interface residues--a homodimer and monomer use case', 'Hou, Qingzhen|Dutilh, Bas E|Huynen, Martijn A|Heringa, Jaap|Feenstra, K Anton', 'Center for Integrative Bioinformatics VU (IBIVU), Vrije University Amsterdam, De Boelelaan 1081A, 1081 HV, Amsterdam, The Netherlands. q.hou@vu.nl.|Theoretical Biology and Bioinformatics, Utrecht University, Padualaan 8, 3584 CH, Utrecht, The Netherlands. bedutilh@gmail.com.|Centre for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Centre, Geert Grooteplein 28, 6525 GA, Nijmegen, The Netherlands. m.huynen@cmbi.ru.nl.|Center for Integrative Bioinformatics VU (IBIVU), Vrije University Amsterdam, De Boelelaan 1081A, 1081 HV, Amsterdam, The Netherlands. j.heringa@vu.nl.|Center for Integrative Bioinformatics VU (IBIVU), Vrije University Amsterdam, De Boelelaan 1081A, 1081 HV, Amsterdam, The Netherlands. k.a.feenstra@vu.nl.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 4, 'Area Under Curve|Dimerization|Protein Interaction Domains and Motifs|Proteins|ROC Curve', 'Journal Article', '10.1186/s12859-015-0758-y', '26449222', 'Protein families participating in protein-protein interactions may contain sub-families that have different binding characteristics, ranging from right binding to showing no interaction at all. Composition differences at the sequence level in these sub-families are often decisive to their differential functional interaction. Methods to predict interface sites from protein sequences typically exploit conservation as a signal. Here, instead, we provide proof of concept that the sequence specificity between interacting versus non-interacting groups can be exploited to recognise interaction sites.'),
(96935, 2015, 'bmc', 'SPINGO: a rapid species-classifier for microbial amplicon sequences', 'Allard, Guy|Ryan, Feargal J|Jeffery, Ian B|Claesson, Marcus J', 'School of Microbiology and APC Microbiome Institute, University College Cork, Cork, Ireland. guyallard01@gmail.com.|School of Microbiology and APC Microbiome Institute, University College Cork, Cork, Ireland. feargalr@gmail.com.|School of Microbiology and APC Microbiome Institute, University College Cork, Cork, Ireland. i.jeffery@ucc.ie.|School of Microbiology and APC Microbiome Institute, University College Cork, Cork, Ireland. m.claesson@ucc.ie.', 'Ireland|Ireland|Ireland|Ireland', 50, 'Bacteria|High-Throughput Nucleotide Sequencing|Metagenomics|RNA, Ribosomal, 16S|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/s12859-015-0747-1', '26450747', 'Taxonomic classification is a corner stone for the characterisation and comparison of microbial communities. Currently, most existing methods are either slow, restricted to specific communities, highly sensitive to taxonomic inconsistencies, or limited to genus level classification. As crucial microbiota information is hinging on high-level resolution it is imperative to increase taxonomic resolution to species level wherever possible.'),
(96936, 2015, 'bmc', 'Moments of genome evolution by Double Cut-and-Join', 'Biller, Priscila|Guéguen, Laurent|Tannier, Eric', 'School of Microbiology and APC Microbiome Institute, University College Cork, Cork, Ireland. m.claesson@ucc.ie.|School of Microbiology and APC Microbiome Institute, University College Cork, Cork, Ireland. m.claesson@ucc.ie.|School of Microbiology and APC Microbiome Institute, University College Cork, Cork, Ireland. m.claesson@ucc.ie.', 'Ireland|Ireland|Ireland', 9, 'Algorithms|Computer Simulation|Evolution, Molecular|Gene Rearrangement|Genome|Genomics|Humans|Models, Genetic', 'Journal Article', '10.1186/1471-2105-16-S14-S7', '26451469', 'We study statistical estimators of the number of genomic events separating two genomes under a Double Cut-and Join (DCJ) rearrangement model, by a method of moment estimation. We first propose an exact, closed, analytically invertible formula for the expected number of breakpoints after a given number of DCJs. This improves over the heuristic, recursive and computationally slower previously proposed one. Then we explore the analogies of genome evolution by DCJ with evolution of binary sequences under substitutions, permutations under transpositions, and random graphs. Each of these are presented in the literature with intuitive justifications, and are used to import results from better known fields. We formalize the relations by proving a correspondence between moments in sequence and genome evolution, provided substitutions appear four by four in the corresponding model. Eventually we prove a bounded error on two estimators of the number of cycles in the breakpoint graph after a given number of rearrangements, by an analogy with cycles in permutations and components in random graphs. '),
(96937, 2015, 'bmc', 'Event inference in multidomain families with phylogenetic reconciliation', 'Stolzer, Maureen|Siewert, Katherine|Lai, Han|Xu, Minli|Durand, Dannie', 'School of Microbiology and APC Microbiome Institute, University College Cork, Cork, Ireland. m.claesson@ucc.ie.|School of Microbiology and APC Microbiome Institute, University College Cork, Cork, Ireland. m.claesson@ucc.ie.|School of Microbiology and APC Microbiome Institute, University College Cork, Cork, Ireland. m.claesson@ucc.ie.|School of Microbiology and APC Microbiome Institute, University College Cork, Cork, Ireland. m.claesson@ucc.ie.|School of Microbiology and APC Microbiome Institute, University College Cork, Cork, Ireland. m.claesson@ucc.ie.', 'Ireland|Ireland|Ireland|Ireland|Ireland', 0, 'Algorithms|Animals|Evolution, Molecular|Gene Duplication|Guanylate Kinases|Multigene Family|Phylogeny|Protein Structure, Tertiary|Software|Vertebrates', 'Journal Article', '10.1186/1471-2105-16-S14-S8', '26451642', 'Reconstructing evolution provides valuable insights into the processes of gene evolution and function. However, while there have been great advances in algorithms and software to reconstruct the history of gene families, these tools do not model the domain shuffling events (domain duplication, insertion, transfer, and deletion) that drive the evolution of multidomain protein families. Protein evolution through domain shuffling events allows for rapid exploration of functions by introducing new combinations of existing folds. This powerful mechanism was key to some significant evolutionary innovations, such as multicellularity and the vertebrate immune system. A method for reconstructing this important evolutionary process is urgently needed.'),
(96938, 2015, 'bmc', 'Prediction of microbial phenotypes based on comparative genomics', 'Feldbauer, Roman|Schulz, Frederik|Horn, Matthias|Rattei, Thomas', 'School of Microbiology and APC Microbiome Institute, University College Cork, Cork, Ireland. m.claesson@ucc.ie.|School of Microbiology and APC Microbiome Institute, University College Cork, Cork, Ireland. m.claesson@ucc.ie.|School of Microbiology and APC Microbiome Institute, University College Cork, Cork, Ireland. m.claesson@ucc.ie.|School of Microbiology and APC Microbiome Institute, University College Cork, Cork, Ireland. m.claesson@ucc.ie.', 'Ireland|Ireland|Ireland|Ireland', 14, 'Algorithms|Chromosome Mapping|Genome, Microbial|Machine Learning|Metagenome|Metagenomics|Phenotype|Sequence Analysis, DNA|Software|Support Vector Machine', 'Comparative Study', '10.1186/1471-2105-16-S14-S1', '26451672', 'The accessibility of almost complete genome sequences of uncultivable microbial species from metagenomes necessitates computational methods predicting microbial phenotypes solely based on genomic data. Here we investigate how comparative genomics can be utilized for the prediction of microbial phenotypes. The PICA framework facilitates application and comparison of different machine learning techniques for phenotypic trait prediction. We have improved and extended PICAs support vector machine plug-in and suggest its applicability to large-scale genome databases and incomplete genome sequences. We have demonstrated the stability of the predictive power for phenotypic traits, not perturbed by the rapid growth of genome databases. A new software tool facilitates the in-depth analysis of phenotype models, which associate expected and unexpected protein functions with particular traits. Most of the traits can be reliably predicted in only 60-70% complete genomes. We have established a new phenotypic model that predicts intracellular microorganisms. Thereby we could demonstrate that also independently evolved phenotypic traits, characterized by genome reduction, can be reliably predicted based on comparative genomics. Our results suggest that the extended PICA framework can be used to automatically annotate phenotypes in near-complete microbial genome sequences, as generated in large numbers in current metagenomics studies.'),
(96939, 2015, 'bmc', 'Exact approaches for scaffolding', 'Weller, Mathias|Chateau, Annie|Giroudeau, Rodolphe', 'School of Microbiology and APC Microbiome Institute, University College Cork, Cork, Ireland. m.claesson@ucc.ie.|School of Microbiology and APC Microbiome Institute, University College Cork, Cork, Ireland. m.claesson@ucc.ie.|School of Microbiology and APC Microbiome Institute, University College Cork, Cork, Ireland. m.claesson@ucc.ie.', 'Ireland|Ireland|Ireland', 0, 'Algorithms|Animals|Bacteria|Contig Mapping|Genome|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-16-S14-S2', '26451725', 'This paper presents new structural and algorithmic results around the scaffolding problem, which occurs prominently in next generation sequencing. The problem can be formalized as an optimization problem on a special graph, the scaffold graph. We prove that the problem is polynomial if this graph is a tree by providing a dynamic programming algorithm for this case. This algorithm serves as a basis to deduce an exact algorithm for general graphs using a tree decomposition of the input. We explore other structural parameters, proving a linear-size problem kernel with respect to the size of a feedback-edge set on a restricted version of Scaffolding. Finally, we examine some parameters of scaffold graphs, which are based on real-world genomes, revealing that the feedback edge set is significantly smaller than the input size. '),
(96940, 2015, 'bmc', 'Reconstruction of ancestral gene orders using intermediate genomes', 'Feijão, Pedro', 'School of Microbiology and APC Microbiome Institute, University College Cork, Cork, Ireland. m.claesson@ucc.ie.', 'Ireland', 13, 'Algorithms|Animals|Computational Biology|Evolution, Molecular|Gene Order|Genome|Genomics|Humans|Models, Genetic|Phylogeny', 'Journal Article', '10.1186/1471-2105-16-S14-S3', '26451811', 'The problem of reconstructing ancestral genomes in a given phylogenetic tree arises in many different comparative genomics fields. Here, we focus on reconstructing the gene order of ancestral genomes, a problem that has been largely studied in the past 20 years, especially with the increasing availability of whole genome DNA sequences. There are two main approaches to this problem: event-based methods, that try to find the ancestral genomes that minimize the number of rearrangement events in the tree, and homology-based, that look for conserved structures, such as adjacent genes in the extant genomes, to build the ancestral genomes.'),
(96941, 2015, 'bmc', 'Reconstructing a SuperGeneTree minimizing reconciliation', 'Lafond, Manuel|Ouangraoua, Axc3xafda|El-Mabrouk, Nadia', 'School of Microbiology and APC Microbiome Institute, University College Cork, Cork, Ireland. m.claesson@ucc.ie.|School of Microbiology and APC Microbiome Institute, University College Cork, Cork, Ireland. m.claesson@ucc.ie.|School of Microbiology and APC Microbiome Institute, University College Cork, Cork, Ireland. m.claesson@ucc.ie.', 'Ireland|Ireland|Ireland', 5, 'Algorithms|Animals|Computational Biology|Evolution, Molecular|Genetic Speciation|Humans|Models, Genetic|Phylogeny|Software', 'Journal Article', '10.1186/1471-2105-16-S14-S4', '26451911', 'Combining a set of trees on partial datasets into a single tree is a classical method for inferring large phylogenetic trees. Ideally, the combined tree should display each input partial tree, which is only possible if input trees do not contain contradictory phylogenetic information. The simplest version of the supertree problem is thus to state whether a set of trees is compatible, and if so, construct a tree displaying them all. Classically, supertree methods have been applied to the reconstruction of species trees. Here we rather consider reconstructing a super gene tree in light of a known species tree S. We define the supergenetree problem as finding, among all supertrees displaying a set of input gene trees, one supertree minimizing a reconciliation distance with S. We first show how classical exact methods to the supertree problem can be extended to the supergenetree problem. As all these methods are highly exponential, we also exhibit a natural greedy heuristic for the duplication cost, based on minimizing the set of duplications preceding the first speciation event. We then show that both the supergenetree problem and its restriction to minimizing duplications preceding the first speciation are NP-hard to approximate within a n1-xcfxb5 factor, for any 0 < xcfxb5 < 1. Finally, we show that a restriction of this problem to uniquely labeled speciation gene trees, which is relevant to many biological applications, is also NP-hard. Therefore, we introduce new avenues in the field of supertrees, and set the theoretical basis for the exploration of various algorithmic aspects of the problems. '),
(96942, 2015, 'bmc', 'Probabilistic modeling of the evolution of gene synteny within reconciled phylogenies', 'Semeria, Magali|Tannier, Eric|Guéguen, Laurent', 'School of Microbiology and APC Microbiome Institute, University College Cork, Cork, Ireland. m.claesson@ucc.ie.|School of Microbiology and APC Microbiome Institute, University College Cork, Cork, Ireland. m.claesson@ucc.ie.|School of Microbiology and APC Microbiome Institute, University College Cork, Cork, Ireland. m.claesson@ucc.ie.', 'Ireland|Ireland|Ireland', 0, 'Algorithms|Animals|Drosophila|Evolution, Molecular|Gene Order|Genome|Models, Genetic|Phylogeny|Synteny', 'Journal Article', '10.1186/1471-2105-16-S14-S5', '26452018', 'Most models of genome evolution concern either genetic sequences, gene content or gene order. They sometimes integrate two of the three levels, but rarely the three of them. Probabilistic models of gene order evolution usually have to assume constant gene content or adopt a presence/absence coding of gene neighborhoods which is blind to complex events modifying gene content.'),
(96943, 2015, 'bmc', 'Sampling and counting genome rearrangement scenarios', 'Miklós, István|Smith, Heather', 'School of Microbiology and APC Microbiome Institute, University College Cork, Cork, Ireland. m.claesson@ucc.ie.|School of Microbiology and APC Microbiome Institute, University College Cork, Cork, Ireland. m.claesson@ucc.ie.', 'Ireland|Ireland', 4, 'Evolution, Molecular|Gene Rearrangement|Genome|Humans|Models, Genetic|Software', 'Journal Article', '10.1186/1471-2105-16-S14-S6', '26452124', 'Even for moderate size inputs, there are a tremendous number of optimal rearrangement scenarios, regardless what the model is and which specific question is to be answered. Therefore giving one optimal solution might be misleading and cannot be used for statistical inferring. Statistically well funded methods are necessary to sample uniformly from the solution space and then a small number of samples are sufficient for statistical inferring.'),
(96944, 2015, 'bmc', 'Differential analysis of biological networks', 'Ruan, Da|Young, Alastair|Montana, Giovanni', 'Department of Biomedical Engineering, Kings College London, London, SE1 7EH, UK. giovanni.montana@kcl.ac.uk.|Department of Biomedical Engineering, Kings College London, London, SE1 7EH, UK. alastair.young@imperial.ac.uk.|Department of Mathematics, Imperial College London, London, SW7 2AZ, UK. giovanni.montana@kcl.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom', 20, 'Algorithms|DNA Methylation|Gene Expression|Gene Expression Profiling|Humans', 'Journal Article', '10.1186/s12859-015-0735-5', '26453322', 'In cancer research, the comparison of gene expression or DNA methylation networks inferred from healthy controls and patients can lead to the discovery of biological pathways associated to the disease. As a cancer progresses, its signalling and control networks are subject to some degree of localised re-wiring. Being able to detect disrupted interaction patterns induced by the presence or progression of the disease can lead to the discovery of novel molecular diagnostic and prognostic signatures. Currently there is a lack of scalable statistical procedures for two-network comparisons aimed at detecting localised topological differences.'),
(96945, 2015, 'bmc', 'Indexing a protein-protein interaction network expedites network alignment', 'Hasan, Md Mahmudul|Kahveci, Tamer', 'Department of Computer & Information Science and Engineering, University of Florida, Gainesville FL, 32611, USA. mmhasan@cise.ufl.edu.|Department of Computer & Information Science and Engineering, University of Florida, Gainesville FL, 32611, USA. tamer@cise.ufl.edu.', 'United States|United States', 5, 'Algorithms|Protein Interaction Domains and Motifs|Protein Interaction Maps', 'Journal Article', '10.1186/s12859-015-0756-0', '26453444', 'Network query problem aligns a small query network with an arbitrarily large target network. The complexity of this problem grows exponentially with the number of nodes in the query network if confidence in the optimality of result is desired. Scaling this problem to large query and target networks remains to be a challenge.'),
(96946, 2015, 'bmc', 'An automated real-time integration and interoperability framework for bioinformatics', 'Lopes, Pedro|Oliveira, José Luís', 'DETI/IEETA, Universidade de Aveiro, Campus Universitario de Santiago, Aveiro, 3810-193, Portugal. pedrolopes@ua.pt.|DETI/IEETA, Universidade de Aveiro, Campus Universitario de Santiago, Aveiro, 3810-193, Portugal. jlo@ua.pt.', 'Portugal|Portugal', 5, 'Automation|Cloud Computing|Computational Biology|Humans|Software', 'Journal Article', '10.1186/s12859-015-0761-3', '26464306', 'In recent years data integration has become an everyday undertaking for life sciences researchers. Aggregating and processing data from disparate sources, whether through specific developed software or via manual processes, is a common task for scientists. However, the scope and usability of the majority of current integration tools fail to deal with the fast growing and highly dynamic nature of biomedical data.'),
(96947, 2015, 'bmc', 'Estimating cumulative pathway effects on risk for age-related macular degeneration using mixed linear models', 'Hall, Jacob B|Cooke Bailey, Jessica N|Hoffman, Joshua D|Pericak-Vance, Margaret A|Scott, William K|Kovach, Jaclyn L|Schwartz, Stephen G|Agarwal, Anita|Brantley, Milam A|Haines, Jonathan L|Bush, William S', 'Graduate Program in Human Genetics, Vanderbilt University, Nashville, TN, USA. jacobhall@case.edu.|Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA. jnc43@case.edu.|Graduate Program in Human Genetics, Vanderbilt University, Nashville, TN, USA. joshua.hoffman@vanderbilt.edu.|John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA. mpericak@med.miami.edu.|John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA. bscott@med.miami.edu.|Department of Ophthalmology at Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL, USA. j.kovach@miami.edu.|Department of Ophthalmology at Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL, USA. sschwartz2@med.miami.edu.|Department of Ophthalmology and Visual Sciences, Vanderbilt University, Nashville, TN, USA. anita.agarwal@vanderbilt.edu.|Department of Ophthalmology and Visual Sciences, Vanderbilt University, Nashville, TN, USA. milam.brantley@vanderbilt.edu.|Institute for Computational Biology, Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA. jlh213@case.edu.|Institute for Computational Biology, Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA. wsb36@case.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 8, 'Case-Control Studies|Female|Gene Regulatory Networks|Genome-Wide Association Study|Genotype|Humans|Linear Models|Linkage Disequilibrium|Macular Degeneration|Male|Phenotype|Polymorphism, Single Nucleotide|Risk', 'Journal Article', '10.1186/s12859-015-0760-4', '26467978', 'Age-related macular degeneration (AMD) is the leading cause of irreversible visual loss in the elderly in developed countries and typically affects more than 10% of individuals over age 80. AMD has a large genetic component, with heritability estimated to be between 45% and 70%. Numerous variants have been identified and implicate various molecular mechanisms and pathways for AMD pathogenesis but those variants only explain a portion of AMDs heritability. The goal of our study was to estimate the cumulative genetic contribution of common variants on AMD risk for multiple pathways related to the etiology of AMD, including angiogenesis, antioxidant activity, apoptotic signaling, complement activation, inflammatory response, response to nicotine, oxidative phosphorylation, and the tricarboxylic acid cycle. While these mechanisms have been associated with AMD in literature, the overall extent of the contribution to AMD risk for each is unknown.'),
(96948, 2015, 'bmc', 'Survey statistics of automated segmentations applied to optical imaging of mammalian cells', 'Bajcsy, Peter|Cardone, Antonio|Chalfoun, Joe|Halter, Michael|Juba, Derek|Kociolek, Marcin|Majurski, Michael|Peskin, Adele|Simon, Carl|Simon, Mylene|Vandecreme, Antoine|Brady, Mary', 'Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, USA. peter.bajcsy@nist.gov.|Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, USA. antonio.cardone@nist.gov.|Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, USA. joe.chalfoun@nist.gov.|Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, USA. michael.halter@nist.gov.|Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, USA. derek.juba@nist.gov.|Lodz University of Technology, Lodz, Poland. marcin.kociolek@gmail.com.|Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, USA. michael.majurski@nist.gov.|Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, USA. adele.peskin@nist.gov.|Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, USA. carl.simon@nist.gov.|Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, USA. mylene.simon@nist.gov.|Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, USA. antoine.vandecreme@nist.gov.|Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, USA. mary.brady@nist.gov.', 'United States|United States|United States|United States|United States|Poland|United States|United States|United States|United States|United States|United States', 21, 'Algorithms|Animals|Automation|Humans|Microscopy|Optical Imaging', 'Journal Article', '10.1186/s12859-015-0762-2', '26472075', 'The goal of this survey paper is to overview cellular measurements using optical microscopy imaging followed by automated image segmentation. The cellular measurements of primary interest are taken from mammalian cells and their components. They are denoted as two- or three-dimensional (2D or 3D) image objects of biological interest. In our applications, such cellular measurements are important for understanding cell phenomena, such as cell counts, cell-scaffold interactions, cell colony growth rates, or cell pluripotency stability, as well as for establishing quality metrics for stem cell therapies. In this context, this survey paper is focused on automated segmentation as a software-based measurement leading to quantitative cellular measurements.'),
(96949, 2015, 'bmc', 'cnvCurator: an interactive visualization and editing tool for somatic copy number variations', 'Ma, Lingnan|Qin, Maochun|Liu, Biao|Hu, Qiang|Wei, Lei|Wang, Jianmin|Liu, Song', 'Department of Biostatistics and Bioinformatics, Roswell Park Cancer Institute, Buffalo, NY, 14263, USA. lm69@buffalo.edu.|Department of Biostatistics and Bioinformatics, Roswell Park Cancer Institute, Buffalo, NY, 14263, USA. Maochun.Qin@RoswellPark.org.|Department of Biostatistics and Bioinformatics, Roswell Park Cancer Institute, Buffalo, NY, 14263, USA. Biao.Liu@RoswellPark.org.|Department of Biostatistics and Bioinformatics, Roswell Park Cancer Institute, Buffalo, NY, 14263, USA. Qiang.Hu@RoswellPark.org.|Department of Biostatistics and Bioinformatics, Roswell Park Cancer Institute, Buffalo, NY, 14263, USA. Lei.Wei@RoswellPark.org.|Department of Biostatistics and Bioinformatics, Roswell Park Cancer Institute, Buffalo, NY, 14263, USA. Jianmin.Wang@RoswellPark.org.|Department of Biostatistics and Bioinformatics, Roswell Park Cancer Institute, Buffalo, NY, 14263, USA. Song.Liu@RoswellPark.org.', 'United States|United States|United States|United States|United States|United States|United States', 2, 'DNA Copy Number Variations|Genome, Human|Genomics|Humans|Internet|Neoplasms|User-Computer Interface', 'Journal Article', '10.1186/s12859-015-0766-y', '26472134', 'One of the most important somatic aberrations, copy number variations (CNVs) in tumor genomes is believed to have a high probability of harboring oncotargets. Detection of somatic CNVs is an essential part of cancer genome sequencing analysis, but the accuracy is usually limited due to various factors. A post-processing procedure including manual review and refinement of CNV segments is often needed in practice to achieve better accuracy.'),
(96950, 2015, 'bmc', 'Improving RNA-Seq expression estimation by modeling isoform- and exon-specific read sequencing rate', 'Liu, Xuejun|Shi, Xinxin|Chen, Chunlin|Zhang, Li', 'College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, 29 Jiangjun Rd., Nanjing, 211106, China. xuejun.liu@nuaa.edu.cn.|College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, 29 Jiangjun Rd., Nanjing, 211106, China. shixinxin61@126.com.|College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, 29 Jiangjun Rd., Nanjing, 211106, China. 524126860@qq.com.|College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, 29 Jiangjun Rd., Nanjing, 211106, China. leo.zhang@nuaa.edu.cn.', 'China|China|China|China', 7, 'Alternative Splicing|Exons|High-Throughput Nucleotide Sequencing|Humans|Internet|Models, Genetic|Protein Isoforms|RNA|Sequence Analysis, RNA|Transcriptome|User-Computer Interface', 'Journal Article', '10.1186/s12859-015-0750-6', '26475308', 'The high-throughput sequencing technology, RNA-Seq, has been widely used to quantify gene and isoform expression in the study of transcriptome in recent years. Accurate expression measurement from the millions or billions of short generated reads is obstructed by difficulties. One is ambiguous mapping of reads to reference transcriptome caused by alternative splicing. This increases the uncertainty in estimating isoform expression. The other is non-uniformity of read distribution along the reference transcriptome due to positional, sequencing, mappability and other undiscovered sources of biases. This violates the uniform assumption of read distribution for many expression calculation approaches, such as the direct RPKM calculation and Poisson-based models. Many methods have been proposed to address these difficulties. Some approaches employ latent variable models to discover the underlying pattern of read sequencing. However, most of these methods make bias correction based on surrounding sequence contents and share the bias models by all genes. They therefore cannot estimate gene- and isoform-specific biases as revealed by recent studies.'),
(96951, 2015, 'bmc', 'A straightforward method to compute average stochastic oscillations from data samples', 'Júlvez, Jorge', 'Cambridge Systems Biology Centre, University of Cambridge, Tennis Court RoadCB2 1GA, Cambridge, United Kingdom. jj425@cam.ac.uk.', 'United Kingdom', 0, 'Algorithms|Computational Biology|Markov Chains|Population Dynamics', 'Journal Article', '10.1186/s12859-015-0765-z', '26482438', 'Many biological systems exhibit sustained stochastic oscillations in their steady state. Assessing these oscillations is usually a challenging task due to the potential variability of the amplitude and frequency of the oscillations over time. As a result of this variability, when several stochastic replications are averaged, the oscillations are flattened and can be overlooked. This can easily lead to the erroneous conclusion that the system reaches a constant steady state.'),
(96952, 2015, 'bmc', 'Comparative study on gene set and pathway topology-based enrichment methods', 'Bayerlová, Michaela|Jung, Klaus|Kramer, Frank|Klemm, Florian|Bleckmann, Annalen|Beißbarth, Tim', 'Department of Medical Statistics, University Medical Center Göttingen, 37099, Göttingen, Germany. Michaela.Bayerlova@med.uni-goettingen.de.|Department of Medical Statistics, University Medical Center Göttingen, 37099, Göttingen, Germany. Klaus.Jung@ams.med.uni-goettingen.de.|Department of Medical Statistics, University Medical Center Göttingen, 37099, Göttingen, Germany. Frank.Kramer@med.uni-goettingen.de.|Department of Hematology and Medical Oncology, University Medical Center Göttingen, 37099, Göttingen, Germany. Florian.Klemm@med.uni-goettingen.de.|Department of Medical Statistics, University Medical Center Göttingen, 37099, Göttingen, Germany. A.Bleckmann@med.uni-goettingen.de.|Department of Medical Statistics, University Medical Center Göttingen, 37099, Göttingen, Germany. Tim.Beissbarth@ams.med.uni-goettingen.de.', 'Germany|Germany|Germany|Germany|Germany|Germany', 31, 'Algorithms|Computer Simulation|Feedback, Physiological|Gene Expression|Humans|Metabolic Networks and Pathways|Models, Genetic', 'Comparative Study', '10.1186/s12859-015-0751-5', '26489510', 'Enrichment analysis is a popular approach to identify pathways or sets of genes which are significantly enriched in the context of differentially expressed genes. The traditional gene set enrichment approach considers a pathway as a simple gene list disregarding any knowledge of gene or protein interactions. In contrast, the new group of so called pathway topology-based methods integrates the topological structure of a pathway into the analysis.'),
(96953, 2015, 'bmc', 'AlloPred: prediction of allosteric pockets on proteins using normal mode perturbation analysis', 'Greener, Joe G|Sternberg, Michael J E', 'Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK. joe.greener13@imperial.ac.uk.|Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK.', 'United Kingdom|United Kingdom', 30, 'Algorithms|Allosteric Site|Ligands|Protein Conformation|Proteins', 'Journal Article', '10.1186/s12859-015-0771-1', '26493317', 'Despite being hugely important in biological processes, allostery is poorly understood and no universal mechanism has been discovered. Allosteric drugs are a largely unexplored prospect with many potential advantages over orthosteric drugs. Computational methods to predict allosteric sites on proteins are needed to aid the discovery of allosteric drugs, as well as to advance our fundamental understanding of allostery.'),
(96954, 2015, 'bmc', 'Iterative reconstruction of three-dimensional models of human chromosomes from chromosomal contact data', 'Nowotny, Jackson|Ahmed, Sharif|Xu, Lingfei|Oluwadare, Oluwatosin|Chen, Hannah|Hensley, Noelan|Trieu, Tuan|Cao, Renzhi|Cheng, Jianlin', 'Department of Computer Science, Informatics Institute, University of Missouri, Columbia, MO, 65211, USA. jn9qc@mail.missouri.edu.|Department of Computer Science, Informatics Institute, University of Missouri, Columbia, MO, 65211, USA. sa262@mail.missouri.edu.|Department of Computer Science, Informatics Institute, University of Missouri, Columbia, MO, 65211, USA. lxq35@mail.missouri.edu.|Department of Computer Science, Informatics Institute, University of Missouri, Columbia, MO, 65211, USA. oeow39@mail.missouri.edu.|Department of Computer Science, Informatics Institute, University of Missouri, Columbia, MO, 65211, USA. hannahc9596@gmail.com.|Department of Computer Science, Informatics Institute, University of Missouri, Columbia, MO, 65211, USA. ndhhfb@mail.missouri.edu.|Department of Computer Science, Informatics Institute, University of Missouri, Columbia, MO, 65211, USA. tuantrieu@mail.missouri.edu.|Department of Computer Science, Informatics Institute, University of Missouri, Columbia, MO, 65211, USA. rcrg4@mail.missouri.edu.|Department of Computer Science, Informatics Institute, University of Missouri, Columbia, MO, 65211, USA. chengji@missouri.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 7, 'B-Lymphocytes|Chromosomes, Human|Genome|Humans|Models, Theoretical|Sequence Alignment|Templates, Genetic', 'Journal Article', '10.1186/s12859-015-0772-0', '26493399', 'The entire collection of genetic information resides within the chromosomes, which themselves reside within almost every cell nucleus of eukaryotic organisms. Each individual chromosome is found to have its own preferred three-dimensional (3D) structure independent of the other chromosomes. The structure of each chromosome plays vital roles in controlling certain genome operations, including gene interaction and gene regulation. As a result, knowing the structure of chromosomes assists in the understanding of how the genome functions. Fortunately, the 3D structure of chromosomes proves possible to construct through computational methods via contact data recorded from the chromosome. We developed a unique computational approach based on optimization procedures known as adaptation, simulated annealing, and genetic algorithm to construct 3D models of human chromosomes, using chromosomal contact data.'),
(96955, 2015, 'bmc', 'Phylogenomics and sequence-structure-function relationships in the GmrSD family of Type IV restriction enzymes', 'Machnicka, Magdalena A|Kaminska, Katarzyna H|Dunin-Horkawicz, Stanislaw|Bujnicki, Janusz M', 'Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, PL-02-109, Warsaw, Poland.|Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, PL-02-109, Warsaw, Poland.|Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, PL-02-109, Warsaw, Poland.|Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, PL-02-109, Warsaw, Poland. iamb@genesilico.pl.', 'Poland|Poland|Poland|Poland', 8, 'Catalytic Domain|DNA|DNA Restriction Enzymes|Genomics|Phylogeny|Protein Conformation|Protein Structure, Tertiary|Structure-Activity Relationship', 'Journal Article', '10.1186/s12859-015-0773-z', '26493560', 'GmrSD is a modification-dependent restriction endonuclease that specifically targets and cleaves glucosylated hydroxymethylcytosine (glc-HMC) modified DNA. It is encoded either as two separate single-domain GmrS and GmrD proteins or as a single protein carrying both domains. Previous studies suggested that GmrS acts as endonuclease and NTPase whereas GmrD binds DNA.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(96956, 2015, 'bmc', 'A large-scale conformation sampling and evaluation server for protein tertiary structure prediction and its assessment in CASP11', 'Li, Jilong|Cao, Renzhi|Cheng, Jianlin', 'Department of Computer Science, University of Missouri-Columbia, Columbia, MO, 65211, USA. jl482@mail.missouri.edu.|Department of Computer Science, University of Missouri-Columbia, Columbia, MO, 65211, USA. rcrg4@mail.missouri.edu.|Department of Computer Science, University of Missouri-Columbia, Columbia, MO, 65211, USA. chengji@missouri.edu.', 'United States|United States|United States', 10, 'Amino Acid Sequence|Genomics|Models, Chemical|Protein Conformation|Protein Structure, Tertiary|Proteins', 'Journal Article', '10.1186/s12859-015-0775-x', '26493701', 'With more and more protein sequences produced in the genomic era, predicting protein structures from sequences becomes very important for elucidating the molecular details and functions of these proteins for biomedical research. Traditional template-based protein structure prediction methods tend to focus on identifying the best templates, generating the best alignments, and applying the best energy function to rank models, which often cannot achieve the best performance because of the difficulty of obtaining best templates, alignments, and models.'),
(96957, 2015, 'bmc', 'PDB-Explorer: a web-based interactive map of the protein data bank in shape space', 'Jin, Xian|Awale, Mahendra|Zasso, Michaxc3xabl|Kostro, Daniel|Patiny, Luc|Reymond, Jean-Louis', 'Department of Chemistry and Biochemistry, University of Berne, Freiestrasse 3, 3012, Berne, Switzerland. xian.jin@dcb.unibe.ch.|Department of Chemistry and Biochemistry, University of Berne, Freiestrasse 3, 3012, Berne, Switzerland. mahendra.awale@dcb.unibe.ch.|Ecole Polytechnique Fxc3xa9dxc3xa9rale de Lausanne (EPFL), Institute of Chemical Sciences and Engineering (ISIC), Lausanne, 1015, Switzerland. mic.besace@gmail.com.|Ecole Polytechnique Fxc3xa9dxc3xa9rale de Lausanne (EPFL), Institute of Chemical Sciences and Engineering (ISIC), Lausanne, 1015, Switzerland. kostro.d@gmail.com.|Ecole Polytechnique Fxc3xa9dxc3xa9rale de Lausanne (EPFL), Institute of Chemical Sciences and Engineering (ISIC), Lausanne, 1015, Switzerland. luc.patiny@epfl.ch.|Department of Chemistry and Biochemistry, University of Berne, Freiestrasse 3, 3012, Berne, Switzerland. jean-louis.reymond@dcb.unibe.ch.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 18, 'Algorithms|Cluster Analysis|Databases, Protein|Models, Molecular|Proteins', 'Journal Article', '10.1186/s12859-015-0776-9', '26493835', 'The RCSB Protein Data Bank (PDB) provides public access to experimentally determined 3D-structures of biological macromolecules (proteins, peptides and nucleic acids). While various tools are available to explore the PDB, options to access the global structural diversity of the entire PDB and to perceive relationships between PDB structures remain very limited.'),
(96958, 2015, 'bmc', 'Whole genome SNP genotype piecemeal imputation', 'Wang, Yining|Wylie, Tim|Stothard, Paul|Lin, Guohui', 'Department of Computing Science, University of Alberta, Edmonton, Alberta T6G 2E8, Canada. yining@ualberta.ca.|Department of Computing Science, University of Alberta, Edmonton, Alberta T6G 2E8, Canada. timothy.wylie@utrgv.edu.|Department of Agricultural, Food, and Nutritional Science, University of Alberta, Edmonton, T6G 2C8, Alberta, Canada. stothard@ualberta.ca.|Department of Computing Science, University of Alberta, Edmonton, Alberta T6G 2E8, Canada. guohui@ualberta.ca.', 'Canada|Canada|Canada|Canada', 0, 'Animals|Cattle|Cluster Analysis|Genome|Genome-Wide Association Study|Genotype|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1186/s12859-015-0770-2', '26498158', 'Despite ongoing reductions in the cost of sequencing technologies, whole genome SNP genotype imputation is often used as an alternative for obtaining abundant SNP genotypes for genome wide association studies. Several existing genotype imputation methods can be efficient for this purpose, while achieving various levels of imputation accuracy. Recent empirical results have shown that the two-step imputation may improve accuracy by imputing the low density genotyped study animals to a medium density array first and then to the target density. We are interested in building a series of staircase arrays that lead the low density array to the high density array or even the whole genome, such that genotype imputation along these staircases can achieve the highest accuracy.'),
(96959, 2015, 'bmc', 'Neuron anatomy structure reconstruction based on a sliding filter', 'Luo, Gongning|Sui, Dong|Wang, Kuanquan|Chae, Jinseok', 'Research Center of Perception and Computing, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China. luogongning@hit.edu.cn.|Research Center of Perception and Computing, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China. suidong10@gmail.com.|Research Center of Perception and Computing, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China. wangkq@hit.edu.cn.|Department of Computer Science and Engineering, Incheon National University, Incheon, Korea. jschae@inu.ac.kr.', 'China|China|China|-', 8, 'Algorithms|Humans|Imaging, Three-Dimensional|Microscopy|Neurons', 'Journal Article', '10.1186/s12859-015-0780-0', '26498293', 'Reconstruction of neuron anatomy structure is a challenging and important task in neuroscience. However, few algorithms can automatically reconstruct the full structure well without manual assistance, making it essential to develop new methods for this task.'),
(96960, 2015, 'bmc', 'QCScreen: a software tool for data quality control in LC-HRMS based metabolomics', 'Simader, Alexandra Maria|Kluger, Bernhard|Neumann, Nora Katharina Nicole|Bueschl, Christoph|Lemmens, Marc|Lirk, Gerald|Krska, Rudolf|Schuhmacher, Rainer', 'Center for Analytical Chemistry, Department of Agrobiotechnology (IFA-Tulln), University of Natural Resources and Life Sciences, Vienna (BOKU), Konrad Lorenz Strasse 20, 3430, Tulln, Austria. alexandra.simader@boku.ac.at.|Center for Analytical Chemistry, Department of Agrobiotechnology (IFA-Tulln), University of Natural Resources and Life Sciences, Vienna (BOKU), Konrad Lorenz Strasse 20, 3430, Tulln, Austria. bernhard.kluger@boku.ac.at.|Center for Analytical Chemistry, Department of Agrobiotechnology (IFA-Tulln), University of Natural Resources and Life Sciences, Vienna (BOKU), Konrad Lorenz Strasse 20, 3430, Tulln, Austria. nora.neumann@boku.ac.at.|Center for Analytical Chemistry, Department of Agrobiotechnology (IFA-Tulln), University of Natural Resources and Life Sciences, Vienna (BOKU), Konrad Lorenz Strasse 20, 3430, Tulln, Austria. christoph.bueschl@boku.ac.at.|Institute for Biotechnology in Plant Production, Department of Agrobiotechnology (IFA-Tulln), University of Natural Resources and Life Sciences, Vienna (BOKU), Konrad Lorenz Strasse 20, 3430, Tulln, Austria. marc.lemmens@boku.ac.at.|University of Applied Sciences Upper Austria, School of Informatics, Communications and Media, Softwarepark 11, 4232, Hagenberg, Austria. gerald.lirk@fh-hagenberg.at.|Center for Analytical Chemistry, Department of Agrobiotechnology (IFA-Tulln), University of Natural Resources and Life Sciences, Vienna (BOKU), Konrad Lorenz Strasse 20, 3430, Tulln, Austria. rudolf.krska@boku.ac.at.|Center for Analytical Chemistry, Department of Agrobiotechnology (IFA-Tulln), University of Natural Resources and Life Sciences, Vienna (BOKU), Konrad Lorenz Strasse 20, 3430, Tulln, Austria. rainer.schuhmacher@boku.ac.at.', 'Austria|Austria|Austria|Austria|Austria|Austria|Austria|Austria', 9, 'Chromatography, High Pressure Liquid|Information Storage and Retrieval|Mass Spectrometry|Metabolomics|Quality Control|Software', 'Journal Article', '10.1186/s12859-015-0783-x', '26498454', 'Metabolomics experiments often comprise large numbers of biological samples resulting in huge amounts of data. This data needs to be inspected for plausibility before data evaluation to detect putative sources of error e.g. retention time or mass accuracy shifts. Especially in liquid chromatography-high resolution mass spectrometry (LC-HRMS) based metabolomics research, proper quality control checks (e.g. for precision, signal drifts or offsets) are crucial prerequisites to achieve reliable and comparable results within and across experimental measurement sequences. Software tools can support this process.'),
(96961, 2015, 'bmc', 'DiSNPindel: improved intra-individual SNP and InDel detection in direct amplicon sequencing of a diploid', 'Deng, Jizhong|Huang, Huasheng|Yu, Xiaoli|Jin, Ji|Lin, Weisen|Li, Fagen|Song, Zhijiao|Li, Mei|Gan, Siming', 'College of Engineering, South China Agricultural University, Wushan Road, Guangzhou, 510642, China. jz-deng@scau.edu.cn.|College of Engineering, South China Agricultural University, Wushan Road, Guangzhou, 510642, China. huanghsheng@126.com.|State Key Laboratory of Tree Genetics and Breeding, Chinese Academy of Forestry, Xiangshan Road, Beijing, 100091, China. zouxiaqu99@163.com.|College of Engineering, South China Agricultural University, Wushan Road, Guangzhou, 510642, China. ji_king314@aliyun.com.|College of Engineering, South China Agricultural University, Wushan Road, Guangzhou, 510642, China. 576920627@qq.com.|Research Institute of Tropical Forestry, Chinese Academy of Forestry, Longdong, Guangzhou, 510520, China. lifagen2002@126.com.|Research Institute of Tropical Forestry, Chinese Academy of Forestry, Longdong, Guangzhou, 510520, China. 277856054@qq.com.|Research Institute of Tropical Forestry, Chinese Academy of Forestry, Longdong, Guangzhou, 510520, China. limei3043@126.com.|State Key Laboratory of Tree Genetics and Breeding, Chinese Academy of Forestry, Xiangshan Road, Beijing, 100091, China. Siming_Gan@126.com.', 'China|China|China|China|China|China|China|China|China', 0, 'DNA|Diploidy|Expressed Sequence Tags|INDEL Mutation|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-015-0790-y', '26498606', 'Amplicon re-sequencing based on the automated Sanger method remains popular for detection of single nucleotide polymorphisms (SNPs) and insertion-deletion polymorphisms (InDels) for a spectrum of genetics applications. However, existing software tools for detecting intra-individual SNPs and InDels in direct amplicon sequencing of diploid samples are insufficient in analyzing single traces and their accuracy is still limited.'),
(96962, 2015, 'bmc', 'CytoSpectre: a tool for spectral analysis of oriented structures on cellular and subcellular levels', 'Kartasalo, Kimmo|Pölönen, Risto-Pekka|Ojala, Marisa|Rasku, Jyrki|Lekkala, Jukka|Aalto-Setälä, Katriina|Kallio, Pasi', 'Department of Automation Science and Engineering, Tampere University of Technology, BioMediTech, Tampere, Finland. kimmo.kartasalo@uta.fi.|University of Tampere, BioMediTech, Tampere, Finland. risto-pekka.polonen@staff.uta.fi.|University of Tampere, BioMediTech, Tampere, Finland. marisa.ojala@uta.fi.|School of Information Sciences, University of Tampere, Tampere, Finland. jyrki.rasku@uta.fi.|Department of Automation Science and Engineering, Tampere University of Technology, BioMediTech, Tampere, Finland. jukka.lekkala@tut.fi.|University of Tampere, BioMediTech, Tampere, Finland. katriina.aalto-setala@uta.fi.|Department of Automation Science and Engineering, Tampere University of Technology, BioMediTech, Tampere, Finland. pasi.kallio@tut.fi.', 'Finland|Finland|Finland|Finland|Finland|Finland|Finland', 24, 'Cell Differentiation|Coculture Techniques|Cytoskeleton|Fourier Analysis|Humans|Image Processing, Computer-Assisted|Induced Pluripotent Stem Cells|Microscopy, Fluorescence|Myocytes, Cardiac|Software|Stress, Mechanical', 'Journal Article', '10.1186/s12859-015-0782-y', '26503371', 'Orientation and the degree of isotropy are important in many biological systems such as the sarcomeres of cardiomyocytes and other fibrillar structures of the cytoskeleton. Image based analysis of such structures is often limited to qualitative evaluation by human experts, hampering the throughput, repeatability and reliability of the analyses. Software tools are not readily available for this purpose and the existing methods typically rely at least partly on manual operation.'),
(96963, 2015, 'bmc', 'Extended notions of sign consistency to relate experimental data to signaling and regulatory network topologies', 'Thiele, Sven|Cerone, Luca|Saez-Rodriguez, Julio|Siegel, Anne|Guzioxc5x82owski, Carito|Klamt, Steffen', 'Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, 39106, Germany. thieles@mpi-magdeburg.mpg.de.|European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton CB101SD, UK. luca.cerone@gmail.com.|European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton CB101SD, UK. saezrodriguez@gmail.com.|CNRS, UMR 6074 IRISA, Campus de Beaulieu, Rennes, 35042, France. anne.siegel@irisa.fr.|xc3x89cole Centrale de Nantes, IRCCyN UMR 6597, 1 rue de la Noxc3xab, Nantes, 44321, France. carito.guziolowski@irccyn.ec-nantes.fr.|Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, 39106, Germany. klamt@mpi-magdeburg.mpg.de.', 'Germany|United Kingdom|United Kingdom|France|France|Germany', 4, 'Algorithms|Escherichia coli|Gene Regulatory Networks|Signal Transduction', 'Journal Article', '10.1186/s12859-015-0733-7', '26510976', 'A rapidly growing amount of knowledge about signaling and gene regulatory networks is available in databases such as KEGG, Reactome, or RegulonDB. There is an increasing need to relate this knowledge to high-throughput data in order to (in)validate network topologies or to decide which interactions are present or inactive in a given cell type under a particular environmental condition. Interaction graphs provide a suitable representation of cellular networks with information flows and methods based on sign consistency approaches have been shown to be valuable tools to (i) predict qualitative responses, (ii) to test the consistency of network topologies and experimental data, and (iii) to apply repair operations to the network model suggesting missing or wrong interactions.'),
(96964, 2015, 'bmc', 'Proceedings of the 14th Annual UT-KBRIN Bioinformatics Summit 2015', '-', '-', '-', 4, '-', 'Journal Article', '10.1186/s12859-015-0733-7', '26510995', '-'),
(96965, 2015, 'bmc', 'GOTA: GO term annotation of biomedical literature', 'Di Lena, Pietro|Domeniconi, Giacomo|Margara, Luciano|Moro, Gianluca', 'Department of Computer Science and Engineering, University of Bologna, Cesena Campus, Via Sacchi 3, Cesena, 47521, Italy. pietro.dilena@unibo.it.|Department of Computer Science and Engineering, University of Bologna, Cesena Campus, Via Sacchi 3, Cesena, 47521, Italy. giacomo.domeniconi@unibo.it.|Department of Computer Science and Engineering, University of Bologna, Cesena Campus, Via Sacchi 3, Cesena, 47521, Italy. luciano.margara@unibo.it.|Department of Computer Science and Engineering, University of Bologna, Cesena Campus, Via Sacchi 3, Cesena, 47521, Italy. gianluca.moro@unibo.it.', 'Italy|Italy|Italy|Italy', 14, 'Animals|Data Mining|Gene Ontology|Humans|Internet|Molecular Sequence Annotation|User-Computer Interface', 'Journal Article', '10.1186/s12859-015-0777-8', '26511083', 'Functional annotation of genes and gene products is a major challenge in the post-genomic era. Nowadays, gene function curation is largely based on manual assignment of Gene Ontology (GO) annotations to genes by using published literature. The annotation task is extremely time-consuming, therefore there is an increasing interest in automated tools that can assist human experts.'),
(96966, 2015, 'bmc', 'Comparing the normalization methods for the differential analysis of Illumina high-throughput RNA-Seq data', 'Li, Peipei|Piao, Yongjun|Shon, Ho Sun|Ryu, Keun Ho', 'College of Electrical and Computer Engineering, Chungbuk National University, Cheongju-si, South Korea. lipeipei0611@gmail.com.|College of Electrical and Computer Engineering, Chungbuk National University, Cheongju-si, South Korea. pyz@dblab.chungbuk.ac.kr.|College of Electrical and Computer Engineering, Chungbuk National University, Cheongju-si, South Korea. shon0621@gmail.com.|College of Electrical and Computer Engineering, Chungbuk National University, Cheongju-si, South Korea. khryu@dblab.chungbuk.ac.kr.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Base Sequence|Databases, Genetic|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Polyadenylation|RNA|Real-Time Polymerase Chain Reaction|Sequence Alignment|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-015-0778-7', '26511205', 'Recently, rapid improvements in technology and decrease in sequencing costs have made RNA-Seq a widely used technique to quantify gene expression levels. Various normalization approaches have been proposed, owing to the importance of normalization in the analysis of RNA-Seq data. A comparison of recently proposed normalization methods is required to generate suitable guidelines for the selection of the most appropriate approach for future experiments.'),
(96967, 2015, 'bmc', 'Novel gene sets improve set-level classification of prokaryotic gene expression data', 'Holec, Matxc4x9bj|Kuxc5xbeelka, Ondxc5x99ej|xc5xbdeleznxc3xbd, Filip', 'Faculty of Electrical Engineering, Czech Technical University, Technickxc3xa1 2, Prague, 166 27, Czech Republic. holecm@fel.cvut.cz.|Faculty of Electrical Engineering, Czech Technical University, Technickxc3xa1 2, Prague, 166 27, Czech Republic. kuzelo1@gmail.com.|Faculty of Electrical Engineering, Czech Technical University, Technickxc3xa1 2, Prague, 166 27, Czech Republic. zelezny@fel.cvut.cz.', '-|-|-', 0, 'Gene Expression Regulation|Gene Ontology|Machine Learning|Metabolic Networks and Pathways|Operon|Prokaryotic Cells|Transcription Factors', 'Journal Article', '10.1186/s12859-015-0786-7', '26511329', 'Set-level classification of gene expression data has received significant attention recently. In this setting, high-dimensional vectors of features corresponding to genes are converted into lower-dimensional vectors of features corresponding to biologically interpretable gene sets. The dimensionality reduction brings the promise of a decreased risk of overfitting, potentially resulting in improved accuracy of the learned classifiers. However, recent empirical research has not confirmed this expectation. Here we hypothesize that the reported unfavorable classification results in the set-level framework were due to the adoption of unsuitable gene sets defined typically on the basis of the Gene ontology and the KEGG database of metabolic networks. We explore an alternative approach to defining gene sets, based on regulatory interactions, which we expect to collect genes with more correlated expression. We hypothesize that such more correlated gene sets will enable to learn more accurate classifiers.'),
(96968, 2015, 'bmc', 'Peak shape clustering reveals biological insights', 'Cremona, Marzia A|Sangalli, Laura M|Vantini, Simone|Dellino, Gaetano I|Pelicci, Pier Giuseppe|Secchi, Piercesare|Riva, Laura', 'MOX - Dipartimento di Matematica, Politecnico di Milano, Milan, Italy. marziaangela.cremona@polimi.it.|MOX - Dipartimento di Matematica, Politecnico di Milano, Milan, Italy. laura.sangalli@polimi.it.|MOX - Dipartimento di Matematica, Politecnico di Milano, Milan, Italy. simone.vantini@polimi.it.|Department of Experimental Oncology, European Institute of Oncology, Milan, Italy. gaetano.dellino@ieo.eu.|Department of Experimental Oncology, European Institute of Oncology, Milan, Italy. piergiuseppe.pelicci@ieo.eu.|MOX - Dipartimento di Matematica, Politecnico di Milano, Milan, Italy. piercesare.secchi@polimi.it.|Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia, Milan, Italy. laura.riva@iit.it.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy', 9, 'Chromatin Immunoprecipitation|Cluster Analysis|Computational Biology|DNA|GATA1 Transcription Factor|Gene Knockdown Techniques|Humans|K562 Cells|Megakaryocytes|Protein Binding|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-015-0787-6', '26511446', 'ChIP-seq experiments are widely used to detect and study DNA-protein interactions, such as transcription factor binding and chromatin modifications. However, downstream analysis of ChIP-seq data is currently restricted to the evaluation of signal intensity and the detection of enriched regions (peaks) in the genome. Other features of peak shape are almost always neglected, despite the remarkable differences shown by ChIP-seq for different proteins, as well as by distinct regions in a single experiment.'),
(96969, 2015, 'bmc', 'ToPASeq: an R package for topology-based pathway analysis of microarray and RNA-Seq data', 'Ihnatova, Ivana|Budinska, Eva', 'Institute of Biostatistics and Analyses, Faculty of Medicine, Masarykova Univerzita, Brno, Czech Republic. ihnatova@iba.muni.cz.|Institute of Biostatistics and Analyses, Faculty of Medicine, Masarykova Univerzita, Brno, Czech Republic. budinska@iba.muni.cz.', '-|-', 0, 'Computer Graphics|Gene Expression Profiling|Gene Regulatory Networks|High-Throughput Nucleotide Sequencing|Humans|RNA|Sequence Analysis, RNA|Signal Transduction|Software', 'Journal Article', '10.1186/s12859-015-0763-1', '26514335', 'Pathway analysis methods, in which differentially expressed genes are mapped to databases of reference pathways and relative enrichment is assessed, help investigators to propose biologically relevant hypotheses. The last generation of pathway analysis methods takes into account the topological structure of a pathway, which helps to increase both specificity and sensitivity of the findings. Simultaneously, the RNA-Seq technology is gaining popularity and becomes widely used for gene expression profiling. Unfortunately, majority of topological pathway analysis methods remains without implementation and if an implementation exists, it is limited in various factors.'),
(96970, 2015, 'bmc', 'HMMvar-func: a new method for predicting the functional outcome of genetic variants', 'Liu, Mingming|Watson, Layne T|Zhang, Liqing', 'Department of Computer Science, Virginia Polytechnic Institute & State University, Blacksburg, USA. mingml@cs.vt.edu.|Department of Computer Science, Virginia Polytechnic Institute & State University, Blacksburg, USA. john.RS.Smith@cambridge.co.uk.|Department of Computer Science, Virginia Polytechnic Institute & State University, Blacksburg, USA. lqzhang@cs.vt.edu.', 'United Kingdom|United Kingdom|United Kingdom', 3, 'Computational Biology|Genetic Variation|Humans|Internet|Markov Chains|Mutation|Neoplasms|Receptors, Thyrotropin|User-Computer Interface', 'Journal Article', '10.1186/s12859-015-0781-z', '26518340', 'Numerous tools have been developed to predict the fitness effects (i.e., neutral, deleterious, or beneficial) of genetic variants on corresponding proteins. However, prediction in terms of whether a variant causes the variant bearing protein to lose the original function or gain new function is also needed for better understanding of how the variant contributes to disease/cancer. To address this problem, the present work introduces and computationally defines four types of functional outcome of a variant: gain, loss, switch, and conservation of function. The deployment of multiple hidden Markov models is proposed to computationally classify mutations by the four functional impact types.'),
(96971, 2015, 'bmc', 'Use and mis-use of supplementary material in science publications', 'Pop, Mihai|Salzberg, Steven L', 'Department of Computer Science and Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, 20742, USA. mpop@umiacs.umd.edu.|Center for Computational Biology McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA. salzberg@jhu.edu.', 'United States|United States', 19, 'Editorial Policies|Publications', 'Editorial', '10.1186/s12859-015-0668-z', '26525146', 'Supplementary material is a ubiquitous feature of scientific articles, particularly in journals that limit the length of the articles. While the judicious use of supplementary material can improve the readability of scientific articles, its excessive use threatens the scientific review process and by extension the integrity of the scientific literature. In many cases supplementary material today is so extensive that it is reviewed superficially or not at all. Furthermore, citations buried within supplementary files rob other scientists of recognition of their contribution to the scientific record. These issues are exacerbated by the lack of guidance on the use of supplementary information from the journals to authors and reviewers. We propose that the removal of artificial length restrictions plus the use of interactive features made possible by modern electronic media can help to alleviate these problems. Many journals, in fact, have already removed article length limitations (as is the case for BMC Bioinformatics and other BioMed Central journals). We hope that the issues raised in our article will encourage publishers and scientists to work together towards a better use of supplementary information in scientific publishing. '),
(96972, 2015, 'bmc', 'Cnidaria: fast, reference-free clustering of raw and assembled genome and transcriptome NGS data', 'Aflitos, Saulo Alves|Severing, Edouard|Sanchez-Perez, Gabino|Peters, Sander|de Jong, Hans|de Ridder, Dick', 'Applied Bioinformatics, Plant Research International, Wageningen, The Netherlands. sauloalves.aflitos@wur.nl.|Laboratory of Genetics, Wageningen University, Wageningen, The Netherlands. severing@mpipz.mpg.de.|Applied Bioinformatics, Plant Research International, Wageningen, The Netherlands. gabino.sanchezperez@wur.nl.|Applied Bioinformatics, Plant Research International, Wageningen, The Netherlands. sander.peters@wur.nl.|Laboratory of Genetics, Wageningen University, Wageningen, The Netherlands. hans.deJong@wur.nl.|Bioinformatics Group, Department of Plant Sciences, Wageningen University, Wageningen, The Netherlands. dick.deridder@wur.nl.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 6, 'Animals|Cluster Analysis|Genome|Genomics|High-Throughput Nucleotide Sequencing|Insecta|Internet|Phylogeny|Sequence Analysis, DNA|Sequence Analysis, RNA|Solanaceae|Transcriptome|User-Computer Interface', 'Journal Article', '10.1186/s12859-015-0806-7', '26525298', 'Identification of biological specimens is a requirement for a range of applications. Reference-free methods analyse unprocessed sequencing data without relying on prior knowledge, but generally do not scale to arbitrarily large genomes and arbitrarily large phylogenetic distances.'),
(96973, 2015, 'bmc', 'A Lattice-Boltzmann scheme for the simulation of diffusion in intracellular crowded systems', 'Angeles-Martinez, Liliana|Theodoropoulos, Constantinos', 'School of Chemical Engineering and Analytical Science, University of Manchester, Manchester, M13 9PL, UK. liliana.angelesmartinez@manchester.ac.uk.|School of Chemical Engineering and Analytical Science, University of Manchester, Manchester, M13 9PL, UK. k.theodoropoulos@manchester.ac.uk.', 'United Kingdom|United Kingdom', 0, 'Algorithms|Computer Simulation|Diffusion|Intracellular Space|Kinetics|Monte Carlo Method|Reproducibility of Results', 'Journal Article', '10.1186/s12859-015-0769-8', '26530635', 'The intracellular environment is a complex and crowded medium where the diffusion of proteins, metabolites and other molecules can be decreased. One of the most popular methodologies for the simulation of diffusion in crowding systems is the Monte Carlo algorithm (MC) which tracks the movement of each particle. This can, however, be computationally expensive for a system comprising a large number of molecules. On the other hand, the Lattice Boltzmann Method (LBM) tracks the movement of collections of molecules, which represents significant savings in computational time. Nevertheless in the classical manifestation of such scheme the crowding conditions are neglected.'),
(96974, 2015, 'bmc', 'tDRmapper: challenges and solutions to mapping, naming, and quantifying tRNA-derived RNAs from human small RNA-sequencing data', 'Selitsky, Sara R|Sethupathy, Praveen', 'Bioinformatics and Computational Biology Curriculum, University of North Carolina, Chapel Hill, NC, USA. SaraRSelitsky@gmail.com.|Bioinformatics and Computational Biology Curriculum, University of North Carolina, Chapel Hill, NC, USA. praveen_sethupathy@med.unc.edu.', 'United States|United States', 27, 'Base Sequence|Cell Differentiation|Databases, Nucleic Acid|Humans|MicroRNAs|Molecular Sequence Data|Organ Specificity|RNA Precursors|RNA, Transfer|Sequence Alignment|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/s12859-015-0800-0', '26530785', 'Small RNA-sequencing has revealed the diversity and high abundance of small RNAs derived from tRNAs, referred to as tRNA-derived RNAs. However, at present, there is no standardized nomenclature and there are no methods for accurate annotation and quantification of these small RNAs. tRNA-derived RNAs have unique features that limit the utility of conventional alignment tools and quantification methods.'),
(96975, 2015, 'bmc', 'Estimation of evolutionary parameters using short, random and partial sequences from mixed samples of anonymous individuals', 'Wu, Steven H|Rodrigo, Allen G', 'Biodesign Institute, Arizona State University, Tempe, AZ, 85287, USA. stevenwu@asu.edu.|Department of Biology, Duke University, Box 90338, Durham, NC, 27708, USA. a.rodrigo@nescent.org.', 'United States|United States', 0, 'Algorithms|Base Sequence|Bayes Theorem|Computer Simulation|Confidence Intervals|Evolution, Molecular|High-Throughput Nucleotide Sequencing|Humans|Least-Squares Analysis|Markov Chains|Monte Carlo Method|Population Density', 'Journal Article', '10.1186/s12859-015-0810-y', '26536860', 'Over the last decade, next generation sequencing (NGS) has become widely available, and is now the sequencing technology of choice for most researchers. Nonetheless, NGS presents a challenge for the evolutionary biologists who wish to estimate evolutionary genetic parameters from a mixed sample of unlabelled or untagged individuals, especially when the reconstruction of full length haplotypes can be unreliable. We propose two novel approaches, least squares estimation (LS) and Approximate Bayesian Computation Markov chain Monte Carlo estimation (ABC-MCMC), to infer evolutionary genetic parameters from a collection of short-read sequences obtained from a mixed sample of anonymous DNA using the frequencies of nucleotides at each site only without reconstructing the full-length alignment nor the phylogeny.'),
(96976, 2015, 'bmc', 'BRANE Cut: biologically-related a priori network enhancement with graph cuts for gene regulatory network inference', 'Pirayre, Aurélie|Couprie, Camille|Bidard, Frédérique|Duval, Laurent|Pesquet, Jean-Christophe', 'IFP Energies Nouvelles, 1-4 avenue de Bois-Préau, Rueil-Malmaison, 92852, France. aurelie.pirayre@ifpen.fr.|IFP Energies Nouvelles, 1-4 avenue de Bois-Préau, Rueil-Malmaison, 92852, France. camille.couprie@ifpen.fr.|IFP Energies Nouvelles, 1-4 avenue de Bois-Préau, Rueil-Malmaison, 92852, France. frederique.bidard-michelot@ifp.fr.|IFP Energies Nouvelles, 1-4 avenue de Bois-Préau, Rueil-Malmaison, 92852, France. laurent.duval@ifpen.fr.|Université Paris-Est, Laboratoire dInformatique Gaspard-Monge, 5 boulevard Descartes - Champs-sur-Marne, Marne-la-Vallée, 77454, France. jean-christophe.pesquet@univ-paris-est.fr.', 'France|France|France|France|France', 6, 'Algorithms|Area Under Curve|Computer Simulation|Databases, Genetic|Escherichia coli|Gene Regulatory Networks|Reproducibility of Results', 'Journal Article', '10.1186/s12859-015-0754-2', '26537179', 'Inferring gene networks from high-throughput data constitutes an important step in the discovery of relevant regulatory relationships in organism cells. Despite the large number of available Gene Regulatory Network inference methods, the problem remains challenging: the underdetermination in the space of possible solutions requires additional constraints that incorporate a priori information on gene interactions.'),
(96977, 2015, 'bmc', 'CellProfiler Tracer: exploring and validating high-throughput, time-lapse microscopy image data', 'Bray, Mark-Anthony|Carpenter, Anne E', 'Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA, 02142, USA. mbray@broadinstitute.org.|Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA, 02142, USA. anne@broadinstitute.org.', 'United States|United States', 13, 'Cell Tracking|Humans|Image Processing, Computer-Assisted|MCF-7 Cells|Microscopy|Reproducibility of Results|Software|Time-Lapse Imaging|User-Computer Interface', 'Journal Article', '10.1186/s12859-015-0759-x', '26537300', 'Time-lapse analysis of cellular images is an important and growing need in biology. Algorithms for cell tracking are widely available, what researchers have been missing is a single open-source software package to visualize standard tracking output (from software like CellProfiler) in a way that allows convenient assessment of track quality, especially for researchers tuning tracking parameters for high-content time-lapse experiments. This makes quality assessment and algorithm adjustment a substantial challenge, particularly when dealing with hundreds of time-lapse movies collected in a high-throughput manner.'),
(96978, 2015, 'bmc', 'ODTbrain: a Python library for full-view, dense diffraction tomography', 'Müller, Paul|Schürmann, Mirjam|Guck, Jochen', 'Biotechnology Center of the TU Dresden, Tatzberg 47-51, Dresden, 01307, Germany. paul.mueller@biotec.tu-dresden.de.|Biotechnology Center of the TU Dresden, Tatzberg 47-51, Dresden, 01307, Germany. mirjam.schuermann@biotec.tu-dresden.de.|Biotechnology Center of the TU Dresden, Tatzberg 47-51, Dresden, 01307, Germany. jochen.guck@biotec.tu-dresden.de.', 'Germany|Germany|Germany', 11, 'Algorithms|Computer Simulation|Imaging, Three-Dimensional|Programming Languages|Refractometry|Time Factors|Tomography', 'Journal Article', '10.1186/s12859-015-0764-0', '26537417', 'Analyzing the three-dimensional (3D) refractive index distribution of a single cell makes it possible to describe and characterize its inner structure in a marker-free manner. A dense, full-view tomographic data set is a set of images of a cell acquired for multiple rotational positions, densely distributed from 0 to 360 degrees. The reconstruction is commonly realized by projection tomography, which is based on the inversion of the Radon transform. The reconstruction quality of projection tomography is greatly improved when first order scattering, which becomes relevant when the imaging wavelength is comparable to the characteristic object size, is taken into account. This advanced reconstruction technique is called diffraction tomography. While many implementations of projection tomography are available today, there is no publicly available implementation of diffraction tomography so far.'),
(96979, 2015, 'bmc', 'Iterative sub-network component analysis enables reconstruction of large scale genetic networks', 'Jayavelu, Naresh Doni|Aasgaard, Lasse S|Bar, Nadav', 'Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU), Sem Salandsvei 4, Trondheim, Norway. nareshd@ntnu.no.|Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU), Sem Salandsvei 4, Trondheim, Norway. lsaas@statoil.com.|Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU), Sem Salandsvei 4, Trondheim, Norway. nadi.bar@ntnu.no.', 'Norway|Norway|Norway', 8, 'Algorithms|Gene Expression Regulation|Gene Regulatory Networks|Humans|MCF-7 Cells|Transcription Factors', 'Journal Article', '10.1186/s12859-015-0768-9', '26537518', 'Network component analysis (NCA) became a popular tool to understand complex regulatory networks. The method uses high-throughput gene expression data and a priori topology to reconstruct transcription factor activity profiles. Current NCA algorithms are constrained by several conditions posed on the network topology, to guarantee unique reconstruction (termed compliancy). However, the restrictions these conditions pose are not necessarily true from biological perspective and they force network size reduction, pruning potentially important components.'),
(96980, 2015, 'bmc', 'Predicting drug side effects by multi-label learning and ensemble learning', 'Zhang, Wen|Liu, Feng|Luo, Longqiang|Zhang, Jingxia', 'School of Computer, Wuhan University, Wuhan, 430072, China. zhangwen@whu.edu.cn.|International School of software, Wuhan University, Wuhan, 430072, China. fliuwhu@whu.edu.cn.|School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, China. lqluo@whu.edu.cn.|School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, China. jxz_job@163.com.', 'China|China|China|China', 65, 'Algorithms|Area Under Curve|Cluster Analysis|Databases as Topic|Drug-Related Side Effects and Adverse Reactions|Humans|Machine Learning', 'Journal Article', '10.1186/s12859-015-0774-y', '26537615', 'Predicting drug side effects is an important topic in the drug discovery. Although several machine learning methods have been proposed to predict side effects, there is still space for improvements. Firstly, the side effect prediction is a multi-label learning task, and we can adopt the multi-label learning techniques for it. Secondly, drug-related features are associated with side effects, and feature dimensions have specific biological meanings. Recognizing critical dimensions and reducing irrelevant dimensions may help to reveal the causes of side effects.'),
(96981, 2015, 'bmc', 'BitTorious volunteer: server-side extensions for centrally-managed volunteer storage in BitTorrent swarms', 'Lee, Preston V|Dinu, Valentin', 'Department of Biomedical Informatics, Arizona State University, 13212 East Shea Boulevard, Scottsdale, AZ, 85259, USA. preston@asu.edu.|Department of Biomedical Informatics, Arizona State University, 13212 East Shea Boulevard, Scottsdale, AZ, 85259, USA. valentin.dinu@asu.edu.', 'United States|United States', 0, 'Archives|Computational Biology|Humans|Information Storage and Retrieval|Software|Volunteers', 'Journal Article', '10.1186/s12859-015-0779-6', '26537733', 'Our publication of the BitTorious portal [1] demonstrated the ability to create a privatized distributed data warehouse of sufficient magnitude for real-world bioinformatics studies using minimal changes to the standard BitTorrent tracker protocol. In this second phase, we release a new server-side specification to accept anonymous philantropic storage donations by the general public, wherein a small portion of each users local disk may be used for archival of scientific data. We have implementated the server-side announcement and control portions of this BitTorrent extension into v3.0.0 of the BitTorious portal, upon which compatible clients may be built.'),
(96982, 2015, 'bmc', 'Joint use of over- and under-sampling techniques and cross-validation for the development and assessment of prediction models', 'Blagus, Rok|Lusa, Lara', 'Institute for Biostatistics and Medical Informatics, University of Ljubljana, Vrazov trg 2, Ljubljana, Slovenia. rok.blagus@mf.uni-lj.si.|Institute for Biostatistics and Medical Informatics, University of Ljubljana, Vrazov trg 2, Ljubljana, Slovenia. lara.lusa@mf.uni-lj.si.', 'Slovenia|Slovenia', 26, 'Area Under Curve|Databases as Topic|Humans|Models, Theoretical|Probability|Reproducibility of Results', 'Journal Article', '10.1186/s12859-015-0784-9', '26537827', 'Prediction models are used in clinical research to develop rules that can be used to accurately predict the outcome of the patients based on some of their characteristics. They represent a valuable tool in the decision making process of clinicians and health policy makers, as they enable them to estimate the probability that patients have or will develop a disease, will respond to a treatment, or that their disease will recur. The interest devoted to prediction models in the biomedical community has been growing in the last few years. Often the data used to develop the prediction models are class-imbalanced as only few patients experience the event (and therefore belong to minority class).'),
(96983, 2015, 'bmc', 'Evaluation of shotgun metagenomics sequence classification methods using in silico and in vitro simulated communities', 'Peabody, Michael A|Van Rossum, Thea|Lo, Raymond|Brinkman, Fiona S L', 'Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada. map1@sfu.ca.|Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada. tva4@sfu.ca.|Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada. raymondl@sfu.ca.|Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada. brinkman@sfu.ca.', 'Canada|Canada|Canada|Canada', 55, 'Bacteria|Base Sequence|Computational Biology|Computer Simulation|Databases, Genetic|Metagenomics|Phylogeny|Species Specificity', 'Journal Article', '10.1186/s12859-015-0788-5', '26537885', 'The field of metagenomics (study of genetic material recovered directly from an environment) has grown rapidly, with many bioinformatics analysis methods being developed. To ensure appropriate use of such methods, robust comparative evaluation of their accuracy and features is needed. For taxonomic classification of sequence reads, such evaluation should include use of clade exclusion, which better evaluates a methods accuracy when identical sequences are not present in any reference database, as is common in metagenomic analysis. To date, relatively small evaluations have been performed, with evaluation approaches like clade exclusion limited to assessment of new methods by the authors of the given method. What is needed is a rigorous, independent comparison between multiple major methods, using the same in silico and in vitro test datasets, with and without approaches like clade exclusion, to better characterize accuracy under different conditions.'),
(96984, 2015, 'bmc', 'Inferring plant microRNA functional similarity using a weighted protein-protein interaction network', 'Meng, Jun|Liu, Dong|Luan, Yushi', 'School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning, China. mengjun@dlut.edu.cn.|School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning, China. kobe_liudong@mail.dlut.edu.cn.|School of Life Science and Biotechnology, Dalian University of Technology, Dalian, Liaoning, China. luanyush@dlut.edu.cn.', 'China|China|China', 0, 'Arabidopsis|Genes, Plant|MicroRNAs|Protein Interaction Maps|ROC Curve|Sodium Chloride|Software|Stress, Physiological', 'Journal Article', '10.1186/s12859-015-0789-4', '26538106', 'MiRNAs play a critical role in the response of plants to abiotic and biotic stress. However, the functions of most plant miRNAs remain unknown. Inferring these functions from miRNA functional similarity would thus be useful. This study proposes a new method, called PPImiRFS, for inferring miRNA functional similarity.'),
(96985, 2015, 'bmc', 'The Gap Procedure: for the identification of phylogenetic clusters in HIV-1 sequence data', 'Vrbik, Irene|Stephens, David A|Roger, Michel|Brenner, Bluma G', 'Department of Mathematics & Statistics, McGill University, 805 Sherbrooke Street West, Montreal, H3A 0B9, Canada. irene.vrbik@mcgill.ca.|Department of Mathematics & Statistics, McGill University, 805 Sherbrooke Street West, Montreal, H3A 0B9, Canada. d.stephens@math.mcgill.ca.|Centre de recherche du CHUM, 900 rue Saint-Denis Tour Viger, H2X 0A9, Montreal, Canada. michel.roger.chum@ssss.gouv.qc.ca.|Faculty of Surgery (Medicine), McGill University, 1010 Sherbrooke Street West, Montreal, H3A 2R7, Canada. bluma.brenner@mcgill.ca.', 'Canada|Canada|Canada|Canada', 0, 'Algorithms|Base Sequence|Bayes Theorem|Cluster Analysis|Computer Simulation|Databases, Nucleic Acid|HIV-1|Humans|Likelihood Functions|Phylogeny', 'Journal Article', '10.1186/s12859-015-0791-x', '26538192', 'In the context of infectious disease, sequence clustering can be used to provide important insights into the dynamics of transmission. Cluster analysis is usually performed using a phylogenetic approach whereby clusters are assigned on the basis of sufficiently small genetic distances and high bootstrap support (or posterior probabilities). The computational burden involved in this phylogenetic threshold approach is a major drawback, especially when a large number of sequences are being considered. In addition, this method requires a skilled user to specify the appropriate threshold values which may vary widely depending on the application.'),
(96986, 2015, 'bmc', 'Fizzy: feature subset selection for metagenomics', 'Ditzler, Gregory|Morrison, J Calvin|Lan, Yemin|Rosen, Gail L', 'Department of Electrical & Computer Engineering, The University of Arizona, 1230 E Speedway Blvd., ECE Bldg., Tucson, 85721, AZ, USA. gregory.ditzler@gmail.com.|Department of Electrical & Computer Engineering, Drexel University, 3141 Chestnut St., Philadelphia, 19104, PA, USA. mutantturkey@gmail.com.|School of Biomedical Engineering, Science and Health, Drexel University, 3141 Chestnut St., Philadelphia, 19104, PA, USA. yeminlan@gmail.com.|Department of Electrical & Computer Engineering, Drexel University, 3141 Chestnut St., Philadelphia, 19104, PA, USA. gailr@ece.drexel.edu.', 'United States|United States|United States|United States', 0, 'Algorithms|Computational Biology|Databases, Genetic|Humans|Metagenomics|Microbiota|Software|Vegetarians', 'Journal Article', '10.1186/s12859-015-0793-8', '26538306', 'Some of the current software tools for comparative metagenomics provide ecologists with the ability to investigate and explore bacterial communities using xcexb1- & xcexb2-diversity. Feature subset selection--a sub-field of machine learning--can also provide a unique insight into the differences between metagenomic or 16S phenotypes. In particular, feature subset selection methods can obtain the operational taxonomic units (OTUs), or functional features, that have a high-level of influence on the condition being studied. For example, in a previous study we have used information-theoretic feature selection to understand the differences between protein family abundances that best discriminate between age groups in the human gut microbiome.'),
(96987, 2015, 'bmc', 'Evaluation of methods for differential expression analysis on multi-group RNA-seq count data', 'Tang, Min|Sun, Jianqiang|Shimizu, Kentaro|Kadota, Koji', 'Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657, Japan. tm2015@bi.a.u-tokyo.ac.jp.|Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657, Japan. wukong@bi.a.u-tokyo.ac.jp.|Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657, Japan. shimizu@bi.a.u-tokyo.ac.jp.|Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657, Japan. kadota@bi.a.u-tokyo.ac.jp.', 'Japan|Japan|Japan|Japan', 0, 'Animals|Area Under Curve|Computer Simulation|Female|Gene Expression Profiling|Gene Expression Regulation|Humans|Macaca mulatta|Male|Pan troglodytes|Reproducibility of Results|Sequence Analysis, RNA|Software|Transcriptome', 'Journal Article', '10.1186/s12859-015-0794-7', '26538400', 'RNA-seq is a powerful tool for measuring transcriptomes, especially for identifying differentially expressed genes or transcripts (DEGs) between sample groups. A number of methods have been developed for this task, and several evaluation studies have also been reported. However, those evaluations so far have been restricted to two-group comparisons. Accumulations of comparative studies for multi-group data are also desired.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(96988, 2015, 'bmc', 'Revisiting operons: an analysis of the landscape of transcriptional units in E coli', 'Mao, Xizeng|Ma, Qin|Liu, Bingqiang|Chen, Xin|Zhang, Hanyuan|Xu, Ying', 'Computational Systems Biology Lab, Department of Biochemistry and Molecular Biology, and Institute of Bioinformatics, University of Georgia, Athens, USA. xizeng.mao@gmail.com.|Computational Systems Biology Lab, Department of Biochemistry and Molecular Biology, and Institute of Bioinformatics, University of Georgia, Athens, USA. qin.ma@sdstate.edu.|School of Mathematics, Shandong University, Jinan, Shandong, China. bingqiangsdu@gmail.com.|Computational Systems Biology Lab, Department of Biochemistry and Molecular Biology, and Institute of Bioinformatics, University of Georgia, Athens, USA. chenxin@uga.edu.|Computational Systems Biology Lab, Department of Biochemistry and Molecular Biology, and Institute of Bioinformatics, University of Georgia, Athens, USA. jluzhhy@gmail.com.|Computational Systems Biology Lab, Department of Biochemistry and Molecular Biology, and Institute of Bioinformatics, University of Georgia, Athens, USA. xyn@bmb.uga.edu.', 'Georgia|Georgia|China|Georgia|Georgia|Georgia', 11, 'Binding Sites|Conserved Sequence|Escherichia coli K12|Escherichia coli Proteins|Genes, Bacterial|Nucleotide Motifs|Operon|Promoter Regions, Genetic|Regulon|Terminator Regions, Genetic|Transcription Factors|Transcription, Genetic', 'Journal Article', '10.1186/s12859-015-0805-8', '26538447', 'Bacterial operons are considerably more complex than what were thought. At least their components are dynamically rather than statically defined as previously assumed. Here we present a computational study of the landscape of the transcriptional units (TUs) of E. coli K12, revealed by the available genomic and transcriptomic data, providing new understanding about the complexity of TUs as a whole encoded in the genome of E. coli K12.'),
(96989, 2015, 'bmc', 'An assessment of catalytic residue 3D ensembles for the prediction of enzyme function', 'xc5xbdvxc3xa1xc4x8dek, Clemens|Friedrichs, Gerald|Heizinger, Leonhard|Merkl, Rainer', 'Faculty of Mathematics and Computer Science, University of Hagen, D-58084, Hagen, Germany. clemens@zvacek.de.|Faculty of Mathematics and Computer Science, University of Hagen, D-58084, Hagen, Germany. gerald.friedrichs@fernuni-hagen.de.|Institute of Biophysics and Physical Biochemistry, University of Regensburg, D-93040, Regensburg, Germany. leonhard.heizinger@ur.de.|Institute of Biophysics and Physical Biochemistry, University of Regensburg, D-93040, Regensburg, Germany. rainer.merkl@ur.de.', 'Germany|Germany|Germany|Germany', 1, 'Amino Acids|Catalysis|Catalytic Domain|Computational Biology|Databases, Protein|Enzymes|Evolution, Molecular|Gene Ontology|Sequence Homology, Amino Acid|Substrate Specificity', 'Journal Article', '10.1186/s12859-015-0807-6', '26538500', 'The central element of each enzyme is the catalytic site, which commonly catalyzes a single biochemical reaction with high specificity. It was unclear to us how often sites that catalyze the same or highly similar reactions evolved on different, i. e. non-homologous protein folds and how similar their 3D poses are. Both similarities are key criteria for assessing the usability of pose comparison for function prediction.'),
(96990, 2015, 'bmc', 'Erratum to: MONALISA for stochastic simulations of Petri net models of biochemical systems', 'Balazki, Pavel|Lindauer, Klaus|Einloft, Jens|Ackermann, Jörg|Koch, Ina', 'Department of Molecular Bioinformatics, Institute of Computer Science, Cluster of Excellence Macromolecular Complexes, Johann Wolfgang Goethe-University Frankfurt am Main, Robert-Mayer-Straße 11-15, Frankfurt am Main, 60325, Germany. Pavel.Balazki@gmail.com.|Sanofi Aventis Deutschland GmbH, Industriepark Höchst H831, Frankfurt am Main, 65926, Germany. Klaus.Lindauer@sanofi-aventis.com.|Department of Molecular Bioinformatics, Institute of Computer Science, Cluster of Excellence Macromolecular Complexes, Johann Wolfgang Goethe-University Frankfurt am Main, Robert-Mayer-Straße 11-15, Frankfurt am Main, 60325, Germany. Einloft@bioinformatik.uni-frankfurt.de.|Department of Molecular Bioinformatics, Institute of Computer Science, Cluster of Excellence Macromolecular Complexes, Johann Wolfgang Goethe-University Frankfurt am Main, Robert-Mayer-Straße 11-15, Frankfurt am Main, 60325, Germany. J.Ackermann@bioinformatik.uni-frankfurt.de.|Department of Molecular Bioinformatics, Institute of Computer Science, Cluster of Excellence Macromolecular Complexes, Johann Wolfgang Goethe-University Frankfurt am Main, Robert-Mayer-Straße 11-15, Frankfurt am Main, 60325, Germany. Ina.koch@bioinformatik.uni-frankfurt.de.', 'Germany|Germany|Germany|Germany|Germany', 0, '-', 'Published Erratum', '10.1186/s12859-015-0725-7', '26542386', '-'),
(96991, 2015, 'bmc', 'miRA: adaptable novel miRNA identification in plants using small RNA sequencing data', 'Evers, Maurits|Huttner, Michael|Dueck, Anne|Meister, Gunter|Engelmann, Julia C', 'Institute of Functional Genomics, University of Regensburg, Regensburg, Germany. maurits.evers@anu.edu.au.|Institute of Functional Genomics, University of Regensburg, Regensburg, Germany.|Biochemistry Center Regensburg (BZR), Laboratory for RNA Biology, University of Regensburg, Regensburg, Germany.|Biochemistry Center Regensburg (BZR), Laboratory for RNA Biology, University of Regensburg, Regensburg, Germany.|Institute of Functional Genomics, University of Regensburg, Regensburg, Germany.', 'Germany|Germany|Germany|Germany|Germany', 27, 'Animals|Arabidopsis|Base Sequence|Chlamydomonas|Computer Simulation|Databases, Genetic|MicroRNAs|Plants|RNA Precursors|RNA, Plant|Sequence Analysis, RNA|Thermodynamics|Volvox', 'Journal Article', '10.1186/s12859-015-0798-3', '26542525', 'MicroRNAs (miRNAs) are short regulatory RNAs derived from longer precursor RNAs. miRNA biogenesis has been studied in animals and plants, recently elucidating more complex aspects, such as non-conserved, species-specific, and heterogeneous miRNA precursor populations. Small RNA sequencing data can help in computationally identifying genomic loci of miRNA precursors. The challenge is to predict a valid miRNA precursor from inhomogeneous read coverage from a complex RNA library: while the mature miRNA typically produces many sequence reads, the remaining part of the precursor is covered very sparsely. As recent results suggest, alternative miRNA biogenesis pathways may lead to a more diverse miRNA precursor population than previously assumed. In plants, the latter manifests itself in e.g. complex secondary structures and expression from multiple loci within precursors. Current miRNA identification algorithms often depend on already existing gene annotation, and/or make use of specific miRNA precursor features such as precursor lengths, secondary structures etc. Consequently and in view of the emerging new understanding of a more complex miRNA biogenesis in plants, current tools may fail to characterise organism-specific and heterogeneous miRNA populations.'),
(96992, 2015, 'bmc', 'Practical impacts of genomic data cleaning on biological discovery using surrogate variable analysis', 'Jaffe, Andrew E|Hyde, Thomas|Kleinman, Joel|Weinbergern, Daniel R|Chenoweth, Joshua G|McKay, Ronald D|Leek, Jeffrey T|Colantuoni, Carlo', 'Lieber Institute for Brain Development, 855 N Wolfe St, Ste 300, Baltimore, MD, 21205, USA. andrew.jaffe@libd.org.|Lieber Institute for Brain Development, 855 N Wolfe St, Ste 300, Baltimore, MD, 21205, USA. Thomas.Hyde@libd.org.|Lieber Institute for Brain Development, 855 N Wolfe St, Ste 300, Baltimore, MD, 21205, USA. Joel.Kleinman@libd.org.|Lieber Institute for Brain Development, 855 N Wolfe St, Ste 300, Baltimore, MD, 21205, USA. drweinberger@libd.org.|Lieber Institute for Brain Development, 855 N Wolfe St, Ste 300, Baltimore, MD, 21205, USA. josh.chenoweth@libd.org.|Lieber Institute for Brain Development, 855 N Wolfe St, Ste 300, Baltimore, MD, 21205, USA. ronald.mckay@libd.org.|Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD, 21205, USA. ljeek@jhsph.edu.|Lieber Institute for Brain Development, 855 N Wolfe St, Ste 300, Baltimore, MD, 21205, USA. carlo.colantuoni@libd.org.', 'United States|United States|United States|United States|United States|United States|United States|United States', 24, 'Algorithms|Artifacts|Brain|Cell Differentiation|Computational Biology|Gene Expression Profiling|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Pluripotent Stem Cells|Regression Analysis', 'Journal Article', '10.1186/s12859-015-0808-5', '26545828', 'Genomic data production is at its highest level and continues to increase, making available novel primary data and existing public data to researchers for exploration. Here we explore the consequences of batch correction for biological discovery in two publicly available expression datasets. We consider this to include the estimation of and adjustment for wide-spread systematic heterogeneity in genomic measurements that is unrelated to the effects under study, whether it be technical or biological in nature.'),
(96993, 2015, 'bmc', 'Sieve-based relation extraction of gene regulatory networks from biological literature', 'Žitnik, Slavko|Žitnik, Marinka|Zupan, Blaž|Bajec, Marko', 'Lieber Institute for Brain Development, 855 N Wolfe St, Ste 300, Baltimore, MD, 21205, USA. carlo.colantuoni@libd.org.|Lieber Institute for Brain Development, 855 N Wolfe St, Ste 300, Baltimore, MD, 21205, USA. carlo.colantuoni@libd.org.|Lieber Institute for Brain Development, 855 N Wolfe St, Ste 300, Baltimore, MD, 21205, USA. carlo.colantuoni@libd.org.|Lieber Institute for Brain Development, 855 N Wolfe St, Ste 300, Baltimore, MD, 21205, USA. carlo.colantuoni@libd.org.', 'United States|United States|United States|United States', 5, 'Algorithms|Gene Regulatory Networks|Information Storage and Retrieval|Models, Theoretical|Publications', 'Journal Article', '10.1186/1471-2105-16-S16-S1', '26551454', 'Relation extraction is an essential procedure in literature mining. It focuses on extracting semantic relations between parts of text, called mentions. Biomedical literature includes an enormous amount of textual descriptions of biological entities, their interactions and results of related experiments. To extract them in an explicit, computer readable format, these relations were at first extracted manually from databases. Manual curation was later replaced with automatic or semi-automatic tools with natural language processing capabilities. The current challenge is the development of information extraction procedures that can directly infer more complex relational structures, such as gene regulatory networks.'),
(96994, 2015, 'bmc', 'Optimizing graph-based patterns to extract biomedical events from the literature', 'Liu, Haibin|Verspoor, Karin|Comeau, Donald C|MacKinlay, Andrew D|Wilbur, W', 'Lieber Institute for Brain Development, 855 N Wolfe St, Ste 300, Baltimore, MD, 21205, USA. carlo.colantuoni@libd.org.|Lieber Institute for Brain Development, 855 N Wolfe St, Ste 300, Baltimore, MD, 21205, USA. carlo.colantuoni@libd.org.|Lieber Institute for Brain Development, 855 N Wolfe St, Ste 300, Baltimore, MD, 21205, USA. carlo.colantuoni@libd.org.|Lieber Institute for Brain Development, 855 N Wolfe St, Ste 300, Baltimore, MD, 21205, USA. carlo.colantuoni@libd.org.|Lieber Institute for Brain Development, 855 N Wolfe St, Ste 300, Baltimore, MD, 21205, USA. carlo.colantuoni@libd.org.', 'United States|United States|United States|United States|United States', 2, 'Algorithms|Databases as Topic|Information Storage and Retrieval|Natural Language Processing|Publications|Statistics as Topic', 'Journal Article', '10.1186/1471-2105-16-S16-S2', '26551594', 'IN BIONLP-ST 2013: We participated in the BioNLP 2013 shared tasks on event extraction. Our extraction method is based on the search for an approximate subgraph isomorphism between key context dependencies of events and graphs of input sentences. Our system was able to address both the GENIA (GE) task focusing on 13 molecular biology related event types and the Cancer Genetics (CG) task targeting a challenging group of 40 cancer biology related event types with varying arguments concerning 18 kinds of biological entities. In addition to adapting our system to the two tasks, we also attempted to integrate semantics into the graph matching scheme using a distributional similarity model for more events, and evaluated the event extraction impact of using paths of all possible lengths as key context dependencies beyond using only the shortest paths in our system. We achieved a 46.38% F-score in the CG task (ranking 3rd) and a 48.93% F-score in the GE task (ranking 4th). AFTER BIONLP-ST 2013: We explored three ways to further extend our event extraction system in our previously published work: (1) We allow non-essential nodes to be skipped, and incorporated a node skipping penalty into the subgraph distance function of our approximate subgraph matching algorithm. (2) Instead of assigning a unified subgraph distance threshold to all patterns of an event type, we learned a customized threshold for each pattern. (3) We implemented the well-known Empirical Risk Minimization (ERM) principle to optimize the event pattern set by balancing prediction errors on training data against regularization. When evaluated on the official GE task test data, these extensions help to improve the extraction precision from 62% to 65%. However, the overall F-score stays equivalent to the previous performance due to a 1% drop in recall. '),
(96995, 2015, 'bmc', 'Application of the EVEX resource to event extraction and network construction: Shared Task entry and result analysis', 'Hakala, Kai|Van Landeghem, Sofie|Salakoski, Tapio|Van de Peer, Yves|Ginter, Filip', 'Lieber Institute for Brain Development, 855 N Wolfe St, Ste 300, Baltimore, MD, 21205, USA. carlo.colantuoni@libd.org.|Lieber Institute for Brain Development, 855 N Wolfe St, Ste 300, Baltimore, MD, 21205, USA. carlo.colantuoni@libd.org.|Lieber Institute for Brain Development, 855 N Wolfe St, Ste 300, Baltimore, MD, 21205, USA. carlo.colantuoni@libd.org.|Lieber Institute for Brain Development, 855 N Wolfe St, Ste 300, Baltimore, MD, 21205, USA. carlo.colantuoni@libd.org.|Lieber Institute for Brain Development, 855 N Wolfe St, Ste 300, Baltimore, MD, 21205, USA. carlo.colantuoni@libd.org.', 'United States|United States|United States|United States|United States', 4, 'Data Mining|Gene Regulatory Networks|Molecular Sequence Annotation|Natural Language Processing', 'Journal Article', '10.1186/1471-2105-16-S16-S3', '26551766', 'Modern methods for mining biomolecular interactions from literature typically make predictions based solely on the immediate textual context, in effect a single sentence. No prior work has been published on extending this context to the information automatically gathered from the whole biomedical literature. Thus, our motivation for this study is to explore whether mutually supporting evidence, aggregated across several documents can be utilized to improve the performance of the state-of-the-art event extraction systems.'),
(96996, 2015, 'bmc', 'TEES 22: Biomedical Event Extraction for Diverse Corpora', 'Björne, Jari|Salakoski, Tapio', 'Lieber Institute for Brain Development, 855 N Wolfe St, Ste 300, Baltimore, MD, 21205, USA. carlo.colantuoni@libd.org.|Lieber Institute for Brain Development, 855 N Wolfe St, Ste 300, Baltimore, MD, 21205, USA. carlo.colantuoni@libd.org.', 'United States|United States', 0, 'Algorithms|Data Mining|Software', 'Journal Article', '10.1186/1471-2105-16-S16-S4', '26551925', 'The Turku Event Extraction System (TEES) is a text mining program developed for the extraction of events, complex biomedical relationships, from scientific literature. Based on a graph-generation approach, the system detects events with the use of a rich feature set built via dependency parsing. The TEES system has achieved record performance in several of the shared tasks of its domain, and continues to be used in a variety of biomedical text mining tasks.'),
(96997, 2015, 'bmc', 'TOGGLE: toolbox for generic NGS analyses', 'Monat, Cécile|Tranchant-Dubreuil, Christine|Kougbeadjo, Ayité|Farcy, Cédric|Ortega-Abboud, Enrique|Amanzougarene, Souhila|Ravel, Sébastien|Agbessi, Mawussé|Orjuela-Bouniol, Julie|Summo, Maryline|Sabot, François', 'UMR DIADE IRD/UM, 911 Avenue Agropolis, Montpellier Cedex 5, F-34934, France. cecile.monat@ird.fr.|UMR DIADE IRD/UM, 911 Avenue Agropolis, Montpellier Cedex 5, F-34934, France. christine.tranchant@ird.fr.|UMR AGAP CIRAD/INRA/SupAgro, TA A-108/03 - Avenue Agropolis, Montpellier Cedex 5, F-34398, France. ayite.kougbeadjo@cirad.fr.|UMR AGAP CIRAD/INRA/SupAgro, TA A-108/03 - Avenue Agropolis, Montpellier Cedex 5, F-34398, France. cedric.farcy@cirad.fr.|UMR AGAP CIRAD/INRA/SupAgro, TA A-108/03 - Avenue Agropolis, Montpellier Cedex 5, F-34398, France. enrique.ortega-abboud@cirad.fr.|UMR DIADE IRD/UM, 911 Avenue Agropolis, Montpellier Cedex 5, F-34934, France. souhila.amanzougarene@ird.fr.|UMR-BGPI CIRAD TA A-54/K, Campus International de Baillarguet, Montpellier Cedex 5, F-34398, France. sebastien.ravel@cirad.fr.|UMR DIADE IRD/UM, 911 Avenue Agropolis, Montpellier Cedex 5, F-34934, France. mawusse.agbessi@ird.fr.|ADNid, Cap Alpha, Avenue de lEurope, Clapiers, F-34830, France. julie.orjuela@adnid.fr.|UMR AGAP CIRAD/INRA/SupAgro, TA A-108/03 - Avenue Agropolis, Montpellier Cedex 5, F-34398, France. maryline.summo@cirad.fr.|UMR DIADE IRD/UM, 911 Avenue Agropolis, Montpellier Cedex 5, F-34934, France. francois.sabot@ird.fr.', 'France|France|France|France|France|France|France|France|France|France|France', 0, 'Computational Biology|Data Mining|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Software|Transcriptome', 'Journal Article', '10.1186/s12859-015-0795-6', '26552596', 'The explosion of NGS (Next Generation Sequencing) sequence data requires a huge effort in Bioinformatics methods and analyses. The creation of dedicated, robust and reliable pipelines able to handle dozens of samples from raw FASTQ data to relevant biological data is a time-consuming task in all projects relying on NGS. To address this, we created a generic and modular toolbox for developing such pipelines.'),
(96998, 2015, 'bmc', 'From raw data to data-analysis for magnetic resonance spectroscopy--the missing link: jMRUI2XML', 'Mocioiu, Victor|Ortega-Martorell, Sandra|Olier, Iván|Jablonski, Michal|Starcukova, Jana|Lisboa, Paulo|Arús, Carles|Julià-Sapé, Margarida', 'Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, UAB, Cerdanyola del Vallès, Barcelona, 08193, Spain. victor.mocioiu@uab.cat.|Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina CIBER-BBN, Cerdanyola del Vallès, Barcelona, Spain. S.OrtegaMartorell@ljmu.ac.uk.|Institute of Biotechnology, The University of Manchester, Manchester, UK. Ivan.Olier@manchester.ac.uk.|Institute of Scientific Instruments of the CAS, v. v. i, Brno, Czech Republic. jmj@isibrno.cz.|Institute of Scientific Instruments of the CAS, v. v. i, Brno, Czech Republic. jana@isibrno.cz.|School of Computing and Mathematical Sciences, Liverpool John Moores University, Liverpool, UK. P.J.Lisboa@ljmu.ac.uk.|Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, UAB, Cerdanyola del Vallès, Barcelona, 08193, Spain. carles.arus@uab.es.|Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, UAB, Cerdanyola del Vallès, Barcelona, 08193, Spain. Margarita.Julia@uab.cat.', 'Spain|Spain|United Kingdom|United Kingdom|United Kingdom|United Kingdom|Spain|Spain', 0, 'Algorithms|Electronic Data Processing|Humans|Image Processing, Computer-Assisted|Magnetic Resonance Imaging|Magnetic Resonance Spectroscopy|Software', 'Journal Article', '10.1186/s12859-015-0796-5', '26552737', 'Magnetic resonance spectroscopy provides metabolic information about living tissues in a non-invasive way. However, there are only few multi-centre clinical studies, mostly performed on a single scanner model or data format, as there is no flexible way of documenting and exchanging processed magnetic resonance spectroscopy data in digital format. This is because the DICOM standard for spectroscopy deals with unprocessed data. This paper proposes a plugin tool developed for jMRUI, namely jMRUI2XML, to tackle the latter limitation. jMRUI is a software tool for magnetic resonance spectroscopy data processing that is widely used in the magnetic resonance spectroscopy community and has evolved into a plugin platform allowing for implementation of novel features.'),
(96999, 2015, 'bmc', 'Inferring intra-motif dependencies of DNA binding sites from ChIP-seq data', 'Eggeling, Ralf|Roos, Teemu|Myllymäki, Petri|Grosse, Ivo', 'Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle, Germany. eggeling@cs.helsinki.fi.|Department of Computer Science, Helsinki Institute for Information Technology HIIT, University of Helsinki, Helsinki, Finland. teemu.roos@cs.helsinki.fi.|Department of Computer Science, Helsinki Institute for Information Technology HIIT, University of Helsinki, Helsinki, Finland. petri.myllymaki@cs.helsinki.fi.|Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle, Germany. grosse@informatik.uni-halle.de.', 'Germany|Finland|Finland|Germany', 22, 'Algorithms|Binding Sites|Chromatin Immunoprecipitation|Computational Biology|DNA|High-Throughput Nucleotide Sequencing|Humans|Models, Theoretical|Nucleotide Motifs|Position-Specific Scoring Matrices|Protein Binding|Transcription Factors', 'Journal Article', '10.1186/s12859-015-0797-4', '26552868', 'Statistical modeling of transcription factor binding sites is one of the classical fields in bioinformatics. The position weight matrix (PWM) model, which assumes statistical independence among all nucleotides in a binding site, used to be the standard model for this task for more than three decades but its simple assumptions are increasingly put into question. Recent high-throughput sequencing methods have provided data sets of sufficient size and quality for studying the benefits of more complex models. However, learning more complex models typically entails the danger of overfitting, and while model classes that dynamically adapt the model complexity to data have been developed, effective model selection is to date only possible for fully observable data, but not, e.g., within de novo motif discovery.'),
(97000, 2015, 'bmc', 'Reconstruction of 3D genome architecture via a two-stage algorithm', 'Segal, Mark R|Bengtsson, Henrik L', 'Division of Bioinformatics, Department of Epidemiology and Biostatistics, University of California, 550 16th Street, San Francisco, 94158, CA, USA. mark@biostat.ucsf.edu.|Division of Bioinformatics, Department of Epidemiology and Biostatistics, University of California, 550 16th Street, San Francisco, 94158, CA, USA. henrik.bengtsson@ucsf.edu.', 'United States|United States', 12, 'Algorithms|Animals|Chromatin|Chromosomes|Embryonic Stem Cells|Genome|High-Throughput Nucleotide Sequencing|Humans|Lymphocytes|Mice|Models, Molecular|Molecular Conformation|Reproducibility of Results', 'Journal Article', '10.1186/s12859-015-0799-2', '26553003', 'The three-dimensional (3D) configuration of chromosomes within the eukaryote nucleus is an important factor for several cellular functions, including gene expression regulation, and has also been linked with cancer-causing translocation events. While visualization of such architecture remains limited to low resolutions, the ability to infer structures at increasing resolutions has been enabled by recently-devised chromosome conformation capture techniques. In particular, when coupled with next generation sequencing, such methods yield an inventory of genome-wide chromatin contacts or interactions. Various algorithms have been advanced to operate on such contact data to produce reconstructed 3D configurations. Studies have shown that these reconstructions can provide added value over raw interaction data with respect to downstream biological insights. However, only limited, low-resolution reconstructions have been realized for mammals due to computational bottlenecks.'),
(97001, 2015, 'bmc', 'Sequence information gain based motif analysis', 'Maynou, Joan|Pairó, Erola|Marco, Santiago|Perera, Alexandre', 'Departament dEnginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, Pau Gargallo, 5, Barcelona, 08028, Spain. joan.maynou@upc.edu.|Institute for BioEngineering of Catalonia, balidiri Reixach 4-6, Barcelona, 08028, Spain. epairo@ibecbarcelona.eu.|Institute for BioEngineering of Catalonia, balidiri Reixach 4-6, Barcelona, 08028, Spain. smarco@el.ub.es.|Departament dEnginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, Pau Gargallo, 5, Barcelona, 08028, Spain. alexandre.perera@upc.edu.', 'Spain|Spain|Spain|Spain', 0, 'Algorithms|Animals|Binding Sites|Genome|Genomics|Humans|Mice|Nonlinear Dynamics|Nucleotide Motifs|Protein Binding|ROC Curve|Software|Transcription Factors', 'Journal Article', '10.1186/s12859-015-0811-x', '26553056', 'The detection of regulatory regions in candidate sequences is essential for the understanding of the regulation of a particular gene and the mechanisms involved. This paper proposes a novel methodology based on information theoretic metrics for finding regulatory sequences in promoter regions.'),
(97002, 2015, 'bmc', 'AlignWise: a tool for identifying protein-coding sequence and correcting frame-shifts', 'Evans, Teri|Loose, Matthew', 'School of Life Sciences, University of Nottingham, Nottingham, NG7 2UH, UK. teri.evans@nottingham.ac.uk.|School of Life Sciences, University of Nottingham, Nottingham, NG7 2UH, UK. matt.loose@nottingham.ac.uk.', 'United Kingdom|United Kingdom', 0, 'Algorithms|Base Sequence|Frameshift Mutation|Gene Expression Profiling|Genome, Human|Humans|Molecular Sequence Data|Open Reading Frames|Phylogeny|Sequence Alignment|Sequence Homology, Nucleic Acid|Software', 'Journal Article', '10.1186/s12859-015-0813-8', '26553107', 'Identifying protein-coding genes from species without a reference genome sequence can be complicated by the presence of sequencing errors, particularly insertions and deletions. A number of tools capable of correcting erroneous frame-shifts within assembled transcripts are available but often do not report back DNA sequences required for subsequent phylogenetic analysis. Amongst those that do, the Genewise algorithm is the most effective. However, it requires a homology wrapper to be used in this way, and here we demonstrate it perfectly corrects frame-shifts only 60% of the time.'),
(97003, 2015, 'bmc', 'QQ-SNV: single nucleotide variant detection at low frequency by comparing the quality quantiles', 'Van der Borght, Koen|Thys, Kim|Wetzels, Yves|Clement, Lieven|Verbist, Bie|Reumers, Joke|van Vlijmen, Herman|Aerssens, Jeroen', 'Janssen Infectious Diseases-Diagnostics BVBA, B-2340, Beerse, Belgium. kvdborgh@its.jnj.com.|Janssen Infectious Diseases-Diagnostics BVBA, B-2340, Beerse, Belgium. kthys@its.jnj.com.|Janssen Infectious Diseases-Diagnostics BVBA, B-2340, Beerse, Belgium. ywetzel@its.jnj.com.|Ghent University, Applied Mathematics, Informatics and Statistics, B-9000, Ghent, Belgium. lieven.clement@ugent.be.|Janssen Infectious Diseases-Diagnostics BVBA, B-2340, Beerse, Belgium. bverbis2@its.jnj.com.|Janssen Infectious Diseases-Diagnostics BVBA, B-2340, Beerse, Belgium. jreumers@its.jnj.com.|Janssen Infectious Diseases-Diagnostics BVBA, B-2340, Beerse, Belgium. hvvlijme@its.jnj.com.|Janssen Infectious Diseases-Diagnostics BVBA, B-2340, Beerse, Belgium. jaerssen@its.jnj.com.', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 0, 'Algorithms|Cluster Analysis|Computer Simulation|Genome, Viral|HIV Infections|HIV-1|Hepacivirus|Hepatitis C|High-Throughput Nucleotide Sequencing|Humans|Plasmids|Polymorphism, Single Nucleotide|Regression Analysis|Software', 'Comparative Study', '10.1186/s12859-015-0812-9', '26554718', 'Next generation sequencing enables studying heterogeneous populations of viral infections. When the sequencing is done at high coverage depth (deep sequencing), low frequency variants can be detected. Here we present QQ-SNV (http://sourceforge.net/projects/qqsnv), a logistic regression classifier model developed for the Illumina sequencing platforms that uses the quantiles of the quality scores, to distinguish true single nucleotide variants from sequencing errors based on the estimated SNV probability. To train the model, we created a dataset of an in silico mixture of five HIV-1 plasmids. Testing of our method in comparison to the existing methods LoFreq, ShoRAH, and V-Phaser 2 was performed on two HIV and four HCV plasmid mixture datasets and one influenza H1N1 clinical dataset.'),
(97004, 2015, 'bmc', 'A census of xcexb1-helical membrane proteins in double-stranded DNA viruses infecting bacteria and archaea', 'Kristensen, David M|Saeed, Usman|Frishman, Dmitrij|Koonin, Eugene V', 'National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA. david-kristensen@uiowa.edu.|Department of Genome Oriented Bioinformatics, Technische Universitxc3xa4t Mxc3xbcnchen, Wissenschaftzentrum Weihenstephan, Maximus-von-Imhof-Forum 3, D-85354, Freising, Germany. usman.saeed08@gmail.com.|Department of Genome Oriented Bioinformatics, Technische Universitxc3xa4t Mxc3xbcnchen, Wissenschaftzentrum Weihenstephan, Maximus-von-Imhof-Forum 3, D-85354, Freising, Germany. d.frishman@wzw.tum.de.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA. koonin@ncbi.nlm.nih.gov.', 'United States|Germany|Germany|United States', 0, 'Archaea|Archaeal Viruses|Bacteria|DNA Viruses|Host-Pathogen Interactions|Membrane Proteins|Viral Proteins|Virion|Virus Integration', 'Journal Article', '10.1186/s12859-015-0817-4', '26554846', 'Viruses are the most abundant and genetically diverse biological entities on earth, yet the repertoire of viral proteins remains poorly explored. As the number of sequenced virus genomes grows into the thousands, and the number of viral proteins into the hundreds of thousands, we report a systematic computational analysis of the point of first-contact between viruses and their hosts, namely viral transmembrane (TM) proteins.'),
(97005, 2015, 'bmc', 'A large scale prediction of bacteriocin gene blocks suggests a wide functional spectrum for bacteriocins', 'Morton, James T|Freed, Stefan D|Lee, Shaun W|Friedberg, Iddo', 'Department of Computer Science and Software engineering, Miami University, Oxford, OH, USA. jamietmorton@gmail.com.|Eck Institute for Global Health, Department of Biological Sciences, University of Notre Dame, South Bend, IN, USA. stefan_freed@nd.edu.|Eck Institute for Global Health, Department of Biological Sciences, University of Notre Dame, South Bend, IN, USA. shaun_lee@nd.edu.|Department of Computer Science and Software engineering, Miami University, Oxford, OH, USA. idoerg@iastate.edu.', 'United States|United States|United States|United States', 20, 'Anti-Bacterial Agents|Bacteriocins|Chromosome Mapping|Genome, Archaeal|Genome, Bacterial|Operon|Software', 'Journal Article', '10.1186/s12859-015-0792-9', '26558535', 'Bacteriocins are peptide-derived molecules produced by bacteria, whose recently-discovered functions include virulence factors and signaling molecules as well as their better known roles as antibiotics. To date, close to five hundred bacteriocins have been identified and classified. Recent discoveries have shown that bacteriocins are highly diverse and widely distributed among bacterial species. Given the heterogeneity of bacteriocin compounds, many tools struggle with identifying novel bacteriocins due to their vast sequence and structural diversity. Many bacteriocins undergo post-translational processing or modifications necessary for the biosynthesis of the final mature form. Enzymatic modification of bacteriocins as well as their export is achieved by proteins whose genes are often located in a discrete gene cluster proximal to the bacteriocin precursor gene, referred to as context genes in this study. Although bacteriocins themselves are structurally diverse, context genes have been shown to be largely conserved across unrelated species.'),
(97006, 2015, 'bmc', 'An investigation of causes of false positive single nucleotide polymorphisms using simulated reads from a small eukaryote genome', 'Ribeiro, Antonio|Golicz, Agnieszka|Hackett, Christine Anne|Milne, Iain|Stephen, Gordon|Marshall, David|Flavell, Andrew J|Bayer, Micha', 'The James Hutton Institute, Invergowrie, Dundee, DD2 5DA, Scotland, UK. antonio.ribeiro@hutton.ac.uk.|School of Agriculture and Food Sciences, University of Queensland, Brisbane, Queensland, 4072, Australia. agnieszka.golicz@uq.net.au.|Biomathematics and Statistics Scotland, Invergowrie, Dundee, DD2 5DA, Scotland, UK. Christine.Hackett@bioss.ac.uk.|The James Hutton Institute, Invergowrie, Dundee, DD2 5DA, Scotland, UK. iain.milne@hutton.ac.uk.|The James Hutton Institute, Invergowrie, Dundee, DD2 5DA, Scotland, UK. gordon.stephen@hutton.ac.uk.|The James Hutton Institute, Invergowrie, Dundee, DD2 5DA, Scotland, UK. david.marshall@hutton.ac.uk.|Division of Plant Sciences, University of Dundee at JHI, Invergowrie, Dundee, DD2 5DA, Scotland, UK. as.j.flavell@dundee.ac.uk.|The James Hutton Institute, Invergowrie, Dundee, DD2 5DA, Scotland, UK. micha.bayer@hutton.ac.uk.', 'United Kingdom|Australia|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 21, 'Arabidopsis|Databases, Genetic|Eukaryota|Genome|Genomics|High-Throughput Nucleotide Sequencing|Molecular Sequence Annotation|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-015-0801-z', '26558718', 'Single Nucleotide Polymorphisms (SNPs) are widely used molecular markers, and their use has increased massively since the inception of Next Generation Sequencing (NGS) technologies, which allow detection of large numbers of SNPs at low cost. However, both NGS data and their analysis are error-prone, which can lead to the generation of false positive (FP) SNPs. We explored the relationship between FP SNPs and seven factors involved in mapping-based variant calling - quality of the reference sequence, read length, choice of mapper and variant caller, mapping stringency and filtering of SNPs by read mapping quality and read depth. This resulted in 576 possible factor level combinations. We used error- and variant-free simulated reads to ensure that every SNP found was indeed a false positive.'),
(97007, 2015, 'bmc', 'Two molecular measures of relatedness based on haplotype sharing', 'Edwards, David', 'Centre for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Blichers Allé 20, Tjele, 8830, DK, Denmark. david.edwards@mbg.au.dk.', 'Denmark', 0, 'Animals|Cattle|Computer Simulation|Female|Genetic Variation|Genetics, Population|Genome|Genomics|Haplotypes|Humans|Linear Models|Male|Models, Genetic|Pedigree|Software', 'Journal Article', '10.1186/s12859-015-0802-y', '26558877', 'Measuring the extent of shared ancestry between individuals or organisms is important in many fields, including forensic science, conservation genetics and animal breeding. The traditional approach is to calculate the expected degree of relatedness between individuals in a pedigree. This assumes that the founders of the pedigree are non-inbred and unrelated to each other, which is rarely the case. In contrast, molecular data allow measurement of actual relatedness without knowledge of a pedigree. Methods to do this have been proposed, but generally do not take the lengths of the genomic regions shared between individuals into account.'),
(97008, 2015, 'bmc', 'A fast method for calculating reliable event supports in tree reconciliations via Pareto optimality', 'To, Thu-Hien|Jacox, Edwin|Ranwez, Vincent|Scornavacca, Celine', 'ISEM - Université de Montpellier, CNRS, IRD, EPHE, Place Eugène Bataillon, Montpellier, 34392, France. thu-hien.to@univ-montp2.fr.|ISEM - Université de Montpellier, CNRS, IRD, EPHE, Place Eugène Bataillon, Montpellier, 34392, France. edwin.jacox@unvi-montp2.fr.|Montpellier SupAgro, UMR AGAP, 2 Place P. Viala, Montpellier, 34060, France. vincent.ranwez@supagro.inra.fr.|ISEM - Université de Montpellier, CNRS, IRD, EPHE, Place Eugène Bataillon, Montpellier, 34392, France. celine.scornavacca@unvi-montp2.fr.', 'France|France|France|France', 7, 'Algorithms|Computer Simulation|Evolution, Molecular|Genes, Bacterial|Phylogeny|Proteobacteria|Reproducibility of Results', 'Journal Article', '10.1186/s12859-015-0803-x', '26573665', 'Given a gene and a species tree, reconciliation methods attempt to retrieve the macro-evolutionary events that best explain the discrepancies between the two tree topologies. The DTL parsimonious approach searches for a most parsimonious reconciliation between a gene tree and a (dated) species tree, considering four possible macro-evolutionary events (speciation, duplication, transfer, and loss) with specific costs. Unfortunately, many events are erroneously predicted due to errors in the input trees, inappropriate input cost values or because of the existence of several equally parsimonious scenarios. It is thus crucial to provide a measure of the reliability for predicted events. It has been recently proposed that the reliability of an event can be estimated via its frequency in the set of most parsimonious reconciliations obtained using a variety of reasonable input cost vectors. To compute such a support, a straightforward but time-consuming approach is to generate the costs slightly departing from the original ones, independently compute the set of all most parsimonious reconciliations for each vector, and combine these sets a posteriori. Another proposed approach uses Pareto-optimality to partition cost values into regions which induce reconciliations with the same number of DTL events. The support of an event is then defined as its frequency in the set of regions. However, often, the number of regions is not large enough to provide reliable supports.'),
(97009, 2015, 'bmc', 'A new network representation of the metabolism to detect chemical transformation modules', 'Sorokina, Maria|Medigue, Claudine|Vallenet, David', 'Direction des Sciences du Vivant, Commissariat à lEnergie Atomique et aux Energies Alternatives (CEA), Institut de Génomique, Genoscope, Laboratoire dAnalyses Bioinformatiques pour la Génomique et le Métabolisme, 2 rue Gaston Crémieux, Evry, 91057, France. msorokina@genoscope.cns.fr.|Direction des Sciences du Vivant, Commissariat à lEnergie Atomique et aux Energies Alternatives (CEA), Institut de Génomique, Genoscope, Laboratoire dAnalyses Bioinformatiques pour la Génomique et le Métabolisme, 2 rue Gaston Crémieux, Evry, 91057, France. cmedigue@genoscope.cns.fr.|Direction des Sciences du Vivant, Commissariat à lEnergie Atomique et aux Energies Alternatives (CEA), Institut de Génomique, Genoscope, Laboratoire dAnalyses Bioinformatiques pour la Génomique et le Métabolisme, 2 rue Gaston Crémieux, Evry, 91057, France. vallenet@genoscope.cns.fr.', 'France|France|France', 0, 'Genome|Metabolic Networks and Pathways|Models, Chemical|Multigene Family', 'Journal Article', '10.1186/s12859-015-0809-4', '26573681', 'Metabolism is generally modeled by directed networks where nodes represent reactions and/or metabolites. In order to explore metabolic pathway conservation and divergence among organisms, previous studies were based on graph alignment to find similar pathways. Few years ago, the concept of chemical transformation modules, also called reaction modules, was introduced and correspond to sequences of chemical transformations which are conserved in metabolism. We propose here a novel graph representation of the metabolic network where reactions sharing a same chemical transformation type are grouped in Reaction Molecular Signatures (RMS).'),
(97010, 2015, 'bmc', 'misFinder: identify mis-assemblies in an unbiased manner using reference and paired-end reads', 'Zhu, Xiao|Leung, Henry C M|Wang, Rongjie|Chin, Francis Y L|Yiu, Siu Ming|Quan, Guangri|Li, Yajie|Zhang, Rui|Jiang, Qinghua|Liu, Bo|Dong, Yucui|Zhou, Guohui|Wang, Yadong', 'College of Computer Sciences and Information Engineering, Harbin Normal University, Harbin, Heilongjiang, China. zhuxiao.hit@gmail.com.|Department of Computer Science, University of Hong Kong, Pokfulam Road, Hong Kong, China. cmleung2@cs.hku.hk.|Center for Bioinformatics, School of Computer Sciences and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China. rjwang.hit@gmail.com.|Department of Computer Science, University of Hong Kong, Pokfulam Road, Hong Kong, China. chin@cs.hku.hk.|Department of Computer Science, University of Hong Kong, Pokfulam Road, Hong Kong, China. smyiu@cs.hku.hk.|Center for Bioinformatics, School of Computer Sciences and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China. grquan@hit.edu.cn.|The Fourth Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China. 148077246@qq.com.|The Fourth Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China. 282661708@qq.com.|School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China. qhjiang@hit.edu.cn.|Center for Bioinformatics, School of Computer Sciences and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China. bo.liu@hit.edu.cn.|Department of Immunology, Harbin Medical University, Harbin, Heilongjiang, China. dongyucui521@yeah.net.|College of Computer Sciences and Information Engineering, Harbin Normal University, Harbin, Heilongjiang, China. zhou_ghui@163.com.|Center for Bioinformatics, School of Computer Sciences and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China. ydwang@hit.edu.cn.', 'China|China|China|China|China|China|China|China|China|China|China|China|China', 5, 'Computer Simulation|Escherichia coli|High-Throughput Nucleotide Sequencing|Schizosaccharomyces|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-015-0818-3', '26573684', 'Because of the short read length of high throughput sequencing data, assembly errors are introduced in genome assembly, which may have adverse impact to the downstream data analysis. Several tools have been developed to eliminate these errors by either 1) comparing the assembled sequences with some similar reference genome, or 2) analyzing paired-end reads aligned to the assembled sequences and determining inconsistent features alone mis-assembled sequences. However, the former approach cannot distinguish real structural variations between the target genome and the reference genome while the latter approach could have many false positive detections (correctly assembled sequence being considered as mis-assembled sequence).'),
(97011, 2015, 'bmc', 'DiffLogo: a comparative visualization of sequence motifs', 'Nettling, Martin|Treutler, Hendrik|Grau, Jan|Keilwagen, Jens|Posch, Stefan|Grosse, Ivo', 'Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany. martin.nettling@informatik.uni-halle.de.|Leibniz Institute of Plant Biochemistry, Halle (Saale), Germany. hendrik.treutler@ipb-halle.de.|Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany. jan.grau@informatik.uni-halle.de.|Institute for Biosafety in Plant Biotechnology, Julius Kühn-Institut (JKI), Federal Research Centre for Cultivated Plants, Quedlinburg, Germany. jens.keilwagen@jki.bund.de.|Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany. stefan.posch@informatik.uni-halle.de.|Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany. ivo.grosse@informatik.uni-halle.de.', 'Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Amino Acid Motifs|Basic Helix-Loop-Helix Transcription Factors|CCCTC-Binding Factor|Computational Biology|Computer Graphics|Humans|Nucleotide Motifs|Protein Structure, Tertiary|Repressor Proteins|Sequence Analysis, DNA|Software|Tumor Cells, Cultured', 'Comparative Study', '10.1186/s12859-015-0767-x', '26577052', 'For three decades, sequence logos are the de facto standard for the visualization of sequence motifs in biology and bioinformatics. Reasons for this success story are their simplicity and clarity. The number of inferred and published motifs grows with the number of data sets and motif extraction algorithms. Hence, it becomes more and more important to perceive differences between motifs. However, motif differences are hard to detect from individual sequence logos in case of multiple motifs for one transcription factor, highly similar binding motifs of different transcription factors, or multiple motifs for one protein domain.'),
(97012, 2015, 'bmc', 'antaRNA--Multi-objective inverse folding of pseudoknot RNA using ant-colony optimization', 'Kleinkauf, Robert|Houwaart, Torsten|Backofen, Rolf|Mann, Martin', 'Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Köhler-Allee 106, Freiburg, 79110, Germany. robertk@informatik.uni-freiburg.de.|Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Köhler-Allee 106, Freiburg, 79110, Germany. houwaart@informatik.uni-freiburg.de.|Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Köhler-Allee 106, Freiburg, 79110, Germany. backofen@informatik.uni-freiburg.de.|Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Köhler-Allee 106, Freiburg, 79110, Germany. mmann@informatik.uni-freiburg.de.', 'Germany|Germany|Germany|Germany', 13, 'Algorithms|Humans|Models, Molecular|RNA|RNA Folding|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/s12859-015-0815-6', '26581440', 'Many functional RNA molecules fold into pseudoknot structures, which are often essential for the formation of an RNAs 3D structure. Currently the design of RNA molecules, which fold into a specific structure (known as RNA inverse folding) within biotechnological applications, is lacking the feature of incorporating pseudoknot structures into the design. Hairpin-(H)- and kissing hairpin-(K)-type pseudoknots cover a wide range of biologically functional pseudoknots and can be represented on a secondary structure level.'),
(97013, 2015, 'bmc', 'Proposal of supervised data analysis strategy of plasma miRNAs from hybridisation array data with an application to assess hemolysis-related deregulation', 'Landoni, Elena|Miceli, Rosalba|Callari, Maurizio|Tiberio, Paola|Appierto, Valentina|Angeloni, Valentina|Mariani, Luigi|Daidone, Maria Grazia', 'Clinical Epidemiology Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Via Venezian 1, 20133, Milan, Italy. elena.landoni@istitutotumori.mi.it.|Clinical Epidemiology Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Via Venezian 1, 20133, Milan, Italy. rosalba.miceli@istitutotumori.mi.it.|Biomarkers Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Via Venezian 1, 20133, Milan, Italy.|Biomarkers Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Via Venezian 1, 20133, Milan, Italy.|Biomarkers Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Via Venezian 1, 20133, Milan, Italy.|Biomarkers Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Via Venezian 1, 20133, Milan, Italy.|Clinical Epidemiology Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Via Venezian 1, 20133, Milan, Italy.|Biomarkers Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Via Venezian 1, 20133, Milan, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 0, 'Biomarkers, Tumor|Breast Neoplasms|Case-Control Studies|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Hemolysis|Humans|MicroRNAs', 'Comparative Study', '10.1186/s12859-015-0820-9', '26581577', 'Plasma miRNAs have the potential as cancer biomarkers but no consolidated guidelines for data mining in this field are available. The purpose of the study was to apply a supervised data analysis strategy in a context where prior knowledge is available, i.e., that of hemolysis-related miRNAs deregulation, so as to compare our results with existing evidence.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(97014, 2015, 'bmc', 'An affinity-structure database of helix-turn-helix: DNA complexes with a universal coordinate system', 'AlQuraishi, Mohammed|Tang, Shengdong|Xia, Xide', 'Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA. alquraishi@hms.harvard.edu.|Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA.|Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA.', 'United States|United States|United States', 1, 'Algorithms|DNA|DNA-Binding Proteins|Databases, Factual|Transcription Factors', 'Journal Article', '10.1186/s12859-015-0819-2', '26586237', 'Molecular interactions between proteins and DNA molecules underlie many cellular processes, including transcriptional regulation, chromosome replication, and nucleosome positioning. Computational analyses of protein-DNA interactions rely on experimental data characterizing known protein-DNA interactions structurally and biochemically. While many databases exist that contain either structural or biochemical data, few integrate these two data sources in a unified fashion. Such integration is becoming increasingly critical with the rapid growth of structural and biochemical data, and the emergence of algorithms that rely on the synthesis of multiple data types to derive computational models of molecular interactions.'),
(97015, 2015, 'bmc', 'FoCo: a simple and robust quantification algorithm of nuclear foci', 'Lapytsko, Anastasiya|Kollarovic, Gabriel|Ivanova, Lyubomira|Studencka, Maja|Schaber, Jxc3xb6rg', 'Medical Faculty, Institute for Experimental Internal Medicine, Otto von Guericke University, Pfxc3xa4lzer Platz 2, 39106, Magdeburg, Germany. anastasiya.lapytsko@med.ovgu.de.|Medical Faculty, Institute for Experimental Internal Medicine, Otto von Guericke University, Pfxc3xa4lzer Platz 2, 39106, Magdeburg, Germany. kollarovic.gabor@gmail.com.|Medical Faculty, Institute for Experimental Internal Medicine, Otto von Guericke University, Pfxc3xa4lzer Platz 2, 39106, Magdeburg, Germany. lu.ivanova@googlemail.com.|Medical Faculty, Institute for Experimental Internal Medicine, Otto von Guericke University, Pfxc3xa4lzer Platz 2, 39106, Magdeburg, Germany. maja.studencka@med.ovgu.de.|Medical Faculty, Institute for Experimental Internal Medicine, Otto von Guericke University, Pfxc3xa4lzer Platz 2, 39106, Magdeburg, Germany. schaber@med.ovgu.de.', 'Germany|Germany|Germany|Germany|Germany', 0, 'Cell Nucleus|DNA Breaks, Double-Stranded|Microscopy, Fluorescence|Single-Cell Analysis', 'Journal Article', '10.1186/s12859-015-0816-5', '26589438', 'The number of xcexb3H2AX foci per nucleus is an accepted measure of the number of DNA double-strand breaks in single cells. One of the experimental techniques for xcexb3H2AX detection in cultured cells is immunofluorescent labelling of xcexb3H2AX and nuclei followed by microscopy imaging and analysis.'),
(97016, 2015, 'bmc', 'Coev-web: a web platform designed to simulate and evaluate coevolving positions along a phylogenetic tree', 'Dib, Linda|Meyer, Xavier|Artimo, Panu|Ioannidis, Vassilios|Stockinger, Heinz|Salamin, Nicolas', 'Department of Ecology and Evolution, University of Lausanne, Lausanne, 1015, Switzerland. linda.dib@unil.ch.|Department of Ecology and Evolution, University of Lausanne, Lausanne, 1015, Switzerland. xavier.meyer@iphc.cnrs.fr.|SIB Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland. Panu.Artimo@isb-sib.ch.|SIB Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland. Vassilios.Ioannidis@isb-sib.ch.|SIB Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland. Heinz.Stockinger@isb-sib.ch.|Department of Ecology and Evolution, University of Lausanne, Lausanne, 1015, Switzerland. nicolas.salamin@unil.ch.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 6, 'Algorithms|Amino Acids|Bayes Theorem|Computational Biology|Evolution, Molecular|Humans|Internet|Phylogeny|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-015-0785-8', '26597459', 'Available methods to simulate nucleotide or amino acid data typically use Markov models to simulate each position independently. These approaches are not appropriate to assess the performance of combinatorial and probabilistic methods that look for coevolving positions in nucleotide or amino acid sequences.'),
(97017, 2015, 'bmc', 'An heuristic filtering tool to identify phenotype-associated genetic variants applied to human intellectual disability and canine coat colors', 'Broeckx, Bart J G|Coopman, Frank|Verhoeven, Geert|Bosmans, Tim|Gielen, Ingrid|Dingemanse, Walter|Saunders, Jimmy H|Deforce, Dieter|Van Nieuwerburgh, Filip', 'Laboratory of Pharmaceutical Biotechnology, Faculty of Pharmaceutical Sciences, Ghent University, 9000, Ghent, Belgium. Bart.broeckx@ugent.be.|Department of Applied Biosciences, Faculty of Bioscience Engineering, Ghent University, 9000, Ghent, Belgium. Frank.coopman@ugent.be.|Department of Medical Imaging and Small Animal Orthopaedics, Faculty of Veterinary Medicine, Ghent University, 9820, Merelbeke, Belgium. Geertverhoeven@hotmail.com.|Department of Medicine and Clinical Biology of Small Animals, Faculty of Veterinary Medicine, Ghent University, 9820, Merelbeke, Belgium. Tim.Bosmans@ugent.be.|Department of Medical Imaging and Small Animal Orthopaedics, Faculty of Veterinary Medicine, Ghent University, 9820, Merelbeke, Belgium. Ingrid.Gielen@ugent.be.|Department of Medical Imaging and Small Animal Orthopaedics, Faculty of Veterinary Medicine, Ghent University, 9820, Merelbeke, Belgium. Walter.Dingemanse@ugent.be.|Department of Medical Imaging and Small Animal Orthopaedics, Faculty of Veterinary Medicine, Ghent University, 9820, Merelbeke, Belgium. Jimmy.Saunders@ugent.be.|Laboratory of Pharmaceutical Biotechnology, Faculty of Pharmaceutical Sciences, Ghent University, 9000, Ghent, Belgium. Dieter.Deforce@ugent.be.|Laboratory of Pharmaceutical Biotechnology, Faculty of Pharmaceutical Sciences, Ghent University, 9000, Ghent, Belgium. Filip.VanNieuwerburgh@UGent.be.', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 0, 'Animals|Computational Biology|Databases, Genetic|Dogs|Exome|Genetic Variation|Hair Color|Heuristics|High-Throughput Nucleotide Sequencing|Humans|Intellectual Disability|Phenotype|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-015-0822-7', '26597515', 'Identification of one or several disease causing variant(s) from the large collection of variants present in an individual is often achieved by the sequential use of heuristic filters. The recent development of whole exome sequencing enrichment designs for several non-model species created the need for a species-independent, fast and versatile analysis tool, capable of tackling a wide variety of standard and more complex inheritance models. With this aim, we developed Mendelian, an R-package that can be used for heuristic variant filtering.'),
(97018, 2015, 'bmc', 'MSAIndelFR: a scheme for multiple protein sequence alignment using information on indel flanking regions', 'Al-Shatnawi, Mufleh|Ahmad, M Omair|Swamy, M N S', 'Department of Electrical and Computer Engineering, Concordia University, 1455 De Maisonneuve Blvd. W., Montreal, H3G 1M8, Quebec, Canada. m_alshat@ece.concordia.ca.|Department of Electrical and Computer Engineering, Concordia University, 1455 De Maisonneuve Blvd. W., Montreal, H3G 1M8, Quebec, Canada. omair@ece.concordia.ca.|Department of Electrical and Computer Engineering, Concordia University, 1455 De Maisonneuve Blvd. W., Montreal, H3G 1M8, Quebec, Canada. swamy@ece.concordia.ca.', 'Canada|Canada|Canada', 3, 'Algorithms|Computational Biology|Humans|INDEL Mutation|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/s12859-015-0826-3', '26597571', 'The alignment of multiple protein sequences is one of the most commonly performed tasks in bioinformatics. In spite of considerable research and efforts that have been recently deployed for improving the performance of multiple sequence alignment (MSA) algorithms, finding a highly accurate alignment between multiple protein sequences is still a challenging problem.'),
(97019, 2015, 'bmc', 'Fully automated registration of vibrational microspectroscopic images in histologically stained tissue sections', 'Yang, Chen|Niedieker, Daniel|Grosserüschkamp, Frederik|Horn, Melanie|Tannapfel, Andrea|Kallenbach-Thieltges, Angela|Gerwert, Klaus|Mosig, Axel', 'Department of Biophysics, CAS-MPG Partner Institute and Key Laboratory for Computational Biology, 320 Yueyang Road, Shanghai, 200031, China. yangchen@picb.ac.cn.|Department of Biophysics, Ruhr-University Bochum, Universitätsstraße 150, Bochum, 44780, Germany. daniel.niedieker@bph.rub.de.|Department of Biophysics, Ruhr-University Bochum, Universitätsstraße 150, Bochum, 44780, Germany. fred@bph.rub.de.|Department of Biophysics, Ruhr-University Bochum, Universitätsstraße 150, Bochum, 44780, Germany. melanie.horn@bph.rub.de.|Institute of Pathology, Ruhr-University Bochum, Bochum, Germany, Bürkle-de-la-Camp-Platz 1, Bochum, 44789, Germany. andrea.tannapfel@rub.de.|Department of Biophysics, Ruhr-University Bochum, Universitätsstraße 150, Bochum, 44780, Germany. angela@bph.rub.de.|Department of Biophysics, CAS-MPG Partner Institute and Key Laboratory for Computational Biology, 320 Yueyang Road, Shanghai, 200031, China. gerwert@bph.rub.de.|Department of Biophysics, Ruhr-University Bochum, Universitätsstraße 150, Bochum, 44780, Germany. axel.mosig@bph.rub.de.', 'China|Germany|Germany|Germany|Germany|Germany|China|Germany', 0, 'Algorithms|Colon|Humans|Image Processing, Computer-Assisted|Lung|Microscopy|Pattern Recognition, Automated|Spectroscopy, Fourier Transform Infrared|Spectrum Analysis, Raman|Tissue Array Analysis|Urinary Bladder', 'Journal Article', '10.1186/s12859-015-0804-9', '26607812', 'In recent years, hyperspectral microscopy techniques such as infrared or Raman microscopy have been applied successfully for diagnostic purposes. In many of the corresponding studies, it is common practice to measure one and the same sample under different types of microscopes. Any joint analysis of the two image modalities requires to overlay the images, so that identical positions in the sample are located at the same coordinate in both images. This step, commonly referred to as image registration, has typically been performed manually in the lack of established automated computational registration tools.'),
(97020, 2015, 'bmc', 'Analysis of in vivo single cell behavior by high throughput, human-in-the-loop segmentation of three-dimensional images', 'Chiang, Michael|Hallman, Sam|Cinquin, Amanda|de Mochel, Nabora Reyes|Paz, Adrian|Kawauchi, Shimako|Calof, Anne L|Cho, Ken W|Fowlkes, Charless C|Cinquin, Olivier', 'Department of Developmental & Cell Biology, University of California at Irvine, Irvine, USA. mchiang3@uci.edu.|Center for Complex Biological Systems, University of California at Irvine, Irvine, USA. shallman@uci.edu.|Department of Developmental & Cell Biology, University of California at Irvine, Irvine, USA. acinquin@uci.edu.|Department of Developmental & Cell Biology, University of California at Irvine, Irvine, USA. nreyesde@uci.edu.|Department of Developmental & Cell Biology, University of California at Irvine, Irvine, USA. apaz@uci.edu.|Center for Complex Biological Systems, University of California at Irvine, Irvine, USA. skawauch@uci.edu.|Department of Developmental & Cell Biology, University of California at Irvine, Irvine, USA. alcalof@uci.edu.|Department of Developmental & Cell Biology, University of California at Irvine, Irvine, USA. kwcho@uci.edu.|Center for Complex Biological Systems, University of California at Irvine, Irvine, USA. fowlkes@uci.edu.|Department of Developmental & Cell Biology, University of California at Irvine, Irvine, USA. ocinquin@uci.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 8, 'Algorithms|Animals|Blastocyst|Caenorhabditis elegans|Cell Cycle|Cells, Cultured|Computational Biology|Female|Gonads|High-Throughput Screening Assays|Humans|Image Processing, Computer-Assisted|Imaging, Three-Dimensional|Male|Mice|Microscopy, Confocal|Olfactory Mucosa|Single-Cell Analysis|Software', 'Journal Article', '10.1186/s12859-015-0814-7', '26607933', 'Analysis of single cells in their native environment is a powerful method to address key questions in developmental systems biology. Confocal microscopy imaging of intact tissues, followed by automatic image segmentation, provides a means to conduct cytometric studies while at the same time preserving crucial information about the spatial organization of the tissue and morphological features of the cells. This technique is rapidly evolving but is still not in widespread use among research groups that do not specialize in technique development, perhaps in part for lack of tools that automate repetitive tasks while allowing experts to make the best use of their time in injecting their domain-specific knowledge.'),
(97021, 2015, 'bmc', 'High-order dynamic Bayesian Network learning with hidden common causes for causal gene regulatory network', 'Lo, Leung-Yau|Wong, Man-Leung|Lee, Kin-Hong|Leung, Kwong-Sak', 'Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong. lylo@cse.cuhk.edu.hk.|Department of Computing and Decision Sciences, Lingnan University, Tuen Mun, Hong Kong. mlwong@ln.edu.hk.|Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong. leekinhong@hotmail.com.|Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong. ksleung@cse.cuhk.edu.hk.', 'Hong Kong|Hong Kong|Hong Kong|Hong Kong', 9, 'Algorithms|Bayes Theorem|Computational Biology|Gene Expression Profiling|Gene Expression Regulation|Gene Regulatory Networks|Humans', 'Journal Article', '10.1186/s12859-015-0823-6', '26608050', 'Inferring gene regulatory network (GRN) has been an important topic in Bioinformatics. Many computational methods infer the GRN from high-throughput expression data. Due to the presence of time delays in the regulatory relationships, High-Order Dynamic Bayesian Network (HO-DBN) is a good model of GRN. However, previous GRN inference methods assume causal sufficiency, i.e. no unobserved common cause. This assumption is convenient but unrealistic, because it is possible that relevant factors have not even been conceived of and therefore un-measured. Therefore an inference method that also handles hidden common cause(s) is highly desirable. Also, previous methods for discovering hidden common causes either do not handle multi-step time delays or restrict that the parents of hidden common causes are not observed genes.'),
(97022, 2015, 'bmc', 'Reads2Type: a web application for rapid microbial taxonomy identification', 'Saputra, Dhany|Rasmussen, Simon|Larsen, Mette V|Haddad, Nizar|Sperotto, Maria Maddalena|Aarestrup, Frank M|Lund, Ole|Sicheritz-Pontén, Thomas', 'Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Kemitorvet, Kgs. Lyngby, DK-2800, Denmark. dhany@cbs.dtu.dk.|Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Kemitorvet, Kgs. Lyngby, DK-2800, Denmark. simon@cbs.dtu.dk.|Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Kemitorvet, Kgs. Lyngby, DK-2800, Denmark. metteb@cbs.dtu.dk.|Bee Research Department, National Centre for Agricultural Research and Extension, P.O. Box 639, Baqa, 19381, Jordan. drnizarh@yahoo.com.|Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Kemitorvet, Kgs. Lyngby, DK-2800, Denmark. maria@cbs.dtu.dk.|National Food Institute, Division for Epidemiology and Microbial Genomics, Technical University of Denmark, Kemitorvet, Kgs. Lyngby, DK-2800, Denmark. fmaa@food.dtu.dk.|Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Kemitorvet, Kgs. Lyngby, DK-2800, Denmark. lund@cbs.dtu.dk.|Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Kemitorvet, Kgs. Lyngby, DK-2800, Denmark. thomas@cbs.dtu.dk.', 'Denmark|Denmark|Denmark|Jordan|Denmark|Denmark|Denmark|Denmark', 0, 'Bacteria|Bacterial Proteins|Benchmarking|Classification|DNA, Bacterial|Databases, Genetic|Genome, Bacterial|Internet|Multilocus Sequence Typing|RNA, Ribosomal, 16S|Software', 'Journal Article', '10.1186/s12859-015-0829-0', '26608174', 'Identification of bacteria may be based on sequencing and molecular analysis of a specific locus such as 16S rRNA, or a set of loci such as in multilocus sequence typing. In the near future, healthcare institutions and routine diagnostic microbiology laboratories may need to sequence the entire genome of microbial isolates. Therefore we have developed Reads2Type, a web-based tool for taxonomy identification based on whole bacterial genome sequence data.'),
(97023, 2015, 'bmc', 'PC-TraFF: identification of potentially collaborating transcription factors using pointwise mutual information', 'Meckbach, Cornelia|Tacke, Rebecca|Hua, Xu|Waack, Stephan|Wingender, Edgar|Gültas, Mehmet', 'Institute of Bioinformatics, University of Göttingen, Goldschmidtstr. 1, Göttingen, 37077, Germany. cornelia.meckbach@bioinf.med.uni-goettingen.de.|Institute of Bioinformatics, University of Göttingen, Goldschmidtstr. 1, Göttingen, 37077, Germany. rebecca.tacke@stud.uni-goettingen.de.|Institute of Bioinformatics, University of Göttingen, Goldschmidtstr. 1, Göttingen, 37077, Germany. xu.hua@bioinf.med.uni-goettingen.de.|Institute of Computer Science, University of Göttingen, Goldschmidtstr. 7, Göttingen, 37077, Germany. waack@cs.uni-goettingen.de.|Institute of Bioinformatics, University of Göttingen, Goldschmidtstr. 1, Göttingen, 37077, Germany. edgar.wingender@bioinf.med.uni-goettingen.de.|Institute of Bioinformatics, University of Göttingen, Goldschmidtstr. 1, Göttingen, 37077, Germany. mehmet.gueltas@bioinf.med.uni-goettingen.de.', 'Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Binding Sites|Breast Neoplasms|Computational Biology|Female|Gene Expression Regulation, Neoplastic|Genome, Human|Humans|MicroRNAs|Promoter Regions, Genetic|Protein Binding|Transcription Factors', 'Journal Article', '10.1186/s12859-015-0827-2', '26627005', 'Transcription factors (TFs) are important regulatory proteins that govern transcriptional regulation. Today, it is known that in higher organisms different TFs have to cooperate rather than acting individually in order to control complex genetic programs. The identification of these interactions is an important challenge for understanding the molecular mechanisms of regulating biological processes. In this study, we present a new method based on pointwise mutual information, PC-TraFF, which considers the genome as a document, the sequences as sentences, and TF binding sites (TFBSs) as words to identify interacting TFs in a set of sequences.'),
(97024, 2015, 'bmc', 'Scalable analysis of Big pathology image data cohorts using efficient methods and high-performance computing strategies', 'Kurc, Tahsin|Qi, Xin|Wang, Daihou|Wang, Fusheng|Teodoro, George|Cooper, Lee|Nalisnik, Michael|Yang, Lin|Saltz, Joel|Foran, David J', 'Department of Biomedical Informatics, Stony Brook University, Stony Brook, USA. tahsin.kurc@stonybrook.edu.|Department of Pathology & Laboratory Medicine, Rutgers -- Robert Wood Johnson Medical School, New Brunswick, USA. qixi@rutgers.edu.|Department of Electrical and Computer Engineering, Rutgers University, New Brunswick, USA. daihou.wang@rutgers.edu.|Department of Biomedical Informatics, Stony Brook University, Stony Brook, USA. fusheng.wang@stonybrook.edu.|Department of Biomedical Informatics, Stony Brook University, Stony Brook, USA. teodoro@unb.br.|Department of Biomedical Informatics, Emory University, Atlanta, USA. lee.cooper@emory.edu.|Department of Biomedical Informatics, Emory University, Atlanta, USA. mnalisn@emory.edu.|Department of Biomedical Engineering, University of Florida, Gainesville, USA. lin.yang@bme.ufl.edu.|Department of Biomedical Informatics, Stony Brook University, Stony Brook, USA. joel.saltz@stonybrookmedicine.edu.|Department of Pathology & Laboratory Medicine, Rutgers -- Robert Wood Johnson Medical School, New Brunswick, USA. foran@cinj.rutgers.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Cluster Analysis|Diagnostic Imaging|Humans|Image Interpretation, Computer-Assisted|Information Storage and Retrieval|Male|Neoplasm Grading|Pattern Recognition, Automated|Prostatic Neoplasms|Tissue Array Analysis', 'Journal Article', '10.1186/s12859-015-0831-6', '26627175', 'We describe a suite of tools and methods that form a core set of capabilities for researchers and clinical investigators to evaluate multiple analytical pipelines and quantify sensitivity and variability of the results while conducting large-scale studies in investigative pathology and oncology. The overarching objective of the current investigation is to address the challenges of large data sizes and high computational demands.'),
(97025, 2015, 'bmc', 'GigaTON: an extensive publicly searchable database providing a new reference transcriptome in the pacific oyster Crassostrea gigas', 'Riviere, Guillaume|Klopp, Christophe|Ibouniyamine, Nabihoudine|Huvet, Arnaud|Boudry, Pierre|Favrel, Pascal', 'Institute for fundamental and applied biology, University of Caen Basse-Normandie, Caen, France. guillaume.riviere@unicaen.fr.|INRA, Sigenae UR875 Biométrie et Intelligence Artificielle, BP 52627, Castanet-Tolosan Cedex, 31326, France. christophe.klopp@toulouse.inra.fr.|INRA, Sigenae UR875 Biométrie et Intelligence Artificielle, BP 52627, Castanet-Tolosan Cedex, 31326, France. nabihoudine.ibounyamine@toulouse.inra.fr.|Ifremer, UMR CNRS 6539 (CNRS/UBO/IRD/Ifremer), Laboratoire des Sciences de lEnvironnement Marin, ZI de la Pointe du Diable, CS 10070, Plouzané, 29280, France. arnaud.huvet@ifremer.fr.|Ifremer, UMR CNRS 6539 (CNRS/UBO/IRD/Ifremer), Laboratoire des Sciences de lEnvironnement Marin, ZI de la Pointe du Diable, CS 10070, Plouzané, 29280, France. pierre.boudry@ifremer.fr.|Institute for fundamental and applied biology, University of Caen Basse-Normandie, Caen, France. pascal.favrel@unicaen.fr.', 'France|France|France|France|France|France', 21, 'Animals|Base Sequence|Computational Biology|Crassostrea|Databases, Genetic|Gene Library|Gene Ontology|Genome|High-Throughput Nucleotide Sequencing|Molecular Sequence Data|Sequence Analysis, RNA|Software|Transcriptome', 'Journal Article', '10.1186/s12859-015-0833-4', '26627443', 'The Pacific oyster, Crassostrea gigas, is one of the most important aquaculture shellfish resources worldwide. Important efforts have been undertaken towards a better knowledge of its genome and transcriptome, which makes now C. gigas becoming a model organism among lophotrochozoans, the under-described sister clade of ecdysozoans within protostomes. These massive sequencing efforts offer the opportunity to assemble gene expression data and make such resource accessible and exploitable for the scientific community. Therefore, we undertook this assembly into an up-to-date publicly available transcriptome database: the GigaTON (Gigas TranscriptOme pipeliNe) database.'),
(97026, 2015, 'bmc', 'Accurate prediction of nuclear receptors with conjoint triad feature', 'Wang, Hongchu|Hu, Xuehai', 'Department of Mathemaitcs, South China Normal University, Guangzhou, 510631, P.R. of China.|College of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, 430070, P.R. of China. huxuehai@mail.hzau.edu.cn.', 'China|China', 0, 'Algorithms|Amino Acids|Computational Biology|Databases, Protein|Humans|Receptors, Cytoplasmic and Nuclear|Support Vector Machine', 'Journal Article', '10.1186/s12859-015-0828-1', '26630876', 'Nuclear receptors (NRs) form a large family of ligand-inducible transcription factors that regulate gene expressions involved in numerous physiological phenomena, such as embryogenesis, homeostasis, cell growth and death. These nuclear receptors-related pathways are important targets of marketed drugs. Therefore, the design of a reliable computational model for predicting NRs from amino acid sequence has now been a significant biomedical problem.'),
(97027, 2015, 'bmc', 'M(3)-S: a genotype calling method incorporating information from samples with known genotypes', 'Li, Gengxin|Zhao, Hongyu', 'Department of Mathematics and Statistics, Wright State University, 3640 Colonel Glenn Hwy, Dayton, 45435, USA. gengxin.li@wright.edu.|Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, 06520, USA. hongyu.zhao@yale.edu.', 'United States|United States', 0, 'Algorithms|Gene Frequency|Genome-Wide Association Study|Genotype|Genotyping Techniques|Humans|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/s12859-015-0824-5', '26634345', 'A key challenge in analyzing high throughput Single Nucleotide Polymorphism (SNP) arrays is the accurate inference of genotypes for SNPs with low minor allele frequencies. A number of calling algorithms have been developed to infer genotypes for common SNPs, but they are limited in their performance in calling rare SNPs. The existing algorithms can be broadly classified into three categories, including: population-based methods, SNP-based methods, and a hybrid of the two approaches. Despite the relatively better performance of the hybrid approach, it is still challenging to analyze rare SNPs.'),
(97028, 2015, 'bmc', 'DHOEM: a statistical simulation software for simulating new markers in real SNP marker data', 'Jacquin, Laval|Cao, Tuong-Vi|Grenier, Cécile|Ahmadi, Nourollah', 'CIRAD, UMR AGAP, Centre de Coopération Internationale en Recherche Agronomique pour le Développement, Avenue Agropolis, Montpellier Cedex 5, 34398, France. laval.jacquin@cirad.fr.|CIRAD, UMR AGAP, Centre de Coopération Internationale en Recherche Agronomique pour le Développement, Avenue Agropolis, Montpellier Cedex 5, 34398, France.|CIRAD, UMR AGAP, Centre de Coopération Internationale en Recherche Agronomique pour le Développement, Avenue Agropolis, Montpellier Cedex 5, 34398, France.|CIRAD, UMR AGAP, Centre de Coopération Internationale en Recherche Agronomique pour le Développement, Avenue Agropolis, Montpellier Cedex 5, 34398, France.', 'France|France|France|France', 0, 'Computer Simulation|Gene Frequency|Haplotypes|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1186/s12859-015-0830-7', '26634451', 'Numerous simulation tools based on specific assumptions have been proposed to simulate populations. Here we present a simulation tool named DHOEM (densification of haplotypes by loess regression and maximum likelihood) which is free from population assumptions and simulates new markers in real SNP marker data. The main objective of DHOEM is to generate a new population, which incorporates real and simulated SNP by statistical learning from an initial population, which match the realized features of the latter.'),
(97029, 2015, 'bmc', 'Rare variants analysis using penalization methods for whole genome sequence data', 'Yazdani, Akram|Yazdani, Azam|Boerwinkle, Eric', 'Human Genetics Center, University of Texas Health Science Center at Houston, TX, USA. akram.yazdani@uth.tmc.edu.|Human Genetics Center, University of Texas Health Science Center at Houston, TX, USA. azam.yazdani@uth.tmc.edu.|Human Genetics Center, University of Texas Health Science Center at Houston, TX, USA. Eric.Boerwinkle@uth.tmc.edu.', 'United States|United States|United States', 9, 'Algorithms|Atherosclerosis|Genetic Association Studies|Genetic Variation|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Linkage Disequilibrium|Phenotype|Principal Component Analysis', 'Journal Article', '10.1186/s12859-015-0825-4', '26637205', 'Availability of affordable and accessible whole genome sequencing for biomedical applications poses a number of statistical challenges and opportunities, particularly related to the analysis of rare variants and sparseness of the data. Although efforts have been devoted to address these challenges, the performance of statistical methods for rare variants analysis still needs further consideration.'),
(97030, 2015, 'bmc', 'Parameterizing sequence alignment with an explicit evolutionary model', 'Rivas, Elena|Eddy, Sean R', 'Department of Molecular and Cellular Biology, Harvard University, Cambridge, 02138, MA, USA. elenarivas@fas.harvard.edu.|Department of Molecular and Cellular Biology, Harvard University, Cambridge, 02138, MA, USA. sean@eddylab.com.', 'United States|United States', 0, 'Algorithms|Evolution, Molecular|Humans|Models, Statistical|Models, Theoretical|Probability|Sequence Alignment', 'Journal Article', '10.1186/s12859-015-0832-5', '26652060', 'Inference of sequence homology is inherently an evolutionary question, dependent upon evolutionary divergence. However, the insertion and deletion penalties in the most widely used methods for inferring homology by sequence alignment, including BLAST and profile hidden Markov models (profile HMMs), are not based on any explicitly time-dependent evolutionary model. Using one fixed score system (BLOSUM62 with some gap open/extend costs, for example) corresponds to making an unrealistic assumption that all sequence relationships have diverged by the same time. Adoption of explicit time-dependent evolutionary models for scoring insertions and deletions in sequence alignments has been hindered by algorithmic complexity and technical difficulty.'),
(97031, 2015, 'bmc', 'Penalized weighted low-rank approximation for robust recovery of recurrent copy number variations', 'Gao, Xiaoli', 'Department of Mathematics and Statistics, University of North Carolina at Greensboro, 1400 Spring Garden St, Greensoboro, NC, USA. x_gao2@uncg.edu.', 'United States', 0, 'Algorithms|Chromosomes, Human, Pair 11|Chromosomes, Human, Pair 17|Comparative Genomic Hybridization|Computational Biology|DNA Copy Number Variations|Genome, Human|Humans|Models, Statistical', 'Journal Article', '10.1186/s12859-015-0835-2', '26652207', 'Copy number variation (CNV) analysis has become one of the most important research areas for understanding complex disease. With increasing resolution of array-based comparative genomic hybridization (aCGH) arrays, more and more raw copy number data are collected for multiple arrays. It is natural to realize the co-existence of both recurrent and individual-specific CNVs, together with the possible data contamination during the data generation process. Therefore, there is a great need for an efficient and robust statistical model for simultaneous recovery of both recurrent and individual-specific CNVs.'),
(97032, 2015, 'bmc', 'Bioinformatics approach to predict target genes for dysregulated microRNAs in hepatocellular carcinoma: study on a chemically-induced HCC mouse model', 'Del Vecchio, Filippo|Gallo, Francesco|Di Marco, Antinisca|Mastroiaco, Valentina|Caianiello, Pasquale|Zazzeroni, Francesca|Alesse, Edoardo|Tessitore, Alessandra', 'Department of Biotechnological and Applied Clinical Sciences, University of LAquila, Via Vetoio, Coppito 2, 67100, LAquila, Italy. filippo.delvecchio1@graduate.univaq.it.|Department of Computer Engineering and Science, and Mathematics, University of LAquila, Via Vetoio, Coppito 1, LAquila, 67100, Italy. francesco.gallo@univaq.it.|Department of Computer Engineering and Science, and Mathematics, University of LAquila, Via Vetoio, Coppito 1, LAquila, 67100, Italy. antinisca.dimarco@univaq.it.|Department of Biotechnological and Applied Clinical Sciences, University of LAquila, Via Vetoio, Coppito 2, 67100, LAquila, Italy. valentina.mastroiaco@graduate.univaq.it.|Department of Computer Engineering and Science, and Mathematics, University of LAquila, Via Vetoio, Coppito 1, LAquila, 67100, Italy. pasquale.caianiello@di.univaq.it.|Department of Biotechnological and Applied Clinical Sciences, University of LAquila, Via Vetoio, Coppito 2, 67100, LAquila, Italy. francesca.zazzeroni@univaq.it.|Department of Biotechnological and Applied Clinical Sciences, University of LAquila, Via Vetoio, Coppito 2, 67100, LAquila, Italy. edoardo.alesse@univaq.it.|Department of Biotechnological and Applied Clinical Sciences, University of LAquila, Via Vetoio, Coppito 2, 67100, LAquila, Italy. alessandra.tessitore@univaq.it.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 0, 'Animals|Carcinoma, Hepatocellular|Computational Biology|Gene Regulatory Networks|Liver Neoplasms|Mice|Mice, Inbred C57BL|MicroRNAs|Protein Interaction Maps|RNA, Messenger', 'Journal Article', '10.1186/s12859-015-0836-1', '26652480', 'Hepatocellular carcinoma (HCC) is an aggressive epithelial tumor which shows very poor prognosis and high rate of recurrence, representing an urgent problem for public healthcare. MicroRNAs (miRNAs/miRs) are a class of small, non-coding RNAs that attract great attention because of their role in regulation of processes such as cellular growth, proliferation, apoptosis. Because of the thousands of potential interactions between a single miR and target mRNAs, bioinformatics prediction tools are very useful to facilitate the task for individuating and selecting putative target genes. In this study, we present a chemically-induced HCC mouse model to identify differential expression of miRNAs during the progression of the hepatic injury up to HCC onset. In addition, we describe an established bioinformatics approach to highlight putative target genes and protein interaction networks where they are involved.'),
(97033, 2015, 'bmc', 'MIA: Mutual Information Analyzer, a graphic user interface program that calculates entropy, vertical and horizontal mutual information of molecular sequence sets', 'Lichtenstein, Flavio|Antoneli, Fernando|Briones, Marcelo R S', 'Departamento de Informática em Saúde, Escola Paulista de Medicina, Universidade Federal de Sao Paulo, Rua Botucatu, 862, Ed. José Leal Prado, andar térreo, Vila Clementino, CEP 04023-062, Sao Paulo, SP, Brazil. flalix@gmail.com.|Departamento de Informática em Saúde, Escola Paulista de Medicina, Universidade Federal de Sao Paulo, Rua Botucatu, 862, Ed. José Leal Prado, andar térreo, Vila Clementino, CEP 04023-062, Sao Paulo, SP, Brazil. fernando.antoneli@unifesp.br.|Departamento de Microbiologia, Immunologia and Parasitologia, Escola Paulista de Medicina, Universidade Federal de Sao Paulo, Rua Botucatu, 862, Ed. Ciências Biomédicas, 3 andar, Vila Clementino, CEP 04023-062, Sao Paulo, SP, Brazil. marcelo.briones@unifesp.br.', 'Brazil|Brazil|Brazil', 1, 'Algorithms|Animals|Arabidopsis|Arabidopsis Proteins|Computational Biology|Computer Graphics|Drosophila|Drosophila Proteins|Entropy|Evolution, Molecular|Genome|Genomics|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|Phylogeny|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-015-0837-0', '26652707', 'Short and long range correlations in biological sequences are central in genomic studies of covariation. These correlations can be studied using mutual information because it measures the amount of information one random variable contains about the other. Here we present MIA (Mutual Information Analyzer) a user friendly graphic interface pipeline that calculates spectra of vertical entropy (VH), vertical mutual information (VMI) and horizontal mutual information (HMI), since currently there is no user friendly integrated platform that in a single package perform all these calculations. MIA also calculates Jensen-Shannon Divergence (JSD) between pair of different species spectra, herein called informational distances. Thus, the resulting distance matrices can be presented by distance histograms and informational dendrograms, giving support to discrimination of closely related species.'),
(97034, 2015, 'bmc', 'MRMPlus: an open source quality control and assessment tool for SRM/MRM assay development', 'Aiyetan, Paul|Thomas, Stefani N|Zhang, Zhen|Zhang, Hui', 'Department of Pathology, The Johns Hopkins University School of Medicine, Robert H and Clarice Smith Building, Room 4000C, 400 North Broadway, Baltimore, MD, 21287, USA. paul.aytton@gmail.com.|Department of Pathology, The Johns Hopkins University School of Medicine, Robert H and Clarice Smith Building, Room 4000C, 400 North Broadway, Baltimore, MD, 21287, USA. sthoma92@jhmi.edu.|Department of Pathology, The Johns Hopkins University School of Medicine, Robert H and Clarice Smith Building, Room 4000C, 400 North Broadway, Baltimore, MD, 21287, USA. zzhang7@jhmi.edu.|Department of Pathology, The Johns Hopkins University School of Medicine, Robert H and Clarice Smith Building, Room 4000C, 400 North Broadway, Baltimore, MD, 21287, USA. huizhang@jhu.edu.', 'United States|United States|United States|United States', 7, 'Humans|Mass Spectrometry|Peptide Fragments|Proteins|Proteome|Proteomics|Quality Control|Reproducibility of Results|Software', 'Journal Article', '10.1186/s12859-015-0838-z', '26652794', 'Selected and multiple reaction monitoring involves monitoring a multiplexed assay of proteotypic peptides and associated transitions in mass spectrometry runs. To describe peptide and associated transitions as stable, quantifiable, and reproducible representatives of proteins of interest, experimental and analytical validation is required. However, inadequate and disparate analytical tools and validation methods predispose assay performance measures to errors and inconsistencies.'),
(97035, 2015, 'bmc', 'Erratum to: Efficient experimental design for uncertainty reduction in gene regulatory networks', 'Dehghannasiri, Roozbeh|Yoon, Byung-Jun|Dougherty, Edward R', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.', 'United States|United States|United States', 0, '-', 'Published Erratum', '10.1186/s12859-015-0839-y', '26652981', '-'),
(97036, 2015, 'bmc', 'Identification of aberrant gene expression associated with aberrant promoter methylation in primordial germ cells between E13 and E16 rat F3 generation vinclozolin lineage', 'Taguchi, Y-h', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.', 'United States', 17, 'Algorithms|Animals|DNA Methylation|Embryo, Mammalian|Epigenomics|Female|Germ Cells|Oligonucleotide Array Sequence Analysis|Oxazoles|Principal Component Analysis|Promoter Regions, Genetic|Rats|Signal Transduction|Transcriptome', 'Journal Article', '10.1186/1471-2105-16-S18-S16', '26677731', 'Transgenerational epigenetics (TGE) are currently considered important in disease, but the mechanisms involved are not yet fully understood. TGE abnormalities expected to cause disease are likely to be initiated during development and to be mediated by aberrant gene expression associated with aberrant promoter methylation that is heritable between generations. However, because methylation is removed and then re-established during development, it is not easy to identify promoter methylation abnormalities by comparing normal lineages with those expected to exhibit TGE abnormalities.'),
(97037, 2015, 'bmc', 'PCTFPeval: a web tool for benchmarking newly developed algorithms for predicting cooperative transcription factor pairs in yeast', 'Lai, Fu-Jou|Chang, Hong-Tsun|Wu, Wei-Sheng', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.', 'United States|United States|United States', 5, 'Algorithms|Benchmarking|Internet|Saccharomyces cerevisiae|Transcription Factors|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-16-S18-S2', '26677932', 'Computational identification of cooperative transcription factor (TF) pairs helps understand the combinatorial regulation of gene expression in eukaryotic cells. Many advanced algorithms have been proposed to predict cooperative TF pairs in yeast. However, it is still difficult to conduct a comprehensive and objective performance comparison of different algorithms because of lacking sufficient performance indices and adequate overall performance scores. To solve this problem, in our previous study (published in BMC Systems Biology 2014), we adopted/proposed eight performance indices and designed two overall performance scores to compare the performance of 14 existing algorithms for predicting cooperative TF pairs in yeast. Most importantly, our performance comparison framework can be applied to comprehensively and objectively evaluate the performance of a newly developed algorithm. However, to use our framework, researchers have to put a lot of effort to construct it first. To save researchers time and effort, here we develop a web tool to implement our performance comparison framework, featuring fast data processing, a comprehensive performance comparison and an easy-to-use web interface.'),
(97038, 2015, 'bmc', 'Accurate reconstruction of viral quasispecies spectra through improved estimation of strain richness', 'Jayasundara, Duleepa|Saeed, I|Chang, B C|Tang, Sen-Lin|Halgamuge, Saman K', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Benchmarking|High-Throughput Nucleotide Sequencing|Internet|Metagenomics|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-16-S18-S3', '26678073', 'Estimating the number of different species (richness) in a mixed microbial population has been a main focus in metagenomic research. Existing methods of species richness estimation ride on the assumption that the reads in each assembled contig correspond to only one of the microbial genomes in the population. This assumption and the underlying probabilistic formulations of existing methods are not useful for quasispecies populations where the strains are highly genetically related.'),
(97039, 2015, 'bmc', 'Automated identification of copepods using digital image processing and artificial neural network', 'Leow, Lee Kien|Chew, Li-Lee|Chong, Ving Ching|Dhillon, Sarinder Kaur', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.', 'United States|United States|United States|United States', 18, 'Animals|Automation|Copepoda|Databases, Factual|Discriminant Analysis|Image Processing, Computer-Assisted|Neural Networks (Computer)', 'Journal Article', '10.1186/1471-2105-16-S18-S4', '26678287', 'Copepods are planktonic organisms that play a major role in the marine food chain. Studying the community structure and abundance of copepods in relation to the environment is essential to evaluate their contribution to mangrove trophodynamics and coastal fisheries. The routine identification of copepods can be very technical, requiring taxonomic expertise, experience and much effort which can be very time-consuming. Hence, there is an urgent need to introduce novel methods and approaches to automate identification and classification of copepod specimens. This study aims to apply digital image processing and machine learning methods to build an automated identification and classification technique.'),
(97040, 2015, 'bmc', 'Identifying representative drug resistant mutants of HIV', 'Yu, Xiaxia|Weber, Irene T|Harrison, Robert W', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.', 'United States|United States|United States', 0, 'Cluster Analysis|Computational Biology|Drug Resistance, Multiple, Viral|Drug Resistance, Viral|HIV Infections|HIV Protease|HIV Protease Inhibitors|HIV-1|Humans|Mutation|Regression Analysis|Reverse Transcriptase Inhibitors', 'Journal Article', '10.1186/1471-2105-16-S17-S1', '26678327', 'Drug resistance is one of the most important causes for failure of anti-AIDS treatment. During therapy, multiple mutations accumulate in the HIV genome, eventually rendering the drugs ineffective in blocking replication of the mutant virus. The huge number of possible mutants precludes experimental analysis to explore the molecular mechanisms of resistance and develop improved antiviral drugs.'),
(97041, 2015, 'bmc', 'Analysis of genomic rearrangements by using the Burrows-Wheeler transform of short-read data', 'Kimura, Kouichi|Koike, Asako', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.', 'United States|United States', 5, 'Algorithms|Databases, Genetic|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-16-S18-S5', '26678411', 'The potential utility of the Burrows-Wheeler transform (BWT) of a large amount of short-read data (reads) has not been fully studied. The BWT basically serves as a lossless dictionary of reads, unlike the heuristic and lossy reads-to-genome mapping results conventionally obtained in the first step of sequence analysis. Thus, it is naturally expected to lead to development of sensitive methods for analysis of short-read data. Recently, one of the most active areas of research in sequence analysis is sensitive detection of rare genomic rearrangements from whole-genome sequencing (WGS) data of heterogeneous cancer samples. The application the BWT of reads to the analysis of genomic rearrangements is addressed in this study.'),
(97042, 2015, 'bmc', 'SpliceJumper: a classification-based approach for calling splicing junctions from RNA-seq data', 'Chu, Chong|Li, Xin|Wu, Yufeng', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.', 'United States|United States|United States', 2, 'Computer Simulation|Databases, Genetic|RNA Splice Sites|RNA Splicing|Sequence Analysis, RNA|Software|Time Factors', 'Journal Article', '10.1186/1471-2105-16-S17-S10', '26678515', 'Next-generation RNA sequencing technologies have been widely applied in transcriptome profiling. This facilitates further studies of gene structure and expression on the genome wide scale. It is an important step to align reads to the reference genome and call out splicing junctions for the following analysis, such as the analysis of alternative splicing and isoform construction. However, because of the existence of introns, when RNA-seq reads are aligned to the reference genome, reads can not be fully mapped at splicing sites. Thus, it is challenging to align reads and call out splicing junctions accurately.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(97043, 2015, 'bmc', 'Privacy-preserving search for chemical compound databases', 'Shimizu, Kana|Nuida, Koji|Arai, Hiromi|Mitsunari, Shigeo|Attrapadung, Nuttapong|Hamada, Michiaki|Tsuda, Koji|Hirokawa, Takatsugu|Sakuma, Jun|Hanaoka, Goichiro|Asai, Kiyoshi', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 6, 'Algorithms|Cloud Computing|Computer Security|Databases, Chemical', 'Journal Article', '10.1186/1471-2105-16-S18-S6', '26678650', 'Searching for similar compounds in a database is the most important process for in-silico drug screening. Since a query compound is an important starting point for the new drug, a query holder, who is afraid of the query being monitored by the database server, usually downloads all the records in the database and uses them in a closed network. However, a serious dilemma arises when the database holder also wants to output no information except for the search results, and such a dilemma prevents the use of many important data resources.'),
(97044, 2015, 'bmc', 'EC: an efficient error correction algorithm for short reads', 'Saha, Subrata|Rajasekaran, Sanguthevar', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.', 'United States|United States', 5, 'Algorithms|Computer Simulation|Databases, Nucleic Acid|High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-16-S17-S2', '26678663', 'In highly parallel next-generation sequencing (NGS) techniques millions to billions of short reads are produced from a genomic sequence in a single run. Due to the limitation of the NGS technologies, there could be errors in the reads. The error rate of the reads can be reduced with trimming and by correcting the erroneous bases of the reads. It helps to achieve high quality data and the computational complexity of many biological applications will be greatly reduced if the reads are first corrected. We have developed a novel error correction algorithm called EC and compared it with four other state-of-the-art algorithms using both real and simulated sequencing reads.'),
(97045, 2015, 'bmc', 'Principal component analysis for designed experiments', 'Konishi, Tomokazu', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.', 'United States', 17, 'Algorithms|Principal Component Analysis|Research Design', 'Journal Article', '10.1186/1471-2105-16-S18-S7', '26678818', 'Principal component analysis is used to summarize matrix data, such as found in transcriptome, proteome or metabolome and medical examinations, into fewer dimensions by fitting the matrix to orthogonal axes. Although this methodology is frequently used in multivariate analyses, it has disadvantages when applied to experimental data. First, the identified principal components have poor generality, since the size and directions of the components are dependent on the particular data set, the components are valid only within the data set. Second, the method is sensitive to experimental noise and bias between sample groups. It cannot reflect the experimental design that is planned to manage the noise and bias, rather, it estimates the same weight and independence to all the samples in the matrix. Third, the resulting components are often difficult to interpret. To address these issues, several options were introduced to the methodology. First, the principal axes were identified using training data sets and shared across experiments. These training data reflect the design of experiments, and their preparation allows noise to be reduced and group bias to be removed. Second, the center of the rotation was determined in accordance with the experimental design. Third, the resulting components were scaled to unify their size unit.'),
(97046, 2015, 'bmc', 'How genome complexity can explain the difficulty of aligning reads to genomes', 'Phan, Vinhthuy|Gao, Shanshan|Tran, Quang|Vo, Nam S', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.', 'United States|United States|United States|United States', 0, 'Animals|Base Sequence|Genome|Humans|Sequence Alignment|Sequence Analysis, DNA|Software|Time Factors', 'Journal Article', '10.1186/1471-2105-16-S17-S3', '26678826', 'Although it is frequently observed that aligning short reads to genomes becomes harder if they contain complex repeat patterns, there has not been much effort to quantify the relationship between complexity of genomes and difficulty of short-read alignment. Existing measures of sequence complexity seem unsuitable for the understanding and quantification of this relationship.'),
(97047, 2015, 'bmc', 'ORBiT: Oak Ridge biosurveillance toolkit for public health dynamics', 'Ramanathan, Arvind|Pullum, Laura L|Hobson, Tanner C|Steed, Chad A|Quinn, Shannon P|Chennubhotla, Chakra S|Valkova, Silvia', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.', 'United States|United States|United States|United States|United States|United States|United States', 5, 'Biosurveillance|Electronic Health Records|Humans|Incidence|Influenza A Virus, H1N1 Subtype|Influenza, Human|Pandemics|Public Health|Seasons|Software|Time Factors|United States', 'Journal Article', '10.1186/1471-2105-16-S17-S4', '26679008', 'The digitization of health-related information through electronic health records (EHR) and electronic healthcare reimbursement claims and the continued growth of self-reported health information through social media provides both tremendous opportunities and challenges in developing effective biosurveillance tools. With novel emerging infectious diseases being reported across different parts of the world, there is a need to build systems that can track, monitor and report such events in a timely manner. Further, it is also important to identify susceptible geographic regions and populations where emerging diseases may have a significant impact.'),
(97048, 2015, 'bmc', 'Discrete structural features among interface residue-level classes', 'Sowmya, Gopichandran|Ranganathan, Shoba', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.', 'United States|United States', 4, 'Databases, Protein|Dimerization|Hydrogen Bonding|Internet|Protein Interaction Maps|Protein Structure, Tertiary|Proteins|Static Electricity|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-16-S18-S8', '26679043', 'Protein-protein interaction (PPI) is essential for molecular functions in biological cells. Investigation on protein interfaces of known complexes is an important step towards deciphering the driving forces of PPIs. Each PPI complex is specific, sensitive and selective to binding. Therefore, we have estimated the relative difference in percentage of polar residues between surface and the interface for each complex in a non-redundant heterodimer dataset of 278 complexes to understand the predominant forces driving binding.'),
(97049, 2015, 'bmc', 'A computational method for drug repositioning using publicly available gene expression data', 'Shabana, K M|Abdul Nazeer, K A|Pradhan, Meeta|Palakal, Mathew', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.', 'United States|United States|United States|United States', 3, 'Antineoplastic Agents|Breast Neoplasms|Computational Biology|Databases, Genetic|Drug Repositioning|Female|Gene Expression Regulation, Neoplastic|Hepatitis C|Humans|Letrozole|Lung Neoplasms|Nitriles|Triazoles', 'Journal Article', '10.1186/1471-2105-16-S17-S5', '26679199', 'The identification of new therapeutic uses of existing drugs, or drug repositioning, offers the possibility of faster drug development, reduced risk, lesser cost and shorter paths to approval. The advent of high throughput microarray technology has enabled comprehensive monitoring of transcriptional response associated with various disease states and drug treatments. This data can be used to characterize disease and drug effects and thereby give a measure of the association between a given drug and a disease. Several computational methods have been proposed in the literature that make use of publicly available transcriptional data to reposition drugs against diseases.'),
(97050, 2015, 'bmc', 'Prediction of neddylation sites from protein sequences and sequence-derived properties', 'Yavuz, Ahmet Sinan|Sxc3xb6zer, Namxc4xb1k Berk|Sezerman, Osman Uxc4x9fur', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.', 'United States|United States|United States', 6, 'Amino Acid Sequence|Area Under Curve|Computational Biology|Lysine|Position-Specific Scoring Matrices|Protein Processing, Post-Translational|ROC Curve|Support Vector Machine|Ubiquitins', 'Journal Article', '10.1186/1471-2105-16-S18-S9', '26679222', 'Neddylation is a reversible post-translational modification that plays a vital role in maintaining cellular machinery. It is shown to affect localization, binding partners and structure of target proteins. Disruption of protein neddylation was observed in various diseases such as Alzheimers and cancer. Therefore, understanding the neddylation mechanism and determining neddylation targets possibly bears a huge importance in further understanding the cellular processes. This study is the first attempt to predict neddylated sites from protein sequences by using several sequence and sequence-based structural features.'),
(97051, 2015, 'bmc', 'Signal reachability facilitates characterization of probabilistic signaling networks', 'Gabr, Haitham|Kahveci, Tamer', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.', 'United States|United States', 3, 'Gene Ontology|Humans|Models, Theoretical|Probability|Signal Transduction', 'Journal Article', '10.1186/1471-2105-16-S17-S6', '26679404', 'Studying biological networks is of extreme importance in understanding cellular functions. These networks model interactions between molecules in each cell. A large volume of research has been done to uncover different characteristics of biological networks, such as large-scale organization, node centrality and network robustness. Nevertheless, the vast majority of research done in this area assume that biological networks have deterministic topologies. Biological interactions are however probabilistic events that may or may not appear at different cells or even in the same cell at different times.'),
(97052, 2015, 'bmc', 'In search of perfect reads', 'Pal, Soumitra|Aluru, Srinivas', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.', 'United States|United States', 3, 'Algorithms|Databases, Nucleic Acid|High-Throughput Nucleotide Sequencing|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-16-S17-S7', '26679555', 'Continued advances in next generation short-read sequencing technologies are increasing throughput and read lengths, while driving down error rates. Taking advantage of the high coverage sampling used in many applications, several error correction algorithms have been developed to improve data quality further. However, correcting errors in high coverage sequence data requires significant computing resources.'),
(97053, 2015, 'bmc', 'Automated parameter estimation for biological models using Bayesian statistical model checking', 'Hussain, Faraz|Langmead, Christopher J|Mi, Qi|Dutta-Moscato, Joyeeta|Vodovotz, Yoram|Jha, Sumit K', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.', 'United States|United States|United States|United States|United States|United States', 0, 'Acute Disease|Algorithms|Automation|Bayes Theorem|Computer Simulation|Humans|Inflammation|Models, Biological|Models, Statistical|Models, Theoretical|Probability|Reproducibility of Results|Stochastic Processes|Systems Biology', 'Journal Article', '10.1186/1471-2105-16-S17-S8', '26679759', 'Probabilistic models have gained widespread acceptance in the systems biology community as a useful way to represent complex biological systems. Such models are developed using existing knowledge of the structure and dynamics of the system, experimental observations, and inferences drawn from statistical analysis of empirical data. A key bottleneck in building such models is that some system variables cannot be measured experimentally. These variables are incorporated into the model as numerical parameters. Determining values of these parameters that justify existing experiments and provide reliable predictions when model simulations are performed is a key research problem.'),
(97054, 2015, 'bmc', 'Structural vs functional mechanisms of duplicate gene loss following whole genome doubling', 'Sankoff, David|Zheng, Chunfang|Wang, Baoyong|Abad Najar, Carlos', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.', 'United States|United States|United States|United States', 1, 'Computer Simulation|Gene Deletion|Gene Dosage|Gene Duplication|Genes, Duplicate|Genome', 'Journal Article', '10.1186/1471-2105-16-S17-S9', '26680009', 'The loss of duplicate genes - fractionation - after whole genome doubling (WGD) is the subject to a debate as to whether it proceeds gene by gene or through deletion of multi-gene chromosomal segments.'),
(97055, 2015, 'bmc', 'An adaptive genetic algorithm for selection of blood-based biomarkers for prediction of Alzheimers disease progression', 'Vandewater, Luke|Brusic, Vladimir|Wilson, William|Macaulay, Lance|Zhang, Ping', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Alzheimer Disease|Area Under Curve|Australia|Biomarkers|Disease Progression|Humans|Logistic Models|Neuropsychological Tests|ROC Curve', 'Journal Article', '10.1186/1471-2105-16-S18-S1', '26680269', 'Alzheimers disease is a multifactorial disorder that may be diagnosed earlier using a combination of tests rather than any single test. Search algorithms and optimization techniques in combination with model evaluation techniques have been used previously to perform the selection of suitable feature sets. Previously we successfully applied GA with LR to neuropsychological data contained within the The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging, to select cognitive tests for prediction of progression of AD. This research addresses an Adaptive Genetic Algorithm (AGA) in combination with LR for identifying the best biomarker combination for prediction of the progression to AD.'),
(97056, 2015, 'bmc', 'A two-layered machine learning method to identify protein O-GlcNAcylation sites with O-GlcNAc transferase substrate motifs', 'Kao, Hui-Ju|Huang, Chien-Hsun|Bretaxc3xb1a, Neil Arvin|Lu, Cheng-Tsung|Huang, Kai-Yao|Weng, Shun-Long|Lee, Tzong-Yi', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.', 'United States|United States|United States|United States|United States|United States|United States', 15, 'Acetylglucosamine|Algorithms|Amino Acid Motifs|Glycosylation|Internet|Machine Learning|Mass Spectrometry|N-Acetylglucosaminyltransferases|Peptides|Proteins|Substrate Specificity|Support Vector Machine|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-16-S18-S10', '26680539', 'Protein O-GlcNAcylation, involving the xcexb2-attachment of single N-acetylglucosamine (GlcNAc) to the hydroxyl group of serine or threonine residues, is an O-linked glycosylation catalyzed by O-GlcNAc transferase (OGT). Molecular level investigation of the basis for OGTs substrate specificity should aid understanding how O-GlcNAc contributes to diverse cellular processes. Due to an increasing number of O-GlcNAcylated peptides with site-specific information identified by mass spectrometry (MS)-based proteomics, we were motivated to characterize substrate site motifs of O-GlcNAc transferases. In this investigation, a non-redundant dataset of 410 experimentally verified O-GlcNAcylation sites were manually extracted from dbOGAP, OGlycBase and UniProtKB. After detection of conserved motifs by using maximal dependence decomposition, profile hidden Markov model (profile HMM) was adopted to learn a first-layered model for each identified OGT substrate motif. Support Vector Machine (SVM) was then used to generate a second-layered model learned from the output values of profile HMMs in first layer. The two-layered predictive model was evaluated using a five-fold cross validation which yielded a sensitivity of 85.4%, a specificity of 84.1%, and an accuracy of 84.7%. Additionally, an independent testing set from PhosphoSitePlus, which was really non-homologous to the training data of predictive model, was used to demonstrate that the proposed method could provide a promising accuracy (84.05%) and outperform other O-GlcNAcylation site prediction tools. A case study indicated that the proposed method could be a feasible means of conducting preliminary analyses of protein O-GlcNAcylation and has been implemented as a web-based system, OGTSite, which is now freely available at http://csb.cse.yzu.edu.tw/OGTSite/. '),
(97057, 2015, 'bmc', 'Light-RCV: a lightweight read coverage viewer for next generation sequencing data', 'Chang, Che-Wei|Lee, Wen-Bin|Chen-Deng, An|Liu, Tsunglin|Tseng, Joseph T|Chang, Darby Tien-Hao', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.', 'United States|United States|United States|United States|United States|United States', 1, 'Algorithms|Genome, Fungal|Genomics|High-Throughput Nucleotide Sequencing|Internet|Polymorphism, Single Nucleotide|RNA Splicing|Saccharomyces cerevisiae|Sequence Analysis, DNA|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-16-S18-S11', '26680734', 'Next-generation sequencing (NGS) technologies has brought an unprecedented amount of genomic data for analysis. Unlike array-based profiling technologies, NGS can reveal the expression profile across a transcript at the base level. Such a base-level read coverage provides further insights for alternative mRNA splicing, single-nucleotide polymorphism (SNP), novel transcript discovery, etc. However, to our best knowledge, none of existing NGS viewers can timely visualize genome-wide base-level read coverages in an interactive environment.'),
(97058, 2015, 'bmc', 'Positive-unlabeled learning for the prediction of conformational B-cell epitopes', 'Ren, Jing|Liu, Qian|Ellis, John|Li, Jinyan', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.', 'United States|United States|United States|United States', 14, 'Algorithms|Antibodies|Antigens|Databases, Protein|Epitopes, B-Lymphocyte|Protein Structure, Tertiary|Software', 'Journal Article', '10.1186/1471-2105-16-S18-S12', '26681157', 'The incomplete ground truth of training data of B-cell epitopes is a demanding issue in computational epitope prediction. The challenge is that only a small fraction of the surface residues of an antigen are confirmed as antigenic residues (positive training data), the remaining residues are unlabeled. As some of these uncertain residues can possibly be grouped to form novel but currently unknown epitopes, it is misguided to unanimously classify all the unlabeled residues as negative training data following the traditional supervised learning scheme.'),
(97059, 2015, 'bmc', 'Obtaining long 16S rDNA sequences using multiple primers and its application on dioxin-containing samples', 'Chen, Yi-Lin|Lee, Chuan-Chun|Lin, Ya-Lan|Yin, Kai-Min|Ho, Chung-Liang|Liu, Tsunglin', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.', 'United States|United States|United States|United States|United States|United States', 11, 'Bacteria|DNA Primers|Dioxins|High-Throughput Nucleotide Sequencing|RNA, Ribosomal, 16S|Sequence Analysis, DNA|Soil Microbiology', 'Journal Article', '10.1186/1471-2105-16-S18-S13', '26681335', 'Next-generation sequencing (NGS) technology has transformed metagenomics because the high-throughput data allow an in-depth exploration of a complex microbial community. However, accurate species identification with NGS data is challenging because NGS sequences are relatively short. Assembling 16S rDNA segments into longer sequences has been proposed for improving species identification. Current approaches, however, either suffer from amplification bias due to one single primer or insufficient 16S rDNA reads in whole genome sequencing data.'),
(97060, 2015, 'bmc', 'Characterizing informative sequence descriptors and predicting binding affinities of heterodimeric protein complexes', 'Srinivasulu, Yerukala Sathipati|Wang, Jyun-Rong|Hsu, Kai-Ti|Tsai, Ming-Ju|Charoenkwan, Phasit|Huang, Wen-Lin|Huang, Hui-Ling|Ho, Shinn-Ying', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States', 6, 'Area Under Curve|Dimerization|Hydrogen Bonding|Principal Component Analysis|Protein Binding|Protein Interaction Maps|Protein Structure, Tertiary|Proteins|ROC Curve|Support Vector Machine', 'Journal Article', '10.1186/1471-2105-16-S18-S14', '26681483', 'Protein-protein interactions (PPIs) are involved in various biological processes, and underlying mechanism of the interactions plays a crucial role in therapeutics and protein engineering. Most machine learning approaches have been developed for predicting the binding affinity of protein-protein complexes based on structure and functional information. This work aims to predict the binding affinity of heterodimeric protein complexes from sequences only.'),
(97061, 2015, 'bmc', 'Condensing Raman spectrum for single-cell phenotype analysis', 'Sun, Shiwei|Wang, Xuetao|Gao, Xin|Ren, Lihui|Su, Xiaoquan|Bu, Dongbo|Ning, Kang', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.', 'United States|United States|United States|United States|United States|United States|United States', 9, 'Bacteria|Databases, Factual|Discriminant Analysis|Phenotype|Principal Component Analysis|Signal Processing, Computer-Assisted|Single-Cell Analysis|Spectrum Analysis, Raman', 'Journal Article', '10.1186/1471-2105-16-S18-S15', '26681607', 'In recent years, high throughput and non-invasive Raman spectrometry technique has matured as an effective approach to identification of individual cells by species, even in complex, mixed populations. Raman profiling is an appealing optical microscopic method to achieve this. To fully utilize Raman proling for single-cell analysis, an extensive understanding of Raman spectra is necessary to answer questions such as which filtering methodologies are effective for pre-processing of Raman spectra, what strains can be distinguished by Raman spectra, and what features serve best as Raman-based biomarkers for single-cells, etc.'),
(97062, 2015, 'bmc', 'On the distribution of cycles and paths in multichromosomal breakpoint graphs and the expected value of rearrangement distance', 'Feijão, Pedro|Martinez, Fábio|Thévenin, Annelyse', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.', 'United States|United States|United States', 0, 'Algorithms|Chromosome Breakage|Chromosomes|DNA, Circular|Gene Rearrangement|Genome|Genome Size', 'Journal Article', '10.1186/1471-2105-16-S19-S1', '26695008', 'Finding the smallest sequence of operations to transform one genome into another is an important problem in comparative genomics. The breakpoint graph is a discrete structure that has proven to be effective in solving distance problems, and the number of cycles in a cycle decomposition of this graph is one of the remarkable parameters to help in the solution of related problems. For a fixed k, the number of linear unichromosomal genomes (signed or unsigned) with n elements such that the induced breakpoint graphs have k disjoint cycles, known as the Hultman number, has been already determined. In this work we extend these results to multichromosomal genomes, providing formulas to compute the number of multichromosal genomes having a fixed number of cycles and/or paths. We obtain an explicit formula for circular multichromosomal genomes and recurrences for general multichromosomal genomes, and discuss how these series can be used to calculate the distribution and expected value of the rearrangement distance between random genomes. '),
(97063, 2015, 'bmc', 'Structural studies on molecular mechanisms of Nelfinavir resistance caused by non-active site mutation V77I in HIV-1 protease', 'Gupta, Ankita|Jamal, Salma|Goyal, Sukriti|Jain, Ritu|Wahi, Divya|Grover, Abhinav', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.', 'United States|United States|United States|United States|United States|United States', 15, 'Binding Sites|Catalytic Domain|Drug Resistance, Viral|HIV Infections|HIV Protease|HIV Protease Inhibitors|HIV-1|Humans|Hydrogen Bonding|Hydrophobic and Hydrophilic Interactions|Ligands|Molecular Docking Simulation|Molecular Dynamics Simulation|Mutation|Nelfinavir|Thermodynamics', 'Journal Article', '10.1186/1471-2105-16-S19-S10', '26695135', 'The human immunodeficiency virus (HIV-1) is a retrovirus causing acquired immunodeficiency syndrome (AIDS), which has become a serious problem across the world and has no cure reported to date. Human immunodeficiency virus (HIV-1) protease is an attractive target for antiviral treatment and a number of therapeutically useful inhibitors have been designed against it. The emergence of drug resistant mutants of HIV-1 poses a serious problem for conventional therapies that have been used so far. Until now, thirteen protease inhibitors (PIs), major mutation sites and many secondary mutations have been listed in the HIV Drug Resistance Database. In this study, we have studied the effect of the V77I mutation in HIV-PR along with the co-occurring mutations L33F and K20T through multi-nanosecond molecular dynamics simulations. V77I is known to cause Nelfinavir (NFV) resistance in the subtype B population of HIV-1 protease. We have for the first time reported the effect of this clinically relevant mutation on the binding of Nelfinavir and the conformational flexibility of the protease.'),
(97064, 2015, 'bmc', 'Algebraic Dynamic Programming over general data structures', 'zu Siederdissen, Christian Höner|Prohaska, Sonja J|Stadler, Peter F', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.', 'United States|United States|United States', 10, 'Algorithms|Computational Biology|Databases, Genetic|Genes, Homeobox|Markov Chains|Multigene Family|Probability|RNA Folding|Sequence Alignment|Software', 'Journal Article', '10.1186/1471-2105-16-S19-S2', '26695390', 'Dynamic programming algorithms provide exact solutions to many problems in computational biology, such as sequence alignment, RNA folding, hidden Markov models (HMMs), and scoring of phylogenetic trees. Structurally analogous algorithms compute optimal solutions, evaluate score distributions, and perform stochastic sampling. This is explained in the theory of Algebraic Dynamic Programming (ADP) by a strict separation of state space traversal (usually represented by a context free grammar), scoring (encoded as an algebra), and choice rule. A key ingredient in this theory is the use of yield parsers that operate on the ordered input data structure, usually strings or ordered trees. The computation of ensemble properties, such as a posteriori probabilities of HMMs or partition functions in RNA folding, requires the combination of two distinct, but intimately related algorithms, known as the inside and the outside recursion. Only the inside recursions are covered by the classical ADP theory.'),
(97065, 2015, 'bmc', 'Sorting by weighted inversions considering length and symmetry', 'Baudet, Christian|Dias, Ulisses|Dias, Zanoni', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.', 'United States|United States|United States', 4, 'Algorithms|Bacteria|Chromosome Breakage|Chromosome Inversion|Computational Biology|Genome|Time Factors', 'Journal Article', '10.1186/1471-2105-16-S19-S3', '26695591', 'Large-scale mutational events that occur when stretches of DNA sequence move throughout genomes are called genome rearrangements. In bacteria, inversions are one of the most frequently observed rearrangements. In some bacterial families, inversions are biased in favor of symmetry as shown by recent research. In addition, several results suggest that short segment inversions are more frequent in the evolution of microbial genomes. Despite the fact that symmetry and length of the reversed segments seem very important, they have not been considered together in any problem in the genome rearrangement field. Here, we define the problem of sorting genomes (or permutations) using inversions whose costs are assigned based on their lengths and asymmetries. We consider two formulations of the same problem depending on whether we know the orientation of the genes. Several procedures are presented and we assess these procedure performances on a large set of more than 4.4 × 109 permutations. The ideas presented in this paper provide insights to solve the problem and set the stage for a proper theoretical analysis. '),
(97066, 2015, 'bmc', 'An innovative electronic health records system for rare and complex diseases', 'Faria-Campos, Alessandra C|Hanke, Lucas A|Batista, Paulo H S|Garcia, Vinicius|Campos, Sérgio V A', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.', 'United States|United States|United States|United States|United States', 0, 'Electronic Health Records|Humans|Internet|Inventions|Rare Diseases|Software|User-Computer Interface|Workflow', 'Journal Article', '10.1186/1471-2105-16-S19-S4', '26695733', 'There exists a large number of rare and complex diseases that are neglected due to the difficulty in diagnosis and treatment. Being rare, they normally do not justify the costs of developing an especialized Electronic Health Record (EHR) system to assist doctors and patients of these diseases. In this work we propose the use of Computer applications known as Laboratory Information Management Systems (LIMS) to address this issue.'),
(97067, 2015, 'bmc', 'The impact of sequence length and number of sequences on promoter prediction performance', 'Carvalho, Sávio G|Guerra-Sá, Renata|de C Merschmann, Luiz H', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.', 'United States|United States|United States', 0, 'Base Sequence|Computational Biology|Databases, Genetic|Oligonucleotides|Promoter Regions, Genetic|Sequence Analysis, DNA|Support Vector Machine', 'Journal Article', '10.1186/1471-2105-16-S19-S5', '26695879', 'The advent of rapid evolution on sequencing capacity of new genomes has evidenced the need for data analysis automation aiming at speeding up the genomic annotation process and reducing its cost. Given that one important step for functional genomic annotation is the promoter identification, several studies have been taken in order to propose computational approaches to predict promoters. Different classifiers and characteristics of the promoter sequences have been used to deal with this prediction problem. However, several works in literature have addressed the promoter prediction problem using datasets containing sequences of 250 nucleotides or more. As the sequence length defines the amount of dataset attributes, even considering a limited number of properties to characterize the sequences, datasets with a high number of attributes are generated for training classifiers. Once high-dimensional datasets can degrade the classifiers predictive performance or even require an infeasible processing time, predicting promoters by training classifiers from datasets with a reduced number of attributes, it is essential to obtain good predictive performance with low computational cost. To the best of our knowledge, there is no work in literature that verified in a systematic way the relation between the sequences length and the predictive performance of classifiers. Thus, in this work, we have evaluated the impact of sequence length variation and training dataset size (number of sequences) on the predictive performance of classifiers.'),
(97068, 2015, 'bmc', 'Evolution of genes neighborhood within reconciled phylogenies: an ensemble approach', 'Chauve, Cedric|Ponty, Yann|Zanetti, João', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.', 'United States|United States|United States', 0, 'Algorithms|Animals|Computational Biology|Databases, Genetic|Evolution, Molecular|Genome|Mammals|Phylogeny|Probability|Temperature', 'Journal Article', '10.1186/1471-2105-16-S19-S6', '26696141', 'The reconstruction of evolutionary scenarios for whole genomes in terms of genome rearrangements is a fundamental problem in evolutionary and comparative genomics. The DeCo algorithm, recently introduced by Bérard et al., computes parsimonious evolutionary scenarios for gene adjacencies, from pairs of reconciled gene trees. However, as for many combinatorial optimization algorithms, there can exist many co-optimal, or slightly sub-optimal, evolutionary scenarios that deserve to be considered.'),
(97069, 2015, 'bmc', 'Classification epitopes in groups based on their protein family', 'Kozlova, Edgar|Viart, Benjamin|de Avila, Ricardo|Felicori, Liza|Chavez-Olortegui, Carlos', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.', 'United States|United States|United States|United States|United States', 0, 'Amino Acid Sequence|Amino Acids|Area Under Curve|Computational Biology|Data Mining|Databases, Protein|Decision Trees|Epitopes, B-Lymphocyte|Proteins|Regression Analysis|Support Vector Machine', 'Journal Article', '10.1186/1471-2105-16-S19-S7', '26696329', 'The humoral immune system response is based on the interaction between antibodies and antigens for the clearance of pathogens and foreign molecules. The interaction between these proteins occurs at specific positions known as antigenic determinants or B-cell epitopes. The experimental identification of epitopes is costly and time consuming. Therefore the use of in silico methods, to help discover new epitopes, is an appealing alternative due the importance of biomedical applications such as vaccine design, disease diagnostic, anti-venoms and immune-therapeutics. However, the performance of predictions is not optimal been around 70% of accuracy. Further research could increase our understanding of the biochemical and structural properties that characterize a B-cell epitope.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(97070, 2015, 'bmc', 'FluxCTTX: A LIMS-based tool for management and analysis of cytotoxicity assays data', 'Faria-Campos, Alessandra C|Balottin, Luciene B|Zuin, Gianlucca|Garcia, Vinicius|Batista, Paulo H S|Granjeiro, José M|Campos, Sérgio V A', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.', 'United States|United States|United States|United States|United States|United States|United States', 1, 'Absorption, Radiation|Animals|Biological Assay|Cell Death|Cell Survival|Humans|Information Management|Inhibitory Concentration 50|Laboratories|Regression Analysis|Software|Statistics as Topic|User-Computer Interface|Workflow', 'Journal Article', '10.1186/1471-2105-16-S19-S8', '26696462', 'Cytotoxicity assays have been used by researchers to screen for cytotoxicity in compound libraries. Researchers can either look for cytotoxic compounds or screen hits from initial high-throughput drug screens for unwanted cytotoxic effects before investing in their development as a pharmaceutical. These assays may be used as an alternative to animal experimentation and are becoming increasingly important in modern laboratories. However, the execution of these assays in large scale and different laboratories requires, among other things, the management of protocols, reagents, cell lines used as well as the data produced, which can be a challenge. The management of all this information is greatly improved by the utilization of computational tools to save time and guarantee quality. However, a tool that performs this task designed specifically for cytotoxicity assays is not yet available.'),
(97071, 2015, 'bmc', 'NERI: network-medicine based integrative approach for disease gene prioritization by relative importance', 'Simões, Sérgio N|Martins, David C|Pereira, Carlos A B|Hashimoto, Ronaldo F|Brentani, Helena', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. edward@ece.tamu.edu.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Computational Biology|Databases, Protein|Disease|Genes|Humans|Protein Interaction Maps|Reproducibility of Results', 'Journal Article', '10.1186/1471-2105-16-S19-S9', '26696568', 'Complex diseases are characterized as being polygenic and multifactorial, so this poses a challenge regarding the search for genes related to them. With the advent of high-throughput technologies for genome sequencing, gene expression measurements (transcriptome), and protein-protein interactions, complex diseases have been sistematically investigated. Particularly, Protein-Protein Interaction (PPI) networks have been used to prioritize genes related to complex diseases according to its topological features. However, PPI networks are affected by ascertainment bias, in which more studied proteins tend to have more connections, degrading the results quality. Additionally, methods using only PPI networks can provide only static and non-specific results, since the topologies of these networks are not specific of a given disease.'),
(97072, 2015, 'bmc', 'Unsupervised image segmentation for microarray spots with irregular contours and inner holes', 'Belean, Bogdan|Borda, Monica|Ackermann, Jörg|Koch, Ina|Balacescu, Ovidiu', 'CETATEA Research Centre, National Institute for Research and Development of Isotopic and Molecular Technologies - INCDTIM, 67 - 103 Donat, Cluj-Napoca, Romania. bogdan.belean@itim-cj.ro.|Department of Communication, Technical University of Cluj-Napoca, Baritiu 26-28, Cluj-Napoca, Romania. monica.borda@com.utcluj.ro.|Molecular Bioinformatics Group, Institute of Computer Science, Faculty of Computer Science and Mathematics, Cluster of Excellence Frankfurt Macromolecular Complexes, Johann Wolfgang Goethe-University, Baritiu 26-28, Frankfurt am Main, Germany. j.ackermann@bioinformatik.uni-frankfurt.de.|Molecular Bioinformatics Group, Institute of Computer Science, Faculty of Computer Science and Mathematics, Cluster of Excellence Frankfurt Macromolecular Complexes, Johann Wolfgang Goethe-University, Baritiu 26-28, Frankfurt am Main, Germany. ina.koch@bioinformatik.uni-frankfurt.de.|Department of Functional Genomics and Experimental Pathology, The Oncology Institute Prof. Dr. Ion Chiricuta, Cluj-Napoca, Romania. ovidiubalacescu@iocn.ro.', 'Romania|Romania|Germany|Germany|Romania', 6, 'Algorithms|Cluster Analysis|Gene Expression|Microarray Analysis|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1186/s12859-015-0842-3', '26698293', 'Microarray analysis represents a powerful way to test scientific hypotheses on the functionality of cells. The measurements consider the whole genome, and the large number of generated data requires sophisticated analysis. To date, no gold-standard for the analysis of microarray images has been established. Due to the lack of a standard approach there is a strong need to identify new processing algorithms.'),
(97073, 2015, 'bmc', 'MixChIP: a probabilistic method for cell type specific protein-DNA binding analysis', 'Rautio, Sini|Lähdesmäki, Harri', 'Department of Computer Science, Aalto University, Aalto, FI-00076, Finland. sini.rautio@aalto.fi.|Department of Computer Science, Aalto University, Aalto, FI-00076, Finland. harri.lahdesmaki@aalto.fi.', 'Finland|Finland', 1, 'Area Under Curve|Binding Sites|Chromatin Immunoprecipitation|DNA|Hep G2 Cells|Humans|Internet|K562 Cells|Protein Binding|ROC Curve|Sequence Analysis, DNA|Transcription Factors|User-Computer Interface', 'Journal Article', '10.1186/s12859-015-0834-3', '26703974', 'Transcription factors (TFs) are proteins that bind to DNA and regulate gene expression. To understand details of gene regulation, characterizing TF binding sites in different cell types, diseases and among individuals is essential. However, sometimes TF binding can only be measured from biological samples that contain multiple cell or tissue types. Sample heterogeneity can have a considerable effect on TF binding site detection. While manual separation techniques can be used to isolate a cell type of interest from heterogeneous samples, such techniques are challenging and can change intra-cellular interactions, including protein-DNA binding. Computational deconvolution methods have emerged as an alternative strategy to study heterogeneous samples and numerous methods have been proposed to analyze gene expression. However, no computational method exists to deconvolve cell type specific TF binding from heterogeneous samples.'),
(97074, 2015, 'bmc', 'Pathosphereorg: pathogen detection and characterization through a web-based, open source informatics platform', 'Kilianski, Andy|Carcel, Patrick|Yao, Shijie|Roth, Pierce|Schulte, Josh|Donarum, Greg B|Fochler, Ed T|Hill, Jessica M|Liem, Alvin T|Wiley, Michael R|Ladner, Jason T|Pfeffer, Bradley P|Elliot, Oliver|Petrosov, Alexandra|Jima, Dereje D|Vallard, Tyghe G|Melendrez, Melanie C|Skowronski, Evan|Quan, Phenix-Lan|Lipkin, W Ian|Gibbons, Henry S|Hirschberg, David L|Palacios, Gustavo F|Rosenzweig, C Nicole', 'Biosciences Division, Edgewood Chemical and Biological Center, 5183 Blackhawk Rd, Aberdeen Proving Ground, Edgewood, MD, 21010, USA. andrew.kilianski.ctr@mail.mil.|OptiMetrics, Inc, Abingdon, MD, USA. pcarcel@omi.com.|OptiMetrics, Inc, Abingdon, MD, USA. syao1979@yahoo.com.|Biosciences Division, Edgewood Chemical and Biological Center, 5183 Blackhawk Rd, Aberdeen Proving Ground, Edgewood, MD, 21010, USA. proth@omi.com.|OptiMetrics, Inc, Abingdon, MD, USA. jschulte@omi.com.|OptiMetrics, Inc, Abingdon, MD, USA. gregory.b.donarum2.ctr@mail.mil.|OptiMetrics, Inc, Abingdon, MD, USA. edward.t.fochler.ctr@mail.mil.|Biosciences Division, Edgewood Chemical and Biological Center, 5183 Blackhawk Rd, Aberdeen Proving Ground, Edgewood, MD, 21010, USA. jessica.m.hill58.ctr@mail.mil.|Biosciences Division, Edgewood Chemical and Biological Center, 5183 Blackhawk Rd, Aberdeen Proving Ground, Edgewood, MD, 21010, USA. alvin.t.liem.ctr@mail.mil.|Center for Genome Sciences, United States Medical Research Institute of Infectious Diseases, Ft. Detrick, Frederick, MD, USA. michael.r.wiley19.ctr@mail.mil.|Center for Genome Sciences, United States Medical Research Institute of Infectious Diseases, Ft. Detrick, Frederick, MD, USA. jason.t.ladner.ctr@mail.mil.|Center for Genome Sciences, United States Medical Research Institute of Infectious Diseases, Ft. Detrick, Frederick, MD, USA. bradley.p.pfeffer.ctr@mail.mil.|Department of Biomedical Informatics, Columbia University, New York, NY, USA. oe2118@columbia.edu.|The Center for Infection and Immunity, Columbia University, New York, NY, USA. ap2811ap@gmail.com.|Walter Reed Army Institute of Research, Viral Diseases Branch, Silver Spring, MD, USA. dereje.d.jima.ctr@mail.mil.|Walter Reed Army Institute of Research, Viral Diseases Branch, Silver Spring, MD, USA. tyghe.g.vallard.ctr@mail.mil.|Walter Reed Army Institute of Research, Viral Diseases Branch, Silver Spring, MD, USA. melanie.c.melendrez.ctr@mail.mil.|TMG Biosciences LLC, Austin, TX, USA. eskowronski@tmgbiosciences.com.|The Center for Infection and Immunity, Columbia University, New York, NY, USA. pq2106@gmail.com.|The Center for Infection and Immunity, Columbia University, New York, NY, USA. ilipkin@me.com.|Biosciences Division, Edgewood Chemical and Biological Center, 5183 Blackhawk Rd, Aberdeen Proving Ground, Edgewood, MD, 21010, USA. henry.s.gibbons.civ@mail.mil.|The Center for Infection and Immunity, Columbia University, New York, NY, USA. dlhirschberg@gmail.com.|Center for Genome Sciences, United States Medical Research Institute of Infectious Diseases, Ft. Detrick, Frederick, MD, USA. gustavo.f.palacios.ctr@mail.mil.|Biosciences Division, Edgewood Chemical and Biological Center, 5183 Blackhawk Rd, Aberdeen Proving Ground, Edgewood, MD, 21010, USA. carolyn.n.rosenzweig.civ@mail.mil.', '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', 0, 'Algorithms|Animals|Arenavirus|Computational Biology|Coronavirus|Databases, Factual|Flavivirus|High-Throughput Nucleotide Sequencing|Humans|Internet|RNA, Viral|Sequence Analysis, RNA|User-Computer Interface', 'Journal Article', '10.1186/s12859-015-0840-5', '26714571', 'The detection of pathogens in complex sample backgrounds has been revolutionized by wide access to next-generation sequencing (NGS) platforms. However, analytical methods to support NGS platforms are not as uniformly available. Pathosphere (found at Pathosphere.org) is a cloud - based open - sourced community tool that allows for communication, collaboration and sharing of NGS analytical tools and data amongst scientists working in academia, industry and government. The architecture allows for users to upload data and run available bioinformatics pipelines without the need for onsite processing hardware or technical support.'),
(97075, 2015, 'bmc', 'Treelink: data integration, clustering and visualization of phylogenetic trees', 'Allende, Christian|Sohn, Erik|Little, Cedric', 'Faculty of Engineering and Sciences, Universidad Adolfo Ibañez, Diagonal las Torres 2640, Santiago, 7941169, Chile. christian.allende.cid@gmail.com.|Faculty of Engineering and Sciences, Universidad Adolfo Ibañez, Diagonal las Torres 2640, Santiago, 7941169, Chile. erik.a.sohn@gmail.com.|Faculty of Engineering and Sciences, Universidad Adolfo Ibañez, Diagonal las Torres 2640, Santiago, 7941169, Chile. cedric.little@uai.cl.', 'Chile|Chile|Chile', 4, 'Base Sequence|Cluster Analysis|Genomics|HIV|Humans|Internet|Phylogeny|Phylogeography|Serogroup|User-Computer Interface', 'Journal Article', '10.1186/s12859-015-0860-1', '26714661', 'Phylogenetic trees are central to a wide range of biological studies. In many of these studies, tree nodes need to be associated with a variety of attributes. For example, in studies concerned with viral relationships, tree nodes are associated with epidemiological information, such as location, age and subtype. Gene trees used in comparative genomics are usually linked with taxonomic information, such as functional annotations and events. A wide variety of tree visualization and annotation tools have been developed in the past, however none of them are intended for an integrative and comparative analysis.'),
(97076, 2015, 'bmc', 'BigQ: a NoSQL based framework to handle genomic variants in i2b2', 'Gabetta, Matteo|Limongelli, Ivan|Rizzo, Ettore|Riva, Alberto|Segagni, Daniele|Bellazzi, Riccardo', 'Dipartimento di Ingegneria Industriale e dellInformazione and Center for Health Technologies, Università di Pavia, Pavia, Italy. matteo.gabetta@unipv.it.|Dipartimento di Ingegneria Industriale e dellInformazione and Center for Health Technologies, Università di Pavia, Pavia, Italy. ivan.limongelli@unipv.it.|Dipartimento di Ingegneria Industriale e dellInformazione and Center for Health Technologies, Università di Pavia, Pavia, Italy. ettore.rizzo@unipv.it.|Interdisciplinary Center for Biotechnology Research, University of Florida, Gainesville, FL, USA. ariva@ufl.edu.|IRCCS Fondazione S. Maugeri, Pavia, Italy. daniele.segagni@fsm.it.|Dipartimento di Ingegneria Industriale e dellInformazione and Center for Health Technologies, Università di Pavia, Pavia, Italy. riccardo.bellazzi@unipv.it.', 'Italy|Italy|Italy|United States|Italy|Italy', 10, 'Databases, Factual|Genomics|High-Throughput Nucleotide Sequencing|Humans|Information Storage and Retrieval|Software', 'Journal Article', '10.1186/s12859-015-0861-0', '26714792', 'Precision medicine requires the tight integration of clinical and molecular data. To this end, it is mandatory to define proper technological solutions able to manage the overwhelming amount of high throughput genomic data needed to test associations between genomic signatures and human phenotypes. The i2b2 Center (Informatics for Integrating Biology and the Bedside) has developed a widely internationally adopted framework to use existing clinical data for discovery research that can help the definition of precision medicine interventions when coupled with genetic data. i2b2 can be significantly advanced by designing efficient management solutions of Next Generation Sequencing data.'),
(97077, 2015, 'bmc', 'Multi-label multi-instance transfer learning for simultaneous reconstruction and cross-talk modeling of multiple human signaling pathways', 'Mei, Suyu|Zhu, Hao', 'Software College, Shenyang Normal University, Shenyang, China. meisygle@gmail.com.|Bioinformatics Section, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China. zhuhao@smu.edu.cn.', 'China|China', 5, 'Databases, Protein|Gene Ontology|Humans|Models, Biological|Protein Interaction Mapping|Protein Transport|Reproducibility of Results|Signal Transduction|Staining and Labeling', 'Journal Article', '10.1186/s12859-015-0841-4', '26718335', 'Signaling pathways play important roles in the life processes of cell growth, cell apoptosis and organism development. At present the signal transduction networks are far from complete. As an effective complement to experimental methods, computational modeling is suited to rapidly reconstruct the signaling pathways at low cost. To our knowledge, the existing computational methods seldom simultaneously exploit more than three signaling pathways into one predictive model for the discovery of novel signaling components and the cross-talk modeling between signaling pathways.'),
(97078, 2016, 'bmc', 'Diffany: an ontology-driven framework to infer, visualise and analyse differential molecular networks', 'Van Landeghem, Sofie|Van Parys, Thomas|Dubois, Marieke|Inzé, Dirk|Van de Peer, Yves', 'Department of Plant Systems Biology, VIB, Technologiepark 927, Ghent, 9052, Belgium. sofie.van.landeghem@gmail.com.|Department of Plant Systems Biology, VIB, Technologiepark 927, Ghent, 9052, Belgium. thomas.vanparys@psb.ugent.be.|Department of Plant Systems Biology, VIB, Technologiepark 927, Ghent, 9052, Belgium. marieke.dubois@psb.ugent.be.|Department of Plant Systems Biology, VIB, Technologiepark 927, Ghent, 9052, Belgium. dirk.inze@psb.ugent.be.|Department of Plant Systems Biology, VIB, Technologiepark 927, Ghent, 9052, Belgium. yves.vandepeer@psb.ugent.be.', 'Belgium|Belgium|Belgium|Belgium|Belgium', 17, 'Algorithms|Arabidopsis|Arabidopsis Proteins|Calcium-Binding Proteins|Computational Biology|DNA-Binding Proteins|Gene Expression Regulation, Plant|Gene Regulatory Networks|Models, Molecular|Systems Biology|Transcription Factors', 'Journal Article', '10.1186/s12859-015-0863-y', '26729218', 'Differential networks have recently been introduced as a powerful way to study the dynamic rewiring capabilities of an interactome in response to changing environmental conditions or stimuli. Currently, such differential networks are generated and visualised using ad hoc methods, and are often limited to the analysis of only one condition-specific response or one interaction type at a time.'),
(97079, 2016, 'bmc', 'ChainRank, a chain prioritisation method for contextualisation of biological networks', 'Tényi, Ákos|de Atauri, Pedro|Gomez-Cabrero, David|Cano, Isaac|Clarke, Kim|Falciani, Francesco|Cascante, Marta|Roca, Josep|Maier, Dieter', 'Hospital Clínic-Institut dInvestigacions Biomediques August Pi i Sunyer (IDIBAPS), Research Institute, Universitat de Barcelona, C/Villarroel 170, 08036, Barcelona, Spain. tenyi.akos@ub.edu.|Hospital Clínic-Institut dInvestigacions Biomediques August Pi i Sunyer (IDIBAPS), Research Institute, Universitat de Barcelona, C/Villarroel 170, 08036, Barcelona, Spain. pde_atauri@ub.edu.|Unit of computational Medicine, Center for Molecular Medicine, Department of Medicine, Karolinska Institute and Karolinska University Hospital, SE-171 76, Stockholm, Sweden. david.gomezcabrero@ki.se.|Hospital Clínic-Institut dInvestigacions Biomediques August Pi i Sunyer (IDIBAPS), Research Institute, Universitat de Barcelona, C/Villarroel 170, 08036, Barcelona, Spain. iscano@clinic.ub.es.|Integrative Systems Biology, University of Liverpool, L69 3BX, Liverpool, UK. kim.clarke@liverpool.ac.uk.|Integrative Systems Biology, University of Liverpool, L69 3BX, Liverpool, UK. f.falciani@liverpool.ac.uk.|Hospital Clínic-Institut dInvestigacions Biomediques August Pi i Sunyer (IDIBAPS), Research Institute, Universitat de Barcelona, C/Villarroel 170, 08036, Barcelona, Spain. martacascante@ub.edu.|Hospital Clínic-Institut dInvestigacions Biomediques August Pi i Sunyer (IDIBAPS), Research Institute, Universitat de Barcelona, C/Villarroel 170, 08036, Barcelona, Spain. jroca@clinic.ub.es.|Biomax Informatics AG, D-82152, Planegg, Germany. dieter.maier@biomax.com.', 'Spain|Spain|Sweden|Spain|United Kingdom|United Kingdom|Spain|Spain|Germany', 4, 'Computational Biology|Databases, Protein|Humans|Metabolomics|Models, Biological|Muscle, Skeletal|Protein Interaction Maps|Proteins|Proteomics|Pulmonary Disease, Chronic Obstructive|Signal Transduction', 'Journal Article', '10.1186/s12859-015-0864-x', '26729273', 'Advances in high throughput technologies and growth of biomedical knowledge have contributed to an exponential increase in associative data. These data can be represented in the form of complex networks of biological associations, which are suitable for systems analyses. However, these networks usually lack both, context specificity in time and space as well as the distinctive borders, which are usually assigned in the classical pathway view of molecular events (e.g. signal transduction). This complexity and high interconnectedness call for automated techniques that can identify smaller targeted subnetworks specific to a given research context (e.g. a disease scenario).'),
(97080, 2016, 'bmc', 'An efficient genome-wide association test for multivariate phenotypes based on the Fisher combination function', 'Yang, James J|Li, Jia|Williams, L Keoki|Buu, Anne', 'School of Nursing, University of Michigan, Ann Arbor, Michigan, USA. jjyang@umich.edu.|Department of Public Health Sciences, Henry Ford Health System, Detroit, Michigan, USA. jli4@hfhs.org.|Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit, Michigan, USA. kwillia5@hfhs.org.|Department of Systems, Populations and Leadership, University of Michigan, Ann Arbor, Michigan, USA. buu@umich.edu.', 'United States|United States|United States|United States', 21, 'Computer Simulation|Databases, Genetic|Gene-Environment Interaction|Genome-Wide Association Study|Humans|Models, Molecular|Multivariate Analysis|Phenotype|Polymorphism, Single Nucleotide|Principal Component Analysis', 'Journal Article', '10.1186/s12859-015-0868-6', '26729364', 'In genome-wide association studies (GWAS) for complex diseases, the association between a SNP and each phenotype is usually weak. Combining multiple related phenotypic traits can increase the power of gene search and thus is a practically important area that requires methodology work. This study provides a comprehensive review of existing methods for conducting GWAS on complex diseases with multiple phenotypes including the multivariate analysis of variance (MANOVA), the principal component analysis (PCA), the generalizing estimating equations (GEE), the trait-based association test involving the extended Simes procedure (TATES), and the classical Fisher combination test. We propose a new method that relaxes the unrealistic independence assumption of the classical Fisher combination test and is computationally efficient. To demonstrate applications of the proposed method, we also present the results of statistical analysis on the Study of Addiction: Genetics and Environment (SAGE) data.'),
(97081, 2016, 'bmc', 'Efficient and automated large-scale detection of structural relationships in proteins with a flexible aligner', 'Gutiérrez, Fernando I|Rodriguez-Valenzuela, Felipe|Ibarra, Ignacio L|Devos, Damien P|Melo, Francisco', 'Departamento de Genética Molecular y Microbiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Alameda 340, Santiago, Chile.|Departamento de Genética Molecular y Microbiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Alameda 340, Santiago, Chile.|Departamento de Genética Molecular y Microbiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Alameda 340, Santiago, Chile.|Centre for Organismal Studies (COS), Heidelberg University, Heidelberg, Germany. damienpdevos@gmail.com.|Departamento de Genética Molecular y Microbiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Alameda 340, Santiago, Chile. fmelo@bio.puc.cl.', 'Chile|Chile|Chile|Germany|Chile', 2, 'Algorithms|Amino Acid Sequence|Calibration|Computational Biology|Databases, Protein|Gene Rearrangement|Humans|Molecular Sequence Data|Protein Structure, Secondary|Proteins', 'Journal Article', '10.1186/s12859-015-0866-8', '26732380', 'The total number of known three-dimensional protein structures is rapidly increasing. Consequently, the need for fast structural search against complete databases without a significant loss of accuracy is increasingly demanding. Recently, TopSearch, an ultra-fast method for finding rigid structural relationships between a query structure and the complete Protein Data Bank (PDB), at the multi-chain level, has been released. However, comparable accurate flexible structural aligners to perform efficient whole database searches of multi-domain proteins are not yet available. The availability of such a tool is critical for a sustainable boosting of biological discovery.'),
(97082, 2016, 'bmc', 'TRAPLINE: a standardized and automated pipeline for RNA sequencing data analysis, evaluation and annotation', 'Wolfien, Markus|Rimmbach, Christian|Schmitz, Ulf|Jung, Julia Jeannine|Krebs, Stefan|Steinhoff, Gustav|David, Robert|Wolkenhauer, Olaf', 'Department of Systems Biology and Bioinformatics, University of Rostock, 18057, Rostock, Germany. markus.wolfien@uni-rostock.de.|Reference und Translation Center for Cardiac Stem Cell Therapy (RTC), University of Rostock, Rostock, 18057, Germany. christian.rimmbach@med.uni-rostock.de.|Gene & Stem Cell Therapy Program, Centenary Institute, 2050, Camperdown, Australia. u.schmitz@centenary.org.au.|Reference und Translation Center for Cardiac Stem Cell Therapy (RTC), University of Rostock, Rostock, 18057, Germany. julia.jung@med.uni-rostock.de.|Gene Center Munich, LMU Munich, 81377, Munich, Germany. krebs@genzentrum.lmu.de.|Reference und Translation Center for Cardiac Stem Cell Therapy (RTC), University of Rostock, Rostock, 18057, Germany. gustav.steinhoff@med.uni-rostock.de.|Reference und Translation Center for Cardiac Stem Cell Therapy (RTC), University of Rostock, Rostock, 18057, Germany. robert.david@med.uni-rostock.de.|Department of Systems Biology and Bioinformatics, University of Rostock, 18057, Rostock, Germany. olaf.wolkenhauer@uni-rostock.de.', 'Germany|Germany|Australia|Germany|Germany|Germany|Germany|Germany', 17, 'Computational Biology|High-Throughput Nucleotide Sequencing|MicroRNAs|Molecular Sequence Annotation|Polymorphism, Single Nucleotide|Protein Interaction Domains and Motifs|Sequence Alignment|Sequence Analysis, RNA|Transcriptome', 'Journal Article', '10.1186/s12859-015-0873-9', '26738481', 'Technical advances in Next Generation Sequencing (NGS) provide a means to acquire deeper insights into cellular functions. The lack of standardized and automated methodologies poses a challenge for the analysis and interpretation of RNA sequencing data. We critically compare and evaluate state-of-the-art bioinformatics approaches and present a workflow that integrates the best performing data analysis, data evaluation and annotation methods in a Transparent, Reproducible and Automated PipeLINE (TRAPLINE) for RNA sequencing data processing (suitable for Illumina, SOLiD and Solexa).'),
(97083, 2016, 'bmc', 'A novel semi-supervised algorithm for the taxonomic assignment of metagenomic reads', 'Le, Vinh Van|Tran, Lang Van|Tran, Hoai Van', 'Faculty of Computer Science and Engineering, HCMC University of Technology, 268 Ly Thuong Kiet, Q10, HCM City, Vietnam. vinhlv@fit.hcmute.edu.vn.|Institute of Applied Mechanics and Informatics, Vietnam Academy of Science and Technology, 01 Mac Dinh Chi, Q1, HCM City, Vietnam. langtv@vast.vn.|Faculty of Computer Science and Engineering, HCMC University of Technology, 268 Ly Thuong Kiet, Q10, HCM City, Vietnam. hoai@cse.hcmut.edu.vn.', '-|-|-', 5, 'Algorithms|Base Composition|Cluster Analysis|Databases, Factual|Humans|Metagenomics|Models, Molecular|Multigene Family|Reproducibility of Results', 'Journal Article', '10.1186/s12859-015-0872-x', '26740458', 'Taxonomic assignment is a crucial step in a metagenomic project which aims to identify the origin of sequences in an environmental sample. Among the existing methods, since composition-based algorithms are not sufficient for classifying short reads, recent algorithms use only the feature of similarity, or similarity-based combined features. However, those algorithms suffer from the computational expense because the task of similarity search is very time-consuming. Besides, the lack of similarity information between reads and reference sequences due to the length of short reads reduces significantly the classification quality.'),
(97084, 2016, 'bmc', 'Fast and accurate branch lengths estimation for phylogenomic trees', 'Binet, Manuel|Gascuel, Olivier|Scornavacca, Celine|Douzery, Emmanuel J P|Pardi, Fabio', 'Laboratoire dInformatique de Robotique et de Microélectronique de Montpellier (LIRMM), CNRS, Université de Montpellier, Montpellier, France. binet@lirmm.fr.|Laboratoire dInformatique de Robotique et de Microélectronique de Montpellier (LIRMM), CNRS, Université de Montpellier, Montpellier, France. gascuel@lirmm.fr.|Institut de Biologie Computationnelle, Montpellier, France. celine.scornavacca@umontpellier.fr.|Institut des Sciences de lEvolution de Montpellier, CNRS, IRD, EPHE, Université de Montpellier, France. emmanuel.douzery@umontpellier.fr.|Laboratoire dInformatique de Robotique et de Microélectronique de Montpellier (LIRMM), CNRS, Université de Montpellier, Montpellier, France. pardi@lirmm.fr.', 'France|France|France|France|France', 9, 'Animals|Computer Simulation|Databases, Factual|Genomics|Mammals|Models, Molecular|Phylogeny|Sequence Alignment', 'Journal Article', '10.1186/s12859-015-0821-8', '26744021', 'Branch lengths are an important attribute of phylogenetic trees, providing essential information for many studies in evolutionary biology. Yet, part of the current methodology to reconstruct a phylogeny from genomic information - namely supertree methods - focuses on the topology or structure of the phylogenetic tree, rather than the evolutionary divergences associated to it. Moreover, accurate methods to estimate branch lengths - typically based on probabilistic analysis of a concatenated alignment - are limited by large demands in memory and computing time, and may become impractical when the data sets are too large.'),
(97085, 2016, 'bmc', 'Integrating 400 million variants from 80,000 human samples with extensive annotations: towards a knowledge base to analyze disease cohorts', 'Hakenberg, Jörg|Cheng, Wei-Yi|Thomas, Philippe|Wang, Ying-Chih|Uzilov, Andrew V|Chen, Rong', 'Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1425 Madison Ave, Box 1498, New York, 10029, USA. joerg.hakenberg@gmail.com.|Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1425 Madison Ave, Box 1498, New York, 10029, USA. ninpy.weiyi@gmail.com.|Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1425 Madison Ave, Box 1498, New York, 10029, USA. thomas@informatik.hu-berlin.de.|Department of Computer Science, Humboldt-Universität zu Berlin, Unter den Linden 6, Berlin, 10099, Germany. ying-chih.wang@mssm.edu.|Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1425 Madison Ave, Box 1498, New York, 10029, USA. andrew.uzilov@mssm.edu.|Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1425 Madison Ave, Box 1498, New York, 10029, USA. rong.chen@mssm.edu.', 'United States|United States|United States|Germany|United States|United States', 11, 'Databases, Genetic|Disease|Genetic Variation|Genome, Human|Genotyping Techniques|High-Throughput Nucleotide Sequencing|Humans|Knowledge Bases|Molecular Sequence Annotation|Reference Values|Risk Factors', 'Journal Article', '10.1186/s12859-015-0865-9', '26746786', 'Data from a plethora of high-throughput sequencing studies is readily available to researchers, providing genetic variants detected in a variety of healthy and disease populations. While each individual cohort helps gain insights into polymorphic and disease-associated variants, a joint perspective can be more powerful in identifying polymorphisms, rare variants, disease-associations, genetic burden, somatic variants, and disease mechanisms.'),
(97086, 2016, 'bmc', 'Combining location-and-scale batch effect adjustment with data cleaning by latent factor adjustment', 'Hornung, Roman|Boulesteix, Anne-Laure|Causeur, David', 'Department of Medical Informatics, Biometry and Epidemiology, University of Munich, Marchioninistr. 15, Munich, D-81377, Germany. hornung@ibe.med.uni-muenchen.de.|Department of Medical Informatics, Biometry and Epidemiology, University of Munich, Marchioninistr. 15, Munich, D-81377, Germany. boulesteix@ibe.med.uni-muenchen.de.|Applied Mathematics Department, Agrocampus Ouest, 65 rue de St. Brieuc, Rennes, 35042, France. David.Causeur@agrocampus-ouest.fr.', 'Germany|Germany|France', 15, 'Computational Biology|Datasets as Topic|Humans', 'Journal Article', '10.1186/s12859-015-0870-z', '26753519', 'In the context of high-throughput molecular data analysis it is common that the observations included in a dataset form distinct groups, for example, measured at different times, under different conditions or even in different labs. These groups are generally denoted as batches. Systematic differences between these batches not attributable to the biological signal of interest are denoted as batch effects. If ignored when conducting analyses on the combined data, batch effects can lead to distortions in the results. In this paper we present FAbatch, a general, model-based method for correcting for such batch effects in the case of an analysis involving a binary target variable. It is a combination of two commonly used approaches: location-and-scale adjustment and data cleaning by adjustment for distortions due to latent factors. We compare FAbatch extensively to the most commonly applied competitors on the basis of several performance metrics. FAbatch can also be used in the context of prediction modelling to eliminate batch effects from new test data. This important application is illustrated using real and simulated data. We implemented FAbatch and various other functionalities in the R package bapred available online from CRAN.'),
(97087, 2016, 'bmc', 'MS3ALIGN: an efficient molecular surface aligner using the topology of surface curvature', 'Shivashankar, Nithin|Patil, Sonali|Bhosle, Amrisha|Chandra, Nagasuma|Natarajan, Vijay', 'Department of Computer Science and Automation, Indian Institute of Science, Bangalore, 560012, India. nithin@csa.iisc.ernet.in.|Department of Computer Science and Automation, Indian Institute of Science, Bangalore, 560012, India.|Department of Biochemistry, Indian Institute of Science, Bangalore, 560012, India.|Department of Biochemistry, Indian Institute of Science, Bangalore, 560012, India.|Department of Computer Science and Automation, and Supercomputer Education and Research Centre, Indian Institute of Science, Bangalore, 560012, India. vijayn@csa.iisc.ernet.in.', 'India|India|India|India|India', 1, 'Algorithms|Binding Sites|Computational Biology|Computer Simulation|Models, Molecular|Molecular Structure|Sequence Alignment|Software', 'Journal Article', '10.1186/s12859-015-0874-8', '26753741', 'Aligning similar molecular structures is an important step in the process of bio-molecular structure and function analysis. Molecular surfaces are simple representations of molecular structure that are easily constructed from various forms of molecular data such as 3D atomic coordinates (PDB) and Electron Microscopy (EM) data.'),
(97088, 2016, 'bmc', 'Molecular dynamics simulation and bioinformatics study on chloroplast stromal ridge complex from rice (Oryza sativa L)', 'Zhang, Yubo|Ding, Yi', 'State Key Laboratory of Hybrid Rice, Department of Genetics, College of Life Sciences, Wuhan University, Wuhan, 430072, Peoples Republic of China. zhyubo7@gmail.com.|State Key Laboratory of Hybrid Rice, Department of Genetics, College of Life Sciences, Wuhan University, Wuhan, 430072, Peoples Republic of China. yiding@whu.edu.cn.', 'China|China', 7, 'Amino Acid Sequence|Chloroplasts|Computational Biology|Hydrogen Bonding|Molecular Dynamics Simulation|Molecular Sequence Data|Oryza|Photosynthetic Reaction Center Complex Proteins|Protein Structure, Quaternary|Sequence Alignment|Thermodynamics', 'Journal Article', '10.1186/s12859-016-0877-0', '26753869', 'Rice (Oryza sativa L.) is one of the most important cereal crops in the world and its yield is closely related to the photosynthesis efficiency. The chloroplast stromal ridge complex consisting of PsaC-PsaD-PsaE plays an important role in plant photosynthesis, which has been a subject of many studies. Till now, the recognition mechanism between PsaC and PsaD in rice is still not fully understood.'),
(97089, 2016, 'bmc', 'GBS-SNP-CROP: a reference-optional pipeline for SNP discovery and plant germplasm characterization using variable length, paired-end genotyping-by-sequencing data', 'Melo, Arthur T O|Bartaula, Radhika|Hale, Iago', 'College of Life Sciences and Agriculture, Department of Biological Sciences, University of New Hampshire, Durham, NH, USA.|College of Life Sciences and Agriculture, Genetics Graduate Program, University of New Hampshire, Durham, NH, USA.|College of Life Sciences and Agriculture, Department of Biological Sciences, University of New Hampshire, Durham, NH, USA. iago.hale@unh.edu.', 'United States|United States|United States', 45, 'Actinidia|Computational Biology|DNA Mutational Analysis|Gene Library|Genome, Plant|Genotyping Techniques|Polymorphism, Single Nucleotide|Polyploidy|Sensitivity and Specificity', 'Journal Article', '10.1186/s12859-016-0879-y', '26754002', 'With its simple library preparation and robust approach to genome reduction, genotyping-by-sequencing (GBS) is a flexible and cost-effective strategy for SNP discovery and genotyping, provided an appropriate reference genome is available. For resource-limited curation, research, and breeding programs of underutilized plant genetic resources, however, even low-depth references may not be within reach, despite declining sequencing costs. Such programs would find value in an open-source bioinformatics pipeline that can maximize GBS data usage and perform high-density SNP genotyping in the absence of a reference.'),
(97090, 2016, 'bmc', 'BayesFlow: latent modeling of flow cytometry cell populations', 'Johnsson, Kerstin|Wallin, Jonas|Fontes, Magnus', 'Centre for Mathematical Sciences, Lund University, Box 118, Lund, S-221 00, Sweden. johnsson@maths.lth.se.|Mathematical Sciences, Chalmers and University of Gothenburg, Gothenburg, S-412 58, Sweden. jonwal@chalmers.se.|Centre for Mathematical Sciences, Lund University, Box 118, Lund, S-221 00, Sweden. fontes@maths.lth.se.', 'Sweden|Sweden|Sweden', 11, 'Bayes Theorem|Computer Simulation|Databases, Factual|Flow Cytometry|Humans|Models, Molecular', 'Journal Article', '10.1186/s12859-015-0862-z', '26755197', 'Flow cytometry is a widespread single-cell measurement technology with a multitude of clinical and research applications. Interpretation of flow cytometry data is hard, the instrumentation is delicate and can not render absolute measurements, hence samples can only be interpreted in relation to each other while at the same time comparisons are confounded by inter-sample variation. Despite this, most automated flow cytometry data analysis methods either treat samples individually or ignore the variation by for example pooling the data. A key requirement for models that include multiple samples is the ability to visualize and assess inferred variation, since what could be technical variation in one setting would be different phenotypes in another.'),
(97091, 2016, 'bmc', 'Aligning the unalignable: bacteriophage whole genome alignments', 'Bérard, Sèverine|Chateau, Annie|Pompidor, Nicolas|Guertin, Paul|Bergeron, Anne|Swenson, Krister M', 'ISEM, CNRS - Univ. Montpellier, Montpellier, France. severine.berard@univ-montp2.fr.|LIRMM, CNRS - Univ. Montpellier, 161 rue Ada, Montpellier, 34392, France. annie.chateau@gmail.com.|LIRMM, CNRS - Univ. Montpellier, 161 rue Ada, Montpellier, 34392, France. nicolas.pompidor@gmail.com.|LaCIM, Université du Québec à Montréal, Montréal, Canada. pguertin@grasset.qc.ca.|LaCIM, Université du Québec à Montréal, Montréal, Canada. bergeron.anne@uqam.ca.|LIRMM, CNRS - Univ. Montpellier, 161 rue Ada, Montpellier, 34392, France. swenson@lirmm.fr.', 'France|France|France|Canada|Canada|France', 6, 'Algorithms|Bacteriophages|Computational Biology|Genome, Viral|Genomics|Mycobacterium|Sequence Alignment|Staphylococcus aureus', 'Journal Article', '10.1186/s12859-015-0869-5', '26757899', 'In recent years, many studies focused on the description and comparison of large sets of related bacteriophage genomes. Due to the peculiar mosaic structure of these genomes, few informative approaches for comparing whole genomes exist: dot plots diagrams give a mostly qualitative assessment of the similarity/dissimilarity between two or more genomes, and clustering techniques are used to classify genomes. Multiple alignments are conspicuously absent from this scene. Indeed, whole genome aligners interpret lack of similarity between sequences as an indication of rearrangements, insertions, or losses. This behavior makes them ill-prepared to align bacteriophage genomes, where even closely related strains can accomplish the same biological function with highly dissimilar sequences.'),
(97092, 2016, 'bmc', 'PTESFinder: a computational method to identify post-transcriptional exon shuffling (PTES) events', 'Izuogu, Osagie G|Alhasan, Abd A|Alafghani, Hani M|Santibanez-Koref, Mauro|Elliott, David J|Elliot, David J|Jackson, Michael S', 'Institute of Genetic Medicine, Newcastle University, Newcastle Upon Tyne, UK. g.izuogu@ncl.ac.uk.|Institute of Genetic Medicine, Newcastle University, Newcastle Upon Tyne, UK. abd.al-hasan@newcastle.ac.uk.|Security Forces Hostpital, P. O. Box 2748-24268-8541, Makkah, Kingdom of Saudi Arabia. hani940@hotmail.com.|Institute of Genetic Medicine, Newcastle University, Newcastle Upon Tyne, UK. mauro.santibanez-koref@ncl.ac.uk.|Institute of Genetic Medicine, Newcastle University, Newcastle Upon Tyne, UK. mauro.santibanez-koref@ncl.ac.uk.|Institute of Genetic Medicine, Newcastle University, Newcastle Upon Tyne, UK. david.elliot@ncl.ac.uk.|Institute of Genetic Medicine, Newcastle University, Newcastle Upon Tyne, UK. michael.jackson@ncl.ac.uk.', 'United Kingdom|United Kingdom|Saudi Arabia|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 22, 'Alternative Splicing|Computational Biology|Exons|Gene Expression Regulation|Genome|Genomics|Humans|RNA|Software', 'Comparative Study', '10.1186/s12859-016-0881-4', '26758031', 'Transcripts, which have been subject to Post-transcriptional exon shuffling (PTES), have an exon order inconsistent with the underlying genomic sequence. These have been identified in a wide variety of tissues and cell types from many eukaryotes, and are now known to be mostly circular, cytoplasmic, and non-coding. Although there is no uniformly ascribed function, several have been shown to be involved in gene regulation. Accurate identification of these transcripts can, however, be difficult due to artefacts from a wide variety of sources.'),
(97093, 2016, 'bmc', 'Variable selection for binary classification using error rate p-values applied to metabolomics data', 'van Reenen, Mari|Reinecke, Carolus J|Westerhuis, Johan A|Venter, J Hendrik', 'Centre for Human Metabolomics, Faculty of Natural Sciences, North-West University (Potchefstroom Campus), Private Bag X6001, Potchefstroom, South Africa. 12791733@nwu.ac.za.|Centre for Human Metabolomics, Faculty of Natural Sciences, North-West University (Potchefstroom Campus), Private Bag X6001, Potchefstroom, South Africa. carools.reinecke@nwu.ac.za.|Biosystems Data Analysis, Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, The Netherlands. J.A.Westerhuis@uva.nl.|Centre for Business Mathematics and Informatics, Faculty of Natural Sciences, North-West University (Potchefstroom Campus), Private Bag X6001, Potchefstroom, South Africa. hennie.venter@nwu.ac.za.', 'South Africa|South Africa|Netherlands|South Africa', 4, 'Computational Biology|Humans|Metabolomics|Mycobacterium tuberculosis|Tuberculosis', 'Journal Article', '10.1186/s12859-015-0867-7', '26763892', 'Metabolomics datasets are often high-dimensional though only a limited number of variables are expected to be informative given a specific research question. The important task of selecting informative variables can therefore become complex. In this paper we look at discriminating between two groups. Two tasks need to be performed: (i) finding variables which differ between the two groups, and (ii) determining how the selected variables can be used to classify new subjects. We introduce an approach using minimum classification error rates as test statistics to find discriminatory and therefore informative variables. The thresholds resulting in the minimum error rates can be used to classify new subjects. This approach transforms error rates into p-values and is referred to as ERp.'),
(97094, 2016, 'bmc', 'NOBLE - Flexible concept recognition for large-scale biomedical natural language processing', 'Tseytlin, Eugene|Mitchell, Kevin|Legowski, Elizabeth|Corrigan, Julia|Chavan, Girish|Jacobson, Rebecca S', 'Department of Biomedical Informatics, University of Pittsburgh School of Medicine, The Offices at Baum, 5607 Baum Boulevard, BAUM 423, Rm 523, Pittsburgh, PA, 15206-3701, USA. eugene.tseytlin@gmail.com.|Department of Biomedical Informatics, University of Pittsburgh School of Medicine, The Offices at Baum, 5607 Baum Boulevard, BAUM 423, Rm 523, Pittsburgh, PA, 15206-3701, USA. mitchellkj@upmc.edu.|Department of Biomedical Informatics, University of Pittsburgh School of Medicine, The Offices at Baum, 5607 Baum Boulevard, BAUM 423, Rm 523, Pittsburgh, PA, 15206-3701, USA. legowski@bsc.gwu.edu.|Department of Biomedical Informatics, University of Pittsburgh School of Medicine, The Offices at Baum, 5607 Baum Boulevard, BAUM 423, Rm 523, Pittsburgh, PA, 15206-3701, USA. juc61@pitt.edu.|Department of Biomedical Informatics, University of Pittsburgh School of Medicine, The Offices at Baum, 5607 Baum Boulevard, BAUM 423, Rm 523, Pittsburgh, PA, 15206-3701, USA. chavang@upmc.edu.|Department of Biomedical Informatics, University of Pittsburgh School of Medicine, The Offices at Baum, 5607 Baum Boulevard, BAUM 423, Rm 523, Pittsburgh, PA, 15206-3701, USA. rebeccaj@pitt.edu.', 'United States|United States|United States|United States|United States|United States', 46, 'Algorithms|Humans|Natural Language Processing|Software', 'Comparative Study', '10.1186/s12859-015-0871-y', '26763894', 'Natural language processing (NLP) applications are increasingly important in biomedical data analysis, knowledge engineering, and decision support. Concept recognition is an important component task for NLP pipelines, and can be either general-purpose or domain-specific. We describe a novel, flexible, and general-purpose concept recognition component for NLP pipelines, and compare its speed and accuracy against five commonly used alternatives on both a biological and clinical corpus. NOBLE Coder implements a general algorithm for matching terms to concepts from an arbitrary vocabulary set. The systems matching options can be configured individually or in combination to yield specific system behavior for a variety of NLP tasks. The software is open source, freely available, and easily integrated into UIMA or GATE. We benchmarked speed and accuracy of the system against the CRAFT and ShARe corpora as reference standards and compared it to MMTx, MGrep, Concept Mapper, cTAKES Dictionary Lookup Annotator, and cTAKES Fast Dictionary Lookup Annotator.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(97095, 2016, 'bmc', 'Predicting the functions of a protein from its ability to associate with other molecules', 'Taha, Kamal|Yoo, Paul D', 'Department of Electrical and Computer Engineering, Khalifa University, Abu Dhabi, United Arab Emirates. kamal.taha@kustar.ac.ae.|Faculty of Science and Technology, Bournemouth University, Bournemouth, UK. pyoo@bournemouth.ac.uk.', 'United Arab Emirates|United Kingdom', 6, 'Databases, Factual|Genome, Fungal|Information Storage and Retrieval|Molecular Sequence Annotation|Multiprotein Complexes|Proteins|PubMed|Saccharomyces|Semantics|Software', 'Comparative Study', '10.1186/s12859-016-0882-3', '26767846', 'All proteins associate with other molecules. These associated molecules are highly predictive of the potential functions of proteins. The association of a protein and a molecule can be determined from their co-occurrences in biomedical abstracts. Extensive semantically related co-occurrences of a proteins name and a molecules name in the sentences of biomedical abstracts can be considered as indicative of the association between the protein and the molecule. Dependency parsers extract textual relations from a text by determining the grammatical relations between words in a sentence. They can be used for determining the textual relations between proteins and molecules. Despite their success, they may extract textual relations with low precision. This is because they do not consider the semantic relationships between terms in a sentence (i.e., they consider only the structural relationships between the terms). Moreover, they may not be well suited for complex sentences and for long-distance textual relations.'),
(97096, 2016, 'bmc', 'IPLaminator: an ImageJ plugin for automated binning and quantification of retinal lamination', 'Li, Shuai|Woodfin, Michael|Long, Seth S|Fuerst, Peter G', 'Department of Biological Sciences, University of Idaho, 145 Life Science South, Moscow, ID, 83844, USA.|Department of Computer Sciences, Lewis-Clark State College, Lewiston, ID, 83501, USA.|Department of Computer Sciences, Lewis-Clark State College, Lewiston, ID, 83501, USA.|Department of Biological Sciences, University of Idaho, 145 Life Science South, Moscow, ID, 83844, USA. fuerst@uidaho.edu.', 'United States|United States|United States|United States', 1, 'Amacrine Cells|Animals|Computational Biology|Humans|Nerve Net|Neurites|Retinal Ganglion Cells|Software|Synapses', 'Journal Article', '10.1186/s12859-016-0876-1', '26772546', 'Information in the brain is often segregated into spatially organized layers that reflect the function of the embedded circuits. This is perhaps best exemplified in the layering, or lamination, of the retinal inner plexiform layer (IPL). The neurites of the retinal ganglion, amacrine and bipolar cell subtypes that form synapses in the IPL are precisely organized in highly refined strata within the IPL. Studies focused on developmental organization and cell morphology often use this layered stratification to characterize cells and identify the function of genes in development of the retina. A current limitation to such analysis is the lack of standardized tools to quantitatively analyze this complex structure. Most previous work on neuron stratification in the IPL is qualitative and descriptive.'),
(97097, 2016, 'bmc', 'A computational method for genotype calling in family-based sequencing data', 'Chang, Lun-Ching|Li, Bingshan|Fang, Zhou|Vrieze, Scott|McGue, Matt|Iacono, William G|Tseng, George C|Chen, Wei', 'Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD, 20892, USA. lun-ching.chang@nih.gov.|Department of Molecular Physiology & Biophysics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA. bingshan.li@vanderbilt.edu.|Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, 15261, USA. zhf9@pitt.edu.|Department of Psychology & Neuroscience, Institute for Behavioral Genetics, University of Colorado, Boulder, CO, 80309, USA. scott.vrieze@colorado.edu.|Department of Psychology, University of Minnesota, Minneapolis, MN, 55455, USA. mcgue001@umn.edu.|Department of Psychology, University of Minnesota, Minneapolis, MN, 55455, USA. wiacono@umn.edu.|Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, 15261, USA. ctseng@pitt.edu.|Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, 15261, USA. wei.chen@chp.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States', 1, 'Algorithms|Computational Biology|Computer Simulation|Databases, Genetic|Genome, Human|Genotyping Techniques|Haplotypes|Humans|Linkage Disequilibrium|Polymorphism, Single Nucleotide|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-016-0880-5', '26772743', 'As sequencing technologies can help researchers detect common and rare variants across the human genome in many individuals, it is known that jointly calling genotypes across multiple individuals based on linkage disequilibrium (LD) can facilitate the analysis of low to modest coverage sequence data. However, genotype-calling methods for family-based sequence data, particularly for complex families beyond parent-offspring trios, are still lacking.'),
(97098, 2016, 'bmc', 'Deciphering transcriptional regulations coordinating the response to environmental changes', 'Acuña, Vicente|Aravena, Andrés|Guziolowski, Carito|Eveillard, Damien|Siegel, Anne|Maass, Alejandro', 'Center for Mathematical Modeling (UMI-CNRS 2807), Universidad de Chile, Santiago, Chile. viacuna@dim.uchile.cl.|Department of Molecular Biology and Genetics, Istanbul University, Istanbul, Turkey. andres.aravena@istanbul.edu.tr.|IRCCyN (UMR CNRS 6597), École Centrale de Nantes, Nantes, France. carito.guziolowski@irccyn.ec-nantes.fr.|LINA (UMR CNRS 6241), Université de Nantes, École des Mines de Nantes, Nantes, France. damien.eveillard@univ-nantes.fr.|IRISA Project Dyliss (UMR CNRS 6074), Université de Rennes 1, Rennes, France. anne.siegel@irisa.fr.|Center for Mathematical Modeling (UMI-CNRS 2807), Universidad de Chile, Santiago, Chile. amaass@dim.uchile.cl.', 'Chile|Turkey|France|France|France|Chile', 1, 'Computational Biology|Environment|Escherichia coli|Gene Expression Regulation|Gene Regulatory Networks|Transcription Factors|Transcription, Genetic', 'Journal Article', '10.1186/s12859-016-0885-0', '26772805', 'Gene co-expression evidenced as a response to environmental changes has shown that transcriptional activity is coordinated, which pinpoints the role of transcriptional regulatory networks (TRNs). Nevertheless, the prediction of TRNs based on the affinity of transcription factors (TFs) with binding sites (BSs) generally produces an over-estimation of the observable TF/BS relations within the network and therefore many of the predicted relations are spurious.'),
(97099, 2016, 'bmc', 'Assessment of k-mer spectrum applicability for metagenomic dissimilarity analysis', 'Dubinkina, Veronika B|Ischenko, Dmitry S|Ulyantsev, Vladimir I|Tyakht, Alexander V|Alexeev, Dmitry G', 'Research Institute of Physico-Chemical Medicine, Malaya Pirogovskaya, Moscow, 119435, Russia. dubinkina@phystech.edu.|Research Institute of Physico-Chemical Medicine, Malaya Pirogovskaya, Moscow, 119435, Russia. ischenko.dmitry@gmail.com.|ITMO University, Kronverksky Pr., St. Petersburg, 197101, Russia. vl.ulyantsev@gmail.com.|Research Institute of Physico-Chemical Medicine, Malaya Pirogovskaya, Moscow, 119435, Russia. at@niifhm.ru.|Research Institute of Physico-Chemical Medicine, Malaya Pirogovskaya, Moscow, 119435, Russia. alexeev@niifhm.ru.', '-|-|-|-|-', 25, 'Chromosome Mapping|Cluster Analysis|Computational Biology|Computer Simulation|Databases, Genetic|Gastrointestinal Microbiome|Gastrointestinal Tract|Humans|Metagenome|Metagenomics|Models, Molecular|Polymorphism, Single Nucleotide|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-015-0875-7', '26774270', 'A rapidly increasing flow of genomic data requires the development of efficient methods for obtaining its compact representation. Feature extraction facilitates classification, clustering and model analysis for testing and refining biological hypotheses. Shotgun metagenome is an analytically challenging type of genomic data - containing sequences of all genes from the totality of a complex microbial community. Recently, researchers started to analyze metagenomes using reference-free methods based on the analysis of oligonucleotides (k-mers) frequency spectrum previously applied to isolated genomes. However, little is known about their correlation with the existing approaches for metagenomic feature extraction, as well as the limits of applicability. Here we evaluated a metagenomic pairwise dissimilarity measure based on short k-mer spectrum using the example of human gut microbiota, a biomedically significant object of study.'),
(97100, 2016, 'bmc', 'Algorithms for reconstruction of chromosomal structures', 'Lyubetsky, Vassily|Gershgorin, Roman|Seliverstov, Alexander|Gorbunov, Konstantin', 'Institute for Information Transmission Problems of the Russian Academy of Sciences (Kharkevich Institute), Bolshoi Karetnyi lane, 19, 127051, Moscow, Russia. lyubetsk@iitp.ru.|Institute for Information Transmission Problems of the Russian Academy of Sciences (Kharkevich Institute), Bolshoi Karetnyi lane, 19, 127051, Moscow, Russia. gershr@mail.ru.|Institute for Information Transmission Problems of the Russian Academy of Sciences (Kharkevich Institute), Bolshoi Karetnyi lane, 19, 127051, Moscow, Russia. slvstv@iitp.ru.|Institute for Information Transmission Problems of the Russian Academy of Sciences (Kharkevich Institute), Bolshoi Karetnyi lane, 19, 127051, Moscow, Russia. gorbunov@iitp.ru.', '-|-|-|-', 13, 'Algorithms|Chromosome Structures|Computational Biology|Computer Simulation|Models, Molecular|Phylogeny|Software', 'Journal Article', '10.1186/s12859-016-0878-z', '26780836', 'One of the main aims of phylogenomics is the reconstruction of objects defined in the leaves along the whole phylogenetic tree to minimize the specified functional, which may also include the phylogenetic tree generation. Such objects can include nucleotide and amino acid sequences, chromosomal structures, etc. The structures can have any set of linear and circular chromosomes, variable gene composition and include any number of paralogs, as well as any weights of individual evolutionary operations to transform a chromosome structure. Many heuristic algorithms were proposed for this purpose, but there are just a few exact algorithms with low (linear, cubic or similar) polynomial computational complexity among them to our knowledge. The algorithms naturally start from the calculation of both the distance between two structures and the shortest sequence of operations transforming one structure into another. Such calculation per se is an NP-hard problem.'),
(97101, 2016, 'bmc', 'Erratum to: MetCap: A bioinformatics probe design pipeline for large-scale targeted metagenomics', 'Kushwaha, Sandeep K|Manoharan, Lokeshwaran|Meerupati, Tejashwari|Hedlund, Katarina|Ahren, Dag', 'Department of Biology, Lund University, Ecology Building, Lund, 223 62, Sweden. sandeep.kushwaha@biol.lu.se.|Department of Biology, Lund University, Ecology Building, Lund, 223 62, Sweden. lokeshwaran.manoharan@biol.lu.se.|Department of Biology, Lund University, Ecology Building, Lund, 223 62, Sweden. tejashwari.meerupati@biol.lu.se.|Department of Biology, Lund University, Ecology Building, Lund, 223 62, Sweden. katarina.hedlund@biol.lu.se.|Department of Biology, Lund University, Ecology Building, Lund, 223 62, Sweden. dag.ahren@biol.lu.se.', 'Sweden|Sweden|Sweden|Sweden|Sweden', 0, '-', 'Published Erratum', '10.1186/s12859-015-0843-2', '26782982', '-'),
(97102, 2016, 'bmc', 'An integrative analysis of gene expression and molecular interaction data to identify dys-regulated sub-networks in inflammatory bowel disease', 'Muraro, Daniele|Simmons, Alison', 'Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, OX3 9DS Oxford, UK. Daniele.Muraro@ndm.ox.ac.uk.|Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, OX3 9DS Oxford, UK. Alison.Simmons@ndm.ox.ac.uk.', 'United Kingdom|United Kingdom', 0, 'Algorithms|Colitis, Ulcerative|Crohn Disease|Databases, Genetic|ErbB Receptors|Evolution, Molecular|Gene Expression Profiling|Gene Regulatory Networks|Genetic Association Studies|Humans|Janus Kinase 1|MAP Kinase Signaling System|Models, Molecular|STAT Transcription Factors|Signal Transduction', 'Journal Article', '10.1186/s12859-016-0886-z', '26787018', 'Inflammatory bowel disease (IBD) consists of two main disease-subtypes, Crohns disease (CD) and ulcerative colitis (UC), these subtypes share overlapping genetic and clinical features. Genome-wide microarray data enable unbiased documentation of alterations in gene expression that may be disease-specific. As genetic diseases are believed to be caused by genetic alterations affecting the function of signalling pathways, module-centric optimisation algorithms, whose aim is to identify sub-networks that are dys-regulated in disease, are emerging as promising approaches.'),
(97103, 2016, 'bmc', '4Pipe4--A 454 data analysis pipeline for SNP detection in datasets with no reference sequence or strain information', 'Pina-Martins, Francisco|Vieira, Bruno M|Seabra, Sofia G|Batista, Dora|Paulo, Octávio S', 'Departamento de Biologia Animal, Faculdade de Ciências, Computational Biology and Population Genomics Group, cE3c - Centre for Ecology, Evolution and Environmental Changes, Universidade de Lisboa, Campo Grande, 1749-016, Lisboa, Portugal. f.pinamartins@gmail.com.|Departamento de Biologia Animal, Faculdade de Ciências, Computational Biology and Population Genomics Group, cE3c - Centre for Ecology, Evolution and Environmental Changes, Universidade de Lisboa, Campo Grande, 1749-016, Lisboa, Portugal.|Departamento de Biologia Animal, Faculdade de Ciências, Computational Biology and Population Genomics Group, cE3c - Centre for Ecology, Evolution and Environmental Changes, Universidade de Lisboa, Campo Grande, 1749-016, Lisboa, Portugal.|Departamento de Biologia Animal, Faculdade de Ciências, Computational Biology and Population Genomics Group, cE3c - Centre for Ecology, Evolution and Environmental Changes, Universidade de Lisboa, Campo Grande, 1749-016, Lisboa, Portugal.|Departamento de Biologia Animal, Faculdade de Ciências, Computational Biology and Population Genomics Group, cE3c - Centre for Ecology, Evolution and Environmental Changes, Universidade de Lisboa, Campo Grande, 1749-016, Lisboa, Portugal.', 'Portugal|Portugal|Portugal|Portugal|Portugal', 1, 'Computer Simulation|Databases, Genetic|Genome, Human|Genotyping Techniques|High-Throughput Nucleotide Sequencing|Humans|Models, Molecular|Polymorphism, Single Nucleotide|Reproducibility of Results|Software', 'Journal Article', '10.1186/s12859-016-0892-1', '26787189', 'Next-generation sequencing datasets are becoming more frequent, and their use in population studies is becoming widespread. For non-model species, without a reference genome, it is possible from a panel of individuals to identify a set of SNPs that can be used for further population genotyping. However the lack of a reference genome to which the sequenced data could be compared makes the finding of SNPs more troublesome. Additionally when the data sources (strains) are not identified (e.g. in datasets of pooled individuals), the problem of finding reliable variation in these datasets can become much more difficult due to the lack of specialized software for this specific task.'),
(97104, 2016, 'bmc', 'Bioconductors EnrichmentBrowser: seamless navigation through combined results of set- & network-based enrichment analysis', 'Geistlinger, Ludwig|Csaba, Gergely|Zimmer, Ralf', 'Institute of Bioinformatics, Department of Informatics, Ludwig-Maximilians-Universität München, Amalienstrasse 1780333, Munich, Germany. Ludwig.Geistlinger@bio.ifi.lmu.de.|Institute of Bioinformatics, Department of Informatics, Ludwig-Maximilians-Universität München, Amalienstrasse 1780333, Munich, Germany. Gergely.Csaba@bio.ifi.lmu.de.|Institute of Bioinformatics, Department of Informatics, Ludwig-Maximilians-Universität München, Amalienstrasse 1780333, Munich, Germany. Ralf.Zimmer@bio.ifi.lmu.de.', 'Germany|Germany|Germany', 0, 'Databases, Factual|Gene Expression Profiling|Gene Regulatory Networks|Microarray Analysis|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/s12859-016-0884-1', '26791995', 'Enrichment analysis of gene expression data is essential to find functional groups of genes whose interplay can explain experimental observations. Numerous methods have been published that either ignore (set-based) or incorporate (network-based) known interactions between genes. However, the often subtle benefits and disadvantages of the individual methods are confusing for most biological end users and there is currently no convenient way to combine methods for an enhanced result interpretation.'),
(97105, 2016, 'bmc', 'Protein Sequence Annotation Tool (PSAT): a centralized web-based meta-server for high-throughput sequence annotations', 'Leung, Elo|Huang, Amy|Cadag, Eithon|Montana, Aldrin|Soliman, Jan Lorenz|Zhou, Carol L Ecale', 'Computing Applications and Research, Global Security Computing Applications Division, Lawrence Livermore National Security, Livermore, CA, 94550, USA. elo.leung@personalis.com.|Computing Applications and Research, Global Security Computing Applications Division, Lawrence Livermore National Security, Livermore, CA, 94550, USA. huang30@llnl.gov.|Computing Applications and Research, Global Security Computing Applications Division, Lawrence Livermore National Security, Livermore, CA, 94550, USA. eithon@capellabio.com.|Computing Applications and Research, Global Security Computing Applications Division, Lawrence Livermore National Security, Livermore, CA, 94550, USA. aldrin.montana@personalis.com.|Computing Applications and Research, Global Security Computing Applications Division, Lawrence Livermore National Security, Livermore, CA, 94550, USA. jan.v.soliman@gmail.com.|Computing Applications and Research, Global Security Computing Applications Division, Lawrence Livermore National Security, Livermore, CA, 94550, USA. zhou4@llnl.gov.', 'United States|United States|United States|United States|United States|United States', 4, 'Computational Biology|Computers|Genome, Bacterial|Herbaspirillum|High-Throughput Nucleotide Sequencing|Internet|Molecular Sequence Annotation|Software|Water Microbiology', 'Journal Article', '10.1186/s12859-016-0887-y', '26792120', 'Here we introduce the Protein Sequence Annotation Tool (PSAT), a web-based, sequence annotation meta-server for performing integrated, high-throughput, genome-wide sequence analyses. Our goals in building PSAT were to (1) create an extensible platform for integration of multiple sequence-based bioinformatics tools, (2) enable functional annotations and enzyme predictions over large input protein fasta data sets, and (3) provide a web interface for convenient execution of the tools.'),
(97106, 2016, 'bmc', 'Informative gene selection and the direct classification of tumors based on relative simplicity', 'Chen, Yuan|Wang, Lifeng|Li, Lanzhi|Zhang, Hongyan|Yuan, Zheming', 'Hunan Provincial Key Laboratory for Biology and Control of Plant Diseases and Insect Pests, Changsha, China. chenyuan0510@126.com.|Biotechnology Research Center, Hunan Academy of Agricultural Sciences, Changsha, China. wanglifeng112358@163.com.|Hunan Provincial Key Laboratory for Germplasm Innovation and Utilization of Crop, Hunan Agricultural University, Changsha, China. lancy0829@163.com.|Hunan Provincial Key Laboratory for Germplasm Innovation and Utilization of Crop, Hunan Agricultural University, Changsha, China. hongyan_zhang6@aliyun.com.|Hunan Provincial Key Laboratory for Biology and Control of Plant Diseases and Insect Pests, Changsha, China. zhmyuan@sina.com.', 'China|China|China|China|China', 13, 'Bayes Theorem|Databases, Genetic|Genes, Neoplasm|Humans|Models, Molecular|Neoplasms|Support Vector Machine', 'Journal Article', '10.1186/s12859-016-0893-0', '26792270', 'Selecting a parsimonious set of informative genes to build highly generalized performance classifier is the most important task for the analysis of tumor microarray expression data. Many existing gene pair evaluation methods cannot highlight diverse patterns of gene pairs only used one strategy of vertical comparison and horizontal comparison, while individual-gene-ranking method ignores redundancy and synergy among genes.'),
(97107, 2016, 'bmc', 'EchinoDB, an application for comparative transcriptomics of deeply-sampled clades of echinoderms', 'Janies, Daniel A|Witter, Zach|Linchangco, Gregorio V|Foltz, David W|Miller, Allison K|Kerr, Alexander M|Jay, Jeremy|Reid, Robert W|Wray, Gregory A', 'Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223-0001, USA. djanies@uncc.edu.|Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223-0001, USA.|Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223-0001, USA.|Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, 70803, USA.|Marine Laboratory, University of Guam, University Dr, Mangilao, 96923, Guam.|Marine Laboratory, University of Guam, University Dr, Mangilao, 96923, Guam.|Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223-0001, USA.|Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223-0001, USA.|Department of Biology, Duke University, Durham, NC, 27708, USA.', 'United States|United States|United States|United States|Guam|Guam|United States|United States|United States', 0, 'Animals|Databases, Factual|Genome|High-Throughput Nucleotide Sequencing|Phylogeny|Sea Urchins|Transcriptome', 'Comparative Study', '10.1186/s12859-016-0883-2', '26800861', 'One of our goals for the echinoderm tree of life project (http://echinotol.org) is to identify orthologs suitable for phylogenetic analysis from next-generation transcriptome data. The current dataset is the largest assembled for echinoderm phylogeny and transcriptomics. We used RNA-Seq to profile adult tissues from 42 echinoderm specimens from 24 orders and 37 families. In order to achieve sampling members of clades that span key evolutionary divergence, many of our exemplars were collected from deep and polar seas.'),
(97108, 2016, 'bmc', 'An individualized predictor of health and disease using paired reference and target samples', 'Liu, Tzu-Yu|Burke, Thomas|Park, Lawrence P|Woods, Christopher W|Zaas, Aimee K|Ginsburg, Geoffrey S|Hero, Alfred O', 'Electrical Engineering and Computer Science Department, University of California, Berkeley CA, USA. tyliu@eecs.berkeley.edu.|Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University, Durham NC, USA. thomas.burke@duke.edu.|Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University, Durham NC, USA. larry.park@duke.edu.|Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University, Durham NC, USA. woods004@mc.duke.edu.|Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University, Durham NC, USA. aimee.zaas@duke.edu.|Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University, Durham NC, USA. geoffrey.ginsburg@duke.edu.|Electrical Engineering and Computer Science Department, University of Michigan, Ann Arbor MI, USA. hero@eecs.umich.edu.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Gene Expression|Genes, Essential|Genetic Markers|Humans|Influenza A Virus, H1N1 Subtype|Influenza A Virus, H3N2 Subtype|Microarray Analysis|Models, Molecular|Respiratory Syncytial Viruses|Rhinovirus|Virus Diseases', 'Journal Article', '10.1186/s12859-016-0889-9', '26801061', 'Consider the problem of designing a panel of complex biomarkers to predict a patients health or disease state when one can pair his or her current test sample, called a target sample, with the patients previously acquired healthy sample, called a reference sample. As contrasted to a population averaged reference this reference sample is individualized. Automated predictor algorithms that compare and contrast the paired samples to each other could result in a new generation of test panels that compare to a persons healthy reference to enhance predictive accuracy. This paper develops such an individualized predictor and illustrates the added value of including the healthy reference for design of predictive gene expression panels.'),
(97109, 2016, 'bmc', 'A multiple kernel learning algorithm for drug-target interaction prediction', 'Nascimento, André C A|Prudêncio, Ricardo B C|Costa, Ivan G', 'Center of Informatics, UFPE, Recife, Brazil. acan@cin.ufpe.br.|Center of Informatics, UFPE, Recife, Brazil. rbcp@cin.ufpe.br.|Center of Informatics, UFPE, Recife, Brazil. ivan.costa@rwth-aachen.de.', 'Brazil|Brazil|Brazil', 64, 'Algorithms|Computational Biology|Databases, Factual|Drug Delivery Systems|Empirical Research|Humans|Models, Theoretical|Protein Interaction Domains and Motifs|Proteins', 'Journal Article', '10.1186/s12859-016-0890-3', '26801218', 'Drug-target networks are receiving a lot of attention in late years, given its relevance for pharmaceutical innovation and drug lead discovery. Different in silico approaches have been proposed for the identification of new drug-target interactions, many of which are based on kernel methods. Despite technical advances in the latest years, these methods are not able to cope with large drug-target interaction spaces and to integrate multiple sources of biological information.'),
(97110, 2016, 'bmc', 'CP-CHARM: segmentation-free image classification made accessible', 'Uhlmann, Virginie|Singh, Shantanu|Carpenter, Anne E', 'Biomedical Imaging Group, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland. virginie.uhlmann@epfl.ch.|Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, MA, USA. shsingh@broadinstitute.org.|Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, MA, USA. anne@broadinstitute.org.', 'Switzerland|United States|United States', 0, 'Algorithms|Artifacts|Cells|Computational Biology|Humans|Image Interpretation, Computer-Assisted|Microscopy, Fluorescence|Pattern Recognition, Automated|Software', 'Journal Article', '10.1186/s12859-016-0895-y', '26817459', 'Automated classification using machine learning often relies on features derived from segmenting individual objects, which can be difficult to automate. WND-CHARM is a previously developed classification algorithm in which features are computed on the whole image, thereby avoiding the need for segmentation. The algorithm obtained encouraging results but requires considerable computational expertise to execute. Furthermore, some benchmark sets have been shown to be subject to confounding artifacts that overestimate classification accuracy.'),
(97111, 2016, 'bmc', 'Arabidopsis Motif Scanner', 'Mele, Giovanni', 'Institute of Agricultural Biology and Biotechnology (IBBA), National Council of Research (CNR), Via Salaria Km. 29.300, Monterotondo Scalo (Roma), 00015, Italy. melegio@ibba.cnr.it.', 'Italy', 1, 'Amino Acid Motifs|Arabidopsis|Arabidopsis Proteins|Binding Sites|Gene Expression Regulation, Plant|Gene Regulatory Networks|High-Throughput Nucleotide Sequencing|Oligonucleotide Array Sequence Analysis|Software|Transcription Factors', 'Journal Article', '10.1186/s12859-016-0896-x', '26817596', 'The major mechanism driving cellular differentiation and organism development is the regulation of gene expression. Cis-acting enhancers and silencers have key roles in controlling gene transcription. The genomic era allowed the transition from single gene analysis to the investigation of full transcriptomes. This transition increased the complexity of the analyses and the difficulty in the interpretation of the results. In this context, there is demand for new tools aimed at the creation of gene networks that can facilitate the interpretation of Next Generation Sequencing (NGS) data.'),
(97112, 2016, 'bmc', 'Weakly supervised learning of biomedical information extraction from curated data', 'Jain, Suvir|Tumkur, Kashyap R|Kuo, Tsung-Ting|Bhargava, Shitij|Lin, Gordon|Hsu, Chun-Nan', 'Department of Computer Science and Engineering, Jacobs School of Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, 92093, USA. suj011@eng.ucsd.edu.|Department of Computer Science and Engineering, Jacobs School of Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, 92093, USA. ktumkur@eng.ucsd.edu.|Department of Biomedical Informatics, School of Medicine, University of California, San Diego, 9500 Gilman Drive, La Jolla, 92093, USA. tskuo@ucsd.edu.|Department of Computer Science and Engineering, Jacobs School of Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, 92093, USA. shbharga@eng.ucsd.edu.|Department of Computer Science and Engineering, Jacobs School of Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, 92093, USA. golin@eng.ucsd.edu.|Department of Biomedical Informatics, School of Medicine, University of California, San Diego, 9500 Gilman Drive, La Jolla, 92093, USA. chunnan@ucsd.edu.', 'United States|United States|United States|United States|United States|United States', 6, 'Abstracting and Indexing as Topic|Data Curation|Data Mining|Databases, Factual|Disease|Genetic Predisposition to Disease|Genome-Wide Association Study|Humans|Risk Assessment', 'Journal Article', '10.1186/s12859-015-0844-1', '26817711', 'Numerous publicly available biomedical databases derive data by curating from literatures. The curated data can be useful as training examples for information extraction, but curated data usually lack the exact mentions and their locations in the text required for supervised machine learning. This paper describes a general approach to information extraction using curated data as training examples. The idea is to formulate the problem as cost-sensitive learning from noisy labels, where the cost is estimated by a committee of weak classifiers that consider both curated data and the text.'),
(97113, 2016, 'bmc', 'Drug repositioning for non-small cell lung cancer by using machine learning algorithms and topological graph theory', 'Huang, Chien-Hung|Chang, Peter Mu-Hsin|Hsu, Chia-Wei|Huang, Chi-Ying F|Ng, Ka-Lok', 'Department of Computer Science and Information Engineering, National Formosa University, Hu-Wei, 63205, Taiwan. chhuang@nfu.edu.tw.|Division of Hematology and Oncology, Department of Medicine, Taipei Veterans General Hospital, Faculty of Medicine, National Yang Ming University, Taipei, 112, Taiwan. ptchang@vghtpe.gov.tw.|Department of Computer Science and Information Engineering, National Formosa University, Hu-Wei, 63205, Taiwan. 10163138@gm.nfu.edu.tw.|Institute of Biopharmaceutical Sciences, National Yang-Ming University, Taipei, 112, Taiwan. cyhuang5@ym.edu.tw.|Department of Bioinformatics and Medical Engineering, Asia University, Taichung, 41354, Taiwan. ppiddi@gmail.com.', '-|-|-|-|-', 10, 'Algorithms|Antineoplastic Agents|Carcinoma, Non-Small-Cell Lung|Drug Discovery|Drug Repositioning|Gene Expression Regulation, Neoplastic|Humans|Lung Neoplasms|Machine Learning|Microarray Analysis|Models, Theoretical|Neoplasm Proteins|Signal Transduction', 'Journal Article', '10.1186/s12859-015-0845-0', '26817825', 'Non-small cell lung cancer (NSCLC) is one of the leading causes of death globally, and research into NSCLC has been accumulating steadily over several years. Drug repositioning is the current trend in the pharmaceutical industry for identifying potential new uses for existing drugs and accelerating the development process of drugs, as well as reducing side effects.'),
(97114, 2016, 'bmc', 'Predicting transcription factor site occupancy using DNA sequence intrinsic and cell-type specific chromatin features', 'Kumar, Sunil|Bucher, Philipp', 'Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, EPFL, Station 15, Lausanne, CH-1015, Switzerland. kumar.sunil@epfl.ch.|Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, EPFL, Station 15, Lausanne, CH-1015, Switzerland. philipp.bucher@epfl.ch.', 'Switzerland|Switzerland', 14, 'Algorithms|Binding Sites|Cell Lineage|Chromatin|Chromatin Immunoprecipitation|DNA|Gene Expression Regulation|Genome, Human|Histones|Humans|Machine Learning|Nucleosomes|Protein Processing, Post-Translational|Transcription Factors', 'Journal Article', '10.1186/s12859-015-0846-z', '26818008', 'Understanding the mechanisms by which transcription factors (TF) are recruited to their physiological target sites is crucial for understanding gene regulation. DNA sequence intrinsic features such as predicted binding affinity are often not very effective in predicting in vivo site occupancy and in any case could not explain cell-type specific binding events. Recent reports show that chromatin accessibility, nucleosome occupancy and specific histone post-translational modifications greatly influence TF site occupancy in vivo. In this work, we use machine-learning methods to build predictive models and assess the relative importance of different sequence-intrinsic and chromatin features in the TF-to-target-site recruitment process.'),
(97115, 2016, 'bmc', 'A semi-parametric statistical model for integrating gene expression profiles across different platforms', 'Lyu, Yafei|Li, Qunhua', 'The Huck Institute of Life Science, Pennsylvania State University, University Park, PA, 16802, USA. yul199@psu.edu.|Department of Statistics, Pennsylvania State University, University Park, PA, 16802, USA. qunhua.li@psu.edu.', 'United States|United States', 0, 'Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Humans|Models, Statistical|Oligonucleotide Array Sequence Analysis|RNA|Reproducibility of Results|Sequence Analysis, RNA|Transcriptome', 'Comparative Study', '10.1186/s12859-015-0847-y', '26818110', 'Determining differentially expressed genes (DEGs) between biological samples is the key to understand how genotype gives rise to phenotype. RNA-seq and microarray are two main technologies for profiling gene expression levels. However, considerable discrepancy has been found between DEGs detected using the two technologies. Integration data across these two platforms has the potential to improve the power and reliability of DEG detection.'),
(97116, 2016, 'bmc', 'Gene expression profiling identifies candidate biomarkers for active and latent tuberculosis', 'Lee, Shih-Wei|Wu, Lawrence Shih-Hsin|Huang, Guan-Mau|Huang, Kai-Yao|Lee, Tzong-Yi|Weng, Julia Tzu-Ya', 'Taoyuan General Hospital, Ministry of Health and Welfare, Taoyuan, Taiwan. swl@mail.tygh.gov.tw.|Institute of Medical Sciences, Tzu Chi University, Hualien, Taiwan. lshwu@hotmail.com.|Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, Taiwan. koko5696@gmail.com.|Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, Taiwan. kaiyao.tw@gmail.com.|Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, Taiwan. francis@saturn.yzu.edu.tw.|Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, Taiwan. julweng@saturn.yzu.edu.tw.', '-|-|-|-|-|-', 20, 'Bayes Theorem|Biomarkers|Case-Control Studies|Gene Expression Profiling|Humans|Latent Tuberculosis|Leukocytes, Mononuclear|Microarray Analysis|Mycobacterium tuberculosis|ROC Curve|Reproducibility of Results|Transcriptome|Tuberculosis', 'Comparative Study', '10.1186/s12859-015-0848-x', '26818387', 'Tuberculosis (TB) is a serious infectious disease in that 90% of those latently infected with Mycobacterium tuberculosis present no symptoms, but possess a 10% lifetime chance of developing active TB. To prevent the spread of the disease, early diagnosis is crucial. However, current methods of detection require improvement in sensitivity, efficiency or specificity. In the present study, we conducted a microarray experiment, comparing the gene expression profiles in the peripheral blood mononuclear cells among individuals with active TB, latent infection, and healthy conditions in a Taiwanese population.'),
(97117, 2016, 'bmc', 'The diagnostic application of RNA sequencing in patients with thyroid cancer: an analysis of 851 variants and 133 fusions in 524 genes', 'Pagan, Moraima|Kloos, Richard T|Lin, Chu-Fang|Travers, Kevin J|Matsuzaki, Hajime|Tom, Ed Y|Kim, Su Yeon|Wong, Mei G|Stewart, Andrew C|Huang, Jing|Walsh, P Sean|Monroe, Robert J|Kennedy, Giulia C', 'Veracyte, Inc., South San Francisco, CA, USA.|Veracyte, Inc., South San Francisco, CA, USA.|Veracyte, Inc., South San Francisco, CA, USA.|Veracyte, Inc., South San Francisco, CA, USA.|Veracyte, Inc., South San Francisco, CA, USA.|Veracyte, Inc., South San Francisco, CA, USA.|Veracyte, Inc., South San Francisco, CA, USA.|Veracyte, Inc., South San Francisco, CA, USA.|Veracyte, Inc., South San Francisco, CA, USA.|Veracyte, Inc., South San Francisco, CA, USA.|Veracyte, Inc., South San Francisco, CA, USA.|Veracyte, Inc., South San Francisco, CA, USA.|Veracyte, Inc., South San Francisco, CA, USA. giulia@veracyte.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', 20, 'Adenocarcinoma, Follicular|Biomarkers, Tumor|Biopsy, Fine-Needle|Carcinoma|Carcinoma, Neuroendocrine|Carcinoma, Papillary|Gene Fusion|Genetic Variation|High-Throughput Nucleotide Sequencing|Humans|Prospective Studies|ROC Curve|Sequence Analysis, RNA|Thyroid Cancer, Papillary|Thyroid Neoplasms|Thyroid Nodule', 'Journal Article', '10.1186/s12859-015-0849-9', '26818556', 'Thyroid carcinomas are known to harbor oncogenic driver mutations and advances in sequencing technology now allow the detection of these in fine needle aspiration biopsies (FNA). Recent work by The Cancer Genome Atlas (TCGA) Research Network has expanded the number of genetic alterations detected in papillary thyroid carcinomas (PTC). We sought to investigate the prevalence of these and other genetic alterations in diverse subtypes of thyroid nodules beyond PTC, including a variety of samples with benign histopathology. This is the first clinical evaluation of a large panel of TCGA-reported genomic alterations in thyroid FNAs.'),
(97118, 2016, 'bmc', 'A full Bayesian partition model for identifying hypo- and hyper-methylated loci from single nucleotide resolution sequencing data', 'Wang, Henan|He, Chong|Kushwaha, Garima|Xu, Dong|Qiu, Jing', 'Department of Statistics, University of Missouri, Columbia, Missouri, USA. hwg58@mail.missouri.edu.|Department of Statistics, University of Missouri, Columbia, Missouri, USA. hezh@missouri.edu.|Department of Computer Science and Informatics Institute, Columbia, USA. gk3kc@mail.missouri.edu.|Department of Computer Science and Informatics Institute, Columbia, USA. xudong@missouri.edu.|Department of Applied Economics and Statistics, University of Delaware, Newark, DE, USA. qiujing@udel.edu.', 'United States|United States|United States|United States|United States', 1, 'Bayes Theorem|Computational Biology|Computer Simulation|CpG Islands|DNA Methylation|Epigenesis, Genetic|Gene Expression Regulation|Humans|Polymorphism, Single Nucleotide|Sample Size|Sulfites', 'Journal Article', '10.1186/s12859-015-0850-3', '26818685', 'DNA methylation is an epigenetic modification that plays important roles on gene regulation. Study of whole-genome bisulfite sequencing and reduced representation bisulfite sequencing brings the availability of DNA methylation at single CpG resolution. The main interest of study on DNA methylation data is to test the methylation difference under two conditions of biological samples. However, the high cost and complexity of this sequencing experiment limits the number of biological replicates, which brings challenges to the development of statistical methods.'),
(97119, 2016, 'bmc', 'PredRSA: a gradient boosted regression trees approach for predicting protein solvent accessibility', 'Fan, Chao|Liu, Diwei|Huang, Rui|Chen, Zhigang|Deng, Lei', 'School of Software, Central South University, No.22 Shaoshan South Road, Changsha, 410075, China. chaofan0427@csu.edu.cn.|School of Software, Central South University, No.22 Shaoshan South Road, Changsha, 410075, China. csu.ldw@csu.edu.cn.|School of Software, Central South University, No.22 Shaoshan South Road, Changsha, 410075, China. ryanhwang1124@csu.edu.cn.|School of Software, Central South University, No.22 Shaoshan South Road, Changsha, 410075, China. czg@csu.edu.cn.|School of Software, Central South University, No.22 Shaoshan South Road, Changsha, 410075, China. leideng@csu.edu.cn.', 'China|China|China|China|China', 27, 'Algorithms|Amino Acids|Computer Simulation|Data Interpretation, Statistical|Humans|Linear Models|Proteins|Software|Solvents', 'Journal Article', '10.1186/s12859-015-0851-2', '26818760', 'Protein solvent accessibility prediction is a pivotal intermediate step towards modeling protein tertiary structures directly from one-dimensional sequences. It also plays an important part in identifying protein folds and domains. Although some methods have been presented to the protein solvent accessibility prediction in recent years, the performance is far from satisfactory. In this work, we propose PredRSA, a computational method that can accurately predict relative solvent accessible surface area (RSA) of residues by exploring various local and global sequence features which have been observed to be associated with solvent accessibility. Based on these features, a novel and efficient approach, Gradient Boosted Regression Trees (GBRT), is first adopted to predict RSA.'),
(97120, 2016, 'bmc', 'Learning a hierarchical representation of the yeast transcriptomic machinery using an autoencoder model', 'Chen, Lujia|Cai, Chunhui|Chen, Vicky|Lu, Xinghua', 'Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Blvd, 15237, Pittsburgh, PA, USA. luc17@pitt.edu.|Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Blvd, 15237, Pittsburgh, PA, USA. chunhuic@pitt.edu.|Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Blvd, 15237, Pittsburgh, PA, USA. vic14@pitt.edu.|Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Blvd, 15237, Pittsburgh, PA, USA. xinghua@pitt.edu.', 'United States|United States|United States|United States', 28, 'Computer Simulation|Gene Expression Profiling|Gene Expression Regulation, Fungal|Models, Theoretical|Neural Networks (Computer)|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Signal Transduction|Systems Biology|Transcription Factors', 'Journal Article', '10.1186/s12859-015-0852-1', '26818848', 'A living cell has a complex, hierarchically organized signaling system that encodes and assimilates diverse environmental and intracellular signals, and it further transmits signals that control cellular responses, including a tightly controlled transcriptional program. An important and yet challenging task in systems biology is to reconstruct cellular signaling system in a data-driven manner. In this study, we investigate the utility of deep hierarchical neural networks in learning and representing the hierarchical organization of yeast transcriptomic machinery.'),
(97121, 2016, 'bmc', 'Missing value imputation for microRNA expression data by using a GO-based similarity measure', 'Yang, Yang|Xu, Zhuangdi|Song, Dandan', 'Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Rd., Shanghai, 200240, China. yangyang@cs.sjtu.edu.cn.|Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Rd., Shanghai, 200240, China. xzdandy@gmail.com.|School of Computer Science and Technology, Beijing Institute of Technology, Beijing100081, China. sdd@bit.edu.cn.', 'China|China|China', 16, 'Algorithms|Computational Biology|Gene Expression Profiling|Gene Ontology|Humans|MicroRNAs|Models, Statistical|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/s12859-015-0853-0', '26818962', 'Missing values are commonly present in microarray data profiles. Instead of discarding genes or samples with incomplete expression level, missing values need to be properly imputed for accurate data analysis. The imputation methods can be roughly categorized as expression level-based and domain knowledge-based. The first type of methods only rely on expression data without the help of external data sources, while the second type incorporates available domain knowledge into expression data to improve imputation accuracy. In recent years, microRNA (miRNA) microarray has been largely developed and used for identifying miRNA biomarkers in complex human disease studies. Similar to mRNA profiles, miRNA expression profiles with missing values can be treated with the existing imputation methods. However, the domain knowledge-based methods are hard to be applied due to the lack of direct functional annotation for miRNAs. With the rapid accumulation of miRNA microarray data, it is increasingly needed to develop domain knowledge-based imputation algorithms specific to miRNA expression profiles to improve the quality of miRNA data analysis.'),
(97122, 2016, 'bmc', 'DNAism: exploring genomic datasets on the web with Horizon Charts', 'Rio Deiros, David|Gibbs, Richard A|Rogers, Jeffrey', 'Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, Houston, USA. driodeiros@gmail.com.|Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, Houston, USA. deiros@bcm.edu.|Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, Houston, USA. deiros@bcm.edu.', 'United States|United States|United States', 0, 'Computational Biology|Computer Graphics|Datasets as Topic|Gene Library|Genome, Human|Genomics|Humans|Internet|Software', 'Journal Article', '10.1186/s12859-016-0891-2', '26819101', 'Computational biologists daily face the need to explore massive amounts of genomic data. New visualization techniques can help researchers navigate and understand these big data. Horizon Charts are a relatively new visualization method that, under the right circumstances, maximizes data density without losing graphical perception.'),
(97123, 2016, 'bmc', 'Methods for the integration of multi-omics data: mathematical aspects', 'Bersanelli, Matteo|Mosca, Ettore|Remondini, Daniel|Giampieri, Enrico|Sala, Claudia|Castellani, Gastone|Milanesi, Luciano', 'Department of Physics and Astronomy, Universita di Bologna, Via B. Pichat 6/2, Bologna, 40127, Italy. matteo.bersanelli@studio.unibo.it.|Institute of Biomedical Technologies - CNR, Via Fratelli Cervi 93, Segrate MI, 20090, Italy. ettore.mosca@itb.cnr.it.|Department of Physics and Astronomy, Universita di Bologna, Via B. Pichat 6/2, Bologna, 40127, Italy. daniel.remondini@unibo.it.|Department of Physics and Astronomy, Universita di Bologna, Via B. Pichat 6/2, Bologna, 40127, Italy. enrico.giampieri@unibo.it.|Department of Physics and Astronomy, Universita di Bologna, Via B. Pichat 6/2, Bologna, 40127, Italy. claudia.sala@studio.unibo.it.|Department of Physics and Astronomy, Universita di Bologna, Via B. Pichat 6/2, Bologna, 40127, Italy. gastone.castellani@unibo.it.|Institute of Biomedical Technologies - CNR, Via Fratelli Cervi 93, Segrate MI, 20090, Italy. luciano.milanesi@itb.cnr.it.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy', 124, 'Algorithms|Bayes Theorem|Genomics|Humans|Least-Squares Analysis|Models, Genetic|Software', 'Journal Article', '10.1186/s12859-015-0857-9', '26821531', 'Methods for the integrative analysis of multi-omics data are required to draw a more complete and accurate picture of the dynamics of molecular systems. The complexity of biological systems, the technological limits, the large number of biological variables and the relatively low number of biological samples make the analysis of multi-omics datasets a non-trivial problem.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(97124, 2016, 'bmc', 'Stochastic neutral modelling of the Gut Microbiotas relative species abundance from next generation sequencing data', 'Sala, Claudia|Vitali, Silvia|Giampieri, Enrico|do Valle, Ìtalo Faria|Remondini, Daniel|Garagnani, Paolo|Bersanelli, Matteo|Mosca, Ettore|Milanesi, Luciano|Castellani, Gastone', 'Department of Physics and Astronomy, University of Bologna, Bologna, 40127, Italy. claudia.sala3@unibo.it.|Department of Physics and Astronomy, University of Bologna, Bologna, 40127, Italy.|Department of Physics and Astronomy, University of Bologna, Bologna, 40127, Italy.|Department of Physics and Astronomy, University of Bologna, Bologna, 40127, Italy.|Department of Physics and Astronomy, University of Bologna, Bologna, 40127, Italy.|Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, 40138, Italy.|Institute of Biomedical Technologies, National Research Council, Milan, 20090, Segrate, Italy.|Institute of Biomedical Technologies, National Research Council, Milan, 20090, Segrate, Italy.|Institute of Biomedical Technologies, National Research Council, Milan, 20090, Segrate, Italy.|Department of Physics and Astronomy, University of Bologna, Bologna, 40127, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 9, 'Animals|Bacteria|Biodiversity|Cattle|Chickens|Gastrointestinal Microbiome|High-Throughput Nucleotide Sequencing|Humans|Phylogeny|RNA, Bacterial|RNA, Ribosomal, 16S|Sequence Analysis, RNA|Sus scrofa', 'Journal Article', '10.1186/s12859-015-0858-8', '26821617', 'Interest in understanding the mechanisms that lead to a particular composition of the Gut Microbiota is highly increasing, due to the relationship between this ecosystem and the host health state. Particularly relevant is the study of the Relative Species Abundance (RSA) distribution, that is a component of biodiversity and measures the number of species having a given number of individuals. It is the universal behaviour of RSA that induced many ecologists to look for theoretical explanations. In particular, a simple stochastic neutral model was proposed by Volkov et al. relying on population dynamics and was proved to fit the coral-reefs and rain forests RSA. Our aim is to ascertain if this model also describes the Microbiota RSA and if it can help in explaining the Microbiota plasticity.'),
(97125, 2016, 'bmc', 'Effects of Mecp2 loss of function in embryonic cortical neurons: a bioinformatics strategy to sort out non-neuronal cells variability from transcriptome profiling', 'Vacca, Marcella|Tripathi, Kumar Parijat|Speranza, Luisa|Aiese Cigliano, Riccardo|Scalabrì, Francesco|Marracino, Federico|Madonna, Michele|Sanseverino, Walter|Perrone-Capano, Carla|Guarracino, Mario Rosario|DEsposito, Maurizio', 'Institute of Genetics and Biophysics A. Buzzati Traverso, National Research Council (CNR)-80131, Naples, Italy. marcella.vacca@igb.cnr.it.|Laboratory for Genomics, Transcriptomics and Proteomics (LAB-GTP), High Performance Computing and Networking Institute (ICAR), National Research Council (CNR)-80131, Naples, Italy. kumpar@na.icar.cnr.it.|Institute of Genetics and Biophysics A. Buzzati Traverso, National Research Council (CNR)-80131, Naples, Italy. speranza@igb.cnr.it.|Sequentia Biotech SL, Calle Comte DUrgell, 240 08036, Barcelona, Spain. raiesecigliano@sequentiabiotech.com.|IRCCS Neuromed, via dellElettronica, Pozzilli (Is), Italy. francesco.scalabri@neuromed.it.|IRCCS Neuromed, via dellElettronica, Pozzilli (Is), Italy. federico.marracino@neuromed.it.|IRCCS Neuromed, via dellElettronica, Pozzilli (Is), Italy. stabulario@neuromed.it.|Sequentia Biotech SL, Calle Comte DUrgell, 240 08036, Barcelona, Spain. wsanseverino@sequentiabiotech.com.|Institute of Genetics and Biophysics A. Buzzati Traverso, National Research Council (CNR)-80131, Naples, Italy. carla.perrone@igb.cnr.it.|Laboratory for Genomics, Transcriptomics and Proteomics (LAB-GTP), High Performance Computing and Networking Institute (ICAR), National Research Council (CNR)-80131, Naples, Italy. mario.guarracino@cnr.it.|Institute of Genetics and Biophysics A. Buzzati Traverso, National Research Council (CNR)-80131, Naples, Italy. maurizio.desposito@igb.cnr.it.', 'Italy|Italy|Italy|Spain|Italy|Italy|Italy|Spain|Italy|Italy|Italy', 5, 'Animals|Astrocytes|Brain|Cerebral Cortex|Computational Biology|Dendrites|Disease Models, Animal|Female|Gene Expression Profiling|Methyl-CpG-Binding Protein 2|Mice|Neuroglia|Neurons|Rett Syndrome|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-015-0859-7', '26821710', 'Mecp2 null mice model Rett syndrome (RTT) a human neurological disorder affecting females after apparent normal pre- and peri-natal developmental periods. Neuroanatomical studies in cerebral cortex of RTT mouse models revealed delayed maturation of neuronal morphology and autonomous as well as non-cell autonomous reduction in dendritic complexity of postnatal cortical neurons. However, both morphometric parameters and high-resolution expression profile of cortical neurons at embryonic developmental stage have not yet been studied. Here we address these topics by using embryonic neuronal primary cultures from Mecp2 loss of function mouse model.'),
(97126, 2016, 'bmc', 'A two-phase procedure for non-normal quantitative trait genetic association study', 'Zhang, Wei|Li, Huiyun|Li, Zhaohai|Li, Qizhai', 'Key Laboratory of Systems Control, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China. zhangwei@amss.ac.cn.|School of Management and Economics, Beijing Institute of Technology, Beijing, 100081, China. lihuiyun@bit.edu.cn.|Department of Statistics, George Washington University, Washington, 20052, DC, USA. zli@gwu.edu.|Key Laboratory of Systems Control, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China. liqz@amss.ac.cn.', 'China|China|United States|China', 0, 'Autoantibodies|Axonemal Dyneins|Genetic Association Studies|Genetic Variation|Humans|Models, Genetic|Models, Statistical|Peptides, Cyclic|Phenotype', 'Journal Article', '10.1186/s12859-016-0888-x', '26821800', 'The nonparametric trend test (NPT) is well suitable for identifying the genetic variants associated with quantitative traits when the trait values do not satisfy the normal distribution assumption. If the genetic model, defined according to the mode of inheritance, is known, the NPT derived under the given genetic model is optimal. However, in practice, the genetic model is often unknown beforehand. The NPT derived from an uncorrected model might result in loss of power. When the underlying genetic model is unknown, a robust test is preferred to maintain satisfactory power.'),
(97127, 2016, 'bmc', 'Evaluation of O2PLS in Omics data integration', 'Bouhaddani, Said El|Houwing-Duistermaat, Jeanine|Salo, Perttu|Perola, Markus|Jongbloed, Geurt|Uh, Hae-Won', 'Department of Medical Statistics and Bioinformatics, LUMC, Albinusdreef 2, Leiden, 2300, RC, The Netherlands. s.el_bouhaddani@lumc.nl.|Department of Medical Statistics and Bioinformatics, LUMC, Albinusdreef 2, Leiden, 2300, RC, The Netherlands. J.J.Houwing@lumc.nl.|National Institute for Health and Welfare (THL), Mannerheimintie 166, Helsinki, FI-00271, Finland. perttu.salo@thl.fi.|National Institute for Health and Welfare (THL), Mannerheimintie 166, Helsinki, FI-00271, Finland. markus.perola@thl.fi.|Department of Statistics, EEMCS, TU Delft, Mekelweg 4, Delft, 2628, CD, The Netherlands. G.Jongbloed@tudelft.nl.|Department of Medical Statistics and Bioinformatics, LUMC, Albinusdreef 2, Leiden, 2300, RC, The Netherlands. H.Uh@lumc.nl.', 'Netherlands|Netherlands|Finland|Finland|Netherlands|Netherlands', 30, 'Adult|Aged|Diet|Female|Genomics|Humans|Least-Squares Analysis|Male|Metabolomics|Middle Aged|Obesity|Statistics as Topic|Systems Biology|Transcriptome', 'Journal Article', '10.1186/s12859-015-0854-z', '26822911', 'Rapid computational and technological developments made large amounts of omics data available in different biological levels. It is becoming clear that simultaneous data analysis methods are needed for better interpretation and understanding of the underlying systems biology. Different methods have been proposed for this task, among them Partial Least Squares (PLS) related methods. To also deal with orthogonal variation, systematic variation in the data unrelated to one another, we consider the Two-way Orthogonal PLS (O2PLS): an integrative data analysis method which is capable of modeling systematic variation, while providing more parsimonious models aiding interpretation.'),
(97128, 2016, 'bmc', 'Dissecting protein architecture with communication blocks and communicating segment pairs', 'Karami, Yasaman|Laine, Elodie|Carbone, Alessandra', 'Sorbonne Universités, UPMC-Univ P6, CNRS, Laboratoire de Biologie Computationnelle et Quantitative - UMR 7238, 15 rue de lEcole de Médecine, Paris, 75006, France. Yasaman.Karami@upmc.fr.|Sorbonne Universités, UPMC-Univ P6, CNRS, Laboratoire de Biologie Computationnelle et Quantitative - UMR 7238, 15 rue de lEcole de Médecine, Paris, 75006, France. elodie.laine@upmc.fr.|Sorbonne Universités, UPMC-Univ P6, CNRS, Laboratoire de Biologie Computationnelle et Quantitative - UMR 7238, 15 rue de lEcole de Médecine, Paris, 75006, France. Alessandra.Carbone@lip6.fr.', 'France|France|France', 0, 'Allosteric Regulation|Automation|Humans|Molecular Dynamics Simulation|Motion|Protein Conformation|Protein Structure, Secondary|Proteins|Proto-Oncogene Proteins c-kit|Software|Staphylococcal Protein A|Tumor Suppressor Protein p53', 'Journal Article', '10.1186/s12859-015-0855-y', '26823083', 'Proteins adapt to environmental conditions by changing their shape and motions. Characterising protein conformational dynamics is increasingly recognised as necessary to understand how proteins function. Given a conformational ensemble, computational tools are needed to extract in a systematic way pertinent and comprehensive biological information.'),
(97129, 2016, 'bmc', '3off2: A network reconstruction algorithm based on 2-point and 3-point information statistics', 'Affeldt, Séverine|Verny, Louis|Isambert, Hervé', 'Institut Curie, PSL Research University, CNRS, UMR168, 26 rue dUlm, Paris, 75005, France.|Institut Curie, PSL Research University, CNRS, UMR168, 26 rue dUlm, Paris, 75005, France.|Institut Curie, PSL Research University, CNRS, UMR168, 26 rue dUlm, Paris, 75005, France. herve.isambert@curie.fr.', 'France|France|France', 0, 'Algorithms|Animals|Bayes Theorem|Computational Biology|Gene Expression Profiling|Gene Regulatory Networks|Hematopoiesis|Mice|Models, Genetic|Single-Cell Analysis', 'Journal Article', '10.1186/s12859-015-0856-x', '26823190', 'The reconstruction of reliable graphical models from observational data is important in bioinformatics and other computational fields applying network reconstruction methods to large, yet finite datasets. The main network reconstruction approaches are either based on Bayesian scores, which enable the ranking of alternative Bayesian networks, or rely on the identification of structural independencies, which correspond to missing edges in the underlying network. Bayesian inference methods typically require heuristic search strategies, such as hill-climbing algorithms, to sample the super-exponential space of possible networks. By contrast, constraint-based methods, such as the PC and IC algorithms, are expected to run in polynomial time on sparse underlying graphs, provided that a correct list of conditional independencies is available. Yet, in practice, conditional independencies need to be ascertained from the available observational data, based on adjustable statistical significance levels, and are not robust to sampling noise from finite datasets.'),
(97130, 2016, 'bmc', 'Impact of imputation methods on the amount of genetic variation captured by a single-nucleotide polymorphism panel in soybeans', 'Xavier, A|Muir, William M|Rainey, Katy M', 'Department of Agronomy, Purdue University, Lilly Hall of Life Sciences, 915 W. State St., West Lafayette, Indiana, 47907, USA. xaviera@purdue.edu.|Department of Animal Science, Purdue University, Lilly Hall of Life Sciences, 915 W. State St., West Lafayette, Indiana, 47907, USA. bmuir@purdue.edu.|Department of Agronomy, Purdue University, Lilly Hall of Life Sciences, 915 W. State St., West Lafayette, Indiana, 47907, USA. krainey@purdue.edu.', 'India|India|India', 0, 'Algorithms|Genetic Variation|Genome, Plant|Genome-Wide Association Study|Genomics|Models, Genetic|Pedigree|Phenotype|Polymorphism, Single Nucleotide|Quantitative Trait Loci|Sequence Analysis, DNA|Soybeans', 'Journal Article', '10.1186/s12859-016-0899-7', '26830693', 'Success in genome-wide association studies and marker-assisted selection depends on good phenotypic and genotypic data. The more complete this data is, the more powerful will be the results of analysis. Nevertheless, there are next-generation technologies that seek to provide genotypic information in spite of great proportions of missing data. The procedures these technologies use to impute genetic data, therefore, greatly affect downstream analyses. This study aims to (1) compare the genetic variance in a single-nucleotide polymorphism panel of soybean with missing data imputed using various methods, (2) evaluate the imputation accuracy and post-imputation quality associated with these methods, and (3) evaluate the impact of imputation method on heritability and the accuracy of genome-wide prediction of soybean traits. The imputation methods we evaluated were as follows: multivariate mixed model, hidden Markov model, logical algorithm, k-nearest neighbor, single value decomposition, and random forest. We used raw genotypes from the SoyNAM project and the following phenotypes: plant height, days to maturity, grain yield, and seed protein composition.'),
(97131, 2016, 'bmc', 'NEAT: a framework for building fully automated NGS pipelines and analyses', 'Schorderet, Patrick', 'Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, 02114, USA. patrick.schorderet@molbio.mgh.harvard.edu.', 'United States', 5, 'Computational Biology|Data Mining|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Metagenomics|Reproducibility of Results|Software|Transcriptome|Workflow', 'Journal Article', '10.1186/s12859-016-0902-3', '26830846', 'The analysis of next generation sequencing (NGS) has become a standard task for many laboratories in the life sciences. Though there exists several tools to support users in the manipulation of such datasets on various levels, few are built on the basis of vertical integration. Here, we present the NExt generation Analysis Toolbox (NEAT) that allows non-expert users including wet-lab scientists to comprehensively build, run and analyze NGS data through double-clickable executables without the need of any programming experience.'),
(97132, 2016, 'bmc', 'ClinQC: a tool for quality control and cleaning of Sanger and NGS data in clinical research', 'Pandey, Ram Vinay|Pabinger, Stephan|Kriegner, Albert|Weinhäusel, Andreas', 'Health & Environment Department, Molecular Diagnostics, AIT Austrian Institute of Technology GmbH, Vienna, Austria. ramvinay.pandey@gmail.com.|Health & Environment Department, Molecular Diagnostics, AIT Austrian Institute of Technology GmbH, Vienna, Austria. Stephan.Pabinger@ait.ac.at.|Health & Environment Department, Molecular Diagnostics, AIT Austrian Institute of Technology GmbH, Vienna, Austria. albert.kriegner@outlook.com.|Health & Environment Department, Molecular Diagnostics, AIT Austrian Institute of Technology GmbH, Vienna, Austria. andreas.weinhaeusel@ait.ac.at.', 'Austria|Austria|Austria|Austria', 0, 'Biomedical Research|Computational Biology|DNA Primers|High-Throughput Nucleotide Sequencing|Humans|Mutation|Quality Control|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-016-0915-y', '26830926', 'Traditional Sanger sequencing has been used as a gold standard method for genetic testing in clinic to perform single gene test, which has been a cumbersome and expensive method to test several genes in heterogeneous disease such as cancer. With the advent of Next Generation Sequencing technologies, which produce data on unprecedented speed in a cost effective manner have overcome the limitation of Sanger sequencing. Therefore, for the efficient and affordable genetic testing, Next Generation Sequencing has been used as a complementary method with Sanger sequencing for disease causing mutation identification and confirmation in clinical research. However, in order to identify the potential disease causing mutations with great sensitivity and specificity it is essential to ensure high quality sequencing data. Therefore, integrated software tools are lacking which can analyze Sanger and NGS data together and eliminate platform specific sequencing errors, low quality reads and support the analysis of several sample/patients data set in a single run.'),
(97133, 2016, 'bmc', 'An experimental study of the intrinsic stability of random forest variable importance measures', 'Wang, Huazhen|Yang, Fan|Luo, Zhiyuan', 'College of Computer Science and Technology, Huaqiao University, Jimei Avenue, Xiamen, 361021, China. wanghuazhen@hqu.edu.cn.|Automation Department, Xiamen University, Siming South Road, Xiamen, 361005, China. yang@xmu.edu.cn.|Computer Learning Research Centre, Royal Holloway, University of London, Egham, Surrey, TW20 0EX, UK. zhiyuan@cs.rhul.ac.uk.', 'China|China|United Kingdom', 25, 'Algorithms|Cluster Analysis|Computational Biology|Gene Expression Profiling|Gene Frequency|Humans|Models, Genetic|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1186/s12859-016-0900-5', '26842629', 'The stability of Variable Importance Measures (VIMs) based on random forest has recently received increased attention. Despite the extensive attention on traditional stability of data perturbations or parameter variations, few studies include influences coming from the intrinsic randomness in generating VIMs, i.e. bagging, randomization and permutation. To address these influences, in this paper we introduce a new concept of intrinsic stability of VIMs, which is defined as the self-consistence among feature rankings in repeated runs of VIMs without data perturbations and parameter variations. Two widely used VIMs, i.e., Mean Decrease Accuracy (MDA) and Mean Decrease Gini (MDG) are comprehensively investigated. The motivation of this study is two-fold. First, we empirically verify the prevalence of intrinsic stability of VIMs over many real-world datasets to highlight that the instability of VIMs does not originate exclusively from data perturbations or parameter variations, but also stems from the intrinsic randomness of VIMs. Second, through Spearman and Pearson tests we comprehensively investigate how different factors influence the intrinsic stability.'),
(97134, 2016, 'bmc', 'The exceptional genomic word symmetry along DNA sequences', 'Afreixo, Vera|Rodrigues, João M O S|Bastos, Carlos A C|Silva, Raquel M', 'Department of Mathematics, University of Aveiro, Campus Universitário de Santiago, Aveiro, Portugal. vera@ua.pt.|Department of Electronics, Telecommunications and Informatics, University of Aveiro, Campus Universitário de Santiago, Aveiro, Portugal. cbastos@ua.pt.|Department of Electronics, Telecommunications and Informatics, University of Aveiro, Campus Universitário de Santiago, Aveiro, Portugal. cbastos@ua.pt.|Department of Medical Sciences and Institute of Biomedicine - iBiMED, University of Aveiro, 3810-193 Aveiro, Portugal, Campus Universitário de Santiago, Aveiro, Portugal. raquelsilva@ua.pt.', 'Portugal|Portugal|Portugal|Portugal', 4, 'Chromosomes, Human|DNA|Genome, Human|Genomics|Humans|Models, Genetic|Models, Statistical|Transcriptome', 'Journal Article', '10.1186/s12859-016-0905-0', '26842742', 'The second Chargaffs parity rule and its extensions are recognized as universal phenomena in DNA sequences. However, parity of the frequencies of reverse complementary oligonucleotides could be a mere consequence of the single nucleotide parity rule, if nucleotide independence is assumed. Exceptional symmetry (symmetry beyond that expected under an independent nucleotide assumption) was proposed previously as a meaningful measure of the extension of the second parity rule to oligonucleotides. The global exceptional symmetry was detected in long and short genomes.'),
(97135, 2016, 'bmc', 'Measure transcript integrity using RNA-seq data', 'Wang, Liguo|Nie, Jinfu|Sicotte, Hugues|Li, Ying|Eckel-Passow, Jeanette E|Dasari, Surendra|Vedell, Peter T|Barman, Poulami|Wang, Liewei|Weinshiboum, Richard|Jen, Jin|Huang, Haojie|Kohli, Manish|Kocher, Jean-Pierre A', 'Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, 55905, USA. wang.liguo@mayo.edu.|Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, 55905, USA. nie.jinfujeff@mayo.edu.|Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, 55905, USA. Sicotte.Hugues@mayo.edu.|Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, 55905, USA. Li.Ying@mayo.edu.|Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, 55905, USA. eckelpassow.jeanette@mayo.edu.|Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, 55905, USA. Dasari.Surendra@mayo.edu.|Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, 55905, USA. Vedell.Peter@mayo.edu.|Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, 55905, USA. Barman.Poulami@mayo.edu.|Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, 55905, USA. Wang.Liewei@mayo.edu.|Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, 55905, USA. Weinshilboum.Richard@mayo.edu.|Department of laboratory medicine and pathology, Mayo Clinic, Rochester, MN, 55905, USA. Jen.Jin@mayo.edu.|Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, MN, 55905, USA. Huang.Haojie@mayo.edu.|Department of Oncology, Mayo Clinic, Rochester, MN, 55905, USA. kohli.manish@mayo.edu.|Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, 55905, USA. kocher.jeanpierre@mayo.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Male|Prostatic Neoplasms|Quality Control|RNA Stability|RNA, Messenger|RNA, Neoplasm|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-016-0922-z', '26842848', 'Stored biological samples with pathology information and medical records are invaluable resources for translational medical research. However, RNAs extracted from the archived clinical tissues are often substantially degraded. RNA degradation distorts the RNA-seq read coverage in a gene-specific manner, and has profound influences on whole-genome gene expression profiling.'),
(97136, 2016, 'bmc', 'The role of structural parameters in DNA cyclization', 'Alexandrov, Ludmil B|Bishop, Alan R|Rasmussen, Kim Ø|Alexandrov, Boian S', 'Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA. lba@lanl.gov.|Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA. arb@lanl.gov.|Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA. kor@lanl.gov.|Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA. boian@lanl.gov.', 'United States|United States|United States|United States', 0, 'Biophysical Phenomena|Cyclization|DNA|Humans|Models, Molecular|Nucleic Acid Conformation|Pliability', 'Journal Article', '10.1186/s12859-016-0897-9', '26846597', 'The intrinsic bendability of DNA plays an important role with relevance for myriad of essential cellular mechanisms. The flexibility of a DNA fragment can be experimentally and computationally examined by its propensity for cyclization, quantified by the Jacobson-Stockmayer J factor. In this study, we use a well-established coarse-grained three-dimensional model of DNA and seven distinct sets of experimentally and computationally derived conformational parameters of the double helix to evaluate the role of structural parameters in calculating DNA cyclization.'),
(97137, 2016, 'bmc', 'Broadwick: a framework for computational epidemiology', 'OHare, Anthony|Lycett, Samantha J|Doherty, Thomas|Salvador, Liliana C M|Kao, Rowland R', 'Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, UK. anthony.ohare@stir.ac.uk.|Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, UK. samantha.lycett@ed.ac.uk.|Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, UK. Tom.doherty@glasgow.ac.uk.|Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, UK. liliana.salvador@glasgow.ac.uk.|Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, UK. rowland.kao@glasgow.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Bayes Theorem|Computer Simulation|Epidemiologic Studies|Genetics, Population|Humans|Markov Chains|Models, Theoretical|Monte Carlo Method', 'Journal Article', '10.1186/s12859-016-0903-2', '26846686', 'Modelling disease outbreaks often involves integrating the wealth of data that are gathered during modern outbreaks into complex mathematical or computational models of transmission. Incorporating these data into simple compartmental epidemiological models is often challenging, requiring the use of more complex but also more efficient computational models. In this paper we introduce a new framework that allows for a more systematic and user-friendly way of building and running epidemiological models that efficiently handles disease data and reduces much of the boilerplate code that usually associated to these models. We introduce the framework by developing an SIR model on a simple network as an example.'),
(97138, 2016, 'bmc', 'GenAp: a distributed SQL interface for genomic data', 'Kozanitis, Christos|Patterson, David A', 'Department of Computer Science, University of California Berkeley, Soda Hall, Berkeley, 94720, California, USA. kozanitis@eecs.berkeley.edu.|Department of Computer Science, University of California Berkeley, Soda Hall, Berkeley, 94720, California, USA. pattersn@eecs.berkeley.edu.', 'United States|United States', 7, 'Algorithms|Chromosome Mapping|Computational Biology|Databases, Nucleic Acid|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1186/s12859-016-0904-1', '26846841', 'The impressively low cost and improved quality of genome sequencing provides to researchers of genetic diseases, such as cancer, a powerful tool to better understand the underlying genetic mechanisms of those diseases and treat them with effective targeted therapies. Thus, a number of projects today sequence the DNA of large patient populations each of which produces at least hundreds of terra-bytes of data. Now the challenge is to provide the produced data on demand to interested parties.'),
(97139, 2016, 'bmc', 'CABeRNET: a Cytoscape app for augmented Boolean models of gene regulatory NETworks', 'Paroni, Andrea|Graudenzi, Alex|Caravagna, Giulio|Damiani, Chiara|Mauri, Giancarlo|Antoniotti, Marco', 'Department of Informatics, Systems and Communication, University of Milan-Bicocca, Viale Sarca 336, Milan, I-20126, Italy.|Department of Informatics, Systems and Communication, University of Milan-Bicocca, Viale Sarca 336, Milan, I-20126, Italy. alex.graudenzi@unimib.it.|Department of Informatics, Systems and Communication, University of Milan-Bicocca, Viale Sarca 336, Milan, I-20126, Italy. giulio.caravagna@unimib.it.|Department of Informatics, Systems and Communication, University of Milan-Bicocca, Viale Sarca 336, Milan, I-20126, Italy. chiara.damiani@unimib.it.|Department of Informatics, Systems and Communication, University of Milan-Bicocca, Viale Sarca 336, Milan, I-20126, Italy. giancarlo.mauri@unimib.it.|Department of Informatics, Systems and Communication, University of Milan-Bicocca, Viale Sarca 336, Milan, I-20126, Italy. marco.antoniotti@unimib.it.', 'Italy|Italy|Italy|Italy|Italy|Italy', 0, 'Cell Differentiation|Cell Lineage|Cells|Computational Biology|Computer Simulation|Gene Regulatory Networks|Humans|Models, Genetic|Software', 'Journal Article', '10.1186/s12859-016-0914-z', '26846964', 'Dynamical models of gene regulatory networks (GRNs) are highly effective in describing complex biological phenomena and processes, such as cell differentiation and cancer development. Yet, the topological and functional characterization of real GRNs is often still partial and an exhaustive picture of their functioning is missing.'),
(97140, 2016, 'bmc', 'sciReptor: analysis of single-cell level immunoglobulin repertoires', 'Imkeller, Katharina|Arndt, Peter F|Wardemann, Hedda|Busse, Christian E', 'Division of B Cell Immunology, German Cancer Research Center, Feld 280, Heidelberg, 69120, Germany. k.imkeller@dkfz-heidelberg.de.|Department for Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, Berlin, 14195, Germany. arndt@molgen.mpg.de.|Division of B Cell Immunology, German Cancer Research Center, Feld 280, Heidelberg, 69120, Germany. h.wardemann@dkfz-heidelberg.de.|Division of B Cell Immunology, German Cancer Research Center, Feld 280, Heidelberg, 69120, Germany. christian.busse@dkfz-heidelberg.de.', 'Germany|Germany|Germany|Germany', 15, 'Computational Biology|Genes, Immunoglobulin|High-Throughput Nucleotide Sequencing|Humans|Immunoglobulins|Molecular Sequence Annotation|Receptors, Immunologic|Single-Cell Analysis|Software', 'Journal Article', '10.1186/s12859-016-0920-1', '26847109', 'The sequencing of immunoglobulin (Ig) transcripts from single B cells yields essential information about Ig heavy:light chain pairing, which is lost in conventional bulk sequencing experiments. The previously limited throughput of single-cell approaches has recently been overcome by the introduction of multiple next-generation sequencing (NGS)-based platforms. Furthermore, single-cell techniques allow the assignment of additional data types (e.g. cell surface marker expression), which are crucial for biological interpretation. However, the currently available computational tools are not designed to handle single-cell data and do not provide integral solutions for linking of sequence data to other biological data.'),
(97141, 2016, 'bmc', 'SPARTA: Simple Program for Automated reference-based bacterial RNA-seq Transcriptome Analysis', 'Johnson, Benjamin K|Scholz, Matthew B|Teal, Tracy K|Abramovitch, Robert B', 'Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, MI, 48824, USA. john3434@msu.edu.|VANTAGE, Vanderbilt University, Nashville, TN, 37235, USA. matthew.b.scholz@vanderbilt.edu.|Data Carpentry, xe1x85x9f, xe1x85x9f. tkteal@datacarpentry.org.|Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, MI, 48824, USA. abramov5@msu.edu.', 'United States|United States|United States|United States', 18, 'Automation|Computational Biology|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|RNA, Bacterial|Reference Standards|Sequence Analysis, RNA|Software|Transcriptome', 'Journal Article', '10.1186/s12859-016-0923-y', '26847232', 'Many tools exist in the analysis of bacterial RNA sequencing (RNA-seq) transcriptional profiling experiments to identify differentially expressed genes between experimental conditions. Generally, the workflow includes quality control of reads, mapping to a reference, counting transcript abundance, and statistical tests for differentially expressed genes. In spite of the numerous tools developed for each component of an RNA-seq analysis workflow, easy-to-use bacterially oriented workflow applications to combine multiple tools and automate the process are lacking. With many tools to choose from for each step, the task of identifying a specific tool, adapting the input/output options to the specific use-case, and integrating the tools into a coherent analysis pipeline is not a trivial endeavor, particularly for microbiologists with limited bioinformatics experience.'),
(97142, 2016, 'bmc', 'Design, structure prediction and molecular dynamics simulation of a fusion construct containing malaria pre-erythrocytic vaccine candidate, PfCelTOS, and human interleukin 2 as adjuvant', 'Shamriz, Shabnam|Ofoghi, Hamideh', 'Department of Biotechnology, Iranian Research Organization for Science and Technology, Tehran, Iran. Shamriz.sh@irost.ir.|Department of Biotechnology, Iranian Research Organization for Science and Technology, Tehran, Iran. Ofoghi@irost.ir.', '-|-', 15, 'Adjuvants, Immunologic|Antigens, Protozoan|Drug Design|Erythrocytes|Humans|Interleukin-2|Malaria|Malaria Vaccines|Molecular Dynamics Simulation|Plasmodium falciparum|Protein Conformation|Protozoan Proteins|Recombinant Proteins|Sporozoites', 'Journal Article', '10.1186/s12859-016-0918-8', '26851942', 'Malaria infection is still widespread in some parts of the world and threatens the lives of millions of people every year. Vaccines, especially oral vaccines are considered to be effective in reducing the burden of malaria morbidity and mortality. By using recombinant technology, suitable oral hosts could serve as antigen delivering vehicles in developing oral vaccines. This study was aimed towards designing and computational analysis of a fusion protein consisting of Plasmodium falciparum cell-traversal protein for ookinetes and sporozoites (PfCelTOS) fused to human interleukin-2 (IL-2) and M cell-specific peptide ligand (Co1), as a step toward developing a vaccine candidate.'),
(97143, 2016, 'bmc', 'Gene set analysis using sufficient dimension reduction', 'Hsueh, Huey-Miin|Tsai, Chen-An', 'Department of Statistics, National Chengchi UniversityZhinan Road, Taipei116, Taiwan, Taipei, 116, Taiwan. hsueh@nccu.edu.tw.|Department of Agronomy, National Taiwan University, No. 1, Section 4, Roosevelt Road, Taipei, 106, Taiwan. catsai@ntu.edu.tw.', '-|-', 4, 'African Americans|Computational Biology|Computer Simulation|Gene Expression Profiling|Gene Regulatory Networks|Genotype|Humans|Male|Oligonucleotide Array Sequence Analysis|Phenotype|Prostatic Neoplasms|Tumor Suppressor Protein p53', 'Journal Article', '10.1186/s12859-016-0928-6', '26852017', 'Gene set analysis (GSA) aims to evaluate the association between the expression of biological pathways, or a priori defined gene sets, and a particular phenotype. Numerous GSA methods have been proposed to assess the enrichment of sets of genes. However, most methods are developed with respect to a specific alternative scenario, such as a differential mean pattern or a differential coexpression. Moreover, a very limited number of methods can handle either binary, categorical, or continuous phenotypes. In this paper, we develop two novel GSA tests, called SDRs, based on the sufficient dimension reduction technique, which aims to capture sufficient information about the relationship between genes and the phenotype. The advantages of our proposed methods are that they allow for categorical and continuous phenotypes, and they are also able to identify a variety of enriched gene sets.'),
(97144, 2016, 'bmc', 'Impact of data resolution on three-dimensional structure inference methods', 'Park, Jincheol|Lin, Shili', 'Department of Statistics, Keimyung University, 1095 Dalgubeol-daero, Dalseo-gu, Daegu 42601, South Korea. park.jincheol@gw.kmu.ac.kr.|Department of Statistics, The Ohio State University, 1958 Neil Avenue, Columbus, OH, USA. shili@stat.osu.edu.', 'United States|United States', 0, 'Cells, Cultured|Chromatin|Chromosomes, Human|Computer Simulation|Genome, Human|Humans|In Situ Hybridization, Fluorescence|Lymphocytes|Models, Theoretical|Software', 'Journal Article', '10.1186/s12859-016-0894-z', '26852142', 'Assays that are capable of detecting genome-wide chromatin interactions have produced massive amount of data and led to great understanding of the chromosomal three-dimensional (3D) structure. As technology becomes more sophisticated, higher-and-higher resolution data are being produced, going from the initial 1 Megabases (Mb) resolution to the current 10 Kilobases (Kb) or even 1 Kb resolution. The availability of genome-wide interaction data necessitates development of analytical methods to recover the underlying 3D spatial chromatin structure, but challenges abound. Most of the methods were proposed for analyzing data at low resolution (1 Mb). Their behaviors are thus unknown for higher resolution data. For such data, one of the key features is the high proportion of 0 contact counts among all available data, in other words, the excess of zeros.'),
(97145, 2016, 'bmc', 'Method to represent the distribution of QTL additive and dominance effects associated with quantitative traits in computer simulation', 'Sun, Xiaochun|Mumm, Rita H', 'Department of Crop Sciences and the Illinois Plant Breeding Center, University of Illinois at Urbana-Champaign, 1102 S. Goodwin Ave., Urbana, IL, 61801, USA. sun1@dow.com.|Department of Crop Sciences and the Illinois Plant Breeding Center, University of Illinois at Urbana-Champaign, 1102 S. Goodwin Ave., Urbana, IL, 61801, USA. ritamumm@illinois.edu.', 'United States|United States', 0, 'Breeding|Chromosome Mapping|Chromosomes, Plant|Computer Simulation|Crosses, Genetic|Genes, Dominant|Genes, Plant|Genome, Plant|Molecular Sequence Annotation|Phenotype|Quantitative Trait Loci|Zea mays', 'Journal Article', '10.1186/s12859-016-0906-z', '26852240', 'Computer simulation is a resource which can be employed to identify optimal breeding strategies to effectively and efficiently achieve specific goals in developing improved cultivars. In some instances, it is crucial to assess in silico the options as well as the impact of various crossing schemes and breeding approaches on performance for traits of interest such as grain yield. For this, a means by which gene effects can be represented in the genome model is critical.'),
(97146, 2016, 'bmc', 'Web-TCGA: an online platform for integrated analysis of molecular cancer data sets', 'Deng, Mario|Brägelmann, Johannes|Schultze, Joachim L|Perner, Sven', 'Pathology of the University Hospital of Luebeck and Leibniz Research Center Borstel, Luebeck and Borstel, Germany. mario.deng@uksh.de.|Department of Internal Medicine III Section of Hematology/Oncology, University Hospital of Bonn, Sigmund-Freud-Str. 25, 53127, Bonn, Germany. j.braegelmann@gmx.de.|Genomics and Immunoregulation, LIMES-Institute, University Bonn, Carl-Troll-Straße 31, 53115, Bonn, Germany. j.schultze@uni-bonn.de.|Pathology of the University Hospital of Luebeck and Leibniz Research Center Borstel, Luebeck and Borstel, Germany. sven.perner@uksh.de.', 'Germany|Germany|Germany|Germany', 0, 'Computational Biology|DNA Copy Number Variations|Databases, Genetic|Genomics|Humans|Internet|Neoplasms|Software|User-Computer Interface', 'Journal Article', '10.1186/s12859-016-0917-9', '26852330', 'The Cancer Genome Atlas (TCGA) is a pool of molecular data sets publicly accessible and freely available to cancer researchers anywhere around the world. However, wide spread use is limited since an advanced knowledge of statistics and statistical software is required.'),
(97147, 2016, 'bmc', 'Resistance related metabolic pathways for drug target identification in Mycobacterium tuberculosis', 'Cloete, Ruben|Oppon, Ekow|Murungi, Edwin|Schubert, Wolf-Dieter|Christoffels, Alan', 'South African Medical Research Council Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Bellville, South Africa. ruben@sanbi.ac.za.|South African Medical Research Council Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Bellville, South Africa. ekow@sanbi.ac.za.|South African Medical Research Council Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Bellville, South Africa. eddkimm@gmail.com.|Department of Biotechnology, University of the Western Cape, Bellville, South Africa. wolf-dieter.schubert@up.ac.za.|South African Medical Research Council Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Bellville, South Africa. alan@sanbi.ac.za.', 'South Africa|South Africa|South Africa|South Africa|South Africa', 11, 'Anti-Bacterial Agents|Drug Resistance, Bacterial|Gene Expression Regulation, Bacterial|Genes, Bacterial|Genome, Bacterial|Humans|Metabolic Networks and Pathways|Mycobacterium tuberculosis|Quantitative Structure-Activity Relationship|Tuberculosis', 'Journal Article', '10.1186/s12859-016-0898-8', '26856535', 'Increasing resistance to anti-tuberculosis drugs has driven the need for developing new drugs. Resources such as the tropical disease research (TDR) target database and AssessDrugTarget can help to prioritize putative drug targets. Hower, these resources do not necessarily map to metabolic pathways and the targets are not involved in dormancy. In this study, we specifically identify drug resistance pathways to allow known drug resistant mutations in one target to be offset by inhibiting another enzyme of the same metabolic pathway. One of the putative targets, Rv1712, was analysed by modelling its three dimensional structure and docking potential inhibitors.'),
(97148, 2016, 'bmc', 'Analysing multiple types of molecular profiles simultaneously: connecting the needles in the haystack', 'Menezes, Renée X|Mohammadi, Leila|Goeman, Jelle J|Boer, Judith M', 'Department of Epidemiology and Biostatistics, VU University Medical Center, De Boelelaan 1089a, HV Amsterdam, 1081, The Netherlands. r.menezes@vumc.nl.|Department of Statistics, Shiraz University, Shiraz, Iran. mohammadileila@yahoo.com.|Biostatistics, Department for Health Evidence, Radboud University Medical Center, Nijmegen, The Netherlands. jelle.goeman@radboudumc.nl.|Department of Pediatric Oncology and Hematology, Erasmus MC-Sophia Childrens Hospital, Rotterdam, The Netherlands. j.m.boer@erasmusmc.nl.', 'Netherlands|-|Netherlands|Netherlands', 0, 'Breast Neoplasms|Colonic Neoplasms|Computational Biology|Computer Simulation|DNA Methylation|Female|Gene Dosage|Gene Expression Regulation|Gene Regulatory Networks|Genotype|Humans|Loss of Heterozygosity|Polymorphism, Single Nucleotide|Transcriptome', 'Journal Article', '10.1186/s12859-016-0926-8', '26860128', 'It has been shown that a random-effects framework can be used to test the association between a genes expression level and the number of DNA copies of a set of genes. This gene-set modelling framework was later applied to find associations between mRNA expression and microRNA expression, by defining the gene sets using target prediction information.'),
(97149, 2016, 'bmc', 'ksRepo: a generalized platform for computational drug repositioning', 'Brown, Adam S|Kong, Sek Won|Kohane, Isaac S|Patel, Chirag J', 'Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA. adambrown@fas.harvard.edu.|Boston Childrens Hospital, Boston, MA, 02115, USA. sekwon.kong@childrens.harvard.edu.|Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA. isaac_kohane@harvard.edu.|Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA. Chirag_Patel@hms.harvard.edu.', 'United States|United States|United States|United States', 0, 'Computational Biology|Computer Simulation|Databases, Factual|Drug Repositioning|Humans|Male|Models, Theoretical|Pharmaceutical Preparations|Prostatic Neoplasms|Software|Toxicogenetics', 'Journal Article', '10.1186/s12859-016-0931-y', '26860211', 'Repositioning approved drug and small molecules in novel therapeutic areas is of key interest to the pharmaceutical industry. A number of promising computational techniques have been developed to aid in repositioning, however, the majority of available methodologies require highly specific data inputs that preclude the use of many datasets and databases. There is a clear unmet need for a generalized methodology that enables the integration of multiple types of both gene expression data and database schema.'),
(97150, 2016, 'bmc', 'LowMACA: exploiting protein family analysis for the identification of rare driver mutations in cancer', 'Melloni, Giorgio E M|de Pretis, Stefano|Riva, Laura|Pelizzola, Mattia|Céol, Arnaud|Costanza, Jole|Müller, Heiko|Zammataro, Luca', 'Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia (IIT), Via Adamello 16, 20139, Milan, Italy. giorgio.melloni@iit.it.|Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia (IIT), Via Adamello 16, 20139, Milan, Italy. stefano.depretis@iit.it.|Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia (IIT), Via Adamello 16, 20139, Milan, Italy. laura.riva@iit.it.|Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia (IIT), Via Adamello 16, 20139, Milan, Italy. mattia.pelizzola@iit.it.|Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia (IIT), Via Adamello 16, 20139, Milan, Italy. arnaud.ceol@iit.it.|Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia (IIT), Via Adamello 16, 20139, Milan, Italy. jole.costanza@iit.it.|Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia (IIT), Via Adamello 16, 20139, Milan, Italy. heiko.muller@iit.it.|Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia (IIT), Via Adamello 16, 20139, Milan, Italy. luca.zammataro@iit.it.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 8, 'DNA Mutational Analysis|Humans|Mutation|Neoplasms|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/s12859-016-0935-7', '26860319', 'The increasing availability of resequencing data has led to a better understanding of the most important genes in cancer development. Nevertheless, the mutational landscape of many tumor types is heterogeneous and encompasses a long tail of potential driver genes that are systematically excluded by currently available methods due to the low frequency of their mutations. We developed LowMACA (Low frequency Mutations Analysis via Consensus Alignment), a method that combines the mutations of various proteins sharing the same functional domains to identify conserved residues that harbor clustered mutations in multiple sequence alignments. LowMACA is designed to visualize and statistically assess potential driver genes through the identification of their mutational hotspots.'),
(97151, 2016, 'bmc', 'Netter: re-ranking gene network inference predictions using structural network properties', 'Ruyssinck, Joeri|Demeester, Piet|Dhaene, Tom|Saeys, Yvan', 'Department of Information Technology, Ghent University - iMinds, IBCN research group iGent Technologiepark 15, Ghent, B-9052, Belgium. joeri.ruyssinck@intec.ugent.be.|Department of Information Technology, Ghent University - iMinds, IBCN research group iGent Technologiepark 15, Ghent, B-9052, Belgium. piet.demeester@intec.ugent.be.|Department of Information Technology, Ghent University - iMinds, IBCN research group iGent Technologiepark 15, Ghent, B-9052, Belgium. tom.dhaene@intec.ugent.be.|Data Mining and Modelling for Biomedicine group, VIB Inflammation Research Center, Ghent, Belgium. yvan.saeys@ugent.be.', 'Belgium|Belgium|Belgium|Belgium', 0, 'Algorithms|Benchmarking|Computational Biology|Escherichia coli|Gene Expression Regulation|Gene Regulatory Networks|Humans', 'Journal Article', '10.1186/s12859-016-0913-0', '26862054', 'Many algorithms have been developed to infer the topology of gene regulatory networks from gene expression data. These methods typically produce a ranking of links between genes with associated confidence scores, after which a certain threshold is chosen to produce the inferred topology. However, the structural properties of the predicted network do not resemble those typical for a gene regulatory network, as most algorithms only take into account connections found in the data and do not include known graph properties in their inference process. This lowers the prediction accuracy of these methods, limiting their usability in practice.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(97152, 2016, 'bmc', 'Explaining the geographic spread of emerging epidemics: a framework for comparing viral phylogenies and environmental landscape data', 'Dellicour, Simon|Rose, Rebecca|Pybus, Oliver G', 'Department of Zoology, University of Oxford, Oxford, OX1 3PS, UK. simon.dellicour@rega.kuleuven.be.|Department of Zoology, University of Oxford, Oxford, OX1 3PS, UK. rebeccarosetm@gmail.com.|Department of Zoology, University of Oxford, Oxford, OX1 3PS, UK. oliver.pybus@zoo.ox.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom', 39, 'Animals|Disease Outbreaks|Environment|Models, Statistical|Phylogeny|Phylogeography|Rabies|Rabies virus|Raccoons', 'Comparative Study', '10.1186/s12859-016-0924-x', '26864798', 'Phylogenetic analysis is now an important tool in the study of viral outbreaks. It can reconstruct epidemic history when surveillance epidemiology data are sparse, and can indicate transmission linkages among infections that may not otherwise be evident. However, a remaining challenge is to develop an analytical framework that can test hypotheses about the effect of environmental variables on pathogen spatial spread. Recent phylogeographic approaches can reconstruct the history of virus dispersal from sampled viral genomes and infer the locations of ancestral infections. Such methods provide a unique source of spatio-temporal information, and are exploited here.'),
(97153, 2016, 'bmc', 'Parasail: SIMD C library for global, semi-global, and local pairwise sequence alignments', 'Daily, Jeff', 'Pacific Northwest National Laboratory, High Performance Computing Group, 902 Battelle Boulevard, P.O. Box 999, MSIN J4-30, Richland, 99352, WA, USA. jeff.daily@pnnl.gov.', 'United States', 41, 'Algorithms|Computational Biology|Databases, Factual|Humans|Programming Languages|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/s12859-016-0930-z', '26864881', 'Sequence alignment algorithms are a key component of many bioinformatics applications. Though various fast Smith-Waterman local sequence alignment implementations have been developed for x86 CPUs, most are embedded into larger database search tools. In addition, fast implementations of Needleman-Wunsch global sequence alignment and its semi-global variants are not as widespread. This article presents the first software library for local, global, and semi-global pairwise intra-sequence alignments and improves the performance of previous intra-sequence implementations.'),
(97154, 2016, 'bmc', 'A powerful score-based statistical test for group difference in weighted biological networks', 'Ji, Jiadong|Yuan, Zhongshang|Zhang, Xiaoshuai|Xue, Fuzhong', 'Department of Biostatistics, School of Public Health, Shandong University, PO Box 100, Jinan, 250012, Shandong, China. jjdjijiadong@163.com.|Department of Biostatistics, School of Public Health, Shandong University, PO Box 100, Jinan, 250012, Shandong, China. yuanzhongshang@sdu.edu.cn.|Department of Biostatistics, School of Public Health, Shandong University, PO Box 100, Jinan, 250012, Shandong, China. zhxiaoshuai@hotmail.com.|Department of Biostatistics, School of Public Health, Shandong University, PO Box 100, Jinan, 250012, Shandong, China. xuefzh@sdu.edu.cn.', 'China|China|China|China', 6, 'Epistasis, Genetic|Female|Gene Regulatory Networks|Humans|Leprosy|Models, Statistical|Ovarian Neoplasms|Signal Transduction', 'Journal Article', '10.1186/s12859-016-0916-x', '26867929', 'Complex disease is largely determined by a number of biomolecules interwoven into networks, rather than a single biomolecule. A key but inadequately addressed issue is how to test possible differences of the networks between two groups. Group-level comparison of network properties may shed light on underlying disease mechanisms and benefit the design of drug targets for complex diseases. We therefore proposed a powerful score-based statistic to detect group difference in weighted networks, which simultaneously capture the vertex changes and edge changes.'),
(97155, 2016, 'bmc', 'Erratum to: Weakly supervised learning of biomedical information extraction from curated data', 'Jain, Suvir|Tumkur, Kashyap R|Kuo, Tsung-Ting|Bhargava, Shitij|Lin, Gordon|Hsu, Chun-Nan', 'Department of Computer Science and Engineering, Jacobs School of Engineering, University of California, 9500 Gilman Drive, La Jolla, San Diego, 92093, USA.|Department of Computer Science and Engineering, Jacobs School of Engineering, University of California, 9500 Gilman Drive, La Jolla, San Diego, 92093, USA.|Department of Biomedical Informatics, School of Medicine, University of California, 9500 Gilman Drive, La Jolla, San Diego, 92093, USA.|Department of Computer Science and Engineering, Jacobs School of Engineering, University of California, 9500 Gilman Drive, La Jolla, San Diego, 92093, USA.|Department of Computer Science and Engineering, Jacobs School of Engineering, University of California, 9500 Gilman Drive, La Jolla, San Diego, 92093, USA.|Department of Biomedical Informatics, School of Medicine, University of California, 9500 Gilman Drive, La Jolla, San Diego, 92093, USA. chunnan@ucsd.edu.', 'United States|United States|United States|United States|United States|United States', 0, '-', 'Published Erratum', '10.1186/s12859-016-0925-9', '26868016', '-'),
(97156, 2016, 'bmc', 'Seqinspector: position-based navigation through the ChIP-seq data landscape to identify gene expression regulators', 'Piechota, Marcin|Korostynski, Michal|Ficek, Joanna|Tomski, Andrzej|Przewlocki, Ryszard', 'Department of Molecular Neuropharmacology, Institute of Pharmacology Polish Academy of Sciences, Krakow, 31-344, Poland. marpiech@if-pan.krakow.pl.|Department of Molecular Neuropharmacology, Institute of Pharmacology Polish Academy of Sciences, Krakow, 31-344, Poland.|Department of Molecular Neuropharmacology, Institute of Pharmacology Polish Academy of Sciences, Krakow, 31-344, Poland.|Department of Molecular Neuropharmacology, Institute of Pharmacology Polish Academy of Sciences, Krakow, 31-344, Poland.|Department of Molecular Neuropharmacology, Institute of Pharmacology Polish Academy of Sciences, Krakow, 31-344, Poland.', 'Poland|Poland|Poland|Poland|Poland', 0, 'Animals|Chromatin Immunoprecipitation|DNA-Directed RNA Polymerases|Epigenesis, Genetic|Gene Expression Regulation|Genomics|High-Throughput Nucleotide Sequencing|Humans|Mice|MicroRNAs|RNA, Long Noncoding|Sequence Analysis, DNA|Software|Transcription Factors|Transcriptome', 'Journal Article', '10.1186/s12859-016-0938-4', '26868127', 'The regulation of gene expression in eukaryotic cells is a complex process that involves epigenetic modifications and the interaction of DNA with multiple transcription factors. This process can be studied with unprecedented sensitivity using a combination of chromatin immunoprecipitation and next-generation DNA sequencing (ChIP-seq). Available ChIP-seq data can be further utilized to interpret new gene expression profiling experiments.'),
(97157, 2016, 'bmc', 'A new method to measure complexity in binary or weighted networks and applications to functional connectivity in the human brain', 'Hahn, Klaus|Massopust, Peter R|Prigarin, Sergei', 'Institute of Computational Biology, HMGU-German Research Center for Environmental Health, Ingolstädter Landstraße 1, Neuherberg, 85764, Germany. hahn@helmholtz-muenchen.de.|Institute of Computational Biology, HMGU-German Research Center for Environmental Health, Ingolstädter Landstraße 1, Neuherberg, 85764, Germany. massopust@ma.tum.de.|Novosibirsk State University, Institute of Computational Mathematics and Mathematical Geophysics, Siberian Branch of Russian Academy of Sciences, Novosibirsk, Russia. sergeim.prigarin@gmail.com.', 'Germany|Germany|-', 10, 'Algorithms|Brain|Depression|Fractals|Gene Regulatory Networks|Humans|Models, Theoretical|Nerve Net', 'Comparative Study', '10.1186/s12859-016-0933-9', '26873589', 'Networks or graphs play an important role in the biological sciences. Protein interaction networks and metabolic networks support the understanding of basic cellular mechanisms. In the human brain, networks of functional or structural connectivity model the information-flow between cortex regions. In this context, measures of network properties are needed. We propose a new measure, Ndim, estimating the complexity of arbitrary networks. This measure is based on a fractal dimension, which is similar to recently introduced box-covering dimensions. However, box-covering dimensions are only applicable to fractal networks. The construction of these network-dimensions relies on concepts proposed to measure fractality or complexity of irregular sets in [Formula: see text].'),
(97158, 2016, 'bmc', 'CellECT: cell evolution capturing tool', 'Delibaltov, Diana L|Gaur, Utkarsh|Kim, Jennifer|Kourakis, Matthew|Newman-Smith, Erin|Smith, William|Belteton, Samuel A|Szymanski, Daniel B|Manjunath, B S', 'Department of Electrical and Computer Engineering, University of California, Santa Barbara, Santa Barbara, CA, USA. diana@ece.ucsb.edu.|Department of Electrical and Computer Engineering, University of California, Santa Barbara, Santa Barbara, CA, USA.|Department of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA, USA.|Department of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA, USA.|Department of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA, USA.|Department of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA, USA.|Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN, USA.|Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN, USA.|Department of Electrical and Computer Engineering, University of California, Santa Barbara, Santa Barbara, CA, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Animals|Arabidopsis|Biological Evolution|Cell Nucleus|Computational Biology|Humans|Image Interpretation, Computer-Assisted|Imaging, Three-Dimensional|Microscopy|Plant Leaves|Urochordata', 'Journal Article', '10.1186/s12859-016-0927-7', '26887436', 'Robust methods for the segmentation and analysis of cells in 3D time sequences (3D+t) are critical for quantitative cell biology. While many automated methods for segmentation perform very well, few generalize reliably to diverse datasets. Such automated methods could significantly benefit from at least minimal user guidance. Identification and correction of segmentation errors in time-series data is of prime importance for proper validation of the subsequent analysis. The primary contribution of this work is a novel method for interactive segmentation and analysis of microscopy data, which learns from and guides user interactions to improve overall segmentation.'),
(97159, 2016, 'bmc', 'Erratum to: MINE: Module Identification in Networks', 'Rhrissorrakrai, Kahn|Gunsalus, Kristin C', 'IBM Computational Biology Center, 1101 Kitchawan Rd., Yorktown Heights, NY, 10598, USA.|Center for Genomics and Systems Biology, Department of Biology, New York University, 12 Waverly Place, New York, NY, 10003, USA. kcg1@nyu.edu.', 'United States|United States', 1, '-', 'Published Erratum', '10.1186/s12859-016-0929-5', '26887572', '-'),
(97160, 2016, 'bmc', 'Drug repurposing to target Ebola virus replication and virulence using structural systems pharmacology', 'Zhao, Zheng|Martin, Che|Fan, Raymond|Bourne, Philip E|Xie, Lei', 'High Magnetic Field Laboratory, Chinese Academy of Sciences, Hefei, P. R. China.|The Graduate Center, The City University of New York, New York, USA.|Department of Chemistry, Hunter College, The City University of New York, New York, USA.|Office of the Director, National Institutes of Health, Bethesda, MD, USA.|The Graduate Center, The City University of New York, New York, USA. lxie@iscb.org.', 'China|United States|United States|United States|United States', 0, 'DNA Replication|Drug Repositioning|Ebolavirus|Humans|Virulence|Virus Replication', 'Journal Article', '10.1186/s12859-016-0941-9', '26887654', 'The recent outbreak of Ebola has been cited as the largest in history. Despite this global health crisis, few drugs are available to efficiently treat Ebola infections. Drug repurposing provides a potentially efficient solution to accelerating the development of therapeutic approaches in response to Ebola outbreak. To identify such candidates, we use an integrated structural systems pharmacology pipeline which combines proteome-scale ligand binding site comparison, protein-ligand docking, and Molecular Dynamics (MD) simulation.'),
(97161, 2016, 'bmc', 'An effective approach for annotation of protein families with low sequence similarity and conserved motifs: identifying GDSL hydrolases across the plant kingdom', 'Vujaklija, Ivan|Bielen, Ana|Paradxc5xbeik, Tina|Bixc4x91in, Sinixc5xa1a|Goldstein, Pavle|Vujaklija, Duxc5xa1ica', 'Faculty of Electrical Engineering and Computing, Unska 3, Zagreb, 10000, Croatia. ivan.vujaklija@gmail.com.|Department of Biochemical Engineering, Faculty of Food Technology and Biotechnology, University of Zagreb, Pierrotijeva 6, Zagreb, 10000, Croatia. abielen@pbf.hr.|Division of Molecular Biology, Ruxc4x91er Boxc5xa1kovixc4x87 Institute, Bijenixc4x8dka 54, Zagreb, 10000, Croatia. Tina.Simunov@irb.hr.|Faculty of Electrical Engineering and Computing, Unska 3, Zagreb, 10000, Croatia. sinisa@bidin.eu.|Department of Mathematics, Faculty of Science, University of Zagreb, Bijenixc4x8dka 30, Zagreb, 10000, Croatia. payo@math.hr.|Division of Molecular Biology, Ruxc4x91er Boxc5xa1kovixc4x87 Institute, Bijenixc4x8dka 54, Zagreb, 10000, Croatia. vujaklij@irb.hr.', 'Croatia|Croatia|Croatia|Croatia|Croatia|Croatia', 0, 'Hydrolases|Phylogeny|Plants|Protein Structure, Tertiary|Proteins', 'Journal Article', '10.1186/s12859-016-0919-7', '26892257', 'The massive accumulation of protein sequences arising from the rapid development of high-throughput sequencing, coupled with automatic annotation, results in high levels of incorrect annotations. In this study, we describe an approach to decrease annotation errors of protein families characterized by low overall sequence similarity. The GDSL lipolytic family comprises proteins with multifunctional properties and high potential for pharmaceutical and industrial applications. The number of proteins assigned to this family has increased rapidly over the last few years. In particular, the natural abundance of GDSL enzymes reported recently in plants indicates that they could be a good source of novel GDSL enzymes. We noticed that a significant proportion of annotated sequences lack specific GDSL motif(s) or catalytic residue(s). Here, we applied motif-based sequence analyses to identify enzymes possessing conserved GDSL motifs in selected proteomes across the plant kingdom.'),
(97162, 2016, 'bmc', 'Intra- and interspecies gene expression models for predicting drug response in canine osteosarcoma', 'Fowles, Jared S|Brown, Kristen C|Hess, Ann M|Duval, Dawn L|Gustafson, Daniel L', 'Cell and Molecular Biology Program, Department of Clinical Sciences, Colorado State University, Fort Collins, CO, USA.|Cell and Molecular Biology Program, Department of Biology, Colorado State University, Fort Collins, CO, USA.|Department of Statistics, Colorado State University, Fort Collins, CO, USA.|Cell and Molecular Biology Program, Department of Clinical Sciences, Colorado State University, Fort Collins, CO, USA.|Cell and Molecular Biology Program, Department of Clinical Sciences, Colorado State University, Fort Collins, CO, USA. Daniel.Gustafson@ColoState.edu.', 'United States|United States|United States|United States|United States', 15, 'Animals|Biomarkers, Pharmacological|Bone Neoplasms|Dog Diseases|Dogs|Doxorubicin|Gene Expression|Humans|Osteosarcoma|Treatment Outcome', 'Journal Article', '10.1186/s12859-016-0942-8', '26892349', 'Genomics-based predictors of drug response have the potential to improve outcomes associated with cancer therapy. Osteosarcoma (OS), the most common primary bone cancer in dogs, is commonly treated with adjuvant doxorubicin or carboplatin following amputation of the affected limb. We evaluated the use of gene-expression based models built in an intra- or interspecies manner to predict chemosensitivity and treatment outcome in canine OS. Models were built and evaluated using microarray gene expression and drug sensitivity data from human and canine cancer cell lines, and canine OS tumor datasets. The COXEN method was utilized to filter gene signatures between human and dog datasets based on strong co-expression patterns. Models were built using linear discriminant analysis via the misclassification penalized posterior algorithm.'),
(97163, 2016, 'bmc', 'Erratum to: PTESFinder: a computational method to identify post-transcriptional exon shuffling (PTES) events', 'Izuogu, Osagie G|Alhasan, Abd A|Alafghani, Hani M|Santibanez-Koref, Mauro|Elliott, David J|Jackson, Michael S', 'Institute of Genetic Medicine, Newcastle University, Newcastle Upon Tyne, UK. g.izuogu@ncl.ac.uk.|Institute of Genetic Medicine, Newcastle University, Newcastle Upon Tyne, UK.|Security Forces Hostpital, P. O. Box 2748-24268-8541, Makkah, Kingdom of Saudi Arabia.|Institute of Genetic Medicine, Newcastle University, Newcastle Upon Tyne, UK.|Institute of Genetic Medicine, Newcastle University, Newcastle Upon Tyne, UK.|Institute of Genetic Medicine, Newcastle University, Newcastle Upon Tyne, UK.', 'United Kingdom|United Kingdom|Saudi Arabia|United Kingdom|United Kingdom|United Kingdom', 0, '-', 'Published Erratum', '10.1186/s12859-016-0949-1', '26892454', '-'),
(97164, 2016, 'bmc', 'MetaCRAM: an integrated pipeline for metagenomic taxonomy identification and compression', 'Kim, Minji|Zhang, Xiejia|Ligo, Jonathan G|Farnoud, Farzad|Veeravalli, Venugopal V|Milenkovic, Olgica', 'Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, 61801, USA. mkim158@illinois.edu.|Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, 61801, USA. xzhan121@illinois.edu.|Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, 61801, USA. ligo2@illinois.edu.|Department of Electrical Engineering, California Institute of Technology, Pasadena, 91125, USA. farnoud@caltech.edu.|Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, 61801, USA. vvv@illinois.edu.|Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, 61801, USA. milenkov@illinois.edu.', 'United States|United States|United States|United States|United States|United States', 0, 'Classification|Data Compression|Genomics|High-Throughput Nucleotide Sequencing|Humans|Metagenomics', 'Journal Article', '10.1186/s12859-016-0932-x', '26895947', 'Metagenomics is a genomics research discipline devoted to the study of microbial communities in environmental samples and human and animal organs and tissues. Sequenced metagenomic samples usually comprise reads from a large number of different bacterial communities and hence tend to result in large file sizes, typically ranging between 1-10 GB. This leads to challenges in analyzing, transferring and storing metagenomic data. In order to overcome these data processing issues, we introduce MetaCRAM, the first de novo, parallelized software suite specialized for FASTA and FASTQ format metagenomic read processing and lossless compression.'),
(97165, 2016, 'bmc', 'Systematic evaluation of the impact of ChIP-seq read designs on genome coverage, peak identification, and allele-specific binding detection', 'Zhang, Qi|Zeng, Xin|Younkin, Sam|Kawli, Trupti|Snyder, Michael P|Kelexc5x9f, Sxc3xbcndxc3xbcz', 'Department of Statistics, University of Nebraska Lincoln, Lincoln, Nebraska, USA. qi.zhang@unl.edu.|Department of Statistics, University of Wisconsin Madison, Madison, Wisconsin, USA.|Department of Biostatistics and Medical Informatics, University of Wisconsin Madison, Madison, Wisconsin, USA.|Department of Genetics, Stanford University School of Medicine, Palo Alto, California, USA. trupti@stanford.edu.|Department of Genetics, Stanford University School of Medicine, Palo Alto, California, USA. mpsnyder@stanford.edu.|Department of Statistics, University of Wisconsin Madison, Madison, Wisconsin, USA. keles@stat.wisc.edu.', 'United States|United States|United States|United States|United States|United States', 1, 'Alleles|Chromatin Immunoprecipitation|Genomics|Humans|Sequence Alignment|Transcription Factors', 'Journal Article', '10.1186/s12859-016-0957-1', '26908256', 'Chromatin immunoprecipitation followed by sequencing (ChIP-seq) experiments revolutionized genome-wide profiling of transcription factors and histone modifications. Although maturing sequencing technologies allow these experiments to be carried out with short (36-50 bps), long (75-100 bps), single-end, or paired-end reads, the impact of these read parameters on the downstream data analysis are not well understood. In this paper, we evaluate the effects of different read parameters on genome sequence alignment, coverage of different classes of genomic features, peak identification, and allele-specific binding detection.'),
(97166, 2016, 'bmc', 'GEMINI: a computationally-efficient search engine for large gene expression datasets', 'DeFreitas, Timothy|Saddiki, Hachem|Flaherty, Patrick', 'Computer Science Department, Worcester Polytechnic Institute, 100 Institute Rd, Worcester, 01609, USA. tmdefreitas@wpi.edu.|Department of Mathematics and Statistics, University of Massachusetts, Amherst, 710 N. Pleasant St, Amherst, 01003, USA. hsaddiki@umass.edu.|Program in Bioinformatics and Computational Biology, 100 Institute Rd, Worcester, 01609, USA. flaherty@math.umass.edu.', 'United States|United States|United States', 6, 'Cluster Analysis|Databases, Factual|Gene Expression|Genomics|Humans|Search Engine|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-016-0934-8', '26911289', 'Low-cost DNA sequencing allows organizations to accumulate massive amounts of genomic data and use that data to answer a diverse range of research questions. Presently, users must search for relevant genomic data using a keyword, accession number of meta-data tag. However, in this search paradigm the form of the query - a text-based string - is mismatched with the form of the target - a genomic profile.'),
(97167, 2016, 'bmc', 'A two-layer integration framework for protein complex detection', 'Ou-Yang, Le|Wu, Min|Zhang, Xiao-Fei|Dai, Dao-Qing|Li, Xiao-Li|Yan, Hong', 'College of Information Engineering, Shenzhen University, Shenzhen, 518060, China. ouyangle@mail2.sysu.edu.cn.|Institute for Infocomm Research (I2R), A*STAR, 1 Fusionopolis Way, Singapore, Singapore. wumin@i2r.a-star.edu.sg.|School of Mathematics and Statistics & Hubei Key Laboratory of Mathematical Sciences, Central China Normal University, Wuhan, 430079, China. zhangxf9@mail2.sysu.edu.cn.|Intelligent Data Center and Department of Mathematics, Sun Yat-Sen University, Guangzhou, 510275, China. stsddq@mail.sysu.edu.cn.|Institute for Infocomm Research (I2R), A*STAR, 1 Fusionopolis Way, Singapore, Singapore. xlli@i2r.a-star.edu.sg.|Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China. h.yan@cityu.edu.hk.', 'China|Singapore|China|China|Singapore|China', 10, 'Humans|Protein Interaction Mapping|Proteins|Proteomics', 'Journal Article', '10.1186/s12859-016-0939-3', '26911324', 'Protein complexes carry out nearly all signaling and functional processes within cells. The study of protein complexes is an effective strategy to analyze cellular functions and biological processes. With the increasing availability of proteomics data, various computational methods have recently been developed to predict protein complexes. However, different computational methods are based on their own assumptions and designed to work on different data sources, and various biological screening methods have their unique experiment conditions, and are often different in scale and noise level. Therefore, a single computational method on a specific data source is generally not able to generate comprehensive and reliable prediction results.'),
(97168, 2016, 'bmc', 'Sparse regressions for predicting and interpreting subcellular localization of multi-label proteins', 'Wan, Shibiao|Mak, Man-Wai|Kung, Sun-Yuan', 'Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong, SAR, China. shibiao.wan@connect.polyu.hk.|Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong, SAR, China. enmwmak@polyu.edu.hk.|Department of Electrical Engineering, Princeton University, New Jersey, USA. kung@princeton.edu.', 'China|China|Jersey', 18, 'Biological Phenomena|Computational Biology|Humans|Protein Transport|Proteins', 'Journal Article', '10.1186/s12859-016-0940-x', '26911432', 'Predicting protein subcellular localization is indispensable for inferring protein functions. Recent studies have been focusing on predicting not only single-location proteins, but also multi-location proteins. Almost all of the high performing predictors proposed recently use gene ontology (GO) terms to construct feature vectors for classification. Despite their high performance, their prediction decisions are difficult to interpret because of the large number of GO terms involved.'),
(97169, 2016, 'bmc', 'Structure-based Markov random field model for representing evolutionary constraints on functional sites', 'Jeong, Chan-Seok|Kim, Dongsup', 'Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea.|Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea. kds@kaist.ac.kr.', '-|-', 3, 'Allosteric Site|Biological Evolution|Evolution, Molecular|Proteins|Sequence Alignment', 'Journal Article', '10.1186/s12859-016-0948-2', '26911566', 'Elucidating the cooperative mechanism of interconnected residues is an important component toward understanding the biological function of a protein. Coevolution analysis has been developed to model the coevolutionary information reflecting structural and functional constraints. Recently, several methods have been developed based on a probabilistic graphical model called the Markov random field (MRF), which have led to significant improvements for coevolution analysis, however, thus far, the performance of these models has mainly been assessed by focusing on the aspect of protein structure.'),
(97170, 2016, 'bmc', 'MethPat: a tool for the analysis and visualisation of complex methylation patterns obtained by massively parallel sequencing', 'Wong, Nicholas C|Pope, Bernard J|Candiloro, Ida L|Korbie, Darren|Trau, Matt|Wong, Stephen Q|Mikeska, Thomas|Zhang, Xinmin|Pitman, Mark|Eggers, Stefanie|Doyle, Stephen R|Dobrovic, Alexander', 'Translational Genomics and Epigenomics Laboratory, Olivia Newton-John Cancer Research Institute, Heidelberg, Victoria, 3084, Australia. nwon@unimelb.edu.au.|Victorian Life Sciences Computation Initiative (VLSCI), The University of Melbourne, Parkville, Victoria, 3052, Australia. bjpope@unimelb.edu.au.|Department of Pathology, The University of Melbourne, Parkville, Victoria, 3010, Australia. ic85@hotmail.com.|Centre for Personalised NanoMedicine, Australian Institute of Nanotechnology and Bioengineering, The University of Queensland, Brisbane, Queensland, 4072, Australia. d.korbie@uq.edu.au.|Centre for Personalised NanoMedicine, Australian Institute of Nanotechnology and Bioengineering, The University of Queensland, Brisbane, Queensland, 4072, Australia. m.trau@uq.edu.au.|Molecular Pathology Research and Development Laboratory, Department of Pathology, Peter MacCallum Cancer Centre, East Melbourne, Victoria, 3002, Australia. Stephen.Wong@petermac.org.|Translational Genomics and Epigenomics Laboratory, Olivia Newton-John Cancer Research Institute, Heidelberg, Victoria, 3084, Australia. Thomas.Mikeska@onjcri.org.au.|BioInfoRx Inc., Madison, WI, USA. xinmin@bioinforx.com.|BioResearch Software Consultants, Battle Ground, WA, USA. mark@bioresearchsoftware.com.|Murdoch Childrens Research Institute, The Royal Childrens Hospital, Parkville, Victoria, 3052, Australia. steffi.eggers@gmail.com.|Department of Animal, Plant and Soil Sciences, La Trobe University, Bundoora, Victoria, 3086, Australia. s.doyle@latrobe.edu.au.|Translational Genomics and Epigenomics Laboratory, Olivia Newton-John Cancer Research Institute, Heidelberg, Victoria, 3084, Australia. alex.dobrovic@onjcri.org.au.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia|United States|United States|Australia|Australia|Austra', 0, 'DNA Methylation|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-016-0950-8', '26911705', 'DNA methylation at a gene promoter region has the potential to regulate gene transcription. Patterns of methylation over multiple CpG sites in a region are often complex and cell type specific, with the region showing multiple allelic patterns in a sample. This complexity is commonly obscured when DNA methylation data is summarised as an average percentage value for each CpG site (or aggregated across CpG sites). True representation of methylation patterns can only be fully characterised by clonal analysis. Deep sequencing provides the ability to investigate clonal DNA methylation patterns in unprecedented detail and scale, enabling the proper characterisation of the heterogeneity of methylation patterns. However, the sheer amount and complexity of sequencing data requires new synoptic approaches to visualise the distribution of allelic patterns.'),
(97171, 2016, 'bmc', 'Detecting false positive sequence homology: a machine learning approach', 'Fujimoto, M Stanley|Suvorov, Anton|Jensen, Nicholas O|Clement, Mark J|Bybee, Seth M', 'Computer Science Department, Brigham Young University, Provo, Utah, 84602, USA.|Department of Biology, Brigham Young University, Provo, Utah, 84602, USA. antony.suvorov@byu.edu.|Department of Biology, Brigham Young University, Provo, Utah, 84602, USA.|Computer Science Department, Brigham Young University, Provo, Utah, 84602, USA.|Department of Biology, Brigham Young University, Provo, Utah, 84602, USA.', 'United States|United States|United States|United States|United States', 0, 'False Positive Reactions|Machine Learning|Sequence Alignment|Sequence Homology', 'Journal Article', '10.1186/s12859-016-0955-3', '26911862', 'Accurate detection of homologous relationships of biological sequences (DNA or amino acid) amongst organisms is an important and often difficult task that is essential to various evolutionary studies, ranging from building phylogenies to predicting functional gene annotations. There are many existing heuristic tools, most commonly based on bidirectional BLAST searches that are used to identify homologous genes and combine them into two fundamentally distinct classes: orthologs and paralogs. Due to only using heuristic filtering based on significance score cutoffs and having no cluster post-processing tools available, these methods can often produce multiple clusters constituting unrelated (non-homologous) sequences. Therefore sequencing data extracted from incomplete genome/transcriptome assemblies originated from low coverage sequencing or produced by de novo processes without a reference genome are susceptible to high false positive rates of homology detection.'),
(97172, 2016, 'bmc', 'Trimming of sequence reads alters RNA-Seq gene expression estimates', 'Williams, Claire R|Baccarella, Alyssa|Parrish, Jay Z|Kim, Charles C', 'Department of Biology, University of Washington, Seattle, WA, 98195, USA.|Division of Experimental Medicine, Department of Medicine, University of California San Francisco, San Francisco, CA, 94110, USA.|Department of Biology, University of Washington, Seattle, WA, 98195, USA. jzp2@uw.edu.|Division of Experimental Medicine, Department of Medicine, University of California San Francisco, San Francisco, CA, 94110, USA. cckim47@gmail.com.', 'United States|United States|United States|United States', 0, 'Animals|Drosophila melanogaster|Gene Expression|Gene Expression Profiling|Genome|High-Throughput Nucleotide Sequencing|RNA', 'Journal Article', '10.1186/s12859-016-0956-2', '26911985', 'High-throughput RNA-Sequencing (RNA-Seq) has become the preferred technique for studying gene expression differences between biological samples and for discovering novel isoforms, though the techniques to analyze the resulting data are still immature. One pre-processing step that is widely but heterogeneously applied is trimming, in which low quality bases, identified by the probability that they are called incorrectly, are removed. However, the impact of trimming on subsequent alignment to a genome could influence downstream analyses including gene expression estimation, we hypothesized that this might occur in an inconsistent manner across different genes, resulting in differential bias.'),
(97173, 2016, 'bmc', 'A multiple testing procedure for multi-dimensional pairwise comparisons with application to gene expression studies', 'Grandhi, Anjana|Guo, Wenge|Peddada, Shyamal D', 'BARDS, Merck Research Laboratories, RY34-A3086h, 126 E. Lincoln Avenue, Rahway, 07065, NJ, USA. ag454@njit.edu.|Department of Mathematical Sciences, New Jersey Institute of Technology, Newark, 07102, NJ, USA. wenge.guo@njit.edu.|Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, T.W.Alexander Drive, Research Triangle Park, 27709, NC, USA. peddada@niehs.nih.gov.', 'United States|Jersey|United States', 0, 'Female|Gene Expression|Humans|Leiomyoma|Uterine Neoplasms', 'Journal Article', '10.1186/s12859-016-0937-5', '26917217', 'Often researchers are interested in comparing multiple experimental groups (e.g. tumor size) with a reference group (e.g. normal tissue) on the basis of thousands of features (e.g. genes) and determine if a differentially expressed feature is up or down regulated in a pairwise comparison. There are two sources of false discoveries, one due to multiple testing involving several pairwise comparisons and the second due to falsely declaring a feature to be up (or down) regulated when it is not (known as directional error). Together, the total error rate is called the mixed directional false discovery rate (mdFDR).'),
(97174, 2016, 'bmc', 'CUDAMPF: a multi-tiered parallel framework for accelerating protein sequence search in HMMER on CUDA-enabled GPU', 'Jiang, Hanyu|Ganesan, Narayan', 'Department of Elec. and Comp. Engg, Stevens Institute of Technology, Hoboken, NJ, 07030, USA. hjiang5@stevens.edu.|Department of Elec. and Comp. Engg, Stevens Institute of Technology, Hoboken, NJ, 07030, USA. nganesan@stevens.edu.', 'United States|United States', 7, 'Amino Acid Sequence|Proteins|Sequence Alignment', 'Journal Article', '10.1186/s12859-016-0946-4', '26920848', 'HMMER software suite is widely used for analysis of homologous protein and nucleotide sequences with high sensitivity. The latest version of hmmsearch in HMMER 3.x, utilizes heuristic-pipeline which consists of MSV/SSV (Multiple/Single ungapped Segment Viterbi) stage, P7Viterbi stage and the Forward scoring stage to accelerate homology detection. Since the latest version is highly optimized for performance on modern multi-core CPUs with SSE capabilities, only a few acceleration attempts report speedup. However, the most compute intensive tasks within the pipeline (viz., MSV/SSV and P7Viterbi stages) still stand to benefit from the computational capabilities of massively parallel processors.'),
(97175, 2016, 'bmc', 'Regularized logistic regression with network-based pairwise interaction for biomarker identification in breast cancer', 'Wu, Meng-Yun|Zhang, Xiao-Fei|Dai, Dao-Qing|Ou-Yang, Le|Zhu, Yuan|Yan, Hong', 'School of Statistics and Management, Shanghai University of Finance and Economics, Guoding Road, Shanghai, 200433, China. wu.mengyun@mail.shufe.edu.cn.|School of Mathematics and Statistics & Hubei Key Laboratory of Mathematical Sciences, Central China Normal University, Luoyu Road, Wuhan, 430079, China. zhangxf@mail.ccnu.edu.cn.|Intelligent Data Center and Department of Mathematics, Sun Yat-Sen University, Xingang West Road, Guangzhou, 510275, China. stsddq@mail.sysu.edu.cn.|College of Information Engineering, Shenzhen University, Nanhai Avenue, Shenzhen, 518060, China. ouyangle@mail2.sysu.edu.cn.|School of Automation, China University of Geosciences, Lumo Road, Wuhan, 430074, China. zhuyuan7@mail2.sysu.edu.cn.|Department of Electronic and Engineering, City University of Hong Kong, Tat Chee Avenue, Hong Kong, 999077, China. h.yan@cityu.edu.hk.', 'China|China|China|China|China|China', 11, 'Biomarkers|Breast Neoplasms|Female|Humans|Logistic Models|Models, Theoretical|Precision Medicine|Protein Interaction Maps|Reproducibility of Results', 'Journal Article', '10.1186/s12859-016-0951-7', '26921029', 'To facilitate advances in personalized medicine, it is important to detect predictive, stable and interpretable biomarkers related with different clinical characteristics. These clinical characteristics may be heterogeneous with respect to underlying interactions between genes. Usually, traditional methods just focus on detection of differentially expressed genes without taking the interactions between genes into account. Moreover, due to the typical low reproducibility of the selected biomarkers, it is difficult to give a clear biological interpretation for a specific disease. Therefore, it is necessary to design a robust biomarker identification method that can predict disease-associated interactions with high reproducibility.'),
(97176, 2016, 'bmc', 'Erratum to: Predicting the functions of a protein from its ability to associate with other molecules', 'Taha, Kamal|Yoo, Paul D', 'Department of Electrical and Computer Engineering, Khalifa University, Abu Dhabi, United Arab Emirates. kamal.taha@kustar.ac.ae.|Faculty of Science and Technology, Bournemouth University, Bournemouth, UK.', 'United Arab Emirates|United Kingdom', 0, '-', 'Published Erratum', '10.1186/s12859-016-0953-5', '26921164', '-'),
(97177, 2016, 'bmc', 'HPG pore: an efficient and scalable framework for nanopore sequencing data', 'Tarraga, Joaquin|Gallego, Asunción|Arnau, Vicente|Medina, Ignacio|Dopazo, Joaquin', 'Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, 46012, Spain. jtarraga@cipf.es.|Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, 46012, Spain. agallego@cipf.es.|Departamento de Informática, ETSE, Universidad de Valencia, Valencia, Spain. Vicente.Arnau@uv.es.|HPC Service, University Information Services, University of Cambridge, Cambridge, UK. im411@cam.ac.uk.|Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, 46012, Spain. jdopazo@cipf.es.', 'Spain|Spain|Spain|United Kingdom|Spain', 4, 'DNA|Nanopores|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-016-0966-0', '26921234', 'The use of nanopore technologies is expected to spread in the future because they are portable and can sequence long fragments of DNA molecules without prior amplification. The first nanopore sequencer available, the MinION™ from Oxford Nanopore Technologies, is a USB-connected, portable device that allows real-time DNA analysis. In addition, other new instruments are expected to be released soon, which promise to outperform the current short-read technologies in terms of throughput. Despite the flood of data expected from this technology, the data analysis solutions currently available are only designed to manage small projects and are not scalable.'),
(97178, 2016, 'bmc', 'Discrete distributional differential expression (D3E)--a tool for gene expression analysis of single-cell RNA-seq data', 'Delmans, Mihails|Hemberg, Martin', 'Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge, CB2 3EA, UK. md656@cam.ac.uk.|Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK. mh26@sanger.ac.uk.', 'United Kingdom|United Kingdom', 0, 'Algorithms|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Humans|RNA|Sequence Analysis, RNA|Single-Cell Analysis|Statistical Distributions', 'Journal Article', '10.1186/s12859-016-0944-6', '26927822', 'The advent of high throughput RNA-seq at the single-cell level has opened up new opportunities to elucidate the heterogeneity of gene expression. One of the most widespread applications of RNA-seq is to identify genes which are differentially expressed between two experimental conditions.'),
(97179, 2016, 'bmc', 'Computational modeling of phagocyte transmigration for foreign body responses to subcutaneous biomaterial implants in mice', 'Kang, Mingon|Tang, Liping|Gao, Jean', 'Department of Computer Science and Engineering, University of Texas at Arlington, 500 UTA Blvd., Arlington, 76019, USA. mingon.kang@mavs.uta.edu.|Department of Bioengineering, University of Texas at Arlington, 500 UTA Blvd., Arlington, 76019, USA. ltang@uta.edu.|Department of Computer Science and Engineering, University of Texas at Arlington, 500 UTA Blvd., Arlington, 76019, USA. gao@uta.edu.', 'United States|United States|United States', 0, 'Algorithms|Animals|Biocompatible Materials|Cell Movement|Computer Simulation|Foreign Bodies|Foreign-Body Reaction|Implants, Experimental|Mice|Phagocytes|Subcutaneous Tissue', 'Journal Article', '10.1186/s12859-016-0947-3', '26927968', 'Computational modeling and simulation play an important role in analyzing the behavior of complex biological systems in response to the implantation of biomedical devices. Quantitative computational modeling discloses the nature of foreign body responses. Such understanding will shed insight on the cause of foreign body responses, which will lead to improved biomaterial design and will reduce foreign body reactions. One of the major obstacles in computational modeling is to build a mathematical model that represents the biological system and to quantitatively define the model parameters.'),
(97180, 2016, 'bmc', 'Analysis of secondary structural elements in human microRNA hairpin precursors', 'Liu, Biao|Childs-Disney, Jessica L|Znosko, Brent M|Wang, Dan|Fallahi, Mohammad|Gallo, Steven M|Disney, Matthew D', 'Department of Chemistry, Scripps Florida, The Scripps Research Institute, 130 Scripps Way 3A1, Jupiter, FL, 33458, USA. biaoliu19@gmail.com.|Department of Chemistry, Scripps Florida, The Scripps Research Institute, 130 Scripps Way 3A1, Jupiter, FL, 33458, USA. jdisney@scripps.edu.|Department of Chemistry, Saint Louis University, 3501 Laclede Ave, St. Louis, MO, 63103, USA. znoskob@slu.edu.|Department of Biostatistics, Roswell Park Cancer Institute, 701 Ellicott St, Buffalo, NY, 14203, USA. Dan.Wang@Roswellpark.org.|Informatics Core, The Scripps Research Institute, 130 Scripps Way 2B2, Jupiter, FL, 33458, USA. mfallahi@scripps.edu.|NYS Center of Excellence in Bioinformatics and Life Sciences, 701 Ellicott St, Buffalo, NY, 14203, USA. smgallo@buffalo.edu.|Department of Chemistry, Scripps Florida, The Scripps Research Institute, 130 Scripps Way 3A1, Jupiter, FL, 33458, USA. Disney@scripps.edu.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Gene Expression Regulation|Humans|MicroRNAs|Nucleic Acid Conformation|Nucleotide Motifs|RNA Precursors', 'Journal Article', '10.1186/s12859-016-0960-6', '26928172', 'MicroRNAs (miRNAs) regulate gene expression by targeting complementary mRNAs for destruction or translational repression. Aberrant expression of miRNAs has been associated with various diseases including cancer, thus making them interesting therapeutic targets. The composite of secondary structural elements that comprise miRNAs could aid the design of small molecules that modulate their function.'),
(97181, 2016, 'bmc', 'ADEPT, a dynamic next generation sequencing data error-detection program with trimming', 'Feng, Shihai|Lo, Chien-Chi|Li, Po-E|Chain, Patrick S G', 'Genome Science Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. sfeng@lanl.gov.|Genome Science Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. chienchi@lanl.gov.|Genome Science Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. po-e@lanl.gov.|Genome Science Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. pchain@lanl.gov.', 'United States|United States|United States|United States', 1, 'Algorithms|Bacteria|Computational Biology|High-Throughput Nucleotide Sequencing|Humans|Metagenomics|Polymorphism, Single Nucleotide|Quality Control|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-016-0967-z', '26928302', 'Illumina is the most widely used next generation sequencing technology and produces millions of short reads that contain errors. These sequencing errors constitute a major problem in applications such as de novo genome assembly, metagenomics analysis and single nucleotide polymorphism discovery.'),
(97182, 2016, 'bmc', 'Enumeration method for tree-like chemical compounds with benzene rings and naphthalene rings by breadth-first search order', 'Jindalertudomdee, Jira|Hayashida, Morihiro|Zhao, Yang|Akutsu, Tatsuya', 'Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Gokasho, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Gokasho, Japan. morihiro@kuicr.kyoto-u.ac.jp.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Gokasho, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Gokasho, Japan. takutsu@kuicr.kyoto-u.ac.jp.', 'Japan|Japan|Japan|Japan', 0, 'Algorithms|Benzene|Chemistry, Pharmaceutical|Computational Biology|Naphthalenes|Stereoisomerism', 'Journal Article', '10.1186/s12859-016-0962-4', '26932529', 'Drug discovery and design are important research fields in bioinformatics. Enumeration of chemical compounds is essential not only for the purpose, but also for analysis of chemical space and structure elucidation. In our previous study, we developed enumeration methods BfsSimEnum and BfsMulEnum for tree-like chemical compounds using a tree-structure to represent a chemical compound, which is limited to acyclic chemical compounds only.'),
(97183, 2016, 'bmc', 'MaGuS: a tool for quality assessment and scaffolding of genome assemblies with Whole Genome Profiling™ Data', 'Madoui, Mohammed-Amin|Dossat, Carole|dAgata, Léo|van Oeveren, Jan|van der Vossen, Edwin|Aury, Jean-Marc', 'CEA, DSV, Institut de Génomique, Genoscope, 2 rue Gaston Crémieux, CP5706, 91057, Evry, France. amadoui@genoscope.cns.fr.|CEA, DSV, Institut de Génomique, Genoscope, 2 rue Gaston Crémieux, CP5706, 91057, Evry, France. cdossat@genoscope.cns.fr.|CEA, DSV, Institut de Génomique, Genoscope, 2 rue Gaston Crémieux, CP5706, 91057, Evry, France. ldataga@genoscope.cns.fr.|Keygene NV, Agro Business Park 90, 6708 PW, Wageningen, The Netherlands. jan.van-oeveren@keygene.com.|Keygene NV, Agro Business Park 90, 6708 PW, Wageningen, The Netherlands. edwin.van-der-vossen@keygene.com.|CEA, DSV, Institut de Génomique, Genoscope, 2 rue Gaston Crémieux, CP5706, 91057, Evry, France. jmaury@genoscope.cns.fr.', 'France|France|France|Netherlands|Netherlands|France', 4, 'Arabidopsis|Chromosomes, Artificial, Bacterial|Chromosomes, Plant|Contig Mapping|Genome, Plant|High-Throughput Nucleotide Sequencing|Physical Chromosome Mapping|Repetitive Sequences, Nucleic Acid|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-016-0969-x', '26936254', 'Scaffolding is an essential step in the genome assembly process. Current methods based on large fragment paired-end reads or long reads allow an increase in contiguity but often lack consistency in repetitive regions, resulting in fragmented assemblies. Here, we describe a novel tool to link assemblies to a genome map to aid complex genome reconstruction by detecting assembly errors and allowing scaffold ordering and anchoring.');
