INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(79273, 2015, 'bio', 'The pervasiveness and plasticity of circadian oscillations: the coupled circadian-oscillators framework', 'Patel, Vishal R|Ceglia, Nicholas|Zeller, Michael|Eckel-Mahan, Kristin|Sassone-Corsi, Paolo|Baldi, Pierre', 'Department of Computer Science, Institute for Genomics and Bioinformatics.|Department of Computer Science, Institute for Genomics and Bioinformatics.|Department of Computer Science, Institute for Genomics and Bioinformatics.|Department of Biological Chemistry and Center for Epigenetics and Metabolism, University of California, Irvine (UCI), Irvine, CA - 92697, USA.|Institute for Genomics and Bioinformatics, Department of Biological Chemistry and Center for Epigenetics and Metabolism, University of California, Irvine (UCI), Irvine, CA - 92697, USA.|Department of Computer Science, Institute for Genomics and Bioinformatics, Department of Biological Chemistry and Center for Epigenetics and Metabolism, University of California, Irvine (UCI), Irvine, CA - 92697, USA.', 'United States|United States|United States|United States|United States|United States', 13, 'Animals|Circadian Rhythm|Circadian Rhythm Signaling Peptides and Proteins|Gene Expression Regulation|Metabolomics|Mice|Organ Specificity|Time Factors|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btv353', '26049162', 'Circadian oscillations have been observed in animals, plants, fungi and cyanobacteria and play a fundamental role in coordinating the homeostasis and behavior of biological systems. Genetically encoded molecular clocks found in nearly every cell, based on negative transcription/translation feedback loops and involving only a dozen genes, play a central role in maintaining these oscillations. However, high-throughput gene expression experiments reveal that in a typical tissue, a much larger fraction ([Formula: see text]) of all transcripts oscillate with the day-night cycle and the oscillating species vary with tissue type suggesting that perhaps a much larger fraction of all transcripts, and perhaps also other molecular species, may bear the potential for circadian oscillations.'),
(79274, 2015, 'bio', 'UniAlign: protein structure alignment meets evolution', 'Zhao, Chunyu|Sacan, Ahmet', 'Center for Integrated Bioinformatics, School of Biomedical Engineering, Science and Health System, Drexel University, Philadelphia, PA 19104, USA.|Center for Integrated Bioinformatics, School of Biomedical Engineering, Science and Health System, Drexel University, Philadelphia, PA 19104, USA.', 'United States|United States', 5, 'Amino Acid Sequence|Conserved Sequence|Databases, Protein|Evolution, Molecular|Humans|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Homology, Amino Acid|Software|Structural Homology, Protein', 'Journal Article', '10.1093/bioinformatics/btv354', '26059715', 'During the evolution, functional sites on the surface of the protein as well as the hydrophobic core maintaining the structural integrity are well-conserved. However, available protein structure alignment methods align protein structures based solely on the 3D geometric similarity, limiting their ability to detect functionally relevant correspondences between the residues of the proteins, especially for distantly related homologous proteins.'),
(79275, 2015, 'bio', 'DISTMIX: direct imputation of summary statistics for unmeasured SNPs from mixed ethnicity cohorts', 'Lee, Donghyung|Bigdeli, T Bernard|Williamson, Vernell S|Vladimirov, Vladimir I|Riley, Brien P|Fanous, Ayman H|Bacanu, Silviu-Alin', 'Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA 23298, USA, Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA 23298, USA.|Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA 23298, USA.|Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA 23298, USA.|Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA 23298, USA, Center for Biomarker Research & Personalized Medicine, Virginia Commonwealth University, Richmond, VA 23298, USA and Lieber Institute for Brain Development, Johns Hopkins University, Baltimore, MD 21205, USA.|Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA 23298, USA.|Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA 23298, USA.|Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA 23298, USA.', 'United States|United States|United States|United States|United States|United States|United States', 14, 'Cohort Studies|Computational Biology|Computer Simulation|Databases, Genetic|Ethnic Groups|Genome-Wide Association Study|Humans|Polymorphism, Single Nucleotide|Software|Statistics as Topic', 'Journal Article', '10.1093/bioinformatics/btv348', '26059716', 'To increase the signal resolution for large-scale meta-analyses of genome-wide association studies, genotypes at unmeasured single nucleotide polymorphisms (SNPs) are commonly imputed using large multi-ethnic reference panels. However, the ever increasing size and ethnic diversity of both reference panels and cohorts makes genotype imputation computationally challenging for moderately sized computer clusters. Moreover, genotype imputation requires subject-level genetic data, which unlike summary statistics provided by virtually all studies, is not publicly available. While there are much less demanding methods which avoid the genotype imputation step by directly imputing SNP statistics, e.g. Directly Imputing summary STatistics (DIST) proposed by our group, their implicit assumptions make them applicable only to ethnically homogeneous cohorts.'),
(79276, 2015, 'bio', 'BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs', 'Simão, Felipe A|Waterhouse, Robert M|Ioannidis, Panagiotis|Kriventseva, Evgenia V|Zdobnov, Evgeny M', 'Department of Genetic Medicine and Development, University of Geneva Medical School and Swiss Institute of Bioinformatics, rue Michel-Servet 1, 1211 Geneva, Switzerland.|Department of Genetic Medicine and Development, University of Geneva Medical School and Swiss Institute of Bioinformatics, rue Michel-Servet 1, 1211 Geneva, Switzerland.|Department of Genetic Medicine and Development, University of Geneva Medical School and Swiss Institute of Bioinformatics, rue Michel-Servet 1, 1211 Geneva, Switzerland.|Department of Genetic Medicine and Development, University of Geneva Medical School and Swiss Institute of Bioinformatics, rue Michel-Servet 1, 1211 Geneva, Switzerland.|Department of Genetic Medicine and Development, University of Geneva Medical School and Swiss Institute of Bioinformatics, rue Michel-Servet 1, 1211 Geneva, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 2619, 'Animals|Computational Biology|Gene Dosage|Genome|Genomics|Humans|Molecular Sequence Annotation|Software', 'Journal Article', '10.1093/bioinformatics/btv351', '26059717', 'Genomics has revolutionized biological research, but quality assessment of the resulting assembled sequences is complicated and remains mostly limited to technical measures like N50.'),
(79277, 2015, 'bio', 'Automated profiling of individual cell-cell interactions from high-throughput time-lapse imaging microscopy in nanowell grids (TIMING)', 'Merouane, Amine|Rey-Villamizar, Nicolas|Lu, Yanbin|Liadi, Ivan|Romain, Gabrielle|Lu, Jennifer|Singh, Harjeet|Cooper, Laurence J N|Varadarajan, Navin|Roysam, Badrinath', 'Department of Electrical and Computer Engineering and.|Department of Electrical and Computer Engineering and.|Department of Electrical and Computer Engineering and.|Department of Chemical and Biomolecular Engineering, University of Houston, Houston, TX, USA and.|Department of Chemical and Biomolecular Engineering, University of Houston, Houston, TX, USA and.|Department of Chemical and Biomolecular Engineering, University of Houston, Houston, TX, USA and.|Division of Pediatrics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.|Division of Pediatrics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.|Department of Chemical and Biomolecular Engineering, University of Houston, Houston, TX, USA and.|Department of Electrical and Computer Engineering and.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 22, 'Algorithms|Cell Communication|Cell Movement|Cell Tracking|Cells, Cultured|Coculture Techniques|High-Throughput Screening Assays|Humans|Image Processing, Computer-Assisted|K562 Cells|Killer Cells, Natural|Nanostructures|T-Lymphocytes|Time-Lapse Imaging', 'Journal Article', '10.1093/bioinformatics/btv355', '26059718', 'There is a need for effective automated methods for profiling dynamic cell-cell interactions with single-cell resolution from high-throughput time-lapse imaging data, especially, the interactions between immune effector cells and tumor cells in adoptive immunotherapy.'),
(79278, 2015, 'bio', 'oposSOM: R-package for high-dimensional portraying of genome-wide expression landscapes on bioconductor', 'Löffler-Wirth, Henry|Kalcher, Martin|Binder, Hans', 'Interdisciplinary Centre for Bioinformatics, Leipzig University, Leipzig 04107, Germany.|Interdisciplinary Centre for Bioinformatics, Leipzig University, Leipzig 04107, Germany.|Interdisciplinary Centre for Bioinformatics, Leipzig University, Leipzig 04107, Germany.', 'Germany|Germany|Germany', 0, 'Algorithms|Biomarkers, Tumor|Computer Graphics|Genome, Human|Genomics|Humans|Lymphoma, B-Cell|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btv342', '26063839', 'Comprehensive analysis of genome-wide molecular data challenges bioinformatics methodology in terms of intuitive visualization with single-sample resolution, biomarker selection, functional information mining and highly granular stratification of sample classes. oposSOM combines those functionalities making use of a comprehensive analysis and visualization strategy based on self-organizing maps (SOM) machine learning which we call high-dimensional data portraying. The method was successfully applied in a series of studies using mostly transcriptome data but also data of other OMICs realms.'),
(79279, 2015, 'bio', 'EXIMS: an improved data analysis pipeline based on a new peak picking method for EXploring Imaging Mass Spectrometry data', 'Wijetunge, Chalini D|Saeed, Isaam|Boughton, Berin A|Spraggins, Jeffrey M|Caprioli, Richard M|Bacic, Antony|Roessner, Ute|Halgamuge, Saman K', 'Department of Mechanical Engineering.|Department of Mechanical Engineering.|Metabolomics Australia, University of Melbourne, Parkville, VIC 3010, Australia.|Department of Biochemistry, Mass Spectrometry Research Centre.|Department of Biochemistry, Mass Spectrometry Research Centre, Department of Chemistry and Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA.|Metabolomics Australia, University of Melbourne, Parkville, VIC 3010, Australia, ARC Centre of Excellence, School of Biosciences and Bio21 Molecular Science and Biotechnology Institute, University of Melbourne, Parkville, VIC 3010, Australia.|Metabolomics Australia, University of Melbourne, Parkville, VIC 3010, Australia.|Department of Mechanical Engineering.', 'United States|United States|Australia|United States|United States|Australia|Australia|United States', 18, 'Algorithms|Animals|Brain|Eucalyptus|Metabolomics|Plant Leaves|Proteomics|Rats|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Journal Article', '10.1093/bioinformatics/btv356', '26063840', 'Matrix Assisted Laser Desorption Ionization-Imaging Mass Spectrometry (MALDI-IMS) in omics data acquisition generates detailed information about the spatial distribution of molecules in a given biological sample. Various data processing methods have been developed for exploring the resultant high volume data. However, most of these methods process data in the spectral domain and do not make the most of the important spatial information available through this technology. Therefore, we propose a novel streamlined data analysis pipeline specifically developed for MALDI-IMS data utilizing significant spatial information for identifying hidden significant molecular distribution patterns in these complex datasets.'),
(79280, 2015, 'bio', 'IBS: an illustrator for the presentation and visualization of biological sequences', 'Liu, Wenzhong|Xie, Yubin|Ma, Jiyong|Luo, Xiaotong|Nie, Peng|Zuo, Zhixiang|Lahrmann, Urs|Zhao, Qi|Zheng, Yueyuan|Zhao, Yong|Xue, Yu|Ren, Jian', 'State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong 510275, China, Collaborative Innovation Center of High Performance Computing, National University of Defense Technology, Changsha 410073, China.|State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong 510275, China.|State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong 510275, China.|State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong 510275, China.|State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong 510275, China.|Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China.|Fraunhofer Institute for Toxicology and Experimental Medicine, Project Group Personalized Tumor Therapy, 93053 Regensburg, Germany and.|State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong 510275, China.|State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong 510275, China.|State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong 510275, China.|Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.|State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong 510275, China, Collaborative Innovation Center of High Performance Computing, National University of Defense Technology, Changsha 410073, China, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China.', 'China|China|China|China|China|China|Germany|China|China|China|China|China', 0, 'Computational Biology|Computer Graphics|Gene Expression Profiling|Genes|Humans|Molecular Sequence Annotation|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btv362', '26069263', 'Biological sequence diagrams are fundamental for visualizing various functional elements in protein or nucleotide sequences that enable a summarization and presentation of existing information as well as means of intuitive new discoveries. Here, we present a software package called illustrator of biological sequences (IBS) that can be used for representing the organization of either protein or nucleotide sequences in a convenient, efficient and precise manner. Multiple options are provided in IBS, and biological sequences can be manipulated, recolored or rescaled in a user-defined mode. Also, the final representational artwork can be directly exported into a publication-quality figure.'),
(79281, 2015, 'bio', 'A parallel and sensitive software tool for methylation analysis on multicore platforms', 'Tárraga, Joaquín|Pérez, Mariano|Orduña, Juan M|Duato, José|Medina, Ignacio|Dopazo, Joaquín', 'Department of Computational Genomics, Centro de Investigación Príncipe Felipe.|Departamento de Informática, Universidad de Valencia and.|Departamento de Informática, Universidad de Valencia and.|DISCA, Universidad Politécnica de Valencia, Valencia, Spain.|Department of Computational Genomics, Centro de Investigación Príncipe Felipe.|Department of Computational Genomics, Centro de Investigación Príncipe Felipe.', '-|-|-|Spain|-|-', 0, 'Algorithms|Base Sequence|DNA Methylation|Databases, Genetic|Genome|Genomics|High-Throughput Nucleotide Sequencing|Humans|Molecular Sequence Data|Mutation|Mutation Rate|Software|Sulfites|Time Factors', 'Journal Article', '10.1093/bioinformatics/btv357', '26069264', 'DNA methylation analysis suffers from very long processing time, as the advent of Next-Generation Sequencers has shifted the bottleneck of genomic studies from the sequencers that obtain the DNA samples to the software that performs the analysis of these samples. The existing software for methylation analysis does not seem to scale efficiently neither with the size of the dataset nor with the length of the reads to be analyzed. As it is expected that the sequencers will provide longer and longer reads in the near future, efficient and scalable methylation software should be developed.'),
(79282, 2015, 'bio', 'Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data', 'Gupta, Namita T|Vander Heiden, Jason A|Uduman, Mohamed|Gadala-Maria, Daniel|Yaari, Gur|Kleinstein, Steven H', 'Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA.|Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA.|Department of Pathology, Yale University School of Medicine, New Haven, CT 06511, USA and.|Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA.|Bioengineering Program, Faculty of Engineering, Bar-Ilan University, Ramat Gan 52900, Israel.|Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA, Department of Pathology, Yale University School of Medicine, New Haven, CT 06511, USA and.', 'United States|United States|United States|United States|Israel|United States', 0, 'Alleles|B-Lymphocytes|Databases, Genetic|Gene Rearrangement, B-Lymphocyte|Genes, Immunoglobulin|High-Throughput Nucleotide Sequencing|Humans|Immunoglobulin Variable Region|Mutation|Software', 'Journal Article', '10.1093/bioinformatics/btv359', '26069265', 'Advances in high-throughput sequencing technologies now allow for large-scale characterization of B cell immunoglobulin (Ig) repertoires. The high germline and somatic diversity of the Ig repertoire presents challenges for biologically meaningful analysis, which requires specialized computational methods. We have developed a suite of utilities, Change-O, which provides tools for advanced analyses of large-scale Ig repertoire sequencing data. Change-O includes tools for determining the complete set of Ig variable region gene segment alleles carried by an individual (including novel alleles), partitioning of Ig sequences into clonal populations, creating lineage trees, inferring somatic hypermutation targeting models, measuring repertoire diversity, quantifying selection pressure, and calculating sequence chemical properties. All Change-O tools utilize a common data format, which enables the seamless integration of multiple analyses into a single workflow.'),
(79283, 2015, 'bio', 'SoloDel: a probabilistic model for detecting low-frequent somatic deletions from unmatched sequencing data', 'Kim, Junho|Kim, Sanghyeon|Nam, Hojung|Kim, Sangwoo|Lee, Doheon', 'Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul 120-752, Korea, Department of Bio and Brain Engineering, KAIST, Yuseong-Gu, Daejeon 305-701, Korea.|Stanley Brain Research Laboratory, Stanley Medical Research Institute, Rockville, MD 20850, USA and.|School of Information and Communications, Gwangju Institute of Science and Technology, Gwangju 500-712, Korea.|Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul 120-752, Korea.|Department of Bio and Brain Engineering, KAIST, Yuseong-Gu, Daejeon 305-701, Korea.', '-|-|-|-|-', 2, 'Computer Simulation|Databases, Genetic|Humans|Mental Disorders|Models, Statistical|Reproducibility of Results|Sequence Analysis, DNA|Sequence Deletion|Software', 'Journal Article', '10.1093/bioinformatics/btv358', '26071141', 'Finding somatic mutations from massively parallel sequencing data is becoming a standard process in genome-based biomedical studies. There are a number of robust methods developed for detecting somatic single nucleotide variations However, detection of somatic copy number alteration has been substantially less explored and remains vulnerable to frequently raised sampling issues: low frequency in cell population and absence of the matched control samples.'),
(79284, 2015, 'bio', 'ISMB/ECCB 2015', 'Moreau, Yves|Beerenwinkel, Niko', 'SymBioSys Center for Computational Systems Biology, University of Leuven, Leuven, Belgium.|Department of Biosystems Science and Engineering, ETH Zürich, Basel, SwitzerlandProceedings Chairs ISMB/ECCB 2015.', 'Belgium|Switzerland', 0, '-', 'Editorial', '10.1093/bioinformatics/btv303', '26072471', '-'),
(79285, 2015, 'bio', 'MSProGene: integrative proteogenomics beyond six-frames and single nucleotide polymorphisms', 'Zickmann, Franziska|Renard, Bernhard Y', 'Research Group Bioinformatics (NG4), Robert Koch Institute, 13353 Berlin, Germany.|Research Group Bioinformatics (NG4), Robert Koch Institute, 13353 Berlin, Germany.', 'Germany|Germany', 0, 'Algorithms|Animals|Bartonella|Databases, Genetic|Filarioidea|Gene Expression Profiling|Genomics|Mass Spectrometry|Peptides|Polymorphism, Single Nucleotide|Proteins|Proteomics|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btv236', '26072472', 'Ongoing advances in high-throughput technologies have facilitated accurate proteomic measurements and provide a wealth of information on genomic and transcript level. In proteogenomics, this multi-omics data is combined to analyze unannotated organisms and to allow more accurate sample-specific predictions. Existing analysis methods still mainly depend on six-frame translations or reference protein databases that are extended by transcriptomic information or known single nucleotide polymorphisms (SNPs). However, six-frames introduce an artificial sixfold increase of the target database and SNP integration requires a suitable database summarizing results from previous experiments. We overcome these limitations by introducing MSProGene, a new method for integrative proteogenomic analysis based on customized RNA-Seq driven transcript databases. MSProGene is independent from existing reference databases or annotated SNPs and avoids large six-frame translated databases by constructing sample-specific transcripts. In addition, it creates a network combining RNA-Seq and peptide information that is optimized by a maximum-flow algorithm. It thereby also allows resolving the ambiguity of shared peptides for protein inference. We applied MSProGene on three datasets and show that it facilitates a database-independent reliable yet accurate prediction on gene and protein level and additionally identifies novel genes.'),
(79286, 2015, 'bio', 'Large-scale model quality assessment for improving protein tertiary structure prediction', 'Cao, Renzhi|Bhattacharya, Debswapna|Adhikari, Badri|Li, Jilong|Cheng, Jianlin', 'Computer Science Department, University of Missouri, Columbia, Missouri, 65211, USA, Informatics Institute, University of Missouri, Columbia, Missouri, 65211, USA and C. Bond Life Science Center, University of Missouri, Columbia, Missouri, 65211, USA.|Computer Science Department, University of Missouri, Columbia, Missouri, 65211, USA, Informatics Institute, University of Missouri, Columbia, Missouri, 65211, USA and C. Bond Life Science Center, University of Missouri, Columbia, Missouri, 65211, USA.|Computer Science Department, University of Missouri, Columbia, Missouri, 65211, USA, Informatics Institute, University of Missouri, Columbia, Missouri, 65211, USA and C. Bond Life Science Center, University of Missouri, Columbia, Missouri, 65211, USA.|Computer Science Department, University of Missouri, Columbia, Missouri, 65211, USA, Informatics Institute, University of Missouri, Columbia, Missouri, 65211, USA and C. Bond Life Science Center, University of Missouri, Columbia, Missouri, 65211, USA.|Computer Science Department, University of Missouri, Columbia, Missouri, 65211, USA, Informatics Institute, University of Missouri, Columbia, Missouri, 65211, USA and C. Bond Life Science Center, University of Missouri, Columbia, Missouri, 65211, USA Computer Science Department, University of Missouri, Columbia, Missouri, 65211, USA, Informatics Institute, University of Missouri, Columbia, Missouri, 65211, USA and C. Bond Life Science Center, University of Missouri, Columbia, Missouri, 65211, USA Computer Science Department, University of Missouri, Columbia, Missouri, 65211, USA, Informatics Institute, University of Missouri, Columbia, Missouri, 65211, USA and C. Bond Life Science Center, University of Missouri, Columbia, Missouri, 65211, USA.', 'United States|United States|United States|United States|United States', 37, 'Cluster Analysis|Humans|Models, Molecular|Protein Structure, Tertiary|Software', 'Journal Article', '10.1093/bioinformatics/btv235', '26072473', 'Sampling structural models and ranking them are the two major challenges of protein structure prediction. Traditional protein structure prediction methods generally use one or a few quality assessment (QA) methods to select the best-predicted models, which cannot consistently select relatively better models and rank a large number of models well.'),
(79287, 2015, 'bio', 'Using kernelized partial canonical correlation analysis to study directly coupled side chains and allostery in small G proteins', 'Soltan Ghoraie, Laleh|Burkowski, Forbes|Zhu, Mu', 'Department of Computer Science and Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada.|Department of Computer Science and Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada.|Department of Computer Science and Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada.', 'Canada|Canada|Canada', 1, 'Algorithms|Allosteric Site|Data Interpretation, Statistical|Monomeric GTP-Binding Proteins|Motion|Protein Conformation|rab GTP-Binding Proteins|ras Proteins|rho GTP-Binding Proteins', 'Journal Article', '10.1093/bioinformatics/btv241', '26072474', 'Inferring structural dependencies among a proteins side chains helps us understand their coupled motions. It is known that coupled fluctuations can reveal pathways of communication used for information propagation in a molecule. Side-chain conformations are commonly represented by multivariate angular variables, but existing partial correlation methods that can be applied to this inference task are not capable of handling multivariate angular data. We propose a novel method to infer direct couplings from this type of data, and show that this method is useful for identifying functional regions and their interactions in allosteric proteins.'),
(79288, 2015, 'bio', 'Finding optimal interaction interface alignments between biological complexes', 'Cui, Xuefeng|Naveed, Hammad|Gao, Xin', 'Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.|Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.|Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.', 'Saudi Arabia|Saudi Arabia|Saudi Arabia', 0, 'Algorithms|Binding Sites|DNA|DNA-Binding Proteins|Models, Molecular|Molecular Mimicry|Multiprotein Complexes|Nucleic Acid Conformation|Protein Conformation|RNA|RNA-Binding Proteins|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btv242', '26072475', 'Biological molecules perform their functions through interactions with other molecules. Structure alignment of interaction interfaces between biological complexes is an indispensable step in detecting their structural similarities, which are key S: to understanding their evolutionary histories and functions. Although various structure alignment methods have been developed to successfully access the similarities of protein structures or certain types of interaction interfaces, existing alignment tools cannot directly align arbitrary types of interfaces formed by protein, DNA or RNA molecules. Specifically, they require a : blackbox preprocessing : to standardize interface types and chain identifiers. Yet their performance is limited and sometimes unsatisfactory.'),
(79289, 2015, 'bio', 'Deconvolving molecular signatures of interactions between microbial colonies', 'Harn, Y-C|Powers, M J|Shank, E A|Jojic, V', 'Department of Biology, University of North Carolina, Chapel Hill, NC 27599-3280, USA, Department of Biology, University of North Carolina, Chapel Hill, NC 27599-32800, USA, Department of Microbiology and Immunology, University of North Carolina, Chapel Hill, NC 27599-7290, USA and Curriculum of Genetics and Molecular Biology, University of North Carolina, Chapel Hill, NC, USA.|Department of Biology, University of North Carolina, Chapel Hill, NC 27599-3280, USA, Department of Biology, University of North Carolina, Chapel Hill, NC 27599-32800, USA, Department of Microbiology and Immunology, University of North Carolina, Chapel Hill, NC 27599-7290, USA and Curriculum of Genetics and Molecular Biology, University of North Carolina, Chapel Hill, NC, USA.|Department of Biology, University of North Carolina, Chapel Hill, NC 27599-3280, USA, Department of Biology, University of North Carolina, Chapel Hill, NC 27599-32800, USA, Department of Microbiology and Immunology, University of North Carolina, Chapel Hill, NC 27599-7290, USA and Curriculum of Genetics and Molecular Biology, University of North Carolina, Chapel Hill, NC, USA Department of Biology, University of North Carolina, Chapel Hill, NC 27599-3280, USA, Department of Biology, University of North Carolina, Chapel Hill, NC 27599-32800, USA, Department of Microbiology and Immunology, University of North Carolina, Chapel Hill, NC 27599-7290, USA and Curriculum of Genetics and Molecular Biology, University of North Carolina, Chapel Hill, NC, USA Department of Biology, University of North Carolina, Chapel Hill, NC 27599-3280, USA, Department of Biology, University of North Carolina, Chapel Hill, NC 27599-32800, USA, Department of Microbiology and Immunology, University of North Carolina, Chapel Hill, NC 27599-7290, USA and Curriculum of Genetics and Molecular Biology, University of North Carolina, Chapel Hill, NC, USA.|Department of Biology, University of North Carolina, Chapel Hill, NC 27599-3280, USA, Department of Biology, University of North Carolina, Chapel Hill, NC 27599-32800, USA, Department of Microbiology and Immunology, University of North Carolina, Chapel Hill, NC 27599-7290, USA and Curriculum of Genetics and Molecular Biology, University of North Carolina, Chapel Hill, NC, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Bacillus cereus|Bacillus subtilis|Microbial Interactions|Poisson Distribution|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Journal Article', '10.1093/bioinformatics/btv251', '26072476', 'The interactions between microbial colonies through chemical signaling are not well understood. A microbial colony can use different molecules to inhibit or accelerate the growth of other colonies. A better understanding of the molecules involved in these interactions could lead to advancements in health and medicine. Imaging mass spectrometry (IMS) applied to co-cultured microbial communities aims to capture the spatial characteristics of the colonies molecular fingerprints. These data are high-dimensional and require computational analysis methods to interpret.'),
(79290, 2015, 'bio', 'cNMA: a framework of encounter complex-based normal mode analysis to model conformational changes in protein interactions', 'Oliwa, Tomasz|Shen, Yang', 'Toyota Technological Institute at Chicago, Chicago, IL 60637, USA and Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.|Toyota Technological Institute at Chicago, Chicago, IL 60637, USA and Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA Toyota Technological Institute at Chicago, Chicago, IL 60637, USA and Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.', 'United States|United States', 0, 'Molecular Docking Simulation|Motion|Multiprotein Complexes|Protein Conformation|Protein Interaction Mapping', 'Journal Article', '10.1093/bioinformatics/btv252', '26072477', 'It remains both a fundamental and practical challenge to understand and anticipate motions and conformational changes of proteins during their associations. Conventional normal mode analysis (NMA) based on anisotropic network model (ANM) addresses the challenge by generating normal modes reflecting intrinsic flexibility of proteins, which follows a conformational selection model for protein-protein interactions. But earlier studies have also found cases where conformational selection alone could not adequately explain conformational changes and other models have been proposed. Moreover, there is a pressing demand of constructing a much reduced but still relevant subset of protein conformational space to improve computational efficiency and accuracy in protein docking, especially for the difficult cases with significant conformational changes.'),
(79291, 2015, 'bio', 'Metabolome-scale de novo pathway reconstruction using regioisomer-sensitive graph alignments', 'Yamanishi, Yoshihiro|Tabei, Yasuo|Kotera, Masaaki', 'Division of System Cohort, Medical Institute of Bioregulation, Kyushu University, Higashi-ku, Fukuoka, Fukuoka 812-8582, Institute for Advanced Study, Kyushu University, Higashi-ku, Fukuoka, Fukuoka 812-8581, PRESTO, Japan Science and Technology Agency, Kawaguchi, Saitama 332-0012 and Graduate School of Bioscience and Biotechnology, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan Division of System Cohort, Medical Institute of Bioregulation, Kyushu University, Higashi-ku, Fukuoka, Fukuoka 812-8582, Institute for Advanced Study, Kyushu University, Higashi-ku, Fukuoka, Fukuoka 812-8581, PRESTO, Japan Science and Technology Agency, Kawaguchi, Saitama 332-0012 and Graduate School of Bioscience and Biotechnology, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan.|Division of System Cohort, Medical Institute of Bioregulation, Kyushu University, Higashi-ku, Fukuoka, Fukuoka 812-8582, Institute for Advanced Study, Kyushu University, Higashi-ku, Fukuoka, Fukuoka 812-8581, PRESTO, Japan Science and Technology Agency, Kawaguchi, Saitama 332-0012 and Graduate School of Bioscience and Biotechnology, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan.|Division of System Cohort, Medical Institute of Bioregulation, Kyushu University, Higashi-ku, Fukuoka, Fukuoka 812-8582, Institute for Advanced Study, Kyushu University, Higashi-ku, Fukuoka, Fukuoka 812-8581, PRESTO, Japan Science and Technology Agency, Kawaguchi, Saitama 332-0012 and Graduate School of Bioscience and Biotechnology, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan.', 'Japan|Japan|Japan', 0, 'Algorithms|Metabolic Networks and Pathways|Metabolome|Metabolomics', 'Evaluation Studies', '10.1093/bioinformatics/btv224', '26072478', 'Recent advances in mass spectrometry and related metabolomics technologies have enabled the rapid and comprehensive analysis of numerous metabolites. However, biosynthetic and biodegradation pathways are only known for a small portion of metabolites, with most metabolic pathways remaining uncharacterized.'),
(79292, 2015, 'bio', 'A hierarchical Bayesian model for flexible module discovery in three-way time-series data', 'Amar, David|Yekutieli, Daniel|Maron-Katz, Adi|Hendler, Talma|Shamir, Ron', 'The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel, Department of Statistics and OR, School of Mathematical Sciences, Tel Aviv University, Tel Aviv 69978, Israel, Functional Brain Center, Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel Aviv 64239, Israel and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.|The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel, Department of Statistics and OR, School of Mathematical Sciences, Tel Aviv University, Tel Aviv 69978, Israel, Functional Brain Center, Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel Aviv 64239, Israel and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.|The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel, Department of Statistics and OR, School of Mathematical Sciences, Tel Aviv University, Tel Aviv 69978, Israel, Functional Brain Center, Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel Aviv 64239, Israel and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel, Department of Statistics and OR, School of Mathematical Sciences, Tel Aviv University, Tel Aviv 69978, Israel, Functional Brain Center, Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel Aviv 64239, Israel and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel, Department of Statistics and OR, School of Mathematical Sciences, Tel Aviv University, Tel Aviv 69978, Israel, Functional Brain Center, Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel Aviv 64239, Israel and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.|The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel, Department of Statistics and OR, School of Mathematical Sciences, Tel Aviv University, Tel Aviv 69978, Israel, Functional Brain Center, Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel Aviv 64239, Israel and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel, Department of Statistics and OR, School of Mathematical Sciences, Tel Aviv University, Tel Aviv 69978, Israel, Functional Brain Center, Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel Aviv 64239, Israel and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.|The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel, Department of Statistics and OR, School of Mathematical Sciences, Tel Aviv University, Tel Aviv 69978, Israel, Functional Brain Center, Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel Aviv 64239, Israel and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.', 'Israel|Israel|Israel|Israel|Israel', 7, 'Algorithms|Bayes Theorem|Brain Mapping|Cluster Analysis|Diffusion Magnetic Resonance Imaging|Gene Expression Profiling|Gene Expression Regulation|Humans|Male|Shock, Septic|Time Factors', 'Journal Article', '10.1093/bioinformatics/btv228', '26072479', 'Detecting modules of co-ordinated activity is fundamental in the analysis of large biological studies. For two-dimensional data (e.g. genes × patients), this is often done via clustering or biclustering. More recently, studies monitoring patients over time have added another dimension. Analysis is much more challenging in this case, especially when time measurements are not synchronized. New methods that can analyze three-way data are thus needed.'),
(79293, 2015, 'bio', 'Exploring the structure and function of temporal networks with dynamic graphlets', 'Hulovatyy, Y|Chen, H|Milenkovixc4x87, T', 'Department of Computer Science and Engineering, Interdisciplinary Center for Network Science and Applications, and ECK Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, USA.|Department of Computer Science and Engineering, Interdisciplinary Center for Network Science and Applications, and ECK Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, USA.|Department of Computer Science and Engineering, Interdisciplinary Center for Network Science and Applications, and ECK Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, USA.', 'United States|United States|United States', 0, 'Aging|Gene Expression Profiling|Humans|Metabolic Networks and Pathways|Models, Biological|Protein Interaction Mapping', 'Journal Article', '10.1093/bioinformatics/btv227', '26072480', 'With increasing availability of temporal real-world networks, how to efficiently study these data? One can model a temporal network as a single aggregate static network, or as a series of time-specific snapshots, each being an aggregate static network over the corresponding time window. Then, one can use established methods for static analysis on the resulting aggregate network(s), but losing in the process valuable temporal information either completely, or at the interface between different snapshots, respectively. Here, we develop a novel approach for studying a temporal network more explicitly, by capturing inter-snapshot relationships.'),
(79294, 2015, 'bio', 'Adapt-Mix: learning local genetic correlation structure improves summary statistics-based analyses', 'Park, Danny S|Brown, Brielin|Eng, Celeste|Huntsman, Scott|Hu, Donglei|Torgerson, Dara G|Burchard, Esteban G|Zaitlen, Noah', 'Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, Department of Computer Science, University of California Berkeley, Berkeley and Department of Medicine, University of California San Francisco, San Francisco, CA, USA.|Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, Department of Computer Science, University of California Berkeley, Berkeley and Department of Medicine, University of California San Francisco, San Francisco, CA, USA.|Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, Department of Computer Science, University of California Berkeley, Berkeley and Department of Medicine, University of California San Francisco, San Francisco, CA, USA.|Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, Department of Computer Science, University of California Berkeley, Berkeley and Department of Medicine, University of California San Francisco, San Francisco, CA, USA.|Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, Department of Computer Science, University of California Berkeley, Berkeley and Department of Medicine, University of California San Francisco, San Francisco, CA, USA.|Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, Department of Computer Science, University of California Berkeley, Berkeley and Department of Medicine, University of California San Francisco, San Francisco, CA, USA.|Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, Department of Computer Science, University of California Berkeley, Berkeley and Department of Medicine, University of California San Francisco, San Francisco, CA, USA Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, Department of Computer Science, University of California Berkeley, Berkeley and Department of Medicine, University of California San Francisco, San Francisco, CA, USA.|Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, Department of Computer Science, University of California Berkeley, Berkeley and Department of Medicine, University of California San Francisco, San Francisco, CA, USA Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, Department of Computer Science, University of California Berkeley, Berkeley and Department of Medicine, University of California San Francisco, San Francisco, CA, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 8, 'Algorithms|Coronary Artery Disease|Data Interpretation, Statistical|Genome-Wide Association Study|Genotype|Humans|Phenotype|Polymorphism, Single Nucleotide|Software', 'Evaluation Studies', '10.1093/bioinformatics/btv230', '26072481', 'Approaches to identifying new risk loci, training risk prediction models, imputing untyped variants and fine-mapping causal variants from summary statistics of genome-wide association studies are playing an increasingly important role in the human genetics community. Current summary statistics-based methods rely on global best guess reference panels to model the genetic correlation structure of the dataset being studied. This approach, especially in admixed populations, has the potential to produce misleading results, ignores variation in local structure and is not feasible when appropriate reference panels are missing or small. Here, we develop a method, Adapt-Mix, that combines information across all available reference panels to produce estimates of local genetic correlation structure for summary statistics-based methods in arbitrary populations.'),
(79295, 2015, 'bio', 'Inferring parental genomic ancestries using pooled semi-Markov processes', 'Zou, James Y|Halperin, Eran|Burchard, Esteban|Sankararaman, Sriram', 'Microsoft Research, One Memorial Drive, Cambridge, MA 02142, USA, Blavatnik School of Computer Science, Tel Aviv University, Tel-Aviv 69978, Israel, Department of Bioengineering and Therapeutic Sciences and Department of Medicine, University of California, San Francisco, CA 94158 and Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.|Microsoft Research, One Memorial Drive, Cambridge, MA 02142, USA, Blavatnik School of Computer Science, Tel Aviv University, Tel-Aviv 69978, Israel, Department of Bioengineering and Therapeutic Sciences and Department of Medicine, University of California, San Francisco, CA 94158 and Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.|Microsoft Research, One Memorial Drive, Cambridge, MA 02142, USA, Blavatnik School of Computer Science, Tel Aviv University, Tel-Aviv 69978, Israel, Department of Bioengineering and Therapeutic Sciences and Department of Medicine, University of California, San Francisco, CA 94158 and Department of Genetics, Harvard Medical School, Boston, MA 02115, USA Microsoft Research, One Memorial Drive, Cambridge, MA 02142, USA, Blavatnik School of Computer Science, Tel Aviv University, Tel-Aviv 69978, Israel, Department of Bioengineering and Therapeutic Sciences and Department of Medicine, University of California, San Francisco, CA 94158 and Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.|Microsoft Research, One Memorial Drive, Cambridge, MA 02142, USA, Blavatnik School of Computer Science, Tel Aviv University, Tel-Aviv 69978, Israel, Department of Bioengineering and Therapeutic Sciences and Department of Medicine, University of California, San Francisco, CA 94158 and Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.', 'Israel|Israel|Israel|Israel', 3, 'Algorithms|Child|Continental Population Groups|Female|Genetics, Population|Genomics|Genotyping Techniques|Humans|Male|Markov Chains|Mexico|Parents|Puerto Rico', 'Journal Article', '10.1093/bioinformatics/btv239', '26072482', 'A basic problem of broad public and scientific interest is to use the DNA of an individual to infer the genomic ancestries of the parents. In particular, we are often interested in the fraction of each parents genome that comes from specific ancestries (e.g. European, African, Native American, etc). This has many applications ranging from understanding the inheritance of ancestry-related risks and traits to quantifying human assortative mating patterns.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(79296, 2015, 'bio', 'Integrative random forest for gene regulatory network inference', 'Petralia, Francesca|Wang, Pei|Yang, Jialiang|Tu, Zhidong', 'Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.|Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.|Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.|Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.', 'United States|United States|United States|United States', 71, 'Algorithms|Gene Regulatory Networks|Saccharomyces cerevisiae|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btv268', '26072483', 'Gene regulatory network (GRN) inference based on genomic data is one of the most actively pursued computational biological problems. Because different types of biological data usually provide complementary information regarding the underlying GRN, a model that integrates big data of diverse types is expected to increase both the power and accuracy of GRN inference. Towards this goal, we propose a novel algorithm named iRafNet: integrative random forest for gene regulatory network inference.'),
(79297, 2015, 'bio', 'Identification of causal genes for complex traits', 'Hormozdiari, Farhad|Kichaev, Gleb|Yang, Wen-Yun|Pasaniuc, Bogdan|Eskin, Eleazar', 'Department of Computer Science, Inter-Departmental Program in Bioinformatics, Department of Human Genetics and Department of Pathology and Laboratory Medicine, University of California, Los Angeles, CA 90095, USA.|Department of Computer Science, Inter-Departmental Program in Bioinformatics, Department of Human Genetics and Department of Pathology and Laboratory Medicine, University of California, Los Angeles, CA 90095, USA.|Department of Computer Science, Inter-Departmental Program in Bioinformatics, Department of Human Genetics and Department of Pathology and Laboratory Medicine, University of California, Los Angeles, CA 90095, USA.|Department of Computer Science, Inter-Departmental Program in Bioinformatics, Department of Human Genetics and Department of Pathology and Laboratory Medicine, University of California, Los Angeles, CA 90095, USA Department of Computer Science, Inter-Departmental Program in Bioinformatics, Department of Human Genetics and Department of Pathology and Laboratory Medicine, University of California, Los Angeles, CA 90095, USA Department of Computer Science, Inter-Departmental Program in Bioinformatics, Department of Human Genetics and Department of Pathology and Laboratory Medicine, University of California, Los Angeles, CA 90095, USA.|Department of Computer Science, Inter-Departmental Program in Bioinformatics, Department of Human Genetics and Department of Pathology and Laboratory Medicine, University of California, Los Angeles, CA 90095, USA Department of Computer Science, Inter-Departmental Program in Bioinformatics, Department of Human Genetics and Department of Pathology and Laboratory Medicine, University of California, Los Angeles, CA 90095, USA.', 'United States|United States|United States|United States|United States', 36, 'Algorithms|Animals|Apolipoprotein A-II|Data Interpretation, Statistical|Genes|Genome-Wide Association Study|Humans|Linkage Disequilibrium|Mice|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btv240', '26072484', 'Although genome-wide association studies (GWAS) have identified thousands of variants associated with common diseases and complex traits, only a handful of these variants are validated to be causal. We consider causal variants as variants which are responsible for the association signal at a locus. As opposed to association studies that benefit from linkage disequilibrium (LD), the main challenge in identifying causal variants at associated loci lies in distinguishing among the many closely correlated variants due to LD. This is particularly important for model organisms such as inbred mice, where LD extends much further than in human populations, resulting in large stretches of the genome with significantly associated variants. Furthermore, these model organisms are highly structured and require correction for population structure to remove potential spurious associations.'),
(79298, 2015, 'bio', 'Refined elasticity sampling for Monte Carlo-based identification of stabilizing network patterns', 'Childs, Dorothee|Grimbs, Sergio|Selbig, Joachim', 'Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany, Bioinformatics Group, University of Potsdam and Max-Planck Institute for Molecular Plant Physiology, Potsdam, Germany and Computational Systems Biology Group, School of Engineering and Science, Jacobs University Bremen, Bremen, Germany Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany, Bioinformatics Group, University of Potsdam and Max-Planck Institute for Molecular Plant Physiology, Potsdam, Germany and Computational Systems Biology Group, School of Engineering and Science, Jacobs University Bremen, Bremen, Germany.|Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany, Bioinformatics Group, University of Potsdam and Max-Planck Institute for Molecular Plant Physiology, Potsdam, Germany and Computational Systems Biology Group, School of Engineering and Science, Jacobs University Bremen, Bremen, Germany.|Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany, Bioinformatics Group, University of Potsdam and Max-Planck Institute for Molecular Plant Physiology, Potsdam, Germany and Computational Systems Biology Group, School of Engineering and Science, Jacobs University Bremen, Bremen, Germany.', 'Germany|Germany|Germany', 1, 'Citric Acid Cycle|Kinetics|Metabolic Networks and Pathways|Models, Biological|Monte Carlo Method', 'Journal Article', '10.1093/bioinformatics/btv243', '26072485', 'Structural kinetic modelling (SKM) is a framework to analyse whether a metabolic steady state remains stable under perturbation, without requiring detailed knowledge about individual rate equations. It provides a representation of the systems Jacobian matrix that depends solely on the network structure, steady state measurements, and the elasticities at the steady state. For a measured steady state, stability criteria can be derived by generating a large number of SKMs with randomly sampled elasticities and evaluating the resulting Jacobian matrices. The elasticity space can be analysed statistically in order to detect network positions that contribute significantly to the perturbation response. Here, we extend this approach by examining the kinetic feasibility of the elasticity combinations created during Monte Carlo sampling.'),
(79299, 2015, 'bio', 'Improving compound-protein interaction prediction by building up highly credible negative samples', 'Liu, Hui|Sun, Jianjiang|Guan, Jihong|Zheng, Jie|Zhou, Shuigeng', 'Lab of Information Management, Changzhou University, Jiangsu 213164, China, School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore, Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science, Fudan University, Shanghai 200433, China and Department of Computer Science and Technology, Tongji University, Shanghai 201804, China Lab of Information Management, Changzhou University, Jiangsu 213164, China, School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore, Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science, Fudan University, Shanghai 200433, China and Department of Computer Science and Technology, Tongji University, Shanghai 201804, China.|Lab of Information Management, Changzhou University, Jiangsu 213164, China, School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore, Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science, Fudan University, Shanghai 200433, China and Department of Computer Science and Technology, Tongji University, Shanghai 201804, China.|Lab of Information Management, Changzhou University, Jiangsu 213164, China, School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore, Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science, Fudan University, Shanghai 200433, China and Department of Computer Science and Technology, Tongji University, Shanghai 201804, China.|Lab of Information Management, Changzhou University, Jiangsu 213164, China, School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore, Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science, Fudan University, Shanghai 200433, China and Department of Computer Science and Technology, Tongji University, Shanghai 201804, China.|Lab of Information Management, Changzhou University, Jiangsu 213164, China, School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore, Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science, Fudan University, Shanghai 200433, China and Department of Computer Science and Technology, Tongji University, Shanghai 201804, China.', 'China|China|China|China|China', 0, 'Amino Acid Sequence|Bayes Theorem|Computer Simulation|Databases, Protein|Drug Discovery|Humans|Pharmaceutical Preparations|Protein Interaction Mapping|Proteins|Support Vector Machine', 'Evaluation Studies', '10.1093/bioinformatics/btv256', '26072486', 'Computational prediction of compound-protein interactions (CPIs) is of great importance for drug design and development, as genome-scale experimental validation of CPIs is not only time-consuming but also prohibitively expensive. With the availability of an increasing number of validated interactions, the performance of computational prediction approaches is severely impended by the lack of reliable negative CPI samples. A systematic method of screening reliable negative sample becomes critical to improving the performance of in silico prediction methods.'),
(79300, 2015, 'bio', 'Gene network inference by fusing data from diverse distributions', 'Žitnik, Marinka|Zupan, Blaž', 'Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia and Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.|Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia and Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia and Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.', 'Slovenia|Slovenia', 16, 'Algorithms|Breast Neoplasms|Female|Gene Expression Profiling|Gene Regulatory Networks|High-Throughput Nucleotide Sequencing|Humans|Markov Chains|Poisson Distribution|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btv258', '26072487', 'Markov networks are undirected graphical models that are widely used to infer relations between genes from experimental data. Their state-of-the-art inference procedures assume the data arise from a Gaussian distribution. High-throughput omics data, such as that from next generation sequencing, often violates this assumption. Furthermore, when collected data arise from multiple related but otherwise nonidentical distributions, their underlying networks are likely to have common features. New principled statistical approaches are needed that can deal with different data distributions and jointly consider collections of datasets.'),
(79301, 2015, 'bio', 'Genome-wide detection of intervals of genetic heterogeneity associated with complex traits', 'Llinares-López, Felipe|Grimm, Dominik G|Bodenham, Dean A|Gieraths, Udo|Sugiyama, Mahito|Rowan, Beth|Borgwardt, Karsten', 'Machine Learning and Computational Biology Lab, Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland, The Institute of Scientific and Industrial Research, Osaka University, Osaka, Japan, JST, PRESTO, Japan and Department of Molecular Biology, Max Planck Institute for Developmental Biology, Tübingen, Germany.|Machine Learning and Computational Biology Lab, Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland, The Institute of Scientific and Industrial Research, Osaka University, Osaka, Japan, JST, PRESTO, Japan and Department of Molecular Biology, Max Planck Institute for Developmental Biology, Tübingen, Germany.|Machine Learning and Computational Biology Lab, Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland, The Institute of Scientific and Industrial Research, Osaka University, Osaka, Japan, JST, PRESTO, Japan and Department of Molecular Biology, Max Planck Institute for Developmental Biology, Tübingen, Germany.|Machine Learning and Computational Biology Lab, Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland, The Institute of Scientific and Industrial Research, Osaka University, Osaka, Japan, JST, PRESTO, Japan and Department of Molecular Biology, Max Planck Institute for Developmental Biology, Tübingen, Germany.|Machine Learning and Computational Biology Lab, Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland, The Institute of Scientific and Industrial Research, Osaka University, Osaka, Japan, JST, PRESTO, Japan and Department of Molecular Biology, Max Planck Institute for Developmental Biology, Tübingen, Germany Machine Learning and Computational Biology Lab, Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland, The Institute of Scientific and Industrial Research, Osaka University, Osaka, Japan, JST, PRESTO, Japan and Department of Molecular Biology, Max Planck Institute for Developmental Biology, Tübingen, Germany.|Machine Learning and Computational Biology Lab, Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland, The Institute of Scientific and Industrial Research, Osaka University, Osaka, Japan, JST, PRESTO, Japan and Department of Molecular Biology, Max Planck Institute for Developmental Biology, Tübingen, Germany.|Machine Learning and Computational Biology Lab, Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland, The Institute of Scientific and Industrial Research, Osaka University, Osaka, Japan, JST, PRESTO, Japan and Department of Molecular Biology, Max Planck Institute for Developmental Biology, Tübingen, Germany.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 13, 'Algorithms|Arabidopsis|Genetic Heterogeneity|Genome-Wide Association Study|Phenotype|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btv263', '26072488', 'Genetic heterogeneity, the fact that several sequence variants give rise to the same phenotype, is a phenomenon that is of the utmost interest in the analysis of complex phenotypes. Current approaches for finding regions in the genome that exhibit genetic heterogeneity suffer from at least one of two shortcomings: (i) they require the definition of an exact interval in the genome that is to be tested for genetic heterogeneity, potentially missing intervals of high relevance, or (ii) they suffer from an enormous multiple hypothesis testing problem due to the large number of potential candidate intervals being tested, which results in either many false positives or a lack of power to detect true intervals.'),
(79302, 2015, 'bio', 'PAGER: constructing PAGs and new PAG-PAG relationships for network biology', 'Yue, Zongliang|Kshirsagar, Madhura M|Nguyen, Thanh|Suphavilai, Chayaporn|Neylon, Michael T|Zhu, Liugen|Ratliff, Timothy|Chen, Jake Y', 'Indiana University School of Informatics and Computing, Department of Computer and Information Science, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, Purdue University Center for Cancer Research, West Lafayette, IN 47906 and Institute of Biopharmaceutical Informatics and Technology, Wenzhou Medical University, WenZhou, Zhe Jiang Province, China.|Indiana University School of Informatics and Computing, Department of Computer and Information Science, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, Purdue University Center for Cancer Research, West Lafayette, IN 47906 and Institute of Biopharmaceutical Informatics and Technology, Wenzhou Medical University, WenZhou, Zhe Jiang Province, China.|Indiana University School of Informatics and Computing, Department of Computer and Information Science, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, Purdue University Center for Cancer Research, West Lafayette, IN 47906 and Institute of Biopharmaceutical Informatics and Technology, Wenzhou Medical University, WenZhou, Zhe Jiang Province, China.|Indiana University School of Informatics and Computing, Department of Computer and Information Science, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, Purdue University Center for Cancer Research, West Lafayette, IN 47906 and Institute of Biopharmaceutical Informatics and Technology, Wenzhou Medical University, WenZhou, Zhe Jiang Province, China.|Indiana University School of Informatics and Computing, Department of Computer and Information Science, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, Purdue University Center for Cancer Research, West Lafayette, IN 47906 and Institute of Biopharmaceutical Informatics and Technology, Wenzhou Medical University, WenZhou, Zhe Jiang Province, China.|Indiana University School of Informatics and Computing, Department of Computer and Information Science, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, Purdue University Center for Cancer Research, West Lafayette, IN 47906 and Institute of Biopharmaceutical Informatics and Technology, Wenzhou Medical University, WenZhou, Zhe Jiang Province, China.|Indiana University School of Informatics and Computing, Department of Computer and Information Science, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, Purdue University Center for Cancer Research, West Lafayette, IN 47906 and Institute of Biopharmaceutical Informatics and Technology, Wenzhou Medical University, WenZhou, Zhe Jiang Province, China.|Indiana University School of Informatics and Computing, Department of Computer and Information Science, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, Purdue University Center for Cancer Research, West Lafayette, IN 47906 and Institute of Biopharmaceutical Informatics and Technology, Wenzhou Medical University, WenZhou, Zhe Jiang Province, China Indiana University School of Informatics and Computing, Department of Computer and Information Science, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, Purdue University Center for Cancer Research, West Lafayette, IN 47906 and Institute of Biopharmaceutical Informatics and Technology, Wenzhou Medical University, WenZhou, Zhe Jiang Province, China Indiana University School of Informatics and Computing, Department of Computer and Information Science, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, Purdue University Center for Cancer Research, West Lafayette, IN 47906 and Institute of Biopharmaceutical Informatics and Technology, Wenzhou Medical University, WenZhou, Zhe Jiang Province, China.', 'China|China|China|China|China|China|China|China', 0, 'Databases, Genetic|Gene Regulatory Networks|Humans|Software', 'Journal Article', '10.1093/bioinformatics/btv265', '26072489', 'In this article, we described a new database framework to perform integrative gene-set, network, and pathway analysis (GNPA). In this framework, we integrated heterogeneous data on pathways, annotated list, and gene-sets (PAGs) into a PAG electronic repository (PAGER). PAGs in the PAGER database are organized into P-type, A-type and G-type PAGs with a three-letter-code standard naming convention. The PAGER database currently compiles 44 313 genes from 5 species including human, 38 663 PAGs, 324 830 gene-gene relationships and two types of 3 174 323 PAG-PAG regulatory relationships-co-membership based and regulatory relationship based. To help users assess each PAGs biological relevance, we developed a cohesion measure called Cohesion Coefficient (CoCo), which is capable of disambiguating between biologically significant PAGs and random PAGs with an area-under-curve performance of 0.98. PAGER database was set up to help users to search and retrieve PAGs from its online web interface. PAGER enable advanced users to build PAG-PAG regulatory networks that provide complementary biological insights not found in gene set analysis or individual gene network analysis. We provide a case study using cancer functional genomics data sets to demonstrate how integrative GNPA help improve network biology data coverage and therefore biological interpretability. The PAGER database can be accessible openly at http://discovery.informatics.iupui.edu/PAGER/.'),
(79303, 2015, 'bio', 'Inferring models of multiscale copy number evolution for single-tumor phylogenetics', 'Chowdhury, Salim Akhter|Gertz, E Michael|Wangsa, Darawalee|Heselmeyer-Haddad, Kerstin|Ried, Thomas|Schäffer, Alejandro A|Schwartz, Russell', 'Joint Carnegie Mellon/University of Pittsburgh PhD Program in Computational Biology, Pittsburgh, PA, USA, Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA, Computational Biology Branch, National Center for Biotechnology Information, U.S. National Institutes of Health, Bethesda, MD, USA, Section of Cancer Genomics, Genetics Branch, Center for Cancer Research, National Cancer Institute, U.S. National Institutes of Health, Bethesda, MD, USA and Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA Joint Carnegie Mellon/University of Pittsburgh PhD Program in Computational Biology, Pittsburgh, PA, USA, Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA, Computational Biology Branch, National Center for Biotechnology Information, U.S. National Institutes of Health, Bethesda, MD, USA, Section of Cancer Genomics, Genetics Branch, Center for Cancer Research, National Cancer Institute, U.S. National Institutes of Health, Bethesda, MD, USA and Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA.|Joint Carnegie Mellon/University of Pittsburgh PhD Program in Computational Biology, Pittsburgh, PA, USA, Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA, Computational Biology Branch, National Center for Biotechnology Information, U.S. National Institutes of Health, Bethesda, MD, USA, Section of Cancer Genomics, Genetics Branch, Center for Cancer Research, National Cancer Institute, U.S. National Institutes of Health, Bethesda, MD, USA and Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA.|Joint Carnegie Mellon/University of Pittsburgh PhD Program in Computational Biology, Pittsburgh, PA, USA, Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA, Computational Biology Branch, National Center for Biotechnology Information, U.S. National Institutes of Health, Bethesda, MD, USA, Section of Cancer Genomics, Genetics Branch, Center for Cancer Research, National Cancer Institute, U.S. National Institutes of Health, Bethesda, MD, USA and Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA.|Joint Carnegie Mellon/University of Pittsburgh PhD Program in Computational Biology, Pittsburgh, PA, USA, Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA, Computational Biology Branch, National Center for Biotechnology Information, U.S. National Institutes of Health, Bethesda, MD, USA, Section of Cancer Genomics, Genetics Branch, Center for Cancer Research, National Cancer Institute, U.S. National Institutes of Health, Bethesda, MD, USA and Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA.|Joint Carnegie Mellon/University of Pittsburgh PhD Program in Computational Biology, Pittsburgh, PA, USA, Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA, Computational Biology Branch, National Center for Biotechnology Information, U.S. National Institutes of Health, Bethesda, MD, USA, Section of Cancer Genomics, Genetics Branch, Center for Cancer Research, National Cancer Institute, U.S. National Institutes of Health, Bethesda, MD, USA and Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA.|Joint Carnegie Mellon/University of Pittsburgh PhD Program in Computational Biology, Pittsburgh, PA, USA, Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA, Computational Biology Branch, National Center for Biotechnology Information, U.S. National Institutes of Health, Bethesda, MD, USA, Section of Cancer Genomics, Genetics Branch, Center for Cancer Research, National Cancer Institute, U.S. National Institutes of Health, Bethesda, MD, USA and Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA.|Joint Carnegie Mellon/University of Pittsburgh PhD Program in Computational Biology, Pittsburgh, PA, USA, Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA, Computational Biology Branch, National Center for Biotechnology Information, U.S. National Institutes of Health, Bethesda, MD, USA, Section of Cancer Genomics, Genetics Branch, Center for Cancer Research, National Cancer Institute, U.S. National Institutes of Health, Bethesda, MD, USA and Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA Joint Carnegie Mellon/University of Pittsburgh PhD Program in Computational Biology, Pittsburgh, PA, USA, Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA, Computational Biology Branch, National Center for Biotechnology Information, U.S. National Institutes of Health, Bethesda, MD, USA, Section of Cancer Genomics, Genetics Branch, Center for Cancer Research, National Cancer Institute, U.S. National Institutes of Health, Bethesda, MD, USA and Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA.', 'United States|United States|United States|United States|United States|United States|United States', 16, 'Algorithms|Disease Progression|Evolution, Molecular|Female|Gene Dosage|Genomics|Humans|Models, Genetic|Neoplasms|Phylogeny|Software|Uterine Cervical Neoplasms', 'Journal Article', '10.1093/bioinformatics/btv233', '26072490', 'Phylogenetic algorithms have begun to see widespread use in cancer research to reconstruct processes of evolution in tumor progression. Developing reliable phylogenies for tumor data requires quantitative models of cancer evolution that include the unusual genetic mechanisms by which tumors evolve, such as chromosome abnormalities, and allow for heterogeneity between tumor types and individual patients. Previous work on inferring phylogenies of single tumors by copy number evolution assumed models of uniform rates of genomic gain and loss across different genomic sites and scales, a substantial oversimplification necessitated by a lack of algorithms and quantitative parameters for fitting to more realistic tumor evolution models.'),
(79304, 2015, 'bio', 'Integrating different data types by regularized unsupervised multiple kernel learning with application to cancer subtype discovery', 'Speicher, Nora K|Pfeifer, Nico', 'Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Campus E1.4, 66123 Saarbrücken and Saarbrücken Graduate School of Computer Science, Saarland University, 66123 Saarbrücken Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Campus E1.4, 66123 Saarbrücken and Saarbrücken Graduate School of Computer Science, Saarland University, 66123 Saarbrücken.|Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Campus E1.4, 66123 Saarbrücken and Saarbrücken Graduate School of Computer Science, Saarland University, 66123 Saarbrücken.', '-|-', 45, 'Algorithms|Cluster Analysis|Humans|Machine Learning|Neoplasms|Survival Analysis', 'Journal Article', '10.1093/bioinformatics/btv244', '26072491', 'Despite ongoing cancer research, available therapies are still limited in quantity and effectiveness, and making treatment decisions for individual patients remains a hard problem. Established subtypes, which help guide these decisions, are mainly based on individual data types. However, the analysis of multidimensional patient data involving the measurements of various molecular features could reveal intrinsic characteristics of the tumor. Large-scale projects accumulate this kind of data for various cancer types, but we still lack the computational methods to reliably integrate this information in a meaningful manner. Therefore, we apply and extend current multiple kernel learning for dimensionality reduction approaches. On the one hand, we add a regularization term to avoid overfitting during the optimization procedure, and on the other hand, we show that one can even use several kernels per data type and thereby alleviate the user from having to choose the best kernel functions and kernel parameters for each data type beforehand.'),
(79305, 2015, 'bio', 'Cypiripi: exact genotyping of CYP2D6 using high-throughput sequencing data', 'Numanagixc4x87, Ibrahim|Malikixc4x87, Salem|Pratt, Victoria M|Skaar, Todd C|Flockhart, David A|Sahinalp, S Cenk', 'School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada, Department of Medicine, Division of Clinical Pharmacology, Indiana University School of Medicine, Indianapolis, IN 46202, USA and School of Informatics and Computing, Indiana University, Bloomington, IN 47401, USA School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada, Department of Medicine, Division of Clinical Pharmacology, Indiana University School of Medicine, Indianapolis, IN 46202, USA and School of Informatics and Computing, Indiana University, Bloomington, IN 47401, USA.|School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada, Department of Medicine, Division of Clinical Pharmacology, Indiana University School of Medicine, Indianapolis, IN 46202, USA and School of Informatics and Computing, Indiana University, Bloomington, IN 47401, USA School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada, Department of Medicine, Division of Clinical Pharmacology, Indiana University School of Medicine, Indianapolis, IN 46202, USA and School of Informatics and Computing, Indiana University, Bloomington, IN 47401, USA.|School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada, Department of Medicine, Division of Clinical Pharmacology, Indiana University School of Medicine, Indianapolis, IN 46202, USA and School of Informatics and Computing, Indiana University, Bloomington, IN 47401, USA.|School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada, Department of Medicine, Division of Clinical Pharmacology, Indiana University School of Medicine, Indianapolis, IN 46202, USA and School of Informatics and Computing, Indiana University, Bloomington, IN 47401, USA.|School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada, Department of Medicine, Division of Clinical Pharmacology, Indiana University School of Medicine, Indianapolis, IN 46202, USA and School of Informatics and Computing, Indiana University, Bloomington, IN 47401, USA.|School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada, Department of Medicine, Division of Clinical Pharmacology, Indiana University School of Medicine, Indianapolis, IN 46202, USA and School of Informatics and Computing, Indiana University, Bloomington, IN 47401, USA School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada, Department of Medicine, Division of Clinical Pharmacology, Indiana University School of Medicine, Indianapolis, IN 46202, USA and School of Informatics and Computing, Indiana University, Bloomington, IN 47401, USA.', 'Canada|Canada|Canada|Canada|Canada|Canada', 18, 'Alleles|Cytochrome P-450 CYP2D6|DNA Copy Number Variations|Genotype|High-Throughput Nucleotide Sequencing|Humans|Polymorphism, Genetic|Pseudogenes|Software', 'Journal Article', '10.1093/bioinformatics/btv232', '26072492', 'CYP2D6 is highly polymorphic gene which encodes the (CYP2D6) enzyme, involved in the metabolism of 20-25% of all clinically prescribed drugs and other xenobiotics in the human body. CYP2D6 genotyping is recommended prior to treatment decisions involving one or more of the numerous drugs sensitive to CYP2D6 allelic composition. In this context, high-throughput sequencing (HTS) technologies provide a promising time-efficient and cost-effective alternative to currently used genotyping techniques. To achieve accurate interpretation of HTS data, however, one needs to overcome several obstacles such as high sequence similarity and genetic recombinations between CYP2D6 and evolutionarily related pseudogenes CYP2D7 and CYP2D8, high copy number variation among individuals and short read lengths generated by HTS technologies.'),
(79306, 2015, 'bio', 'Phenome-driven disease genetics prediction toward drug discovery', 'Chen, Yang|Li, Li|Zhang, Guo-Qiang|Xu, Rong', 'Department of Electrical Engineering and Computer Science, Department of Epidemiology and Biostatistics and Department of Family Medicine and Community Health, Case Western Reserve University, Cleveland, OH 44106, USA.|Department of Electrical Engineering and Computer Science, Department of Epidemiology and Biostatistics and Department of Family Medicine and Community Health, Case Western Reserve University, Cleveland, OH 44106, USA Department of Electrical Engineering and Computer Science, Department of Epidemiology and Biostatistics and Department of Family Medicine and Community Health, Case Western Reserve University, Cleveland, OH 44106, USA.|Department of Electrical Engineering and Computer Science, Department of Epidemiology and Biostatistics and Department of Family Medicine and Community Health, Case Western Reserve University, Cleveland, OH 44106, USA.|Department of Electrical Engineering and Computer Science, Department of Epidemiology and Biostatistics and Department of Family Medicine and Community Health, Case Western Reserve University, Cleveland, OH 44106, USA.', 'United States|United States|United States|United States', 26, 'Crohn Disease|Databases, Genetic|Disease|Drug Discovery|Genes|Genomics|Humans|Phenotype|Protein Interaction Mapping', 'Journal Article', '10.1093/bioinformatics/btv245', '26072493', 'Discerning genetic contributions to diseases not only enhances our understanding of disease mechanisms, but also leads to translational opportunities for drug discovery. Recent computational approaches incorporate disease phenotypic similarities to improve the prediction power of disease gene discovery. However, most current studies used only one data source of human disease phenotype. We present an innovative and generic strategy for combining multiple different data sources of human disease phenotype and predicting disease-associated genes from integrated phenotypic and genomic data.'),
(79307, 2015, 'bio', 'MEMCover: integrated analysis of mutual exclusivity and functional network reveals dysregulated pathways across multiple cancer types', 'Kim, Yoo-Ah|Cho, Dong-Yeon|Dao, Phuong|Przytycka, Teresa M', 'National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, 20894, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, 20894, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, 20894, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, 20894, USA.', 'United States|United States|United States|United States', 51, 'Algorithms|Gene Expression Regulation, Neoplastic|Gene Regulatory Networks|Humans|Mutation|Neoplasms', 'Journal Article', '10.1093/bioinformatics/btv247', '26072494', 'The data gathered by the Pan-Cancer initiative has created an unprecedented opportunity for illuminating common features across different cancer types. However, separating tissue-specific features from across cancer signatures has proven to be challenging. One of the often-observed properties of the mutational landscape of cancer is the mutual exclusivity of cancer driving mutations. Even though studies based on individual cancer types suggested that mutually exclusive pairs often share the same functional pathway, the relationship between across cancer mutual exclusivity and functional connectivity has not been previously investigated.'),
(79308, 2015, 'bio', 'Dynamic changes of RNA-sequencing expression for precision medicine: N-of-1-pathways Mahalanobis distance within pathways of single subjects predicts breast cancer survival', 'Schissler, A Grant|Gardeux, Vincent|Li, Qike|Achour, Ikbel|Li, Haiquan|Piegorsch, Walter W|Lussier, Yves A', 'University of Arizona Center for Biomedical Informatics and Biostatistics (CB2), Tucson, AZ, USA, Graduate Interdisciplinary Program in Statistics, Department of Medicine and BIO5 Institute, University of Arizona, Tucson, AZ, USA University of Arizona Center for Biomedical Informatics and Biostatistics (CB2), Tucson, AZ, USA, Graduate Interdisciplinary Program in Statistics, Department of Medicine and BIO5 Institute, University of Arizona, Tucson, AZ, USA University of Arizona Center for Biomedical Informatics and Biostatistics (CB2), Tucson, AZ, USA, Graduate Interdisciplinary Program in Statistics, Department of Medicine and BIO5 Institute, University of Arizona, Tucson, AZ, USA University of Arizona Center for Biomedical Informatics and Biostatistics (CB2), Tucson, AZ, USA, Graduate Interdisciplinary Program in Statistics, Department of Medicine and BIO5 Institute, University of Arizona, Tucson, AZ, USA.|University of Arizona Center for Biomedical Informatics and Biostatistics (CB2), Tucson, AZ, USA, Graduate Interdisciplinary Program in Statistics, Department of Medicine and BIO5 Institute, University of Arizona, Tucson, AZ, USA University of Arizona Center for Biomedical Informatics and Biostatistics (CB2), Tucson, AZ, USA, Graduate Interdisciplinary Program in Statistics, Department of Medicine and BIO5 Institute, University of Arizona, Tucson, AZ, USA University of Arizona Center for Biomedical Informatics and Biostatistics (CB2), Tucson, AZ, USA, Graduate Interdisciplinary Program in Statistics, Department of Medicine and BIO5 Institute, University of Arizona, Tucson, AZ, USA University of Arizona Center for Biomedical Informatics and Biostatistics (CB2), Tucson, AZ, USA, Graduate Interdisciplinary Program in Statistics, Department of Medicine and BIO5 Institute, University of Arizona, Tucson, AZ, USA.|University of Arizona Center for Biomedical Informatics and Biostatistics (CB2), Tucson, AZ, USA, Graduate Interdisciplinary Program in Statistics, Department of Medicine and BIO5 Institute, University of Arizona, Tucson, AZ, USA University of Arizona Center for Biomedical Informatics and Biostatistics (CB2), Tucson, AZ, USA, Graduate Interdisciplinary Program in Statistics, Department of Medicine and BIO5 Institute, University of Arizona, Tucson, AZ, USA University of Arizona Center for Biomedical Informatics and Biostatistics (CB2), Tucson, AZ, USA, Graduate Interdisciplinary Program in Statistics, Department of Medicine and BIO5 Institute, University of Arizona, Tucson, AZ, USA University of Arizona Center for Biomedical Informatics and Biostatistics (CB2), Tucson, AZ, USA, Graduate Interdisciplinary Program in Statistics, Department of Medicine and BIO5 Institute, University of Arizona, Tucson, AZ, USA University of Arizona Center for Biomedical Informatics and Biostatistics (CB2), Tucson, AZ, USA, Graduate Interdisciplinary Program in Statistics, Department of Medicine and BIO5 Institute, University of Arizona, Tucson, AZ, USA.|University of Arizona Center for Biomedical Informatics and Biostatistics (CB2), Tucson, AZ, USA, Graduate Interdisciplinary Program in Statistics, Department of Medicine and BIO5 Institute, University of Arizona, Tucson, AZ, USA University of Arizona Center for Biomedical Informatics and Biostatistics (CB2), Tucson, AZ, USA, Graduate Interdisciplinary Program in Statistics, Department of Medicine and BIO5 Institute, University of Arizona, Tucson, AZ, USA University of Arizona Center for Biomedical Informatics and Biostatistics (CB2), Tucson, AZ, USA, Graduate Interdisciplinary Program in Statistics, Department of Medicine and BIO5 Institute, University of Arizona, Tucson, AZ, USA.|University of Arizona Center for Biomedical Informatics and Biostatistics (CB2), Tucson, AZ, USA, Graduate Interdisciplinary Program in Statistics, Department of Medicine and BIO5 Institute, University of Arizona, Tucson, AZ, USA University of Arizona Center for Biomedical Informatics and Biostatistics (CB2), Tucson, AZ, USA, Graduate Interdisciplinary Program in Statistics, Department of Medicine and BIO5 Institute, University of Arizona, Tucson, AZ, USA University of Arizona Center for Biomedical Informatics and Biostatistics (CB2), Tucson, AZ, USA, Graduate Interdisciplinary Program in Statistics, Department of Medicine and BIO5 Institute, University of Arizona, Tucson, AZ, USA.|University of Arizona Center for Biomedical Informatics and Biostatistics (CB2), Tucson, AZ, USA, Graduate Interdisciplinary Program in Statistics, Department of Medicine and BIO5 Institute, University of Arizona, Tucson, AZ, USA University of Arizona Center for Biomedical Informatics and Biostatistics (CB2), Tucson, AZ, USA, Graduate Interdisciplinary Program in Statistics, Department of Medicine and BIO5 Institute, University of Arizona, Tucson, AZ, USA University of Arizona Center for Biomedical Informatics and Biostatistics (CB2), Tucson, AZ, USA, Graduate Interdisciplinary Program in Statistics, Department of Medicine and BIO5 Institute, University of Arizona, Tucson, AZ, USA.|University of Arizona Center for Biomedical Informatics and Biostatistics (CB2), Tucson, AZ, USA, Graduate Interdisciplinary Program in Statistics, Department of Medicine and BIO5 Institute, University of Arizona, Tucson, AZ, USA University of Arizona Center for Biomedical Informatics and Biostatistics (CB2), Tucson, AZ, USA, Graduate Interdisciplinary Program in Statistics, Department of Medicine and BIO5 Institute, University of Arizona, Tucson, AZ, USA University of Arizona Center for Biomedical Informatics and Biostatistics (CB2), Tucson, AZ, USA, Graduate Interdisciplinary Program in Statistics, Department of Medicine and BIO5 Institute, University of Arizona, Tucson, AZ, USA University of Arizona Center for Biomedical Informatics and Biostatistics (CB2), Tucson, AZ, USA, Graduate Interdisciplinary Program in Statistics, Department of Medicine and BIO5 Institute, University of Arizona, Tucson, AZ, USA.', 'United States|United States|United States|United States|United States|United States|United States', 15, 'Breast Neoplasms|Data Interpretation, Statistical|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Kaplan-Meier Estimate|Precision Medicine|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btv253', '26072495', 'The conventional approach to personalized medicine relies on molecular data analytics across multiple patients. The path to precision medicine lies with molecular data analytics that can discover interpretable single-subject signals (N-of-1). We developed a global framework, N-of-1-pathways, for a mechanistic-anchored approach to single-subject gene expression data analysis. We previously employed a metric that could prioritize the statistical significance of a deregulated pathway in single subjects, however, it lacked in quantitative interpretability (e.g. the equivalent to a gene expression fold-change).');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(79309, 2015, 'bio', 'In silico phenotyping via co-training for improved phenotype prediction from genotype', 'Roqueiro, Damian|Witteveen, Menno J|Anttila, Verneri|Terwindt, Gisela M|van den Maagdenberg, Arn M J M|Borgwardt, Karsten', 'Machine Learning and Computational Biology Lab, Department of Biosystems Science and Engineering, ETH Zurich, Switzerland, Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA, Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA, Department of Neurology and Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands.|Machine Learning and Computational Biology Lab, Department of Biosystems Science and Engineering, ETH Zurich, Switzerland, Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA, Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA, Department of Neurology and Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands.|Machine Learning and Computational Biology Lab, Department of Biosystems Science and Engineering, ETH Zurich, Switzerland, Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA, Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA, Department of Neurology and Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands Machine Learning and Computational Biology Lab, Department of Biosystems Science and Engineering, ETH Zurich, Switzerland, Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA, Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA, Department of Neurology and Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands Machine Learning and Computational Biology Lab, Department of Biosystems Science and Engineering, ETH Zurich, Switzerland, Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA, Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA, Department of Neurology and Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands.|Machine Learning and Computational Biology Lab, Department of Biosystems Science and Engineering, ETH Zurich, Switzerland, Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA, Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA, Department of Neurology and Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands.|Machine Learning and Computational Biology Lab, Department of Biosystems Science and Engineering, ETH Zurich, Switzerland, Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA, Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA, Department of Neurology and Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands Machine Learning and Computational Biology Lab, Department of Biosystems Science and Engineering, ETH Zurich, Switzerland, Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA, Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA, Department of Neurology and Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands.|Machine Learning and Computational Biology Lab, Department of Biosystems Science and Engineering, ETH Zurich, Switzerland, Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA, Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA, Department of Neurology and Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 3, 'Algorithms|Computer Simulation|Disease|Genotype|Genotyping Techniques|Humans|Migraine with Aura|Migraine without Aura|Phenotype', 'Journal Article', '10.1093/bioinformatics/btv254', '26072497', 'Predicting disease phenotypes from genotypes is a key challenge in medical applications in the postgenomic era. Large training datasets of patients that have been both genotyped and phenotyped are the key requisite when aiming for high prediction accuracy. With current genotyping projects producing genetic data for hundreds of thousands of patients, large-scale phenotyping has become the bottleneck in disease phenotype prediction.'),
(79310, 2015, 'bio', 'FERAL: network-based classifier with application to breast cancer outcome prediction', 'Allahyar, Amin|de Ridder, Jeroen', 'Delft Bioinformatics Lab, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands.|Delft Bioinformatics Lab, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands.', 'Netherlands|Netherlands', 24, 'Breast Neoplasms|Female|Gene Expression Profiling|Gene Regulatory Networks|Humans|Prognosis|Protein Interaction Mapping', 'Journal Article', '10.1093/bioinformatics/btv255', '26072498', 'Breast cancer outcome prediction based on gene expression profiles is an important strategy for personalize patient care. To improve performance and consistency of discovered markers of the initial molecular classifiers, network-based outcome prediction methods (NOPs) have been proposed. In spite of the initial claims, recent studies revealed that neither performance nor consistency can be improved using these methods. NOPs typically rely on the construction of meta-genes by averaging the expression of several genes connected in a network that encodes protein interactions or pathway information. In this article, we expose several fundamental issues in NOPs that impede on the prediction power, consistency of discovered markers and obscures biological interpretation.'),
(79311, 2015, 'bio', 'A generic methodological framework for studying single cell motility in high-throughput time-lapse data', 'Schoenauer Sebag, Alice|Plancade, Sandra|Raulet-Tomkiewicz, Cxc3xa9line|Barouki, Robert|Vert, Jean-Philippe|Walter, Thomas', 'MINES ParisTech, PSL-Research University, CBIO-Centre for Computational Biology, Fontainebleau, Institut Curie, Paris, INSERM U900, Paris, Universitxc3xa9 Paris Descartes, Paris, INSERM UMR-S 1124, Paris, Agro ParisTech, Paris and Mathxc3xa9matiques et Informatique Appliquxc3xa9es, INRA, Jouy-en-Josas, France MINES ParisTech, PSL-Research University, CBIO-Centre for Computational Biology, Fontainebleau, Institut Curie, Paris, INSERM U900, Paris, Universitxc3xa9 Paris Descartes, Paris, INSERM UMR-S 1124, Paris, Agro ParisTech, Paris and Mathxc3xa9matiques et Informatique Appliquxc3xa9es, INRA, Jouy-en-Josas, France MINES ParisTech, PSL-Research University, CBIO-Centre for Computational Biology, Fontainebleau, Institut Curie, Paris, INSERM U900, Paris, Universitxc3xa9 Paris Descartes, Paris, INSERM UMR-S 1124, Paris, Agro ParisTech, Paris and Mathxc3xa9matiques et Informatique Appliquxc3xa9es, INRA, Jouy-en-Josas, France MINES ParisTech, PSL-Research University, CBIO-Centre for Computational Biology, Fontainebleau, Institut Curie, Paris, INSERM U900, Paris, Universitxc3xa9 Paris Descartes, Paris, INSERM UMR-S 1124, Paris, Agro ParisTech, Paris and Mathxc3xa9matiques et Informatique Appliquxc3xa9es, INRA, Jouy-en-Josas, France MINES ParisTech, PSL-Research University, CBIO-Centre for Computational Biology, Fontainebleau, Institut Curie, Paris, INSERM U900, Paris, Universitxc3xa9 Paris Descartes, Paris, INSERM UMR-S 1124, Paris, Agro ParisTech, Paris and Mathxc3xa9matiques et Informatique Appliquxc3xa9es, INRA, Jouy-en-Josas, France MINES ParisTech, PSL-Research University, CBIO-Centre for Computational Biology, Fontainebleau, Institut Curie, Paris, INSERM U900, Paris, Universitxc3xa9 Paris Descartes, Paris, INSERM UMR-S 1124, Paris, Agro ParisTech, Paris and Mathxc3xa9matiques et Informatique Appliquxc3xa9es, INRA, Jouy-en-Josas, France.|MINES ParisTech, PSL-Research University, CBIO-Centre for Computational Biology, Fontainebleau, Institut Curie, Paris, INSERM U900, Paris, Universitxc3xa9 Paris Descartes, Paris, INSERM UMR-S 1124, Paris, Agro ParisTech, Paris and Mathxc3xa9matiques et Informatique Appliquxc3xa9es, INRA, Jouy-en-Josas, France.|MINES ParisTech, PSL-Research University, CBIO-Centre for Computational Biology, Fontainebleau, Institut Curie, Paris, INSERM U900, Paris, Universitxc3xa9 Paris Descartes, Paris, INSERM UMR-S 1124, Paris, Agro ParisTech, Paris and Mathxc3xa9matiques et Informatique Appliquxc3xa9es, INRA, Jouy-en-Josas, France MINES ParisTech, PSL-Research University, CBIO-Centre for Computational Biology, Fontainebleau, Institut Curie, Paris, INSERM U900, Paris, Universitxc3xa9 Paris Descartes, Paris, INSERM UMR-S 1124, Paris, Agro ParisTech, Paris and Mathxc3xa9matiques et Informatique Appliquxc3xa9es, INRA, Jouy-en-Josas, France.|MINES ParisTech, PSL-Research University, CBIO-Centre for Computational Biology, Fontainebleau, Institut Curie, Paris, INSERM U900, Paris, Universitxc3xa9 Paris Descartes, Paris, INSERM UMR-S 1124, Paris, Agro ParisTech, Paris and Mathxc3xa9matiques et Informatique Appliquxc3xa9es, INRA, Jouy-en-Josas, France MINES ParisTech, PSL-Research University, CBIO-Centre for Computational Biology, Fontainebleau, Institut Curie, Paris, INSERM U900, Paris, Universitxc3xa9 Paris Descartes, Paris, INSERM UMR-S 1124, Paris, Agro ParisTech, Paris and Mathxc3xa9matiques et Informatique Appliquxc3xa9es, INRA, Jouy-en-Josas, France.|MINES ParisTech, PSL-Research University, CBIO-Centre for Computational Biology, Fontainebleau, Institut Curie, Paris, INSERM U900, Paris, Universitxc3xa9 Paris Descartes, Paris, INSERM UMR-S 1124, Paris, Agro ParisTech, Paris and Mathxc3xa9matiques et Informatique Appliquxc3xa9es, INRA, Jouy-en-Josas, France MINES ParisTech, PSL-Research University, CBIO-Centre for Computational Biology, Fontainebleau, Institut Curie, Paris, INSERM U900, Paris, Universitxc3xa9 Paris Descartes, Paris, INSERM UMR-S 1124, Paris, Agro ParisTech, Paris and Mathxc3xa9matiques et Informatique Appliquxc3xa9es, INRA, Jouy-en-Josas, France MINES ParisTech, PSL-Research University, CBIO-Centre for Computational Biology, Fontainebleau, Institut Curie, Paris, INSERM U900, Paris, Universitxc3xa9 Paris Descartes, Paris, INSERM UMR-S 1124, Paris, Agro ParisTech, Paris and Mathxc3xa9matiques et Informatique Appliquxc3xa9es, INRA, Jouy-en-Josas, France.|MINES ParisTech, PSL-Research University, CBIO-Centre for Computational Biology, Fontainebleau, Institut Curie, Paris, INSERM U900, Paris, Universitxc3xa9 Paris Descartes, Paris, INSERM UMR-S 1124, Paris, Agro ParisTech, Paris and Mathxc3xa9matiques et Informatique Appliquxc3xa9es, INRA, Jouy-en-Josas, France MINES ParisTech, PSL-Research University, CBIO-Centre for Computational Biology, Fontainebleau, Institut Curie, Paris, INSERM U900, Paris, Universitxc3xa9 Paris Descartes, Paris, INSERM UMR-S 1124, Paris, Agro ParisTech, Paris and Mathxc3xa9matiques et Informatique Appliquxc3xa9es, INRA, Jouy-en-Josas, France MINES ParisTech, PSL-Research University, CBIO-Centre for Computational Biology, Fontainebleau, Institut Curie, Paris, INSERM U900, Paris, Universitxc3xa9 Paris Descartes, Paris, INSERM UMR-S 1124, Paris, Agro ParisTech, Paris and Mathxc3xa9matiques et Informatique Appliquxc3xa9es, INRA, Jouy-en-Josas, France.', 'France|France|France|France|France|France', 10, 'Cell Movement|Cell Tracking|HeLa Cells|Humans|Single-Cell Analysis|Software|Time-Lapse Imaging|Workflow', 'Journal Article', '10.1093/bioinformatics/btv225', '26072499', 'Motility is a fundamental cellular attribute, which plays a major part in processes ranging from embryonic development to metastasis. Traditionally, single cell motility is often studied by live cell imaging. Yet, such studies were so far limited to low throughput. To systematically study cell motility at a large scale, we need robust methods to quantify cell trajectories in live cell imaging data.'),
(79312, 2015, 'bio', 'Comparing genomes with rearrangements and segmental duplications', 'Shao, Mingfu|Moret, Bernard M E', 'School of Computer and Communication Sciences, EPFL, CH-1015, Lausanne, Switzerland.|School of Computer and Communication Sciences, EPFL, CH-1015, Lausanne, Switzerland.', 'Switzerland|Switzerland', 8, 'Algorithms|Animals|Chromosomes|Evolution, Molecular|Genomics|Humans|Mice|Programming, Linear|Rats|Segmental Duplications, Genomic', 'Journal Article', '10.1093/bioinformatics/btv229', '26072500', 'Large-scale evolutionary events such as genomic rearrange.ments and segmental duplications form an important part of the evolution of genomes and are widely studied from both biological and computational perspectives. A basic computational problem is to infer these events in the evolutionary history for given modern genomes, a task for which many algorithms have been proposed under various constraints. Algorithms that can handle both rearrangements and content-modifying events such as duplications and losses remain few and limited in their applicability.'),
(79313, 2015, 'bio', 'MeSHLabeler: improving the accuracy of large-scale MeSH indexing by integrating diverse evidence', 'Liu, Ke|Peng, Shengwen|Wu, Junqiu|Zhai, Chengxiang|Mamitsuka, Hiroshi|Zhu, Shanfeng', 'School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China, School of Information Science & Engineering, Central South University, Changsha 410083, China, Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA, Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji 611-0011, Japan and Center for Computational System Biology, Fudan University, Shanghai 200433, China School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China, School of Information Science & Engineering, Central South University, Changsha 410083, China, Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA, Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji 611-0011, Japan and Center for Computational System Biology, Fudan University, Shanghai 200433, China.|School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China, School of Information Science & Engineering, Central South University, Changsha 410083, China, Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA, Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji 611-0011, Japan and Center for Computational System Biology, Fudan University, Shanghai 200433, China School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China, School of Information Science & Engineering, Central South University, Changsha 410083, China, Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA, Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji 611-0011, Japan and Center for Computational System Biology, Fudan University, Shanghai 200433, China.|School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China, School of Information Science & Engineering, Central South University, Changsha 410083, China, Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA, Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji 611-0011, Japan and Center for Computational System Biology, Fudan University, Shanghai 200433, China.|School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China, School of Information Science & Engineering, Central South University, Changsha 410083, China, Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA, Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji 611-0011, Japan and Center for Computational System Biology, Fudan University, Shanghai 200433, China.|School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China, School of Information Science & Engineering, Central South University, Changsha 410083, China, Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA, Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji 611-0011, Japan and Center for Computational System Biology, Fudan University, Shanghai 200433, China.|School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China, School of Information Science & Engineering, Central South University, Changsha 410083, China, Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA, Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji 611-0011, Japan and Center for Computational System Biology, Fudan University, Shanghai 200433, China School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China, School of Information Science & Engineering, Central South University, Changsha 410083, China, Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA, Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji 611-0011, Japan and Center for Computational System Biology, Fudan University, Shanghai 200433, China School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China, School of Information Science & Engineering, Central South University, Changsha 410083, China, Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA, Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji 611-0011, Japan and Center for Computational System Biology, Fudan University, Shanghai 200433, China.', 'China|China|China|China|China|China', 31, 'Abstracting and Indexing as Topic|Algorithms|Data Mining|MEDLINE|Medical Subject Headings|Reproducibility of Results|Software', 'Journal Article', '10.1093/bioinformatics/btv237', '26072501', 'Medical Subject Headings (MeSHs) are used by National Library of Medicine (NLM) to index almost all citations in MEDLINE, which greatly facilitates the applications of biomedical information retrieval and text mining. To reduce the time and financial cost of manual annotation, NLM has developed a software package, Medical Text Indexer (MTI), for assisting MeSH annotation, which uses k-nearest neighbors (KNN), pattern matching and indexing rules. Other types of information, such as prediction by MeSH classifiers (trained separately), can also be used for automatic MeSH annotation. However, existing methods cannot effectively integrate multiple evidence for MeSH annotation.'),
(79314, 2015, 'bio', 'Knowledge-driven geospatial location resolution for phylogeographic models of virus migration', 'Weissenbacher, Davy|Tahsin, Tasnia|Beard, Rachel|Figaro, Mari|Rivera, Robert|Scotch, Matthew|Gonzalez, Graciela', 'Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ 85259, USA and Center for Environmental Security, Biodesign Institute, Arizona State University, Tempe, AZ 85287-5904, USA.|Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ 85259, USA and Center for Environmental Security, Biodesign Institute, Arizona State University, Tempe, AZ 85287-5904, USA.|Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ 85259, USA and Center for Environmental Security, Biodesign Institute, Arizona State University, Tempe, AZ 85287-5904, USA Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ 85259, USA and Center for Environmental Security, Biodesign Institute, Arizona State University, Tempe, AZ 85287-5904, USA.|Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ 85259, USA and Center for Environmental Security, Biodesign Institute, Arizona State University, Tempe, AZ 85287-5904, USA Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ 85259, USA and Center for Environmental Security, Biodesign Institute, Arizona State University, Tempe, AZ 85287-5904, USA.|Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ 85259, USA and Center for Environmental Security, Biodesign Institute, Arizona State University, Tempe, AZ 85287-5904, USA.|Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ 85259, USA and Center for Environmental Security, Biodesign Institute, Arizona State University, Tempe, AZ 85287-5904, USA Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ 85259, USA and Center for Environmental Security, Biodesign Institute, Arizona State University, Tempe, AZ 85287-5904, USA.|Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ 85259, USA and Center for Environmental Security, Biodesign Institute, Arizona State University, Tempe, AZ 85287-5904, USA.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Databases, Nucleic Acid|Phylogeography|Sequence Analysis|Viruses', 'Journal Article', '10.1093/bioinformatics/btv259', '26072502', 'Diseases caused by zoonotic viruses (viruses transmittable between humans and animals) are a major threat to public health throughout the world. By studying virus migration and mutation patterns, the field of phylogeography provides a valuable tool for improving their surveillance. A key component in phylogeographic analysis of zoonotic viruses involves identifying the specific locations of relevant viral sequences. This is usually accomplished by querying public databases such as GenBank and examining the geospatial metadata in the record. When sufficient detail is not available, a logical next step is for the researcher to conduct a manual survey of the corresponding published articles.'),
(79315, 2015, 'bio', 'Reconstructing 16S rRNA genes in metagenomic data', 'Yuan, Cheng|Lei, Jikai|Cole, James|Sun, Yanni', 'Computer Science and Engineering, Michigan State Univerisity, 428 South Shaw Rd East Lansing, MI 48824, USA and Center for Microbial Ecology, Michigan State University, East Lansing, MI 48824, USA.|Computer Science and Engineering, Michigan State Univerisity, 428 South Shaw Rd East Lansing, MI 48824, USA and Center for Microbial Ecology, Michigan State University, East Lansing, MI 48824, USA.|Computer Science and Engineering, Michigan State Univerisity, 428 South Shaw Rd East Lansing, MI 48824, USA and Center for Microbial Ecology, Michigan State University, East Lansing, MI 48824, USA.|Computer Science and Engineering, Michigan State Univerisity, 428 South Shaw Rd East Lansing, MI 48824, USA and Center for Microbial Ecology, Michigan State University, East Lansing, MI 48824, USA.', 'United States|United States|United States|United States', 0, 'Computational Biology|Databases, Nucleic Acid|Genes, rRNA|Metagenomics|Microbiota|Phylogeny|RNA, Bacterial|RNA, Ribosomal, 16S|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btv231', '26072503', 'Metagenomic data, which contains sequenced DNA reads of uncultured microbial species from environmental samples, provide a unique opportunity to thoroughly analyze microbial species that have never been identified before. Reconstructing 16S ribosomal RNA, a phylogenetic marker gene, is usually required to analyze the composition of the metagenomic data. However, massive volume of dataset, high sequence similarity between related species, skewed microbial abundance and lack of reference genes make 16S rRNA reconstruction difficult. Generic de novo assembly tools are not optimized for assembling 16S rRNA genes. In this work, we introduce a targeted rRNA assembly tool, REAGO (REconstruct 16S ribosomal RNA Genes from metagenOmic data). It addresses the above challenges by combining secondary structure-aware homology search, zproperties of rRNA genes and de novo assembly. Our experimental results show that our tool can correctly recover more rRNA genes than several popular generic metagenomic assembly tools and specially designed rRNA construction tools.'),
(79316, 2015, 'bio', 'Exploiting ontology graph for predicting sparsely annotated gene function', 'Wang, Sheng|Cho, Hyunghoon|Zhai, ChengXiang|Berger, Bonnie|Peng, Jian', 'Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA and Department of Mathematics, MIT, Cambridge, MA, USA.|Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA and Department of Mathematics, MIT, Cambridge, MA, USA.|Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA and Department of Mathematics, MIT, Cambridge, MA, USA.|Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA and Department of Mathematics, MIT, Cambridge, MA, USA Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA and Department of Mathematics, MIT, Cambridge, MA, USA.|Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA and Department of Mathematics, MIT, Cambridge, MA, USA.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Animals|Computational Biology|Gene Ontology|Gene Regulatory Networks|Humans|Mice|Molecular Sequence Annotation|Proteins|Saccharomyces cerevisiae Proteins|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btv260', '26072504', 'Systematically predicting gene (or protein) function based on molecular interaction networks has become an important tool in refining and enhancing the existing annotation catalogs, such as the Gene Ontology (GO) database. However, functional labels with only a few (<10) annotated genes, which constitute about half of the GO terms in yeast, mouse and human, pose a unique challenge in that any prediction algorithm that independently considers each label faces a paucity of information and thus is prone to capture non-generalizable patterns in the data, resulting in poor predictive performance. There exist a variety of algorithms for function prediction, but none properly address this overfitting issue of sparsely annotated functions, or do so in a manner scalable to tens of thousands of functions in the human catalog.'),
(79317, 2015, 'bio', 'Protein (multi-)location prediction: utilizing interdependencies via a generative model', 'Simha, Ramanuja|Briesemeister, Sebastian|Kohlbacher, Oliver|Shatkay, Hagit', 'Department of Computer and Information Sciences, University of Delaware, Newark, DE, USA, Applied Bioinformatics, Center for Bioinformatics, University of Tuebingen, Germany, Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA and School of Computing, Queens University, Kingston, ON, Canada.|Department of Computer and Information Sciences, University of Delaware, Newark, DE, USA, Applied Bioinformatics, Center for Bioinformatics, University of Tuebingen, Germany, Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA and School of Computing, Queens University, Kingston, ON, Canada.|Department of Computer and Information Sciences, University of Delaware, Newark, DE, USA, Applied Bioinformatics, Center for Bioinformatics, University of Tuebingen, Germany, Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA and School of Computing, Queens University, Kingston, ON, Canada.|Department of Computer and Information Sciences, University of Delaware, Newark, DE, USA, Applied Bioinformatics, Center for Bioinformatics, University of Tuebingen, Germany, Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA and School of Computing, Queens University, Kingston, ON, Canada Department of Computer and Information Sciences, University of Delaware, Newark, DE, USA, Applied Bioinformatics, Center for Bioinformatics, University of Tuebingen, Germany, Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA and School of Computing, Queens University, Kingston, ON, Canada Department of Computer and Information Sciences, University of Delaware, Newark, DE, USA, Applied Bioinformatics, Center for Bioinformatics, University of Tuebingen, Germany, Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA and School of Computing, Queens University, Kingston, ON, Canada.', 'Canada|Canada|Canada|Canada', 9, 'Bayes Theorem|Databases, Protein|Humans|Models, Theoretical|Protein Transport|Proteins|Subcellular Fractions', 'Journal Article', '10.1093/bioinformatics/btv264', '26072505', 'Proteins are responsible for a multitude of vital tasks in all living organisms. Given that a proteins function and role are strongly related to its subcellular location, protein location prediction is an important research area. While proteins move from one location to another and can localize to multiple locations, most existing location prediction systems assign only a single location per protein. A few recent systems attempt to predict multiple locations for proteins, however, their performance leaves much room for improvement. Moreover, such systems do not capture dependencies among locations and usually consider locations as independent. We hypothesize that a multi-location predictor that captures location inter-dependencies can improve location predictions for proteins.'),
(79318, 2015, 'bio', 'Using collective expert judgements to evaluate quality measures of mass spectrometry images', 'Palmer, Andrew|Ovchinnikova, Ekaterina|Thuné, Mikael|Lavigne, Régis|Guével, Blandine|Dyatlov, Andrey|Vitek, Olga|Pineau, Charles|Borén, Mats|Alexandrov, Theodore', 'European Molecular Biology Laboratory, Heidelberg, Germany, Center for Industrial Mathematics, University of Bremen, Bremen, Germany, High Performance Humanoid Technologies Lab, Institute for Anthropomatics, Karlsruhe Institute of Technology, Karlsruhe, Germany, Denator, Uppsala, Sweden, Protim, Inserm U1085 - Irset, University of Rennes 1, Rennes, France, SCiLS GmbH, Bremen, Germany, College of Computer and Information Science, Northeastern University, Boston, MA, USA and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA European Molecular Biology Laboratory, Heidelberg, Germany, Center for Industrial Mathematics, University of Bremen, Bremen, Germany, High Performance Humanoid Technologies Lab, Institute for Anthropomatics, Karlsruhe Institute of Technology, Karlsruhe, Germany, Denator, Uppsala, Sweden, Protim, Inserm U1085 - Irset, University of Rennes 1, Rennes, France, SCiLS GmbH, Bremen, Germany, College of Computer and Information Science, Northeastern University, Boston, MA, USA and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA.|European Molecular Biology Laboratory, Heidelberg, Germany, Center for Industrial Mathematics, University of Bremen, Bremen, Germany, High Performance Humanoid Technologies Lab, Institute for Anthropomatics, Karlsruhe Institute of Technology, Karlsruhe, Germany, Denator, Uppsala, Sweden, Protim, Inserm U1085 - Irset, University of Rennes 1, Rennes, France, SCiLS GmbH, Bremen, Germany, College of Computer and Information Science, Northeastern University, Boston, MA, USA and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA European Molecular Biology Laboratory, Heidelberg, Germany, Center for Industrial Mathematics, University of Bremen, Bremen, Germany, High Performance Humanoid Technologies Lab, Institute for Anthropomatics, Karlsruhe Institute of Technology, Karlsruhe, Germany, Denator, Uppsala, Sweden, Protim, Inserm U1085 - Irset, University of Rennes 1, Rennes, France, SCiLS GmbH, Bremen, Germany, College of Computer and Information Science, Northeastern University, Boston, MA, USA and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA.|European Molecular Biology Laboratory, Heidelberg, Germany, Center for Industrial Mathematics, University of Bremen, Bremen, Germany, High Performance Humanoid Technologies Lab, Institute for Anthropomatics, Karlsruhe Institute of Technology, Karlsruhe, Germany, Denator, Uppsala, Sweden, Protim, Inserm U1085 - Irset, University of Rennes 1, Rennes, France, SCiLS GmbH, Bremen, Germany, College of Computer and Information Science, Northeastern University, Boston, MA, USA and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA.|European Molecular Biology Laboratory, Heidelberg, Germany, Center for Industrial Mathematics, University of Bremen, Bremen, Germany, High Performance Humanoid Technologies Lab, Institute for Anthropomatics, Karlsruhe Institute of Technology, Karlsruhe, Germany, Denator, Uppsala, Sweden, Protim, Inserm U1085 - Irset, University of Rennes 1, Rennes, France, SCiLS GmbH, Bremen, Germany, College of Computer and Information Science, Northeastern University, Boston, MA, USA and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA.|European Molecular Biology Laboratory, Heidelberg, Germany, Center for Industrial Mathematics, University of Bremen, Bremen, Germany, High Performance Humanoid Technologies Lab, Institute for Anthropomatics, Karlsruhe Institute of Technology, Karlsruhe, Germany, Denator, Uppsala, Sweden, Protim, Inserm U1085 - Irset, University of Rennes 1, Rennes, France, SCiLS GmbH, Bremen, Germany, College of Computer and Information Science, Northeastern University, Boston, MA, USA and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA.|European Molecular Biology Laboratory, Heidelberg, Germany, Center for Industrial Mathematics, University of Bremen, Bremen, Germany, High Performance Humanoid Technologies Lab, Institute for Anthropomatics, Karlsruhe Institute of Technology, Karlsruhe, Germany, Denator, Uppsala, Sweden, Protim, Inserm U1085 - Irset, University of Rennes 1, Rennes, France, SCiLS GmbH, Bremen, Germany, College of Computer and Information Science, Northeastern University, Boston, MA, USA and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA European Molecular Biology Laboratory, Heidelberg, Germany, Center for Industrial Mathematics, University of Bremen, Bremen, Germany, High Performance Humanoid Technologies Lab, Institute for Anthropomatics, Karlsruhe Institute of Technology, Karlsruhe, Germany, Denator, Uppsala, Sweden, Protim, Inserm U1085 - Irset, University of Rennes 1, Rennes, France, SCiLS GmbH, Bremen, Germany, College of Computer and Information Science, Northeastern University, Boston, MA, USA and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA.|European Molecular Biology Laboratory, Heidelberg, Germany, Center for Industrial Mathematics, University of Bremen, Bremen, Germany, High Performance Humanoid Technologies Lab, Institute for Anthropomatics, Karlsruhe Institute of Technology, Karlsruhe, Germany, Denator, Uppsala, Sweden, Protim, Inserm U1085 - Irset, University of Rennes 1, Rennes, France, SCiLS GmbH, Bremen, Germany, College of Computer and Information Science, Northeastern University, Boston, MA, USA and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA.|European Molecular Biology Laboratory, Heidelberg, Germany, Center for Industrial Mathematics, University of Bremen, Bremen, Germany, High Performance Humanoid Technologies Lab, Institute for Anthropomatics, Karlsruhe Institute of Technology, Karlsruhe, Germany, Denator, Uppsala, Sweden, Protim, Inserm U1085 - Irset, University of Rennes 1, Rennes, France, SCiLS GmbH, Bremen, Germany, College of Computer and Information Science, Northeastern University, Boston, MA, USA and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA.|European Molecular Biology Laboratory, Heidelberg, Germany, Center for Industrial Mathematics, University of Bremen, Bremen, Germany, High Performance Humanoid Technologies Lab, Institute for Anthropomatics, Karlsruhe Institute of Technology, Karlsruhe, Germany, Denator, Uppsala, Sweden, Protim, Inserm U1085 - Irset, University of Rennes 1, Rennes, France, SCiLS GmbH, Bremen, Germany, College of Computer and Information Science, Northeastern University, Boston, MA, USA and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA.|European Molecular Biology Laboratory, Heidelberg, Germany, Center for Industrial Mathematics, University of Bremen, Bremen, Germany, High Performance Humanoid Technologies Lab, Institute for Anthropomatics, Karlsruhe Institute of Technology, Karlsruhe, Germany, Denator, Uppsala, Sweden, Protim, Inserm U1085 - Irset, University of Rennes 1, Rennes, France, SCiLS GmbH, Bremen, Germany, College of Computer and Information Science, Northeastern University, Boston, MA, USA and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA European Molecular Biology Laboratory, Heidelberg, Germany, Center for Industrial Mathematics, University of Bremen, Bremen, Germany, High Performance Humanoid Technologies Lab, Institute for Anthropomatics, Karlsruhe Institute of Technology, Karlsruhe, Germany, Denator, Uppsala, Sweden, Protim, Inserm U1085 - Irset, University of Rennes 1, Rennes, France, SCiLS GmbH, Bremen, Germany, College of Computer and Information Science, Northeastern University, Boston, MA, USA and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA European Molecular Biology Laboratory, Heidelberg, Germany, Center for Industrial Mathematics, University of Bremen, Bremen, Germany, High Performance Humanoid Technologies Lab, Institute for Anthropomatics, Karlsruhe Institute of Technology, Karlsruhe, Germany, Denator, Uppsala, Sweden, Protim, Inserm U1085 - Irset, University of Rennes 1, Rennes, France, SCiLS GmbH, Bremen, Germany, College of Computer and Information Science, Northeastern University, Boston, MA, USA and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA European Molecular Biology Laboratory, Heidelberg, Germany, Center for Industrial Mathematics, University of Bremen, Bremen, Germany, High Performance Humanoid Technologies Lab, Institute for Anthropomatics, Karlsruhe Institute of Technolo', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Animals|Brain|Humans|Image Processing, Computer-Assisted|Mice|Molecular Imaging|Observer Variation|Pilot Projects|Quality Control|Rats|Reference Standards|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Journal Article', '10.1093/bioinformatics/btv266', '26072506', 'Imaging mass spectrometry (IMS) is a maturating technique of molecular imaging. Confidence in the reproducible quality of IMS data is essential for its integration into routine use. However, the predominant method for assessing quality is visual examination, a time consuming, unstandardized and non-scalable approach. So far, the problem of assessing the quality has only been marginally addressed and existing measures do not account for the spatial information of IMS data. Importantly, no approach exists for unbiased evaluation of potential quality measures.'),
(79319, 2015, 'bio', 'ASTRAL-II: coalescent-based species tree estimation with many hundreds of taxa and thousands of genes', 'Mirarab, Siavash|Warnow, Tandy', 'Department of Computer Science, The University of Texas at Austin, Austin, TX 78712, USA and Departments of Computer Science and Bioengineering, The University of Illinois at Urbana-Champaign, Champaign, IL 61801, USA.|Department of Computer Science, The University of Texas at Austin, Austin, TX 78712, USA and Departments of Computer Science and Bioengineering, The University of Illinois at Urbana-Champaign, Champaign, IL 61801, USA.', 'United States|United States', 0, 'Algorithms|Computer Simulation|Data Interpretation, Statistical|Genes, Plant|Genetic Speciation|Genomics|Phylogeny|Plants', 'Journal Article', '10.1093/bioinformatics/btv234', '26072508', 'The estimation of species phylogenies requires multiple loci, since different loci can have different trees due to incomplete lineage sorting, modeled by the multi-species coalescent model. We recently developed a coalescent-based method, ASTRAL, which is statistically consistent under the multi-species coalescent model and which is more accurate than other coalescent-based methods on the datasets we examined. ASTRAL runs in polynomial time, by constraining the search space using a set of allowed bipartitions. Despite the limitation to allowed bipartitions, ASTRAL is statistically consistent.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(79320, 2015, 'bio', 'IgRepertoireConstructor: a novel algorithm for antibody repertoire construction and immunoproteogenomics analysis', 'Safonova, Yana|Bonissone, Stefano|Kurpilyansky, Eugene|Starostina, Ekaterina|Lapidus, Alla|Stinson, Jeremy|DePalatis, Laura|Sandoval, Wendy|Lill, Jennie|Pevzner, Pavel A', 'Center for Algorithmic Biotechnology, St. Petersburg State University, St. Petersburg, Russia, Algorithmic Biology Laboratory, St. Petersburg Academic University, St. Petersburg, Russia, Bioinformatics Program, University of California, San Diego, CA, USA, Genentech, South San Francisco, CA, USA and Department of Computer Science and Engineering, University of California, San Diego, CA, USA Center for Algorithmic Biotechnology, St. Petersburg State University, St. Petersburg, Russia, Algorithmic Biology Laboratory, St. Petersburg Academic University, St. Petersburg, Russia, Bioinformatics Program, University of California, San Diego, CA, USA, Genentech, South San Francisco, CA, USA and Department of Computer Science and Engineering, University of California, San Diego, CA, USA.|Center for Algorithmic Biotechnology, St. Petersburg State University, St. Petersburg, Russia, Algorithmic Biology Laboratory, St. Petersburg Academic University, St. Petersburg, Russia, Bioinformatics Program, University of California, San Diego, CA, USA, Genentech, South San Francisco, CA, USA and Department of Computer Science and Engineering, University of California, San Diego, CA, USA.|Center for Algorithmic Biotechnology, St. Petersburg State University, St. Petersburg, Russia, Algorithmic Biology Laboratory, St. Petersburg Academic University, St. Petersburg, Russia, Bioinformatics Program, University of California, San Diego, CA, USA, Genentech, South San Francisco, CA, USA and Department of Computer Science and Engineering, University of California, San Diego, CA, USA.|Center for Algorithmic Biotechnology, St. Petersburg State University, St. Petersburg, Russia, Algorithmic Biology Laboratory, St. Petersburg Academic University, St. Petersburg, Russia, Bioinformatics Program, University of California, San Diego, CA, USA, Genentech, South San Francisco, CA, USA and Department of Computer Science and Engineering, University of California, San Diego, CA, USA Center for Algorithmic Biotechnology, St. Petersburg State University, St. Petersburg, Russia, Algorithmic Biology Laboratory, St. Petersburg Academic University, St. Petersburg, Russia, Bioinformatics Program, University of California, San Diego, CA, USA, Genentech, South San Francisco, CA, USA and Department of Computer Science and Engineering, University of California, San Diego, CA, USA.|Center for Algorithmic Biotechnology, St. Petersburg State University, St. Petersburg, Russia, Algorithmic Biology Laboratory, St. Petersburg Academic University, St. Petersburg, Russia, Bioinformatics Program, University of California, San Diego, CA, USA, Genentech, South San Francisco, CA, USA and Department of Computer Science and Engineering, University of California, San Diego, CA, USA Center for Algorithmic Biotechnology, St. Petersburg State University, St. Petersburg, Russia, Algorithmic Biology Laboratory, St. Petersburg Academic University, St. Petersburg, Russia, Bioinformatics Program, University of California, San Diego, CA, USA, Genentech, South San Francisco, CA, USA and Department of Computer Science and Engineering, University of California, San Diego, CA, USA.|Center for Algorithmic Biotechnology, St. Petersburg State University, St. Petersburg, Russia, Algorithmic Biology Laboratory, St. Petersburg Academic University, St. Petersburg, Russia, Bioinformatics Program, University of California, San Diego, CA, USA, Genentech, South San Francisco, CA, USA and Department of Computer Science and Engineering, University of California, San Diego, CA, USA.|Center for Algorithmic Biotechnology, St. Petersburg State University, St. Petersburg, Russia, Algorithmic Biology Laboratory, St. Petersburg Academic University, St. Petersburg, Russia, Bioinformatics Program, University of California, San Diego, CA, USA, Genentech, South San Francisco, CA, USA and Department of Computer Science and Engineering, University of California, San Diego, CA, USA.|Center for Algorithmic Biotechnology, St. Petersburg State University, St. Petersburg, Russia, Algorithmic Biology Laboratory, St. Petersburg Academic University, St. Petersburg, Russia, Bioinformatics Program, University of California, San Diego, CA, USA, Genentech, South San Francisco, CA, USA and Department of Computer Science and Engineering, University of California, San Diego, CA, USA.|Center for Algorithmic Biotechnology, St. Petersburg State University, St. Petersburg, Russia, Algorithmic Biology Laboratory, St. Petersburg Academic University, St. Petersburg, Russia, Bioinformatics Program, University of California, San Diego, CA, USA, Genentech, South San Francisco, CA, USA and Department of Computer Science and Engineering, University of California, San Diego, CA, USA.|Center for Algorithmic Biotechnology, St. Petersburg State University, St. Petersburg, Russia, Algorithmic Biology Laboratory, St. Petersburg Academic University, St. Petersburg, Russia, Bioinformatics Program, University of California, San Diego, CA, USA, Genentech, South San Francisco, CA, USA and Department of Computer Science and Engineering, University of California, San Diego, CA, USA Center for Algorithmic Biotechnology, St. Petersburg State University, St. Petersburg, Russia, Algorithmic Biology Laboratory, St. Petersburg Academic University, St. Petersburg, Russia, Bioinformatics Program, University of California, San Diego, CA, USA, Genentech, South San Francisco, CA, USA and Department of Computer Science and Engineering, University of California, San Diego, CA, USA Center for Algorithmic Biotechnology, St. Petersburg State University, St. Petersburg, Russia, Algorithmic Biology Laboratory, St. Petersburg Academic University, St. Petersburg, Russia, Bioinformatics Program, University of California, San Diego, CA, USA, Genentech, South San Francisco, CA, USA and Department of Computer Science and Engineering, University of California, San Diego, CA, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Antibodies|Databases, Factual|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Immunoglobulins|Mass Spectrometry|Peptide Fragments|Proteome|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btv238', '26072509', 'The analysis of concentrations of circulating antibodies in serum (antibody repertoire) is a fundamental, yet poorly studied, problem in immunoinformatics. The two current approaches to the analysis of antibody repertoires [next generation sequencing (NGS) and mass spectrometry (MS)] present difficult computational challenges since antibodies are not directly encoded in the germline but are extensively diversified by somatic recombination and hypermutations. Therefore, the protein database required for the interpretation of spectra from circulating antibodies is custom for each individual. Although such a database can be constructed via NGS, the reads generated by NGS are error-prone and even a single nucleotide error precludes identification of a peptide by the standard proteomics tools. Here, we present the IgRepertoireConstructor algorithm that performs error-correction of immunosequencing reads and uses mass spectra to validate the constructed antibody repertoires.'),
(79321, 2015, 'bio', 'Reconstruction of clonal trees and tumor composition from multi-sample sequencing data', 'El-Kebir, Mohammed|Oesper, Layla|Acheson-Field, Hannah|Raphael, Benjamin J', 'Center for Computational Molecular Biology and Department of Computer Science, Brown University, Providence, RI 02912, USA.|Center for Computational Molecular Biology and Department of Computer Science, Brown University, Providence, RI 02912, USA.|Center for Computational Molecular Biology and Department of Computer Science, Brown University, Providence, RI 02912, USA.|Center for Computational Molecular Biology and Department of Computer Science, Brown University, Providence, RI 02912, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Clonal Evolution|Gene Frequency|High-Throughput Nucleotide Sequencing|Humans|Models, Statistical|Mutation|Neoplasms|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btv261', '26072510', 'DNA sequencing of multiple samples from the same tumor provides data to analyze the process of clonal evolution in the population of cells that give rise to a tumor.'),
(79322, 2015, 'bio', 'Robust reconstruction of gene expression profiles from reporter gene data using linear inversion', 'Zulkower, Valentin|Page, Michel|Ropers, Delphine|Geiselmann, Johannes|de Jong, Hidde', 'INRIA Grenoble-Rhône-Alpes, 655 Avenue de lEurope, Montbonnot, 38334 Saint-Ismier Cedex, France, IAE Grenoble, Université Pierre-Mendès-France, Domaine universitaire BP 47, Grenoble Cedex 9, 38040 Saint Martin dHères, France and Laboratoire Interdisciplinaire de Physique (CNRS UMR 5588), Université Joseph Fourier, 140 Avenue de la physique BP 87, 38402 Saint Martin dHères, France.|INRIA Grenoble-Rhône-Alpes, 655 Avenue de lEurope, Montbonnot, 38334 Saint-Ismier Cedex, France, IAE Grenoble, Université Pierre-Mendès-France, Domaine universitaire BP 47, Grenoble Cedex 9, 38040 Saint Martin dHères, France and Laboratoire Interdisciplinaire de Physique (CNRS UMR 5588), Université Joseph Fourier, 140 Avenue de la physique BP 87, 38402 Saint Martin dHères, France INRIA Grenoble-Rhône-Alpes, 655 Avenue de lEurope, Montbonnot, 38334 Saint-Ismier Cedex, France, IAE Grenoble, Université Pierre-Mendès-France, Domaine universitaire BP 47, Grenoble Cedex 9, 38040 Saint Martin dHères, France and Laboratoire Interdisciplinaire de Physique (CNRS UMR 5588), Université Joseph Fourier, 140 Avenue de la physique BP 87, 38402 Saint Martin dHères, France.|INRIA Grenoble-Rhône-Alpes, 655 Avenue de lEurope, Montbonnot, 38334 Saint-Ismier Cedex, France, IAE Grenoble, Université Pierre-Mendès-France, Domaine universitaire BP 47, Grenoble Cedex 9, 38040 Saint Martin dHères, France and Laboratoire Interdisciplinaire de Physique (CNRS UMR 5588), Université Joseph Fourier, 140 Avenue de la physique BP 87, 38402 Saint Martin dHères, France.|INRIA Grenoble-Rhône-Alpes, 655 Avenue de lEurope, Montbonnot, 38334 Saint-Ismier Cedex, France, IAE Grenoble, Université Pierre-Mendès-France, Domaine universitaire BP 47, Grenoble Cedex 9, 38040 Saint Martin dHères, France and Laboratoire Interdisciplinaire de Physique (CNRS UMR 5588), Université Joseph Fourier, 140 Avenue de la physique BP 87, 38402 Saint Martin dHères, France INRIA Grenoble-Rhône-Alpes, 655 Avenue de lEurope, Montbonnot, 38334 Saint-Ismier Cedex, France, IAE Grenoble, Université Pierre-Mendès-France, Domaine universitaire BP 47, Grenoble Cedex 9, 38040 Saint Martin dHères, France and Laboratoire Interdisciplinaire de Physique (CNRS UMR 5588), Université Joseph Fourier, 140 Avenue de la physique BP 87, 38402 Saint Martin dHères, France.|INRIA Grenoble-Rhône-Alpes, 655 Avenue de lEurope, Montbonnot, 38334 Saint-Ismier Cedex, France, IAE Grenoble, Université Pierre-Mendès-France, Domaine universitaire BP 47, Grenoble Cedex 9, 38040 Saint Martin dHères, France and Laboratoire Interdisciplinaire de Physique (CNRS UMR 5588), Université Joseph Fourier, 140 Avenue de la physique BP 87, 38402 Saint Martin dHères, France.', 'France|France|France|France|France', 8, 'Algorithms|Computational Biology|Escherichia coli|Escherichia coli Proteins|Gene Expression Profiling|Gene Expression Regulation, Bacterial|Genes, Bacterial|Genes, Reporter|Kinetics|Regression Analysis', 'Journal Article', '10.1093/bioinformatics/btv246', '26072511', 'Time-series observations from reporter gene experiments are commonly used for inferring and analyzing dynamical models of regulatory networks. The robust estimation of promoter activities and protein concentrations from primary data is a difficult problem due to measurement noise and the indirect relation between the measurements and quantities of biological interest.'),
(79323, 2015, 'bio', 'Misassembly detection using paired-end sequence reads and optical mapping data', 'Muggli, Martin D|Puglisi, Simon J|Ronen, Roy|Boucher, Christina', 'Department of Computer Science, Colorado State University, Fort Collins, CO 80526, USA, Department of Computer Science, University of Helsinki, Finland and Bioinformatics Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA.|Department of Computer Science, Colorado State University, Fort Collins, CO 80526, USA, Department of Computer Science, University of Helsinki, Finland and Bioinformatics Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA.|Department of Computer Science, Colorado State University, Fort Collins, CO 80526, USA, Department of Computer Science, University of Helsinki, Finland and Bioinformatics Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA.|Department of Computer Science, Colorado State University, Fort Collins, CO 80526, USA, Department of Computer Science, University of Helsinki, Finland and Bioinformatics Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA.', 'Finland|Finland|Finland|Finland', 0, 'Algorithms|Animals|Computational Biology|Contig Mapping|Francisella tularensis|Genome|Melopsittacus|Oryza|Pinus|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btv262', '26072512', 'A crucial problem in genome assembly is the discovery and correction of misassembly errors in draft genomes. We develop a method called misSEQuel that enhances the quality of draft genomes by identifying misassembly errors and their breakpoints using paired-end sequence reads and optical mapping data. Our method also fulfills the critical need for open source computational methods for analyzing optical mapping data. We apply our method to various assemblies of the loblolly pine, Francisella tularensis, rice and budgerigar genomes. We generated and used stimulated optical mapping data for loblolly pine and F.tularensis and used real optical mapping data for rice and budgerigar.'),
(79324, 2015, 'bio', 'Reconstructing gene regulatory dynamics from high-dimensional single-cell snapshot data', 'Ocone, Andrea|Haghverdi, Laleh|Mueller, Nikola S|Theis, Fabian J', 'Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany and Department of Mathematics, Technical University Munich, 85747 Garching, Germany.|Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany and Department of Mathematics, Technical University Munich, 85747 Garching, Germany.|Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany and Department of Mathematics, Technical University Munich, 85747 Garching, Germany.|Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany and Department of Mathematics, Technical University Munich, 85747 Garching, Germany Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany and Department of Mathematics, Technical University Munich, 85747 Garching, Germany.', 'Germany|Germany|Germany|Germany', 70, 'Algorithms|Gene Expression Profiling|Gene Regulatory Networks|Hematopoiesis|Hematopoietic Stem Cells|Kinetics|Models, Genetic|Single-Cell Analysis|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btv257', '26072513', 'High-dimensional single-cell snapshot data are becoming widespread in the systems biology community, as a mean to understand biological processes at the cellular level. However, as temporal information is lost with such data, mathematical models have been limited to capture only static features of the underlying cellular mechanisms.'),
(79325, 2015, 'bio', 'De novo meta-assembly of ultra-deep sequencing data', 'Mirebrahim, Hamid|Close, Timothy J|Lonardi, Stefano', 'Department of Computer Science and Engineering and Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA.|Department of Computer Science and Engineering and Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA.|Department of Computer Science and Engineering and Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA.', 'United States|United States|United States', 0, 'Algorithms|Genes, Plant|High-Throughput Nucleotide Sequencing|Hordeum|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btv226', '26072514', 'We introduce a new divide and conquer approach to deal with the problem of de novo genome assembly in the presence of ultra-deep sequencing data (i.e. coverage of 1000x or higher). Our proposed meta-assembler Slicembler partitions the input data into optimal-sized slices and uses a standard assembly tool (e.g. Velvet, SPAdes, IDBA_UD and Ray) to assemble each slice individually. Slicembler uses majority voting among the individual assemblies to identify long contigs that can be merged to the consensus assembly. To improve its efficiency, Slicembler uses a generalized suffix tree to identify these frequent contigs (or fraction thereof). Extensive experimental results on real ultra-deep sequencing data (8000x coverage) and simulated data show that Slicembler significantly improves the quality of the assembly compared with the performance of the base assembler. In fact, most of the times, Slicembler generates error-free assemblies. We also show that Slicembler is much more resistant against high sequencing error rate than the base assembler.'),
(79326, 2015, 'bio', 'Inferring orthologous gene regulatory networks using interspecies data fusion', 'Penfold, Christopher A|Millar, Jonathan B A|Wild, David L', 'Warwick Systems Biology Centre and Biomedical Cell Biology, Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK.|Warwick Systems Biology Centre and Biomedical Cell Biology, Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK.|Warwick Systems Biology Centre and Biomedical Cell Biology, Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Bayes Theorem|Cell Cycle|Computer Simulation|Gene Expression Profiling|Gene Regulatory Networks|Models, Genetic|Saccharomyces cerevisiae|Schizosaccharomyces|Software', 'Journal Article', '10.1093/bioinformatics/btv267', '26072515', 'The ability to jointly learn gene regulatory networks (GRNs) in, or leverage GRNs between related species would allow the vast amount of legacy data obtained in model organisms to inform the GRNs of more complex, or economically or medically relevant counterparts. Examples include transferring information from Arabidopsis thaliana into related crop species for food security purposes, or from mice into humans for medical applications. Here we develop two related Bayesian approaches to network inference that allow GRNs to be jointly inferred in, or leveraged between, several related species: in one framework, network information is directly propagated between species, in the second hierarchical approach, network information is propagated via an unobserved hypernetwork. In both frameworks, information about network similarity is captured via graph kernels, with the networks additionally informed by species-specific time series gene expression data, when available, using Gaussian processes to model the dynamics of gene expression.'),
(79327, 2015, 'bio', 'REDEMPTION: reduced dimension ensemble modeling and parameter estimation', 'Liu, Yang|Manesso, Erica|Gunawan, Rudiyanto', 'Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich, 8093, Zurich, Switzerland and Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland.|Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich, 8093, Zurich, Switzerland and Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland.|Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich, 8093, Zurich, Switzerland and Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland.', 'Switzerland|Switzerland|Switzerland', 5, 'Algorithms|Models, Biological|Software', 'Journal Article', '10.1093/bioinformatics/btv365', '26076722', 'Here, we present REDEMPTION ( RE: duced D: imension E: nsemble M: odeling and P: arameter estima TION: ), a toolbox for parameter estimation and ensemble modeling of ordinary differential equations (ODEs) using time-series data. For models with more reactions than measured species, a common scenario in biological modeling, the parameter estimation is formulated as a nested optimization problem based on incremental parameter estimation strategy. REDEMPTION also includes a tool for the identification of an ensemble of parameter combinations that provide satisfactory goodness-of-fit to the data. The functionalities of REDEMPTION are accessible through a MATLAB user interface (UI), as well as through programming script. For computational speed-up, REDEMPTION provides a numerical parallelization option using MATLAB Parallel Computing toolbox.'),
(79328, 2015, 'bio', 'PPUS: a web server to predict PUS-specific pseudouridine sites', 'Li, Yan-Hui|Zhang, Gaigai|Cui, Qinghua', 'Institute of Cardiovascular Sciences, Peking University Health Science Center.|Department of Geriatrics and Gerontology, Beijing Huaxin Hospital, the First Affiliated Hospital of Tsinghua University and.|Department of Biomedical Informatics, Peking University Health Science Center, Beijing, China.', '-|-|China', 0, 'Algorithms|Humans|Internet|Intramolecular Transferases|Pseudouridine|RNA, Transfer|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Sequence Analysis, RNA|Software|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/btv366', '26076723', 'Pseudouridine (xcexa8), catalyzed by pseudouridine synthase (PUS), is the most abundant RNA modification and has important cellular functions. Developing an algorithm to identify xcexa8 sites is an important work. And it is better if the algorithm could assign which PUS modifies the xcexa8 sites. Here, we developed PPUS (http://lyh.pkmu.cn/ppus/), the first web server to predict PUS-specific xcexa8 sites. PPUS: employed support vector machine as the classifier and used nucleotides around xcexa8 sites as the features. Currently, PPUS: could accurately predict new xcexa8 sites for PUS1, PUS4 and PUS7 in yeast and PUS4 in human. PPUS: is well designed and friendly to user.'),
(79329, 2015, 'bio', 'A note on the false discovery rate of novel peptides in proteogenomics', 'Zhang, Kun|Fu, Yan|Zeng, Wen-Feng|He, Kun|Chi, Hao|Liu, Chao|Li, Yan-Chang|Gao, Yuan|Xu, Ping|He, Si-Min', 'Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190, University of Chinese Academy of Sciences, Beijing 100049.|National Center for Mathematics and Interdisciplinary Sciences, Key Laboratory of Random Complex Structures and Data Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190 and.|Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190, University of Chinese Academy of Sciences, Beijing 100049.|Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190, University of Chinese Academy of Sciences, Beijing 100049.|Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190.|Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190.|State Key Laboratory of Proteomics, National Engineering Research Center for Protein Drugs, Beijing Proteome Research Center, National Center for Protein Sciences Beijing, Beijing Institute of Radiation Medicine, Beijing 102206, China.|State Key Laboratory of Proteomics, National Engineering Research Center for Protein Drugs, Beijing Proteome Research Center, National Center for Protein Sciences Beijing, Beijing Institute of Radiation Medicine, Beijing 102206, China.|State Key Laboratory of Proteomics, National Engineering Research Center for Protein Drugs, Beijing Proteome Research Center, National Center for Protein Sciences Beijing, Beijing Institute of Radiation Medicine, Beijing 102206, China.|Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190.', '-|-|-|-|-|-|China|China|China|-', 0, 'Escherichia coli|Molecular Sequence Annotation|Mycobacterium tuberculosis|Peptides|Proteomics', 'Journal Article', '10.1093/bioinformatics/btv340', '26076724', 'Proteogenomics has been well accepted as a tool to discover novel genes. In most conventional proteogenomic studies, a global false discovery rate is used to filter out false positives for identifying credible novel peptides. However, it has been found that the actual level of false positives in novel peptides is often out of control and behaves differently for different genomes.'),
(79330, 2015, 'bio', 'MetaPathways v25: quantitative functional, taxonomic and usability improvements', 'Konwar, Kishori M|Hanson, Niels W|Bhatia, Maya P|Kim, Dongjae|Wu, Shang-Ju|Hahn, Aria S|Morgan-Lang, Connor|Cheung, Hiu Kan|Hallam, Steven J', 'Department of Microbiology & Immunology, University of British Columbia, 2350 Health Sciences Mall, Vancouver, BC, Canada.|Graduate Program in Bioinformatics, University of British Columbia, Genome Sciences Centre, 100-570 West 7th Avenue, Vancouver, BC, Canada and.|Department of Microbiology & Immunology, University of British Columbia, 2350 Health Sciences Mall, Vancouver, BC, Canada.|Department of Computer Science, University of British Columbia, 2366 Main Mall, Vancouver, BC, Canada.|Department of Computer Science, University of British Columbia, 2366 Main Mall, Vancouver, BC, Canada.|Department of Microbiology & Immunology, University of British Columbia, 2350 Health Sciences Mall, Vancouver, BC, Canada.|Graduate Program in Bioinformatics, University of British Columbia, Genome Sciences Centre, 100-570 West 7th Avenue, Vancouver, BC, Canada and.|Department of Microbiology & Immunology, University of British Columbia, 2350 Health Sciences Mall, Vancouver, BC, Canada.|Department of Microbiology & Immunology, University of British Columbia, 2350 Health Sciences Mall, Vancouver, BC, Canada, Graduate Program in Bioinformatics, University of British Columbia, Genome Sciences Centre, 100-570 West 7th Avenue, Vancouver, BC, Canada and.', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada', 0, 'Algorithms|Databases, Genetic|Genomics|High-Throughput Nucleotide Sequencing|Humans|Information Storage and Retrieval|Molecular Sequence Annotation|Phylogeny|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btv361', '26076725', 'Next-generation sequencing is producing vast amounts of sequence information from natural and engineered ecosystems. Although this data deluge has an enormous potential to transform our lives, knowledge creation and translation need software applications that scale with increasing data processing and analysis requirements. Here, we present improvements to MetaPathways, an annotation and analysis pipeline for environmental sequence information that expedites this transformation. We specifically address pathway prediction hazards through integration of a weighted taxonomic distance and enable quantitative comparison of assembled annotations through a normalized read-mapping measure. Additionally, we improve LAST homology searches through BLAST-equivalent E-values and output formats that are natively compatible with prevailing software applications. Finally, an updated graphical user interface allows for keyword annotation query and projection onto user-defined functional gene hierarchies, including the Carbohydrate-Active Enzyme database.'),
(79331, 2015, 'bio', 'JSBML 10: providing a smorgasbord of options to encode systems biology models', 'Rodriguez, Nicolas|Thomas, Alex|Watanabe, Leandro|Vazirabad, Ibrahim Y|Kofia, Victor|Gómez, Harold F|Mittag, Florian|Matthes, Jakob|Rudolph, Jan|Wrzodek, Finja|Netz, Eugen|Diamantikos, Alexander|Eichner, Johannes|Keller, Roland|Wrzodek, Clemens|Fröhlich, Sebastian|Lewis, Nathan E|Myers, Chris J|Le Novère, Nicolas|Palsson, Bernhard Ø|Hucka, Michael|Dräger, Andreas', 'European Bioinformatics Institute (EBI), Hinxton, UK, Babraham Institute, Babraham Research Campus, Cambridge, UK.|University of California, San Diego, La Jolla, CA, USA, Novo Nordisk Foundation Center for Biosustainability, University of California, San Diego, La Jolla, CA, USA.|The University of Utah, Salt Lake City, UT, USA.|Marquette University, Milwaukee, WI, USA.|The University of Toronto, Toronto, ON, Canada.|Boston University, Boston, MA, USA.|European Bioinformatics Institute (EBI), Hinxton, UK, Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany.|Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany.|Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany, Tel Aviv University, Tel Aviv, Israel.|European Bioinformatics Institute (EBI), Hinxton, UK, Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany.|Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany.|Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany.|Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany.|Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany.|Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany, Roche Pharmaceutical Research and Early Development, pRED Informatics, Roche Innovation Center, Penzberg, Germany.|Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany, Martin Luther University of Halle-Wittenberg, Halle (Saale), Germany and.|University of California, San Diego, La Jolla, CA, USA, Novo Nordisk Foundation Center for Biosustainability, University of California, San Diego, La Jolla, CA, USA.|The University of Utah, Salt Lake City, UT, USA.|European Bioinformatics Institute (EBI), Hinxton, UK, Babraham Institute, Babraham Research Campus, Cambridge, UK.|University of California, San Diego, La Jolla, CA, USA, Novo Nordisk Foundation Center for Biosustainability, University of California, San Diego, La Jolla, CA, USA.|The California Institute of Technology, Pasadena, CA, USA.|University of California, San Diego, La Jolla, CA, USA, Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|Canada|United Kingdom|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|United Kingdom|United Kingdom', 15, 'Computer Simulation|Models, Biological|Programming Languages|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btv341', '26079347', 'JSBML, the official pure Java programming library for the Systems Biology Markup Language (SBML) format, has evolved with the advent of different modeling formalisms in systems biology and their ability to be exchanged and represented via extensions of SBML. JSBML has matured into a major, active open-source project with contributions from a growing, international team of developers who not only maintain compatibility with SBML, but also drive steady improvements to the Java interface and promote ease-of-use with end users.'),
(79332, 2015, 'bio', 'TENET: topological feature-based target characterization in signalling networks', 'Chua, Huey Eng|Bhowmick, Sourav S|Tucker-Kellogg, Lisa|Dewey, C Forbes', 'School of Computer Engineering, Nanyang Technological University.|School of Computer Engineering, Nanyang Technological University.|Duke-NUS Graduate Medical School, National University of Singapore, Singapore and.|Biological Engineering Department, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.', 'United States|United States|Singapore|United States', 0, 'Algorithms|Humans|Protein Interaction Mapping|Signal Transduction|Software|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/btv360', '26079348', 'Target characterization for a biochemical network is a heuristic evaluation process that produces a characterization model that may aid in predicting the suitability of each molecule for drug targeting. These approaches are typically used in drug research to identify novel potential targets using insights from known targets. Traditional approaches that characterize targets based on their molecular characteristics and biological function require extensive experimental study of each protein and are infeasible for evaluating larger networks with poorly understood proteins. Moreover, they fail to exploit network connectivity information which is now available from systems biology methods. Adopting a network-based approach by characterizing targets using network features provides greater insights that complement these traditional techniques. To this end, we present Tenet (Target charactErization using NEtwork Topology), a network-based approach that characterizes known targets in signalling networks using topological features.'),
(79333, 2015, 'bio', 'TPpred3 detects and discriminates mitochondrial and chloroplastic targeting peptides in eukaryotic proteins', 'Savojardo, Castrense|Martelli, Pier Luigi|Fariselli, Piero|Casadio, Rita', 'Biocomputing Group, University of Bologna, Department of Biology, 40126 Bologna, Italy and.|Biocomputing Group, University of Bologna, Department of Biology, 40126 Bologna, Italy and.|Biocomputing Group, University of Bologna, Department of Biology, 40126 Bologna, Italy and Department of Computer Science and Engineering, University of Bologna, 40127 Bologna, Italy.|Biocomputing Group, University of Bologna, Department of Biology, 40126 Bologna, Italy and.', 'Italy|Italy|Italy|Italy', 17, 'Chloroplast Proteins|Chloroplasts|Eukaryota|Machine Learning|Mitochondria|Mitochondrial Proteins|Peptides|Protein Transport|Sequence Analysis, Protein|Software|Support Vector Machine', 'Evaluation Studies', '10.1093/bioinformatics/btv367', '26079349', 'Molecular recognition of N-terminal targeting peptides is the most common mechanism controlling the import of nuclear-encoded proteins into mitochondria and chloroplasts. When experimental information is lacking, computational methods can annotate targeting peptides, and determine their cleavage sites for characterizing protein localization, function, and mature protein sequences. The problem of discriminating mitochondrial from chloroplastic propeptides is particularly relevant when annotating proteomes of photosynthetic Eukaryotes, endowed with both types of sequences.'),
(79334, 2015, 'bio', 'Investigating microbial co-occurrence patterns based on metagenomic compositional data', 'Ban, Yuguang|An, Lingling|Jiang, Hongmei', 'Department of Statistics, Northwestern University, Evanston, IL 60208, USA.|Interdisciplinary Program in Statistics, University of Arizona, Tucson, AZ 85721, USA and Department of Agricultural and Biosystems Engineering, University of Arizona, Tucson, AZ 85721, USA.|Department of Statistics, Northwestern University, Evanston, IL 60208, USA.', 'United States|United States|United States', 0, 'Algorithms|Computational Biology|High-Throughput Nucleotide Sequencing|Humans|Immunization|Metagenomics|Skin|Species Specificity', 'Journal Article', '10.1093/bioinformatics/btv364', '26079350', 'The high-throughput sequencing technologies have provided a powerful tool to study the microbial organisms living in various environments. Characterizing microbial interactions can give us insights into how they live and work together as a community. Metagonomic data are usually summarized in a compositional fashion due to varying sampling/sequencing depths from one sample to another. We study the co-occurrence patterns of microbial organisms using their relative abundance information. Analyzing compositional data using conventional correlation methods has been shown prone to bias that leads to artifactual correlations.'),
(79335, 2015, 'bio', 'libRoadRunner: a high performance SBML simulation and analysis library', 'Somogyi, Endre T|Bouteiller, Jean-Marie|Glazier, James A|König, Matthias|Medley, J Kyle|Swat, Maciej H|Sauro, Herbert M', 'Biocomplexity Institute and Department of Physics, Indiana University, Bloomington, IN, 47405, USA.|Biomedical Engineering Department, University of Southern California, Los Angeles, CA, 90089, USA.|Biocomplexity Institute and Department of Physics, Indiana University, Bloomington, IN, 47405, USA.|Department of Computational Systems Biochemistry, University Medicine Charité Berlin, 10117, Berlin, Germany and.|Department of Bioengineering, University of Washington, Seattle, WA 98195, USA.|Biocomplexity Institute and Department of Physics, Indiana University, Bloomington, IN, 47405, USA.|Department of Bioengineering, University of Washington, Seattle, WA 98195, USA.', 'India|United States|India|Germany|United States|India|United States', 44, 'Cell Adhesion|Computer Simulation|Humans|Liver|Models, Biological|Models, Theoretical|Neurons|Programming Languages|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btv363', '26085503', 'This article presents libRoadRunner, an extensible, high-performance, cross-platform, open-source software library for the simulation and analysis of models expressed using Systems Biology Markup Language (SBML). SBML is the most widely used standard for representing dynamic networks, especially biochemical networks. libRoadRunner is fast enough to support large-scale problems such as tissue models, studies that require large numbers of repeated runs and interactive simulations.'),
(79336, 2015, 'bio', 'Fast-SL: an efficient algorithm to identify synthetic lethal sets in metabolic networks', 'Pratapa, Aditya|Balachandran, Shankar|Raman, Karthik', 'Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences and.|Department of Computer Science and Engineering, Indian Institute of Technology Madras, Chennai 600xe2x80x89036, India.|Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences and.', '-|India|-', 0, 'Algorithms|Genes, Lethal|Genome, Bacterial|Metabolic Networks and Pathways|Programming, Linear', 'Journal Article', '10.1093/bioinformatics/btv352', '26085504', 'Synthetic lethal sets are sets of reactions/genes where only the simultaneous removal of all reactions/genes in the set abolishes growth of an organism. Previous approaches to identify synthetic lethal genes in genome-scale metabolic networks have built on the framework of flux balance analysis (FBA), extending it either to exhaustively analyze all possible combinations of genes or formulate the problem as a bi-level mixed integer linear programming (MILP) problem. We here propose an algorithm, Fast-SL, which surmounts the computational complexity of previous approaches by iteratively reducing the search space for synthetic lethals, resulting in a substantial reduction in running time, even for higher order synthetic lethals.'),
(79337, 2015, 'bio', 'AmyLoad: website dedicated to amyloidogenic protein fragments', 'Wozniak, Pawel P|Kotulska, Malgorzata', 'Department of Biomedical Engineering, Wroclaw University of Technology, Wroclaw, Poland.|Department of Biomedical Engineering, Wroclaw University of Technology, Wroclaw, Poland.', 'Poland|Poland', 0, 'Amyloidogenic Proteins|Databases, Protein|Internet|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btv375', '26088800', 'Analyses of amyloidogenic sequence fragments are essential in studies of neurodegenerative diseases. However, there is no one internet dataset that collects all the sequences that have been investigated for their amyloidogenicity. Therefore, we have created the AmyLoad website which collects the amyloidogenic sequences from all major sources. The website allows for filtration of the fragments and provides detailed information about each of them. Registered users can both personalize their work with the website and submit their own sequences into the database. To maintain database reliability, submitted sequences are reviewed before making them available to the public. Finally, we re-implemented several amyloidogenic sequence predictors, thus the AmyLoad website can be used as a sequence analysis tool. We encourage researchers working on amyloid proteins to contribute to our service.'),
(79338, 2015, 'bio', 'Rchemcpp: a web service for structural analoging in ChEMBL, Drugbank and the Connectivity Map', 'Klambauer, Günter|Wischenbart, Martin|Mahr, Michael|Unterthiner, Thomas|Mayr, Andreas|Hochreiter, Sepp', 'Institute of Bioinformatics, Johannes Kepler University Linz, Altenbergerstr. 69, 4040 Linz, Austria.|Institute of Bioinformatics, Johannes Kepler University Linz, Altenbergerstr. 69, 4040 Linz, Austria.|Institute of Bioinformatics, Johannes Kepler University Linz, Altenbergerstr. 69, 4040 Linz, Austria.|Institute of Bioinformatics, Johannes Kepler University Linz, Altenbergerstr. 69, 4040 Linz, Austria.|Institute of Bioinformatics, Johannes Kepler University Linz, Altenbergerstr. 69, 4040 Linz, Austria.|Institute of Bioinformatics, Johannes Kepler University Linz, Altenbergerstr. 69, 4040 Linz, Austria.', 'Austria|Austria|Austria|Austria|Austria|Austria', 7, 'Databases, Chemical|Drug Discovery|Gene Expression|Internet|Machine Learning|Software', 'Journal Article', '10.1093/bioinformatics/btv373', '26088801', 'We have developed Rchempp, a web service that identifies structurally similar compounds (structural analogs) in large-scale molecule databases. The service allows compounds to be queried in the widely used ChEMBL, DrugBank and the Connectivity Map databases. Rchemcpp utilizes the best performing similarity functions, i.e. molecule kernels, as measures for structural similarity. Molecule kernels have proven superior performance over other similarity measures and are currently excelling at machine learning challenges. To considerably reduce computational time, and thereby make it feasible as a web service, a novel efficient prefiltering strategy has been developed, which maintains the sensitivity of the method. By exploiting information contained in public databases, the web service facilitates many applications crucial for the drug development process, such as prioritizing compounds after screening or reducing adverse side effects during late phases. Rchemcpp was used in the DeepTox pipeline that has won the Tox21 Data Challenge and is frequently used by researchers in pharmaceutical companies.'),
(79339, 2015, 'bio', 'NCC-AUC: an AUC optimization method to identify multi-biomarker panel for cancer prognosis from genomic and clinical data', 'Zou, Meng|Liu, Zhaoqi|Zhang, Xiang-Sun|Wang, Yong', 'Academy of Mathematics and Systems Science, National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences, Beijing 10080, China.|Academy of Mathematics and Systems Science, National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences, Beijing 10080, China.|Academy of Mathematics and Systems Science, National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences, Beijing 10080, China.|Academy of Mathematics and Systems Science, National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences, Beijing 10080, China.', 'China|China|China|China', 9, 'Algorithms|Area Under Curve|Biomarkers|Breast Neoplasms|Carcinoma, Non-Small-Cell Lung|Data Interpretation, Statistical|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Genomics|Humans|Lung Neoplasms|Models, Biological|Pattern Recognition, Automated|Prognosis|Proportional Hazards Models|Support Vector Machine|Survival Rate|Systems Biology|Systems Integration', 'Journal Article', '10.1093/bioinformatics/btv374', '26092859', 'In prognosis and survival studies, an important goal is to identify multi-biomarker panels with predictive power using molecular characteristics or clinical observations. Such analysis is often challenged by censored, small-sample-size, but high-dimensional genomic profiles or clinical data. Therefore, sophisticated models and algorithms are in pressing need.'),
(79340, 2015, 'bio', 'atSNP: transcription factor binding affinity testing for regulatory SNP detection', 'Zuo, Chandler|Shin, Sunyoung|Kelexc5x9f, Sxc3xbcndxc3xbcz', 'Department of Statistics and Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA.|Department of Statistics and Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA.|Department of Statistics and Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA.', 'United States|United States|United States', 0, 'Algorithms|Databases, Genetic|Gene Expression Regulation|Genome, Human|Genome-Wide Association Study|Genomics|Humans|Polymorphism, Single Nucleotide|Protein Binding|Regulatory Sequences, Nucleic Acid|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btv328', '26092860', 'Genome-wide association studies revealed that most disease-associated single nucleotide polymorphisms (SNPs) are located in regulatory regions within introns or in regions between genes. Regulatory SNPs (rSNPs) are such SNPs that affect gene regulation by changing transcription factor (TF) binding affinities to genomic sequences. Identifying potential rSNPs is crucial for understanding disease mechanisms. In silico methods that evaluate the impact of SNPs on TF binding affinities are not scalable for large-scale analysis.'),
(79341, 2015, 'bio', 'IBiSS, a versatile and interactive tool for integrated sequence and 3D structure analysis of large macromolecular complexes', 'Beinsteiner, Brice|Michalon, Jonathan|Klaholz, Bruno P', 'Centre for Integrative Biology (CBI), Department of Integrated Structural Biology, IGBMC (Institute of Genetics and of Molecular and Cellular Biology), Illkirch, France, Centre National de la Recherche Scientifique (CNRS) UMR 7104, Illkirch, France, Institut National de la Santé et de la Recherche Médicale (INSERM) U964, Illkirch, France and Université de Strasbourg, Strasbourg, France.|Centre for Integrative Biology (CBI), Department of Integrated Structural Biology, IGBMC (Institute of Genetics and of Molecular and Cellular Biology), Illkirch, France, Centre National de la Recherche Scientifique (CNRS) UMR 7104, Illkirch, France, Institut National de la Santé et de la Recherche Médicale (INSERM) U964, Illkirch, France and Université de Strasbourg, Strasbourg, France.|Centre for Integrative Biology (CBI), Department of Integrated Structural Biology, IGBMC (Institute of Genetics and of Molecular and Cellular Biology), Illkirch, France, Centre National de la Recherche Scientifique (CNRS) UMR 7104, Illkirch, France, Institut National de la Santé et de la Recherche Médicale (INSERM) U964, Illkirch, France and Université de Strasbourg, Strasbourg, France.', 'France|France|France', 3, 'Amino Acids|Cryoelectron Microscopy|DNA-Directed RNA Polymerases|Databases, Protein|Humans|Imaging, Three-Dimensional|Macromolecular Substances|Models, Molecular|Nucleosomes|Photosystem I Protein Complex|Photosystem II Protein Complex|Proteasome Endopeptidase Complex|Proteins|Ribosomes|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btv347', '26092861', 'In the past few years, an increasing number of crystal and cryo electron microscopy (cryo-EM) structures of large macromolecular complexes, such as the ribosome or the RNA polymerase, have become available from various species. These multi-subunit complexes can be difficult to analyze at the level of amino acid sequence in combination with the 3D structural organization of the complex. Therefore, novel tools for simultaneous analysis of structure and sequence information of complex assemblies are required to better understand the basis of molecular mechanisms and their functional implications.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(79342, 2015, 'bio', 'An efficient Bayesian inference framework for coalescent-based nonparametric phylodynamics', 'Lan, Shiwei|Palacios, Julia A|Karcher, Michael|Minin, Vladimir N|Shahbaba, Babak', 'Department of Statistics, University of Warwick, Coventry CV4 7AL, UK.|Department of Organismic and Evolutionary Biology, Harvard University, MA 02138, US, Department of Ecology and Evolutionary Biology, Brown University, RI 02912, US, Center for Computational Molecular Biology, Brown University.|Department of Statistics, University of Washington, WA 98195, US.|Department of Statistics, University of Washington, WA 98195, US, Department of Biology, University of Washington and.|Department of Statistics, University of California, Irvine, CA 92697, US.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Bayes Theorem|Genetics, Population|Humans|Influenza, Human|Models, Statistical|Monte Carlo Method|Orthomyxoviridae|Population Density|Population Dynamics|Software|Statistics, Nonparametric', 'Journal Article', '10.1093/bioinformatics/btv378', '26093147', 'The field of phylodynamics focuses on the problem of reconstructing population size dynamics over time using current genetic samples taken from the population of interest. This technique has been extensively used in many areas of biology but is particularly useful for studying the spread of quickly evolving infectious diseases agents, e.g. influenza virus. Phylodynamic inference uses a coalescent model that defines a probability density for the genealogy of randomly sampled individuals from the population. When we assume that such a genealogy is known, the coalescent model, equipped with a Gaussian process prior on population size trajectory, allows for nonparametric Bayesian estimation of population size dynamics. Although this approach is quite powerful, large datasets collected during infectious disease surveillance challenge the state-of-the-art of Bayesian phylodynamics and demand inferential methods with relatively low computational cost.'),
(79343, 2015, 'bio', 'LFQC: a lossless compression algorithm for FASTQ files', 'Nicolae, Marius|Pathak, Sudipta|Rajasekaran, Sanguthevar', 'Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269-4155, USA.|Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269-4155, USA.|Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269-4155, USA.', 'United States|United States|United States', 25, 'Algorithms|Data Compression|Genomics|High-Throughput Nucleotide Sequencing|Information Storage and Retrieval|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btv384', '26093148', 'Next Generation Sequencing (NGS) technologies have revolutionized genomic research by reducing the cost of whole genome sequencing. One of the biggest challenges posed by modern sequencing technology is economic storage of NGS data. Storing raw data is infeasible because of its enormous size and high redundancy. In this article, we address the problem of storage and transmission of large FASTQ files using innovative compression techniques.'),
(79344, 2015, 'bio', 'Chimira: analysis of small RNA sequencing data and microRNA modifications', 'Vitsios, Dimitrios M|Enright, Anton J', 'European Molecular Biology Laboratory-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.|European Molecular Biology Laboratory-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.', 'United Kingdom|United Kingdom', 0, 'Humans|MicroRNAs|RNA, Small Untranslated|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btv380', '26093149', 'Chimira is a web-based system for microRNA (miRNA) analysis from small RNA-Seq data. Sequences are automatically cleaned, trimmed, size selected and mapped directly to miRNA hairpin sequences. This generates count-based miRNA expression data for subsequent statistical analysis. Moreover, it is capable of identifying epi-transcriptomic modifications in the input sequences. Supported modification types include multiple types of 3-modifications (e.g. uridylation, adenylation), 5-modifications and also internal modifications or variation (ADAR editing or single nucleotide polymorphisms). Besides cleaning and mapping of input sequences to miRNAs, Chimira provides a simple and intuitive set of tools for the analysis and interpretation of the results (see also Supplementary Material). These allow the visual study of the differential expression between two specific samples or sets of samples, the identification of the most highly expressed miRNAs within sample pairs (or sets of samples) and also the projection of the modification profile for specific miRNAs across all samples. Other tools have already been published in the past for various types of small RNA-Seq analysis, such as UEA workbench, seqBuster, MAGI, OASIS and CAP-miRSeq, CPSS for modifications identification. A comprehensive comparison of Chimira with each of these tools is provided in the Supplementary Material. Chimira outperforms all of these tools in total execution speed and aims to facilitate simple, fast and reliable analysis of small RNA-Seq data allowing also, for the first time, identification of global microRNA modification profiles in a simple intuitive interface.'),
(79345, 2015, 'bio', 'GenomeD3Plot: a library for rich, interactive visualizations of genomic data in web applications', 'Laird, Matthew R|Langille, Morgan G I|Brinkman, Fiona S L', 'Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, British Columbia, V5A 1S6 and.|Department of Pharmacology, Dalhousie University, Halifax, Nova Scotia, B3H 4R2, Canada.|Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, British Columbia, V5A 1S6 and.', '-|Canada|-', 0, 'Computational Biology|Computer Graphics|Genome, Human|Genomic Islands|Genomics|Humans|Internet|Software', 'Journal Article', '10.1093/bioinformatics/btv376', '26093150', 'A simple static image of genomes and associated metadata is very limiting, as researchers expect rich, interactive tools similar to the web applications found in the post-Web 2.0 world. GenomeD3Plot is a light weight visualization library written in javascript using the D3 library. GenomeD3Plot provides a rich API to allow the rapid visualization of complex genomic data using a convenient standards based JSON configuration file. When integrated into existing web services GenomeD3Plot allows researchers to interact with data, dynamically alter the view, or even resize or reposition the visualization in their browser window. In addition GenomeD3Plot has built in functionality to export any resulting genome visualization in PNG or SVG format for easy inclusion in manuscripts or presentations.'),
(79346, 2015, 'bio', 'Forna (force-directed RNA): Simple and effective online RNA secondary structure diagrams', 'Kerpedjiev, Peter|Hammer, Stefan|Hofacker, Ivo L', 'Institute for Theoretical Chemistry, University of Vienna, Währinger Straße 17/3, A-1090 Vienna, Austria and.|Institute for Theoretical Chemistry, University of Vienna, Währinger Straße 17/3, A-1090 Vienna, Austria and Research Group Bioinformatics and Computational Biology, University of Vienna, Währinger Straße 29, A-1090 Vienna, Austria.|Institute for Theoretical Chemistry, University of Vienna, Währinger Straße 17/3, A-1090 Vienna, Austria and Research Group Bioinformatics and Computational Biology, University of Vienna, Währinger Straße 29, A-1090 Vienna, Austria.', 'Austria|Austria|Austria', 86, 'Base Pairing|Internet|Nucleic Acid Conformation|RNA|Software', 'Journal Article', '10.1093/bioinformatics/btv372', '26099263', 'The secondary structure of RNA is integral to the variety of functions it carries out in the cell and its depiction allows researchers to develop hypotheses about which nucleotides and base pairs are functionally relevant. Current approaches to visualizing secondary structure provide an adequate platform for the conversion of static text-based representations to 2D images, but are limited in their offer of interactivity as well as their ability to display larger structures, multiple structures and pseudoknotted structures.'),
(79347, 2015, 'bio', 'Sincell: an R/Bioconductor package for statistical assessment of cell-state hierarchies from single-cell RNA-seq', 'Juliá, Miguel|Telenti, Amalio|Rausell, Antonio', 'Vital-IT group, SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland, University of Lausanne, 1015 Lausanne, Switzerland and.|J. Craig Venter Institute, La Jolla, CA 92037.|Vital-IT group, SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland, University of Lausanne, 1015 Lausanne, Switzerland and.', 'Switzerland|-|Switzerland', 0, 'Algorithms|Cell Differentiation|Data Interpretation, Statistical|Gene Expression Profiling|Sequence Analysis, RNA|Single-Cell Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btv368', '26099264', 'Cell differentiation processes are achieved through a continuum of hierarchical intermediate cell states that might be captured by single-cell RNA seq. Existing computational approaches for the assessment of cell-state hierarchies from single-cell data can be formalized under a general framework composed of (i) a metric to assess cell-to-cell similarities (with or without a dimensionality reduction step) and (ii) a graph-building algorithm (optionally making use of a cell clustering step). The Sincell R package implements a methodological toolbox allowing flexible workflows under such a framework. Furthermore, Sincell contributes new algorithms to provide cell-state hierarchies with statistical support while accounting for stochastic factors in single-cell RNA seq. Graphical representations and functional association tests are provided to interpret hierarchies. The functionalities of Sincell are illustrated in a real case study, which demonstrates its ability to discriminate noisy from stable cell-state hierarchies.'),
(79348, 2015, 'bio', 'Bandage: interactive visualization of de novo genome assemblies', 'Wick, Ryan R|Schultz, Mark B|Zobel, Justin|Holt, Kathryn E', 'Department of Biochemistry and Molecular Biology, Bio21 Molecular Science and Biotechnology Institute, University of Melbourne and.|Department of Biochemistry and Molecular Biology, Bio21 Molecular Science and Biotechnology Institute, University of Melbourne and.|Department of Computing and Information Systems, University of Melbourne, Parkville, Victoria, Australia.|Department of Biochemistry and Molecular Biology, Bio21 Molecular Science and Biotechnology Institute, University of Melbourne and.', '-|-|Australia|-', 354, 'Chromosome Mapping|Computational Biology|Computer Graphics|Genome, Bacterial|Genome, Human|Genomics|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btv383', '26099265', 'Although de novo assembly graphs contain assembled contigs (nodes), the connections between those contigs (edges) are difficult for users to access. Bandage (a Bioinformatics Application for Navigating De novo Assembly Graphs Easily) is a tool for visualizing assembly graphs with connections. Users can zoom in to specific areas of the graph and interact with it by moving nodes, adding labels, changing colors and extracting sequences. BLAST searches can be performed within the Bandage graphical user interface and the hits are displayed as highlights in the graph. By displaying connections between contigs, Bandage presents new possibilities for analyzing de novo assemblies that are not possible through investigation of contigs alone.'),
(79349, 2015, 'bio', 'FoldNucleus: web server for the prediction of RNA and protein folding nuclei from their 3D structures', 'Pereyaslavets, Leonid B|Sokolovsky, Igor V|Galzitskaya, Oxana V', 'Institute of Protein Research, Russian Academy of Sciences, Pushchino, Moscow Region 142290, Russia.|Institute of Protein Research, Russian Academy of Sciences, Pushchino, Moscow Region 142290, Russia.|Institute of Protein Research, Russian Academy of Sciences, Pushchino, Moscow Region 142290, Russia.', '-|-|-', 4, 'Internet|Nucleic Acid Conformation|Protein Conformation|Protein Folding|RNA|RNA Folding|Software', 'Journal Article', '10.1093/bioinformatics/btv369', '26104744', 'To gain insight into how biopolymers fold as quickly as they do, it is useful to determine which structural elements limit the rate of RNA/protein folding.'),
(79350, 2015, 'bio', 'MetaQuery: a web server for rapid annotation and quantitative analysis of specific genes in the human gut microbiome', 'Nayfach, Stephen|Fischbach, Michael A|Pollard, Katherine S', 'Integrative Program in Quantitative Biology, Gladstone Institutes, and Division of Biostatistics, University of California San Francisco and.|Department of Bioengineering and Therapeutic Sciences and California Institute for Quantitative Biosciences, University of California San Francisco, San Francisco, CA, USA.|Integrative Program in Quantitative Biology, Gladstone Institutes, and Division of Biostatistics, University of California San Francisco and.', 'United States|United States|United States', 0, 'Diabetes Mellitus|Feces|Gastrointestinal Microbiome|Genes, Bacterial|Humans|Inflammatory Bowel Diseases|Internet|Metagenomics|Molecular Sequence Annotation|Software', 'Journal Article', '10.1093/bioinformatics/btv382', '26104745', 'Microbiome researchers frequently want to know how abundant a particular microbial gene or pathway is across different human hosts, including its association with disease and its co-occurrence with other genes or microbial taxa. With thousands of publicly available metagenomes, these questions should be easy to answer. However, computational barriers prevent most researchers from conducting such analyses. We address this problem with MetaQuery, a web application for rapid and quantitative analysis of specific genes in the human gut microbiome. The user inputs one or more query genes, and our software returns the estimated abundance of these genes across 1267 publicly available fecal metagenomes from American, European and Chinese individuals. In addition, our application performs downstream statistical analyses to identify features that are associated with gene variation, including other query genes (i.e. gene co-variation), taxa, clinical variables (e.g. inflammatory bowel disease and diabetes) and average genome size. The speed and accessibility of MetaQuery are a step toward democratizing metagenomics research, which should allow many researchers to query the abundance and variation of specific genes in the human gut microbiome.'),
(79351, 2015, 'bio', 'Hyperscape: visualization for complex biological networks', 'Cromar, Graham L|Zhao, Anthony|Yang, Alex|Parkinson, John', 'Program in Molecular Structure and Function, Hospital for Sick Children, 21-9830 PGCRL, 686 Bay Street, Toronto, ON, M5G 0A4, Canada.|Program in Molecular Structure and Function, Hospital for Sick Children, 21-9830 PGCRL, 686 Bay Street, Toronto, ON, M5G 0A4, Canada.|Program in Molecular Structure and Function, Hospital for Sick Children, 21-9830 PGCRL, 686 Bay Street, Toronto, ON, M5G 0A4, Canada, Department of Biochemistry and.|Program in Molecular Structure and Function, Hospital for Sick Children, 21-9830 PGCRL, 686 Bay Street, Toronto, ON, M5G 0A4, Canada, Department of Biochemistry and Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 1A8, Canada.', 'Canada|Canada|Canada|Canada', 2, 'Chromatin|Computer Graphics|Elastic Tissue|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btv385', '26108529', 'Network biology has emerged as a powerful tool to uncover the organizational properties of living systems through the application of graph theoretic approaches. However, due to limitations in underlying data models and visualization software, knowledge relating to large molecular assemblies and biologically active fragments is poorly represented.'),
(79352, 2015, 'bio', 'Data Safe Havens in health research and healthcare', 'Burton, Paul R|Murtagh, Madeleine J|Boyd, Andy|Williams, James B|Dove, Edward S|Wallace, Susan E|Tassé, Anne-Marie|Little, Julian|Chisholm, Rex L|Gaye, Amadou|Hveem, Kristian|Brookes, Anthony J|Goodwin, Pat|Fistein, Jon|Bobrow, Martin|Knoppers, Bartha M', 'Data to Knowledge (D2K) Research Group, University of Bristol, Oakfield House, Oakfield Grove, Clifton, Bristol BS8 2BN, UK, Public Population Project in Genomics and Society (PG), Montreal, QC H3A 0G1, Canada.|Data to Knowledge (D2K) Research Group, University of Bristol, Oakfield House, Oakfield Grove, Clifton, Bristol BS8 2BN, UK.|Data to Knowledge (D2K) Research Group, University of Bristol, Oakfield House, Oakfield Grove, Clifton, Bristol BS8 2BN, UK.|Department of Computer Science, University of Toronto, Sandford Fleming Building, Toronto, ON M5S 3G4, Canada.|JK Mason Institute for Medicine, Life Sciences and the Law, School of Law, University of Edinburgh, Old College, South Bridge, Edinburgh EH8 9YL, UK.|Public Population Project in Genomics and Society (PG), Montreal, QC H3A 0G1, Canada, Department of Health Sciences, University of Leicester, Adrian Building, University Road, Leicester LE1 7RH, UK.|Public Population Project in Genomics and Society (PG), Montreal, QC H3A 0G1, Canada.|School of Epidemiology, Public Health and Preventive Medicine, Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada.|Center for Genetic Medicine and Surgery, Northwestern University, Rubloff Building, 750 N Lake Shore, Chicago, IL 60611, USA.|Data to Knowledge (D2K) Research Group, University of Bristol, Oakfield House, Oakfield Grove, Clifton, Bristol BS8 2BN, UK.|Department of Public Health and General Practice, Norwegian University of Science and Technology, Postboks 8905, 7401 Trondheim, Norway.|Department of Health Sciences, University of Leicester, Adrian Building, University Road, Leicester LE1 7RH, UK.|C3 Collaborating for Health, 7-14 Great Dover Street, London SE1 4YR.|MRC Medical Bioinformatics Centre, Leeds Institute of Health Sciences, University of Leeds, University of Leeds, Charles Thackrah Building,101 Clarendon Road, Leeds LS2 9LJ.|University of Cambridge, Wolfson College, Cambridge CB3 9BB, UK and.|Public Population Project in Genomics and Society (PG), Montreal, QC H3A 0G1, Canada, Centre of Genomics and Policy, McGill University, Montreal, QC H3A 0G1, Canada.', 'Canada|United Kingdom|United Kingdom|Canada|United Kingdom|Canada|Canada|Canada|United Kingdom|United Kingdom|Norway|United Kingdom|United Kingdom|United Kingdom|United Kingdom|Canada', 28, 'Access to Information|Biomedical Research|Confidentiality|Delivery of Health Care|Humans|Research', 'Journal Article', '10.1093/bioinformatics/btv279', '26112289', 'The data that put the evidence into evidence-based medicine are central to developments in public health, primary and hospital care. A fundamental challenge is to site such data in repositories that can easily be accessed under appropriate technical and governance controls which are effectively audited and are viewed as trustworthy by diverse stakeholders. This demands socio-technical solutions that may easily become enmeshed in protracted debate and controversy as they encounter the norms, values, expectations and concerns of diverse stakeholders. In this context, the development of what are called Data Safe Havens has been crucial. Unfortunately, the origins and evolution of the term have led to a range of different definitions being assumed by different groups. There is, however, an intuitively meaningful interpretation that is often assumed by those who have not previously encountered the term: a repository in which useful but potentially sensitive data may be kept securely under governance and informatics systems that are fit-for-purpose and appropriately tailored to the nature of the data being maintained, and may be accessed and utilized by legitimate users undertaking work and research contributing to biomedicine, health and/or to ongoing development of healthcare systems.'),
(79353, 2015, 'bio', 'biRte: Bayesian inference of context-specific regulator activities and transcriptional networks', 'Fröhlich, Holger', 'University of Bonn, Institute for Computer Science, Römerstr. 164, 53117 Bonn, Germany.', 'Germany', 0, 'Bayes Theorem|Computer Simulation|Escherichia coli|Gene Expression|Gene Expression Profiling|Gene Regulatory Networks|Humans|Male|MicroRNAs|Prostatic Neoplasms|RNA, Messenger|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btv379', '26112290', 'In the last years there has been an increasing effort to computationally model and predict the influence of regulators (transcription factors, miRNAs) on gene expression. Here we introduce biRte as a computationally attractive approach combining Bayesian inference of regulator activities with network reverse engineering. biRte integrates target gene predictions with different omics data entities (e.g. miRNA and mRNA data) into a joint probabilistic framework. The utility of our method is tested in extensive simulation studies and demonstrated with applications from prostate cancer and Escherichia coli growth control. The resulting regulatory networks generally show a good agreement with the biological literature.'),
(79354, 2015, 'bio', 'Real-time multi-view deconvolution', 'Schmid, Benjamin|Huisken, Jan', 'Max Planck Institute of Molecular Cell Biology and Genetics, 01307 Dresden, Germany.|Max Planck Institute of Molecular Cell Biology and Genetics, 01307 Dresden, Germany.', 'Germany|Germany', 0, 'Imaging, Three-Dimensional|Microscopy|Software|Time-Lapse Imaging', 'Journal Article', '10.1093/bioinformatics/btv387', '26112291', 'In light-sheet microscopy, overall image content and resolution are improved by acquiring and fusing multiple views of the sample from different directions. State-of-the-art multi-view (MV) deconvolution simultaneously fuses and deconvolves the images in 3D, but processing takes a multiple of the acquisition time and constitutes the bottleneck in the imaging pipeline. Here, we show that MV deconvolution in 3D can finally be achieved in real-time by processing cross-sectional planes individually on the massively parallel architecture of a graphics processing unit (GPU). Our approximation is valid in the typical case where the rotation axis lies in the imaging plane.'),
(79355, 2015, 'bio', 'damidseq_pipeline: an automated pipeline for processing DamID sequencing datasets', 'Marshall, Owen J|Brand, Andrea H', 'Wellcome Trust/Cancer Research UK Gurdon Institute, Cambridge, CB2 1QN, UK.|Wellcome Trust/Cancer Research UK Gurdon Institute, Cambridge, CB2 1QN, UK.', 'United Kingdom|United Kingdom', 0, 'Binding Sites|DNA-Binding Proteins|High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btv386', '26112292', 'DamID is a powerful technique for identifying regions of the genome bound by a DNA-binding (or DNA-associated) protein. Currently, no method exists for automatically processing next-generation sequencing DamID (DamID-seq) data, and the use of DamID-seq datasets with normalization based on read-counts alone can lead to high background and the loss of bound signal. DamID-seq thus presents novel challenges in terms of normalization and background minimization. We describe here damidseq_pipeline, a software pipeline that performs automatic normalization and background reduction on multiple DamID-seq FASTQ datasets.'),
(79356, 2015, 'bio', 'Predicting tumor purity from methylation microarray data', 'Zhang, Naiqian|Wu, Hua-Jun|Zhang, Weiwei|Wang, Jun|Wu, Hao|Zheng, Xiaoqi', 'Department of Mathematics, Shanghai Normal University, Shanghai 200234, China.|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston 02215, MA, USA and.|Department of Mathematics, Shanghai Normal University, Shanghai 200234, China.|Department of Mathematics, Shanghai Normal University, Shanghai 200234, China.|Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA.|Department of Mathematics, Shanghai Normal University, Shanghai 200234, China.', 'China|United States|China|China|United States|China', 0, 'Algorithms|Computational Biology|DNA Copy Number Variations|DNA Methylation|Gene Expression Profiling|Genome, Human|Genomics|Humans|Lung Neoplasms', 'Journal Article', '10.1093/bioinformatics/btv370', '26112293', 'In cancer genomics research, one important problem is that the solid tissue sample obtained from clinical settings is always a mixture of cancer and normal cells. The sample mixture brings complication in data analysis and results in biased findings if not correctly accounted for. Estimating tumor purity is of great interest, and a number of methods have been developed using gene expression, copy number variation or point mutation data.'),
(79357, 2015, 'bio', 'nextflu: real-time tracking of seasonal influenza virus evolution in humans', 'Neher, Richard A|Bedford, Trevor', 'Max Planck Institute for Developmental Biology, 72076 Tübingen, Germany and.|Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.', 'Germany|United States', 78, 'Evolution, Molecular|Humans|Influenza, Human|Mutation|Orthomyxoviridae|Phylogeny|Seasons|Software', 'Journal Article', '10.1093/bioinformatics/btv381', '26115986', 'Seasonal influenza viruses evolve rapidly, allowing them to evade immunity in their human hosts and reinfect previously infected individuals. Similarly, vaccines against seasonal influenza need to be updated frequently to protect against an evolving virus population. We have thus developed a processing pipeline and browser-based visualization that allows convenient exploration and analysis of the most recent influenza virus sequence data. This web-application displays a phylogenetic tree that can be decorated with additional information such as the viral genotype at specific sites, sampling location and derived statistics that have been shown to be predictive of future virus dynamics. In addition, mutation, genotype and clade frequency trajectories are calculated and displayed.'),
(79358, 2015, 'bio', 'Proteny: discovering and visualizing statistically significant syntenic clusters at the proteome level', 'Gehrmann, Thies|Reinders, Marcel J T', 'Delft Bioinformatics Lab, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands.|Delft Bioinformatics Lab, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands.', 'Netherlands|Netherlands', 0, 'Algorithms|Chromosome Mapping|Chromosomes, Fungal|Computer Graphics|Fungal Proteins|Fungi|Gene Duplication|Gene Order|Gene Rearrangement|Genome, Fungal|Genomics|Proteome|Software|Synteny', 'Journal Article', '10.1093/bioinformatics/btv389', '26116928', 'With more and more genomes being sequenced, detecting synteny between genomes becomes more and more important. However, for microorganisms the genomic divergence quickly becomes large, resulting in different codon usage and shuffling of gene order and gene elements such as exons.'),
(79359, 2015, 'bio', 'ISQuest: finding insertion sequences in prokaryotic sequence fragment data', 'Biswas, Abhishek|Gauthier, David T|Ranjan, Desh|Zubair, Mohammad', 'Department of Computer Science and.|Department of Biological Sciences, Old Dominion University, Norfolk, Virginia, USA.|Department of Computer Science and.|Department of Computer Science and.', 'United States|United States|United States|United States', 0, 'Chromosome Mapping|DNA Transposable Elements|Databases, Nucleic Acid|Genome, Archaeal|Genome, Bacterial|Genomics|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btv388', '26116929', 'Insertion sequences (ISs) are transposable elements present in most bacterial and archaeal genomes that play an important role in genomic evolution. The increasing availability of sequenced prokaryotic genomes offers the opportunity to study ISs comprehensively, but development of efficient and accurate tools is required for discovery and annotation. Additionally, prokaryotic genomes are frequently deposited as incomplete, or draft stage because of the substantial cost and effort required to finish genome assembly projects. Development of methods to identify IS directly from raw sequence reads or draft genomes are therefore desirable. Software tools such as Optimized Annotation System for Insertion Sequences and IScan currently identify IS elements in completely assembled and annotated genomes, however, to our knowledge no methods have been developed to identify ISs from raw fragment data or partially assembled genomes. We have developed novel methods to solve this computationally challenging problem, and implemented these methods in the software package ISQuest. This software identifies bacterial ISs and their sequence elements-inverted and direct repeats-in raw read data or contigs using flexible search parameters. ISQuest is capable of finding ISs in hundreds of partially assembled genomes within hours, making it a valuable high-throughput tool for a global search of IS elements. We tested ISQuest on simulated read libraries of 3810 complete bacterial genomes and plasmids in GenBank and were capable of detecting 82% of the ISs and transposases annotated in GenBank with 80% sequence identity.'),
(79360, 2016, 'bio', 'Inference of Markovian properties of molecular sequences from NGS data and applications to comparative genomics', 'Ren, Jie|Song, Kai|Deng, Minghua|Reinert, Gesine|Cannon, Charles H|Sun, Fengzhu', 'Molecular and Computational Biology Program, University of Southern California, Los Angeles, CA, USA.|School of Mathematical Sciences, Peking University, Beijing, China.|School of Mathematical Sciences, Peking University, Beijing, China.|Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK.|Department of Biological Sciences, Texas Tech University, TX 79409-3131, USA, Xishuangbanna Tropical Botanic Garden, Chinese Academy of Sciences, Yunnan, China and.|Molecular and Computational Biology Program, University of Southern California, Los Angeles, CA, USA, Centre for Computational Systems Biology, School of Mathematical Sciences, Fudan University, Shanghai, China.', 'United Kingdom|China|China|United Kingdom|China|China', 15, 'Algorithms|Animals|Cluster Analysis|Computational Biology|Genome|Genomics|High-Throughput Nucleotide Sequencing|Markov Chains|Models, Statistical|Vertebrates', 'Journal Article', '10.1093/bioinformatics/btv395', '26130573', 'Next-generation sequencing (NGS) technologies generate large amounts of short read data for many different organisms. The fact that NGS reads are generally short makes it challenging to assemble the reads and reconstruct the original genome sequence. For clustering genomes using such NGS data, word-count based alignment-free sequence comparison is a promising approach, but for this approach, the underlying expected word counts are essential.A plausible model for this underlying distribution of word counts is given through modeling the DNA sequence as a Markov chain (MC). For single long sequences, efficient statistics are available to estimate the order of MCs and the transition probability matrix for the sequences. As NGS data do not provide a single long sequence, inference methods on Markovian properties of sequences based on single long sequences cannot be directly used for NGS short read data.'),
(79361, 2015, 'bio', 'ProFET: Feature engineering captures high-level protein functions', 'Ofer, Dan|Linial, Michal', 'Department of Biological Chemistry, Institute of Life Sciences, The Edmond J. Safra Campus, The Hebrew University of Jerusalem, Givat Ram, 91904, Israel.|Department of Biological Chemistry, Institute of Life Sciences, The Edmond J. Safra Campus, The Hebrew University of Jerusalem, Givat Ram, 91904, Israel.', 'Israel|Israel', 27, 'Algorithms|Amino Acids|Computational Biology|Databases, Protein|Genome, Human|Humans|Machine Learning|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btv345', '26130574', 'The amount of sequenced genomes and proteins is growing at an unprecedented pace. Unfortunately, manual curation and functional knowledge lag behind. Homologous inference often fails at labeling proteins with diverse functions and broad classes. Thus, identifying high-level protein functionality remains challenging. We hypothesize that a universal feature engineering approach can yield classification of high-level functions and unified properties when combined with machine learning approaches, without requiring external databases or alignment.'),
(79362, 2015, 'bio', 'GDFuzz3D: a method for protein 3D structure reconstruction from contact maps, based on a non-Euclidean distance function', 'Pietal, Michal J|Bujnicki, Janusz M|Kozlowski, Lukasz P', 'Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland, Laboratory of Functional and Structural Genomics, Centre of New Technologies, University of Warsaw, Warsaw, Poland and.|Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland, Bioinformatics Laboratory, Institute of Molecular Biology and Biotechnology, Adam Mickiewicz University, Poznan, Poland.|Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland.', 'Poland|Poland|Poland', 0, 'Algorithms|Amino Acids|Models, Molecular|Protein Conformation|Proteins|Sequence Analysis, Protein|Software', 'Evaluation Studies', '10.1093/bioinformatics/btv390', '26130575', 'To date, only a few distinct successful approaches have been introduced to reconstruct a protein 3D structure from a map of contacts between its amino acid residues (a 2D contact map). Current algorithms can infer structures from information-rich contact maps that contain a limited fraction of erroneous predictions. However, it is difficult to reconstruct 3D structures from predicted contact maps that usually contain a high fraction of false contacts.'),
(79363, 2015, 'bio', 'Estimating beta diversity for under-sampled communities using the variably weighted Odum dissimilarity index and OTUshuff', 'Manter, Daniel K|Bakker, Matthew G', 'USDA-ARS, Soil-Plant-Nutrient Research, Fort Collins, CO 80526, USA and.|USDA-ARS, National Laboratory for Agriculture and the Environment, Ames, IA 50011 USA.', 'United States|United States', 4, 'Bacteria|Biodiversity|Computer Simulation|DNA, Bacterial|Humans|Metagenomics|Models, Theoretical|Polymerase Chain Reaction|RNA, Ribosomal, 16S|Selection Bias|Soil Microbiology', 'Journal Article', '10.1093/bioinformatics/btv394', '26130576', 'In profiling the composition and structure of complex microbial communities via high throughput amplicon sequencing, a very low proportion of community members are typically sampled. As a result of this incomplete sampling, estimates of dissimilarity between communities are often inflated, an issue we term pseudo xcexb2-diversity.'),
(79364, 2015, 'bio', 'A DNA shape-based regulatory score improves position-weight matrix-based recognition of transcription factor binding sites', 'Yang, Jichen|Ramsey, Stephen A', 'Department of Biomedical Sciences and.|Department of Biomedical Sciences and School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR, USA.', 'United States|United States', 0, 'Binding Sites|Computational Biology|DNA|Gene Expression Regulation|Humans|Models, Theoretical|Position-Specific Scoring Matrices|Protein Binding|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btv391', '26130577', 'The position-weight matrix (PWM) is a useful representation of a transcription factor binding site (TFBS) sequence pattern because the PWM can be estimated from a small number of representative TFBS sequences. However, because the PWM probability model assumes independence between individual nucleotide positions, the PWMs for some TFs poorly discriminate binding sites from non-binding-sites that have similar sequence content. Since the local three-dimensional DNA structure (shape) is a determinant of TF binding specificity and since DNA shape has a significant sequence-dependence, we combined DNA shape-derived features into a TF-generalized regulatory score and tested whether the score could improve PWM-based discrimination of TFBS from non-binding-sites.'),
(79365, 2015, 'bio', 'Integrating full spectrum of sequence features into predicting functional microRNA-mRNA interactions', 'Wang, Zixing|Xu, Wenlong|Liu, Yin', 'Department of Neurobiology and Anatomy, University of Texas Health Science Center at Houston and.|Department of Neurobiology and Anatomy, University of Texas Health Science Center at Houston and.|Department of Neurobiology and Anatomy, University of Texas Health Science Center at Houston and University of Texas Graduate School of Biomedical Sciences, Houston, Texas, USA.', 'United States|United States|United States', 8, 'Down-Regulation|Gene Expression Profiling|Humans|MicroRNAs|Models, Genetic|Neoplasms|RNA, Messenger|Sensitivity and Specificity|Sequence Analysis, RNA', 'Evaluation Studies', '10.1093/bioinformatics/btv392', '26130578', 'MicroRNAs (miRNAs) play important roles in general biological processes and diseases pathogenesis. Identifying miRNA target genes is an essential step to fully understand the regulatory effects of miRNAs. Many computational methods based on the sequence complementary rules and the miRNA and mRNA expression profiles have been developed for this purpose. It is noted that there have been many sequence features of miRNA targets available, including the context features of the target sites, the thermodynamic stability and the accessibility energy for miRNA-mRNA interaction. However, most of current computational methods that combine sequence and expression information do not effectively integrate full spectrum of these features, instead, they perceive putative miRNA-mRNA interactions from sequence-based prediction as equally meaningful. Therefore, these sequence features have not been fully utilized for improving miRNA target prediction.'),
(79366, 2015, 'bio', 'A multivariate Bernoulli model to predict DNaseI hypersensitivity status from haplotype data', 'Shi, Huwenbo|Pasaniuc, Bogdan|Lange, Kenneth L', 'Bioinformatics Interdepartmental Program, University of California, Los Angeles.|Bioinformatics Interdepartmental Program, University of California, Los Angeles, Department of Pathology and Laboratory Medicine, Department of Human Genetics and.|Bioinformatics Interdepartmental Program, University of California, Los Angeles, Department of Human Genetics and Department of Biomathematics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90024, USA.', 'United States|United States|United States', 0, 'Algorithms|Cell Line|Deoxyribonuclease I|Haplotypes|Humans|Linear Models|Logistic Models|Models, Statistical|Multivariate Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btv397', '26139633', 'Haplotype models enjoy a wide range of applications in population inference and disease gene discovery. The hidden Markov models traditionally used for haplotypes are hindered by the dubious assumption that dependencies occur only between consecutive pairs of variants. In this article, we apply the multivariate Bernoulli (MVB) distribution to model haplotype data. The MVB distribution relies on interactions among all sets of variants, thus allowing for the detection and exploitation of long-range and higher-order interactions. We discuss penalized estimation and present an efficient algorithm for fitting sparse versions of the MVB distribution to haplotype data. Finally, we showcase the benefits of the MVB model in predicting DNaseI hypersensitivity (DH) status--an epigenetic mark describing chromatin accessibility--from population-scale haplotype data.'),
(79367, 2015, 'bio', 'Functional classification of CATH superfamilies: a domain-based approach for protein function annotation', 'Das, Sayoni|Lee, David|Sillitoe, Ian|Dawson, Natalie L|Lees, Jonathan G|Orengo, Christine A', 'Institute of Structural and Molecular Biology, UCL, Darwin Building, Gower Street, WC1E 6BT, UK.|Institute of Structural and Molecular Biology, UCL, Darwin Building, Gower Street, WC1E 6BT, UK.|Institute of Structural and Molecular Biology, UCL, Darwin Building, Gower Street, WC1E 6BT, UK.|Institute of Structural and Molecular Biology, UCL, Darwin Building, Gower Street, WC1E 6BT, UK.|Institute of Structural and Molecular Biology, UCL, Darwin Building, Gower Street, WC1E 6BT, UK.|Institute of Structural and Molecular Biology, UCL, Darwin Building, Gower Street, WC1E 6BT, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 34, 'Algorithms|Amino Acid Sequence|Databases, Protein|Humans|Molecular Sequence Annotation|Molecular Sequence Data|Protein Structure, Tertiary|Proteins|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Structural Homology, Protein', 'Journal Article', '10.1093/bioinformatics/btv398', '26139634', 'Computational approaches that can predict protein functions are essential to bridge the widening function annotation gap especially since <1.0% of all proteins in UniProtKB have been experimentally characterized. We present a domain-based method for protein function classification and prediction of functional sites that exploits functional sub-classification of CATH superfamilies. The superfamilies are sub-classified into functional families (FunFams) using a hierarchical clustering algorithm supervised by a new classification method, FunFHMMer.'),
(79368, 2015, 'bio', 'LDlink: a web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants', 'Machiela, Mitchell J|Chanock, Stephen J', 'Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20892, USA.|Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20892, USA.', 'United States|United States', 0, 'Alleles|Chromosome Mapping|Genetic Predisposition to Disease|Haplotypes|Humans|Internet|Linkage Disequilibrium|Polymorphism, Single Nucleotide|Population Groups|Software', 'Journal Article', '10.1093/bioinformatics/btv402', '26139635', 'Assessing linkage disequilibrium (LD) across ancestral populations is a powerful approach for investigating population-specific genetic structure as well as functionally mapping regions of disease susceptibility. Here, we present LDlink, a web-based collection of bioinformatic modules that query single nucleotide polymorphisms (SNPs) in population groups of interest to generate haplotype tables and interactive plots. Modules are designed with an emphasis on ease of use, query flexibility, and interactive visualization of results. Phase 3 haplotype data from the 1000 Genomes Project are referenced for calculating pairwise metrics of LD, searching for proxies in high LD, and enumerating all observed haplotypes. LDlink is tailored for investigators interested in mapping common and uncommon disease susceptibility loci by focusing on output linking correlated alleles and highlighting putative functional variants.'),
(79369, 2015, 'bio', 'ERGC: an efficient referential genome compression algorithm', 'Saha, Subrata|Rajasekaran, Sanguthevar', 'Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA.|Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA.', 'United States|United States', 20, 'Algorithms|Chromosome Mapping|Data Compression|Databases, Factual|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Information Storage and Retrieval|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btv399', '26139636', 'Genome sequencing has become faster and more affordable. Consequently, the number of available complete genomic sequences is increasing rapidly. As a result, the cost to store, process, analyze and transmit the data is becoming a bottleneck for research and future medical applications. So, the need for devising efficient data compression and data reduction techniques for biological sequencing data is growing by the day. Although there exists a number of standard data compression algorithms, they are not efficient in compressing biological data. These generic algorithms do not exploit some inherent properties of the sequencing data while compressing. To exploit statistical and information-theoretic properties of genomic sequences, we need specialized compression algorithms. Five different next-generation sequencing data compression problems have been identified and studied in the literature. We propose a novel algorithm for one of these problems known as reference-based genome compression.'),
(79370, 2015, 'bio', 'Error filtering, pair assembly and error correction for next-generation sequencing reads', 'Edgar, Robert C|Flyvbjerg, Henrik', 'Tiburon, CA 94920, USA and.|Department of Micro- and Nanotechnology, Technical University of Denmark, DK-2800 Lyngby, Denmark.', '-|Denmark', 368, 'Algorithms|High-Throughput Nucleotide Sequencing|Software', 'Evaluation Studies', '10.1093/bioinformatics/btv401', '26139637', 'Next-generation sequencing produces vast amounts of data with errors that are difficult to distinguish from true biological variation when coverage is low.'),
(79371, 2015, 'bio', 'Nsite, NsiteH and NsiteM computer tools for studying transcription regulatory elements', 'Shahmuradov, Ilham A|Solovyev, Victor V', 'Computer, Electrical and Mathematical Sciences and Engineering Division, KAUST, Thuwal 23955-6900, KSA, Bioinformatics laboratory, Institute of Botany, ANAS, Baku AZ1073, Azerbaijan and.|Computer, Electrical and Mathematical Sciences and Engineering Division, KAUST, Thuwal 23955-6900, KSA, Bioinformatics Division, Softberry Inc., Mount Kisco, NY 10549, USA.', 'Azerbaijan|United States', 0, 'Animals|Binding Sites|Genomics|Humans|Nucleotide Motifs|Plants|Promoter Regions, Genetic|Regulatory Sequences, Nucleic Acid|Sequence Analysis, DNA|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btv404', '26142184', 'Gene transcription is mostly conducted through interactions of various transcription factors and their binding sites on DNA (regulatory elements, REs). Today, we are still far from understanding the real regulatory content of promoter regions. Computer methods for identification of REs remain a widely used tool for studying and understanding transcriptional regulation mechanisms. The Nsite, NsiteH and NsiteM programs perform searches for statistically significant (non-random) motifs of known human, animal and plant one-box and composite REs in a single genomic sequence, in a pair of aligned homologous sequences and in a set of functionally related sequences, respectively.'),
(79372, 2015, 'bio', 'JASSA: a comprehensive tool for prediction of SUMOylation sites and SIMs', 'Beauclair, Guillaume|Bridier-Nahmias, Antoine|Zagury, Jean-François|Saïb, Ali|Zamborlini, Alessia', 'CNRS UMR7212, Hôpital St Louis, Inserm U944, Institut Universitaire dHématologie, Hôpital St Louis, Université Paris Diderot, Sorbonne Paris Cité, Hôpital St Louis.|CNRS UMR7212, Hôpital St Louis, Inserm U944, Institut Universitaire dHématologie, Hôpital St Louis, Université Paris Diderot, Sorbonne Paris Cité, Hôpital St Louis, Laboratoire PVM, Conservatoire national des arts et métiers (Cnam) and.|Laboratoire Génomique, Bioinformatique, et Applications, EA4627, Chaire de bioinformatique, Conservatoire national des arts et métiers (Cnam), Paris, France.|CNRS UMR7212, Hôpital St Louis, Inserm U944, Institut Universitaire dHématologie, Hôpital St Louis, Université Paris Diderot, Sorbonne Paris Cité, Hôpital St Louis, Laboratoire PVM, Conservatoire national des arts et métiers (Cnam) and.|CNRS UMR7212, Hôpital St Louis, Inserm U944, Institut Universitaire dHématologie, Hôpital St Louis, Université Paris Diderot, Sorbonne Paris Cité, Hôpital St Louis, Laboratoire PVM, Conservatoire national des arts et métiers (Cnam) and.', '-|-|France|-|-', 37, 'Humans|Protein Interaction Domains and Motifs|Protein Structure, Tertiary|Sequence Alignment|Sequence Analysis, Protein|Small Ubiquitin-Related Modifier Proteins|Software|Sumoylation', 'Journal Article', '10.1093/bioinformatics/btv403', '26142185', 'Post-translational modification by the Small Ubiquitin-like Modifier (SUMO) proteins, a process termed SUMOylation, is involved in many fundamental cellular processes. SUMO proteins are conjugated to a protein substrate, creating an interface for the recruitment of cofactors harboring SUMO-interacting motifs (SIMs). Mapping both SUMO-conjugation sites and SIMs is required to study the functional consequence of SUMOylation. To define the best candidate sites for experimental validation we designed JASSA, a Joint Analyzer of SUMOylation site and SIMs.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(79373, 2015, 'bio', 'al3c: high-performance software for parameter inference using Approximate Bayesian Computation', 'Stram, Alexander H|Marjoram, Paul|Chen, Gary K', 'Cancer Center - Research, USC, Los Angeles, CA 90089, USA and.|Division of Biostatistics, Department of Preventive Medicine, USC, Los Angeles, CA 90033, USA.|Division of Biostatistics, Department of Preventive Medicine, USC, Los Angeles, CA 90033, USA.', 'United States|United States|United States', 0, 'Algorithms|Animals|Bayes Theorem|Models, Biological|Monte Carlo Method|Software', 'Journal Article', '10.1093/bioinformatics/btv393', '26142186', 'The development of Approximate Bayesian Computation (ABC) algorithms for parameter inference which are both computationally efficient and scalable in parallel computing environments is an important area of research. Monte Carlo rejection sampling, a fundamental component of ABC algorithms, is trivial to distribute over multiple processors but is inherently inefficient. While development of algorithms such as ABC Sequential Monte Carlo (ABC-SMC) help address the inherent inefficiencies of rejection sampling, such approaches are not as easily scaled on multiple processors. As a result, current Bayesian inference software offerings that use ABC-SMC lack the ability to scale in parallel computing environments.'),
(79374, 2015, 'bio', 'PSIKO2: a fast and versatile tool to infer population stratification on various levels in GWAS', 'Popescu, Andrei-Alin|Huber, Katharina T', 'School of Computing Sciences, University of East Anglia, Norwich, NR4 7TJ, UK.|School of Computing Sciences, University of East Anglia, Norwich, NR4 7TJ, UK.', 'United Kingdom|United Kingdom', 0, 'Genome-Wide Association Study|Genotype|Humans|Population Groups|Software', 'Journal Article', '10.1093/bioinformatics/btv396', '26142187', 'Genome-wide association studies are an invaluable tool for identifying genotypic loci linked with agriculturally important traits or certain diseases. The signal on which such studies rely upon can, however, be obscured by population stratification making it necessary to account for it in some way. Population stratification is dependent on when admixture happened and thus can occur at various levels. To aid in its inference at the genome level, we recently introduced psiko, and comparison with leading methods indicates that it has attractive properties. However, until now, it could not be used for local ancestry inference which is preferable in cases of recent admixture as the genome level tends to be too coarse to properly account for processes acting on small segments of a genome. To also bring the powerful ideas underpinning psiko to bear in such studies, we extended it to psiko2, which we introduce here.'),
(79375, 2015, 'bio', 'Data2Dynamics: a modeling environment tailored to parameter estimation in dynamical systems', 'Raue, A|Steiert, B|Schelker, M|Kreutz, C|Maiwald, T|Hass, H|Vanlier, J|Tönsing, C|Adlung, L|Engesser, R|Mader, W|Heinemann, T|Hasenauer, J|Schilling, M|Höfer, T|Klipp, E|Theis, F|Klingmüller, U|Schöberl, B|Timmer, J', 'Merrimack Pharmaceuticals Inc., Discovery Devision, Cambridge, MA 02139, USA.|University of Freiburg, Institute for Physics, 79104 Freiburg, Germany.|Humboldt-Universität zu Berlin, Theoretical Biophysics, 10115 Berlin, Germany.|University of Freiburg, Institute for Physics, 79104 Freiburg, Germany.|University of Freiburg, Institute for Physics, 79104 Freiburg, Germany.|University of Freiburg, Institute for Physics, 79104 Freiburg, Germany.|University of Freiburg, Institute for Physics, 79104 Freiburg, Germany.|University of Freiburg, Institute for Physics, 79104 Freiburg, Germany.|Systems Biology of Signal Transduction and.|University of Freiburg, Institute for Physics, 79104 Freiburg, Germany.|University of Freiburg, Institute for Physics, 79104 Freiburg, Germany.|Divison of Theoretical Systems Biology, German Cancer Research Center, 69120 Heidelberg, Germany, BioQuant, University of Heidelberg, 69120 Heidelberg, Germany.|Helmholtz Center Munich, Institute of Computational Biology, 85764 Neuherberg, Germany, Technische Universität München, Department of Mathematics, 85748 Garching, Germany and.|Systems Biology of Signal Transduction and.|Divison of Theoretical Systems Biology, German Cancer Research Center, 69120 Heidelberg, Germany, BioQuant, University of Heidelberg, 69120 Heidelberg, Germany.|Humboldt-Universität zu Berlin, Theoretical Biophysics, 10115 Berlin, Germany.|Helmholtz Center Munich, Institute of Computational Biology, 85764 Neuherberg, Germany, Technische Universität München, Department of Mathematics, 85748 Garching, Germany and.|Systems Biology of Signal Transduction and.|Merrimack Pharmaceuticals Inc., Discovery Devision, Cambridge, MA 02139, USA.|University of Freiburg, Institute for Physics, 79104 Freiburg, Germany, BIOSS Centre for Biological Signalling Studies, University of Freiburg, 79104 Freiburg, Germany.', 'United States|Germany|Germany|Germany|Germany|Germany|Germany|Germany|United States|Germany|Germany|Germany|Germany|United States|German', 0, 'Algorithms|Bayes Theorem|Models, Biological|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btv405', '26142188', 'Modeling of dynamical systems using ordinary differential equations is a popular approach in the field of systems biology. Two of the most critical steps in this approach are to construct dynamical models of biochemical reaction networks for large datasets and complex experimental conditions and to perform efficient and reliable parameter estimation for model fitting. We present a modeling environment for MATLAB that pioneers these challenges. The numerically expensive parts of the calculations such as the solving of the differential equations and of the associated sensitivity system are parallelized and automatically compiled into efficient C code. A variety of parameter estimation algorithms as well as frequentist and Bayesian methods for uncertainty analysis have been implemented and used on a range of applications that lead to publications.'),
(79376, 2015, 'bio', 'DINGO: differential network analysis in genomics', 'Ha, Min Jin|Baladandayuthapani, Veerabhadran|Do, Kim-Anh', 'Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.|Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.|Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.', 'United States|United States|United States', 0, 'Algorithms|Computer Simulation|DNA Copy Number Variations|DNA Methylation|Gene Expression Profiling|Gene Regulatory Networks|Genomics|Glioblastoma|Humans|MicroRNAs|Models, Theoretical|RNA, Messenger|Regression Analysis|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btv406', '26148744', 'Cancer progression and development are initiated by aberrations in various molecular networks through coordinated changes across multiple genes and pathways. It is important to understand how these networks change under different stress conditions and/or patient-specific groups to infer differential patterns of activation and inhibition. Existing methods are limited to correlation networks that are independently estimated from separate group-specific data and without due consideration of relationships that are conserved across multiple groups.'),
(79377, 2015, 'bio', 'LayerCake: a tool for the visual comparison of viral deep sequencing data', 'Correll, Michael|Bailey, Adam L|Sarikaya, Alper|OConnor, David H|Gleicher, Michael', 'Department of Computer Sciences and.|Department of Pathology and Laboratory Medicine, University of Wisconsin, Madison, Madison, WI 53706, USA.|Department of Computer Sciences and.|Department of Pathology and Laboratory Medicine, University of Wisconsin, Madison, Madison, WI 53706, USA.|Department of Computer Sciences and.', 'United States|United States|United States|United States|United States', 7, 'Computer Graphics|Genetic Variation|Genome, Viral|Genomics|High-Throughput Nucleotide Sequencing|Software', 'Journal Article', '10.1093/bioinformatics/btv407', '26153515', 'The advent of next-generation sequencing (NGS) has created unprecedented opportunities to examine viral populations within individual hosts, among infected individuals and over time. Comparing sequence variability across viral genomes allows for the construction of complex population structures, the analysis of which can yield powerful biological insights. However, the simultaneous display of sequence variation, coverage depth and quality scores across thousands of bases presents a unique visualization challenge that has not been fully met by current NGS analysis tools.'),
(79378, 2016, 'bio', 'TiMEx: a waiting time model for mutually exclusive cancer alterations', 'Constantinescu, Simona|Szczurek, Ewa|Mohammadi, Pejman|Rahnenführer, Jörg|Beerenwinkel, Niko', 'Department of Biosystems Science and Engineering, ETH Zürich, Swiss Institute of Bioinformatics, Basel 4058, Switzerland and.|Department of Biosystems Science and Engineering, ETH Zürich, Swiss Institute of Bioinformatics, Basel 4058, Switzerland and.|Department of Biosystems Science and Engineering, ETH Zürich, Swiss Institute of Bioinformatics, Basel 4058, Switzerland and.|Faculty of Statistics, Technische Universität Dortmund, Dortmund 44221, Germany.|Department of Biosystems Science and Engineering, ETH Zürich, Swiss Institute of Bioinformatics, Basel 4058, Switzerland and.', 'Switzerland|Switzerland|Switzerland|Germany|Switzerland', 0, 'Algorithms|Genomics|Humans|Models, Theoretical|Mutation|Neoplasms|Software', 'Journal Article', '10.1093/bioinformatics/btv400', '26163509', 'Despite recent technological advances in genomic sciences, our understanding of cancer progression and its driving genetic alterations remains incomplete.'),
(79379, 2015, 'bio', 'C-It-Loci: a knowledge database for tissue-enriched loci', 'Weirick, Tyler|John, David|Dimmeler, Stefanie|Uchida, Shizuka', 'Institute of Cardiovascular Regeneration, Centre for Molecular Medicine, Goethe University Frankfurt and German Center for Cardiovascular Research, Partner side Rhein-Main, Frankfurt am Main, Germany.|Institute of Cardiovascular Regeneration, Centre for Molecular Medicine, Goethe University Frankfurt and German Center for Cardiovascular Research, Partner side Rhein-Main, Frankfurt am Main, Germany.|Institute of Cardiovascular Regeneration, Centre for Molecular Medicine, Goethe University Frankfurt and German Center for Cardiovascular Research, Partner side Rhein-Main, Frankfurt am Main, Germany.|Institute of Cardiovascular Regeneration, Centre for Molecular Medicine, Goethe University Frankfurt and German Center for Cardiovascular Research, Partner side Rhein-Main, Frankfurt am Main, Germany.', 'Germany|Germany|Germany|Germany', 20, 'Animals|Databases, Genetic|Genetic Loci|High-Throughput Nucleotide Sequencing|Humans|Knowledge Bases|Mice|Organ Specificity|Proteins|RNA, Untranslated|Sequence Analysis, RNA|Zebrafish', 'Journal Article', '10.1093/bioinformatics/btv410', '26163692', 'Increasing evidences suggest that most of the genome is transcribed into RNAs, but many of them are not translated into proteins. All those RNAs that do not become proteins are called non-coding RNAs (ncRNAs), which outnumbers protein-coding genes. Interestingly, these ncRNAs are shown to be more tissue specifically expressed than protein-coding genes. Given that tissue-specific expressions of transcripts suggest their importance in the expressed tissue, researchers are conducting biological experiments to elucidate the function of such ncRNAs. Owing greatly to the advancement of next-generation techniques, especially RNA-seq, the amount of high-throughput data are increasing rapidly. However, due to the complexity of the data as well as its high volume, it is not easy to re-analyze such data to extract tissue-specific expressions of ncRNAs from published datasets.'),
(79380, 2015, 'bio', 'Application of learning to rank to protein remote homology detection', 'Liu, Bin|Chen, Junjie|Wang, Xiaolong', 'School of Computer Science and Technology, Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China and Gordon Life Science Institute, Belmont, MA 02478, USA.|School of Computer Science and Technology.|School of Computer Science and Technology, Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China and.', 'China|United States|China', 65, 'Algorithms|Computational Biology|Databases, Protein|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software', 'Evaluation Studies', '10.1093/bioinformatics/btv413', '26163693', 'Protein remote homology detection is one of the fundamental problems in computational biology, aiming to find protein sequences in a database of known structures that are evolutionarily related to a given query protein. Some computational methods treat this problem as a ranking problem and achieve the state-of-the-art performance, such as PSI-BLAST, HHblits and ProtEmbed. This raises the possibility to combine these methods to improve the predictive performance. In this regard, we are to propose a new computational method called ProtDec-LTR for protein remote homology detection, which is able to combine various ranking methods in a supervised manner via using the Learning to Rank (LTR) algorithm derived from natural language processing.'),
(79381, 2015, 'bio', 'SomaticSignatures: inferring mutational signatures from single-nucleotide variants', 'Gehring, Julian S|Fischer, Bernd|Lawrence, Michael|Huber, Wolfgang', 'European Molecular Biology Laboratory, Genome Biology Unit, 69117 Heidelberg, Germany, Department of Bioinformatics and Computational Biology, Genentech Inc., South San Francisco, CA 94080, USA and.|European Molecular Biology Laboratory, Genome Biology Unit, 69117 Heidelberg, Germany, Computational Genome Biology, German Cancer Research Center, 69120 Heidelberg, Germany.|Department of Bioinformatics and Computational Biology, Genentech Inc., South San Francisco, CA 94080, USA and.|European Molecular Biology Laboratory, Genome Biology Unit, 69117 Heidelberg, Germany.', 'Germany|Germany|-|Germany', 136, 'Genome, Human|Humans|Mutation|Neoplasms|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btv408', '26163694', 'Mutational signatures are patterns in the occurrence of somatic single-nucleotide variants that can reflect underlying mutational processes. The SomaticSignatures package provides flexible, interoperable and easy-to-use tools that identify such signatures in cancer sequencing data. It facilitates large-scale, cross-dataset estimation of mutational signatures, implements existing methods for pattern decomposition, supports extension through user-defined approaches and integrates with existing Bioconductor workflows.'),
(79382, 2015, 'bio', 'Generation of sequence-based data for pedigree-segregating Mendelian or Complex traits', 'Li, Biao|Wang, Gao T|Leal, Suzanne M', 'Center for Statistical Genetics, Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA.|Center for Statistical Genetics, Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA.|Center for Statistical Genetics, Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA.', 'United States|United States|United States', 5, 'Base Sequence|Chromosome Segregation|Humans|Pedigree|Quantitative Trait, Heritable|Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btv412', '26177964', 'There is great interest in analyzing next generation sequence data that has been generated for pedigrees. However, unlike for population-based data there are only a limited number of rare variant methods to analyze pedigree data. One limitation is the ability to evaluate type I and II errors for family-based methods, due to lack of software that can simulate realistic sequence data for pedigrees.'),
(79383, 2015, 'bio', 'Karect: accurate correction of substitution, insertion and deletion errors for next-generation sequencing data', 'Allam, Amin|Kalnis, Panos|Solovyev, Victor', 'Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.|Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.|Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.', 'Saudi Arabia|Saudi Arabia|Saudi Arabia', 28, 'Algorithms|Chromosome Mapping|Computational Biology|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|INDEL Mutation|Mutagenesis, Insertional|Sequence Analysis, DNA|Sequence Deletion', 'Journal Article', '10.1093/bioinformatics/btv415', '26177965', 'Next-generation sequencing generates large amounts of data affected by errors in the form of substitutions, insertions or deletions of bases. Error correction based on the high-coverage information, typically improves de novo assembly. Most existing tools can correct substitution errors only, some support insertions and deletions, but accuracy in many cases is low.'),
(79384, 2015, 'bio', 'A Bayesian approach for structure learning in oscillating regulatory networks', 'Trejo Banos, Daniel|Millar, Andrew J|Sanguinetti, Guido', 'School of Informatics, University of Edinburgh, 10 Crichton St, Edinburgh EH8 9AB, UK.|SynthSys-Systems and Synthetic Biology, University of Edinburgh, CH Waddington Building, Kings Buildings, Mayfield Road, Edinburgh EH9 3JD, UK and School of Biological Sciences, University of Edinburgh, Darwin Building, Kings Buildings, Mayfield Road, Edinburgh EH9 3JR, UK.|School of Informatics, University of Edinburgh, 10 Crichton St, Edinburgh EH8 9AB, UK, SynthSys-Systems and Synthetic Biology, University of Edinburgh, CH Waddington Building, Kings Buildings, Mayfield Road, Edinburgh EH9 3JD, UK and.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Arabidopsis|Bayes Theorem|Cell Cycle|Circadian Clocks|Computer Simulation|Databases, Genetic|Gene Expression Profiling|Gene Expression Regulation, Fungal|Gene Expression Regulation, Plant|Gene Regulatory Networks|Saccharomyces cerevisiae', 'Journal Article', '10.1093/bioinformatics/btv414', '26177966', 'Oscillations lie at the core of many biological processes, from the cell cycle, to circadian oscillations and developmental processes. Time-keeping mechanisms are essential to enable organisms to adapt to varying conditions in environmental cycles, from day/night to seasonal. Transcriptional regulatory networks are one of the mechanisms behind these biological oscillations. However, while identifying cyclically expressed genes from time series measurements is relatively easy, determining the structure of the interaction network underpinning the oscillation is a far more challenging problem.'),
(79385, 2015, 'bio', 'RxnSim: a tool to compare biochemical reactions', 'Giri, Varun|Sivakumar, Tadi Venkata|Cho, Kwang Myung|Kim, Tae Yong|Bhaduri, Anirban', 'Bioinformatics Lab, Samsung Advanced Institute of Technology, Bangalore 560037, India and.|Bioinformatics Lab, Samsung Advanced Institute of Technology, Bangalore 560037, India and.|Biomaterials Lab, Materials Center, Samsung Advanced Institute of Technology, Gyeonggi-do 443803, Korea.|Biomaterials Lab, Materials Center, Samsung Advanced Institute of Technology, Gyeonggi-do 443803, Korea.|Bioinformatics Lab, Samsung Advanced Institute of Technology, Bangalore 560037, India and.', 'India|India|-|-|India', 6, 'Algorithms|Biochemical Phenomena|Databases, Chemical|Software', 'Journal Article', '10.1093/bioinformatics/btv416', '26187943', ': Quantitative assessment of chemical reaction similarity aids database searches, classification of reactions and identification of candidate enzymes. Most methods evaluate reaction similarity based on chemical transformation patterns. We describe a tool, RxnSim, which computes reaction similarity based on the molecular signatures of participating molecules. The tool is able to compare reactions based on similarities of substrates and products in addition to their transformation. It allows masking of user-defined chemical moieties for weighted similarity computations.'),
(79386, 2015, 'bio', 'Roary: rapid large-scale prokaryote pan genome analysis', 'Page, Andrew J|Cummins, Carla A|Hunt, Martin|Wong, Vanessa K|Reuter, Sandra|Holden, Matthew T G|Fookes, Maria|Falush, Daniel|Keane, Jacqueline A|Parkhill, Julian', 'Pathogen Genomics, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge.|Pathogen Genomics, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge.|Pathogen Genomics, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge.|Pathogen Genomics, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, Department of Medicine, University of Cambridge, Cambridge.|Department of Medicine, University of Cambridge, Cambridge.|School of Medicine, University of St. Andrews, North Haugh, St Andrews and.|Pathogen Genomics, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge.|College of Medicine, Swansea University, Swansea, UK.|Pathogen Genomics, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge.|Pathogen Genomics, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 894, 'Computer Simulation|Databases, Genetic|Genome, Bacterial|Prokaryotic Cells|Salmonella typhi|Software', 'Journal Article', '10.1093/bioinformatics/btv421', '26198102', 'A typical prokaryote population sequencing study can now consist of hundreds or thousands of isolates. Interrogating these datasets can provide detailed insights into the genetic structure of prokaryotic genomes. We introduce Roary, a tool that rapidly builds large-scale pan genomes, identifying the core and accessory genes. Roary makes construction of the pan genome of thousands of prokaryote samples possible on a standard desktop without compromising on the accuracy of results. Using a single CPU Roary can produce a pan genome consisting of 1000 isolates in 4.5 hours using 13 GB of RAM, with further speedups possible using multiple processors.'),
(79387, 2015, 'bio', 'WATCLUST: a tool for improving the design of drugs based on protein-water interactions', 'López, Elias D|Arcon, Juan Pablo|Gauto, Diego F|Petruk, Ariel A|Modenutti, Carlos P|Dumas, Victoria G|Marti, Marcelo A|Turjanski, Adrian G', 'Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Pabellón II, C1428EHA, Ciudad de Buenos Aires, Argentina and.|Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Pabellón II, C1428EHA, Ciudad de Buenos Aires, Argentina and INQUIMAE-CONICET, Ciudad Universitaria, Pabellón II, C1428EHA, Ciudad de Buenos Aires, Argentina.|INQUIMAE-CONICET, Ciudad Universitaria, Pabellón II, C1428EHA, Ciudad de Buenos Aires, Argentina.|INQUIMAE-CONICET, Ciudad Universitaria, Pabellón II, C1428EHA, Ciudad de Buenos Aires, Argentina.|Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Pabellón II, C1428EHA, Ciudad de Buenos Aires, Argentina and.|Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Pabellón II, C1428EHA, Ciudad de Buenos Aires, Argentina and.|Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Pabellón II, C1428EHA, Ciudad de Buenos Aires, Argentina and INQUIMAE-CONICET, Ciudad Universitaria, Pabellón II, C1428EHA, Ciudad de Buenos Aires, Argentina.|Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Pabellón II, C1428EHA, Ciudad de Buenos Aires, Argentina and INQUIMAE-CONICET, Ciudad Universitaria, Pabellón II, C1428EHA, Ciudad de Buenos Aires, Argentina.', 'Argentina|Argentina|Argentina|Argentina|Argentina|Argentina|Argentina|Argentina', 19, 'Drug Design|Escherichia coli Proteins|Molecular Dynamics Simulation|Proteins|Software|Water', 'Journal Article', '10.1093/bioinformatics/btv411', '26198103', 'Water molecules are key players for protein folding and function. On the protein surface, water is not placed randomly, but display instead a particular structure evidenced by the presence of specific water sites (WS). These WS can be derived and characterized using explicit water Molecular Dynamics simulations, providing useful information for ligand binding prediction and design. Here we present WATCLUST, a WS determination and analysis tool running on the VMD platform. The tool also allows direct transfer of the WS information to Autodock program to perform biased docking.'),
(79388, 2015, 'bio', 'Identifying novel associations between small molecules and miRNAs based on integrated molecular networks', 'Lv, Yingli|Wang, Shuyuan|Meng, Fanlin|Yang, Lei|Wang, Zhifeng|Wang, Jing|Chen, Xiaowen|Jiang, Wei|Li, Yixue|Li, Xia', 'College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China and.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China and.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China and.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China and.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China and.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China and.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China and.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China and.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China and Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China and.', 'China|China|China|China|China|China|China|China|China|China', 0, 'Algorithms|Area Under Curve|Computational Biology|Gene Regulatory Networks|Humans|MicroRNAs|Reproducibility of Results|Small Molecule Libraries', 'Journal Article', '10.1093/bioinformatics/btv417', '26198104', 'miRNAs play crucial roles in human diseases and newly discovered could be targeted by small molecule (SM) drug compounds. Thus, the identification of small molecule drug compounds (SM) that target dysregulated miRNAs in cancers will provide new insight into cancer biology and accelerate drug discovery for cancer therapy.'),
(79389, 2015, 'bio', 'The application of a novel rising activity, multi-level mixed effects, indicator emphasis (RAMMIE) method for syndromic surveillance in England', 'Morbey, Roger A|Elliot, Alex J|Charlett, Andre|Verlander, Neville Q|Andrews, Nick|Smith, Gillian E', 'Real-time Syndromic Surveillance Team, Public Health England, Birmingham B3 2PW, UK and.|Real-time Syndromic Surveillance Team, Public Health England, Birmingham B3 2PW, UK and.|Statistics and Modelling Economics Department, Public Health England, London, UK.|Statistics and Modelling Economics Department, Public Health England, London, UK.|Statistics and Modelling Economics Department, Public Health England, London, UK.|Real-time Syndromic Surveillance Team, Public Health England, Birmingham B3 2PW, UK and.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Emergency Service, Hospital|England|Humans|Population Surveillance|Predictive Value of Tests|Public Health', 'Journal Article', '10.1093/bioinformatics/btv418', '26198105', 'Syndromic surveillance is the real-time collection and interpretation of data to allow the early identification of public health threats and their impact, enabling public health action. The rising activity, multi-level mixed effects, indicator emphasis method was developed to provide a single robust method enabling detection of unusual activity across a wide range of syndromes, nationally and locally.'),
(79390, 2015, 'bio', 'CEMAsuite: open source degenerate PCR primer design', 'Lane, Courtney E|Hulgan, Daniel|OQuinn, Kelly|Benton, Michael G', 'Cain Department of Chemical Engineering, Louisiana State University, Baton Rouge, LA 70803, USA.|Cain Department of Chemical Engineering, Louisiana State University, Baton Rouge, LA 70803, USA.|Cain Department of Chemical Engineering, Louisiana State University, Baton Rouge, LA 70803, USA.|Cain Department of Chemical Engineering, Louisiana State University, Baton Rouge, LA 70803, USA.', 'United States|United States|United States|United States', 6, 'Algorithms|DNA Primers|Polymerase Chain Reaction|Sequence Alignment|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btv420', '26198106', 'The codon-equivalent multiple alignment suite begins conservational analysis for polymerase chain reaction primer design at the protein level, allowing the user to design consensus primers capable of detecting homologous coding sequences even when low-to-moderate sequence information is available. This package also condenses the wealth of information associated with multiple sequence alignments and presents them in an intuitive manner, allowing the user to quickly and effectively address degenerate primer design considerations.'),
(79391, 2015, 'bio', 'GeneCOST: a novel scoring-based prioritization framework for identifying disease causing genes', 'Ozer, Bugra|Saxc4x9fxc4xb1roxc4x9flu, Mahmut|Demirci, Hxc3xbcseyin', 'Advanced Genomics and Bioinformatics Research Center, The Scientific and Technological Research Council of Turkey (TUBITAK), Gebze, Kocaeli, Turkey and Department of Biological Sciences and Bioengineering, Sabanci University, 34956, Istanbul, Turkey.|Advanced Genomics and Bioinformatics Research Center, The Scientific and Technological Research Council of Turkey (TUBITAK), Gebze, Kocaeli, Turkey and.|Advanced Genomics and Bioinformatics Research Center, The Scientific and Technological Research Council of Turkey (TUBITAK), Gebze, Kocaeli, Turkey and.', 'Turkey|Turkey|Turkey', 0, 'Disease|Family|Genetic Association Studies|Humans|Mutation|Software', 'Journal Article', '10.1093/bioinformatics/btv424', '26203168', 'Due to the big data produced by next-generation sequencing studies, there is an evident need for methods to extract the valuable information gathered from these experiments. In this work, we propose GeneCOST, a novel scoring-based method to evaluate every gene for their disease association. Without any prior filtering and any prior knowledge, we assign a disease likelihood score to each gene in correspondence with their variations. Then, we rank all genes based on frequency, conservation, pedigree and detailed variation information to find out the causative reason of the disease state. We demonstrate the usage of GeneCOST with public and real life Mendelian disease cases including recessive, dominant, compound heterozygous and sporadic models. As a result, we were able to identify causative reason behind the disease state in top rankings of our list, proving that this novel prioritization framework provides a powerful environment for the analysis in genetic disease studies alternative to filtering-based approaches.'),
(79392, 2015, 'bio', 'TEtranscripts: a package for including transposable elements in differential expression analysis of RNA-seq datasets', 'Jin, Ying|Tam, Oliver H|Paniagua, Eric|Hammell, Molly', 'Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA.|Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA.|Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA.|Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA.', 'United States|United States|United States|United States', 85, 'Animals|DNA Transposable Elements|Databases, Genetic|Drosophila melanogaster|Gene Expression Profiling|Mice|RNA, Messenger|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btv422', '26206304', 'Most RNA-seq data analysis software packages are not designed to handle the complexities involved in properly apportioning short sequencing reads to highly repetitive regions of the genome. These regions are often occupied by transposable elements (TEs), which make up between 20 and 80% of eukaryotic genomes. They can contribute a substantial portion of transcriptomic and genomic sequence reads, but are typically ignored in most analyses.'),
(79393, 2015, 'bio', 'Identifying kinase dependency in cancer cells by integrating high-throughput drug screening and kinase inhibition data', 'Ryall, Karen A|Shin, Jimin|Yoo, Minjae|Hinz, Trista K|Kim, Jihye|Kang, Jaewoo|Heasley, Lynn E|Tan, Aik Choon', 'Translational Bioinformatics and Cancer Systems Biology Laboratory, Division of Medical Oncology, Department of Medicine.|Translational Bioinformatics and Cancer Systems Biology Laboratory, Division of Medical Oncology, Department of Medicine.|Translational Bioinformatics and Cancer Systems Biology Laboratory, Division of Medical Oncology, Department of Medicine.|Department of Craniofacial Biology, School of Dental Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.|Translational Bioinformatics and Cancer Systems Biology Laboratory, Division of Medical Oncology, Department of Medicine.|Department of Computer Science and Engineering, Korea University, Seoul, Korea and.|Translational Bioinformatics and Cancer Systems Biology Laboratory, Division of Medical Oncology, Department of Medicine.|Translational Bioinformatics and Cancer Systems Biology Laboratory, Division of Medical Oncology, Department of Medicine, Department of Computer Science and Engineering, Korea University, Seoul, Korea and Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 11, 'Algorithms|Antineoplastic Agents|Biomarkers, Tumor|Carcinoma, Non-Small-Cell Lung|Cell Proliferation|Drug Evaluation, Preclinical|Drug Resistance, Neoplasm|Drug Synergism|Gene Expression Profiling|High-Throughput Screening Assays|Humans|Immunoblotting|Leukemia|Lung Neoplasms|Mutation|Protein Kinase Inhibitors|Receptor, Fibroblast Growth Factor, Type 1|TOR Serine-Threonine Kinases|Tumor Cells, Cultured', 'Journal Article', '10.1093/bioinformatics/btv427', '26206305', 'Targeted kinase inhibitors have dramatically improved cancer treatment, but kinase dependency for an individual patient or cancer cell can be challenging to predict. Kinase dependency does not always correspond with gene expression and mutation status. High-throughput drug screens are powerful tools for determining kinase dependency, but drug polypharmacology can make results difficult to interpret.'),
(79394, 2015, 'bio', 'High-order neural networks and kernel methods for peptide-MHC binding prediction', 'Kuksa, Pavel P|Min, Martin Renqiang|Dugar, Rishabh|Gerstein, Mark', 'Institute for Biomedical Informatics, Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA, Department of Machine Learning, NEC Laboratories America, Princeton, NJ 08540, USA.|Department of Machine Learning, NEC Laboratories America, Princeton, NJ 08540, USA.|Department of Machine Learning, NEC Laboratories America, Princeton, NJ 08540, USA.|Program of Computational Biology and Bioinformatics and Department of Molecular Biophysics and Biochemistry and Department of Computer Science, Yale University, New Haven, CT 06511, USA.', 'United States|United States|United States|United States', 17, 'Algorithms|Amino Acid Sequence|Area Under Curve|Databases, Protein|Epitopes|Humans|Major Histocompatibility Complex|Molecular Sequence Data|Neural Networks (Computer)|Peptides|Protein Binding|ROC Curve|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/btv371', '26206306', 'Effective computational methods for peptide-protein binding prediction can greatly help clinical peptide vaccine search and design. However, previous computational methods fail to capture key nonlinear high-order dependencies between different amino acid positions. As a result, they often produce low-quality rankings of strong binding peptides. To solve this problem, we propose nonlinear high-order machine learning methods including high-order neural networks (HONNs) with possible deep extensions and high-order kernel support vector machines to predict major histocompatibility complex-peptide binding.'),
(79395, 2015, 'bio', 'Statistical models for RNA-seq data derived from a two-condition 48-replicate experiment', 'Gierlixc5x84ski, Marek|Cole, Christian|Schofield, Pietxc3xa0|Schurch, Nicholas J|Sherstnev, Alexander|Singh, Vijender|Wrobel, Nicola|Gharbi, Karim|Simpson, Gordon|Owen-Hughes, Tom|Blaxter, Mark|Barton, Geoffrey J', 'Division of Computational Biology and Centre for Gene Regulation and Expression, College of Life Sciences, University of Dundee, Dow Street Dundee, DD1 5EH, UK.|Division of Computational Biology and.|Division of Computational Biology and Centre for Gene Regulation and Expression, College of Life Sciences, University of Dundee, Dow Street Dundee, DD1 5EH, UK.|Division of Computational Biology and.|Division of Computational Biology and.|Centre for Gene Regulation and Expression, College of Life Sciences, University of Dundee, Dow Street Dundee, DD1 5EH, UK.|Edinburgh Genomics and.|Edinburgh Genomics and Institute of Evolutionary Biology, Ashworth Laboratories, University of Edinburgh, Edinburgh, UK.|Division of Plant Sciences and.|Centre for Gene Regulation and Expression, College of Life Sciences, University of Dundee, Dow Street Dundee, DD1 5EH, UK.|Edinburgh Genomics and Institute of Evolutionary Biology, Ashworth Laboratories, University of Edinburgh, Edinburgh, UK.|Division of Computational Biology and Centre for Gene Regulation and Expression, College of Life Sciences, University of Dundee, Dow Street Dundee, DD1 5EH, UK, Biological Chemistry and Drug Discovery, College of Life Sciences, University of Dundee, Dow Street Dundee, DD1 5EH, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 34, 'Base Sequence|Binomial Distribution|Gene Expression Profiling|Models, Statistical|Reproducibility of Results|Saccharomyces cerevisiae|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btv425', '26206307', 'High-throughput RNA sequencing (RNA-seq) is now the standard method to determine differential gene expression. Identifying differentially expressed genes crucially depends on estimates of read-count variability. These estimates are typically based on statistical models such as the negative binomial distribution, which is employed by the tools edgeR, DESeq and cuffdiff. Until now, the validity of these models has usually been tested on either low-replicate RNA-seq data or simulations.'),
(79396, 2015, 'bio', 'Alternative preprocessing of RNA-Sequencing data in The Cancer Genome Atlas leads to improved analysis results', 'Rahman, Mumtahena|Jackson, Laurie K|Johnson, W Evan|Li, Dean Y|Bild, Andrea H|Piccolo, Stephen R', 'Department of Biomedical Informatics.|Department of Pharmacology and Toxicology.|Department of Oncological Sciences, University of Utah, Salt Lake City, UT, USA, Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118, USA.|Department of Oncological Sciences, University of Utah, Salt Lake City, UT, USA, School of Medicine, Department of Human Genetics, University of Utah, Salt Lake City, UT 84132, USA and.|Department of Biomedical Informatics, Department of Pharmacology and Toxicology, Department of Oncological Sciences, University of Utah, Salt Lake City, UT, USA.|Department of Biology, Brigham Young University, Provo, UT 84604, USA.', 'United States|United States|United States|United States|United States|United States', 77, 'Breast Neoplasms|Female|Gene Expression Regulation, Neoplastic|Genome, Human|Humans|ROC Curve|Reproducibility of Results|Sequence Analysis, RNA|Statistics as Topic', 'Journal Article', '10.1093/bioinformatics/btv377', '26209429', 'The Cancer Genome Atlas (TCGA) RNA-Sequencing data are used widely for research. TCGA provides Level 3 data, which have been processed using a pipeline specific to that resource. However, we have found using experimentally derived data that this pipeline produces gene-expression values that vary considerably across biological replicates. In addition, some RNA-Sequencing analysis tools require integer-based read counts, which are not provided with the Level 3 data. As an alternative, we have reprocessed the data for 9264 tumor and 741 normal samples across 24 cancer types using the Rsubread package. We have also collated corresponding clinical data for these samples. We provide these data as a community resource.'),
(79397, 2015, 'bio', 'CRISPR-ERA: a comprehensive design tool for CRISPR-mediated gene editing, repression and activation', 'Liu, Honglei|Wei, Zheng|Dominguez, Antonia|Li, Yanda|Wang, Xiaowo|Qi, Lei S', 'Bioinformatics Division, Center for Synthetic and Systems Biology, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China, Stanford Chemistry, Engineering & Medicine for Human Health (ChEM-H).|Bioinformatics Division, Center for Synthetic and Systems Biology, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China.|Department of Bioengineering and.|Bioinformatics Division, Center for Synthetic and Systems Biology, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China.|Bioinformatics Division, Center for Synthetic and Systems Biology, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China.|Stanford Chemistry, Engineering & Medicine for Human Health (ChEM-H), Department of Bioengineering and Department of Chemical and Systems Biology, Stanford University, 443 Via Ortega, Shriram Center 376, Stanford, CA 94305-4125, USA.', 'China|China|United States|China|China|United States', 68, 'Animals|Clustered Regularly Interspaced Short Palindromic Repeats|Gene Expression Regulation|Humans|RNA Editing|Software', 'Journal Article', '10.1093/bioinformatics/btv423', '26209430', 'The CRISPR/Cas9 system was recently developed as a powerful and flexible technology for targeted genome engineering, including genome editing (altering the genetic sequence) and gene regulation (without altering the genetic sequence). These applications require the design of single guide RNAs (sgRNAs) that are efficient and specific. However, this remains challenging, as it requires the consideration of many criteria. Several sgRNA design tools have been developed for gene editing, but currently there is no tool for the design of sgRNAs for gene regulation. With accumulating experimental data on the use of CRISPR/Cas9 for gene editing and regulation, we implement a comprehensive computational tool based on a set of sgRNA design rules summarized from these published reports. We report a genome-wide sgRNA design tool and provide an online website for predicting sgRNAs that are efficient and specific. We name the tool CRISPR-ERA, for clustered regularly interspaced short palindromic repeat-mediated editing, repression, and activation (ERA).'),
(79398, 2015, 'bio', 'dendextend: an R package for visualizing, adjusting and comparing trees of hierarchical clustering', 'Galili, Tal', 'Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv 6997801, Israel.', 'Israel', 324, 'Cluster Analysis|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/btv428', '26209431', 'dendextend is an R package for creating and comparing visually appealing tree diagrams. dendextend provides utility functions for manipulating dendrogram objects (their color, shape and content) as well as several advanced methods for comparing trees to one another (both statistically and visually). As such, dendextend offers a flexible framework for enhancing Rs rich ecosystem of packages for performing hierarchical clustering of items.'),
(79399, 2015, 'bio', 'A mutation profile for top-k patient search exploiting Gene-Ontology and orthogonal non-negative matrix factorization', 'Kim, Sungchul|Sael, Lee|Yu, Hwanjo', 'Department of Computer Science and Engineering, POSTECH, Pohang, South Korea and.|Department of Computer Science, Stony Brook University, Stony Brook, USA.|Department of Computer Science and Engineering, POSTECH, Pohang, South Korea and.', 'United States|United States|United States', 10, 'Algorithms|Chi-Square Distribution|Gene Ontology|Humans|Mutation|Neoplasms|Reproducibility of Results|Survival Analysis', 'Journal Article', '10.1093/bioinformatics/btv409', '26209432', 'As the quantity of genomic mutation data increases, the likelihood of finding patients with similar genomic profiles, for various disease inferences, increases. However, so does the difficulty in identifying them. Similarity search based on patient mutation profiles can solve various translational bioinformatics tasks, including prognostics and treatment efficacy predictions for better clinical decision making through large volume of data. However, this is a challenging problem due to heterogeneous and sparse characteristics of the mutation data as well as their high dimensionality.'),
(79400, 2015, 'bio', 'GeneVetter: a web tool for quantitative monogenic assessment of rare diseases', 'Gillies, Christopher E|Robertson, Catherine C|Sampson, Matthew G|Kang, Hyun Min', 'Division of Nephrology, Department of Pediatrics and Communicable Diseases, University of Michigan School of Medicine, Ann Arbor, MI, USA and.|Division of Nephrology, Department of Pediatrics and Communicable Diseases, University of Michigan School of Medicine, Ann Arbor, MI, USA and.|Division of Nephrology, Department of Pediatrics and Communicable Diseases, University of Michigan School of Medicine, Ann Arbor, MI, USA and.|Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA.', 'United States|United States|United States|United States', 4, 'Diabetes Mellitus, Type 2|Humans|Internet|Nephrotic Syndrome|Rare Diseases|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btv432', '26209433', 'When performing DNA sequencing to diagnose affected individuals with monogenic forms of rare diseases, accurate attribution of causality to detected variants is imperative but imperfect. Even if a gene has variants already known to cause a disease, rare disruptive variants predicted to be causal are not always so, mainly due to imperfect ability to predict the pathogenicity of variants. Existing population-scale sequence resources such as 1000 Genomes are useful to quantify the background prevalence of an unaffected individual being falsely predicted to carry causal variants. We developed GeneVetter to allow users to quantify the background prevalence of subjects with predicted causal variants within specific genes under user-specified filtering parameters. GeneVetter helps quantify uncertainty in monogenic diagnosis and design genetic studies with support for power and sample size calculations for specific genes with specific filtering criteria. GeneVetter also allows users to analyze their own sequence data without sending genotype information over the Internet. Overall, GeneVetter is an interactive web tool that facilitates quantifying and accounting for the background prevalence of predicted pathogenic variants in a population.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(79401, 2015, 'bio', 'Spaced seeds improve k-mer-based metagenomic classification', 'Bxc5x99inda, Karel|Sykulski, Maciej|Kucherov, Gregory', 'LIGM/CNRS, Universitxc3xa9 Paris-Est, 77454 Marne-la-Vallxc3xa9e, France.|LIGM/CNRS, Universitxc3xa9 Paris-Est, 77454 Marne-la-Vallxc3xa9e, France.|LIGM/CNRS, Universitxc3xa9 Paris-Est, 77454 Marne-la-Vallxc3xa9e, France.', 'France|France|France', 33, 'Algorithms|Bacillus|Databases, Genetic|Genome, Bacterial|Metagenomics|Mycobacterium|Probability|Sequence Alignment|Statistics, Nonparametric', 'Journal Article', '10.1093/bioinformatics/btv419', '26209798', 'Metagenomics is a powerful approach to study genetic content of environmental samples, which has been strongly promoted by next-generation sequencing technologies. To cope with massive data involved in modern metagenomic projects, recent tools rely on the analysis of k-mers shared between the read to be classified and sampled reference genomes.'),
(79402, 2015, 'bio', 'Mapping networks of light-dark transition in LOV photoreceptors', 'Kaur Grewal, Rajdeep|Mitra, Devrani|Roy, Soumen', 'Bose Institute, Kolkata 700xe2x80x89009, India and.|Department of Biological Sciences, Presidency University, Kolkata 700xe2x80x89073, India.|Bose Institute, Kolkata 700xe2x80x89009, India and.', 'India|India|India', 0, 'Amino Acids|Bacterial Proteins|Darkness|Fungal Proteins|Light|Photoreceptor Cells|Plant Proteins|Protein Interaction Mapping|Protein Interaction Maps|Protein Structure, Tertiary|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btv429', '26209799', 'In optogenetics, designing modules of long or short signaling state lifetime is necessary for control over precise cellular events. A critical parameter for designing artificial or synthetic photoreceptors is the signaling state lifetime of photosensor modules. Design and engineering of biologically relevant artificial photoreceptors is based on signaling mechanisms characteristic of naturally occurring photoreceptors. Therefore identifying residues important for light-dark transition is a definite first step towards rational design of synthetic photoreceptors. A thorough grasp of detailed mechanisms of photo induced signaling process would be immensely helpful in understanding the behaviour of organisms.'),
(79403, 2015, 'bio', 'Statistically identifying tumor suppressors and oncogenes from pan-cancer genome-sequencing data', 'Kumar, Runjun D|Searleman, Adam C|Swamidass, S Joshua|Griffith, Obi L|Bose, Ron', 'Division of Oncology, Department of Medicine, Washington University School of Medicine, Computational and Systems Biology Program, Washington University in St Louis.|Division of Oncology, Department of Medicine, Washington University School of Medicine.|Computational and Systems Biology Program, Washington University in St Louis, Department of Pathology and Immunology, Washington University School of Medicine and.|Division of Oncology, Department of Medicine, Washington University School of Medicine, Division of Oncology, Department of Medicine, Washington University School of Medicine.|Division of Oncology, Department of Medicine, Washington University School of Medicine.', '-|-|-|-|-', 16, 'Area Under Curve|Databases, Genetic|Genes, Tumor Suppressor|Genome, Human|Humans|Models, Genetic|Mutation|Neoplasms|Oncogenes|Quality Control|ROC Curve|Reproducibility of Results|Sequence Analysis, DNA|Statistics as Topic', 'Journal Article', '10.1093/bioinformatics/btv430', '26209800', 'Several tools exist to identify cancer driver genes based on somatic mutation data. However, these tools do not account for subclasses of cancer genes: oncogenes, which undergo gain-of-function events, and tumor suppressor genes (TSGs) which undergo loss-of-function. A method which accounts for these subclasses could improve performance while also suggesting a mechanism of action for new putative cancer genes.'),
(79404, 2015, 'bio', 'The MI bundle: enabling network and structural biology in genome visualization tools', 'Céol, Arnaud|Müller, Heiko', 'Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia (IIT), 20139 Milan, Italy.|Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia (IIT), 20139 Milan, Italy.', 'Italy|Italy', 5, 'Core Binding Factor Alpha 2 Subunit|Disease|Gene Regulatory Networks|Genome|Genomics|Humans|Software', 'Journal Article', '10.1093/bioinformatics/btv431', '26209801', 'Prioritization of candidate genes emanating from large-scale screens requires integrated analyses at the genomics, molecular, network and structural biology levels. We have extended the Integrated Genome Browser (IGB) to facilitate these tasks. The graphical user interface greatly simplifies building disease networks and zooming in at atomic resolution to identify variations in molecular complexes that may affect molecular interactions in the context of genomic data. All results are summarized in genome tracks and can be visualized and analyzed at the transcript level.'),
(79405, 2015, 'bio', 'FermiKit: assembly-based variant calling for Illumina resequencing data', 'Li, Heng', 'Genomics Platform, Broad Institute, Cambridge, MA 02142, USA.', 'United States', 0, 'Algorithms|Base Pairing|Genome, Human|Humans|INDEL Mutation|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Sequence Deletion', 'Journal Article', '10.1093/bioinformatics/btv440', '26220959', 'FermiKit is a variant calling pipeline for Illumina whole-genome germline data. It de novo assembles short reads and then maps the assembly against a reference genome to call SNPs, short insertions/deletions and structural variations. FermiKit takes about one day to assemble 30-fold human whole-genome data on a modern 16-core server with 85 GB RAM at the peak, and calls variants in half an hour to an accuracy comparable to the current practice. FermiKit assembly is a reduced representation of raw data while retaining most of the original information.'),
(79406, 2015, 'bio', 'Canonical, stable, general mapping using context schemes', 'Novak, Adam M|Rosen, Yohei|Haussler, David|Paten, Benedict', 'UC Santa Cruz Genomics Institute, 1156 High Street, Santa Cruz, CA 95064 and.|NYU School of Medicine, 550 First Avenue, New York, NY 10016, USA.|UC Santa Cruz Genomics Institute, 1156 High Street, Santa Cruz, CA 95064 and.|UC Santa Cruz Genomics Institute, 1156 High Street, Santa Cruz, CA 95064 and.', 'United States|United States|United States|United States', 0, 'Algorithms|Computer Simulation|Genetic Loci|Genomics|Humans|Major Histocompatibility Complex|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btv435', '26220960', 'Sequence mapping is the cornerstone of modern genomics. However, most existing sequence mapping algorithms are insufficiently general.'),
(79407, 2015, 'bio', 'Designing alternative splicing RNA-seq studies Beyond generic guidelines', 'Stephan-Otto Attolini, Camille|Peña, Victor|Rossell, David', 'Institute for Research in Biomedicine (IRB Barcelona), Barcelona, Spain.|Department of Statistical Science, Duke University, Durham, North Carolina, USA and.|Department of Statistics, University of Warwick, Coventry, UK.', 'Spain|United Kingdom|United Kingdom', 5, 'Algorithms|Alternative Splicing|Animals|Bayes Theorem|Computer Simulation|Guidelines as Topic|Humans|Mice|Research Design|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btv436', '26220961', 'Designing an RNA-seq study depends critically on its specific goals, technology and underlying biology, which renders general guidelines inadequate. We propose a Bayesian framework to customize experiments so that goals can be attained and resources are not wasted, with a focus on alternative splicing.'),
(79408, 2015, 'bio', 'MetTailor: dynamic block summary and intensity normalization for robust analysis of mass spectrometry data in metabolomics', 'Chen, Gengbo|Cui, Liang|Teo, Guo Shou|Ong, Choon Nam|Tan, Chuen Seng|Choi, Hyungwon', 'Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore.|Interdisciplinary Research Group in Infectious Diseases, Singapore-MIT Alliance for Research & Technology, Singapore, Singapore and.|Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore.|Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore, National University of Singapore Environment Research Institute, Singapore, Singapore.|Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore.|Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore.', 'Singapore|Singapore|Singapore|Singapore|Singapore|Singapore', 0, 'Algorithms|Calibration|Chromatography|Databases as Topic|Mass Spectrometry|Metabolomics|Software', 'Journal Article', '10.1093/bioinformatics/btv434', '26220962', 'Accurate cross-sample peak alignment and reliable intensity normalization is a critical step for robust quantitative analysis in untargetted metabolomics since tandem mass spectrometry (MS/MS) is rarely used for compound identification. Therefore shortcomings in the data processing steps can easily introduce false positives due to misalignments and erroneous normalization adjustments in large sample studies.'),
(79409, 2015, 'bio', 'SNPGenie: estimating evolutionary parameters to detect natural selection using pooled next-generation sequencing data', 'Nelson, Chase W|Moncla, Louise H|Hughes, Austin L', 'Department of Biological Sciences, University of South Carolina, Columbia, SC 29208, USA and.|Department of Pathobiological Sciences, University of Wisconsin School of Veterinary Medicine, Madison, WI 53706, USA.|Department of Biological Sciences, University of South Carolina, Columbia, SC 29208, USA and.', 'United States|United States|United States', 0, 'Biological Evolution|Genetics, Population|Hemagglutinins|High-Throughput Nucleotide Sequencing|Humans|Influenza A Virus, H5N1 Subtype|Neuraminidase|Polymorphism, Single Nucleotide|Selection, Genetic|Software', 'Journal Article', '10.1093/bioinformatics/btv449', '26227143', 'New applications of next-generation sequencing technologies use pools of DNA from multiple individuals to estimate population genetic parameters. However, no publicly available tools exist to analyse single-nucleotide polymorphism (SNP) calling results directly for evolutionary parameters important in detecting natural selection, including nucleotide diversity and gene diversity. We have developed SNPGenie to fill this gap. The user submits a FASTA reference sequence(s), a Gene Transfer Format (.GTF) file with CDS information and a SNP report(s) in an increasing selection of formats. The program estimates nucleotide diversity, distance from the reference and gene diversity. Sites are flagged for multiple overlapping reading frames, and are categorized by polymorphism type: nonsynonymous, synonymous, or ambiguous. The results allow single nucleotide, single codon, sliding window, whole gene and whole genome/population analyses that aid in the detection of positive and purifying natural selection in the source population.'),
(79410, 2015, 'bio', 'SynLinker: an integrated system for designing linkers and synthetic fusion proteins', 'Liu, Chengcheng|Chin, Ju Xin|Lee, Dong-Yup', 'Bioprocessing Technology Institute, Agency for Science Technology and Research (A*STAR), Singapore 138668, Singapore.|Bioprocessing Technology Institute, Agency for Science Technology and Research (A*STAR), Singapore 138668, Singapore.|Bioprocessing Technology Institute, Agency for Science Technology and Research (A*STAR), Singapore 138668, Singapore, Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117585, Singapore and NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, 28 Medical Drive, Singapore 117456, Singapore.', 'Singapore|Singapore|Singapore', 14, 'Amino Acid Sequence|Databases, Protein|Humans|Recombinant Fusion Proteins|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btv447', '26227144', 'Synthetic fusion proteins have shown great potential in various biotechnological and (bio)pharmaceutical applications. They usually contain more than two protein domains joined by a linker peptide sequence which is often selected intuitively or in ad hoc manner. Thus, we developed an integrated web-based system, SynLinker, to provide appropriate linker candidates for constructing fusion proteins. We compiled a total of 2260 linker sequences comprising of natural linkers extracted from a set of non-redundant multi-domain proteins in Protein Data Bank and artificial/empirical linkers collected from literature and patents. Multiple query interface allows users to search for the desired linker candidates based on selection criteria and their preferences. In addition, a selected linker can be combined with two domain structures which are uploaded and appended at its N and C terminals, thereby predicting a de novo structure of the fusion protein. Hence, SynLinker can serve as a systematic tool for researchers who are interested in designing synthetic fusion proteins.'),
(79411, 2015, 'bio', 'BackCLIP: a tool to identify common background presence in PAR-CLIP datasets', 'Reyes-Herrera, P H|Speck-Hernandez, C A|Sierra, C A|Herrera, S', 'Colombian Corporation for Agricultural Research (CORPOICA), 250047 Bogotá, Colombia.|Universidad Antonio Nariño, 110311 Bogotá, Colombia.|Universidad Antonio Nariño, 110311 Bogotá, Colombia.|Woods Hole Oceanographic Institution, 02543 Massachusetts, USA and Massachusetts Institute of Technology, 02139 Massachusetts, USA.', 'Colombia|Colombia|Colombia|United States', 0, 'Cross-Linking Reagents|Databases, Genetic|Immunoprecipitation|RNA-Binding Proteins|Software|Ultraviolet Rays', 'Journal Article', '10.1093/bioinformatics/btv442', '26227145', 'PAR-CLIP, a CLIP-seq protocol, derives a transcriptome wide set of binding sites for RNA-binding proteins. Even though the protocol uses stringent washing to remove experimental noise, some of it remains. A recent study measured three sets of non-specific RNA backgrounds which are present in several PAR-CLIP datasets. However, a tool to identify the presence of common background in PAR-CLIP datasets is not yet available.'),
(79412, 2015, 'bio', 'ClicO FS: an interactive web-based service of Circos', 'Cheong, Wei-Hien|Tan, Yung-Chie|Yap, Soon-Joo|Ng, Kee-Peng', 'Department of Science and Technology, Codon Genomics S/B, Taman Dutamas, Balakong, 43200 Seri Kembangan, Malaysia and.|Department of Science and Technology, Codon Genomics S/B, Taman Dutamas, Balakong, 43200 Seri Kembangan, Malaysia and.|Department of Science and Technology, Codon Genomics S/B, Taman Dutamas, Balakong, 43200 Seri Kembangan, Malaysia and.|Department of Medical Microbiology, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.', 'Malaysia|Malaysia|Malaysia|Malaysia', 0, 'Animals|Humans|Internet|Mice|Software', 'Journal Article', '10.1093/bioinformatics/btv433', '26227146', ': We present ClicO Free Service, an online web-service based on Circos, which provides a user-friendly, interactive web-based interface with configurable features to generate Circos circular plots.'),
(79413, 2015, 'bio', 'MICC: an R package for identifying chromatin interactions from ChIA-PET data', 'He, Chao|Zhang, Michael Q|Wang, Xiaowo', 'MOE Key Laboratory of Bioinformatics and Bioinformatics Division, Center for Synthetic and System Biology, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China and.|Department of Molecular and Cell Biology, Center for Systems Biology, The University of Texas, Richardson, TX 75080-3021, USA MOE Key Laboratory of Bioinformatics and Bioinformatics Division, Center for Synthetic and System Biology, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China and.|MOE Key Laboratory of Bioinformatics and Bioinformatics Division, Center for Synthetic and System Biology, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China and.', 'China|China|China', 0, 'Algorithms|Bayes Theorem|Chromatin|Chromatin Immunoprecipitation|Computer Simulation|Humans|Models, Statistical|Programming Languages|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btv445', '26231426', 'ChIA-PET is rapidly emerging as an important experimental approach to detect chromatin long-range interactions at high resolution. Here, we present Model based Interaction Calling from ChIA-PET data (MICC), an easy-to-use R package to detect chromatin interactions from ChIA-PET sequencing data. By applying a Bayesian mixture model to systematically remove random ligation and random collision noise, MICC could identify chromatin interactions with a significantly higher sensitivity than existing methods at the same false discovery rate.'),
(79414, 2015, 'bio', 'An efficient algorithm for the extraction of HGVS variant descriptions from sequences', 'Vis, Jonathan K|Vermaat, Martijn|Taschner, Peter E M|Kok, Joost N|Laros, Jeroen F J', 'Department of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands, Leiden Institute of Advanced Computer Science, Leiden University, Leiden, The Netherlands.|Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands.|Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands, Generade Center of Expertise Genomics, University of Applied Sciences Leiden, Leiden, The Netherlands and.|Department of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands, Leiden Institute of Advanced Computer Science, Leiden University, Leiden, The Netherlands.|Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands, Leiden Genome Technology Center, Leiden University Medical Center, Leiden, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Algorithms|Genetic Variation|Genome, Human|Humans|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btv443', '26231427', 'Unambiguous sequence variant descriptions are important in reporting the outcome of clinical diagnostic DNA tests. The standard nomenclature of the Human Genome Variation Society (HGVS) describes the observed variant sequence relative to a given reference sequence. We propose an efficient algorithm for the extraction of HGVS descriptions from two sequences with three main requirements in mind: minimizing the length of the resulting descriptions, minimizing the computation time and keeping the unambiguous descriptions biologically meaningful.'),
(79415, 2015, 'bio', 'MEPSA: minimum energy pathway analysis for energy landscapes', 'Marcos-Alcalde, Iñigo|Setoain, Javier|Mendieta-Moreno, Jesús I|Mendieta, Jesús|Gómez-Puertas, Paulino', 'Molecular Modelling Group, CBMSO (CSIC-UAM), ES-28049 Madrid, Spain.|Departamento de Arquitectura de Computadores y Automática, UCM, ES-28040 Madrid, Spain.|Molecular Modelling Group, CBMSO (CSIC-UAM), ES-28049 Madrid, Spain, Departamento de Física Teórica de la Materia Condensada and Condensed Matter Physics Center (IFIMAC), UAM and.|Molecular Modelling Group, CBMSO (CSIC-UAM), ES-28049 Madrid, Spain, Biomol-Informatics SL, Campus UAM, ES-28049 Madrid, Spain.|Molecular Modelling Group, CBMSO (CSIC-UAM), ES-28049 Madrid, Spain.', 'Spain|Spain|Spain|Spain|Spain', 29, 'Molecular Conformation|Software', 'Journal Article', '10.1093/bioinformatics/btv453', '26231428', 'From conformational studies to atomistic descriptions of enzymatic reactions, potential and free energy landscapes can be used to describe biomolecular systems in detail. However, extracting the relevant data of complex 3D energy surfaces can sometimes be laborious. In this article, we present MEPSA (Minimum Energy Path Surface Analysis), a cross-platform user friendly tool for the analysis of energy landscapes from a transition state theory perspective. Some of its most relevant features are: identification of all the barriers and minima of the landscape at once, description of maxima edge profiles, detection of the lowest energy path connecting two minima and generation of transition state theory diagrams along these paths. In addition to a built-in plotting system, MEPSA can save most of the generated data into easily parseable text files, allowing more versatile uses of MEPSAs output such as the generation of molecular dynamics restraints from a calculated path.'),
(79416, 2015, 'bio', 'PBAP: a pipeline for file processing and quality control of pedigree data with dense genetic markers', 'Nato, Alejandro Q|Chapman, Nicola H|Sohi, Harkirat K|Nguyen, Hiep D|Brkanac, Zoran|Wijsman, Ellen M', 'Division of Medical Genetics, Department of Medicine.|Division of Medical Genetics, Department of Medicine.|Division of Medical Genetics, Department of Medicine.|Division of Medical Genetics, Department of Medicine.|Department of Psychiatry and Behavioral Sciences.|Division of Medical Genetics, Department of Medicine, Department of Biostatistics and Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.', 'United States|United States|United States|United States|United States|United States', 5, 'Autism Spectrum Disorder|Female|Genetic Linkage|Genetic Markers|Humans|Linkage Disequilibrium|Male|Pedigree|Polymorphism, Single Nucleotide|Quality Control|Software', 'Journal Article', '10.1093/bioinformatics/btv444', '26231429', 'Huge genetic datasets with dense marker panels are now common. With the availability of sequence data and recognition of importance of rare variants, smaller studies based on pedigrees are again also common. Pedigree-based samples often start with a dense marker panel, a subset of which may be used for linkage analysis to reduce computational burden and to limit linkage disequilibrium between single-nucleotide polymorphisms (SNPs). Programs attempting to select markers for linkage panels exist but lack flexibility.'),
(79417, 2015, 'bio', 'stringgaussnet: from differentially expressed genes to semantic and Gaussian networks generation', 'Chaplais, Emmanuel|Garchon, Henri-Jean', 'Inserm U1173 and University of Versailles Saint-Quentin, 78180 Montigny-le-Bretonneux, France and.|Inserm U1173 and University of Versailles Saint-Quentin, 78180 Montigny-le-Bretonneux, France and Division of Genetics, Ambroise Paré Hospital, 92100 Boulogne-Billancourt, France.', 'France|France', 0, 'Gene Expression Profiling|Gene Regulatory Networks|Humans|Normal Distribution|Semantics|Software', 'Journal Article', '10.1093/bioinformatics/btv450', '26231430', 'Knowledge-based and co-expression networks are two kinds of gene networks that can be currently implemented by sophisticated but distinct tools. We developed stringgaussnet, an R package that integrates both approaches, starting from a list of differentially expressed genes.'),
(79418, 2015, 'bio', 'HHalign-Kbest: exploring sub-optimal alignments for remote homology comparative modeling', 'Yu, Jinchao|Picord, Geraldine|Tuffery, Pierre|Guerois, Raphael', 'Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, University Paris-Saclay, CEA-Saclay, 91191 Gif-sur-Yvette.|INSERM U973, MTi, F-75205 Paris, Universitxc3xa9 Paris Diderot, Sorbonne Paris Citxc3xa9 F-75205 Paris and Ressource Parisienne en Bioinformatique Structurale, F-75205 Paris, France.|INSERM U973, MTi, F-75205 Paris, Universitxc3xa9 Paris Diderot, Sorbonne Paris Citxc3xa9 F-75205 Paris and Ressource Parisienne en Bioinformatique Structurale, F-75205 Paris, France.|Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, University Paris-Saclay, CEA-Saclay, 91191 Gif-sur-Yvette.', '-|France|France|-', 0, 'Algorithms|Markov Chains|Models, Molecular|Sequence Alignment|Sequence Analysis, Protein|Software|Structural Homology, Protein', 'Journal Article', '10.1093/bioinformatics/btv441', '26231431', 'The HHsearch algorithm, implementing a hidden Markov model (HMM)-HMM alignment method, has shown excellent alignment performance in the so-called twilight zone (target-template sequence identity with xe2x88xbc20%). However, an optimal alignment by HHsearch may contain small to large errors, leading to poor structure prediction if these errors are located in important structural elements.'),
(79419, 2015, 'bio', 'AlignBucket: a tool to speed up all-against-all protein sequence alignments optimizing length constraints', 'Profiti, Giuseppe|Fariselli, Piero|Casadio, Rita', 'Department of Computer Science and Engineering, via Mura Anteo Zamboni 7, Bologna, Bologna Biocomputing group, via S. Giacomo 9/2, Bologna and Health Sciences and Technologies ICIR, via Tolara di Sopra 41/E, Ozzano dellEmilia, Italy.|Department of Computer Science and Engineering, via Mura Anteo Zamboni 7, Bologna, Bologna Biocomputing group, via S. Giacomo 9/2, Bologna and.|Bologna Biocomputing group, via S. Giacomo 9/2, Bologna and Health Sciences and Technologies ICIR, via Tolara di Sopra 41/E, Ozzano dellEmilia, Italy.', 'Italy|-|Italy', 1, 'Algorithms|Computational Biology|Databases, Protein|Humans|Proteins|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btv451', '26231432', 'The next-generation sequencing era requires reliable, fast and efficient approaches for the accurate annotation of the ever-increasing number of biological sequences and their variations. Transfer of annotation upon similarity search is a standard approach. The procedure of all-against-all protein comparison is a preliminary step of different available methods that annotate sequences based on information already present in databases. Given the actual volume of sequences, methods are necessary to pre-process data to reduce the time of sequence comparison.'),
(79420, 2015, 'bio', 'NAM: association studies in multiple populations', 'Xavier, Alencar|Xu, Shizhong|Muir, William M|Rainey, Katy Martin', 'Department of Agronomy and.|Department of Plant Science, University of California, Riverside, CA 92521, USA.|Department of Animal Science, Purdue University, West Lafayette, IN 47907 and.|Department of Agronomy and.', 'United States|United States|United States|United States', 25, 'Algorithms|Bayes Theorem|Genetic Linkage|Genome-Wide Association Study|Linear Models|Software', 'Journal Article', '10.1093/bioinformatics/btv448', '26243017', 'Mixed linear models provide important techniques for performing genome-wide association studies. However, current models have pitfalls associated with their strong assumptions. Here, we propose a new implementation designed to overcome some of these pitfalls using an empirical Bayes algorithm.'),
(79421, 2015, 'bio', 'Correcting systematic bias and instrument measurement drift with mzRefinery', 'Gibbons, Bryson C|Chambers, Matthew C|Monroe, Matthew E|Tabb, David L|Payne, Samuel H', 'Biological Sciences Division, Pacific Northwest National Laboratory, Richland WA 99354 and.|Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA.|Biological Sciences Division, Pacific Northwest National Laboratory, Richland WA 99354 and.|Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA.|Biological Sciences Division, Pacific Northwest National Laboratory, Richland WA 99354 and.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Calibration|Chromatography, Liquid|Cluster Analysis|Humans|Information Storage and Retrieval|Mass Spectrometry|Peptide Fragments|Proteins|Proteome|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btv437', '26243018', 'Systematic bias in mass measurement adversely affects data quality and negates the advantages of high precision instruments.'),
(79422, 2015, 'bio', 'ERC analysis: web-based inference of gene function via evolutionary rate covariation', 'Wolfe, Nicholas W|Clark, Nathan L', 'Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15260, USA.|Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15260, USA.', 'United States|United States', 6, 'Animals|Drosophila Proteins|Drosophila melanogaster|Evolution, Molecular|Fungal Proteins|Gene Expression Regulation, Fungal|Gene Regulatory Networks|Genome|Genomics|Internet|Metabolic Networks and Pathways|Phylogeny|Species Specificity|Yeasts', 'Journal Article', '10.1093/bioinformatics/btv454', '26243019', 'The recent explosion of comparative genomics data presents an unprecedented opportunity to construct gene networks via the evolutionary rate covariation (ERC) signature. ERC is used to identify genes that experienced similar evolutionary histories, and thereby draws functional associations between them. The ERC Analysis website allows researchers to exploit genome-wide datasets to infer novel genes in any biological function and to explore deep evolutionary connections between distinct pathways and complexes. The website provides five analytical methods, graphical output, statistical support and access to an increasing number of taxonomic groups.'),
(79423, 2015, 'bio', 'ARResT/AssignSubsets: a novel application for robust subclassification of chronic lymphocytic leukemia based on B cell receptor IG stereotypy', 'Bystry, Vojtech|Agathangelidis, Andreas|Bikos, Vasilis|Sutton, Lesley Ann|Baliakas, Panagiotis|Hadzidimitriou, Anastasia|Stamatopoulos, Kostas|Darzentas, Nikos|European Research Initiative on CLL', 'CEITEC-Central European Institute of Technology, Masaryk University, Brno, Czech Republic.|Division of Molecular Oncology and Department of Onco-Hematology, IRCCS San Raffaele Scientific Institute and Universitxc3xa0 Vita-Salute San Raffaele, Milan, Italy.|CEITEC-Central European Institute of Technology, Masaryk University, Brno, Czech Republic.|Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden and.|Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden and.|Institute of Applied Biosciences, Center for Research and Technology Hellas, Thessaloniki, Greece Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden and.|Institute of Applied Biosciences, Center for Research and Technology Hellas, Thessaloniki, Greece Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden and.|CEITEC-Central European Institute of Technology, Masaryk University, Brno, Czech Republic.|CEITEC-Central European Institute of Technology, Masaryk University, Brno, Czech Republic.', '-|Italy|-|Sweden|Sweden|Greece|Greece|-|-', 0, 'Humans|Leukemia, Lymphocytic, Chronic, B-Cell|Models, Statistical|Receptors, Antigen, B-Cell|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btv456', '26249808', 'An ever-increasing body of evidence supports the importance of B cell receptor immunoglobulin (BcR IG) sequence restriction, alias stereotypy, in chronic lymphocytic leukemia (CLL). This phenomenon accounts for xe2x88xbc30% of studied cases, one in eight of which belong to major subsets, and extends beyond restricted sequence patterns to shared biologic and clinical characteristics and, generally, outcome. Thus, the robust assignment of new cases to major CLL subsets is a critical, and yet unmet, requirement.'),
(79424, 2015, 'bio', 'ASSIsT: an automatic SNP scoring tool for in- and outbreeding species', 'Di Guardo, Mario|Micheletti, Diego|Bianco, Luca|Koehorst-van Putten, Herma J J|Longhi, Sara|Costa, Fabrizio|Aranzana, Maria J|Velasco, Riccardo|Arús, Pere|Troggio, Michela|van de Weg, Eric W', 'Wageningen UR Plant Breeding, 6700 AA Wageningen, The Netherlands, Research and Innovation Centre, Fondazione Edmund Mach, Trento, Italy, Graduate School Experimental Plant Sciences, Wageningen University, 6700 AJ Wageningen, The Netherlands and.|Research and Innovation Centre, Fondazione Edmund Mach, Trento, Italy, Wageningen UR Plant Breeding, 6700 AA Wageningen, The Netherlands.|Research and Innovation Centre, Fondazione Edmund Mach, Trento, Italy.|Wageningen UR Plant Breeding, 6700 AA Wageningen, The Netherlands.|Wageningen UR Plant Breeding, 6700 AA Wageningen, The Netherlands.|Research and Innovation Centre, Fondazione Edmund Mach, Trento, Italy.|IRTA, Centre de Recerca en Agrigenómica CSIC-IRTA-UAB, Beellaterra (Cerdanyola del Vallés), 08193 Barcelona, Spain.|Research and Innovation Centre, Fondazione Edmund Mach, Trento, Italy.|IRTA, Centre de Recerca en Agrigenómica CSIC-IRTA-UAB, Beellaterra (Cerdanyola del Vallés), 08193 Barcelona, Spain.|Research and Innovation Centre, Fondazione Edmund Mach, Trento, Italy.|Wageningen UR Plant Breeding, 6700 AA Wageningen, The Netherlands.', 'Italy|Italy|Italy|Netherlands|Netherlands|Italy|Spain|Italy|Spain|Italy|Netherlands', 0, 'Alleles|Animals|Genotyping Techniques|Humans|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btv446', '26249809', 'ASSIsT (Automatic SNP ScorIng Tool) is a user-friendly customized pipeline for efficient calling and filtering of SNPs from Illumina Infinium arrays, specifically devised for custom genotyping arrays. Illumina has developed an integrated software for SNP data visualization and inspection called GenomeStudio (GS). ASSIsT builds on GS-derived data and identifies those markers that follow a bi-allelic genetic model and show reliable genotype calls. Moreover, ASSIsT re-edits SNP calls with null alleles or additional SNPs in the probe annealing site. ASSIsT can be employed in the analysis of different population types such as full-sib families and mating schemes used in the plant kingdom (backcross, F1, F2), and unrelated individuals. The final result can be directly exported in the format required by the most common software for genetic mapping and marker-trait association analysis. ASSIsT is developed in Python and runs in Windows and Linux.'),
(79425, 2015, 'bio', 'NRGsuite: a PyMOL plugin to perform docking simulations in real time using FlexAID', 'Gaudreault, Francis|Morency, Louis-Philippe|Najmanovich, Rafael J', 'Department of Biochemistry, Faculty of Medicine and Health Sciences, University of Sherbrooke, Sherbrooke, Canada.|Department of Biochemistry, Faculty of Medicine and Health Sciences, University of Sherbrooke, Sherbrooke, Canada.|Department of Biochemistry, Faculty of Medicine and Health Sciences, University of Sherbrooke, Sherbrooke, Canada.', 'Canada|Canada|Canada', 23, 'Binding Sites|Ligands|Molecular Docking Simulation|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btv458', '26249810', 'Ligand protein docking simulations play a fundamental role in understanding molecular recognition. Herein we introduce the NRGsuite, a PyMOL plugin that permits the detection of surface cavities in proteins, their refinements, calculation of volume and use, individually or jointly, as target binding-sites for docking simulations with FlexAID. The NRGsuite offers the users control over a large number of important parameters in docking simulations including the assignment of flexible side-chains and definition of geometric constraints. Furthermore, the NRGsuite permits the visualization of the docking simulation in real time. The NRGsuite give access to powerful docking simulations that can be used in structure-guided drug design as well as an educational tool. The NRGsuite is implemented in Python and C/C++ with an easy to use package installer. The NRGsuite is available for Windows, Linux and MacOS.'),
(79426, 2015, 'bio', 'SurvCurv database and online survival analysis platform update', 'Ziehm, Matthias|Ivanov, Dobril K|Bhat, Aditi|Partridge, Linda|Thornton, Janet M', 'European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Department of Genetics, Evolution and Environment, The Institute of Healthy Ageing, University College London, London WC1E 6BT, UK and.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.|Department of Genetics, Evolution and Environment, The Institute of Healthy Ageing, University College London, London WC1E 6BT, UK and Max Planck Institute for Biology of Ageing, 50931 Cologne, Germany.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.', 'United Kingdom|United Kingdom|United Kingdom|Germany|United Kingdom', 7, 'Animals|Databases, Factual|Survival Analysis', 'Journal Article', '10.1093/bioinformatics/btv463', '26249811', 'Understanding the biology of ageing is an important and complex challenge. Survival experiments are one of the primary approaches for measuring changes in ageing. Here, we present a major update to SurvCurv, a database and online resource for survival data in animals. As well as a substantial increase in data and additions to existing graphical and statistical survival analysis features, SurvCurv now includes extended mathematical mortality modelling functions and survival density plots for more advanced representation of groups of survival cohorts.'),
(79427, 2015, 'bio', 'Strategies to improve the performance of rare variant association studies by optimizing the selection of controls', 'Zhu, Na|Heinrich, Verena|Dickhaus, Thorsten|Hecht, Jochen|Robinson, Peter N|Mundlos, Stefan|Kamphans, Tom|Krawitz, Peter M', 'Institute of Medical Genetics and Human Genetics, Charité Universitätsmedizin Berlin, 13353 Berlin, Germany.|Institute of Medical Genetics and Human Genetics, Charité Universitätsmedizin Berlin, 13353 Berlin, Germany.|Institute for Statistics, University of Bremen, 28344 Bremen, Germany.|Berlin-Brandenburg Center for Regenerative Therapies (BCRT), 13353 Berlin, Germany.|Institute of Medical Genetics and Human Genetics, Charité Universitätsmedizin Berlin, 13353 Berlin, Germany.|Institute of Medical Genetics and Human Genetics, Charité Universitätsmedizin Berlin, 13353 Berlin, Germany, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany and.|GeneTalk, 13189 Berlin, Germany.|Institute of Medical Genetics and Human Genetics, Charité Universitätsmedizin Berlin, 13353 Berlin, Germany, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany and.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 1, 'Case-Control Studies|Data Accuracy|Disease|Ethnic Groups|Genetic Association Studies|Genetic Variation|Humans|ROC Curve|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btv457', '26249812', 'When analyzing a case group of patients with ultra-rare disorders the ethnicities are often diverse and the data quality might vary. The population substructure in the case group as well as the heterogeneous data quality can cause substantial inflation of test statistics and result in spurious associations in case-control studies if not properly adjusted for. Existing techniques to correct for confounding effects were especially developed for common variants and are not applicable to rare variants.'),
(79428, 2015, 'bio', 'A statistical approach to virtual cellular experiments: improved causal discovery using accumulation IDA (aIDA)', 'Taruttis, Franziska|Spang, Rainer|Engelmann, Julia C', 'Department of Statistical Bioinformatics, University of Regensburg, 93053 Regensburg, Germany.|Department of Statistical Bioinformatics, University of Regensburg, 93053 Regensburg, Germany.|Department of Statistical Bioinformatics, University of Regensburg, 93053 Regensburg, Germany.', 'Germany|Germany|Germany', 0, 'Algorithms|Causality|Computer Simulation|Gene Expression Profiling|Models, Statistical|Regression Analysis|Reproducibility of Results|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins', 'Journal Article', '10.1093/bioinformatics/btv461', '26249813', 'We address the following question: Does inhibition of the expression of a gene X in a cellular assay affect the expression of another gene Y? Rather than inhibiting gene X experimentally, we aim at answering this question computationally using as the only input observational gene expression data. Recently, a new statistical algorithm called Intervention calculus when the Directed acyclic graph is Absent (IDA), has been proposed for this problem. For several biological systems, IDA has been shown to outcompete regression-based methods with respect to the number of true positives versus the number of false positives for the top 5000 predicted effects. Further improvements in the performance of IDA have been realized by stability selection, a resampling method wrapped around IDA that enhances the discovery of true causal effects. Nevertheless, the rate of false positive and false negative predictions is still unsatisfactorily high.'),
(79429, 2015, 'bio', 'LoopIng: a template-based tool for predicting the structure of protein loops', 'Messih, Mario Abdel|Lepore, Rosalba|Tramontano, Anna', 'Department of Physics, Sapienza University, 00185 Rome, Italy and.|Department of Physics, Sapienza University, 00185 Rome, Italy and.|Department of Physics, Sapienza University, 00185 Rome, Italy and Istituto Pasteur-Fondazione Cenci Bolognetti, Viale Regina Elena 291, 00161 Rome, Italy.', 'Italy|Italy|Italy', 0, 'Databases, Protein|Machine Learning|Models, Molecular|Protein Conformation|Proteins|Software', 'Evaluation Studies', '10.1093/bioinformatics/btv438', '26249814', 'Predicting the structure of protein loops is very challenging, mainly because they are not necessarily subject to strong evolutionary pressure. This implies that, unlike the rest of the protein, standard homology modeling techniques are not very effective in modeling their structure. However, loops are often involved in protein function, hence inferring their structure is important for predicting protein structure as well as function.'),
(79430, 2015, 'bio', 'cgmisc: enhanced genome-wide association analyses and visualization', 'Kierczak, Marcin|Jabxc5x82oxc5x84ska, Jagoda|Forsberg, Simon K G|Bianchi, Matteo|Tengvall, Katarina|Pettersson, Mats|Scholz, Veronika|Meadows, Jennifer R S|Jern, Patric|Carlborg, xc3x96rjan|Lindblad-Toh, Kerstin', 'Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden, Computational Genetics Section, Department of Clinical Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden and.|Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden.|Computational Genetics Section, Department of Clinical Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden and.|Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden.|Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden.|Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden, Computational Genetics Section, Department of Clinical Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden and.|Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden.|Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden.|Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden.|Computational Genetics Section, Department of Clinical Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden and.|Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden, Broad Institute of MIT and Harvard, Boston, MA, USA.', 'Sweden|Sweden|Sweden|Sweden|Sweden|Sweden|Sweden|Sweden|Sweden|Sweden|Sweden', 6, 'Animals|Computer Graphics|Dogs|Genome-Wide Association Study|Genomics|Genotype|Humans|Loss of Heterozygosity|Phenotype|Software', 'Journal Article', '10.1093/bioinformatics/btv426', '26249815', 'High-throughput genotyping and sequencing technologies facilitate studies of complex genetic traits and provide new research opportunities. The increasing popularity of genome-wide association studies (GWAS) leads to the discovery of new associated loci and a better understanding of the genetic architecture underlying not only diseases, but also other monogenic and complex phenotypes. Several softwares are available for performing GWAS analyses, R environment being one of them.'),
(79431, 2015, 'bio', 'Impact of normalization methods on high-throughput screening data with high hit rates and drug testing with dose-response data', 'Mpindi, John-Patrick|Swapnil, Potdar|Dmitrii, Bychkov|Jani, Saarela|Saeed, Khalid|Wennerberg, Krister|Aittokallio, Tero|Östling, Päivi|Kallioniemi, Olli', 'University of Helsinki, Institute for Molecular Medicine, Tukholmankatu 8, FI-00290, Helsinki, Finland.|University of Helsinki, Institute for Molecular Medicine, Tukholmankatu 8, FI-00290, Helsinki, Finland.|University of Helsinki, Institute for Molecular Medicine, Tukholmankatu 8, FI-00290, Helsinki, Finland.|University of Helsinki, Institute for Molecular Medicine, Tukholmankatu 8, FI-00290, Helsinki, Finland.|University of Helsinki, Institute for Molecular Medicine, Tukholmankatu 8, FI-00290, Helsinki, Finland.|University of Helsinki, Institute for Molecular Medicine, Tukholmankatu 8, FI-00290, Helsinki, Finland.|University of Helsinki, Institute for Molecular Medicine, Tukholmankatu 8, FI-00290, Helsinki, Finland.|University of Helsinki, Institute for Molecular Medicine, Tukholmankatu 8, FI-00290, Helsinki, Finland.|University of Helsinki, Institute for Molecular Medicine, Tukholmankatu 8, FI-00290, Helsinki, Finland.', 'Finland|Finland|Finland|Finland|Finland|Finland|Finland|Finland|Finland', 16, 'Algorithms|Antineoplastic Agents|Data Interpretation, Statistical|Dose-Response Relationship, Drug|Drug Evaluation, Preclinical|High-Throughput Screening Assays|Humans|Male|Normal Distribution|Prostatic Neoplasms|Tumor Cells, Cultured', 'Journal Article', '10.1093/bioinformatics/btv455', '26254433', 'Most data analysis tools for high-throughput screening (HTS) seek to uncover interesting hits for further analysis. They typically assume a low hit rate per plate. Hit rates can be dramatically higher in secondary screening, RNAi screening and in drug sensitivity testing using biologically active drugs. In particular, drug sensitivity testing on primary cells is often based on dose-response experiments, which pose a more stringent requirement for data quality and for intra- and inter-plate variation. Here, we compared common plate normalization and noise-reduction methods, including the B-score and the Loess a local polynomial fit method under high hit-rate scenarios of drug sensitivity testing. We generated simulated 384-well plate HTS datasets, each with 71 plates having a range of 20 (5%) to 160 (42%) hits per plate, with controls placed either at the edge of the plates or in a scattered configuration.'),
(79432, 2015, 'bio', 'Improving protein fold recognition with hybrid profiles combining sequence and structure evolution', 'Ghouzam, Yassine|Postic, Guillaume|de Brevern, Alexandre G|Gelly, Jean-Christophe', 'Inserm U1134, Paris, France, Universitxc3xa9 Paris Diderot, Sorbonne Paris Citxc3xa9, UMR_S 1134, Paris, France, Institut National de la Transfusion Sanguine, Paris, France and Laboratory of Excellence GR-Ex, Paris, France.|Inserm U1134, Paris, France, Universitxc3xa9 Paris Diderot, Sorbonne Paris Citxc3xa9, UMR_S 1134, Paris, France, Institut National de la Transfusion Sanguine, Paris, France and Laboratory of Excellence GR-Ex, Paris, France.|Inserm U1134, Paris, France, Universitxc3xa9 Paris Diderot, Sorbonne Paris Citxc3xa9, UMR_S 1134, Paris, France, Institut National de la Transfusion Sanguine, Paris, France and Laboratory of Excellence GR-Ex, Paris, France.|Inserm U1134, Paris, France, Universitxc3xa9 Paris Diderot, Sorbonne Paris Citxc3xa9, UMR_S 1134, Paris, France, Institut National de la Transfusion Sanguine, Paris, France and Laboratory of Excellence GR-Ex, Paris, France.', 'France|France|France|France', 0, 'Algorithms|Evolution, Molecular|Models, Molecular|Protein Conformation|Protein Folding|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btv462', '26254434', 'Template-based modeling, the most successful approach for predicting protein 3D structure, often requires detecting distant evolutionary relationships between the target sequence and proteins of known structure. Developed for this purpose, fold recognition methods use elaborate strategies to exploit evolutionary information, mainly by encoding amino acid sequence into profiles. Since protein structure is more conserved than sequence, the inclusion of structural information can improve the detection of remote homology.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(79433, 2015, 'bio', 'Accurate disulfide-bonding network predictions improve ab initio structure prediction of cysteine-rich proteins', 'Yang, Jing|He, Bao-Ji|Jang, Richard|Zhang, Yang|Shen, Hong-Bin', 'Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China.|State Key Laboratory of Theoretical Physics, Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing 100190, China, Department of Computational Medicine and Bioinformatics and.|Department of Computational Medicine and Bioinformatics and.|Department of Computational Medicine and Bioinformatics and Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109, USA.|Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China, Department of Computational Medicine and Bioinformatics and.', 'China|China|United States|United States|China', 19, 'Algorithms|Cysteine|Disulfides|Proteins|Sequence Analysis, Protein|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/btv459', '26254435', 'Cysteine-rich proteins cover many important families in nature but there are currently no methods specifically designed for modeling the structure of these proteins. The accuracy of disulfide connectivity pattern prediction, particularly for the proteins of higher-order connections, e.g., >3 bonds, is too low to effectively assist structure assembly simulations.'),
(79434, 2015, 'bio', 'BLSSpeller: exhaustive comparative discovery of conserved cis-regulatory elements', 'De Witte, Dieter|Van de Velde, Jan|Decap, Dries|Van Bel, Michiel|Audenaert, Pieter|Demeester, Piet|Dhoedt, Bart|Vandepoele, Klaas|Fostier, Jan', 'Department of Information Technology (INTEC), Ghent University-iMinds, Ghent, Belgium.|Department of Plant Systems Biology, VIB and Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium.|Department of Information Technology (INTEC), Ghent University-iMinds, Ghent, Belgium.|Department of Plant Systems Biology, VIB and Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium.|Department of Information Technology (INTEC), Ghent University-iMinds, Ghent, Belgium.|Department of Information Technology (INTEC), Ghent University-iMinds, Ghent, Belgium.|Department of Information Technology (INTEC), Ghent University-iMinds, Ghent, Belgium.|Department of Plant Systems Biology, VIB and Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium.|Department of Information Technology (INTEC), Ghent University-iMinds, Ghent, Belgium.', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 9, 'Algorithms|Base Sequence|Binding Sites|Conserved Sequence|DNA, Plant|Genome, Plant|Nucleotide Motifs|Promoter Regions, Genetic|Sequence Alignment|Sequence Analysis, DNA|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btv466', '26254488', 'The accurate discovery and annotation of regulatory elements remains a challenging problem. The growing number of sequenced genomes creates new opportunities for comparative approaches to motif discovery. Putative binding sites are then considered to be functional if they are conserved in orthologous promoter sequences of multiple related species. Existing methods for comparative motif discovery usually rely on pregenerated multiple sequence alignments, which are difficult to obtain for more diverged species such as plants. As a consequence, misaligned regulatory elements often remain undetected.'),
(79435, 2015, 'bio', 'TIPR: transcription initiation pattern recognition on a genome scale', 'Morton, Taj|Wong, Weng-Keen|Megraw, Molly', 'Department of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR 97331, USA.|Department of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR 97331, USA.|Department of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR 97331, USA, Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR 97331, USA and Center for Genome Research and Biocomputing, Oregon State University, Corvallis, OR 97331, USA.', 'United States|United States|United States', 0, 'Algorithms|Genomics|Machine Learning|Molecular Sequence Annotation|Sequence Analysis, DNA|Software|Transcription Initiation Site|Transcription Initiation, Genetic', 'Journal Article', '10.1093/bioinformatics/btv464', '26254489', 'The computational identification of gene transcription start sites (TSSs) can provide insights into the regulation and function of genes without performing expensive experiments, particularly in organisms with incomplete annotations. High-resolution general-purpose TSS prediction remains a challenging problem, with little recent progress on the identification and differentiation of TSSs which are arranged in different spatial patterns along the chromosome.'),
(79436, 2015, 'bio', 'GMcloser: closing gaps in assemblies accurately with a likelihood-based selection of contig or long-read alignments', 'Kosugi, Shunichi|Hirakawa, Hideki|Tabata, Satoshi', 'Department of Technology Development, Kazusa DNA Research Institute, Kisarazu, Chiba 292-0818, Japan.|Department of Technology Development, Kazusa DNA Research Institute, Kisarazu, Chiba 292-0818, Japan.|Department of Technology Development, Kazusa DNA Research Institute, Kisarazu, Chiba 292-0818, Japan.', 'Japan|Japan|Japan', 0, 'Contig Mapping|High-Throughput Nucleotide Sequencing|Likelihood Functions|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btv465', '26261222', 'Genome assemblies generated with next-generation sequencing (NGS) reads usually contain a number of gaps. Several tools have recently been developed to close the gaps in these assemblies with NGS reads. Although these gap-closing tools efficiently close the gaps, they entail a high rate of misassembly at gap-closing sites.'),
(79437, 2015, 'bio', 'Vizardous: interactive analysis of microbial populations with single cell resolution', 'Helfrich, Stefan|Azzouzi, Charaf E|Probst, Christopher|Seiffarth, Johannes|Grünberger, Alexander|Wiechert, Wolfgang|Kohlheyer, Dietrich|Nöh, Katharina', 'Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Germany.|Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Germany.|Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Germany.|Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Germany.|Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Germany.|Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Germany.|Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Germany.|Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 8, 'Corynebacterium glutamicum|Microscopy, Fluorescence|Single-Cell Analysis|Software|Time-Lapse Imaging', 'Journal Article', '10.1093/bioinformatics/btv468', '26261223', 'Single cell time-lapse microscopy is a powerful method for investigating heterogeneous cell behavior. Advances in microfluidic lab-on-a-chip technologies and live-cell imaging render the parallel observation of the development of individual cells in hundreds of populations possible. While image analysis tools are available for cell detection and tracking, biologists are still confronted with the challenge of exploring and evaluating this data.'),
(79438, 2015, 'bio', 'SuccFind: a novel succinylation sites online prediction tool via enhanced characteristic strategy', 'Xu, Hao-Dong|Shi, Shao-Ping|Wen, Ping-Ping|Qiu, Jian-Ding', 'Department of Chemistry.|Department of Mathematics, Nanchang University, Nanchang 330031, China and.|Department of Chemistry.|Department of Chemistry, Department of Chemical Engineering, Pingxiang University, Pingxiang 337055, China.', '-|China|-|China', 0, 'Lysine|Protein Processing, Post-Translational|Proteins|Proteomics|Sequence Analysis, Protein|Software|Succinates', 'Journal Article', '10.1093/bioinformatics/btv439', '26261224', 'Lysine succinylation orchestrates a variety of biological processes. Annotation of succinylation in proteomes is the first-crucial step to decipher physiological roles of succinylation implicated in the pathological processes. In this work, we developed a novel succinylation site online prediction tool, called SuccFind, which is constructed to predict the lysine succinylation sites based on two major categories of characteristics: sequence-derived features and evolutionary-derived information of sequence and via an enhanced feature strategy for further optimizations. The assessment results obtained from cross-validation suggest that SuccFind can provide more instructive guidance for further experimental investigation of protein succinylation.'),
(79439, 2015, 'bio', 'cyNeo4j: connecting Neo4j and Cytoscape', 'Summer, Georg|Kelder, Thomas|Ono, Keiichiro|Radonjic, Marijana|Heymans, Stephane|Demchak, Barry', 'Center for Heart Failure Research, Cardiovascular Research Institute Maastricht (CARIM), University Hospital Maastricht, Maastricht, The Netherlands, TNO, Zeist, The Netherlands.|EdgeLeap B.V., Utrecht, The Netherlands and.|Department of Medicine, University of California, San Diego, La Jolla, CA, USA.|EdgeLeap B.V., Utrecht, The Netherlands and.|Center for Heart Failure Research, Cardiovascular Research Institute Maastricht (CARIM), University Hospital Maastricht, Maastricht, The Netherlands.|Department of Medicine, University of California, San Diego, La Jolla, CA, USA.', 'Netherlands|Netherlands|United States|Netherlands|Netherlands|United States', 0, 'Algorithms|Databases, Factual|Software', 'Journal Article', '10.1093/bioinformatics/btv460', '26272981', 'We developed cyNeo4j, a Cytoscape App to link Cytoscape and Neo4j databases to utilize the performance and storage capacities Neo4j offers. We implemented a Neo4j NetworkAnalyzer, ForceAtlas2 layout and Cypher component to demonstrate the possibilities a distributed setup of Cytoscape and Neo4j have.'),
(79440, 2015, 'bio', 'OVA: integrating molecular and physical phenotype data from multiple biomedical domain ontologies with variant filtering for enhanced variant prioritization', 'Antanaviciute, Agne|Watson, Christopher M|Harrison, Sally M|Lascelles, Carolina|Crinnion, Laura|Markham, Alexander F|Bonthron, David T|Carr, Ian M', 'Section of Genetics, Institute of Biomedical and Clinical Sciences, School of Medicine, University of Leeds and.|Section of Genetics, Institute of Biomedical and Clinical Sciences, School of Medicine, University of Leeds and Yorkshire Regional Genetics Service, St Jamess University Hospital, Leeds, UK.|Section of Genetics, Institute of Biomedical and Clinical Sciences, School of Medicine, University of Leeds and.|Section of Genetics, Institute of Biomedical and Clinical Sciences, School of Medicine, University of Leeds and.|Section of Genetics, Institute of Biomedical and Clinical Sciences, School of Medicine, University of Leeds and Yorkshire Regional Genetics Service, St Jamess University Hospital, Leeds, UK.|Section of Genetics, Institute of Biomedical and Clinical Sciences, School of Medicine, University of Leeds and.|Section of Genetics, Institute of Biomedical and Clinical Sciences, School of Medicine, University of Leeds and.|Section of Genetics, Institute of Biomedical and Clinical Sciences, School of Medicine, University of Leeds and.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Biological Ontologies|Computational Biology|Disease|Exome|Genotype|Humans|Phenotype|Polymorphism, Single Nucleotide|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btv473', '26272982', 'Exome sequencing has become a de facto standard method for Mendelian disease gene discovery in recent years, yet identifying disease-causing mutations among thousands of candidate variants remains a non-trivial task.'),
(79441, 2015, 'bio', 'Mutadelic: mutation analysis using description logic inferencing capabilities', 'Holford, Matthew E|Krauthammer, Michael', 'Program in Computational Biology and Bioinformatics and.|Program in Computational Biology and Bioinformatics and Department of Pathology, Yale University School of Medicine, New Haven, CT, USA.', 'United States|United States', 0, 'DNA Mutational Analysis|Genomics|Hematologic Diseases|High-Throughput Nucleotide Sequencing|Humans|Molecular Sequence Annotation|Mutation|Software|Workflow', 'Journal Article', '10.1093/bioinformatics/btv467', '26272983', 'As next generation sequencing gains a foothold in clinical genetics, there is a need for annotation tools to characterize increasing amounts of patient variant data for identifying clinically relevant mutations. While existing informatics tools provide efficient bulk variant annotations, they often generate excess information that may limit their scalability.'),
(79442, 2015, 'bio', 'motifbreakR: an R/Bioconductor package for predicting variant effects at transcription factor binding sites', 'Coetzee, Simon G|Coetzee, Gerhard A|Hazelett, Dennis J', 'Bioinformatics and Computational Biology Research Center, Cedars-Sinai Medical Center, Los Angeles, CA, USA and.|Department of Urology and Preventive Medicine, USC Norris Comprehensive Cancer Center, Los Angeles, CA, USA.|Bioinformatics and Computational Biology Research Center, Cedars-Sinai Medical Center, Los Angeles, CA, USA and.', 'United States|United States|United States', 62, 'Algorithms|Animals|Binding Sites|Genomics|Humans|Mice|Mutation|Polymorphism, Single Nucleotide|Regulatory Elements, Transcriptional|Regulatory Sequences, Nucleic Acid|Sequence Analysis, DNA|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btv470', '26272984', 'Functional annotation represents a key step toward the understanding and interpretation of germline and somatic variation as revealed by genome-wide association studies (GWAS) and The Cancer Genome Atlas (TCGA), respectively. GWAS have revealed numerous genetic risk variants residing in non-coding DNA associated with complex diseases. For sequences that lie within enhancers or promoters of transcription, it is not straightforward to assess the effects of variants on likely transcription factor binding sites. Consequently we introduce motifbreakR, which allows the biologist to judge whether the sequence surrounding a polymorphism or mutation is a good match, and how much information is gained or lost in one allele of the polymorphism or mutation relative to the other. MotifbreakR is flexible, giving a choice of algorithms for interrogation of genomes with motifs from many public sources that users can choose from. MotifbreakR can predict effects for novel or previously described variants in public databases, making it suitable for tasks beyond the scope of its original design. Lastly, it can be used to interrogate any genome curated within bioconductor.'),
(79443, 2015, 'bio', 'Protein contact prediction by integrating joint evolutionary coupling analysis and supervised learning', 'Ma, Jianzhu|Wang, Sheng|Wang, Zhiyong|Xu, Jinbo', 'Toyota Technological Institute at Chicago, 6045 S. Kenwood Ave. Chicago, Illinois 60637 USA.|Toyota Technological Institute at Chicago, 6045 S. Kenwood Ave. Chicago, Illinois 60637 USA.|Toyota Technological Institute at Chicago, 6045 S. Kenwood Ave. Chicago, Illinois 60637 USA.|Toyota Technological Institute at Chicago, 6045 S. Kenwood Ave. Chicago, Illinois 60637 USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Evolution, Molecular|Machine Learning|Models, Statistical|Protein Conformation|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Evaluation Studies', '10.1093/bioinformatics/btv472', '26275894', 'Protein contact prediction is important for protein structure and functional study. Both evolutionary coupling (EC) analysis and supervised machine learning methods have been developed, making use of different information sources. However, contact prediction is still challenging especially for proteins without a large number of sequence homologs.'),
(79444, 2015, 'bio', 'Determining conserved metabolic biomarkers from a million database queries', 'Kurczy, Michael E|Ivanisevic, Julijana|Johnson, Caroline H|Uritboonthai, Winnie|Hoang, Linh|Fang, Mingliang|Hicks, Matthew|Aldebot, Anthony|Rinehart, Duane|Mellander, Lisa J|Tautenhahn, Ralf|Patti, Gary J|Spilker, Mary E|Benton, H Paul|Siuzdak, Gary', 'Scripps Center for Metabolomics, The Scripps Research Institute, La Jolla, CA 92037, USA.|Scripps Center for Metabolomics, The Scripps Research Institute, La Jolla, CA 92037, USA.|Scripps Center for Metabolomics, The Scripps Research Institute, La Jolla, CA 92037, USA.|Scripps Center for Metabolomics, The Scripps Research Institute, La Jolla, CA 92037, USA.|Scripps Center for Metabolomics, The Scripps Research Institute, La Jolla, CA 92037, USA.|Scripps Center for Metabolomics, The Scripps Research Institute, La Jolla, CA 92037, USA.|Scripps Center for Metabolomics, The Scripps Research Institute, La Jolla, CA 92037, USA.|Scripps Center for Metabolomics, The Scripps Research Institute, La Jolla, CA 92037, USA.|Scripps Center for Metabolomics, The Scripps Research Institute, La Jolla, CA 92037, USA.|Department of Physics, University of California San Diego, La Jolla, CA 92093, USA.|Scripps Center for Metabolomics, The Scripps Research Institute, La Jolla, CA 92037, USA.|Department of Chemistry, Washington University in St. Louis, St. Louis, MO 63130, USA, Departments of Genetics and Medicine, Washington University School of Medicine, St. Louis, MO 63130, USA.|Pfizer Worldwide Research and Development, San Diego, CA 92121, USA and.|Scripps Center for Metabolomics, The Scripps Research Institute, La Jolla, CA 92037, USA.|Scripps Center for Metabolomics, The Scripps Research Institute, La Jolla, CA 92037, USA, Departments of Chemistry, Molecular and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 4, 'Biomarkers|Databases, Factual|Humans|Mass Spectrometry|Metabolome|Metabolomics', 'Journal Article', '10.1093/bioinformatics/btv475', '26275895', 'Metabolite databases provide a unique window into metabolome research allowing the most commonly searched biomarkers to be catalogued. Omic scale metabolite profiling, or metabolomics, is finding increased utility in biomarker discovery largely driven by improvements in analytical technologies and the concurrent developments in bioinformatics. However, the successful translation of biomarkers into clinical or biologically relevant indicators is limited.'),
(79445, 2015, 'bio', 'DSviaDRM: an R package for estimating disease similarity via dysfunctional regulation mechanism', 'Yang, Jing|Wu, Su-Juan|Li, Yi-Xue|Li, Yuan-Yuan', 'School of Biotechnology, East China University of Science and Technology, Shanghai 200237, Peoples Republic of China, System biology group, Shanghai Center for Bioinformation Technology, Shanghai 201203, Peoples Republic of China.|School of Biotechnology, East China University of Science and Technology, Shanghai 200237, Peoples Republic of China, System biology group, Shanghai Center for Bioinformation Technology, Shanghai 201203, Peoples Republic of China.|School of Biotechnology, East China University of Science and Technology, Shanghai 200237, Peoples Republic of China, System biology group, Shanghai Center for Bioinformation Technology, Shanghai 201203, Peoples Republic of China, Shanghai Industrial Technology Institute, Shanghai 201203, Peoples Republic of China and Shanghai Engineering Research Center of Pharmaceutical Translation, Shanghai 201203, Peoples Republic of China.|System biology group, Shanghai Center for Bioinformation Technology, Shanghai 201203, Peoples Republic of China, Shanghai Industrial Technology Institute, Shanghai 201203, Peoples Republic of China and Shanghai Engineering Research Center of Pharmaceutical Translation, Shanghai 201203, Peoples Republic of China.', 'China|China|China|China', 0, 'Disease|Gene Expression Profiling|Gene Expression Regulation|Humans|Software', 'Journal Article', '10.1093/bioinformatics/btv474', '26275896', 'Elucidation of human disease similarities has provided new insights into etiology, disease classification and drug repositioning. Since dysfunctional regulation would be manifested as the decoupling of expression correlation, disease similarity (DS) in terms of dysfunctional regulation mechanism (DRM) could be estimated by using a differential coexpression based approach, which is described in a companion paper. Due to the lack of tools for estimating DS from the viewpoint of DRM in public domain, we implemented an R package DSviaDRM to identify significant DS via DRM based on transcriptomic data. DSviaDRM contains five easy-to-use functions, DCEA, DCpathway, DS, comDCGL and comDCGLplot, for identifying disease relationships and showing common differential regulation information shared by similar diseases.'),
(79446, 2015, 'bio', 'Pairwise RNA secondary structure alignment with conserved stem pattern', 'Chiu, Jimmy Ka Ho|Chen, Yi-Ping Phoebe', 'Department of Computer Science and Information Technology, La Trobe University, Melbourne, Victoria 3086, Australia.|Department of Computer Science and Information Technology, La Trobe University, Melbourne, Victoria 3086, Australia.', 'Australia|Australia', 0, 'Algorithms|Nucleic Acid Conformation|RNA, Untranslated|Sequence Alignment|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btv471', '26275897', 'The regulatory functions performed by non-coding RNAs are related to their 3D structures, which are, in turn, determined by their secondary structures. Pairwise secondary structure alignment gives insight into the functional similarity between a pair of RNA sequences. Numerous exact or heuristic approaches have been proposed for computational alignment. However, the alignment becomes intractable when arbitrary pseudoknots are allowed. Also, since non-coding RNAs are, in general, more conserved in structures than sequences, it is more effective to perform alignment based on the common structural motifs discovered.'),
(79447, 2015, 'bio', 'aRrayLasso: a network-based approach to microarray interconversion', 'Brown, Adam S|Patel, Chirag J', 'Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115.|Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115.', '-|-', 0, 'Gene Expression Profiling|Linear Models|Oligonucleotide Array Sequence Analysis|Oligonucleotide Probes', 'Journal Article', '10.1093/bioinformatics/btv469', '26283699', 'Robust conversion between microarray platforms is needed to leverage the wide variety of microarray expression studies that have been conducted to date. Currently available conversion methods rely on manufacturer annotations, which are often incomplete, or on direct alignment of probes from different platforms, which often fail to yield acceptable genewise correlation. Here, we describe aRrayLasso, which uses the Lasso-penalized generalized linear model to model the relationships between individual probes in different probe sets. We have implemented aRrayLasso in a set of five open-source R functions that allow the user to acquire data from public sources such as Gene Expression Omnibus, train a set of Lasso models on that data and directly map one microarray platform to another. aRrayLasso significantly predicts expression levels with similar fidelity to technical replicates of the same RNA pool, demonstrating its utility in the integration of datasets from different platforms.'),
(79448, 2015, 'bio', 'An integrated structure- and system-based framework to identify new targets of metabolites and known drugs', 'Naveed, Hammad|Hameed, Umar S|Harrus, Deborah|Bourguet, William|Arold, Stefan T|Gao, Xin', 'Computer, Electrical and Mathematical Sciences and Engineering Division, Computational Bioscience Research Center.|Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.|Inserm U1054, Centre de Biochimie Structurale and CNRS UMR5048, Universités Montpellier 1 & 2, Montpellier, France.|Inserm U1054, Centre de Biochimie Structurale and CNRS UMR5048, Universités Montpellier 1 & 2, Montpellier, France.|Computational Bioscience Research Center, Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.|Computer, Electrical and Mathematical Sciences and Engineering Division, Computational Bioscience Research Center.', '-|Saudi Arabia|France|France|Saudi Arabia|-', 9, 'Binding Sites|Computational Biology|Drug Discovery|Humans|Metabolism|Pharmaceutical Preparations|Probability|Proteins', 'Journal Article', '10.1093/bioinformatics/btv477', '26286808', 'The inherent promiscuity of small molecules towards protein targets impedes our understanding of healthy versus diseased metabolism. This promiscuity also poses a challenge for the pharmaceutical industry as identifying all protein targets is important to assess (side) effects and repositioning opportunities for a drug.'),
(79449, 2015, 'bio', 'svviz: a read viewer for validating structural variants', 'Spies, Noah|Zook, Justin M|Salit, Marc|Sidow, Arend', 'Department of Genetics, Stanford University, Department of Pathology, Stanford University, Genome Scale Measurements Group, National Institute of Standards and Technology, Stanford, CA, USA and.|Genome Scale Measurements Group, National Institute of Standards and Technology, Gaithersburg, MD, USA.|Genome Scale Measurements Group, National Institute of Standards and Technology, Stanford, CA, USA and.|Department of Genetics, Stanford University, Department of Pathology, Stanford University.', 'United States|United States|United States|United States', 0, 'Alleles|Genomic Structural Variation|Genomics|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btv478', '26286809', 'Visualizing read alignments is the most effective way to validate candidate structural variants (SVs) with existing data. We present svviz, a sequencing read visualizer for SVs that sorts and displays only reads relevant to a candidate SV. svviz works by searching input bam(s) for potentially relevant reads, realigning them against the inferred sequence of the putative variant allele as well as the reference allele and identifying reads that match one allele better than the other. Separate views of the two alleles are then displayed in a scrollable web browser view, enabling a more intuitive visualization of each allele, compared with the single reference genome-based view common to most current read browsers. The browser view facilitates examining the evidence for or against a putative variant, estimating zygosity, visualizing affected genomic annotations and manual refinement of breakpoints. svviz supports data from most modern sequencing platforms.'),
(79450, 2015, 'bio', 'GenomeLaser: fast and accurate haplotyping from pedigree genotypes', 'Li, Wenzhi|Fu, Guoxing|Rao, Weinian|Xu, Wei|Ma, Li|Guo, Shiwen|Song, Qing', 'Department of Neurosurgery, First Affiliated Hospital of Medical School, Xian Jiaotong University, Xian, Shaanxi, 710061 China, Cardiovascular Research Institute and Department of Medicine, Morehouse School of Medicine, Atlanta, GA, 30310 USA.|4DGENOME Inc, Atlanta, GA, 30033 USA and.|4DGENOME Inc, Atlanta, GA, 30033 USA and.|Cardiovascular Research Institute and Department of Medicine, Morehouse School of Medicine, Atlanta, GA, 30310 USA.|Cardiovascular Research Institute and Department of Medicine, Morehouse School of Medicine, Atlanta, GA, 30310 USA, 4DGENOME Inc, Atlanta, GA, 30033 USA and.|Department of Neurosurgery, First Affiliated Hospital of Medical School, Xian Jiaotong University, Xian, Shaanxi, 710061 China.|Cardiovascular Research Institute and Department of Medicine, Morehouse School of Medicine, Atlanta, GA, 30310 USA, 4DGENOME Inc, Atlanta, GA, 30033 USA and Center of Big Data and Bioinformatics, First Affiliated Hospital of Medical School, Xian Jiaotong University, Xian, Shaanxi, 710061 China.', 'China|United States|United States|United States|United States|China|China', 3, 'Female|Genetic Markers|Genotype|Genotyping Techniques|Haplotypes|Humans|Male|Pedigree|Software', 'Journal Article', '10.1093/bioinformatics/btv452', '26286810', 'We present a software tool called GenomeLaser that determines the haplotypes of each person from unphased high-throughput genotypes in family pedigrees. This method features high accuracy, chromosome-range phasing distance, linear computing, flexible pedigree types and flexible genetic marker types.'),
(79451, 2015, 'bio', 'InteractiveROSETTA: a graphical user interface for the PyRosetta protein modeling suite', 'Schenkelberg, Christian D|Bystroff, Christopher', 'Department of Biological Sciences and.|Department of Biological Sciences and Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.', 'United States|United States', 7, 'Models, Molecular|Protein Conformation|Proteins|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btv492', '26315900', 'Modern biotechnical research is becoming increasingly reliant on computational structural modeling programs to develop novel solutions to scientific questions. Rosetta is one such protein modeling suite that has already demonstrated wide applicability to a number of diverse research projects. Unfortunately, Rosetta is largely a command-line-driven software package which restricts its use among non-computational researchers. Some graphical interfaces for Rosetta exist, but typically are not as sophisticated as commercial software. Here, we present InteractiveROSETTA, a graphical interface for the PyRosetta framework that presents easy-to-use controls for several of the most widely used Rosetta protocols alongside a sophisticated selection system utilizing PyMOL as a visualizer. InteractiveROSETTA is also capable of interacting with remote Rosetta servers, facilitating sophisticated protocols that are not accessible in PyRosetta or which require greater computational resources.'),
(79452, 2015, 'bio', 'LncRNA-ID: Long non-coding RNA IDentification using balanced random forests', 'Achawanantakun, Rujira|Chen, Jiao|Sun, Yanni|Zhang, Yuan', 'Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA.|Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA.|Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA.|Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA.', 'United States|United States|United States|United States', 0, 'Animals|Humans|Machine Learning|Mice|Open Reading Frames|Proteins|RNA, Long Noncoding|Ribosomes|Software', 'Evaluation Studies', '10.1093/bioinformatics/btv480', '26315901', 'Long non-coding RNAs (lncRNAs), which are non-coding RNAs of length above 200 nucleotides, play important biological functions such as gene expression regulation. To fully reveal the functions of lncRNAs, a fundamental step is to annotate them in various species. However, as lncRNAs tend to encode one or multiple open reading frames, it is not trivial to distinguish these long non-coding transcripts from protein-coding genes in transcriptomic data.'),
(79453, 2015, 'bio', 'databasebio: a web application for interpreting human variations', 'Ou, Min|Ma, Ricky|Cheung, Jeanno|Lo, Katie|Yee, Patrick|Luo, Tewei|Chan, T L|Au, Chun Hang|Kwong, Ava|Luo, Ruibang|Lam, Tak-Wah', 'HKU-BGI Bioinformatics Algorithms Research Laboratory and Department of Computer Science, University of Hong Kong.|L3 Bioinformatics Limited.|L3 Bioinformatics Limited.|L3 Bioinformatics Limited.|L3 Bioinformatics Limited.|HKU-BGI Bioinformatics Algorithms Research Laboratory and Department of Computer Science, University of Hong Kong.|Department of Pathology, Hong Kong Sanatorium and Hospital.|Department of Pathology, Hong Kong Sanatorium and Hospital.|Department of Surgery, University of Hong Kong and Hong Kong Hereditary Breast Cancer Family Registry, Hong Kong and.|HKU-BGI Bioinformatics Algorithms Research Laboratory and Department of Computer Science, University of Hong Kong, L3 Bioinformatics Limited, United Electronics Co., Ltd., Beijing, China.|HKU-BGI Bioinformatics Algorithms Research Laboratory and Department of Computer Science, University of Hong Kong, L3 Bioinformatics Limited.', 'Hong Kong|-|-|-|-|Hong Kong|Hong Kong|Hong Kong|Hong Kong|China|Hong Kong', 0, 'Databases, Nucleic Acid|Genetic Variation|High-Throughput Nucleotide Sequencing|Humans|Internet|Molecular Sequence Annotation|Software', 'Journal Article', '10.1093/bioinformatics/btv500', '26315902', 'Rapid advances of next-generation sequencing technology have led to the integration of genetic information with clinical care. Genetic basis of diseases and response to drugs provide new ways of disease diagnosis and safer drug usage. This integration reveals the urgent need for effective and accurate tools to analyze genetic variants. Due to the number and diversity of sources for annotation, automating variant analysis is a challenging task. Here, we present database.bio, a web application that combines variant annotation, prioritization and visualization so as to support insight into the individual genetic characteristics. It enhances annotation speed by preprocessing data on a supercomputer, and reduces database space via a unified database representation with compressed fields.'),
(79454, 2015, 'bio', 'Bayexer: an accurate and fast Bayesian demultiplexer for Illumina sequences', 'Yi, Haisi|Li, Zhe|Li, Tao|Zhao, Jindong', 'Key Laboratory of Algal Biology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, Hubei 430072, China, University of Chinese Academy of Sciences, Beijing 100049, China.|State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China and.|Key Laboratory of Algal Biology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, Hubei 430072, China.|Key Laboratory of Algal Biology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, Hubei 430072, China, College of Life Science, Peking University, Beijing 100871, China.', 'China|China|China|China', 7, 'Algorithms|Bayes Theorem|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btv501', '26315903', 'Demultiplexing is used after high-throughput sequencing to in silico assign reads to the samples of origin based on the sequenced reads of the indices. Existing demultiplexing tools based on the similarity between the read index and the reference index sequences may fail to provide satisfactory results on low-quality datasets. We developed Bayexer, a Bayesian demultiplexing algorithm for Illumina sequencers. Bayexer uses the information extracted directly from the contaminant sequences of the targeting reads as the training dataset for a naïve Bayes classifier to assign reads. According to our evaluation, Bayexer provides higher capability, accuracy and speed on various real datasets than other tools.'),
(79455, 2015, 'bio', 'LIBRA: LIgand Binding site Recognition Application', 'Hung, Le Viet|Caprari, Silvia|Bizai, Massimiliano|Toti, Daniele|Polticelli, Fabio', 'Department of Sciences, University of Roma Tre, 00146 Rome, Italy, Department of Science and Technology, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam and.|Department of Sciences, University of Roma Tre, 00146 Rome, Italy.|Department of Sciences, University of Roma Tre, 00146 Rome, Italy.|Department of Sciences, University of Roma Tre, 00146 Rome, Italy.|Department of Sciences, University of Roma Tre, 00146 Rome, Italy, National Institute of Nuclear Physics, Roma Tre Section, 00146 Rome, Italy.', 'Italy|Italy|Italy|Italy|Italy', 11, 'Algorithms|Binding Sites|Catalytic Domain|Databases, Protein|Ligands|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btv489', '26315904', 'In recent years, structural genomics and ab initio molecular modeling activities are leading to the availability of a large number of structural models of proteins whose biochemical function is not known. The aim of this study was the development of a novel software tool that, given a proteins structural model, predicts the presence and identity of active sites and/or ligand binding sites.'),
(79456, 2015, 'bio', 'EPGA2: memory-efficient de novo assembler', 'Luo, Junwei|Wang, Jianxin|Li, Weilong|Zhang, Zhen|Wu, Fang-Xiang|Li, Min|Pan, Yi', 'School of Information Science and Engineering, Central South University, ChangSha, 410083, China, College of Computer Science and Technology, Henan Polytechnic University, JiaoZuo, 454000, China.|School of Information Science and Engineering, Central South University, ChangSha, 410083, China.|School of Information Science and Engineering, Central South University, ChangSha, 410083, China.|School of Information Science and Engineering, Central South University, ChangSha, 410083, China.|Division of Biomedical Engineering, University of Saskatchewan, Saskatchewan, S7N 5A9, Canada and.|School of Information Science and Engineering, Central South University, ChangSha, 410083, China.|Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA.', 'China|China|China|China|Canada|China|Georgia', 16, 'Algorithms|Genomics|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btv487', '26315905', 'In genome assembly, as coverage of sequencing and genome size growing, most current softwares require a large memory for handling a great deal of sequence data. However, most researchers usually cannot meet the requirements of computing resources which prevent most current softwares from practical applications.'),
(79457, 2015, 'bio', 'An interactive genome browser of association results from the UK10K cohorts project', 'Geihs, Matthias|Yan, Ying|Walter, Klaudia|Huang, Jie|Memari, Yasin|Min, Josine L|Mead, Daniel|UK10K Consortium|Hubbard, Tim J|Timpson, Nicholas J|Down, Thomas A|Soranzo, Nicole', 'Wellcome Trust Sanger Institute, Genome Campus, Hinxton CB10 1HH, UK.|Wellcome Trust Sanger Institute, Genome Campus, Hinxton CB10 1HH, UK.|Wellcome Trust Sanger Institute, Genome Campus, Hinxton CB10 1HH, UK.|Wellcome Trust Sanger Institute, Genome Campus, Hinxton CB10 1HH, UK.|Wellcome Trust Sanger Institute, Genome Campus, Hinxton CB10 1HH, UK.|MRC Integrative Epidemiology Unit, University of Bristol, Oakfield Grove, Bristol, UK.|Wellcome Trust Sanger Institute, Genome Campus, Hinxton CB10 1HH, UK.|Wellcome Trust Sanger Institute, Genome Campus, Hinxton CB10 1HH, UK.|Wellcome Trust Sanger Institute, Genome Campus, Hinxton CB10 1HH, UK, Department of Medical and Molecular Genetics, Division of Genetics and Molecular Medicine, Kings College London School of Medicine, Guys Hospital, London SE1 9RT, UK.|MRC Integrative Epidemiology Unit, University of Bristol, Oakfield Grove, Bristol, UK.|EMBL-EBI, Hinxton CB10 1SD, UK and.|Wellcome Trust Sanger Institute, Genome Campus, Hinxton CB10 1HH, UK, Department of Haematology, University of Cambridge, Cambridge CB2 1TN, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Genetic Association Studies|Genetic Variation|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Linkage Disequilibrium|Software', 'Journal Article', '10.1093/bioinformatics/btv491', '26315906', 'High-throughput sequencing technologies survey genetic variation at genome scale and are increasingly used to study the contribution of rare and low-frequency genetic variants to human traits. As part of the Cohorts arm of the UK10K project, genetic variants called from low-read depth (average 7×) whole genome sequencing of 3621 cohort individuals were analysed for statistical associations with 64 different phenotypic traits of biomedical importance. Here, we describe a novel genome browser based on the Biodalliance platform developed to provide interactive access to the association results of the project.'),
(79458, 2015, 'bio', 'Fast and accurate approximate inference of transcript expression from RNA-seq data', 'Hensman, James|Papastamoulis, Panagiotis|Glaus, Peter|Honkela, Antti|Rattray, Magnus', 'Sheffield Institute for Translational Neuroscience (SITraN), Sheffield, UK.|Faculty of Life Sciences.|School of Computer Science, The University of Manchester, Manchester, UK and.|Helsinki Institute for Information Technology (HIIT), Department of Computer Science, University of Helsinki, Helsinki, Finland.|Faculty of Life Sciences.', 'United Kingdom|United Kingdom|United Kingdom|Finland|United Kingdom', 0, 'Algorithms|Bayes Theorem|Gene Expression Profiling|Humans|Markov Chains|Monte Carlo Method|Sequence Analysis, RNA', 'Evaluation Studies', '10.1093/bioinformatics/btv483', '26315907', 'Assigning RNA-seq reads to their transcript of origin is a fundamental task in transcript expression estimation. Where ambiguities in assignments exist due to transcripts sharing sequence, e.g. alternative isoforms or alleles, the problem can be solved through probabilistic inference. Bayesian methods have been shown to provide accurate transcript abundance estimates compared with competing methods. However, exact Bayesian inference is intractable and approximate methods such as Markov chain Monte Carlo and Variational Bayes (VB) are typically used. While providing a high degree of accuracy and modelling flexibility, standard implementations can be prohibitively slow for large datasets and complex transcriptome annotations.'),
(79459, 2015, 'bio', 'HiXCorr: a portable high-speed XCorr engine for high-resolution tandem mass spectrometry', 'Kim, Hyunwoo|Jo, Hosung|Park, Heejin|Paek, Eunok', 'Department of Electronics and Computer Engineering and.|Department of Electronics and Computer Engineering and.|Department of Computer Science and Engineering, Hanyang University, Seoul, Korea.|Department of Computer Science and Engineering, Hanyang University, Seoul, Korea.', '-|-|-|-', 2, 'Algorithms|Peptides|Proteomics|Software|Tandem Mass Spectrometry', 'Journal Article', '10.1093/bioinformatics/btv490', '26315908', 'Peptide identification is an important problem in proteomics. One of the most popular scoring schemes for peptide identification is XCorr (cross-correlation). Since calculating XCorr is computationally intensive, a lot of efforts have been made to develop fast XCorr engines. However, the existing XCorr engines are not suitable for high-resolution MS/MS spectrometry because they are either slow or require a specific type of CPU. We present a portable high-speed XCorr engine for high-resolution tandem mass spectrometry by developing a novel algorithm for calculating XCorr. The algorithm enables XCorr calculation 1.25-49 times faster than previous algorithms for 0.01 Da fragment tolerance. Furthermore, our engine is easily portable to any machine with different types of CPU because it is developed in C language. Hence, our XCorr engine will expedite peptide identification by high-resolution tandem mass spectrometry.'),
(79460, 2015, 'bio', 'glmgraph: an R package for variable selection and predictive modeling of structured genomic data', 'Chen, Li|Liu, Han|Kocher, Jean-Pierre A|Li, Hongzhe|Chen, Jun', 'Division of Biomedical Statistics and Informatics and Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905,USA, Department of Computer Science, Emory University, Atlanta, GA 30322,USA.|Department of Operations Research and Financial Engineering, Princeton University, Princeton, NJ 08544, USA and.|Division of Biomedical Statistics and Informatics and Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905,USA.|Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA 19104, USA.|Division of Biomedical Statistics and Informatics and Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905,USA.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Genomics|Humans|Linear Models|Logistic Models|Microbiota|Sample Size|Software', 'Journal Article', '10.1093/bioinformatics/btv497', '26315909', 'One central theme of modern high-throughput genomic data analysis is to identify relevant genomic features as well as build up a predictive model based on selected features for various tasks such as personalized medicine. Correlating the large number of omics features with a certain phenotype is particularly challenging due to small sample size (n) and high dimensionality (p). To address this small n, large p problem, various forms of sparse regression models have been proposed by exploiting the sparsity assumption. Among these, network-constrained sparse regression model is of particular interest due to its ability to utilize the prior graph/network structure in the omics data. Despite its potential usefulness for omics data analysis, no efficient R implementation is publicly available. Here we present an R software package glmgraph that implements the graph-constrained regularization for both sparse linear regression and sparse logistic regression. We implement both the L1 penalty and minimax concave penalty for variable selection and Laplacian penalty for coefficient smoothing. Efficient coordinate descent algorithm is used to solve the optimization problem. We demonstrate the use of the package by applying it to a human microbiome dataset, where phylogeny structure among bacterial taxa is available.'),
(79461, 2016, 'bio', 'Identification of hierarchical chromatin domains', 'Weinreb, Caleb|Raphael, Benjamin J', 'Center for Computational Molecular Biology and.|Center for Computational Molecular Biology and Department of Computer Science, Brown University, Providence, RI 02912, USA.', 'United States|United States', 0, 'Cell Differentiation|Chromatin|Gene Expression Regulation|Genome', 'Journal Article', '10.1093/bioinformatics/btv485', '26315910', 'The three-dimensional structure of the genome is an important regulator of many cellular processes including differentiation and gene regulation. Recently, technologies such as Hi-C that combine proximity ligation with high-throughput sequencing have revealed domains of self-interacting chromatin, called topologically associating domains (TADs), in many organisms. Current methods for identifying TADs using Hi-C data assume that TADs are non-overlapping, despite evidence for a nested structure in which TADs and sub-TADs form a complex hierarchy.'),
(79462, 2015, 'bio', 'msa: an R package for multiple sequence alignment', 'Bodenhofer, Ulrich|Bonatesta, Enrico|Horejš-Kainrath, Christoph|Hochreiter, Sepp', 'Institute of Bioinformatics, Johannes Kepler University, Linz, Austria.|Institute of Bioinformatics, Johannes Kepler University, Linz, Austria.|Institute of Bioinformatics, Johannes Kepler University, Linz, Austria.|Institute of Bioinformatics, Johannes Kepler University, Linz, Austria.', 'Austria|Austria|Austria|Austria', 90, 'Algorithms|Animals|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btv494', '26315911', 'Although the R platform and the add-on packages of the Bioconductor project are widely used in bioinformatics, the standard task of multiple sequence alignment has been neglected so far. The msa package, for the first time, provides a unified R interface to the popular multiple sequence alignment algorithms ClustalW, ClustalOmega and MUSCLE. The package requires no additional software and runs on all major platforms. Moreover, the msa package provides an R interface to the powerful package shade which allows for flexible and customizable plotting of multiple sequence alignments.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(79463, 2015, 'bio', 'Hierarchical boosting: a machine-learning framework to detect and classify hard selective sweeps in human populations', 'Pybus, Marc|Luisi, Pierre|DallOlio, Giovanni Marco|Uzkudun, Manu|Laayouni, Hafid|Bertranpetit, Jaume|Engelken, Johannes', 'Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona 08003, Spain.|Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona 08003, Spain, Department of Biology, Stanford University, Stanford, CA 94305, USA.|Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona 08003, Spain, Division of Cancer Studies, Kings College of London, London SE1 1UL, UK and.|Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona 08003, Spain.|Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona 08003, Spain, Departament de Genètica i de Microbiologia, Universitat Autonòma de Barcelona, Bellaterra 8193, Spain.|Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona 08003, Spain.|Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona 08003, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain', 0, 'Demography|Genetics, Population|Genomics|Humans|Machine Learning|Polymorphism, Genetic|Selection, Genetic', 'Journal Article', '10.1093/bioinformatics/btv493', '26315912', 'Detecting positive selection in genomic regions is a recurrent topic in natural population genetic studies. However, there is little consistency among the regions detected in several genome-wide scans using different tests and/or populations. Furthermore, few methods address the challenge of classifying selective events according to specific features such as age, intensity or state (completeness).'),
(79464, 2016, 'bio', 'HapCol: accurate and memory-efficient haplotype assembly from long reads', 'Pirola, Yuri|Zaccaria, Simone|Dondi, Riccardo|Klau, Gunnar W|Pisanti, Nadia|Bonizzoni, Paola', 'Dipartimento di Informatica Sistemistica e Comunicazione (DISCo), Univ. degli Studi di Milano-Bicocca, Milan, Italy.|Dipartimento di Informatica Sistemistica e Comunicazione (DISCo), Univ. degli Studi di Milano-Bicocca, Milan, Italy.|Dipartimento di Scienze Umane e Sociali, Univ. degli Studi di Bergamo, Bergamo, Italy.|Life Sciences group, Centrum Wiskunde & Informatica (CWI), Amsterdam, The Netherlands, ERABLE Team, INRIA, Lyon, France and.|ERABLE Team, INRIA, Lyon, France and Dipartimento di Informatica, Univ. degli Studi di Pisa, Pisa, Italy.|Dipartimento di Informatica Sistemistica e Comunicazione (DISCo), Univ. degli Studi di Milano-Bicocca, Milan, Italy.', 'Italy|Italy|Italy|France|France|Italy', 25, 'Algorithms|Diploidy|Haplotypes|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btv495', '26315913', 'Haplotype assembly is the computational problem of reconstructing haplotypes in diploid organisms and is of fundamental importance for characterizing the effects of single-nucleotide polymorphisms on the expression of phenotypic traits. Haplotype assembly highly benefits from the advent of future-generation sequencing technologies and their capability to produce long reads at increasing coverage. Existing methods are not able to deal with such data in a fully satisfactory way, either because accuracy or performances degrade as read length and sequencing coverage increase or because they are based on restrictive assumptions.'),
(79465, 2015, 'bio', 'CoD: inferring immune-cell quantities related to disease states', 'Frishberg, Amit|Steuerman, Yael|Gat-Viks, Irit', 'Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel.|Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel.|Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel.', 'Israel|Israel|Israel', 5, 'Animals|Female|Gene Expression Profiling|Immune System|Immunity|Mammary Neoplasms, Experimental|Mice|Software', 'Journal Article', '10.1093/bioinformatics/btv498', '26315914', 'The immune system comprises a complex network of genes, cells and tissues, coordinated through signaling pathways and cell-cell communications. However, the orchestrated role of the multiple immunological components in disease is still poorly understood. Classifications based on gene-expression data have revealed immune-related signaling pathways in various diseases, but how such pathways describe the immune cellular physiology remains largely unknown.'),
(79466, 2015, 'bio', 'Integrated structure- and ligand-based in silico approach to predict inhibition of cytochrome P450 2D6', 'Martiny, Virginie Y|Carbonell, Pablo|Chevillard, Florent|Moroy, Gautier|Nicot, Arnaud B|Vayer, Philippe|Villoutreix, Bruno O|Miteva, Maria A', 'Université Paris Diderot, Sorbonne Paris Cité, UMR-S 973 Inserm, Paris 75013, France, Inserm UMR-S 973, Molécules Thérapeutiques In Silico, Université Paris Diderot, Sorbonne Paris Cité, Paris 75013, France.|Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences, Universitat Pompeu Fabra, IMIM (Hospital del Mar Medical Research Institute), 08003 Barcelona, Spain.|Université Paris Diderot, Sorbonne Paris Cité, UMR-S 973 Inserm, Paris 75013, France.|Université Paris Diderot, Sorbonne Paris Cité, UMR-S 973 Inserm, Paris 75013, France, Inserm UMR-S 973, Molécules Thérapeutiques In Silico, Université Paris Diderot, Sorbonne Paris Cité, Paris 75013, France.|Inserm U1064/ITUN, CHU, 44093 Nantes Cedex, France and.|BioInformatic Modelling Department, Technologie Servier, 45007 Orléans Cedex1, France.|Université Paris Diderot, Sorbonne Paris Cité, UMR-S 973 Inserm, Paris 75013, France, Inserm UMR-S 973, Molécules Thérapeutiques In Silico, Université Paris Diderot, Sorbonne Paris Cité, Paris 75013, France.|Université Paris Diderot, Sorbonne Paris Cité, UMR-S 973 Inserm, Paris 75013, France, Inserm UMR-S 973, Molécules Thérapeutiques In Silico, Université Paris Diderot, Sorbonne Paris Cité, Paris 75013, France.', 'France|Spain|France|France|France|France|France|France', 26, 'Algorithms|Binding Sites|Cytochrome P-450 CYP2D6|Cytochrome P-450 CYP2D6 Inhibitors|Cytochrome P-450 Enzyme System|Humans|Ligands|Machine Learning|Molecular Dynamics Simulation|Protein Conformation', 'Journal Article', '10.1093/bioinformatics/btv486', '26315915', 'Cytochrome P450 (CYP) is a superfamily of enzymes responsible for the metabolism of drugs, xenobiotics and endogenous compounds. CYP2D6 metabolizes about 30% of drugs and predicting potential CYP2D6 inhibition is important in early-stage drug discovery.'),
(79467, 2015, 'bio', 'BS-SNPer: SNP calling in bisulfite-seq data', 'Gao, Shengjie|Zou, Dan|Mao, Likai|Liu, Huayu|Song, Pengfei|Chen, Youguo|Zhao, Shancen|Gao, Changduo|Li, Xiangchun|Gao, Zhibo|Fang, Xiaodong|Yang, Huanming|Ørntoft, Torben F|Sørensen, Karina D|Bolund, Lars', 'Department of Biomedicine, Aarhus University, Aarhus, Denmark, BGI Co. Ltd., Shenzhen 518083, China, BioInformatics Research Center (BIRC), Aarhus University, Aarhus 8000, Denmark.|BGI Co. Ltd., Shenzhen 518083, China, School of Computer, National University of Defense Technology, Changsha 410073, China.|BGI Co. Ltd., Shenzhen 518083, China, Genomic Biology Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Australia.|Departments of Biostatistics and Informatics, University of Colorado-Anschutz Medical Campus, Denver 80204, USA.|The Fourth Peoples Hospital of Shenzhen (Futian hospital), Shenzhen, 518033, China.|Department of Obsterics and Gynecology,the First Hospital Affiliated to Suzhou University, Jiangsu Suzhou 215006,China.|BGI Co. Ltd., Shenzhen 518083, China.|College of Information Engineering, Qingdao University, Qingdao 266071, China and.|BGI Co. Ltd., Shenzhen 518083, China.|BGI Co. Ltd., Shenzhen 518083, China.|BGI Co. Ltd., Shenzhen 518083, China.|BGI Co. Ltd., Shenzhen 518083, China.|Department of Molecular Medicine, Aarhus University Hospital, Aarhus 8000, Denmark.|Department of Molecular Medicine, Aarhus University Hospital, Aarhus 8000, Denmark.|Department of Biomedicine, Aarhus University, Aarhus, Denmark, BGI Co. Ltd., Shenzhen 518083, China, BioInformatics Research Center (BIRC), Aarhus University, Aarhus 8000, Denmark.', 'China|China|Australia|United States|China|China|China|China|China|China|China|China|Denmark|Denmark|China', 0, 'Alleles|DNA Methylation|Humans|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software|Sulfites', 'Journal Article', '10.1093/bioinformatics/btv507', '26319221', 'Sodium bisulfite conversion followed by sequencing (BS-Seq, such as whole genome bisulfite sequencing or reduced representation bisulfite sequencing) has become popular for studying human epigenetic profiles. Identifying single nucleotide polymorphisms (SNPs) is important for quantification of methylation levels and for study of allele-specific epigenetic events such as imprinting. However, SNP calling in such data is complex and time consuming. Here, we present an ultrafast and memory-efficient package named BS-SNPer for the exploration of SNP sites from BS-Seq data. Compared with Bis-SNP, a popular BS-Seq specific SNP caller, BS-SNPer is over 100 times faster and uses less memory. BS-SNPer also offers higher sensitivity and specificity compared with existing methods.'),
(79468, 2016, 'bio', 'Utilizing de Bruijn graph of metagenome assembly for metatranscriptome analysis', 'Ye, Yuzhen|Tang, Haixu', 'School of Informatics and Computing, Indiana University, Bloomington, IN 47405, USA.|School of Informatics and Computing, Indiana University, Bloomington, IN 47405, USA.', 'India|India', 0, 'Algorithms|Metagenome|Metagenomics', 'Journal Article', '10.1093/bioinformatics/btv510', '26319390', 'Metagenomics research has accelerated the studies of microbial organisms, providing insights into the composition and potential functionality of various microbial communities. Metatranscriptomics (studies of the transcripts from a mixture of microbial species) and other meta-omics approaches hold even greater promise for providing additional insights into functional and regulatory characteristics of the microbial communities. Current metatranscriptomics projects are often carried out without matched metagenomic datasets (of the same microbial communities). For the projects that produce both metatranscriptomic and metagenomic datasets, their analyses are often not integrated. Metagenome assemblies are far from perfect, partially explaining why metagenome assemblies are not used for the analysis of metatranscriptomic datasets.'),
(79469, 2015, 'bio', 'RASER: reads aligner for SNPs and editing sites of RNA', 'Ahn, Jaegyoon|Xiao, Xinshu', 'Department of Integrative Biology and Physiology and the Molecular Biology Institute, University of California Los Angeles, Los Angeles, CA 90095, USA.|Department of Integrative Biology and Physiology and the Molecular Biology Institute, University of California Los Angeles, Los Angeles, CA 90095, USA.', 'United States|United States', 0, 'Algorithms|Alleles|Polymorphism, Single Nucleotide|RNA Editing|Sequence Alignment|Sequence Analysis, RNA|Software', 'Evaluation Studies', '10.1093/bioinformatics/btv505', '26323713', 'Accurate identification of genetic variants such as single-nucleotide polymorphisms (SNPs) or RNA editing sites from RNA-Seq reads is important, yet challenging, because it necessitates a very low false-positive rate in read mapping. Although many read aligners are available, no single aligner was specifically developed or tested as an effective tool for SNP and RNA editing prediction.'),
(79470, 2015, 'bio', 'RNASeqMetaDB: a database and web server for navigating metadata of publicly available mouse RNA-Seq datasets', 'Guo, Zhengyu|Tzvetkova, Boriana|Bassik, Jennifer M|Bodziak, Tara|Wojnar, Brianna M|Qiao, Wei|Obaida, Md A|Nelson, Sacha B|Hu, Bo Hua|Yu, Peng', 'Department of Electrical and Computer Engineering & TEES-AgriLife Center for Bioinformatics and Genomic Systems Engineering, Texas A&M University, College Station, TX 77843, USA.|Department of Biology & Center for Behavioral Genomics, Brandeis University, Waltham, MA 02454, USA and.|Department of Communicative Disorders and Sciences & Center for Hearing and Deafness, University at Buffalo, Buffalo, NY 14214, USA.|Department of Communicative Disorders and Sciences & Center for Hearing and Deafness, University at Buffalo, Buffalo, NY 14214, USA.|Department of Communicative Disorders and Sciences & Center for Hearing and Deafness, University at Buffalo, Buffalo, NY 14214, USA.|Department of Electrical and Computer Engineering &|Department of Electrical and Computer Engineering &|Department of Biology & Center for Behavioral Genomics, Brandeis University, Waltham, MA 02454, USA and.|Department of Communicative Disorders and Sciences & Center for Hearing and Deafness, University at Buffalo, Buffalo, NY 14214, USA.|Department of Electrical and Computer Engineering & TEES-AgriLife Center for Bioinformatics and Genomic Systems Engineering, Texas A&M University, College Station, TX 77843, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Animals|Databases, Nucleic Acid|High-Throughput Nucleotide Sequencing|Mice|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btv503', '26323714', 'Gene targeting is a protocol for introducing a mutation to a specific gene in an organism. Because of the importance of in vivo assessment of gene function and modeling of human diseases, this technique has been widely adopted to generate a large number of mutant mouse models. Due to the recent breakthroughs in high-throughput sequencing technologies, RNA-Seq experiments have been performed on many of these mouse models, leading to hundreds of publicly available datasets. To facilitate the reuse of these datasets, we collected the associated metadata and organized them in a database called RNASeqMetaDB. The metadata were manually curated to ensure annotation consistency. We developed a web server to allow easy database navigation and data querying. Users can search the database using multiple parameters like genes, diseases, tissue types, keywords and associated publications in order to find datasets that match their interests. Summary statistics of the metadata are also presented on the web server showing interesting global patterns of RNA-Seq studies.'),
(79471, 2015, 'bio', 'BigBWA: approaching the Burrows-Wheeler aligner to Big Data technologies', 'Abuín, José M|Pichel, Juan C|Pena, Tomás F|Amigo, Jorge', 'CITIUS, Universidade de Santiago de Compostela, Spain and.|CITIUS, Universidade de Santiago de Compostela, Spain and.|CITIUS, Universidade de Santiago de Compostela, Spain and.|Genomics Medicine Group (GMX), Universidade de Santiago de Compostela, Spain.', 'Spain|Spain|Spain|Spain', 47, 'Algorithms|Genomics|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btv506', '26323715', 'BigBWA is a new tool that uses the Big Data technology Hadoop to boost the performance of the Burrows-Wheeler aligner (BWA). Important reductions in the execution times were observed when using this tool. In addition, BigBWA is fault tolerant and it does not require any modification of the original BWA source code.'),
(79472, 2015, 'bio', 'BiopLib and BiopTools--a C programming library and toolset for manipulating protein structure', 'Porter, Craig T|Martin, Andrew C R', 'Institute of Structural and Molecular Biology, Division of Biosciences, University College London, Darwin Building, Gower Street, London WC1E 6BT, UK.|Institute of Structural and Molecular Biology, Division of Biosciences, University College London, Darwin Building, Gower Street, London WC1E 6BT, UK.', 'United Kingdom|United Kingdom', 8, 'Databases, Protein|Protein Conformation|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btv482', '26323716', 'We describe BiopLib, a mature C programming library for manipulating protein structure, and BiopTools, a set of command-line tools which exploit BiopLib. The library also provides a small number of functions for handling protein sequence and general purpose programming and mathematics. BiopLib transparently handles PDBML (XML) format and standard PDB files. BiopTools provides facilities ranging from renumbering atoms and residues to calculation of solvent accessibility.'),
(79473, 2015, 'bio', 'A hybrid bayesian approach for genome-wide association studies on related individuals', 'Yazdani, A|Dunson, D B', 'Human Genetic Center, University of Texas at Houston Health Science Center, Houston, USA and.|Department of Statistical Science, Duke University, Durham, North Carolina USA.', 'United States|United States', 0, 'Animals|Bayes Theorem|Breeding|Cattle|Genome-Wide Association Study|Genomics|Genotype|Models, Genetic|Polymorphism, Single Nucleotide', 'Evaluation Studies', '10.1093/bioinformatics/btv496', '26323717', 'Both single marker and simultaneous analysis face challenges in GWAS due to the large number of markers genotyped for a small number of subjects. This large p small n problem is particularly challenging when the trait under investigation has low heritability.'),
(79474, 2015, 'bio', 'NgsRelate: a software tool for estimating pairwise relatedness from next-generation sequencing data', 'Korneliussen, Thorfinn Sand|Moltke, Ida', 'Center for GeoGenetics and.|Department of Biology, University of Copenhagen, 2200 Copenhagen, Denmark.', '-|Denmark', 23, 'Genotyping Techniques|High-Throughput Nucleotide Sequencing|Humans|Likelihood Functions|Software', 'Journal Article', '10.1093/bioinformatics/btv509', '26323718', 'Pairwise relatedness estimation is important in many contexts such as disease mapping and population genetics. However, all existing estimation methods are based on called genotypes, which is not ideal for next-generation sequencing (NGS) data of low depth from which genotypes cannot be called with high certainty.'),
(79475, 2015, 'bio', 'Multi-netclust: an efficient tool for finding connected clusters in multi-parametric networks', 'Kuzniar, Arnold|Dhir, Somdutta|Nijveen, Harm|Pongor, Sándor|Leunissen, Jack A M', 'Department of Biology, University of Copenhagen, 2200 Copenhagen, Denmark.|Department of Biology, University of Copenhagen, 2200 Copenhagen, Denmark.|Department of Biology, University of Copenhagen, 2200 Copenhagen, Denmark.|Department of Biology, University of Copenhagen, 2200 Copenhagen, Denmark.|Department of Biology, University of Copenhagen, 2200 Copenhagen, Denmark.', 'Denmark|Denmark|Denmark|Denmark|Denmark', 0, '-', 'Published Erratum', '10.1093/bioinformatics/btv479', '26330601', '-'),
(79476, 2015, 'bio', 'Learning directed acyclic graphical structures with genetical genomics data', 'Gao, Bin|Cui, Yuehua', 'Department of Statistics and Probability, Michigan State University, East Lansing, MI 48824, USA.|Department of Statistics and Probability, Michigan State University, East Lansing, MI 48824, USA.', 'United States|United States', 7, 'Algorithms|Alzheimer Disease|Gene Expression Profiling|Gene Expression Regulation|Gene Regulatory Networks|Genetic Markers|Genomics|Humans|Normal Distribution', 'Journal Article', '10.1093/bioinformatics/btv513', '26338766', 'Large amount of research efforts have been focused on estimating gene networks based on gene expression data to understand the functional basis of a living organism. Such networks are often obtained by considering pairwise correlations between genes, thus may not reflect the true connectivity between genes. By treating gene expressions as quantitative traits while considering genetic markers, genetical genomics analysis has shown its power in enhancing the understanding of gene regulations. Previous works have shown the improved performance on estimating the undirected network graphical structure by incorporating genetic markers as covariates. Knowing that gene expressions are often due to directed regulations, it is more meaningful to estimate the directed graphical network.'),
(79477, 2015, 'bio', 'DIGGIT: a Bioconductor package to infer genetic variants driving cellular phenotypes', 'Alvarez, Mariano J|Chen, James C|Califano, Andrea', 'Department of Systems Biology and.|Department of Systems Biology and Department of Dermatology, Columbia University, New York, NY 10032 USA.|Department of Systems Biology and.', 'United States|United States|United States', 0, 'Gene Expression|Gene Regulatory Networks|Genome, Human|Genomics|Humans|Models, Statistical|Mutation|Phenotype|Software', 'Journal Article', '10.1093/bioinformatics/btv499', '26338767', 'Identification of driver mutations in human diseases is often limited by cohort size and availability of appropriate statistical models. We propose a method for the systematic discovery of genetic alterations that are causal determinants of disease, by prioritizing genes upstream of functional disease drivers, within regulatory networks inferred de novo from experimental data. Here we present the implementation of Driver-gene Inference by Genetical-Genomic Information Theory as an R-system package.'),
(79478, 2015, 'bio', 'ABIF Manager: simple reading and editing of ABIF formatted files', 'Beran, Pavel|Mraz, Ivan', 'Department of Plant Production and Agroecology, Faculty of Agriculture, University of South Bohemia, 370 05 Ceske Budejovice, Czech Republic.|Department of Plant Production and Agroecology, Faculty of Agriculture, University of South Bohemia, 370 05 Ceske Budejovice, Czech Republic.', '-|-', 0, 'High-Throughput Nucleotide Sequencing|Software', 'Journal Article', '10.1093/bioinformatics/btv512', '26338768', 'ABIF Manager is simple application for low-level access to ABIF formatted files. Those are produced by Data Collection software based on the data, collected from capillary sequencing machine. The application shows all the data as readable text, which can be easily edited.'),
(79479, 2015, 'bio', 'Discovering hospital admission patterns using models learnt from electronic hospital records', 'Arandjelovixc4x87, Ognjen', 'School of Computer Science, University of St Andrews, St Andrews, UK.', 'United Kingdom', 18, 'Electronic Health Records|Hospital Records|Humans|Models, Theoretical|Patient Admission|Prognosis', 'Journal Article', '10.1093/bioinformatics/btv508', '26338769', 'Electronic medical records, nowadays routinely collected in many developed countries, open a new avenue for medical knowledge acquisition. In this article, this vast amount of information is used to develop a novel model for hospital admission type prediction.'),
(79480, 2015, 'bio', 'Benchmark analysis of algorithms for determining and quantifying full-length mRNA splice forms from RNA-seq data', 'Hayer, Katharina E|Pizarro, Angel|Lahens, Nicholas F|Hogenesch, John B|Grant, Gregory R', 'University of Pennsylvania, Institute for Translational Medicine and Therapeutics, Philadelphia, PA 19104.|Scientific Computing at Amazon Web Services, Seattle, WA 98108.|Department of Pharmacology and.|Department of Pharmacology and.|University of Pennsylvania, Institute for Translational Medicine and Therapeutics, Philadelphia, PA 19104, Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA.', 'United States|United States|United States|United States|United States', 43, 'Algorithms|Alternative Splicing|Animals|Benchmarking|Gene Expression Profiling|Humans|Mice|RNA Isoforms|RNA, Messenger|Sequence Analysis, RNA', 'Evaluation Studies', '10.1093/bioinformatics/btv488', '26338770', 'Because of the advantages of RNA sequencing (RNA-Seq) over microarrays, it is gaining widespread popularity for highly parallel gene expression analysis. For example, RNA-Seq is expected to be able to provide accurate identification and quantification of full-length splice forms. A number of informatics packages have been developed for this purpose, but short reads make it a difficult problem in principle. Sequencing error and polymorphisms add further complications. It has become necessary to perform studies to determine which algorithms perform best and which if any algorithms perform adequately. However, there is a dearth of independent and unbiased benchmarking studies. Here we take an approach using both simulated and experimental benchmark data to evaluate their accuracy.'),
(79481, 2016, 'bio', 'Large-scale extraction of gene interactions from full-text literature using DeepDive', 'Mallory, Emily K|Zhang, Ce|Ré, Christopher|Altman, Russ B', 'Biomedical Informatics Training Program, Stanford University, Stanford, CA 94305, USA.|Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA.|Department of Computer Science.|Department of Bioengineering, Department of Genetics and Department of Medicine, Stanford University, Stanford, CA 94305, USA.', 'United States|United States|United States|United States', 38, 'Data Curation|Data Mining|Databases, Genetic|Epistasis, Genetic|Humans|Information Storage and Retrieval|Publications|Software', 'Journal Article', '10.1093/bioinformatics/btv476', '26338771', 'A complete repository of gene-gene interactions is key for understanding cellular processes, human disease and drug response. These gene-gene interactions include both protein-protein interactions and transcription factor interactions. The majority of known interactions are found in the biomedical literature. Interaction databases, such as BioGRID and ChEA, annotate these gene-gene interactions, however, curation becomes difficult as the literature grows exponentially. DeepDive is a trained system for extracting information from a variety of sources, including text. In this work, we used DeepDive to extract both protein-protein and transcription factor interactions from over 100,000 full-text PLOS articles.'),
(79482, 2016, 'bio', 'Inter-functional analysis of high-throughput phenotype data by non-parametric clustering and its application to photosynthesis', 'Gao, Qiaozi|Ostendorf, Elisabeth|Cruz, Jeffrey A|Jin, Rong|Kramer, David M|Chen, Jin', 'Department of Computer Science and Engineering.|Department of Energy Plant Research Laboratory and.|Department of Energy Plant Research Laboratory and.|Department of Computer Science and Engineering.|Department of Energy Plant Research Laboratory and Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA.|Department of Computer Science and Engineering, Department of Energy Plant Research Laboratory and.', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Arabidopsis|Chloroplasts|Cluster Analysis|Genotype|Likelihood Functions|Mutation|Phenotype|Photosynthesis|Reproducibility of Results|Statistics as Topic|Statistics, Nonparametric', 'Journal Article', '10.1093/bioinformatics/btv515', '26342101', 'Phenomics is the study of the properties and behaviors of organisms (i.e. their phenotypes) on a high-throughput scale. New computational tools are needed to analyze complex phenomics data, which consists of multiple traits/behaviors that interact with each other and are dependent on external factors, such as genotype and environmental conditions, in a way that has not been well studied.'),
(79483, 2015, 'bio', 'Deciphering the associations between gene expression and copy number alteration using a sparse double Laplacian shrinkage approach', 'Shi, Xingjie|Zhao, Qing|Huang, Jian|Xie, Yang|Ma, Shuangge', 'Department of Statistics, Nanjing University of Finance and Economics, Nanjing, China, School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China.|Department of Biostatistics, Yale University, New Haven, CT, USA.|Department of Statistics and Actuarial Science, University of Iowa, Iowa, IA, USA.|Department of Clinical Science, The University of Texas Southwestern Medical Center, Dallas, TX, USA and.|Department of Biostatistics, Yale University, New Haven, CT, USA, VA Cooperative Studies Program Coordinating Center, West Haven, CT, USA.', 'China|United States|United States|United States|United States', 0, 'Gene Dosage|Gene Expression|Gene Expression Regulation|Humans|Models, Theoretical|Neoplasms', 'Journal Article', '10.1093/bioinformatics/btv518', '26342102', 'Both gene expression levels (GEs) and copy number alterations (CNAs) have important biological implications. GEs are partly regulated by CNAs, and much effort has been devoted to understanding their relations. The regulation analysis is challenging with one gene expression possibly regulated by multiple CNAs and one CNA potentially regulating the expressions of multiple genes. The correlations among GEs and among CNAs make the analysis even more complicated. The existing methods have limitations and cannot comprehensively describe the regulation.'),
(79484, 2016, 'bio', 'BMix: probabilistic modeling of occurring substitutions in PAR-CLIP data', 'Golumbeanu, Monica|Mohammadi, Pejman|Beerenwinkel, Niko', 'Department of Biosystems Science and Engineering and SIB Swiss Institute of Bioinformatics, CH-4058 Basel, Switzerland.|Department of Biosystems Science and Engineering and SIB Swiss Institute of Bioinformatics, CH-4058 Basel, Switzerland.|Department of Biosystems Science and Engineering and SIB Swiss Institute of Bioinformatics, CH-4058 Basel, Switzerland.', 'Switzerland|Switzerland|Switzerland', 10, 'Binding Sites|High-Throughput Nucleotide Sequencing|Humans|Immunoprecipitation|Models, Statistical|RNA|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btv520', '26342229', 'Photoactivatable ribonucleoside-enhanced cross-linking and immunoprecipitation (PAR-CLIP) is an experimental method based on next-generation sequencing for identifying the RNA interaction sites of a given protein. The method deliberately inserts T-to-C substitutions at the RNA-protein interaction sites, which provides a second layer of evidence compared with other CLIP methods. However, the experiment includes several sources of noise which cause both low-frequency errors and spurious high-frequency alterations. Therefore, rigorous statistical analysis is required in order to separate true T-to-C base changes, following cross-linking, from noise. So far, most of the existing PAR-CLIP data analysis methods focus on discarding the low-frequency errors and rely on high-frequency substitutions to report binding sites, not taking into account the possibility of high-frequency false positive substitutions.'),
(79485, 2016, 'bio', 'Computational modeling of development by epithelia, mesenchyme and their interactions: a unified model', 'Marin-Riera, Miquel|Brun-Usan, Miguel|Zimm, Roland|Välikangas, Tommi|Salazar-Ciudad, Isaac', 'Department of Genetics, Universitat Autònoma de Barcelona, Edifici C. 08193, Spain and.|Department of Genetics, Universitat Autònoma de Barcelona, Edifici C. 08193, Spain and.|Center of Excellence in Experimental and Computational Biology, Institute of Biotechnology, University of Helsinki, PO Box 56, FIN-00014 Helsinki, Finland.|Center of Excellence in Experimental and Computational Biology, Institute of Biotechnology, University of Helsinki, PO Box 56, FIN-00014 Helsinki, Finland.|Department of Genetics, Universitat Autònoma de Barcelona, Edifici C. 08193, Spain and Center of Excellence in Experimental and Computational Biology, Institute of Biotechnology, University of Helsinki, PO Box 56, FIN-00014 Helsinki, Finland.', 'Spain|Spain|Finland|Finland|Spain', 0, 'Animals|Computer Simulation|Embryonic Development|Epithelium|Extracellular Matrix|Gene Regulatory Networks|Mesoderm|Models, Biological|Morphogenesis|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btv527', '26342230', 'The transformation of the embryo during development requires complex gene networks, cell signaling and gene-regulated cell behaviors (division, adhesion, polarization, apoptosis, contraction, extracellular matrix secretion, signal secretion and reception, etc.). There are several models of development implementing these phenomena, but none considers at the same time the very different bio-mechanical properties of epithelia, mesenchyme, extracellular matrix and their interactions.'),
(79486, 2016, 'bio', 'Hammock: a hidden Markov model-based peptide clustering algorithm to identify protein-interaction consensus motifs in large datasets', 'Krejci, Adam|Hupp, Ted R|Lexa, Matej|Vojtesek, Borivoj|Muller, Petr', 'RECAMO, Masaryk Memorial Cancer Institute, Zluty kopec 7, 65653, Brno, Czech Republic.|University of Edinburgh, Institute of Genetics and Molecular Medicine, Cancer Research Centre, Edinburgh EH4 2XR, UK and.|Faculty of Informatics, Masaryk University, Botanicka 68a, 60200 Brno, Czech Republic.|RECAMO, Masaryk Memorial Cancer Institute, Zluty kopec 7, 65653, Brno, Czech Republic.|RECAMO, Masaryk Memorial Cancer Institute, Zluty kopec 7, 65653, Brno, Czech Republic.', '-|-|-|-|-', 0, 'Algorithms|Amino Acid Sequence|Antibodies, Monoclonal|Cluster Analysis|Databases, Protein|Epitopes|Humans|Markov Chains|Molecular Sequence Data|Peptides|Protein Interaction Domains and Motifs|Sequence Alignment|Software|src Homology Domains', 'Journal Article', '10.1093/bioinformatics/btv522', '26342231', 'Proteins often recognize their interaction partners on the basis of short linear motifs located in disordered regions on proteins surface. Experimental techniques that study such motifs use short peptides to mimic the structural properties of interacting proteins. Continued development of these methods allows for large-scale screening, resulting in vast amounts of peptide sequences, potentially containing information on multiple protein-protein interactions. Processing of such datasets is a complex but essential task for large-scale studies investigating protein-protein interactions.'),
(79487, 2016, 'bio', 'metaModules identifies key functional subnetworks in microbiome-related disease', 'May, Ali|Brandt, Bernd W|El-Kebir, Mohammed|Klau, Gunnar W|Zaura, Egija|Crielaard, Wim|Heringa, Jaap|Abeln, Sanne', 'Centre for Integrative Bioinformatics VU (IBIVU), VU University Amsterdam, Amsterdam, The Netherlands, Department of Preventive Dentistry, Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and VU University Amsterdam, Amsterdam, The Netherlands, Amsterdam Institute for Molecules Medicines and Systems (AIMMS), VU University Amsterdam, Amsterdam, The Netherlands.|Department of Preventive Dentistry, Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and VU University Amsterdam, Amsterdam, The Netherlands.|Centre for Integrative Bioinformatics VU (IBIVU), VU University Amsterdam, Amsterdam, The Netherlands, Department of Computer Science and Center for Computational Molecular Biology, Brown University, Providence, USA and Life Sciences, Centre for Mathematics and Computer Science (CWI), Amsterdam, The Netherlands.|Centre for Integrative Bioinformatics VU (IBIVU), VU University Amsterdam, Amsterdam, The Netherlands, Amsterdam Institute for Molecules Medicines and Systems (AIMMS), VU University Amsterdam, Amsterdam, The Netherlands, Life Sciences, Centre for Mathematics and Computer Science (CWI), Amsterdam, The Netherlands.|Department of Preventive Dentistry, Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and VU University Amsterdam, Amsterdam, The Netherlands.|Department of Preventive Dentistry, Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and VU University Amsterdam, Amsterdam, The Netherlands.|Centre for Integrative Bioinformatics VU (IBIVU), VU University Amsterdam, Amsterdam, The Netherlands, Amsterdam Institute for Molecules Medicines and Systems (AIMMS), VU University Amsterdam, Amsterdam, The Netherlands.|Centre for Integrative Bioinformatics VU (IBIVU), VU University Amsterdam, Amsterdam, The Netherlands, Amsterdam Institute for Molecules Medicines and Systems (AIMMS), VU University Amsterdam, Amsterdam, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Algorithms|Dental Caries|Humans|Microbiota', 'Journal Article', '10.1093/bioinformatics/btv526', '26342232', 'The human microbiome plays a key role in health and disease. Thanks to comparative metatranscriptomics, the cellular functions that are deregulated by the microbiome in disease can now be computationally explored. Unlike gene-centric approaches, pathway-based methods provide a systemic view of such functions, however, they typically consider each pathway in isolation and in its entirety. They can therefore overlook the key differences that (i) span multiple pathways, (ii) contain bidirectionally deregulated components, (iii) are confined to a pathway region. To capture these properties, computational methods that reach beyond the scope of predefined pathways are needed.'),
(79488, 2016, 'bio', 'ResiCon: a method for the identification of dynamic domains, hinges and interfacial regions in proteins', 'Dziubixc5x84ski, Maciej|Daniluk, Pawexc5x82|Lesyng, Bogdan', 'Department of Biophysics and CoE BioExploratorium, Faculty of Physics, University of Warsaw, 02-089 Warsaw, Poland and.|Department of Biophysics and CoE BioExploratorium, Faculty of Physics, University of Warsaw, 02-089 Warsaw, Poland and Bioinformatics Laboratory, Mossakowski Medical Research Centre, Polish Academy of Sciences, 02-106 Warsaw, Poland.|Department of Biophysics and CoE BioExploratorium, Faculty of Physics, University of Warsaw, 02-089 Warsaw, Poland and Bioinformatics Laboratory, Mossakowski Medical Research Centre, Polish Academy of Sciences, 02-106 Warsaw, Poland.', 'Poland|Poland|Poland', 5, 'Algorithms|Cluster Analysis|Computational Biology|HIV Protease|HIV-1|Magnetic Resonance Spectroscopy|Molecular Dynamics Simulation|Protein Structure, Tertiary|Software|Stochastic Processes', 'Journal Article', '10.1093/bioinformatics/btv525', '26342233', 'Structure of most proteins is flexible. Identification and analysis of intramolecular motions is a complex problem. Breaking a structure into relatively rigid parts, the so-called dynamic domains, may help comprehend the complexity of proteins mobility. We propose a new approach called ResiCon (Residue Contacts analysis), which performs this task by applying a data-mining analysis of an ensemble of protein configurations and recognizes dynamic domains, hinges and interfacial regions, by considering contacts between residues.'),
(79489, 2016, 'bio', 'Improved large-scale prediction of growth inhibition patterns using the NCI60 cancer cell line panel', 'Cortxc3xa9s-Ciriano, Isidro|van Westen, Gerard J P|Bouvier, Guillaume|Nilges, Michael|Overington, John P|Bender, Andreas|Malliavin, Thxc3xa9rxc3xa8se E', 'Unitxc3xa9 de Bioinformatique Structurale, Institut Pasteur and CNRS UMR 3825, Structural Biology and Chemistry Department, 75xe2x80x89724 Paris, France.|Medicinal Chemistry, Leiden Academic Centre for Drug Research, Einsteinweg 55, 2333CC, Leiden.|Unitxc3xa9 de Bioinformatique Structurale, Institut Pasteur and CNRS UMR 3825, Structural Biology and Chemistry Department, 75xe2x80x89724 Paris, France.|Unitxc3xa9 de Bioinformatique Structurale, Institut Pasteur and CNRS UMR 3825, Structural Biology and Chemistry Department, 75xe2x80x89724 Paris, France.|European Molecular Biology Laboratory European Bioinformatics Institute, Wellcome Trust Genome Campus, CB10 1SD, Hinxton, Cambridge, UK and.|Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, CB2 1EW Cambridge, UK.|Unitxc3xa9 de Bioinformatique Structurale, Institut Pasteur and CNRS UMR 3825, Structural Biology and Chemistry Department, 75xe2x80x89724 Paris, France.', 'France|United Kingdom|France|France|United Kingdom|United Kingdom|France', 0, 'Biological Assay|Cell Line, Tumor|Cell Proliferation|Computational Biology|Databases, Protein|Humans|Models, Biological|Neoplasms|Pharmacogenetics|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/btv529', '26351271', 'Recent large-scale omics initiatives have catalogued the somatic alterations of cancer cell line panels along with their pharmacological response to hundreds of compounds. In this study, we have explored these data to advance computational approaches that enable more effective and targeted use of current and future anticancer therapeutics.'),
(79490, 2016, 'bio', 'SHAPE directed RNA folding', 'Lorenz, Ronny|Luntzer, Dominik|Hofacker, Ivo L|Stadler, Peter F|Wolfinger, Michael T', 'Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria, Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria.|Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria.|Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria, Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria.|Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria, Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria.|Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria, Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria.', 'Austria|Austria|Austria|Austria|Austria', 0, 'Algorithms|Base Sequence|Escherichia coli|Molecular Sequence Data|Nucleic Acid Conformation|RNA Folding|RNA, Ribosomal|Thermodynamics', 'Journal Article', '10.1093/bioinformatics/btv523', '26353838', 'Chemical mapping experiments allow for nucleotide resolution assessment of RNA structure. We demonstrate that different strategies of integrating probing data with thermodynamics-based RNA secondary structure prediction algorithms can be implemented by means of soft constraints. This amounts to incorporating suitable pseudo-energies into the standard energy model for RNA secondary structures. As a showcase application for this new feature of the ViennaRNA Package we compare three distinct, previously published strategies to utilize SHAPE reactivities for structure prediction. The new tool is benchmarked on a set of RNAs with known reference structure.'),
(79491, 2016, 'bio', 'RNF: a general framework to evaluate NGS read mappers', 'Bxc5x99inda, Karel|Boeva, Valentina|Kucherov, Gregory', 'LIGM/CNRS, Universitxc3xa9 Paris-Est, 77454 Marne-la-Vallxc3xa9e, France.|Inserm, U900, Bioinformatics, Biostatistics, Epidemiology and Computational Systems Biology of Cancer, 75248 Paris, France, Institut Curie, Centre de Recherche, 26 rue dUlm, 75248 Paris, France and Mines ParisTech, 77300 Fontainebleau, France.|LIGM/CNRS, Universitxc3xa9 Paris-Est, 77454 Marne-la-Vallxc3xa9e, France.', 'France|France|France', 0, 'Computer Simulation|Genome|High-Throughput Nucleotide Sequencing|Humans|Software', 'Journal Article', '10.1093/bioinformatics/btv524', '26353839', 'Read simulators combined with alignment evaluation tools provide the most straightforward way to evaluate and compare mappers. Simulation of reads is accompanied by information about their positions in the source genome. This information is then used to evaluate alignments produced by the mapper. Finally, reports containing statistics of successful read alignments are created.In default of standards for encoding read origins, every evaluation tool has to be made explicitly compatible with the simulator used to generate reads.'),
(79492, 2016, 'bio', 'Reveel: large-scale population genotyping using low-coverage sequencing data', 'Huang, Lin|Wang, Bo|Chen, Ruitang|Bercovici, Sivan|Batzoglou, Serafim', 'Department of Computer Science, Stanford University, CA 94305, USA.|Department of Computer Science, Stanford University, CA 94305, USA.|Department of Computer Science, Stanford University, CA 94305, USA.|Department of Computer Science, Stanford University, CA 94305, USA.|Department of Computer Science, Stanford University, CA 94305, USA.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Genotype|Linkage Disequilibrium|Polymorphism, Single Nucleotide|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btv530', '26353840', 'Population low-coverage whole-genome sequencing is rapidly emerging as a prominent approach for discovering genomic variation and genotyping a cohort. This approach combines substantially lower cost than full-coverage sequencing with whole-genome discovery of low-allele frequency variants, to an extent that is not possible with array genotyping or exome sequencing. However, a challenging computational problem arises of jointly discovering variants and genotyping the entire cohort. Variant discovery and genotyping are relatively straightforward tasks on a single individual that has been sequenced at high coverage, because the inference decomposes into the independent genotyping of each genomic position for which a sufficient number of confidently mapped reads are available. However, in low-coverage population sequencing, the joint inference requires leveraging the complex linkage disequilibrium (LD) patterns in the cohort to compensate for sparse and missing data in each individual. The potentially massive computation time for such inference, as well as the missing data that confound low-frequency allele discovery, need to be overcome for this approach to become practical.'),
(79493, 2016, 'bio', 'Group and sparse group partial least square approaches applied in genomics context', 'Liquet, Benoît|de Micheaux, Pierre Lafaye|Hejblum, Boris P|Thiébaut, Rodolphe', 'School of Mathematics and Physics, The University of Queensland, Brisbane 4066, Australia, ARC Centre of Excellence for Mathematical and Statistical Frontiers, QUT, Brisbane, Australia.|CREST, ENSAI, Campus de Ker-Lann, Rue Blaise Pascal, BP 37203, 35172 Bruz cedex, France.|Inria, SISTM, Talence and Inserm, U897, Bordeaux, Bordeaux University, Bordeaux and Vaccine Research Institute, Creteil, France.|Inria, SISTM, Talence and Inserm, U897, Bordeaux, Bordeaux University, Bordeaux and Vaccine Research Institute, Creteil, France.', 'Australia|France|France|France', 27, 'AIDS Vaccines|Algorithms|Computer Simulation|Genomics|Humans|Least-Squares Analysis|Sample Size', 'Journal Article', '10.1093/bioinformatics/btv535', '26358727', 'The association between two blocks of omics data brings challenging issues in computational biology due to their size and complexity. Here, we focus on a class of multivariate statistical methods called partial least square (PLS). Sparse version of PLS (sPLS) operates integration of two datasets while simultaneously selecting the contributing variables. However, these methods do not take into account the important structural or group effects due to the relationship between markers among biological pathways. Hence, considering the predefined groups of markers (e.g. genesets), this could improve the relevance and the efficacy of the PLS approach.'),
(79494, 2016, 'bio', 'SeDuS: segmental duplication simulator', 'Hartasánchez, Diego A|Brasó-Vives, Marina|Fuentes-Díaz, Juanma|Vallès-Codina, Oriol|Navarro, Arcadi', 'Institute of Evolutionary Biology (Universitat Pompeu Fabra - CSIC), PRBB, 08003.|Institute of Evolutionary Biology (Universitat Pompeu Fabra - CSIC), PRBB, 08003.|Institute of Evolutionary Biology (Universitat Pompeu Fabra - CSIC), PRBB, 08003.|Institute of Evolutionary Biology (Universitat Pompeu Fabra - CSIC), PRBB, 08003.|Institute of Evolutionary Biology (Universitat Pompeu Fabra - CSIC), PRBB, 08003, National Institute for Bioinformatics, 08003, Institució Catalana de Recerca i Estudis Avançats (ICREA), 08010 and Centre for Genomic Regulation (CRG), 08003, Barcelona, Spain.', '-|-|-|-|Spain', 2, 'Evolution, Molecular|Gene Conversion|Humans|Programming Languages|Segmental Duplications, Genomic|Software', 'Journal Article', '10.1093/bioinformatics/btv481', '26358728', 'SeDuS is the first flexible and user-friendly forward-in-time simulator of patterns of molecular evolution within segmental duplications undergoing interlocus gene conversion and crossover. SeDuS introduces known features of interlocus gene conversion such as biased directionality and dependence on local sequence identity. Additionally, it includes aspects such as different selective pressures acting upon copy number and flexible crossover distributions. A graphical user interface allows fast fine-tuning of relevant parameters and straightforward real-time analysis of the evolution of duplicates.');
