INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(79495, 2015, 'bio', 'Cas-Designer: a web-based tool for choice of CRISPR-Cas9 target sites', 'Park, Jeongbin|Bae, Sangsu|Kim, Jin-Soo', 'Department of Chemistry.|Department of Chemistry, Institute for Materials Design, Hanyang University, Seoul, South Korea.|Center for Genome Engineering, Institute for Basic Science, Seoul, South Korea and Department of Chemistry, Seoul National University, Seoul, South Korea.', '-|-|-', 0, 'CRISPR-Associated Proteins|CRISPR-Cas Systems|Clustered Regularly Interspaced Short Palindromic Repeats|Endodeoxyribonucleases|Gene Knockout Techniques|Genome|Internet|RNA|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btv537', '26358729', 'We present Cas-Designer, a user-friendly program to aid researchers in choosing appropriate target sites in a gene of interest for type II CRISPR/Cas-derived RNA-guided endonucleases, which are now widely used for biomedical research and biotechnology. Cas-Designer rapidly provides the list of all possible guide RNA sequences in a given input DNA sequence and their potential off-target sites including bulge-type sites in a genome of choice. In addition, the program assigns an out-of-frame score to each target site to help users choose appropriate sites for gene knockout. Cas-Designer shows the results in an interactive table and provides user-friendly filter functions.'),
(79496, 2016, 'bio', 'Inferred miRNA activity identifies miRNA-mediated regulatory networks underlying multiple cancers', 'Lee, Eunjee|Ito, Koichi|Zhao, Yong|Schadt, Eric E|Irie, Hanna Y|Zhu, Jun', 'Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology.|Department of Medicine, Hematology and Medical Oncology and.|Department of Genetics and Genomic Sciences.|Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.|Department of Medicine, Hematology and Medical Oncology and The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.|Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Breast Neoplasms|Cell Line, Tumor|Cohort Studies|Female|Gene Expression Regulation, Neoplastic|Gene Regulatory Networks|Humans|MicroRNAs|Molecular Sequence Annotation|Neoplasms|Prognosis|RNA, Messenger|Reproducibility of Results|Software|Survival Rate', 'Journal Article', '10.1093/bioinformatics/btv531', '26358730', 'MicroRNAs (miRNAs) play a key role in regulating tumor progression and metastasis. Identifying key miRNAs, defined by their functional activities, can provide a deeper understanding of biology of miRNAs in cancer. However, miRNA expression level cannot accurately reflect miRNA activity.'),
(79497, 2016, 'bio', 'A robust AMMI model for the analysis of genotype-by-environment data', 'Rodrigues, Paulo C|Monteiro, Andreia|Lourenço, Vanda M', 'Department of Statistics, Federal University of Bahia, Salvador, Brazil, CAST-Center for Applied Statistics and Data Analytics, University of Tampere, Finland.|Centro de Matemática e Aplicações (CMA), and.|Centro de Matemática e Aplicações (CMA), and Department of Mathematics, FCT-NOVA University of Lisbon, Lisbon, Portugal.', 'Brazil|-|Portugal', 10, 'Chromosome Mapping|Computer Simulation|Crosses, Genetic|Environment|Genotype|Hordeum|Models, Theoretical|Principal Component Analysis|Triticum', 'Journal Article', '10.1093/bioinformatics/btv533', '26363027', 'One of the most widely used models to analyse genotype-by-environment data is the additive main effects and multiplicative interaction (AMMI) model. Genotype-by-environment data resulting from multi-location trials are usually organized in two-way tables with genotypes in the rows and environments (location-year combinations) in the columns. The AMMI model applies singular value decomposition (SVD) to the residuals of a specific linear model, to decompose the genotype-by-environment interaction (GEI) into a sum of multiplicative terms. However, SVD, being a least squares method, is highly sensitive to contamination and the presence of even a single outlier, if extreme, may draw the leading principal component towards itself resulting in possible misinterpretations and in turn lead to bad practical decisions. Since, as in many other real-life studies the distribution of these data is usually not normal due to the presence of outlying observations, either resulting from measurement errors or sometimes from individual intrinsic characteristics, robust SVD methods have been suggested to help overcome this handicap.'),
(79498, 2016, 'bio', 'BamHash: a checksum program for verifying the integrity of sequence data', 'Óskarsdóttir, Arna|Másson, Gísli|Melsted, Páll', 'deCODE Genetics/Amgen, Reykjavík, Iceland and.|deCODE Genetics/Amgen, Reykjavík, Iceland and.|deCODE Genetics/Amgen, Reykjavík, Iceland and Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland, Reykjavík, Iceland.', 'Iceland|Iceland|Iceland', 0, 'Humans|Reproducibility of Results|Sequence Analysis|Software|Statistics as Topic', 'Journal Article', '10.1093/bioinformatics/btv539', '26363028', 'Large resequencing projects require a significant amount of storage for raw sequences, as well as alignment files. Because the raw sequences are redundant once the alignment has been generated, it is possible to keep only the alignment files. We present BamHash, a checksum based method to ensure that the read pairs in FASTQ files match exactly the read pairs stored in BAM files, regardless of the ordering of reads. BamHash can be used to verify the integrity of the files stored and discover any discrepancies. Thus, BamHash can be used to determine if it is safe to delete the FASTQ files storing raw sequencing read after alignment, without the loss of data.'),
(79499, 2016, 'bio', 'An algorithm for automated layout of process description maps drawn in SBGN', 'Genc, Begum|Dogrusoz, Ugur', 'The Insight Centre for Data Analytics, University College Cork, Western Road, Cork, Ireland, Computer Engineering Department, Faculty of Engineering, Bilkent University, Ankara 06800, Turkey and.|Computer Engineering Department, Faculty of Engineering, Bilkent University, Ankara 06800, Turkey and Sander Lab, Memorial Sloan-Kettering Cancer Center, 417 E68th St., New York, NY 10065, USA.', 'Ireland|Turkey', 0, 'Algorithms|Aspirin|Automation|Heuristics|Signal Transduction|Systems Biology|Vitamin B 6', 'Journal Article', '10.1093/bioinformatics/btv516', '26363029', 'Evolving technology has increased the focus on genomics. The combination of todays advanced techniques with decades of molecular biology research has yielded huge amounts of pathway data. A standard, named the Systems Biology Graphical Notation (SBGN), was recently introduced to allow scientists to represent biological pathways in an unambiguous, easy-to-understand and efficient manner. Although there are a number of automated layout algorithms for various types of biological networks, currently none specialize on process description (PD) maps as defined by SBGN.'),
(79500, 2016, 'bio', 'Human cell structure-driven model construction for predicting protein subcellular location from biological images', 'Shao, Wei|Liu, Mingxia|Zhang, Daoqiang', 'School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.|School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.|School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.', 'China|China|China', 0, 'Algorithms|Cell Compartmentation|Cells|Databases, Protein|Humans|Imaging, Three-Dimensional|Models, Biological|Proteins|Proteome|Subcellular Fractions', 'Journal Article', '10.1093/bioinformatics/btv521', '26363175', 'The systematic study of subcellular location pattern is very important for fully characterizing the human proteome. Nowadays, with the great advances in automated microscopic imaging, accurate bioimage-based classification methods to predict protein subcellular locations are highly desired. All existing models were constructed on the independent parallel hypothesis, where the cellular component classes are positioned independently in a multi-class classification engine. The important structural information of cellular compartments is missed. To deal with this problem for developing more accurate models, we proposed a novel cell structure-driven classifier construction approach (SC-PSorter) by employing the prior biological structural information in the learning model. Specifically, the structural relationship among the cellular components is reflected by a new codeword matrix under the error correcting output coding framework. Then, we construct multiple SC-PSorter-based classifiers corresponding to the columns of the error correcting output coding codeword matrix using a multi-kernel support vector machine classification approach. Finally, we perform the classifier ensemble by combining those multiple SC-PSorter-based classifiers via majority voting.'),
(79501, 2016, 'bio', 'markophylo: Markov chain analysis on phylogenetic trees', 'Dang, Utkarsh J|Golding, G Brian', 'Department of Biology, McMaster University, Hamilton, Ontario, L8S 4K1, Canada.|Department of Biology, McMaster University, Hamilton, Ontario, L8S 4K1, Canada.', 'Canada|Canada', 2, 'Markov Chains|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/btv541', '26363176', 'Continuous-time Markov chain models with finite state space are routinely used for analysis of discrete character data on phylogenetic trees. Examples of such discrete character data include restriction sites, gene family presence/absence, intron presence/absence and gene family size data. While models with constrained substitution rate matrices have been used to good effect, more biologically realistic models have been increasingly implemented in the recent literature combining, e.g., site rate variation, site partitioning, branch-specific rates, allowing for non-stationary prior root probabilities, correcting for sampling bias, etc. to name a few. Here, a flexible and fast R package is introduced that infers evolutionary rates of discrete characters on a tree within a probabilistic framework. The package, markophylo, fits maximum-likelihood models using Markov chains on phylogenetic trees. The package is efficient, with the workhorse functions written in C++ and the interface in user-friendly R.'),
(79502, 2016, 'bio', 'CD30 cell graphs of Hodgkin lymphoma are not scale-free--an image analysis approach', 'Schäfer, Hendrik|Schäfer, Tim|Ackermann, Jörg|Dichter, Norbert|Döring, Claudia|Hartmann, Sylvia|Hansmann, Martin-Leo|Koch, Ina', 'Department of Molecular Bioinformatics, Institute of Computer Science, Cluster of Excellence Macromolecular Complexes, Johann Wolfgang Goethe-University Frankfurt am Main, Robert-Mayer-Str. 11-15, 60325 Frankfurt am Main and.|Department of Molecular Bioinformatics, Institute of Computer Science, Cluster of Excellence Macromolecular Complexes, Johann Wolfgang Goethe-University Frankfurt am Main, Robert-Mayer-Str. 11-15, 60325 Frankfurt am Main and.|Department of Molecular Bioinformatics, Institute of Computer Science, Cluster of Excellence Macromolecular Complexes, Johann Wolfgang Goethe-University Frankfurt am Main, Robert-Mayer-Str. 11-15, 60325 Frankfurt am Main and.|Department of Molecular Bioinformatics, Institute of Computer Science, Cluster of Excellence Macromolecular Complexes, Johann Wolfgang Goethe-University Frankfurt am Main, Robert-Mayer-Str. 11-15, 60325 Frankfurt am Main and.|Dr. Senckenbergisches Institut für Pathologie, Universitätsklinikum Frankfurt, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany.|Dr. Senckenbergisches Institut für Pathologie, Universitätsklinikum Frankfurt, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany.|Dr. Senckenbergisches Institut für Pathologie, Universitätsklinikum Frankfurt, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany.|Department of Molecular Bioinformatics, Institute of Computer Science, Cluster of Excellence Macromolecular Complexes, Johann Wolfgang Goethe-University Frankfurt am Main, Robert-Mayer-Str. 11-15, 60325 Frankfurt am Main and.', '-|-|-|-|Germany|Germany|Germany|-', 0, 'Cell Aggregation|Cell Count|Hodgkin Disease|Humans|Image Processing, Computer-Assisted|Ki-1 Antigen|Reproducibility of Results', 'Journal Article', '10.1093/bioinformatics/btv542', '26363177', 'Hodgkin lymphoma (HL) is a type of B-cell lymphoma. To diagnose the subtypes, biopsies are taken and immunostained. The slides are scanned to produce high-resolution digital whole slide images (WSI). Pathologists manually inspect the spatial distribution of cells, but little is known on the statistical properties of cell distributions in WSIs. Such properties would give valuable information for the construction of theoretical models that describe the invasion of malignant cells in the lymph node and the intercellular interactions.'),
(79503, 2016, 'bio', 'DIVAS: a centralized genetic variant repository representing 150,000 individuals from multiple disease cohorts', 'Cheng, Wei-Yi|Hakenberg, Jörg|Li, Shuyu Dan|Chen, Rong', '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', 7, 'Cohort Studies|Databases, Genetic|Disease|Genetic Variation|Humans|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btv511', '26363178', 'A plethora of sequenced and genotyped disease cohorts is available to the biomedical research community, spread across many portals and represented in various formats.'),
(79504, 2016, 'bio', 'PyPDB: a Python API for the Protein Data Bank', 'Gilpin, William', 'Department of Applied Physics, Stanford University, Stanford, CA 94305, USA.', 'United States', 2, 'Databases, Protein|Programming Languages|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btv543', '26369703', 'We have created a Python programming interface for the RCSB Protein Data Bank (PDB) that allows search and data retrieval for a wide range of result types, including BLAST and sequence motif queries. The API relies on the existing XML-based API and operates by creating custom XML requests from native Python types, allowing extensibility and straightforward modification. The package has the ability to perform many types of advanced search of the PDB that are otherwise only available through the PDB website.'),
(79505, 2016, 'bio', 'A non-negative matrix factorization method for detecting modules in heterogeneous omics multi-modal data', 'Yang, Zi|Michailidis, George', 'Department of Statistics, University of Michigan, Ann Arbor, MI 48109, USA.|Department of Statistics, University of Michigan, Ann Arbor, MI 48109, USA.', 'United States|United States', 48, 'Algorithms|Computer Simulation|DNA Methylation|Databases, Genetic|Female|Genomics|Humans|MicroRNAs|Ovarian Neoplasms|Reproducibility of Results', 'Journal Article', '10.1093/bioinformatics/btv544', '26377073', 'Recent advances in high-throughput omics technologies have enabled biomedical researchers to collect large-scale genomic data. As a consequence, there has been growing interest in developing methods to integrate such data to obtain deeper insights regarding the underlying biological system. A key challenge for integrative studies is the heterogeneity present in the different omics data sources, which makes it difficult to discern the coordinated signal of interest from source-specific noise or extraneous effects.'),
(79506, 2016, 'bio', 'Component-wise gradient boosting and false discovery control in survival analysis with high-dimensional covariates', 'He, Kevin|Li, Yanming|Zhu, Ji|Liu, Hongliang|Lee, Jeffrey E|Amos, Christopher I|Hyslop, Terry|Jin, Jiashun|Lin, Huazhen|Wei, Qinyi|Li, Yi', 'Department of Biostatistics and.|Department of Biostatistics and.|Department of Statistics, University of Michigan, Ann Arbor, Michigan 48109, USA.|Department of Medicine, Duke University School of Medicine and Duke Cancer Institute, Duke University Medical Center, Durham, NC 27710, USA.|Department of Surgical Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA.|Department of Community and Family Medicine, Geisel School of Medicine, Dartmouth College, Hanover, NH 03750, USA.|Department of Biostatistics and Bioinformatics, Duke University and Duke Clinical Research Institute, Durham, NC 27710, USA.|Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213, USA and.|Center of Statistical Research, School of Statistics, Southwestern University of Finance and Economics, Chengdu, Sichuan 611130, China.|Department of Medicine, Duke University School of Medicine and Duke Cancer Institute, Duke University Medical Center, Durham, NC 27710, USA.|Department of Biostatistics and.', 'United States|United States|United States|United States|United States|United States|United States|United States|China|United States|United States', 10, 'Algorithms|BRCA2 Protein|Computer Simulation|Humans|Melanoma|Polymorphism, Single Nucleotide|Risk Factors|Skin Neoplasms|Survival Analysis|Time Factors', 'Journal Article', '10.1093/bioinformatics/btv517', '26382192', 'Technological advances that allow routine identification of high-dimensional risk factors have led to high demand for statistical techniques that enable full utilization of these rich sources of information for genetics studies. Variable selection for censored outcome data as well as control of false discoveries (i.e. inclusion of irrelevant variables) in the presence of high-dimensional predictors present serious challenges. This article develops a computationally feasible method based on boosting and stability selection. Specifically, we modified the component-wise gradient boosting to improve the computational feasibility and introduced random permutation in stability selection for controlling false discoveries.'),
(79507, 2016, 'bio', 'ICT: isotope correction toolbox', 'Jungreuthmayer, Christian|Neubauer, Stefan|Mairinger, Teresa|Zanghellini, Jürgen|Hann, Stephan', 'Austrian Centre of Industrial Biotechnology (ACIB), Vienna, Austria, Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria and.|Austrian Centre of Industrial Biotechnology (ACIB), Vienna, Austria, Department of Chemistry, University of Natural Resources and Life Sciences, Vienna, Austria.|Austrian Centre of Industrial Biotechnology (ACIB), Vienna, Austria, Department of Chemistry, University of Natural Resources and Life Sciences, Vienna, Austria.|Austrian Centre of Industrial Biotechnology (ACIB), Vienna, Austria, Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria and.|Austrian Centre of Industrial Biotechnology (ACIB), Vienna, Austria, Department of Chemistry, University of Natural Resources and Life Sciences, Vienna, Austria.', 'Austria|Austria|Austria|Austria|Austria', 18, 'Algorithms|Humans|Isotope Labeling|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btv514', '26382193', 'Isotope tracer experiments are an invaluable technique to analyze and study the metabolism of biological systems. However, isotope labeling experiments are often affected by naturally abundant isotopes especially in cases where mass spectrometric methods make use of derivatization. The correction of these additive interferences--in particular for complex isotopic systems--is numerically challenging and still an emerging field of research. When positional information is generated via collision-induced dissociation, even more complex calculations for isotopic interference correction are necessary. So far, no freely available tools can handle tandem mass spectrometry data. We present isotope correction toolbox, a program that corrects tandem mass isotopomer data from tandem mass spectrometry experiments. Isotope correction toolbox is written in the multi-platform programming language Perl and, therefore, can be used on all commonly available computer platforms.'),
(79508, 2016, 'bio', 'ScaffoldScaffolder: solving contig orientation via bidirected to directed graph reduction', 'Bodily, Paul M|Fujimoto, M Stanley|Snell, Quinn|Ventura, Dan|Clement, Mark J', 'Computational Sciences Laboratory, Department of Computer Science, Brigham Young University, Provo, UT 84602-6576, USA.|Computational Sciences Laboratory, Department of Computer Science, Brigham Young University, Provo, UT 84602-6576, USA.|Computational Sciences Laboratory, Department of Computer Science, Brigham Young University, Provo, UT 84602-6576, USA.|Computational Sciences Laboratory, Department of Computer Science, Brigham Young University, Provo, UT 84602-6576, USA.|Computational Sciences Laboratory, Department of Computer Science, Brigham Young University, Provo, UT 84602-6576, USA.', 'United States|United States|United States|United States|United States', 4, 'Algorithms|Contig Mapping', 'Journal Article', '10.1093/bioinformatics/btv548', '26382194', 'The contig orientation problem, which we formally define as the MAX-DIR problem, has at times been addressed cursorily and at times using various heuristics. In setting forth a linear-time reduction from the MAX-CUT problem to the MAX-DIR problem, we prove the latter is NP-complete. We compare the relative performance of a novel greedy approach with several other heuristic solutions.'),
(79509, 2016, 'bio', 'P-SAMS: a web site for plant artificial microRNA and synthetic trans-acting small interfering RNA design', 'Fahlgren, Noah|Hill, Steven T|Carrington, James C|Carbonell, Alberto', 'Donald Danforth Plant Science Center, St. Louis, MO 63132, USA.|Donald Danforth Plant Science Center, St. Louis, MO 63132, USA.|Donald Danforth Plant Science Center, St. Louis, MO 63132, USA.|Donald Danforth Plant Science Center, St. Louis, MO 63132, USA.', 'United States|United States|United States|United States', 24, 'Computational Biology|Internet|MicroRNAs|Plants|RNA, Plant|RNA, Small Interfering|Software', 'Journal Article', '10.1093/bioinformatics/btv534', '26382195', 'The Plant Small RNA Maker Site (P-SAMS) is a web tool for the simple and automated design of artificial miRNAs (amiRNAs) and synthetic trans-acting small interfering RNAs (syn-tasiRNAs) for efficient and specific targeted gene silencing in plants. P-SAMS includes two applications, P-SAMS amiRNA Designer and P-SAMS syn-tasiRNA Designer. The navigation through both applications is wizard-assisted, and the job runtime is relatively short. Both applications output the sequence of designed small RNA(s), and the sequence of the two oligonucleotides required for cloning into B/c compatible vectors.'),
(79510, 2016, 'bio', 'CLAMMS: a scalable algorithm for calling common and rare copy number variants from exome sequencing data', 'Packer, Jonathan S|Maxwell, Evan K|ODushlaine, Colm|Lopez, Alexander E|Dewey, Frederick E|Chernomorsky, Rostislav|Baras, Aris|Overton, John D|Habegger, Lukas|Reid, Jeffrey G', 'Regeneron Genetics Center, Tarrytown, NY 10591, USA.|Regeneron Genetics Center, Tarrytown, NY 10591, USA.|Regeneron Genetics Center, Tarrytown, NY 10591, USA.|Regeneron Genetics Center, Tarrytown, NY 10591, USA.|Regeneron Genetics Center, Tarrytown, NY 10591, USA.|Regeneron Genetics Center, Tarrytown, NY 10591, USA.|Regeneron Genetics Center, Tarrytown, NY 10591, USA.|Regeneron Genetics Center, Tarrytown, NY 10591, USA.|Regeneron Genetics Center, Tarrytown, NY 10591, USA.|Regeneron Genetics Center, Tarrytown, NY 10591, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 24, 'Algorithms|DNA Copy Number Variations|Exome|Humans|Markov Chains|Reproducibility of Results|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btv547', '26382196', 'Several algorithms exist for detecting copy number variants (CNVs) from human exome sequencing read depth, but previous tools have not been well suited for large population studies on the order of tens or hundreds of thousands of exomes. Their limitations include being difficult to integrate into automated variant-calling pipelines and being ill-suited for detecting common variants. To address these issues, we developed a new algorithm--Copy number estimation using Lattice-Aligned Mixture Models (CLAMMS)--which is highly scalable and suitable for detecting CNVs across the whole allele frequency spectrum.'),
(79511, 2016, 'bio', 'NanoOK: multi-reference alignment analysis of nanopore sequencing data, quality and error profiles', 'Leggett, Richard M|Heavens, Darren|Caccamo, Mario|Clark, Matthew D|Davey, Robert P', 'The Genome Analysis Centre (TGAC), Norwich NR4 7UH, UK.|The Genome Analysis Centre (TGAC), Norwich NR4 7UH, UK.|The Genome Analysis Centre (TGAC), Norwich NR4 7UH, UK.|The Genome Analysis Centre (TGAC), Norwich NR4 7UH, UK.|The Genome Analysis Centre (TGAC), Norwich NR4 7UH, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 33, 'Base Sequence|Data Accuracy|Escherichia coli K12|Nanopores|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btv540', '26382197', 'The Oxford Nanopore MinION sequencer, currently in pre-release testing through the MinION Access Programme (MAP), promises long reads in real-time from an inexpensive, compact, USB device. Tools have been released to extract FASTA/Q from the MinION base calling output and to provide basic yield statistics. However, no single tool yet exists to provide comprehensive alignment-based quality control and error profile analysis--something that is extremely important given the speed with which the platform is evolving.'),
(79512, 2016, 'bio', 'Exploring peptide/MHC detachment processes using hierarchical natural move Monte Carlo', 'Knapp, Bernhard|Demharter, Samuel|Deane, Charlotte M|Minary, Peter', 'Department of Statistics, University of Oxford, 1 South Parks Road, Oxford, OX1 3TG, UK and.|Department of Computer Science, University of Oxford, Wolfson Building, Parks Road, Oxford, OX1 3QD, UK.|Department of Statistics, University of Oxford, 1 South Parks Road, Oxford, OX1 3TG, UK and.|Department of Computer Science, University of Oxford, Wolfson Building, Parks Road, Oxford, OX1 3QD, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 13, 'Algorithms|HLA Antigens|HLA-A2 Antigen|Humans|Molecular Dynamics Simulation|Monte Carlo Method|Peptides|Protein Binding', 'Journal Article', '10.1093/bioinformatics/btv502', '26395770', 'The binding between a peptide and a major histocompatibility complex (MHC) is one of the most important processes for the induction of an adaptive immune response. Many algorithms have been developed to predict peptide/MHC (pMHC) binding. However, no approach has yet been able to give structural insight into how peptides detach from the MHC.'),
(79513, 2016, 'bio', 'Positive-unlabeled ensemble learning for kinase substrate prediction from dynamic phosphoproteomics data', 'Yang, Pengyi|Humphrey, Sean J|James, David E|Yang, Yee Hwa|Jothi, Raja', 'Systems Biology Section, Epigenetics & Stem Cell Biology Laboratory, National Institute of Environmental Health Sciences, National Institutes of Health, RTP, NC 27709, USA.|Department of Proteomics and Signal Transduction, Max-Planck-Institute of Biochemistry, Martinsried, Germany.|Charles Perkins Centre, School of Molecular Bioscience, Sydney Medical School and.|School of Mathematics and Statistics, The University of Sydney, NSW 2006, Australia.|Systems Biology Section, Epigenetics & Stem Cell Biology Laboratory, National Institute of Environmental Health Sciences, National Institutes of Health, RTP, NC 27709, USA.', 'United States|Germany|United States|Australia|United States', 18, 'Databases, Protein|Humans|Insulin|Mass Spectrometry|Phosphoproteins|Phosphorylation|Protein Kinases|Protein Processing, Post-Translational|Proteome|Proteomics|Signal Transduction|Substrate Specificity', 'Journal Article', '10.1093/bioinformatics/btv550', '26395771', 'Protein phosphorylation is a post-translational modification that underlines various aspects of cellular signaling. A key step to reconstructing signaling networks involves identification of the set of all kinases and their substrates. Experimental characterization of kinase substrates is both expensive and time-consuming. To expedite the discovery of novel substrates, computational approaches based on kinase recognition sequence (motifs) from known substrates, protein structure, interaction and co-localization have been proposed. However, rarely do these methods take into account the dynamic responses of signaling cascades measured from in vivo cellular systems. Given that recent advances in mass spectrometry-based technologies make it possible to quantify phosphorylation on a proteome-wide scale, computational approaches that can integrate static features with dynamic phosphoproteome data would greatly facilitate the prediction of biologically relevant kinase-specific substrates.'),
(79514, 2016, 'bio', 'APTANI: a computational tool to select aptamers through sequence-structure motif analysis of HT-SELEX data', 'Caroli, J|Taccioli, C|De La Fuente, A|Serafini, P|Bicciato, S', 'Center for Genome Research, Department of Life Sciences, University of Modena and Reggio Emilia, Modena, Italy and.|Center for Genome Research, Department of Life Sciences, University of Modena and Reggio Emilia, Modena, Italy and.|Department of Microbiology & Immunology, UM/Sylvester Comprehensive Cancer Center, Leonard M. Miller School of Medicine, University of Miami, Miami, FL 33136, USA.|Department of Microbiology & Immunology, UM/Sylvester Comprehensive Cancer Center, Leonard M. Miller School of Medicine, University of Miami, Miami, FL 33136, USA.|Center for Genome Research, Department of Life Sciences, University of Modena and Reggio Emilia, Modena, Italy and.', 'Italy|Italy|United States|United States|Italy', 28, 'Algorithms|Aptamers, Nucleotide|High-Throughput Nucleotide Sequencing|Nucleotide Motifs|SELEX Aptamer Technique|Software', 'Journal Article', '10.1093/bioinformatics/btv545', '26395772', 'Aptamers are synthetic nucleic acid molecules that can bind biological targets in virtue of both their sequence and three-dimensional structure. Aptamers are selected using SELEX, Systematic Evolution of Ligands by EXponential enrichment, a technique that exploits aptamer-target binding affinity. The SELEX procedure, coupled with high-throughput sequencing (HT-SELEX), creates billions of random sequences capable of binding different epitopes on specific targets. Since this technique produces enormous amounts of data, computational analysis represents a critical step to screen and select the most biologically relevant sequences.'),
(79515, 2016, 'bio', 'Approximately independent linkage disequilibrium blocks in human populations', 'Berisa, Tomaz|Pickrell, Joseph K', 'New York Genome Center, New York, NY 10013, USA and.|New York Genome Center, New York, NY 10013, USA and Department of Biological Sciences, Columbia University, New York, NY 10027, USA.', 'United States|United States', 69, 'Algorithms|Chromosome Mapping|Genetic Markers|Genome, Human|Genome-Wide Association Study|Humans|Linkage Disequilibrium|Software', 'Journal Article', '10.1093/bioinformatics/btv546', '26395773', 'We present a method to identify approximately independent blocks of linkage disequilibrium in the human genome. These blocks enable automated analysis of multiple genome-wide association studies.'),
(79516, 2016, 'bio', 'PathwaysWeb: a gene pathways API with directional interactions, expanded gene ontology, and versioning', 'Melott, James M|Weinstein, John N|Broom, Bradley M', 'Department of Bioinformatics and Computational Biology and.|Department of Bioinformatics and Computational Biology and Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.|Department of Bioinformatics and Computational Biology and.', 'United States|United States|United States', 3, 'Algorithms|Animals|Computational Biology|Databases, Factual|Gene Ontology|Gene Regulatory Networks|Humans|Information Storage and Retrieval|Internet|Mice|Protein Interaction Maps|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btv554', '26400039', 'PathwaysWeb is a resource-based, well-documented web system that provides publicly available information on genes, biological pathways, Gene Ontology (GO) terms, gene-gene interaction networks (importantly, with the directionality of interactions) and links to key-related PubMed documents. The PathwaysWeb API simplifies the construction of applications that need to retrieve and interrelate information across multiple, pathway-related data types from a variety of original data sources. PathwaysBrowser is a companion website that enables users to explore the same integrated pathway data. The PathwaysWeb system facilitates reproducible analyses by providing access to all versions of the integrated datasets. Although its GO subsystem includes data for mouse, PathwaysWeb currently focuses on human data. However, pathways for mouse and many other species can be inferred with a high success rate from human pathways.'),
(79517, 2016, 'bio', 'Xtalk: a path-based approach for identifying crosstalk between signaling pathways', 'Tegge, Allison N|Sharp, Nicholas|Murali, T M', 'Department of Computer Science, Department of Statistics and.|Department of Computer Science.|Department of Computer Science, ICTAS Center for Systems Biology of Engineered Tissues, Virginia Tech, Blacksburg, VA 24061, USA.', 'United States|United States|United States', 0, 'Computational Biology|Databases, Factual|Humans|Models, Biological|Signal Transduction|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btv549', '26400040', 'Cells communicate with their environment via signal transduction pathways. On occasion, the activation of one pathway can produce an effect downstream of another pathway, a phenomenon known as crosstalk. Existing computational methods to discover such pathway pairs rely on simple overlap statistics.'),
(79518, 2016, 'bio', 'Identification of coupling DNA motif pairs on long-range chromatin interactions in human K562 cells', 'Wong, Ka-Chun|Li, Yue|Peng, Chengbin', 'Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong.|CSAIL, Massachusetts Institute of Technology, Cambridge, MA 02139-4307, USA and.|CEMSE Division, King Abdullah University of Science and Technology, Thuwal, Jeddah, Kingdom of Saudi Arabia.', 'Hong Kong|-|Saudi Arabia', 0, 'Binding Sites|Chromatin|Deoxyribonuclease I|Genomics|Humans|K562 Cells|Nucleotide Motifs|Promoter Regions, Genetic|Regulatory Elements, Transcriptional|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btv555', '26411866', 'The protein-DNA interactions between transcription factors (TFs) and transcription factor binding sites (TFBSs, also known as DNA motifs) are critical activities in gene transcription. The identification of the DNA motifs is a vital task for downstream analysis. Unfortunately, the long-range coupling information between different DNA motifs is still lacking. To fill the void, as the first-of-its-kind study, we have identified the coupling DNA motif pairs on long-range chromatin interactions in human.'),
(79519, 2016, 'bio', 'XLmap: an R package to visualize and score protein structure models based on sites of protein cross-linking', 'Schweppe, Devin K|Chavez, Juan D|Bruce, James E', 'Department of Genome Sciences, University of Washington, 850 Republican Ave, Seattle, WA 98109, USA.|Department of Genome Sciences, University of Washington, 850 Republican Ave, Seattle, WA 98109, USA.|Department of Genome Sciences, University of Washington, 850 Republican Ave, Seattle, WA 98109, USA.', 'United States|United States|United States', 0, 'Computer Simulation|Cross-Linking Reagents|Databases, Protein|Humans|Image Processing, Computer-Assisted|Mass Spectrometry|Protein Conformation|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btv519', '26411867', 'Chemical cross-linking with mass spectrometry (XL-MS) provides structural information for proteins and protein complexes in the form of crosslinked residue proximity and distance constraints between reactive residues. Utilizing spatial information derived from cross-linked residues can therefore assist with structural modeling of proteins. Selection of computationally derived model structures of proteins remains a major challenge in structural biology. The comparison of site interactions resulting from XL-MS with protein structure contact maps can assist the selection of structural models.'),
(79520, 2016, 'bio', 'MDD-SOH: exploiting maximal dependence decomposition to identify S-sulfenylation sites with substrate motifs', 'Bui, Van-Minh|Lu, Cheng-Tsung|Ho, Thi-Trang|Lee, Tzong-Yi', 'Department of Computer Science and Engineering and.|Department of Computer Science and Engineering and.|Department of Computer Science and Engineering and.|Department of Computer Science and Engineering and Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan 320, Taiwan.', '-|-|-|-', 24, 'Amino Acid Motifs|Amino Acids|Cysteine|Humans|Protein Processing, Post-Translational|Proteins|Sequence Analysis, Protein|Software|Sulfur Acids|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/btv558', '26411868', 'S-sulfenylation (S-sulphenylation, or sulfenic acid), the covalent attachment of S-hydroxyl (-SOH) to cysteine thiol, plays a significant role in redox regulation of protein functions. Although sulfenic acid is transient and labile, most of its physiological activities occur under control of S-hydroxylation. Therefore, discriminating the substrate site of S-sulfenylated proteins is an essential task in computational biology for the furtherance of protein structures and functions. Research into S-sulfenylated protein is currently very limited, and no dedicated tools are available for the computational identification of SOH sites. Given a total of 1096 experimentally verified S-sulfenylated proteins from humans, this study carries out a bioinformatics investigation on SOH sites based on amino acid composition and solvent-accessible surface area. A TwoSampleLogo indicates that the positively and negatively charged amino acids flanking the SOH sites may impact the formulation of S-sulfenylation in closed three-dimensional environments. In addition, the substrate motifs of SOH sites are studied using the maximal dependence decomposition (MDD). Based on the concept of binary classification between SOH and non-SOH sites, Support vector machine (SVM) is applied to learn the predictive model from MDD-identified substrate motifs. According to the evaluation results of 5-fold cross-validation, the integrated SVM model learned from substrate motifs yields an average accuracy of 0.87, significantly improving the prediction of SOH sites. Furthermore, the integrated SVM model also effectively improves the predictive performance in an independent testing set. Finally, the integrated SVM model is applied to implement an effective web resource, named MDD-SOH, to identify SOH sites with their corresponding substrate motifs.'),
(79521, 2016, 'bio', 'CLUSTERnGO: a user-defined modelling platform for two-stage clustering of time-series data', 'Fidaner, Ixc5x9fxc4xb1k Barxc4xb1xc5x9f|Cankorur-Cetinkaya, Ayca|Dikicioglu, Duygu|Kirdar, Betul|Cemgil, Ali Taylan|Oliver, Stephen G', 'Department of Computer Engineering.|Department of Chemical Engineering, Bogazici University, Istanbul, Turkey and Cambridge Systems Biology Centre and Department of Biochemistry, University of Cambridge, Cambridge, UK.|Department of Chemical Engineering, Bogazici University, Istanbul, Turkey and Cambridge Systems Biology Centre and Department of Biochemistry, University of Cambridge, Cambridge, UK.|Department of Chemical Engineering, Bogazici University, Istanbul, Turkey and.|Department of Computer Engineering.|Cambridge Systems Biology Centre and Department of Biochemistry, University of Cambridge, Cambridge, UK.', 'United Kingdom|Turkey|Turkey|Turkey|United Kingdom|United Kingdom', 4, 'Algorithms|Bayes Theorem|Cluster Analysis|Gene Expression Profiling|Models, Statistical|Software', 'Journal Article', '10.1093/bioinformatics/btv532', '26411869', 'Simple bioinformatic tools are frequently used to analyse time-series datasets regardless of their ability to deal with transient phenomena, limiting the meaningful information that may be extracted from them. This situation requires the development and exploitation of tailor-made, easy-to-use and flexible tools designed specifically for the analysis of time-series datasets.'),
(79522, 2016, 'bio', 'JBASE: Joint Bayesian Analysis of Subphenotypes and Epistasis', 'Colak, Recep|Kim, TaeHyung|Kazan, Hilal|Oh, Yoomi|Cruz, Miguel|Valladares-Salgado, Adan|Peralta, Jesus|Escobedo, Jorge|Parra, Esteban J|Kim, Philip M|Goldenberg, Anna', 'Department of Computer Science, University of Toronto, M5S 2E4, Toronto, ON, Canada, Donnelly Centre for Cellular & Biomolecular Research, University of Toronto, M5S 3E1, Toronto, ON, Canada.|Department of Computer Science, University of Toronto, M5S 2E4, Toronto, ON, Canada, Department of Computer Engineering, Antalya International University, 07190, Antalya, Turkey.|Department of Computer Engineering, Antalya International University, 07190, Antalya, Turkey.|Donnelly Centre for Cellular & Biomolecular Research, University of Toronto, M5S 3E1, Toronto, ON, Canada, Department of Molecular Genetics, University of Toronto, M5S 1A8, Toronto, ON, Canada.|Unidad de Investigacixc3xb3n Mxc3xa9dica en Bioquxc3xadmica, Hospital de Especialidades, IMSS, 06720, Mexico City, Mexico.|Unidad de Investigacixc3xb3n Mxc3xa9dica en Bioquxc3xadmica, Hospital de Especialidades, IMSS, 06720, Mexico City, Mexico.|Unidad de Investigacixc3xb3n Mxc3xa9dica en Bioquxc3xadmica, Hospital de Especialidades, IMSS, 06720, Mexico City, Mexico.|Unidad de Investigacixc3xb3n en Epidemiologxc3xada Clxc3xadnica, Instituto Mexicano del Seguro Social, Mexico City, Mexico.|Department of Anthropology, University of Toronto, L5L 1C6, Mississauga, ON, Canada.|Donnelly Centre for Cellular & Biomolecular Research, University of Toronto, M5S 3E1, Toronto, ON, Canada, Department of Molecular Genetics, University of Toronto, M5S 1A8, Toronto, ON, Canada, Genetics and Genome Biology, Hospital for Sick Children, M5G 0A4, Toronto, ON, Canada and Banting and Best Department of Medical Research, University of Toronto, M5G 1L6, Toronto, ON, Canada.|Department of Computer Science, University of Toronto, M5S 2E4, Toronto, ON, Canada, Genetics and Genome Biology, Hospital for Sick Children, M5G 0A4, Toronto, ON, Canada and.', 'Canada|Canada|Turkey|Canada|Mexico|Mexico|Mexico|Mexico|Canada|Canada|Canada', 0, 'Algorithms|Bayes Theorem|Body Mass Index|Diabetes Mellitus, Type 2|Epistasis, Genetic|Genome-Wide Association Study|Genotype|Genotyping Techniques|Humans|Mexico|Phenotype|Waist-Hip Ratio', 'Journal Article', '10.1093/bioinformatics/btv504', '26411870', 'Rapid advances in genotyping and genome-wide association studies have enabled the discovery of many new genotype-phenotype associations at the resolution of individual markers. However, these associations explain only a small proportion of theoretically estimated heritability of most diseases. In this work, we propose an integrative mixture model called JBASE: joint Bayesian analysis of subphenotypes and epistasis. JBASE explores two major reasons of missing heritability: interactions between genetic variants, a phenomenon known as epistasis and phenotypic heterogeneity, addressed via subphenotyping.'),
(79523, 2016, 'bio', 'Cytoscapejs: a graph theory library for visualisation and analysis', 'Franz, Max|Lopes, Christian T|Huck, Gerardo|Dong, Yue|Sumer, Onur|Bader, Gary D', 'The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada.|The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada.|The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada.|The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada.|The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada.|The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada', 184, 'Computational Biology|Computer Graphics|Humans|Programming Languages|Protein Interaction Maps|Software|Web Browser', 'Journal Article', '10.1093/bioinformatics/btv557', '26415722', 'Cytoscape.js is an open-source JavaScript-based graph library. Its most common use case is as a visualization software component, so it can be used to render interactive graphs in a web browser. It also can be used in a headless manner, useful for graph operations on a server, such as Node.js.'),
(79524, 2016, 'bio', 'ScreenBEAM: a novel meta-analysis algorithm for functional genomics screens via Bayesian hierarchical modeling', 'Yu, Jiyang|Silva, Jose|Califano, Andrea', 'Department of Biomedical Informatics, Department of Systems Biology, Center for Computational Biology and Bioinformatics, Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY 10032, USA and.|Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.|Department of Biomedical Informatics, Department of Systems Biology, Center for Computational Biology and Bioinformatics, Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY 10032, USA and.', 'United States|United States|United States', 0, 'Algorithms|Bayes Theorem|Clustered Regularly Interspaced Short Palindromic Repeats|Genomics|HL-60 Cells|High-Throughput Nucleotide Sequencing|Humans|Meta-Analysis as Topic|Models, Statistical|RNA Interference', 'Journal Article', '10.1093/bioinformatics/btv556', '26415723', 'Functional genomics (FG) screens, using RNAi or CRISPR technology, have become a standard tool for systematic, genome-wide loss-of-function studies for therapeutic target discovery. As in many large-scale assays, however, off-target effects, variable reagents potency and experimental noise must be accounted for appropriately control for false positives. Indeed, rigorous statistical analysis of high-throughput FG screening data remains challenging, particularly when integrative analyses are used to combine multiple sh/sgRNAs targeting the same gene in the library.'),
(79525, 2016, 'bio', 'Drug-set enrichment analysis: a novel tool to investigate drug mode of action', 'Napolitano, Francesco|Sirci, Francesco|Carrella, Diego|di Bernardo, Diego', 'Systems and Synthetic Biology Lab, Telethon Institute of Genetics and Medicine (TIGEM), 80078 Pozzuoli (NA), Italy and.|Systems and Synthetic Biology Lab, Telethon Institute of Genetics and Medicine (TIGEM), 80078 Pozzuoli (NA), Italy and.|Systems and Synthetic Biology Lab, Telethon Institute of Genetics and Medicine (TIGEM), 80078 Pozzuoli (NA), Italy and.|Systems and Synthetic Biology Lab, Telethon Institute of Genetics and Medicine (TIGEM), 80078 Pozzuoli (NA), Italy and Department of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Naples, Italy.', 'Italy|Italy|Italy|Italy', 28, 'Cystic Fibrosis|Cystic Fibrosis Transmembrane Conductance Regulator|Gene Expression Regulation|Humans|Mutation|Phenotype|Small Molecule Libraries|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btv536', '26415724', 'Automated screening approaches are able to rapidly identify a set of small molecules inducing a desired phenotype from large small-molecule libraries. However, the resulting set of candidate molecules is usually very diverse pharmacologically, thus little insight on the shared mechanism of action (MoA) underlying their efficacy can be gained.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(79526, 2016, 'bio', 'XPIWIT--an XML pipeline wrapper for the Insight Toolkit', 'Bartschat, Andreas|Hübner, Eduard|Reischl, Markus|Mikut, Ralf|Stegmaier, Johannes', 'Institute for Applied Computer Science (IAI), Karlsruhe Institute of Technology, Germany.|Institute for Applied Computer Science (IAI), Karlsruhe Institute of Technology, Germany.|Institute for Applied Computer Science (IAI), Karlsruhe Institute of Technology, Germany.|Institute for Applied Computer Science (IAI), Karlsruhe Institute of Technology, Germany.|Institute for Applied Computer Science (IAI), Karlsruhe Institute of Technology, Germany.', 'Germany|Germany|Germany|Germany|Germany', 0, 'Animals|Computational Biology|Embryo, Nonmammalian|Image Processing, Computer-Assisted|Imaging, Three-Dimensional|Software|Zebrafish', 'Journal Article', '10.1093/bioinformatics/btv559', '26415725', 'The Insight Toolkit offers plenty of features for multidimensional image analysis. Current implementations, however, often suffer either from a lack of flexibility due to hard-coded C++ pipelines for a certain task or by slow execution times, e.g. caused by inefficient implementations or multiple read/write operations for separate filter execution. We present an XML-based wrapper application for the Insight Toolkit that combines the performance of a pure C++ implementation with an easy-to-use graphical setup of dynamic image analysis pipelines. Created XML pipelines can be interpreted and executed by XPIWIT in console mode either locally or on large clusters. We successfully applied the software tool for the automated analysis of terabyte-scale, time-resolved 3D image data of zebrafish embryos.'),
(79527, 2016, 'bio', 'Computational probing protein-protein interactions targeting small molecules', 'Wang, Yong-Cui|Chen, Shi-Long|Deng, Nai-Yang|Wang, Yong', 'Key Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining 810001, China.|Key Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining 810001, China.|College of Science, China Agricultural University, Beijing 100083, China and.|National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China.', 'China|China|China|China', 0, 'Algorithms|Drug Discovery|Humans|Pharmaceutical Preparations|Protein Interaction Mapping|Proteins|Sequence Analysis, Protein|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/btv528', '26415726', 'With the booming of interactome studies, a lot of interactions can be measured in a high throughput way and large scale datasets are available. It is becoming apparent that many different types of interactions can be potential drug targets. Compared with inhibition of a single protein, inhibition of protein-protein interaction (PPI) is promising to improve the specificity with fewer adverse side-effects. Also it greatly broadens the drug target search space, which makes the drug target discovery difficult. Computational methods are highly desired to efficiently provide candidates for further experiments and hold the promise to greatly accelerate the discovery of novel drug targets.'),
(79528, 2016, 'bio', 'missMethyl: an R package for analyzing data from Illuminas HumanMethylation450 platform', 'Phipson, Belinda|Maksimovic, Jovana|Oshlack, Alicia', 'Bioinformatics Group, Murdoch Childrens Research Institute, Royal Childrens Hospital, 50 Flemington Road, Parkville, Victoria, 3052, Australia.|Bioinformatics Group, Murdoch Childrens Research Institute, Royal Childrens Hospital, 50 Flemington Road, Parkville, Victoria, 3052, Australia.|Bioinformatics Group, Murdoch Childrens Research Institute, Royal Childrens Hospital, 50 Flemington Road, Parkville, Victoria, 3052, Australia.', 'Australia|Australia|Australia', 134, 'Computational Biology|CpG Islands|DNA Methylation|Genome, Human|Humans|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btv560', '26424855', 'DNA methylation is one of the most commonly studied epigenetic modifications due to its role in both disease and development. The Illumina HumanMethylation450 BeadChip is a cost-effective way to profile >450xe2x80x89000 CpGs across the human genome, making it a popular platform for profiling DNA methylation. Here we introduce missMethyl, an R package with a suite of tools for performing normalization, removal of unwanted variation in differential methylation analysis, differential variability testing and gene set analysis for the 450K array.'),
(79529, 2016, 'bio', 'smallWig: parallel compression of RNA-seq WIG files', 'Wang, Zhiying|Weissman, Tsachy|Milenkovic, Olgica', 'Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA and.|Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA and.|Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA and.', '-|-|-', 4, 'Algorithms|Data Compression|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btv561', '26424856', 'We developed a new lossless compression method for WIG data, named smallWig, offering the best known compression rates for RNA-seq data and featuring random access functionalities that enable visualization, summary statistics analysis and fast queries from the compressed files. Our approach results in order of magnitude improvements compared with bigWig and ensures compression rates only a fraction of those produced by cWig. The key features of the smallWig algorithm are statistical data analysis and a combination of source coding methods that ensure high flexibility and make the algorithm suitable for different applications. Furthermore, for general-purpose file compression, the compression rate of smallWig approaches the empirical entropy of the tested WIG data. For compression with random query features, smallWig uses a simple block-based compression scheme that introduces only a minor overhead in the compression rate. For archival or storage space-sensitive applications, the method relies on context mixing techniques that lead to further improvements of the compression rate. Implementations of smallWig can be executed in parallel on different sets of chromosomes using multiple processors, thereby enabling desirable scaling for future transcriptome Big Data platforms.'),
(79530, 2016, 'bio', 'ANARCI: antigen receptor numbering and receptor classification', 'Dunbar, James|Deane, Charlotte M', 'Department of Statistics, Oxford University, Oxford, UK.|Department of Statistics, Oxford University, Oxford, UK.', 'United Kingdom|United Kingdom', 0, 'Antibodies|Computational Biology|Humans|Immunoglobulin Variable Region|Models, Molecular|Receptors, Antigen, T-Cell|Software', 'Journal Article', '10.1093/bioinformatics/btv552', '26424857', 'Antibody amino-acid sequences can be numbered to identify equivalent positions. Such annotations are valuable for antibody sequence comparison, protein structure modelling and engineering. Multiple different numbering schemes exist, they vary in the nomenclature they use to annotate residue positions, their definitions of position equivalence and their popularity within different scientific disciplines. However, currently no publicly available software exists that can apply all the most widely used schemes or for which an executable can be obtained under an open license.'),
(79531, 2016, 'bio', 'regioneR: an R/Bioconductor package for the association analysis of genomic regions based on permutation tests', 'Gel, Bernat|Díez-Villanueva, Anna|Serra, Eduard|Buschbeck, Marcus|Peinado, Miguel A|Malinverni, Roberto', 'Institute of Predictive and Personalized Medicine of Cancer (IMPPC), Campus Can Ruti, Badalona, Spain and.|Institute of Predictive and Personalized Medicine of Cancer (IMPPC), Campus Can Ruti, Badalona, Spain and.|Institute of Predictive and Personalized Medicine of Cancer (IMPPC), Campus Can Ruti, Badalona, Spain and.|Institute of Predictive and Personalized Medicine of Cancer (IMPPC), Campus Can Ruti, Badalona, Spain and Josep Carreras Institute for Leukaemia Research (IJC), Campus ICO-HGTP, Campus Can Ruti, Badalona, Spain.|Institute of Predictive and Personalized Medicine of Cancer (IMPPC), Campus Can Ruti, Badalona, Spain and.|Institute of Predictive and Personalized Medicine of Cancer (IMPPC), Campus Can Ruti, Badalona, Spain and Institute of Predictive and Personalized Medicine of Cancer (IMPPC), Campus Can Ruti, Badalona, Spain and.', 'Spain|Spain|Spain|Spain|Spain|Spain', 98, 'Genetic Variation|Genome, Human|Genomics|Humans|Molecular Sequence Annotation|Programming Languages|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btv562', '26424858', 'Statistically assessing the relation between a set of genomic regions and other genomic features is a common challenging task in genomic and epigenomic analyses. Randomization based approaches implicitly take into account the complexity of the genome without the need of assuming an underlying statistical model.'),
(79532, 2016, 'bio', 'Warpgroup: increased precision of metabolomic data processing by consensus integration bound analysis', 'Mahieu, Nathaniel G|Spalding, Jonathan L|Patti, Gary J', 'Department of Chemistry, Washington University, St Louis, MO 63130, USA, Department of Medicine and.|Department of Chemistry, Washington University, St Louis, MO 63130, USA, Department of Genetics, Washington University School of Medicine, St Louis, MO 63110, USA.|Department of Chemistry, Washington University, St Louis, MO 63130, USA, Department of Medicine and.', 'United States|United States|United States', 18, 'Algorithms|Chromatography, Liquid|Consensus|Data Interpretation, Statistical|Databases, Chemical|Electronic Data Processing|Humans|Mass Spectrometry|Metabolomics', 'Journal Article', '10.1093/bioinformatics/btv564', '26424859', 'Current informatic techniques for processing raw chromatography/mass spectrometry data break down under several common, non-ideal conditions. Importantly, hydrophilic liquid interaction chromatography (a key separation technology for metabolomics) produces data which are especially challenging to process. We identify three critical points of failure in current informatic workflows: compound specific drift, integration region variance, and naive missing value imputation. We implement the Warpgroup algorithm to address these challenges.'),
(79533, 2016, 'bio', 'Pseudoknots in RNA folding landscapes', 'Kucharík, Marcel|Hofacker, Ivo L|Stadler, Peter F|Qin, Jing', 'Institute for Theoretical Chemistry.|Institute for Theoretical Chemistry, Research Group BCB, Faculty of Computer Science, University of Vienna, Austria, RTH, University of Copenhagen, Frederiksberg, Denmark.|Institute for Theoretical Chemistry, RTH, University of Copenhagen, Frederiksberg, Denmark, Department of Computer Science & IZBI & iDiv & LIFE, Leipzig University, Max Planck Institute for Mathematics in the Sciences, Fraunhofer Institute IZI, Leipzig, Germany, Santa Fe Institute, Santa Fe, NM 87501, USA and.|Institute for Theoretical Chemistry, RTH, University of Copenhagen, Frederiksberg, Denmark, IMADA, University of Southern Denmark, Campusvej 55, Odense, Denmark.', '-|Austria|Germany|Denmark', 0, 'Algorithms|Kinetics|Nucleic Acid Conformation|RNA|RNA Folding', 'Journal Article', '10.1093/bioinformatics/btv572', '26428288', 'The function of an RNA molecule is not only linked to its native structure, which is usually taken to be the ground state of its folding landscape, but also in many cases crucially depends on the details of the folding pathways such as stable folding intermediates or the timing of the folding process itself. To model and understand these processes, it is necessary to go beyond ground state structures. The study of rugged RNA folding landscapes holds the key to answer these questions. Efficient coarse-graining methods are required to reduce the intractably vast energy landscapes into condensed representations such as barrier trees or basin hopping graphs : BHG) that convey an approximate but comprehensive picture of the folding kinetics. So far, exact and heuristic coarse-graining methods have been mostly restricted to the pseudoknot-free secondary structures. Pseudoknots, which are common motifs and have been repeatedly hypothesized to play an important role in guiding folding trajectories, were usually excluded.'),
(79534, 2016, 'bio', 'Generalized empirical Bayesian methods for discovery of differential data in high-throughput biology', 'Hardcastle, Thomas J', 'Department of Plant Sciences, University of Cambridge, Cambridge CB2 3EA, UK.', 'United Kingdom', 19, 'Algorithms|Animals|Bayes Theorem|Binomial Distribution|Gene Expression Profiling|Rats|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btv569', '26428289', 'High-throughput data are now commonplace in biological research. Rapidly changing technologies and application mean that novel methods for detecting differential behaviour that account for a large P, small n setting are required at an increasing rate. The development of such methods is, in general, being done on an ad hoc basis, requiring further development cycles and a lack of standardization between analyses.'),
(79535, 2016, 'bio', 'cl-dash: rapid configuration and deployment of Hadoop clusters for bioinformatics research in the cloud', 'Hodor, Paul|Chawla, Amandeep|Clark, Andrew|Neal, Lauren', 'Booz Allen Hamilton, Rockville, MD 20852, USA.|Booz Allen Hamilton, Rockville, MD 20852, USA.|Booz Allen Hamilton, Rockville, MD 20852, USA.|Booz Allen Hamilton, Rockville, MD 20852, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Biomedical Research|Computational Biology|Genomics|High-Throughput Nucleotide Sequencing|Humans|Information Storage and Retrieval|Programming Languages|Search Engine|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btv553', '26428290', ': One of the solutions proposed for addressing the challenge of the overwhelming abundance of genomic sequence and other biological data is the use of the Hadoop computing framework. Appropriate tools are needed to set up computational environments that facilitate research of novel bioinformatics methodology using Hadoop. Here, we present cl-dash, a complete starter kit for setting up such an environment. Configuring and deploying new Hadoop clusters can be done in minutes. Use of Amazon Web Services ensures no initial investment and minimal operation costs. Two sample bioinformatics applications help the researcher understand and learn the principles of implementing an algorithm using the MapReduce programming pattern.'),
(79536, 2016, 'bio', 'ALP & FALP: C++ libraries for pairwise local alignment E-values', 'Sheetlin, Sergey|Park, Yonil|Frith, Martin C|Spouge, John L', 'National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, MD 20894, USA and.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, MD 20894, USA and.|Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, Koto-ku, Tokyo 135-0064, Japan.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, MD 20894, USA and.', '-|-|Japan|-', 4, 'Computational Biology|DNA|Databases, Factual|Humans|Proteins|Sequence Alignment|Sequence Analysis, DNA|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btv575', '26428291', 'Pairwise local alignment is an indispensable tool for molecular biologists. In real time (i.e. in about 1 s), ALP (Ascending Ladder Program) calculates the E-values for protein-protein or DNA-DNA local alignments of random sequences, for arbitrary substitution score matrix, gap costs and letter abundances, and FALP (Frameshift Ascending Ladder Program) performs a similar task, although more slowly, for frameshifting DNA-protein alignments.'),
(79537, 2016, 'bio', 'Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data', 'Okonechnikov, Konstantin|Conesa, Ana|García-Alcalde, Fernando', 'Department of Molecular Biology, Max Planck Institute for Infection Biology, D-10117, Berlin, Germany.|Genomics of Gene Expression Lab, Centro de Investigación Príncipe Felipe, 46012, Valencia, Spain and Microbiology and Cell Science Department, Institute for Food and Agricultural Research, University of Florida at Gainesville, FL 32611-0700, USA.|Department of Molecular Biology, Max Planck Institute for Infection Biology, D-10117, Berlin, Germany.', 'Germany|Spain|Germany', 302, 'Algorithms|Genomics|High-Throughput Nucleotide Sequencing|Humans|Quality Control|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btv566', '26428292', 'Detection of random errors and systematic biases is a crucial step of a robust pipeline for processing high-throughput sequencing (HTS) data. Bioinformatics software tools capable of performing this task are available, either for general analysis of HTS data or targeted to a specific sequencing technology. However, most of the existing QC instruments only allow processing of one sample at a time.'),
(79538, 2016, 'bio', 'JEPEGMIX: gene-level joint analysis of functional SNPs in cosmopolitan cohorts', 'Lee, Donghyung|Williamson, Vernell S|Bigdeli, T Bernard|Riley, Brien P|Webb, Bradley T|Fanous, Ayman H|Kendler, Kenneth S|Vladimirov, Vladimir I|Bacanu, Silviu-Alin', 'Department of Psychiatry, Virginia Commonwealth University, Richmond, VA 23298, USA.|Department of Psychiatry, Virginia Commonwealth University, Richmond, VA 23298, USA.|Department of Psychiatry, Virginia Commonwealth University, Richmond, VA 23298, USA.|Department of Psychiatry, Virginia Commonwealth University, Richmond, VA 23298, USA.|Department of Psychiatry, Virginia Commonwealth University, Richmond, VA 23298, USA.|Department of Psychiatry, Virginia Commonwealth University, Richmond, VA 23298, USA.|Department of Psychiatry, Virginia Commonwealth University, Richmond, VA 23298, USA.|Department of Psychiatry, Virginia Commonwealth University, Richmond, VA 23298, USA.|Department of Psychiatry, Virginia Commonwealth University, Richmond, VA 23298, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Cohort Studies|Ethnic Groups|Genetic Testing|Genetics, Population|Genome-Wide Association Study|Genomics|Humans|Linkage Disequilibrium|Phenotype|Polymorphism, Single Nucleotide|Schizophrenia|Software', 'Journal Article', '10.1093/bioinformatics/btv567', '26428293', 'To increase detection power, gene level analysis methods are used to aggregate weak signals. To greatly increase computational efficiency, most methods use as input summary statistics from genome-wide association studies (GWAS). Subsequently, gene statistics are constructed using linkage disequilibrium (LD) patterns from a relevant reference panel. However, all methods, including our own Joint Effect on Phenotype of eQTL/functional single nucleotide polymorphisms (SNPs) associated with a Gene (JEPEG), assume homogeneous panels, e.g. European. However, this renders these tools unsuitable for the analysis of large cosmopolitan cohorts.'),
(79539, 2016, 'bio', 'Cell line name recognition in support of the identification of synthetic lethality in cancer from text', 'Kaewphan, Suwisa|Van Landeghem, Sofie|Ohta, Tomoko|Van de Peer, Yves|Ginter, Filip|Pyysalo, Sampo', 'Turku Centre for Computer Science (TUCS), 20520 Turku, Finland, Department of Information Technology, University of Turku, 20014, Finland, University of Turku Graduate School (UTUGS), University of Turku, 20014, Finland.|Department of Plant Systems Biology, VIB, Ghent 9000, Belgium, Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent 9052, Belgium.|Textimi, Tokyo, Japan.|Department of Plant Systems Biology, VIB, Ghent 9000, Belgium, Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent 9052, Belgium, Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium, Genomics Research Institute, University of Pretoria, Pretoria, South Africa and.|Department of Information Technology, University of Turku, 20014, Finland.|Department of Information Technology, University of Turku, 20014, Finland, Language Technology Lab (LTL), University of Cambridge, Cambridge CB3 9DA, United Kingdom.', 'Finland|Belgium|Japan|Belgium|Finland|Finland', 10, 'Artificial Intelligence|Cell Line|Computational Biology|Data Mining|Databases, Factual|Genes, Lethal|Humans|Information Storage and Retrieval|Machine Learning|Neoplasms|Publications|Semantics|Terminology as Topic', 'Journal Article', '10.1093/bioinformatics/btv570', '26428294', 'The recognition and normalization of cell line names in text is an important task in biomedical text mining research, facilitating for instance the identification of synthetically lethal genes from the literature. While several tools have previously been developed to address cell line recognition, it is unclear whether available systems can perform sufficiently well in realistic and broad-coverage applications such as extracting synthetically lethal genes from the cancer literature. In this study, we revisit the cell line name recognition task, evaluating both available systems and newly introduced methods on various resources to obtain a reliable tagger not tied to any specific subdomain. In support of this task, we introduce two text collections manually annotated for cell line names: the broad-coverage corpus Gellus and CLL, a focused target domain corpus.'),
(79540, 2016, 'bio', 'SIMToolbox: a MATLAB toolbox for structured illumination fluorescence microscopy', 'Kxc5x99xc3xadxc5xbeek, Pavel|Lukexc5xa1, Tomxc3xa1xc5xa1|Ovesnxc3xbd, Martin|Fliegel, Karel|Hagen, Guy M', 'First Faculty of Medicine, Charles University in Prague, Prague 12800, Czech Republic.|Department of Radioelectronics, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague 16627, Czech Republic, Laboratoire dOptique Biomxc3xa9dicale, xc3x89cole Polytechnique Fxc3xa9dxc3xa9rale de Lausanne, Lausanne CH-1015, Switzerland and.|First Faculty of Medicine, Charles University in Prague, Prague 12800, Czech Republic.|Department of Radioelectronics, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague 16627, Czech Republic.|UCCS center for the BioFrontiers Institute, University of Colorado at Colorado Springs, Colorado Springs, CO 80918, USA.', 'United States|Switzerland|United States|United States|United States', 0, 'Actin Cytoskeleton|Fluorescence|Hep G2 Cells|Humans|Image Processing, Computer-Assisted|Imaging, Three-Dimensional|Lighting|Microscopy, Fluorescence|Software', 'Journal Article', '10.1093/bioinformatics/btv576', '26446133', 'SIMToolbox is an open-source, modular set of functions for MATLAB equipped with a user-friendly graphical interface and designed for processing two-dimensional and three-dimensional data acquired by structured illumination microscopy (SIM). Both optical sectioning and super-resolution applications are supported. The software is also capable of maximum a posteriori probability image estimation (MAP-SIM), an alternative method for reconstruction of structured illumination images. MAP-SIM can potentially reduce reconstruction artifacts, which commonly occur due to refractive index mismatch within the sample and to imperfections in the illumination.'),
(79541, 2016, 'bio', 'Rust-Bio: a fast and safe bioinformatics library', 'Köster, Johannes', 'Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Harvard School of Public Health and Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, MA02215, Boston, USA.', 'United States', 0, 'Algorithms|Computational Biology|Humans|Programming Languages|Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btv573', '26446134', 'We present Rust-Bio, the first general purpose bioinformatics library for the innovative Rust programming language. Rust-Bio leverages the unique combination of speed, memory safety and high-level syntax offered by Rust to provide a fast and safe set of bioinformatics algorithms and data structures with a focus on sequence analysis.'),
(79542, 2016, 'bio', 'HEALER: homomorphic computation of ExAct Logistic rEgRession for secure rare disease variants analysis in GWAS', 'Wang, Shuang|Zhang, Yuchen|Dai, Wenrui|Lauter, Kristin|Kim, Miran|Tang, Yuzhe|Xiong, Hongkai|Jiang, Xiaoqian', 'Department of Biomedical Informatics, University of California, San Diego, CA 92093.|Department of Biomedical Informatics, University of California, San Diego, CA 92093, Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.|Department of Biomedical Informatics, University of California, San Diego, CA 92093, Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.|Microsoft Research, San Diego, CA 92122, USA.|Seoul National University, Seoul, 151-742, Republic of Korea and.|Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY 13244, USA.|Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.|Department of Biomedical Informatics, University of California, San Diego, CA 92093.', 'United States|China|China|United States|United States|United States|China|United States', 0, 'Algorithms|Genetic Privacy|Genetic Variation|Genome, Human|Genome-Wide Association Study|Humans|Mucocutaneous Lymph Node Syndrome|Rare Diseases', 'Evaluation Studies', '10.1093/bioinformatics/btv563', '26446135', 'Genome-wide association studies (GWAS) have been widely used in discovering the association between genotypes and phenotypes. Human genome data contain valuable but highly sensitive information. Unprotected disclosure of such information might put individuals privacy at risk. It is important to protect human genome data. Exact logistic regression is a bias-reduction method based on a penalized likelihood to discover rare variants that are associated with disease susceptibility. We propose the HEALER framework to facilitate secure rare variants analysis with a small sample size.'),
(79543, 2016, 'bio', 'The new protein topology graph library web server', 'Schäfer, Tim|Scheck, Andreas|Bruneß, Daniel|May, Patrick|Koch, Ina', 'Molecular Bioinformatics, Institute of Computer Science, Johann Wolfgang Goethe-University Frankfurt am Main, Robert-Mayer-Str. 11-15, 60325 Frankfurt am Main, Germany and.|Molecular Bioinformatics, Institute of Computer Science, Johann Wolfgang Goethe-University Frankfurt am Main, Robert-Mayer-Str. 11-15, 60325 Frankfurt am Main, Germany and.|Molecular Bioinformatics, Institute of Computer Science, Johann Wolfgang Goethe-University Frankfurt am Main, Robert-Mayer-Str. 11-15, 60325 Frankfurt am Main, Germany and.|Genome Analysis, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7, avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg.|Molecular Bioinformatics, Institute of Computer Science, Johann Wolfgang Goethe-University Frankfurt am Main, Robert-Mayer-Str. 11-15, 60325 Frankfurt am Main, Germany and.', 'Germany|Germany|Germany|Luxembourg|Germany', 0, 'Algorithms|Amino Acid Motifs|Computational Biology|Computer Graphics|Databases, Factual|Databases, Protein|Humans|Information Storage and Retrieval|Internet|Protein Structure, Secondary|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btv574', '26446136', 'We present a new, extended version of the Protein Topology Graph Library web server. The Protein Topology Graph Library describes the protein topology on the super-secondary structure level. It allows to compute and visualize protein ligand graphs and search for protein structural motifs. The new server features additional information on ligand binding to secondary structure elements, increased usability and an application programming interface (API) to retrieve data, allowing for an automated analysis of protein topology.'),
(79544, 2016, 'bio', 'HapFlow: visualizing haplotypes in sequencing data', 'Sullivan, Mitchell J|Bachmann, Nathan L|Timms, Peter|Polkinghorne, Adam', 'Faculty of Science, Health, Education and Engineering, University of the Sunshine Coast, Sippy Downs, Australia.|Faculty of Science, Health, Education and Engineering, University of the Sunshine Coast, Sippy Downs, Australia.|Faculty of Science, Health, Education and Engineering, University of the Sunshine Coast, Sippy Downs, Australia.|Faculty of Science, Health, Education and Engineering, University of the Sunshine Coast, Sippy Downs, Australia.', 'Australia|Australia|Australia|Australia', 0, 'Animals|Chlamydia|Chlamydia Infections|Computer Graphics|Genomics|Haplotypes|Phascolarctidae|Software|Urinary Tract Infections', 'Journal Article', '10.1093/bioinformatics/btv551', '26449930', 'HapFlow is a python application for visualizing haplotypes present in sequencing data. It identifies variant profiles present and reads and creates an abstract visual representation of these profiles to make haplotypes easier to identify.'),
(79545, 2016, 'bio', 'CpGFilter: model-based CpG probe filtering with replicates for epigenome-wide association studies', 'Chen, Jun|Just, Allan C|Schwartz, Joel|Hou, Lifang|Jafari, Nadereh|Sun, Zhifu|Kocher, Jean-Pierre A|Baccarelli, Andrea|Lin, Xihong', 'Division of Biomedical Statistics and Informatics and Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115.|Department of Environmental Health, Harvard School of Public Health, Boston, MA 02115.|Department of Environmental Health, Harvard School of Public Health, Boston, MA 02115.|Department of Preventive Medicine and the Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL 60208 and.|Center for Genetic Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60208, USA.|Division of Biomedical Statistics and Informatics and Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905.|Division of Biomedical Statistics and Informatics and Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905.|Department of Environmental Health, Harvard School of Public Health, Boston, MA 02115.|Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Computational Biology|CpG Islands|DNA Methylation|Epigenomics|Genome, Human|Genome-Wide Association Study|Humans|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btv577', '26449931', 'The development of the Infinium HumanMethylation450 BeadChip enables epigenome-wide association studies at a reduced cost. One observation of the 450K data is that many CpG sites the beadchip interrogates have very large measurement errors. Including these noisy CpGs will decrease the statistical power of detecting relevant associations due to multiple testing correction. We propose to use intra-class correlation coefficient (ICC), which characterizes the relative contribution of the biological variability to the total variability, to filter CpGs when technical replicates are available. We estimate the ICC based on a linear mixed effects model by pooling all the samples instead of using the technical replicates only. An ultra-fast algorithm has been developed to address the computational complexity and CpG filtering can be completed in minutes on a desktop computer for a 450K data set of over 1000 samples. Our method is very flexible and can accommodate any replicate design. Simulations and a real data application demonstrate that our whole-sample ICC method performs better than replicate-sample ICC or variance-based method.'),
(79546, 2016, 'bio', 'Jflow: a workflow management system for web applications', 'Mariette, Jérôme|Escudié, Frédéric|Bardou, Philippe|Nabihoudine, Ibouniyamine|Noirot, Céline|Trotard, Marie-Stéphane|Gaspin, Christine|Klopp, Christophe', 'Plate-forme Bio-informatique Genotoul, INRA, UR875 Mathmatiques et Informatique Appliques Toulouse, Castanet-Tolosan, France and.|Plate-forme Bio-informatique Genotoul, INRA, UR875 Mathmatiques et Informatique Appliques Toulouse, Castanet-Tolosan, France and.|Plate-forme SIGENAE, INRA, GenPhyse, Castanet-Tolosan Cedex, France.|Plate-forme Bio-informatique Genotoul, INRA, UR875 Mathmatiques et Informatique Appliques Toulouse, Castanet-Tolosan, France and.|Plate-forme Bio-informatique Genotoul, INRA, UR875 Mathmatiques et Informatique Appliques Toulouse, Castanet-Tolosan, France and.|Plate-forme Bio-informatique Genotoul, INRA, UR875 Mathmatiques et Informatique Appliques Toulouse, Castanet-Tolosan, France and.|Plate-forme Bio-informatique Genotoul, INRA, UR875 Mathmatiques et Informatique Appliques Toulouse, Castanet-Tolosan, France and.|Plate-forme Bio-informatique Genotoul, INRA, UR875 Mathmatiques et Informatique Appliques Toulouse, Castanet-Tolosan, France and Plate-forme SIGENAE, INRA, GenPhyse, Castanet-Tolosan Cedex, France.', 'France|France|France|France|France|France|France|France', 0, 'Computational Biology|Database Management Systems|Databases, Factual|Humans|Information Storage and Retrieval|Internet|Software|Workflow', 'Journal Article', '10.1093/bioinformatics/btv589', '26454273', 'Biologists produce large data sets and are in demand of rich and simple web portals in which they can upload and analyze their files. Providing such tools requires to mask the complexity induced by the needed High Performance Computing (HPC) environment. The connection between interface and computing infrastructure is usually specific to each portal. With Jflow, we introduce a Workflow Management System (WMS), composed of jQuery plug-ins which can easily be embedded in any web application and a Python library providing all requested features to setup, run and monitor workflows.'),
(79547, 2016, 'bio', 'FogLight: an efficient matrix-based approach to construct metabolic pathways by search space reduction', 'Khosraviani, Mehrshad|Saheb Zamani, Morteza|Bidkhori, Gholamreza', 'Department of Computer Engineering & IT, Amirkabir University of Technology, Tehran, Iran and.|Department of Computer Engineering & IT, Amirkabir University of Technology, Tehran, Iran and.|Department of Computer Engineering & IT, Amirkabir University of Technology, Tehran, Iran and.', '-|-|-', 9, 'Algorithms|Computational Biology|Humans|Metabolic Engineering|Metabolic Networks and Pathways|Models, Biological|Software', 'Journal Article', '10.1093/bioinformatics/btv578', '26454274', 'A fundamental computational problem in the area of metabolic engineering is finding metabolic pathways between a pair of source and target metabolites efficiently. We present an approach, namely FogLight, for searching metabolic networks utilizing Boolean (AND-OR) operations represented in matrix notation to efficiently reduce the search space. This enables the enumeration of all pathways between metabolites that are too distant for the application of brute-force methods.'),
(79548, 2016, 'bio', 'miTRATA: a web-based tool for microRNA Truncation and Tailing Analysis', 'Patel, Parth|Ramachandruni, S Deepthi|Kakrana, Atul|Nakano, Mayumi|Meyers, Blake C', 'Center for Bioinformatics and Computational Biology and Delaware Biotechnology Institute and.|Delaware Biotechnology Institute and.|Center for Bioinformatics and Computational Biology and Delaware Biotechnology Institute and.|Department of Plant and Soil Sciences, University of Delaware, Newark, DE 19711, USA.|Delaware Biotechnology Institute and Department of Plant and Soil Sciences, University of Delaware, Newark, DE 19711, USA.', 'United States|United States|United States|United States|United States', 9, 'Algorithms|Animals|Computational Biology|Internet|MicroRNAs|Plants|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btv583', '26454275', 'We describe miTRATA, the first web-based tool for microRNA Truncation and Tailing Analysis--the analysis of 3 modifications of microRNAs including the loss or gain of nucleotides relative to the canonical sequence. miTRATA is implemented in Python (version 3) and employs parallel processing modules to enhance its scalability when analyzing multiple small RNA (sRNA) sequencing datasets. It utilizes miRBase, currently version 21, as a source of known microRNAs for analysis. miTRATA notifies user(s) via email to download as well as visualize the results online. miTRATAs strengths lie in (i) its biologist-focused web interface, (ii) improved scalability via parallel processing and (iii) its uniqueness as a webtool to perform microRNA truncation and tailing analysis.'),
(79549, 2016, 'bio', 'Rapid and enhanced remote homology detection by cascading hidden Markov model searches in sequence space', 'Kaushik, Swati|Nair, Anu G|Mutt, Eshita|Subramanian, Hari Prasanna|Sowdhamini, Ramanathan', 'National Centre for Biological Sciences, Tata Institute of Fundamental Research, GKVK Campus, Bangalore 560065, India.|National Centre for Biological Sciences, Tata Institute of Fundamental Research, GKVK Campus, Bangalore 560065, India.|National Centre for Biological Sciences, Tata Institute of Fundamental Research, GKVK Campus, Bangalore 560065, India.|National Centre for Biological Sciences, Tata Institute of Fundamental Research, GKVK Campus, Bangalore 560065, India.|National Centre for Biological Sciences, Tata Institute of Fundamental Research, GKVK Campus, Bangalore 560065, India.', 'India|India|India|India|India', 4, 'Algorithms|Cluster Analysis|Databases, Protein|Markov Chains|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Journal Article', '10.1093/bioinformatics/btv538', '26454276', 'In the post-genomic era, automatic annotation of protein sequences using computational homology-based methods is highly desirable. However, often protein sequences diverge to an extent where detection of homology and automatic annotation transfer is not straightforward. Sophisticated approaches to detect such distant relationships are needed. We propose a new approach to identify deep evolutionary relationships of proteins to overcome shortcomings of the available methods.'),
(79550, 2016, 'bio', 'I-PV: a CIRCOS module for interactive protein sequence visualization', 'Tanyalcin, Ibrahim|Al Assaf, Carla|Gheldof, Alexander|Stouffs, Katrien|Lissens, Willy|Jansen, Anna C', 'Center for Medical Genetics, UZ Brussel, Brussels, Belgium, Neurogenetics Research Group, Vrije Universiteit Brussel, Brussels, Belgium.|Center for Human Genetics, KU Leuven and University Hospitals Leuven, 3000 Leuven, Belgium.|Center for Medical Genetics, UZ Brussel, Brussels, Belgium.|Center for Medical Genetics, UZ Brussel, Brussels, Belgium, Reproduction, Genetics and Regenerative Medicine, Vrije Universiteit Brussel, Brussels, Belgium and.|Center for Medical Genetics, UZ Brussel, Brussels, Belgium, Reproduction, Genetics and Regenerative Medicine, Vrije Universiteit Brussel, Brussels, Belgium and.|Pediatric Neurology Unit, Department of Pediatrics, UZ Brussel, Brussels, Belgium Neurogenetics Research Group, Vrije Universiteit Brussel, Brussels, Belgium.', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 4, 'Amino Acids|Animals|Computer Graphics|Databases, Protein|Humans|Internet|Mice|Polymorphism, Genetic|Proteins|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btv579', '26454277', 'Todays genome browsers and protein databanks supply vast amounts of information about proteins. The challenge is to concisely bring together this information in an interactive and easy to generate format.'),
(79551, 2016, 'bio', 'FALCON@home: a high-throughput protein structure prediction server based on remote homologue recognition', 'Wang, Chao|Zhang, Haicang|Zheng, Wei-Mou|Xu, Dong|Zhu, Jianwei|Wang, Bing|Ning, Kang|Sun, Shiwei|Li, Shuai Cheng|Bu, Dongbo', 'Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China, University of Chinese Academy of Sciences, Beijing, China.|Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China, University of Chinese Academy of Sciences, Beijing, China.|Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing, China.|Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, MO 65211, USA.|Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.|Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.|College of Life Science, Huazhong University of Science and Technology, Wuhan, China and.|Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.|Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong.|Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.', 'China|China|China|United States|China|China|China|China|Hong Kong|China', 0, 'Algorithms|Animals|Computational Biology|Databases, Protein|High-Throughput Screening Assays|Mice|Protein Conformation|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btv581', '26454278', 'The protein structure prediction approaches can be categorized into template-based modeling (including homology modeling and threading) and free modeling. However, the existing threading tools perform poorly on remote homologous proteins. Thus, improving fold recognition for remote homologous proteins remains a challenge. Besides, the proteome-wide structure prediction poses another challenge of increasing prediction throughput. In this study, we presented FALCON@home as a protein structure prediction server focusing on remote homologue identification. The design of FALCON@home is based on the observation that a structural template, especially for remote homologous proteins, consists of conserved regions interweaved with highly variable regions. The highly variable regions lead to vague alignments in threading approaches. Thus, FALCON@home first extracts conserved regions from each template and then aligns a query protein with conserved regions only rather than the full-length template directly. This helps avoid the vague alignments rooted in highly variable regions, improving remote homologue identification. We implemented FALCON@home using the Berkeley Open Infrastructure of Network Computing (BOINC) volunteer computing protocol. With computation power donated from over 20,000 volunteer CPUs, FALCON@home shows a throughput as high as processing of over 1000 proteins per day. In the Critical Assessment of protein Structure Prediction (CASP11), the FALCON@home-based prediction was ranked the 12th in the template-based modeling category. As an application, the structures of 880 mouse mitochondria proteins were predicted, which revealed the significant correlation between protein half-lives and protein structural factors.'),
(79552, 2016, 'bio', 'Fast and accurate non-sequential protein structure alignment using a new asymmetric linear sum assignment heuristic', 'Brown, Peter|Pullan, Wayne|Yang, Yuedong|Zhou, Yaoqi', 'School of ICT, Griffith University, Gold Coast, QLD 4222, Australia.|School of ICT, Griffith University, Gold Coast, QLD 4222, Australia.|Institute for Glycomics, Griffith University, Gold Coast, QLD 4222, Australia.|School of ICT, Griffith University, Gold Coast, QLD 4222, Australia Institute for Glycomics, Griffith University, Gold Coast, QLD 4222, Australia.', 'Australia|Australia|Australia|Australia', 12, 'Databases, Protein|Heuristics|Models, Molecular|Proteins|Sequence Alignment|Software|Structural Homology, Protein', 'Evaluation Studies', '10.1093/bioinformatics/btv580', '26454279', 'The three dimensional tertiary structure of a protein at near atomic level resolution provides insight alluding to its function and evolution. As protein structure decides its functionality, similarity in structure usually implies similarity in function. As such, structure alignment techniques are often useful in the classifications of protein function. Given the rapidly growing rate of new, experimentally determined structures being made available from repositories such as the Protein Data Bank, fast and accurate computational structure comparison tools are required. This paper presents SPalignNS, a non-sequential protein structure alignment tool using a novel asymmetrical greedy search technique.'),
(79553, 2016, 'bio', 'SUPER-FOCUS: a tool for agile functional analysis of shotgun metagenomic data', 'Silva, Genivaldo Gueiros Z|Green, Kevin T|Dutilh, Bas E|Edwards, Robert A', 'Computational Science Research Center.|Department of Biology, San Diego State University, San Diego, CA 92182, USA.|Theoretical Biology and Bioinformatics, Utrecht University, 3584 CH, Utrecht, The Netherlands Centre for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Centre, 6525 GA, Nijmegen, The Netherlands Department of Marine Biology, Institute of Biology, Federal University of Rio de Janeiro, Brazil.|Computational Science Research Center Department of Biology, San Diego State University, San Diego, CA 92182, USA Department of Marine Biology, Institute of Biology, Federal University of Rio de Janeiro, Brazil Department of Computer Science, San Diego State University, San Diego, CA 92182, USA Division of Mathematics and Computer Science, Argonne National Laboratory, Argonne, IL 60439, USA.', 'United States|United States|Brazil|Brazil', 42, 'Coral Reefs|DNA|Metagenomics|Sequence Homology, Nucleic Acid|Software', 'Evaluation Studies', '10.1093/bioinformatics/btv584', '26454280', 'Analyzing the functional profile of a microbial community from unannotated shotgun sequencing reads is one of the important goals in metagenomics. Functional profiling has valuable applications in biological research because it identifies the abundances of the functional genes of the organisms present in the original sample, answering the question what they can do. Currently, available tools do not scale well with increasing data volumes, which is important because both the number and lengths of the reads produced by sequencing platforms keep increasing. Here, we introduce SUPER-FOCUS, SUbsystems Profile by databasE Reduction using FOCUS, an agile homology-based approach using a reduced reference database to report the subsystems present in metagenomic datasets and profile their abundances. SUPER-FOCUS was tested with over 70 real metagenomes, the results showing that it accurately predicts the subsystems present in the profiled microbial communities, and is up to 1000 times faster than other tools.'),
(79554, 2016, 'bio', 'CSSSCL: a python package that uses combined sequence similarity scores for accurate taxonomic classification of long and short sequence reads', 'Borozan, Ivan|Ferretti, Vincent', 'Informatics and Bio-computing, Ontario Institute for Cancer Research, MaRS Centre, 661 University Avenue, Suite 510, Toronto, Ontario, Canada.|Informatics and Bio-computing, Ontario Institute for Cancer Research, MaRS Centre, 661 University Avenue, Suite 510, Toronto, Ontario, Canada.', 'Canada|Canada', 0, 'Algorithms|Bacteria|Metagenomics|Models, Theoretical|Phylogeny|Sequence Alignment|Sequence Analysis, DNA|Software|Viruses', 'Journal Article', '10.1093/bioinformatics/btv587', '26454281', 'Sequence comparison of genetic material between known and unknown organisms plays a crucial role in genomics, metagenomics and phylogenetic analysis. The emerging long-read sequencing technologies can now produce reads of tens of kilobases in length that promise a more accurate assessment of their origin. To facilitate the classification of long and short DNA sequences, we have developed a Python package that implements a new sequence classification model that we have demonstrated to improve the classification accuracy when compared with other state of the art classification methods. For the purpose of validation, and to demonstrate its usefulness, we test the combined sequence similarity score classifier (CSSSCL) using three different datasets, including a metagenomic dataset composed of short reads.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(79555, 2016, 'bio', 'Automatic semantic classification of scientific literature according to the hallmarks of cancer', 'Baker, Simon|Silins, Ilona|Guo, Yufan|Ali, Imran|Hxc3xb6gberg, Johan|Stenius, Ulla|Korhonen, Anna', 'Computer Laboratory, University of Cambridge, Cambridge, UK.|Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden and.|Department of Theoretical and Applied Linguistics, University of Cambridge, Cambridge, UK.|Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden and.|Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden and.|Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden and.|Department of Theoretical and Applied Linguistics, University of Cambridge, Cambridge, UK.', 'United Kingdom|Sweden|United Kingdom|Sweden|Sweden|Sweden|United Kingdom', 14, 'Abstracting and Indexing as Topic|Algorithms|Biomedical Research|Computational Biology|Data Mining|Humans|Neoplasms|Periodicals as Topic|PubMed|Semantics|Software', 'Journal Article', '10.1093/bioinformatics/btv585', '26454282', 'The hallmarks of cancer have become highly influential in cancer research. They reduce the complexity of cancer into 10 principles (e.g. resisting cell death and sustaining proliferative signaling) that explain the biological capabilities acquired during the development of human tumors. Since new research depends crucially on existing knowledge, technology for semantic classification of scientific literature according to the hallmarks of cancer could greatly support literature review, knowledge discovery and applications in cancer research.'),
(79556, 2016, 'bio', 'Unified tests for fine-scale mapping and identifying sparse high-dimensional sequence associations', 'Cao, Shaolong|Qin, Huaizhen|Gossmann, Alexej|Deng, Hong-Wen|Wang, Yu-Ping', 'Department of Biomedical Engineering, Center for Bioinformatics and Genomics.|Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics and.|Center for Bioinformatics and Genomics, Department of Mathematics, Tulane University, New Orleans, LA, USA.|Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics and.|Department of Biomedical Engineering, Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics and.', 'United States|United States|United States|United States|United States', 6, 'Chromosome Mapping|Genetic Variation|Genome-Wide Association Study|Genomics|Genotyping Techniques|High-Throughput Nucleotide Sequencing|Humans|Linear Models|Osteoporosis|Phenotype|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btv586', '26458888', 'In searching for genetic variants for complex diseases with deep sequencing data, genomic marker sets of high-dimensional genotypic data and sparse functional variants are quite common. Existing sequence association tests are incapable of identifying such marker sets or individual causal loci, although they appeared powerful to identify small marker sets with dense functional variants. In sequence association studies of admixed individuals, cryptic relatedness and population structure are known to confound the association analyses.'),
(79557, 2016, 'bio', 'A multi-objective optimization approach accurately resolves protein domain architectures', 'Bernardes, J S|Vieira, F R J|Zaverucha, G|Carbone, A', 'Sorbonne Universités, UPMC Univ-Paris 6, CNRS, UMR 7238, Laboratoire de Biologie Computationnelle et Quantitative, 15 rue de lEcole de Médecine, 75006 Paris.|CNRS, UMR 7606, Laboratoire dInformatique de Paris 6, 75005 Paris, France and COPPE-UFRJ, Programa de Engenharia de Sistemas e Computação, Rio de Janeiro, Brazil.|COPPE-UFRJ, Programa de Engenharia de Sistemas e Computação, Rio de Janeiro, Brazil.|Sorbonne Universités, UPMC Univ-Paris 6, CNRS, UMR 7238, Laboratoire de Biologie Computationnelle et Quantitative, 15 rue de lEcole de Médecine, 75006 Paris, Institut Universitaire de France, 75005 Paris.', '-|Brazil|Brazil|France', 17, 'Algorithms|Genomics|Molecular Sequence Annotation|Plasmodium falciparum|Protein Structure, Tertiary|Protozoan Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btv582', '26458889', 'Given a protein sequence and a number of potential domains matching it, what are the domain content and the most likely domain architecture for the sequence? This problem is of fundamental importance in protein annotation, constituting one of the main steps of all predictive annotation strategies. On the other hand, when potential domains are several and in conflict because of overlapping domain boundaries, finding a solution for the problem might become difficult. An accurate prediction of the domain architecture of a multi-domain protein provides important information for function prediction, comparative genomics and molecular evolution.'),
(79558, 2016, 'bio', 'BiTrinA--multiscale binarization and trinarization with quality analysis', 'Müssel, Christoph|Schmid, Florian|Blätte, Tamara J|Hopfensitz, Martin|Lausser, Ludwig|Kestler, Hans A', 'Medical Systems Biology, Ulm University, 89069 Ulm, Germany.|Medical Systems Biology, Ulm University, 89069 Ulm, Germany.|Medical Systems Biology, Ulm University, 89069 Ulm, Germany, Section of Oncology, Internal Medicine III, Ulm University, 89069 Ulm, Germany and.|Medical Systems Biology, Ulm University, 89069 Ulm, Germany.|Leibniz Institute on Aging-Fritz Lipmann Institute, 07745 Jena, Germany.|Medical Systems Biology, Ulm University, 89069 Ulm, Germany, Leibniz Institute on Aging-Fritz Lipmann Institute, 07745 Jena, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 8, 'Algorithms|Animals|Biomarkers|Computer Simulation|Drosophila melanogaster|Gene Expression Profiling|Gene Expression Regulation, Developmental|Gene Regulatory Networks', 'Journal Article', '10.1093/bioinformatics/btv591', '26468003', 'When processing gene expression profiles or other biological data, it is often required to assign measurements to distinct categories (e.g. high and low and possibly intermediate). Subsequent analyses strongly depend on the results of this quantization. Poor quantization will have potentially misleading effects on further investigations. We propose the BiTrinA package that integrates different multiscale algorithms for binarization and for trinarization of one-dimensional data with methods for quality assessment and visualization of the results. By identifying measurements that show large variations over different time points or conditions, this quality assessment can determine candidates that are related to the specific experimental setting.'),
(79559, 2016, 'bio', '3DRobot: automated generation of diverse and well-packed protein structure decoys', 'Deng, Haiyou|Jia, Ya|Zhang, Yang', 'Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 45108, USA, Department of Physics and Institute of Biophysics, Central China Normal University, Wuhan 430079, China and.|Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 45108, USA.|Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 45108, USA, Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 45108, USA.', 'China|United States|United States', 42, 'Algorithms|Hydrogen Bonding|Protein Conformation|Protein Folding|Proteins', 'Journal Article', '10.1093/bioinformatics/btv601', '26471454', 'Computationally generated non-native protein structure conformations (or decoys) are often used for designing protein folding simulation methods and force fields. However, almost all the decoy sets currently used in literature suffer from uneven root mean square deviation (RMSD) distribution with bias to non-protein like hydrogen-bonding and compactness patterns. Meanwhile, most protein decoy sets are pre-calculated and there is a lack of methods for automated generation of high-quality decoys for any target proteins.'),
(79560, 2016, 'bio', 'A novel bi-level meta-analysis approach: applied to biological pathway analysis', 'Nguyen, Tin|Tagett, Rebecca|Donato, Michele|Mitrea, Cristina|Draghici, Sorin', 'Department of Computer Science and.|Department of Computer Science and.|Department of Computer Science and.|Department of Computer Science and.|Department of Computer Science and Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI 48202, USA.', 'United States|United States|United States|United States|United States', 13, 'Alzheimer Disease|Case-Control Studies|Computational Biology|Data Interpretation, Statistical|Diabetes Mellitus, Type 2|Gene Expression Profiling|Gene Regulatory Networks|Genome, Human|Genomics|Humans|Leukemia, Myeloid, Acute|Meta-Analysis as Topic|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btv588', '26471455', 'The accumulation of high-throughput data in public repositories creates a pressing need for integrative analysis of multiple datasets from independent experiments. However, study heterogeneity, study bias, outliers and the lack of power of available methods present real challenge in integrating genomic data. One practical drawback of many P-value-based meta-analysis methods, including Fishers, Stouffers, minP and maxP, is that they are sensitive to outliers. Another drawback is that, because they perform just one statistical test for each individual experiment, they may not fully exploit the potentially large number of samples within each study.'),
(79561, 2016, 'bio', 'Computational discovery of specificity-conferring sites in non-ribosomal peptide synthetases', 'Knudsen, Michael|Søndergaard, Dan|Tofting-Olesen, Claus|Hansen, Frederik T|Brodersen, Ditlev Egeskov|Pedersen, Christian N S', 'NANORIPES - Centre for Natural Non-Ribosomal Peptide Synthesis, Aarhus University, Aarhus, Denmark Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark.|NANORIPES - Centre for Natural Non-Ribosomal Peptide Synthesis, Aarhus University, Aarhus, Denmark Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark.|NANORIPES - Centre for Natural Non-Ribosomal Peptide Synthesis, Aarhus University, Aarhus, Denmark Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark.|NANORIPES - Centre for Natural Non-Ribosomal Peptide Synthesis, Aarhus University, Aarhus, Denmark Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark.|NANORIPES - Centre for Natural Non-Ribosomal Peptide Synthesis, Aarhus University, Aarhus, Denmark Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark.|NANORIPES - Centre for Natural Non-Ribosomal Peptide Synthesis, Aarhus University, Aarhus, Denmark Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark.', 'Denmark|Denmark|Denmark|Denmark|Denmark|Denmark', 14, 'Amino Acid Motifs|Bacteria|Computer Simulation|Fungi|Peptide Synthases|Sequence Analysis, Protein|Substrate Specificity', 'Journal Article', '10.1093/bioinformatics/btv600', '26471456', 'By using a class of large modular enzymes known as Non-Ribosomal Peptide Synthetases (NRPS), bacteria and fungi are capable of synthesizing a large variety of secondary metabolites, many of which are bioactive and have potential, pharmaceutical applications as e.g. antibiotics. There is thus an interest in predicting the compound synthesized by an NRPS from its primary structure (amino acid sequence) alone, as this would enable an in silico search of whole genomes for NRPS enzymes capable of synthesizing potentially useful compounds.'),
(79562, 2016, 'bio', 'BioSynther: a customized biosynthetic potential explorer', 'Tu, Weizhong|Zhang, Haoran|Liu, Juan|Hu, Qian-Nan', 'Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Wuhan University), Ministry of Education, and Wuhan University School of Pharmaceutical Sciences, Wuhan 430071, Peoples Republic of China.|State Key Laboratory of Software Engineering (Wuhan University) and Wuhan University School of Computer Sciences, Wuhan 430072, Peoples Republic of China and.|State Key Laboratory of Software Engineering (Wuhan University) and Wuhan University School of Computer Sciences, Wuhan 430072, Peoples Republic of China and.|Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Wuhan University), Ministry of Education, and Wuhan University School of Pharmaceutical Sciences, Wuhan 430071, Peoples Republic of China, Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, Peoples Republic of China.', 'China|China|China|China', 11, 'Benzaldehydes|Biochemical Phenomena|Computer Graphics|Computer Simulation|Databases, Chemical|Humans|Metabolic Engineering|Metabolic Networks and Pathways|Software', 'Journal Article', '10.1093/bioinformatics/btv599', '26471457', 'One of the most promising applications of biosynthetic methods is to produce chemical products of high value from the ready-made chemicals. To explore the biosynthetic potentials of a chemical as a synthesis precursor, biosynthetic databases and related chemoinformatics tools are urgently needed. In the present work, a web-based tool, BioSynther, is developed to explore the biosynthetic potentials of precursor chemicals using BKM-react, Rhea, and more than 50,000 in-house RxnFinder reactions manually curated. BioSynther allows researchers to explore biosynthetic potentials, through so far known biochemical reactions, step by step interactively, which could be used as a useful tool in metabolic engineering and synthetic biology.'),
(79563, 2016, 'bio', 'GERV: a statistical method for generative evaluation of regulatory variants for transcription factor binding', 'Zeng, Haoyang|Hashimoto, Tatsunori|Kang, Daniel D|Gifford, David K', 'Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02142, USA and.|Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02142, USA and.|Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02142, USA and.|Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02142, USA and Department of Stem Cell and Regenerative Biology, Harvard University and Harvard Medical School, Cambridge, MA 02138, USA.', 'United States|United States|United States|United States', 18, 'Algorithms|Binding Sites|Chromatin Immunoprecipitation|Computational Biology|Genome, Human|Genome-Wide Association Study|High-Throughput Nucleotide Sequencing|Humans|Models, Statistical|Molecular Sequence Annotation|Polymorphism, Single Nucleotide|Protein Binding|Regulatory Sequences, Nucleic Acid|Transcription Factors', 'Evaluation Studies', '10.1093/bioinformatics/btv565', '26476779', 'The majority of disease-associated variants identified in genome-wide association studies reside in noncoding regions of the genome with regulatory roles. Thus being able to interpret the functional consequence of a variant is essential for identifying causal variants in the analysis of genome-wide association studies.'),
(79564, 2016, 'bio', 'Scalable clustering algorithms for continuous environmental flow cytometry', 'Hyrkas, Jeremy|Clayton, Sophie|Ribalet, Francois|Halperin, Daniel|Armbrust, E Virginia|Howe, Bill', 'Department of Computer Science and Engineering.|School of Oceanography and.|School of Oceanography and.|Department of Computer Science and Engineering, eScience Institute, University of Washington, Seattle, WA 98195, USA.|School of Oceanography and eScience Institute, University of Washington, Seattle, WA 98195, USA.|Department of Computer Science and Engineering, eScience Institute, University of Washington, Seattle, WA 98195, USA.', 'United States|United States|United States|United States|United States|United States', 6, 'Algorithms|Cluster Analysis|Computational Biology|Environment|Flow Cytometry|Humans|Models, Theoretical|Normal Distribution|Software', 'Journal Article', '10.1093/bioinformatics/btv594', '26476780', 'Recent technological innovations in flow cytometry now allow oceanographers to collect high-frequency flow cytometry data from particles in aquatic environments on a scale far surpassing conventional flow cytometers. The SeaFlow cytometer continuously profiles microbial phytoplankton populations across thousands of kilometers of the surface ocean. The data streams produced by instruments such as SeaFlow challenge the traditional sample-by-sample approach in cytometric analysis and highlight the need for scalable clustering algorithms to extract population information from these large-scale, high-frequency flow cytometers.'),
(79565, 2016, 'bio', 'A-DaGO-Fun: an adaptable Gene Ontology semantic similarity-based functional analysis tool', 'Mazandu, Gaston K|Chimusa, Emile R|Mbiyavanga, Mamana|Mulder, Nicola J', 'Computational Biology Group, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa and African Institute for Mathematical Sciences (AIMS), Cape Town, South Africa and Cape Coast, Ghana.|Computational Biology Group, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa and.|Computational Biology Group, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa and.|Computational Biology Group, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa and.', 'Ghana|South Africa|South Africa|South Africa', 16, 'Computational Biology|Databases, Factual|Gene Ontology|Genes|Humans|Molecular Sequence Annotation|Proteins|Semantics|Software', 'Journal Article', '10.1093/bioinformatics/btv590', '26476781', 'Gene Ontology (GO) semantic similarity measures are being used for biological knowledge discovery based on GO annotations by integrating biological information contained in the GO structure into data analyses. To empower users to quickly compute, manipulate and explore these measures, we introduce A-DaGO-Fun (ADaptable Gene Ontology semantic similarity-based Functional analysis). It is a portable software package integrating all known GO information content-based semantic similarity measures and relevant biological applications associated with these measures. A-DaGO-Fun has the advantage not only of handling datasets from the current high-throughput genome-wide applications, but also allowing users to choose the most relevant semantic similarity approach for their biological applications and to adapt a given module to their needs.'),
(79566, 2016, 'bio', 'iEnhancer-2L: a two-layer predictor for identifying enhancers and their strength by pseudo k-tuple nucleotide composition', 'Liu, Bin|Fang, Longyun|Long, Ren|Lan, Xun|Chou, Kuo-Chen', 'School of Computer Science and Technology, Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China, Computational Biology, Gordon Life Science Institute, Belmont, MA 02478, USA.|School of Computer Science and Technology.|School of Computer Science and Technology.|Department of Genetics, Stanford University, Stanford, CA 94305, USA and.|Computational Biology, Gordon Life Science Institute, Belmont, MA 02478, USA, Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah 21589, Saudi Arabia.', 'China|United States|United States|United States|Saudi Arabia', 238, 'DNA|Enhancer Elements, Genetic|Genomics|Nucleotides|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btv604', '26476782', 'Enhancers are of short regulatory DNA elements. They can be bound with proteins (activators) to activate transcription of a gene, and hence play a critical role in promoting gene transcription in eukaryotes. With the avalanche of DNA sequences generated in the post-genomic age, it is a challenging task to develop computational methods for timely identifying enhancers from extremely complicated DNA sequences. Although some efforts have been made in this regard, they were limited at only identifying whether a query DNA element being of an enhancer or not. According to the distinct levels of biological activities and regulatory effects on target genes, however, enhancers should be further classified into strong and weak ones in strength.'),
(79567, 2016, 'bio', 'RNA structure framework: automated transcriptome-wide reconstruction of RNA secondary structures from high-throughput structure probing data', 'Incarnato, Danny|Neri, Francesco|Anselmi, Francesca|Oliviero, Salvatore', 'Human Genetics Foundation (HuGeF), 10126 Torino, Italy and Dipartimento di Scienze della Vita e Biologia dei Sistemi, Università di Torino, 10123 Torino, Italy.|Human Genetics Foundation (HuGeF), 10126 Torino, Italy and.|Human Genetics Foundation (HuGeF), 10126 Torino, Italy and Dipartimento di Scienze della Vita e Biologia dei Sistemi, Università di Torino, 10123 Torino, Italy.|Human Genetics Foundation (HuGeF), 10126 Torino, Italy and Dipartimento di Scienze della Vita e Biologia dei Sistemi, Università di Torino, 10123 Torino, Italy.', 'Italy|Italy|Italy|Italy', 8, 'Computational Biology|Genome, Human|Humans|Nucleic Acid Conformation|RNA|Sequence Analysis, RNA|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btv571', '26487736', 'The rapidly increasing number of discovered non-coding RNAs makes the understanding of their structure a key feature toward a deeper comprehension of gene expression regulation. Various enzymatic- and chemically- based approaches have been recently developed to allow whole-genome studies of RNA secondary structures. Several methods have been recently presented that allow high-throughput RNA structure probing (CIRS-seq, Structure-seq, SHAPE-seq, PARS, etc.) and unbiased structural inference of residues within RNAs in their native conformation. We here present an analysis toolkit, named RNA Structure Framework (RSF), which allows fast and fully-automated analysis of high-throughput structure probing data, from data pre-processing to whole-transcriptome RNA structure inference.'),
(79568, 2016, 'bio', 'Web-based Gene Pathogenicity Analysis (WGPA): a web platform to interpret gene pathogenicity from personal genome data', 'Diaz-Montana, Juan J|Rackham, Owen J L|Diaz-Diaz, Norberto|Petretto, Enrico', 'School of Engineering, Pablo de Olavide University, Seville, 41013 Spain and.|Duke-NUS Graduate Medical School Singapore, Singapore, 169857 Singapore.|School of Engineering, Pablo de Olavide University, Seville, 41013 Spain and.|Duke-NUS Graduate Medical School Singapore, Singapore, 169857 Singapore.', 'Spain|Singapore|Spain|Singapore', 1, 'Disease|Genome, Human|Genomics|Humans|Internet|Mutation|Software|Virulence Factors', 'Journal Article', '10.1093/bioinformatics/btv598', '26490503', 'As the volume of patient-specific genome sequences increases the focus of biomedical research is switching from the detection of disease-mutations to their interpretation. To this end a number of techniques have been developed that use mutation data collected within a population to predict whether individual genes are likely to be disease-causing or not. As both sequence data and associated analysis tools proliferate, it becomes increasingly difficult for the community to make sense of these data and their implications. Moreover, no single analysis tool is likely to capture all relevant genomic features that contribute to the genes pathogenicity. Here, we introduce Web-based Gene Pathogenicity Analysis (WGPA), a web-based tool to analyze genes impacted by mutations and rank them through the integration of existing prioritization tools, which assess different aspects of gene pathogenicity using population-level sequence data. Additionally, to explore the polygenic contribution of mutations to disease, WGPA implements gene set enrichment analysis to prioritize disease-causing genes and gene interaction networks, therefore providing a comprehensive annotation of personal genomes data in disease.'),
(79569, 2016, 'bio', 'An algorithm to detect and communicate the differences in computational models describing biological systems', 'Scharm, Martin|Wolkenhauer, Olaf|Waltemath, Dagmar', 'Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany and.|Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany and Stellenbosch Institute for Advanced Study, Wallenberg Research Centre at Stellenbosch University, Stellenbosch, South Africa.|Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany and.', 'Germany|Germany|Germany', 0, 'Algorithms|Computer Simulation|Databases, Factual|Humans|Models, Biological|Reproducibility of Results|Systems Biology|Workflow', 'Journal Article', '10.1093/bioinformatics/btv484', '26490504', 'Repositories support the reuse of models and ensure transparency about results in publications linked to those models. With thousands of models available in repositories, such as the BioModels database or the Physiome Model Repository, a framework to track the differences between models and their versions is essential to compare and combine models. Difference detection not only allows users to study the history of models but also helps in the detection of errors and inconsistencies. Existing repositories lack algorithms to track a models development over time.'),
(79570, 2016, 'bio', 'funtooNorm: an R package for normalization of DNA methylation data when there are multiple cell or tissue types', 'Oros Klein, Kathleen|Grinek, Stepan|Bernatsky, Sasha|Bouchard, Luigi|Ciampi, Antonio|Colmegna, Ines|Fortin, Jean-Philippe|Gao, Long|Hivert, Marie-France|Hudson, Marie|Kobor, Michael S|Labbe, Aurelie|MacIsaac, Julia L|Meaney, Michael J|Morin, Alexander M|ODonnell, Kieran J|Pastinen, Tomi|Van Ijzendoorn, Marinus H|Voisin, Gregory|Greenwood, Celia M T', 'Lady Davis Institute, Jewish General Hospital, Montreal, QC H3T 1E2, Canada, Ludmer Center for Neuroinformatics and Mental Health.|Lady Davis Institute, Jewish General Hospital, Montreal, QC H3T 1E2, Canada, Ludmer Center for Neuroinformatics and Mental Health.|Divisions of Rheumatology and Clinical Epidemiology, McGill University Health Centre, McGill University, Montreal, QC H4A 3J1, Canada.|ECOGENE-21, Centre intxc3xa9grxc3xa9 universitaire de santxc3xa9 et de service sociaux du Saguenay-Lac-Saint-Jean, QC G8H 3P7, Canada, Department of Biochemistry, Universitxc3xa9 de Sherbrooke, QC J1K 2R1, Canada.|Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC H3A 1A2, Canada.|Division of Experimental Medicine, McGill University Health Centre, McGill University, Montreal, QC H3A 1A3, Canada.|Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21218, USA.|Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC H3A 1A2, Canada.|Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA 02215, USA, Department of Medicine, Division of Endocrinology, Universitxc3xa9 de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada.|Lady Davis Institute, Jewish General Hospital, Montreal, QC H3T 1E2, Canada, Department of Medicine, McGill University Health Center, Montreal, QC H4A 3J1, Canada.|Canadian Institute for Advanced Research, Child, and Brain Development Program, Toronto, ON M5G 1Z8, Canada, Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Vancouver, BC V5Z 4H4, Canada, Department of Medical Genetics, University of British Columbia, Vancouver, BC V6H 3N1, Canada.|Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC H3A 1A2, Canada.|Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Vancouver, BC V5Z 4H4, Canada, Department of Medical Genetics, University of British Columbia, Vancouver, BC V6H 3N1, Canada.|Ludmer Center for Neuroinformatics and Mental Health, Canadian Institute for Advanced Research, Child, and Brain Development Program, Toronto, ON M5G 1Z8, Canada, Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Vancouver, BC V5Z 4H4, Canada, Douglas Mental Health University Institute, McGill University, Montreal, QC H4H 1R3, Canada, Departments of Psychiatry, McGill University, Montreal, QC, Canada H3A 1A1, Department of Neurology and Neurosurgery, McGill University, Montreal, QC H3A 2B4, Canada.|Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Vancouver, BC V5Z 4H4, Canada, Department of Medical Genetics, University of British Columbia, Vancouver, BC V6H 3N1, Canada.|Douglas Mental Health University Institute, McGill University, Montreal, QC H4H 1R3, Canada.|Department of Human Genetics, McGill University, Montreal, QC H3A 1B1, Canada and.|Centre for Child and Family Studies, Leiden University, Leiden 2300 RB, The Netherlands.|Lady Davis Institute, Jewish General Hospital, Montreal, QC H3T 1E2, Canada, Ludmer Center for Neuroinformatics and Mental Health.|Lady Davis Institute, Jewish General Hospital, Montreal, QC H3T 1E2, Canada, Department of Biochemistry, Universitxc3xa9 de Sherbrooke, QC J1K 2R1, Canada, Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC H3A 1A2, Canada, Department of Human Genetics, McGill University, Montreal, QC H3A 1B1, Canada and.', 'Canada|Canada|Canada|Canada|Canada|Canada|United States|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|', 8, 'Autoimmune Diseases|Cell Lineage|DNA Methylation|Diabetes, Gestational|Female|Humans|Oligonucleotide Array Sequence Analysis|Organ Specificity|Pregnancy|Software', 'Journal Article', '10.1093/bioinformatics/btv615', '26500152', 'DNA methylation patterns are well known to vary substantially across cell types or tissues. Hence, existing normalization methods may not be optimal if they do not take this into account. We therefore present a new R package for normalization of data from the Illumina Infinium Human Methylation450 BeadChip (Illumina 450xe2x80x89K) built on the concepts in the recently published funNorm method, and introducing cell-type or tissue-type flexibility.'),
(79571, 2016, 'bio', 'The minimum evolution problem is hard: a link between tree inference and graph clustering problems', 'Bastkowski, Sarah|Moulton, Vincent|Spillner, Andreas|Wu, Taoyang', 'The Genome Analysis Centre, Norwich, UK.|School of Computing Sciences, University of East Anglia, Norwich, UK and.|Department of Mathematics and Computer Science, University of Greifswald, Greifswald, Germany.|School of Computing Sciences, University of East Anglia, Norwich, UK and.', 'United Kingdom|United Kingdom|Germany|United Kingdom', 0, 'Algorithms|Biological Evolution|Cluster Analysis|Computer Simulation|Humans|Models, Theoretical|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/btv623', '26500153', 'Distance methods are well suited for constructing massive phylogenetic trees. However, the computational complexity for Rzhetsky and Neis minimum evolution (ME) approach, one of the earliest methods for constructing a phylogenetic tree from a distance matrix, remains open.'),
(79572, 2016, 'bio', 'BGT: efficient and flexible genotype query across many samples', 'Li, Heng', 'Medical Population Genetics Program, Broad Institute, Cambridge, MA 02142, USA.', 'United States', 0, 'Algorithms|Databases, Factual|Datasets as Topic|Genetic Variation|Genome, Human|Genotype|Haplotypes|Humans|Software', 'Journal Article', '10.1093/bioinformatics/btv613', '26500154', 'BGT is a compact format, a fast command line tool and a simple web application for efficient and convenient query of whole-genome genotypes and frequencies across tens to hundreds of thousands of samples. On real data, it encodes the haplotypes of 32xe2x80x89488 samples across 39.2 million SNPs into a 7.4 GB database and decodes up to 420 million genotypes per CPU second. The high performance enables real-time responses to complex queries.'),
(79573, 2016, 'bio', 'IsoMIF Finder: online detection of binding site molecular interaction field similarities', 'Chartier, Matthieu|Adriansen, Etienne|Najmanovich, Rafael', 'Department of Biochemistry, Faculty of Medicine and Health Sciences, University of Sherbrooke, Sherbrooke, J1H 5N4 QC, Canada and.|Telecommunication and computer network engineering, Télécom Lille, Lille 1 University, 59650 Villeneuve-dAscq, France.|Department of Biochemistry, Faculty of Medicine and Health Sciences, University of Sherbrooke, Sherbrooke, J1H 5N4 QC, Canada and.', 'Canada|France|Canada', 11, 'Binding Sites|Databases, Protein|Humans|Models, Molecular|Pharmaceutical Preparations|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btv616', '26504139', 'IsoMIF Finder is an online server for the identification of molecular interaction field (MIF) similarities. User defined binding site MIFs can be compared to datasets of pre-calculated MIFs or against a user-defined list of PDB entries. The interface can be used for the prediction of function, identification of potential cross-reactivity or polypharmacological targets and drug repurposing. Detected similarities can be viewed in a browser or within a PyMOL session.'),
(79574, 2016, 'bio', 'GenoWAP: GWAS signal prioritization through integrated analysis of genomic functional annotation', 'Lu, Qiongshi|Yao, Xinwei|Hu, Yiming|Zhao, Hongyu', 'Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.|Yale College, New Haven, CT, USA.|Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.|Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA, Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA and VA Cooperative Studies Program Coordinating Center, West Haven, CT, USA.', 'United States|United States|United States|United States', 20, 'Biomarkers|Crohn Disease|Genome-Wide Association Study|Genomics|Haplotypes|Humans|Linkage Disequilibrium|Polymorphism, Single Nucleotide|Quantitative Trait Loci|Schizophrenia|Software', 'Journal Article', '10.1093/bioinformatics/btv610', '26504140', 'Genome-wide association study (GWAS) has been a great success in the past decade. However, significant challenges still remain in both identifying new risk loci and interpreting results. Bonferroni-corrected significance level is known to be conservative, leading to insufficient statistical power when the effect size is moderate at risk locus. Complex structure of linkage disequilibrium also makes it challenging to separate causal variants from nonfunctional ones in large haplotype blocks. Under such circumstances, a computational approach that may increase signal replication rate and identify potential functional sites among correlated markers is urgently needed.'),
(79575, 2016, 'bio', 'MT-HESS: an efficient Bayesian approach for simultaneous association detection in OMICS datasets, with application to eQTL mapping in multiple tissues', 'Lewin, Alex|Saadi, Habib|Peters, James E|Moreno-Moral, Aida|Lee, James C|Smith, Kenneth G C|Petretto, Enrico|Bottolo, Leonardo|Richardson, Sylvia', 'Department of Mathematics, Brunel University London.|Department of Epidemiology and Biostatistics, Imperial College London, London.|Cambridge Institute for Medical Research, University of Cambridge, Cambridge, MRC Biostatistics Unit, Cambridge Institute of Public Health, Cambridge.|MRC Clinical Sciences Centre, Imperial College London, London, UK.|Cambridge Institute for Medical Research, University of Cambridge, Cambridge.|Cambridge Institute for Medical Research, University of Cambridge, Cambridge.|MRC Clinical Sciences Centre, Imperial College London, London, UK, Duke-NUS Graduate Medical School, Singapore, Singapore.|Department of Mathematics, Imperial College London, London, UK and Department of Medical Genetics, University of Cambridge.|MRC Biostatistics Unit, Cambridge Institute of Public Health, Cambridge.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|Singapore|United Kingdom|United Kingdom', 0, 'Algorithms|Animals|Bayes Theorem|Diabetes Mellitus, Type 1|Gene Expression Regulation|Gene Regulatory Networks|Genomics|Humans|Inflammation|Inflammatory Bowel Diseases|Models, Theoretical|Organ Specificity|Polymorphism, Single Nucleotide|Programming Languages|Quantitative Trait Loci|Rats|Software|Tissue Distribution', 'Journal Article', '10.1093/bioinformatics/btv568', '26504141', 'Analysing the joint association between a large set of responses and predictors is a fundamental statistical task in integrative genomics, exemplified by numerous expression Quantitative Trait Loci (eQTL) studies. Of particular interest are the so-called : hotspots : , important genetic variants that regulate the expression of many genes. Recently, attention has focussed on whether eQTLs are common to several tissues, cell-types or, more generally, conditions or whether they are specific to a particular condition.'),
(79576, 2016, 'bio', 'Pycellerator: an arrow-based reaction-like modelling language for biological simulations', 'Shapiro, Bruce E|Mjolsness, Eric', 'Department of Mathematics, California State University, Northridge, CA 91330, USA and.|Department of Computer Science, University of California, Irvine, CA 92697, USA.', 'United States|United States', 2, 'Computer Simulation|Gene Expression Regulation|MAP Kinase Signaling System|Models, Biological|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btv596', '26504142', 'We introduce Pycellerator, a Python library for reading Cellerator arrow notation from standard text files, conversion to differential equations, generating stand-alone Python solvers, and optionally running and plotting the solutions. All of the original Cellerator arrows, which represent reactions ranging from mass action, Michales-Menten-Henri (MMH) and Gene-Regulation (GRN) to Monod-Wyman-Changeaux (MWC), user defined reactions and enzymatic expansions (KMech), were previously represented with the Mathematica extended character set. These are now typed as reaction-like commands in ASCII text files that are read by Pycellerator, which includes a Python command line interface (CLI), a Python application programming interface (API) and an iPython notebook interface.'),
(79577, 2016, 'bio', 'PDID: database of molecular-level putative protein-drug interactions in the structural human proteome', 'Wang, Chen|Hu, Gang|Wang, Kui|Brylinski, Michal|Xie, Lei|Kurgan, Lukasz', 'Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada T6G 2V4.|School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, Peoples Republic of China.|School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, Peoples Republic of China.|Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA.|Department of Computer Science, Hunter College, City University of New York (CUNY), New York, NY 10065, USA and.|Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada T6G 2V4, Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA.', 'Canada|China|China|United States|United States|Canada', 17, 'Binding Sites|Database Management Systems|Databases, Pharmaceutical|Databases, Protein|Drug Design|Drug Interactions|Humans|Information Storage and Retrieval|Pharmaceutical Preparations|Polypharmacology|Protein Conformation|Proteins|Proteome', 'Journal Article', '10.1093/bioinformatics/btv597', '26504143', 'Many drugs interact with numerous proteins besides their intended therapeutic targets and a substantial portion of these interactions is yet to be elucidated. Protein-Drug Interaction Database (PDID) addresses incompleteness of these data by providing access to putative protein-drug interactions that cover the entire structural human proteome.'),
(79578, 2016, 'bio', 'Graphical pan-genome analysis with compressed suffix trees and the Burrows-Wheeler transform', 'Baier, Uwe|Beller, Timo|Ohlebusch, Enno', 'Institute of Theoretical Computer Science, Ulm University, 89069 Ulm, Germany.|Institute of Theoretical Computer Science, Ulm University, 89069 Ulm, Germany.|Institute of Theoretical Computer Science, Ulm University, 89069 Ulm, Germany.', 'Germany|Germany|Germany', 0, 'Algorithms|Computational Biology|Computer Simulation|Genome, Human|Genomics|Humans|Models, Genetic|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btv603', '26504144', 'Low-cost genome sequencing gives unprecedented complete information about the genetic structure of populations, and a population graph captures the variations between many individuals of a population. Recently, Marcus et al. proposed to use a compressed de Bruijn graph for representing an entire population of genomes. They devised an O(n log g) time algorithm called splitMEM that constructs this graph directly (i.e. without using the uncompressed de Bruijn graph) based on a suffix tree, where n is the total length of the genomes and g is the length of the longest genome. Since the applicability of their algorithm is limited to rather small datasets, there is a strong need for space-efficient construction algorithms.'),
(79579, 2016, 'bio', 'NMRe: a web server for NMR protein structure refinement with high-quality structure validation scores', 'Ryu, Hyojung|Lim, GyuTae|Sung, Bong Hyun|Lee, Jinhyuk', 'Korean Bioinformation Center (KOBIC), Korea Research Institute of Bioscience and Biotechnology, Yuseong-gu, Daejeon 305-806, the Republic of Korea, Department of Nanobiotechnology and Bioinformatics, University of Science and Technology, Yuseong-Gu, Daejeon 305-350, the Republic of Korea and.|Korean Bioinformation Center (KOBIC), Korea Research Institute of Bioscience and Biotechnology, Yuseong-gu, Daejeon 305-806, the Republic of Korea, Department of Nanobiotechnology and Bioinformatics, University of Science and Technology, Yuseong-Gu, Daejeon 305-350, the Republic of Korea and.|Bioenergy and Biochemical Research Center, Korea Research Institute of Bioscience and Biotechnology, Yuseong-gu, Daejeon 305-806, the Republic of Korea.|Korean Bioinformation Center (KOBIC), Korea Research Institute of Bioscience and Biotechnology, Yuseong-gu, Daejeon 305-806, the Republic of Korea, Department of Nanobiotechnology and Bioinformatics, University of Science and Technology, Yuseong-Gu, Daejeon 305-350, the Republic of Korea and.', '-|-|-|-', 4, 'Internet|Knowledge Bases|Nuclear Magnetic Resonance, Biomolecular|Protein Conformation|Software', 'Journal Article', '10.1093/bioinformatics/btv595', '26504145', 'Protein structure refinement is a necessary step for the study of protein function. In particular, some nuclear magnetic resonance (NMR) structures are of lower quality than X-ray crystallographic structures. Here, we present NMRe, a web-based server for NMR structure refinement. The previously developed knowledge-based energy function STAP (Statistical Torsion Angle Potential) was used for NMRe refinement. With STAP, NMRe provides two refinement protocols using two types of distance restraints. If a user provides NOE (Nuclear Overhauser Effect) data, the refinement is performed with the NOE distance restraints as a conventional NMR structure refinement. Additionally, NMRe generates NOE-like distance restraints based on the inter-hydrogen distances derived from the input structure. The efficiency of NMRe refinement was validated on 20 NMR structures. Most of the quality assessment scores of the refined NMR structures were better than those of the original structures. The refinement results are provided as a three-dimensional structure view, a secondary structure scheme, and numerical and graphical structure validation scores.'),
(79580, 2016, 'bio', 'DARA: a web server for rapid search of structural neighbours using solution small angle X-ray scattering data', 'Kikhney, Alexey G|Panjkovich, Alejandro|Sokolova, Anna V|Svergun, Dmitri I', 'European Molecular Biology Laboratory, Hamburg Outstation, EMBL c/o DESY, 22607 Hamburg, Germany and.|European Molecular Biology Laboratory, Hamburg Outstation, EMBL c/o DESY, 22607 Hamburg, Germany and.|Bragg Institute, ANSTO, Lucas Heights, 2234 NSW Australia.|European Molecular Biology Laboratory, Hamburg Outstation, EMBL c/o DESY, 22607 Hamburg, Germany and.', 'Germany|Germany|Australia|Germany', 10, 'Databases, Protein|Internet|Macromolecular Substances|Scattering, Small Angle|Software|X-Ray Diffraction', 'Journal Article', '10.1093/bioinformatics/btv611', '26504146', 'Small angle X-ray scattering (SAXS) is an established method for studying biological macromolecules in solution, whereby the experimental scattering patterns relate to the quaternary and tertiary structure of the macromolecule. Here we present DARA, a web-server, that queries over 150xe2x80x89000 scattering profiles pre-computed from the high resolution models of macromolecules and biological assemblies in the Protein Data Bank, to rapidly find nearest neighbours of a given experimental or theoretical SAXS pattern. Identification of the best scattering equivalents provides a straightforward and automated way of structural assessment of macromolecules based on a SAXS profile. DARA results are useful e.g. for fold recognition and finding of biologically active oligomers.'),
(79581, 2016, 'bio', 'Omokage search: shape similarity search service for biomolecular structures in both the PDB and EMDB', 'Suzuki, Hirofumi|Kawabata, Takeshi|Nakamura, Haruki', 'Institute for Protein Research, Osaka University, Suita, Osaka, 565-0871, Japan.|Institute for Protein Research, Osaka University, Suita, Osaka, 565-0871, Japan.|Institute for Protein Research, Osaka University, Suita, Osaka, 565-0871, Japan.', 'Japan|Japan|Japan', 9, 'Databases, Chemical|Databases, Protein|Internet|Macromolecular Substances|Microscopy, Electron|Models, Molecular|Normal Distribution|Software', 'Journal Article', '10.1093/bioinformatics/btv614', '26508754', 'Omokage search is a service to search the global shape similarity of biological macromolecules and their assemblies, in both the Protein Data Bank (PDB) and Electron Microscopy Data Bank (EMDB). The server compares global shapes of assemblies independent of sequence order and number of subunits. As a search query, the user inputs a structure ID (PDB ID or EMDB ID) or uploads an atomic model or 3D density map to the server. The search is performed usually within 1xe2x80x89min, using one-dimensional profiles (incremental distance rank profiles) to characterize the shapes. Using the gmfit (Gaussian mixture model fitting) program, the found structures are fitted onto the query structure and their superimposed structures are displayed on the Web browser. Our service provides new structural perspectives to life science researchers.'),
(79582, 2016, 'bio', 'caRpools: an R package for exploratory data analysis and documentation of pooled CRISPR/Cas9 screens', 'Winter, Jan|Breinig, Marco|Heigwer, Florian|Brügemann, Dirk|Leible, Svenja|Pelz, Oliver|Zhan, Tianzuo|Boutros, Michael', 'German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics and Heidelberg University, Heidelberg, Germany and.|German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics and Heidelberg University, Heidelberg, Germany and.|German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics and Heidelberg University, Heidelberg, Germany and.|German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics and Heidelberg University, Heidelberg, Germany and.|German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics and Heidelberg University, Heidelberg, Germany and.|German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics and Heidelberg University, Heidelberg, Germany and.|German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics and Heidelberg University, Heidelberg, Germany and Department of Medicine II, University Hospital Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.|German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics and Heidelberg University, Heidelberg, Germany and.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, 'CRISPR-Cas Systems|Documentation|Gene Editing|Genome|High-Throughput Nucleotide Sequencing|Software', 'Journal Article', '10.1093/bioinformatics/btv617', '26508755', 'Genetic screens by CRISPR/Cas9-mediated genome engineering have become a powerful tool for functional genomics. However, there is currently a lack of end-to-end software pipelines to analyze CRISPR/Cas9 screens based on next generation sequencing.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(79583, 2016, 'bio', 'PPIXpress: construction of condition-specific protein interaction networks based on transcript expression', 'Will, Thorsten|Helms, Volkhard', 'Center for Bioinformatics and Graduate School of Computer Science, Saarland University, Saarbrücken, Germany.|Center for Bioinformatics and.', 'Germany|-', 14, 'Algorithms|Alternative Splicing|Biomarkers, Tumor|Breast Neoplasms|Computational Biology|Female|Gene Regulatory Networks|Humans|Protein Interaction Maps|Protein Isoforms', 'Journal Article', '10.1093/bioinformatics/btv620', '26508756', 'Protein-protein interaction networks are an important component of modern systems biology. Yet, comparatively few efforts have been made to tailor their topology to the actual cellular condition being studied. Here, we present a network construction method that exploits expression data at the transcript-level and thus reveals alterations in protein connectivity not only caused by differential gene expression but also by alternative splicing. We achieved this by establishing a direct correspondence between individual protein interactions and underlying domain interactions in a complete but condition-unspecific protein interaction network. This knowledge was then used to infer the condition-specific presence of interactions from the dominant protein isoforms. When we compared contextualized interaction networks of matched normal and tumor samples in breast cancer, our transcript-based construction identified more significant alterations that affected proteins associated with cancerogenesis than a method that only uses gene expression data. The approach is provided as the user-friendly tool PPIXpress.'),
(79584, 2016, 'bio', 'LOLA: enrichment analysis for genomic region sets and regulatory elements in R and Bioconductor', 'Sheffield, Nathan C|Bock, Christoph', 'CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences.|CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Department of Laboratory Medicine, Medical University of Vienna, 1090 Vienna, Austria and Max Planck Institute for Informatics, 66123 Saarbrücken, Germany.', 'Austria|Austria', 84, 'Computational Biology|Databases, Factual|Epigenomics|Gene Regulatory Networks|Genome, Human|Genomics|Humans|Regulatory Sequences, Nucleic Acid|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btv612', '26508757', 'Genomic datasets are often interpreted in the context of large-scale reference databases. One approach is to identify significantly overlapping gene sets, which works well for gene-centric data. However, many types of high-throughput data are based on genomic regions. Locus Overlap Analysis (LOLA) provides easy and automatable enrichment analysis for genomic region sets, thus facilitating the interpretation of functional genomics and epigenomics data.'),
(79585, 2016, 'bio', 'Understanding the fabric of protein crystals: computational classification of biological interfaces and crystal contacts', 'Capitani, Guido|Duarte, Jose M|Baskaran, Kumaran|Bliven, Spencer|Somody, Joseph C', 'Laboratory of Biomolecular Research, Paul Scherrer Institute, OFLC/110, 5232 Villigen PSI, Department of Biology, ETH Zurich, 8093 Zurich, Switzerland.|Laboratory of Biomolecular Research, Paul Scherrer Institute, OFLC/110, 5232 Villigen PSI, Department of Biology, ETH Zurich, 8093 Zurich, Switzerland.|Laboratory of Biomolecular Research, Paul Scherrer Institute, OFLC/110, 5232 Villigen PSI.|Laboratory of Biomolecular Research, Paul Scherrer Institute, OFLC/110, 5232 Villigen PSI, Bioinformatics and Systems Biology Program, UC San Diego, La Jolla, CA 92093, National Center for Biotechnology Information, NIH, Bethesda, MD 20894, USA and.|Laboratory of Biomolecular Research, Paul Scherrer Institute, OFLC/110, 5232 Villigen PSI, Department of Computer Science, ETH Zurich, 8092 Zurich, Switzerland.', 'Switzerland|Switzerland|-|-|Switzerland', 0, 'Algorithms|Computational Biology|Crystallography, X-Ray|Humans|Models, Molecular|Protein Binding|Protein Conformation|Proteins', 'Journal Article', '10.1093/bioinformatics/btv622', '26508758', 'Modern structural biology still draws the vast majority of information from crystallography, a technique where the objects being investigated are embedded in a crystal lattice. Given the complexity and variety of those objects, it becomes fundamental to computationally assess which of the interfaces in the lattice are biologically relevant and which are simply crystal contacts. Since the mid-1990s, several approaches have been applied to obtain high-accuracy classification of crystal contacts and biological protein-protein interfaces. This review provides an overview of the concepts and main approaches to protein interface classification: thermodynamic estimation of interface stability, evolutionary approaches based on conservation of interface residues, and co-occurrence of the interface across different crystal forms. Among the three categories, evolutionary approaches offer the strongest promise for improvement, thanks to the incessant growth in sequence knowledge. Importantly, protein interface classification algorithms can also be used on multimeric structures obtained using other high-resolution techniques or for protein assembly design or validation purposes. A key issue linked to protein interface classification is the identification of the biological assembly of a crystal structure and the analysis of its symmetry. Here, we highlight the most important concepts and problems to be overcome in assembly prediction. Over the next few years, tools and concepts of interface classification will probably become more frequently used and integrated in several areas of structural biology and structural bioinformatics. Among the main challenges for the future are better addressing of weak interfaces and the application of interface classification concepts to prediction problems like protein-protein docking.'),
(79586, 2016, 'bio', 'Detecting dispersed duplications in high-throughput sequencing data using a database-free approach', 'Kroon, M|Lameijer, E W|Lakenberg, N|Hehir-Kwa, J Y|Thung, D T|Slagboom, P E|Kok, J N|Ye, K', 'Department of Molecular Epidemiology, Leiden University Medical Center, Leiden.|Department of Molecular Epidemiology, Leiden University Medical Center, Leiden.|Department of Molecular Epidemiology, Leiden University Medical Center, Leiden.|Department of Human Genetics, Nijmegen Center for Molecular Life Sciences, Institute for Genetic and Metabolic Disease, Radboud University Nijmegen Medical Center, Nijmegen, Donders Centre for Neuroscience, Nijmegen, The Netherlands and.|Department of Human Genetics, Nijmegen Center for Molecular Life Sciences, Institute for Genetic and Metabolic Disease, Radboud University Nijmegen Medical Center, Nijmegen.|Department of Molecular Epidemiology, Leiden University Medical Center, Leiden.|Department of Molecular Epidemiology, Leiden University Medical Center, Leiden.|Department of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Genome Institute, Washington University, St Louis, MO 63108, USA.', 'United States|United States|United States|Netherlands|United States|United States|United States|United States', 0, 'Computational Biology|DNA Copy Number Variations|Databases, Factual|Gene Duplication|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btv621', '26508759', 'Dispersed duplications (DDs) such as transposon element insertions and copy number variations are ubiquitous in the human genome. They have attracted the interest of biologists as well as medical researchers due to their role in both evolution and disease. The efforts of discovering DDs in high-throughput sequencing data are currently dominated by database-oriented approaches that require pre-existing knowledge of the DD elements to be detected.'),
(79587, 2016, 'bio', 'RVFam: an R package for rare variant association analysis with family data', 'Chen, Ming-Huei|Yang, Qiong', 'Department of Neurology, Boston University School of Medicine, Boston, MA 02118, USA, Framingham Heart Study, Population Sciences Branch, Division of Intramural Research, National Heart Lung and Blood Institute, National Institutes of Health, Framingham, MA 01702, USA.|Department of Neurology, Boston University School of Medicine, Boston, MA 02118, USA, Framingham Heart Study, Population Sciences Branch, Division of Intramural Research, National Heart Lung and Blood Institute, National Institutes of Health, Framingham, MA 01702, USA.', 'United States|United States', 4, 'Family|Genetic Variation|Genome-Wide Association Study|Humans|Software', 'Journal Article', '10.1093/bioinformatics/btv609', '26508760', 'Family-based designs offer unique advantage for identifying rare risk variants in genetic association studies. There are existing tools for analyzing rare variants in families but lacking components to handle binary traits properly and survival traits. In this report, we introduce an R software package RVFam (Rare Variant association analysis with Family data) designed to analyze continuous, binary and survival traits against rare and common sequencing variants in genome-wide association studies (GWAS) involving family data. Single and multiple variant association tests were implemented while accounting for arbitrary family structures. Extensive simulation studies were performed to evaluate all the approaches implemented in RVFam.'),
(79588, 2016, 'bio', 'openBIS ELN-LIMS: an open-source database for academic laboratories', 'Barillari, Caterina|Ottoz, Diana S M|Fuentes-Serna, Juan Mariano|Ramakrishnan, Chandrasekhar|Rinn, Bernd|Rudolf, Fabian', 'ETH Zurich ID SIS and Swiss Institute of Bioinformatics, 8092 Zurich, Switzerland.|ETH Zurich D-BSSE and Swiss Institute of Bioinformatics, 4058 Basel, Switzerland and Life Science Zurich PhD Program on Molecular and Translational Biomedicine and Competence Center for Personalized Medicine, 8092 Zurich, Switzerland.|ETH Zurich ID SIS and Swiss Institute of Bioinformatics, 8092 Zurich, Switzerland.|ETH Zurich ID SIS and Swiss Institute of Bioinformatics, 8092 Zurich, Switzerland.|ETH Zurich ID SIS and Swiss Institute of Bioinformatics, 8092 Zurich, Switzerland.|ETH Zurich D-BSSE and Swiss Institute of Bioinformatics, 4058 Basel, Switzerland and.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 13, 'Biological Science Disciplines|Computer Systems|Databases, Factual|Information Management|Information Systems|Laboratories|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btv606', '26508761', 'The open-source platform openBIS (open Biology Information System) offers an Electronic Laboratory Notebook and a Laboratory Information Management System (ELN-LIMS) solution suitable for the academic life science laboratories. openBIS ELN-LIMS allows researchers to efficiently document their work, to describe materials and methods and to collect raw and analyzed data. The system comes with a user-friendly web interface where data can be added, edited, browsed and searched.'),
(79589, 2016, 'bio', 'ancGWAS: a post genome-wide association study method for interaction, pathway and ancestry analysis in homogeneous and admixed populations', 'Chimusa, Emile R|Mbiyavanga, Mamana|Mazandu, Gaston K|Mulder, Nicola J', 'Computational Biology Group, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Medical School, 7925, Observatory, South Africa and.|Computational Biology Group, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Medical School, 7925, Observatory, South Africa and African Institute for Mathematical Sciences, 7945 Muizenberg, Cape Town, South Africa.|Computational Biology Group, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Medical School, 7925, Observatory, South Africa and African Institute for Mathematical Sciences, 7945 Muizenberg, Cape Town, South Africa.|Computational Biology Group, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Medical School, 7925, Observatory, South Africa and.', 'South Africa|South Africa|South Africa|South Africa', 11, 'Breast Neoplasms|Female|Gene Regulatory Networks|Genetic Predisposition to Disease|Genetics, Population|Genome-Wide Association Study|Humans|Linkage Disequilibrium|Polymorphism, Single Nucleotide|Protein Interaction Mapping|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btv619', '26508762', 'Despite numerous successful Genome-wide Association Studies (GWAS), detecting variants that have low disease risk still poses a challenge. GWAS may miss disease genes with weak genetic effects or strong epistatic effects due to the single-marker testing approach commonly used. GWAS may thus generate false negative or inconclusive results, suggesting the need for novel methods to combine effects of single nucleotide polymorphisms within a gene to increase the likelihood of fully characterizing the susceptibility gene.'),
(79590, 2016, 'bio', 'EMDomics: a robust and powerful method for the identification of genes differentially expressed between heterogeneous classes', 'Nabavi, Sheida|Schmolze, Daniel|Maitituoheti, Mayinuer|Malladi, Sadhika|Beck, Andrew H', 'Center for Biomedical Informatics, Harvard Medical School, Boston, MA, USA.|Department of Pathology and Cancer Research Institute, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA.|Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA and.|Department of Pathology and Cancer Research Institute, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA, The Harker School, San Jose, CA, USA.|Department of Pathology and Cancer Research Institute, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA.', 'United States|Israel|United States|Israel|Israel', 0, 'Antineoplastic Agents|Biomarkers, Tumor|Databases, Factual|Drug Resistance, Neoplasm|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Gene Regulatory Networks|Humans|Ovarian Neoplasms|Sensitivity and Specificity|Software', 'Journal Article', '10.1093/bioinformatics/btv634', '26515818', 'A major goal of biomedical research is to identify molecular features associated with a biological or clinical class of interest. Differential expression analysis has long been used for this purpose, however, conventional methods perform poorly when applied to data with high within class heterogeneity.'),
(79591, 2016, 'bio', 'Gapped sequence alignment using artificial neural networks: application to the MHC class I system', 'Andreatta, Massimo|Nielsen, Morten', 'Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina and.|Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina and Center for Biological Sequence Analysis, Technical University of Denmark, Kgs. Lyngby, Denmark.', 'Argentina|Argentina', 311, 'Algorithms|Computational Biology|Epitopes|Histocompatibility Antigens Class I|Humans|Neural Networks (Computer)|Peptide Fragments|Protein Binding|Sequence Alignment', 'Journal Article', '10.1093/bioinformatics/btv639', '26515819', 'Many biological processes are guided by receptor interactions with linear ligands of variable length. One such receptor is the MHC class I molecule. The length preferences vary depending on the MHC allele, but are generally limited to peptides of length 8-11 amino acids. On this relatively simple system, we developed a sequence alignment method based on artificial neural networks that allows insertions and deletions in the alignment.'),
(79592, 2016, 'bio', 'MaxBin 20: an automated binning algorithm to recover genomes from multiple metagenomic datasets', 'Wu, Yu-Wei|Simmons, Blake A|Singer, Steven W', 'Joint BioEnergy Institute, Emeryville, CA 94608, USA, Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA and.|Joint BioEnergy Institute, Emeryville, CA 94608, USA, Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA and Biological and Engineering Sciences Center, Sandia National Laboratories, Livermore, CA 94551, USA.|Joint BioEnergy Institute, Emeryville, CA 94608, USA, Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA and.', 'United States|United States|United States', 247, 'Algorithms|Genome, Bacterial|Genome, Microbial|Metagenome|Metagenomics|Software', 'Journal Article', '10.1093/bioinformatics/btv638', '26515820', 'The recovery of genomes from metagenomic datasets is a critical step to defining the functional roles of the underlying uncultivated populations. We previously developed MaxBin, an automated binning approach for high-throughput recovery of microbial genomes from metagenomes. Here we present an expanded binning algorithm, MaxBin 2.0, which recovers genomes from co-assembly of a collection of metagenomic datasets. Tests on simulated datasets revealed that MaxBin 2.0 is highly accurate in recovering individual genomes, and the application of MaxBin 2.0 to several metagenomes from environmental samples demonstrated that it could achieve two complementary goals: recovering more bacterial genomes compared to binning a single sample as well as comparing the microbial community composition between different sampling environments.'),
(79593, 2016, 'bio', 'tcsBU: a tool to extend TCS network layout and visualization', 'Múrias dos Santos, António|Cabezas, Maria Pilar|Tavares, Ana Isabel|Xavier, Raquel|Branco, Madalena', 'Faculdade de Ciências da Universidade do Porto, 4169-007 Porto, Portugal CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Campus Agrário de Vairão, 4485-661 Vairão, Portugal and.|CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Campus Agrário de Vairão, 4485-661 Vairão, Portugal and.|Faculdade de Ciências da Universidade do Porto, 4169-007 Porto, Portugal CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Campus Agrário de Vairão, 4485-661 Vairão, Portugal and.|CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Campus Agrário de Vairão, 4485-661 Vairão, Portugal and School of Biological Sciences, Cardiff University, Cardiff, CF10 3AX, UK.|CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Campus Agrário de Vairão, 4485-661 Vairão, Portugal and.', 'Portugal|Portugal|Portugal|Portugal|Portugal', 102, 'Computer Graphics|Haplotypes|Software', 'Journal Article', '10.1093/bioinformatics/btv636', '26515821', 'TCS is a widely used haplotype network reconstruction software, but lacks the capability of overlapping genetic with geographic structure, which is often a first step in phylogeographic analysis.'),
(79594, 2016, 'bio', 'PRIMUS: improving pedigree reconstruction using mitochondrial and Y haplotypes', 'Staples, Jeffrey|Ekunwe, Lynette|Lange, Ethan|Wilson, James G|Nickerson, Deborah A|Below, Jennifer E', 'Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.|College of Public Service, Jackson State University, Jackson Heart Study, Jackson, MS 39213, USA.|Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA.|Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA and.|Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.|Department of Epidemiology, University of Texas Health Science Center, Houston, TX 77225, USA.', 'United States|United States|United States|United States|United States|United States', 4, 'Algorithms|Chromosomes, Human, Y|Computer Simulation|DNA, Mitochondrial|Genetics, Population|Haplotypes|Humans|Linkage Disequilibrium|Pedigree|Software', 'Journal Article', '10.1093/bioinformatics/btv618', '26515822', 'PRIMUS is a pedigree reconstruction algorithm that uses estimates of genome-wide identity by descent to reconstruct pedigrees consistent with observed genetic data. However, when genetic data for individuals within a pedigree are missing, often multiple pedigrees can be reconstructed that fit the data. We report a major expansion of PRIMUS that uses mitochondrial (mtDNA) and non-recombining Y chromosome (NRY) haplotypes to eliminate many pedigree structures that are inconsistent with the genetic data. We demonstrate that discordances in mtDNA and NRY haplotypes substantially reduce the number of potential pedigrees, and often lead to the identification of the correct pedigree.'),
(79595, 2016, 'bio', 'SylvX: a viewer for phylogenetic tree reconciliations', 'Chevenet, François|Doyon, Jean-Philippe|Scornavacca, Celine|Jacox, Edwin|Jousselin, Emmanuelle|Berry, Vincent', 'MIVEGEC, CNRS 5290, IRD 224, Université de Montpellier, France, Institut de Biologie Computationnelle, LIRMM, UMR 5506 CNRS, Université de Montpellier, France.|Institut de Biologie Computationnelle, LIRMM, UMR 5506 CNRS, Université de Montpellier, France.|ISEM-UMR 5554 CNRS IRD, Université de Montpellier, France and.|ISEM-UMR 5554 CNRS IRD, Université de Montpellier, France and.|INRA-UMR 1062 CBGP, Centre de Biologie pour la Gestion de Populations, Montferrier-sur-Lez, France.|MIVEGEC, CNRS 5290, IRD 224, Université de Montpellier, France.', 'France|France|France|France|France|France', 9, 'Evolution, Molecular|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/btv625', '26515823', 'Reconciliation methods aim at recovering the evolutionary processes that shaped the history of a given gene family including events such as duplications, transfers and losses by comparing the discrepancies between the topologies of the associated gene and species trees. These methods are also used in the framework of host/parasite studies to recover co-diversification scenarios including co-speciation events, host-switches and extinctions. These evolutionary processes can be graphically represented as nested trees. These interconnected graphs can be visually messy and hard to interpret, and despite the fact that reconciliations are increasingly used, there is a shortage of tools dedicated to their graphical management. Here we present SylvX, a reconciliation viewer which implements classical phylogenetic graphic operators (swapping, highlighting, etc.) and new methods to ease interpretation and comparison of reconciliations (multiple maps, moving, shrinking sub-reconciliations).'),
(79596, 2016, 'bio', 'SeqSIMLA2_exact: simulate multiple disease sites in large pedigrees with given disease status for diseases with low prevalence', 'Yao, Po-Ju|Chung, Ren-Hua', 'Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Miaoli County, Taiwan.|Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Miaoli County, Taiwan.', '-|-', 0, 'Computer Simulation|Data Interpretation, Statistical|Disease|Genetic Association Studies|Genetics, Population|High-Throughput Nucleotide Sequencing|Humans|Models, Genetic|Pedigree|Phenotype|Prevalence|Quantitative Trait, Heritable|Software', 'Journal Article', '10.1093/bioinformatics/btv626', '26515824', 'It is difficult for current simulation tools to simulate sequence data in a pre-specified pedigree structure and pre-specified affection status. Previously, we developed a flexible tool, SeqSIMLA2, for simulating sequence data in either unrelated case-control or family samples with different disease and quantitative trait models. Here we extended the tool to efficiently simulate sequences with multiple disease sites in large pedigrees with a given disease status for each pedigree member, assuming that the disease prevalence is low.'),
(79597, 2016, 'bio', 'DelPhiPKa web server: predicting pKa of proteins, RNAs and DNAs', 'Wang, Lin|Zhang, Min|Alexov, Emil', 'Computational Biophysics and Bioinformatics, Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA and.|Department of Computer Sciences, East Carolina University, Greenville, NC 27858, USA.|Computational Biophysics and Bioinformatics, Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA and.', 'United States|United States|United States', 39, 'DNA|Internet|Proteins|RNA|Software|Static Electricity', 'Journal Article', '10.1093/bioinformatics/btv607', '26515825', 'A new pKa prediction web server is released, which implements DelPhi Gaussian dielectric function to calculate electrostatic potentials generated by charges of biomolecules. Topology parameters are extended to include atomic information of nucleotides of RNA and DNA, which extends the capability of pKa calculations beyond proteins. The web server allows the end-user to protonate the biomolecule at particular pH based on calculated pKa values and provides the downloadable file in PQR format. Several tests are performed to benchmark the accuracy and speed of the protocol.'),
(79598, 2016, 'bio', 'Protael: protein data visualization library for the web', 'Sedova, Mayya|Jaroszewski, Lukasz|Godzik, Adam', 'Bioinformatics and Systems Biology Program, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92307, USA and Center for Structural Genomics of Infectious Diseases (CSGID), Chicago, IL 60611, USA.|Bioinformatics and Systems Biology Program, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92307, USA and Center for Structural Genomics of Infectious Diseases (CSGID), Chicago, IL 60611, USA.|Bioinformatics and Systems Biology Program, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92307, USA and Center for Structural Genomics of Infectious Diseases (CSGID), Chicago, IL 60611, USA.', 'United States|United States|United States', 4, 'Computer Graphics|Humans|Internet|Programming Languages|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btv605', '26515826', 'Protael is a JavaScript library for creating interactive visualizations of biological sequences and various associated data. It allows users to generate high-quality vector graphics (SVG) and integrate it into web pages.'),
(79599, 2016, 'bio', 'CAMUR: Knowledge extraction from RNA-seq cancer data through equivalent classification rules', 'Cestarelli, Valerio|Fiscon, Giulia|Felici, Giovanni|Bertolazzi, Paola|Weitschek, Emanuel', 'Institute of Systems Analysis and Computer Science - National Research Council, 00185, Rome, Italy.|Institute of Systems Analysis and Computer Science - National Research Council, 00185, Rome, Italy, Department of Computer, Control, and Management Engineering - Sapienza University, 00185, Rome, Italy and.|Institute of Systems Analysis and Computer Science - National Research Council, 00185, Rome, Italy.|Institute of Systems Analysis and Computer Science - National Research Council, 00185, Rome, Italy.|Institute of Systems Analysis and Computer Science - National Research Council, 00185, Rome, Italy, Department of Engineering - Uninettuno International University, Corso Vittorio Emanuele II, 39 - 00186 Rome, Italy.', 'Italy|Italy|Italy|Italy|Italy', 0, 'Algorithms|High-Throughput Nucleotide Sequencing|Humans|Neoplasms|RNA|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btv635', '26519501', 'Nowadays, knowledge extraction methods from Next Generation Sequencing data are highly requested. In this work, we focus on RNA-seq gene expression analysis and specifically on case-control studies with rule-based supervised classification algorithms that build a model able to discriminate cases from controls. State of the art algorithms compute a single classification model that contains few features (genes). On the contrary, our goal is to elicit a higher amount of knowledge by computing many classification models, and therefore to identify most of the genes related to the predicted class.'),
(79600, 2016, 'bio', 'Bayesian inference with historical data-based informative priors improves detection of differentially expressed genes', 'Li, Ben|Sun, Zhaonan|He, Qing|Zhu, Yu|Qin, Zhaohui S', 'Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA.|Department of Statistics, Purdue University, West Lafayette, IN 47906, USA and.|Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA.|Department of Statistics, Purdue University, West Lafayette, IN 47906, USA and.|Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA, Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA 30322, USA.', 'United States|United States|United States|United States|United States', 5, 'Algorithms|Bayes Theorem|Databases, Factual|Genomics', 'Journal Article', '10.1093/bioinformatics/btv631', '26519502', 'Modern high-throughput biotechnologies such as microarray are capable of producing a massive amount of information for each sample. However, in a typical high-throughput experiment, only limited number of samples were assayed, thus the classical large p, small n problem. On the other hand, rapid propagation of these high-throughput technologies has resulted in a substantial collection of data, often carried out on the same platform and using the same protocol. It is highly desirable to utilize the existing data when performing analysis and inference on a new dataset.'),
(79601, 2016, 'bio', 'MMR: a tool for read multi-mapper resolution', 'Kahles, André|Behr, Jonas|Rätsch, Gunnar', 'Memorial Sloan Kettering Cancer Center, Computational Biology Center, 1275 York Avenue, New York, NY 10065, USA.|Memorial Sloan Kettering Cancer Center, Computational Biology Center, 1275 York Avenue, New York, NY 10065, USA.|Memorial Sloan Kettering Cancer Center, Computational Biology Center, 1275 York Avenue, New York, NY 10065, USA.', 'United States|United States|United States', 24, 'Algorithms|Genome|High-Throughput Nucleotide Sequencing|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btv624', '26519503', 'Mapping high-throughput sequencing data to a reference genome is an essential step for most analysis pipelines aiming at the computational analysis of genome and transcriptome sequencing data. Breaking ties between equally well mapping locations poses a severe problem not only during the alignment phase but also has significant impact on the results of downstream analyses. We present the multi-mapper resolution (MMR) tool that infers optimal mapping locations from the coverage density of other mapped reads.'),
(79602, 2016, 'bio', 'Fit3D: a web application for highly accurate screening of spatial residue patterns in protein structure data', 'Kaiser, Florian|Eisold, Alexander|Bittrich, Sebastian|Labudde, Dirk', 'Department of Applied Computer and Biosciences, University of Applied Sciences Mittweida, 09648 Mittweida, Germany.|Department of Applied Computer and Biosciences, University of Applied Sciences Mittweida, 09648 Mittweida, Germany.|Department of Applied Computer and Biosciences, University of Applied Sciences Mittweida, 09648 Mittweida, Germany.|Department of Applied Computer and Biosciences, University of Applied Sciences Mittweida, 09648 Mittweida, Germany.', 'Germany|Germany|Germany|Germany', 0, 'Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btv637', '26519504', 'The clarification of linkage between protein structure and function is still a demanding process and can be supported by comparison of spatial residue patterns, so-called structural motifs. However, versatile up-to-date resources to search for local structure similarities are rare. We present Fit3D, an easily accessible web application for highly accurate screening of structural motifs in 3D protein data.'),
(79603, 2016, 'bio', 'Two-pass alignment improves novel splice junction quantification', 'Veeneman, Brendan A|Shukla, Sudhanshu|Dhanasekaran, Saravana M|Chinnaiyan, Arul M|Nesvizhskii, Alexey I', 'Department of Computational Medicine and Bioinformatics, Michigan Center for Translational Pathology.|Michigan Center for Translational Pathology, Department of Pathology.|Michigan Center for Translational Pathology, Department of Pathology.|Department of Computational Medicine and Bioinformatics, Michigan Center for Translational Pathology, Department of Pathology, Department of Urology and Howard Hughes Medical Institute, University of Michigan Medical School, Ann Arbor, Michigan 48109, USA.|Department of Computational Medicine and Bioinformatics, Michigan Center for Translational Pathology, Department of Pathology.', 'United States|United States|United States|United States|United States', 0, 'Base Sequence|Cell Line, Tumor|Databases, Nucleic Acid|Humans|RNA Splice Sites|RNA Splicing|Sequence Alignment', 'Journal Article', '10.1093/bioinformatics/btv642', '26519505', 'Discovery of novel splicing from RNA sequence data remains a critical and exciting focus of transcriptomics, but reduced alignment power impedes expression quantification of novel splice junctions.'),
(79604, 2016, 'bio', 'Riboswitch Scanner: an efficient pHMM-based web-server to detect riboswitches in genomic sequences', 'Mukherjee, Sumit|Sengupta, Supratim', 'Department of Physical Sciences, Indian Institute of Science Education and Research Kolkata, Mohanpur-741246, West Bengal, India.|Department of Physical Sciences, Indian Institute of Science Education and Research Kolkata, Mohanpur-741246, West Bengal, India.', 'India|India', 22, 'Genomics|Riboswitch', 'Journal Article', '10.1093/bioinformatics/btv640', '26519506', 'Riboswitches are non-coding RNA located in the 5 untranslated regions where they bind a target metabolite used to specify the riboswitch class and control the expression of associated genes. Accurate identification of riboswitches is the first step towards understanding their regulatory and functional roles in the cell. In this article, we describe a new web application named Riboswitch Scanner which provides an automated pipeline for pHMM-based detection of riboswitches in partial as well as complete genomic sequences rapidly, with high sensitivity and specificity.'),
(79605, 2016, 'bio', 'catRAPID signature: identification of ribonucleoproteins and RNA-binding regions', 'Livi, Carmen Maria|Klus, Petr|Delli Ponti, Riccardo|Tartaglia, Gian Gaetano', 'Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona 08003, Spain, Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain and.|Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona 08003, Spain, Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain and.|Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona 08003, Spain, Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain and.|Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona 08003, Spain, Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain and Institució Catalana de Recerca i Estudis Avançats (ICREA), 08010 Barcelona, Spain.', 'Spain|Spain|Spain|Spain', 0, 'Algorithms|Humans|RNA|Ribonucleoproteins|Software', 'Journal Article', '10.1093/bioinformatics/btv629', '26520853', 'Recent technological advances revealed that an unexpected large number of proteins interact with transcripts even if the RNA-binding domains are not annotated. We introduce catRAPID signature to identify ribonucleoproteins based on physico-chemical features instead of sequence similarity searches. The algorithm, trained on human proteins and tested on model organisms, calculates the overall RNA-binding propensity followed by the prediction of RNA-binding regions. catRAPID signature outperforms other algorithms in the identification of RNA-binding proteins and detection of non-classical RNA-binding regions. Results are visualized on a webpage and can be downloaded or forwarded to catRAPID omics for predictions of RNA targets.'),
(79606, 2016, 'bio', 'Enumeration and extension of non-equivalent deterministic update schedules in Boolean networks', 'Palma, Eduardo|Salinas, Lilian|Aracena, Julio', 'Departamento de Ingenierxc3xada Informxc3xa1tica y Ciencias de la Computacixc3xb3n, Universidad de Concepcixc3xb3n, Piso 3, Concepcixc3xb3n, Chile.|Departamento de Ingenierxc3xada Informxc3xa1tica y Ciencias de la Computacixc3xb3n, Universidad de Concepcixc3xb3n, Piso 3, Concepcixc3xb3n, Chile. Centro de Investigacixc3xb3n en Ingenierxc3xada Matemxc3xa1tica, CIMA, Casilla 160-C, Concepcixc3xb3n, Chile.|Departamento de Ingenierxc3xada Matemxc3xa1tica, Universidad de Concepcixc3xb3n, Casilla 160-C, Concepcixc3xb3n, Chile and Centro de Investigacixc3xb3n en Ingenierxc3xada Matemxc3xa1tica, CIMA, Casilla 160-C, Concepcixc3xb3n, Chile.', 'Chile|Chile|Chile', 0, 'Algorithms|Gene Regulatory Networks|Models, Theoretical', 'Journal Article', '10.1093/bioinformatics/btv628', '26520854', 'Boolean networks (BNs) are commonly used to model genetic regulatory networks (GRNs). Due to the sensibility of the dynamical behavior to changes in the updating scheme (order in which the nodes of a network update their state values), it is increasingly common to use different updating rules in the modeling of GRNs to better capture an observed biological phenomenon and thus to obtain more realistic models.In Aracena etxc2xa0al. equivalence classes of deterministic update schedules in BNs, that yield exactly the same dynamical behavior of the network, were defined according to a certain label function on the arcs of the interaction digraph defined for each scheme. Thus, the interaction digraph so labeled (update digraphs) encode the non-equivalent schemes.'),
(79607, 2016, 'bio', 'The discordant method: a novel approach for differential correlation', 'Siska, Charlotte|Bowler, Russell|Kechris, Katerina', 'Computational Bioscience Program, Department of Pharmacology, University of Colorado Denver.|Department of Medicine, National Jewish, Denver, CO and.|Department of Biostatistics and Informatics, University of Colorado Denver, Denver, CO, USA.', 'United States|United States|United States', 0, 'Algorithms|Computational Biology', 'Journal Article', '10.1093/bioinformatics/btv633', '26520855', 'Current differential correlation methods are designed to determine molecular feature pairs that have the largest magnitude of difference between correlation coefficients. These methods do not easily capture molecular feature pairs that experience no correlation in one group but correlation in another, which may reflect certain types of biological interactions. We have developed a tool, the Discordant method, which categorizes the correlation types for each group to make this possible.'),
(79608, 2016, 'bio', 'Specific small-RNA signatures in the amygdala at premotor and motor stages of Parkinsons disease revealed by deep sequencing analysis', 'Pantano, Lorena|Friedländer, Marc R|Escaramís, Georgia|Lizano, Esther|Pallarès-Albanell, Joan|Ferrer, Isidre|Estivill, Xavier|Martí, Eulàlia', 'Genomics and Disease Group, Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology, Barcelona 08003, Spain, Universitat Pompeu Fabra (UPF), Barcelona 08003, Spain, IMIM, Hospital del Mar Medical Research Institute, Barcelona 08003, Spain, CIBER de Epidemiología y Salud Pública (CIBERESP), CRG, Instituto Carlos III Barcelona 08003, Spain, Universitat Autonoma de Barcelona, Institut de Biotecnologia i de Biomedicina, Bellaterra (Cerdanyola del Valles), Barcelona, Spain and.|Genomics and Disease Group, Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology, Barcelona 08003, Spain, Universitat Pompeu Fabra (UPF), Barcelona 08003, Spain, IMIM, Hospital del Mar Medical Research Institute, Barcelona 08003, Spain, CIBER de Epidemiología y Salud Pública (CIBERESP), CRG, Instituto Carlos III Barcelona 08003, Spain.|Genomics and Disease Group, Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology, Barcelona 08003, Spain, Universitat Pompeu Fabra (UPF), Barcelona 08003, Spain, IMIM, Hospital del Mar Medical Research Institute, Barcelona 08003, Spain, CIBER de Epidemiología y Salud Pública (CIBERESP), CRG, Instituto Carlos III Barcelona 08003, Spain.|Genomics and Disease Group, Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology, Barcelona 08003, Spain, Universitat Pompeu Fabra (UPF), Barcelona 08003, Spain, IMIM, Hospital del Mar Medical Research Institute, Barcelona 08003, Spain, CIBER de Epidemiología y Salud Pública (CIBERESP), CRG, Instituto Carlos III Barcelona 08003, Spain.|Genomics and Disease Group, Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology, Barcelona 08003, Spain, Universitat Pompeu Fabra (UPF), Barcelona 08003, Spain, IMIM, Hospital del Mar Medical Research Institute, Barcelona 08003, Spain, CIBER de Epidemiología y Salud Pública (CIBERESP), CRG, Instituto Carlos III Barcelona 08003, Spain.|Institut Neuropatologia, Servei Anatomia Patològica, IDIBELL-Hospital Universitari de Bellvitge, Universitat de Barcelona, Spain and CIBER de Enfermedades Neurodegenerativas (CIBERNED), Instituto Carlos III, Barcelona, Spain.|Genomics and Disease Group, Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology, Barcelona 08003, Spain, Universitat Pompeu Fabra (UPF), Barcelona 08003, Spain, IMIM, Hospital del Mar Medical Research Institute, Barcelona 08003, Spain, CIBER de Epidemiología y Salud Pública (CIBERESP), CRG, Instituto Carlos III Barcelona 08003, Spain.|Genomics and Disease Group, Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology, Barcelona 08003, Spain, Universitat Pompeu Fabra (UPF), Barcelona 08003, Spain, IMIM, Hospital del Mar Medical Research Institute, Barcelona 08003, Spain, CIBER de Epidemiología y Salud Pública (CIBERESP), CRG, Instituto Carlos III Barcelona 08003, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain', 0, 'Amygdala|High-Throughput Nucleotide Sequencing|Humans|MicroRNAs|Parkinson Disease|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btv632', '26530722', 'Most computational tools for small non-coding RNAs (sRNA) sequencing data analysis focus in microRNAs (miRNAs), overlooking other types of sRNAs that show multi-mapping hits. Here, we have developed a pipeline to non-redundantly quantify all types of sRNAs, and extract patterns of expression in biologically defined groups. We have used our tool to characterize and profile sRNAs in post-mortem brain samples of control individuals and Parkinsons disease (PD) cases at early-premotor and late-symptomatic stages.'),
(79609, 2016, 'bio', 'No Promoter Left Behind (NPLB): learn de novo promoter architectures from genome-wide transcription start sites', 'Mitra, Sneha|Narlikar, Leelavati', 'Chemical Engineering Division, CSIR-National Chemical Laboratory, Pune 411008, India.|Chemical Engineering Division, CSIR-National Chemical Laboratory, Pune 411008, India.', 'India|India', 3, 'Genome|Promoter Regions, Genetic|Transcription Initiation Site', 'Journal Article', '10.1093/bioinformatics/btv645', '26530723', 'Promoters have diverse regulatory architectures and thus activate genes differently. For example, some have a TATA-box, many others do not. Even the ones with it can differ in its position relative to the transcription start site (TSS). No Promoter Left Behind (NPLB) is an efficient, organism-independent method for characterizing such diverse architectures directly from experimentally identified genome-wide TSSs, without relying on known promoter elements. As a test case, we show its application in identifying novel architectures in the fly genome.'),
(79610, 2016, 'bio', 'PHYLUCE is a software package for the analysis of conserved genomic loci', 'Faircloth, Brant C', 'Department of Biological Sciences and Museum of Natural Science, Louisiana State University, Baton Rouge, LA 70803, USA.', 'United States', 182, 'Genetic Loci|Genomics|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btv646', '26530724', 'Targeted enrichment of conserved and ultraconserved genomic elements allows universal collection of phylogenomic data from hundreds of species at multiple time scales (<5xe2x80x89Ma toxc2xa0> 300xe2x80x89Ma). Prior to downstream inference, data from these types of targeted enrichment studies must undergo preprocessing to assemble contigs from sequence data, identify targeted, enriched loci from the off-target background data, align enriched contigs representing conserved loci to one another, and prepare and manipulate these alignments for subsequent phylogenomic inference. PHYLUCE is an efficient and easy-to-install software package that accomplishes these tasks across hundreds of taxa and thousands of enriched loci.'),
(79611, 2016, 'bio', 'TESS: an R package for efficiently simulating phylogenetic trees and performing Bayesian inference of lineage diversification rates', 'Höhna, Sebastian|May, Michael R|Moore, Brian R', 'Department of Integrative Biology, Department of Statistics, University of California, Berkeley, CA 94720, USA, Department of Evolution and Ecology, University of California, Davis, CA 95616, USA and Department of Mathematics, Stockholm University, Stockholm, SE-106 91, Sweden.|Department of Evolution and Ecology, University of California, Davis, CA 95616, USA and.|Department of Evolution and Ecology, University of California, Davis, CA 95616, USA and.', 'Sweden|-|-', 0, 'Bayes Theorem|Biological Evolution|Computer Simulation|Phylogeny|Programming Languages', 'Journal Article', '10.1093/bioinformatics/btv651', '26543171', 'Many fundamental questions in evolutionary biology entail estimating rates of lineage diversification (speciation-extinction) that are modeled using birth-death branching processes. We leverage recent advances in branching-process theory to develop a flexible Bayesian framework for specifying diversification models-where rates are constant, vary continuously, or change episodically through time-and implement numerical methods to estimate parameters of these models from molecular phylogenies, even when species sampling is incomplete. We enable both statistical inference and efficient simulation under these models. We also provide robust methods for comparing the relative and absolute fit of competing branching-process models to a given tree, thereby providing rigorous tests of biological hypotheses regarding patterns and processes of lineage diversification.'),
(79612, 2016, 'bio', 'Fast Optimized Cluster Algorithm for Localizations (FOCAL): a spatial cluster analysis for super-resolved microscopy', 'Mazouchi, A|Milstein, J N', 'Department of Chemical and Physical Sciences, University of Toronto Mississauga, Mississauga, ON L5L 1C6, Canada.|Department of Chemical and Physical Sciences, University of Toronto Mississauga, Mississauga, ON L5L 1C6, Canada Department of Physics, University of Toronto, Toronto, ON M5S 1A7, Canada.', 'Canada|Canada', 17, 'Algorithms|Cluster Analysis|Image Processing, Computer-Assisted|Microscopy|Software', 'Journal Article', '10.1093/bioinformatics/btv630', '26543172', 'Single-molecule localization microscopy (SMLM) microscopy provides images of cellular structure at a resolution an order of magnitude below what can be achieved by conventional diffraction limited techniques. The concomitantly larger data sets generated by SMLM require increasingly efficient image analysis software. Density based clustering algorithms, with the most ubiquitous being DBSCAN, are commonly used to quantitatively assess sub-cellular assemblies. DBSCAN, however, is slow, scaling with the number of localizations like O(n log (n)) at best, and its performance is highly dependent upon a subjectively selected choice of parameters.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(79613, 2016, 'bio', 'Exact quantification of cellular robustness in genome-scale metabolic networks', 'Gerstl, Matthias P|Klamt, Steffen|Jungreuthmayer, Christian|Zanghellini, Jürgen', 'Austrian Centre of Industrial Biotechnology, Vienna, Austria, Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria, and.|Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.|Austrian Centre of Industrial Biotechnology, Vienna, Austria, Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria, and.|Austrian Centre of Industrial Biotechnology, Vienna, Austria, Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria, and.', 'Austria|Germany|Austria|Austria', 0, 'Genome|Metabolic Networks and Pathways|Programming Languages|Reproducibility of Results', 'Journal Article', '10.1093/bioinformatics/btv649', '26543173', 'Robustness, the ability of biological networks to uphold their functionality in spite of perturbations, is a key characteristic of all living systems. Although several theoretical approaches have been developed to formalize robustness, it still eludes an exact quantification. Here, we present a rigorous and quantitative approach for the structural robustness of metabolic networks by measuring their ability to tolerate random reaction (or gene) knockouts.'),
(79614, 2016, 'bio', 'meRanTK: methylated RNA analysis ToolKit', 'Rieder, Dietmar|Amort, Thomas|Kugler, Elisabeth|Lusser, Alexandra|Trajanoski, Zlatko', 'Division of Bioinformatics and.|Division of Molecular Biology, Biocenter, Medical University of Innsbruck, Innsbruck 6020, Austria.|Division of Bioinformatics and.|Division of Molecular Biology, Biocenter, Medical University of Innsbruck, Innsbruck 6020, Austria.|Division of Bioinformatics and.', '-|Austria|-|Austria|-', 21, 'Cytosine|DNA Methylation|RNA|Sequence Analysis, DNA|Software|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btv647', '26543174', 'The significance and function of posttranscriptional cytosine methylation in poly(A)RNA attracts great interest but is still poorly understood. High-throughput sequencing of RNA treated with bisulfite (RNA-BSseq) or subjected to enrichment techniques like Aza-IP or miCLIP enables transcriptome wide studies of this particular modification at single base pair resolution. However, to date, there are no specialized software tools available for the analysis of RNA-BSseq or Aza-IP data. Therefore, we developed meRanTK, the first publicly available tool kit which addresses the special demands of high-throughput RNA cytosine methylation data analysis. It provides fast and easy to use splice-aware bisulfite sequencing read mapping, comprehensive methylation calling and identification of differentially methylated cytosines by statistical analysis of single- and multi-replicate experiments. Application of meRanTK to RNA-BSseq or Aza-IP data produces accurate results in standard compliant formats.'),
(79615, 2016, 'bio', 'A hidden Markov random field-based Bayesian method for the detection of long-range chromosomal interactions in Hi-C data', 'Xu, Zheng|Zhang, Guosheng|Jin, Fulai|Chen, Mengjie|Furey, Terrence S|Sullivan, Patrick F|Qin, Zhaohui|Hu, Ming|Li, Yun', 'Department of Biostatistics, Department of Genetics, Department of Computer Science.|Department of Computer Science, Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, NC 27599, USA.|Department of Genetics, School of Medicine, Case Western Reserve University, Cleveland, Ohio 44016.|Department of Biostatistics, Department of Genetics.|Department of Genetics.|Department of Genetics, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.|Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA and.|Division of Biostatistics, Department of Population Health, New York University School of Medicine, New York, NY 10016, USA.|Department of Biostatistics, Department of Genetics, Department of Computer Science.', 'United States|United States|United States|United States|United States|Sweden|United States|United States|United States', 0, 'Bayes Theorem|Chromosomes|Genome-Wide Association Study|Markov Chains|Reproducibility of Results', 'Journal Article', '10.1093/bioinformatics/btv650', '26543175', 'Advances in chromosome conformation capture and next-generation sequencing technologies are enabling genome-wide investigation of dynamic chromatin interactions. For example, Hi-C experiments generate genome-wide contact frequencies between pairs of loci by sequencing DNA segments ligated from loci in close spatial proximity. One essential task in such studies is peak calling, that is, detecting non-random interactions between loci from the two-dimensional contact frequency matrix. Successful fulfillment of this task has many important implications including identifying long-range interactions that assist interpreting a sizable fraction of the results from genome-wide association studies. The task - distinguishing biologically meaningful chromatin interactions from massive numbers of random interactions - poses great challenges both statistically and computationally. Model-based methods to address this challenge are still lacking. In particular, no statistical model exists that takes the underlying dependency structure into consideration.'),
(79616, 2016, 'bio', 'Shape component analysis: structure-preserving dimension reduction on biological shape spaces', 'Lee, Hao-Chih|Liao, Tao|Zhang, Yongjie Jessica|Yang, Ge', 'Department of Biomedical Engineering.|Department of Mechanical Engineering, and.|Department of Biomedical Engineering, Department of Mechanical Engineering, and.|Department of Biomedical Engineering, Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA.', 'United States|United States|United States|United States', 0, 'Cluster Analysis|Programming Languages', 'Journal Article', '10.1093/bioinformatics/btv648', '26543176', 'Quantitative shape analysis is required by a wide range of biological studies across diverse scales, ranging from molecules to cells and organisms. In particular, high-throughput and systems-level studies of biological structures and functions have started to produce large volumes of complex high-dimensional shape data. Analysis and understanding of high-dimensional biological shape data require dimension-reduction techniques.'),
(79617, 2016, 'bio', 'Probabilistic models of genetic variation in structured populations applied to global human studies', 'Hao, Wei|Song, Minsun|Storey, John D', 'Lewis-Sigler Institute for Integrative Genomics and.|Lewis-Sigler Institute for Integrative Genomics and.|Lewis-Sigler Institute for Integrative Genomics and Center for Statistics and Machine Learning, Princeton University, Princeton, NJ 08544, USA.', 'United States|United States|United States', 15, 'Algorithms|Genetic Variation|Genotype|Humans|Models, Statistical|Probability|Software', 'Journal Article', '10.1093/bioinformatics/btv641', '26545820', 'Modern population genetics studies typically involve genome-wide genotyping of individuals from a diverse network of ancestries. An important problem is how to formulate and estimate probabilistic models of observed genotypes that account for complex population structure. The most prominent work on this problem has focused on estimating a model of admixture proportions of ancestral populations for each individual. Here, we instead focus on modeling variation of the genotypes without requiring a higher-level admixture interpretation.'),
(79618, 2016, 'bio', 'De novo identification of replication-timing domains in the human genome by deep learning', 'Liu, Feng|Ren, Chao|Li, Hao|Zhou, Pingkun|Bo, Xiaochen|Shu, Wenjie', 'Department of Biotechnology, Beijing Institute of Radiation Medicine and.|Department of Biotechnology, Beijing Institute of Radiation Medicine and.|Department of Biotechnology, Beijing Institute of Radiation Medicine and.|Department of Radiation Toxicology and Oncology, Beijing Institute of Radiation Medicine, Beijing 100850, China.|Department of Biotechnology, Beijing Institute of Radiation Medicine and.|Department of Biotechnology, Beijing Institute of Radiation Medicine and.', '-|-|-|China|-|-', 22, 'DNA Replication|Genome, Human|Genomics|Humans|Neural Networks (Computer)|Normal Distribution', 'Journal Article', '10.1093/bioinformatics/btv643', '26545821', 'The de novo identification of the initiation and termination zones-regions that replicate earlier or later than their upstream and downstream neighbours, respectively-remains a key challenge in DNA replication.'),
(79619, 2016, 'bio', 'Comparison of genetic variants in matched samples using thesaurus annotation', 'Konopka, Tomasz|Nijman, Sebastian M B', 'Ludwig Institute for Cancer Research, University of Oxford, Oxford, UK.|Ludwig Institute for Cancer Research, University of Oxford, Oxford, UK.', 'United Kingdom|United Kingdom', 0, 'Genetic Variation|Genome, Human|Genomics|Humans|Software|Vocabulary, Controlled', 'Comparative Study', '10.1093/bioinformatics/btv654', '26545822', 'Calling changes in DNA, e.g. as a result of somatic events in cancer, requires analysis of multiple matched sequenced samples. Events in low-mappability regions of the human genome are difficult to encode in variant call files and have been under-reported as a result. However, they can be described accurately through thesaurus annotation-a technique that links multiple genomic loci together to explicate a single variant.'),
(79620, 2016, 'bio', 'Evaluation of hierarchical models for integrative genomic analyses', 'Denis, Marie|Tadesse, Mahlet G', 'UMR AGAP, CIRAD, Montpellier, France, Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA and.|Department of Mathematics and Statistics, Georgetown University, Washington, DC, USA.', 'France|United States', 3, 'Algorithms|Genome|Genomics|Glioblastoma|Humans|Likelihood Functions', 'Journal Article', '10.1093/bioinformatics/btv653', '26545823', 'Advances in high-throughput technologies have led to the acquisition of various types of -omic data on the same biological samples. Each data type gives independent and complementary information that can explain the biological mechanisms of interest. While several studies performing independent analyses of each dataset have led to significant results, a better understanding of complex biological mechanisms requires an integrative analysis of different sources of data.'),
(79621, 2016, 'bio', 'Automatic generation of bioinformatics tools for predicting protein-ligand binding sites', 'Komiyama, Yusuke|Banno, Masaki|Ueki, Kokoro|Saad, Gul|Shimizu, Kentaro', 'Human Genome Center, The Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo 108-8639, Japan and.|Department of Biotechnology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo-ku, Tokyo 113-8657, Japan.|Department of Biotechnology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo-ku, Tokyo 113-8657, Japan.|Department of Biotechnology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo-ku, Tokyo 113-8657, Japan.|Department of Biotechnology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo-ku, Tokyo 113-8657, Japan.', 'Japan|Japan|Japan|Japan|Japan', 0, 'Algorithms|Binding Sites|Computational Biology|Protein Binding|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btv593', '26545824', 'Predictive tools that model protein-ligand binding on demand are needed to promote ligand research in an innovative drug-design environment. However, it takes considerable time and effort to develop predictive tools that can be applied to individual ligands. An automated production pipeline that can rapidly and efficiently develop user-friendly protein-ligand binding predictive tools would be useful.'),
(79622, 2016, 'bio', 'CopyNumber450kCancer: baseline correction for accurate copy number calling from the 450k methylation array', 'Marzouka, Nour-Al-Dain|Nordlund, Jessica|Bäcklin, Christofer L|Lönnerholm, Gudmar|Syvänen, Ann-Christine|Carlsson Almlöf, Jonas', 'Department of Medical Sciences, Molecular Medicine and Science for Life Laboratory.|Department of Medical Sciences, Molecular Medicine and Science for Life Laboratory.|Department of Medical Sciences, Cancer Pharmacology and Computational Medicine and.|Department of Womens and Childrens Health, Pediatric Oncology, Uppsala University, Uppsala, Sweden.|Department of Medical Sciences, Molecular Medicine and Science for Life Laboratory.|Department of Medical Sciences, Molecular Medicine and Science for Life Laboratory.', '-|-|-|Sweden|-|-', 0, 'CpG Islands|DNA Methylation|DNA, Neoplasm|Genome, Human|Humans|Neoplasms|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btv652', '26553913', 'The Illumina Infinium HumanMethylation450 BeadChip (450k) is widely used for the evaluation of DNA methylation levels in large-scale datasets, particularly in cancer. The 450k design allows copy number variant (CNV) calling using existing bioinformatics tools. However, in cancer samples, numerous large-scale aberrations cause shifting in the probe intensities and thereby may result in erroneous CNV calling. Therefore, a baseline correction process is needed. We suggest the maximum peak of probe segment density to correct the shift in the intensities in cancer samples.'),
(79623, 2016, 'bio', 'Realtime analysis and visualization of MinION sequencing data with npReader', 'Cao, Minh Duc|Ganesamoorthy, Devika|Cooper, Matthew A|Coin, Lachlan J M', 'Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia and.|Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia and.|Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia and.|Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia and Department of Genomics of Common Disease, Imperial College London, London W12 0NN, UK.', 'Australia|Australia|Australia|Australia', 12, 'Algorithms|Computational Biology|Nanopores|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btv658', '26556383', 'The recently released Oxford Nanopore MinION sequencing platform presents many innovative features opening up potential for a range of applications not previously possible. Among these features, the ability to sequence in real-time provides a unique opportunity for many time-critical applications. While many software packages have been developed to analyze its data, there is still a lack of toolkits that support the streaming and real-time analysis of MinION sequencing data.'),
(79624, 2016, 'bio', 'Flexible data integration and curation using a graph-based approach', 'Croset, Samuel|Rupp, Joachim|Romacker, Martin', 'Roche Innovation Center Basel, F. Hoffmann-La Roche AG, CH-4070 Basel, Switzerland.|Roche Innovation Center Basel, F. Hoffmann-La Roche AG, CH-4070 Basel, Switzerland.|Roche Innovation Center Basel, F. Hoffmann-La Roche AG, CH-4070 Basel, Switzerland.', 'Switzerland|Switzerland|Switzerland', 7, 'Data Curation|Humans|Knowledge Bases', 'Journal Article', '10.1093/bioinformatics/btv644', '26556384', 'The increasing diversity of data available to the biomedical scientist holds promise for better understanding of diseases and discovery of new treatments for patients. In order to provide a complete picture of a biomedical question, data from many different origins needs to be combined into a unified representation. During this data integration process, inevitable errors and ambiguities present in the initial sources compromise the quality of the resulting data warehouse, and greatly diminish the scientific value of the content. Expensive and time-consuming manual curation is then required to improve the quality of the information. However, it becomes increasingly difficult to dedicate and optimize the resources for data integration projects as available repositories are growing both in size and in number everyday.'),
(79625, 2016, 'bio', 'Specific identification and quantification of circular RNAs from sequencing data', 'Cheng, Jun|Metge, Franziska|Dieterich, Christoph', 'Max Planck Institute for Biology of Ageing, Joseph-Stelzmann-Strasse 9B, 50931 Cologne, Germany.|Max Planck Institute for Biology of Ageing, Joseph-Stelzmann-Strasse 9B, 50931 Cologne, Germany.|Max Planck Institute for Biology of Ageing, Joseph-Stelzmann-Strasse 9B, 50931 Cologne, Germany.', 'Germany|Germany|Germany', 0, 'Animals|High-Throughput Nucleotide Sequencing|Programming Languages|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btv656', '26556385', 'Circular RNAs (circRNAs) are a poorly characterized class of molecules that have been identified decades ago. Emerging high-throughput sequencing methods as well as first reports on confirmed functions have sparked new interest in this RNA species. However, the computational detection and quantification tools are still limited.'),
(79626, 2016, 'bio', 'contamDE: differential expression analysis of RNA-seq data for contaminated tumor samples', 'Shen, Qi|Hu, Jiyuan|Jiang, Ning|Hu, Xiaohua|Luo, Zewei|Zhang, Hong', 'State Key Laboratory of Genetic Engineering and Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai 200433, Peoples Republic of China and.|State Key Laboratory of Genetic Engineering and Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai 200433, Peoples Republic of China and.|State Key Laboratory of Genetic Engineering and Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai 200433, Peoples Republic of China and.|State Key Laboratory of Genetic Engineering and Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai 200433, Peoples Republic of China and.|School of Biosciences, University of Birmingham, Birmingham B15 2TT, UK.|State Key Laboratory of Genetic Engineering and Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai 200433, Peoples Republic of China and.', 'China|China|China|China|United Kingdom|China', 9, 'Gene Expression Profiling|Humans|Neoplasms|RNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btv657', '26556386', 'Accurate detection of differentially expressed genes between tumor and normal samples is a primary approach of cancer-related biomarker identification. Due to the infiltration of tumor surrounding normal cells, the expression data derived from tumor samples would always be contaminated with normal cells. Ignoring such cellular contamination would deflate the power of detecting DE genes and further confound the biological interpretation of the analysis results. For the time being, there does not exists any differential expression analysis approach for RNA-seq data in literature that can properly account for the contamination of tumor samples.'),
(79627, 2016, 'bio', 'BioNetFit: a fitting tool compatible with BioNetGen, NFsim and distributed computing environments', 'Thomas, Brandon R|Chylek, Lily A|Colvin, Joshua|Sirimulla, Suman|Clayton, Andrew H A|Hlavacek, William S|Posner, Richard G', 'Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA.|Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY, USA, Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA.|Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA.|Department of Basic Sciences, Saint Louis College of Pharmacy, Saint Louis, MO, USA.|Center for Microphotonics, Faculty of Science, Engineering and Technology, Cell Biophysics Laboratory, Swinburne University of Technology, Hawthorn, VIC, Australia and.|Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA.|Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA.', 'United States|United States|United States|United States|Australia|United States|United States', 0, 'Software', 'Journal Article', '10.1093/bioinformatics/btv655', '26556387', 'Rule-based models are analyzed with specialized simulators, such as those provided by the BioNetGen and NFsim open-source software packages. Here, we present BioNetFit, a general-purpose fitting tool that is compatible with BioNetGen and NFsim. BioNetFit is designed to take advantage of distributed computing resources. This feature facilitates fitting (i.e. optimization of parameter values for consistency with data) when simulations are computationally expensive.'),
(79628, 2016, 'bio', 'SpectralTDF: transition densities of diffusion processes with time-varying selection parameters, mutation rates and effective population sizes', 'Steinrücken, Matthias|Jewett, Ethan M|Song, Yun S', 'Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003, USA.|Department of Statistics.|Department of Statistics, Department of EECS, Department of Integrative Biology, University of California, Berkeley, CA 94720, USA, Department of Mathematics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA.', 'United States|United States|United States', 0, 'Genetics, Population|Models, Genetic|Mutation|Mutation Rate|Selection, Genetic', 'Journal Article', '10.1093/bioinformatics/btv627', '26556388', 'In the Wright-Fisher diffusion, the transition density function describes the time evolution of the population-wide frequency of an allele. This function has several practical applications in population genetics and computing it for biologically realistic scenarios with selection and demography is an important problem.'),
(79629, 2016, 'bio', 'Identification of differentially methylated loci using wavelet-based functional mixed models', 'Lee, Wonyul|Morris, Jeffrey S', 'Department of Biostatistics, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA.|Department of Biostatistics, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA.', 'United States|United States', 11, 'CpG Islands|DNA Methylation|Epigenesis, Genetic|Epigenomics|Software', 'Journal Article', '10.1093/bioinformatics/btv659', '26559505', 'DNA methylation is a key epigenetic modification that can modulate gene expression. Over the past decade, a lot of studies have focused on profiling DNA methylation and investigating its alterations in complex diseases such as cancer. While early studies were mostly restricted to CpG islands or promoter regions, recent findings indicate that many of important DNA methylation changes can occur in other regions and DNA methylation needs to be examined on a genome-wide scale. In this article, we apply the wavelet-based functional mixed model methodology to analyze the high-throughput methylation data for identifying differentially methylated loci across the genome. Contrary to many commonly-used methods that model probes independently, this framework accommodates spatial correlations across the genome through basis function modeling as well as correlations between samples through functional random effects, which allows it to be applied to many different settings and potentially leads to more power in detection of differential methylation.'),
(79630, 2016, 'bio', 'WormExp: a web-based application for a Caenorhabditis elegans-specific gene expression enrichment analysis', 'Yang, Wentao|Dierking, Katja|Schulenburg, Hinrich', 'Evolutionary Ecology and Genetics, Zoological Institute, CAU Kiel, 24118 Kiel, Germany.|Evolutionary Ecology and Genetics, Zoological Institute, CAU Kiel, 24118 Kiel, Germany.|Evolutionary Ecology and Genetics, Zoological Institute, CAU Kiel, 24118 Kiel, Germany.', 'Germany|Germany|Germany', 0, 'Animals|Caenorhabditis elegans|Databases, Factual|Gene Expression Profiling|Internet|Software', 'Journal Article', '10.1093/bioinformatics/btv667', '26559506', 'A particular challenge of the current omics age is to make sense of the inferred differential expression of genes and proteins. The most common approach is to perform a gene ontology (GO) enrichment analysis, thereby relying on a database that has been extracted from a variety of organisms and that can therefore only yield reliable information on evolutionary conserved functions.'),
(79631, 2016, 'bio', 'BRAKER1: Unsupervised RNA-Seq-Based Genome Annotation with GeneMark-ET and AUGUSTUS', 'Hoff, Katharina J|Lange, Simone|Lomsadze, Alexandre|Borodovsky, Mark|Stanke, Mario', 'Ernst Moritz Arndt Universität Greifswald, Institute for Mathematics and Computer Science, 17487 Greifswald, Germany.|Ernst Moritz Arndt Universität Greifswald, Institute for Mathematics and Computer Science, 17487 Greifswald, Germany.|Joint Georgia Tech and Emory University Wallace H Coulter Department of Biomedical Engineering, Atlanta, GA 30332, USA and.|School of Computational Science and Engineering, Atlanta, GA 30332, USA, Joint Georgia Tech and Emory University Wallace H Coulter Department of Biomedical Engineering, Atlanta, GA 30332, USA and Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, Russia.|Ernst Moritz Arndt Universität Greifswald, Institute for Mathematics and Computer Science, 17487 Greifswald, Germany.', 'Germany|Germany|Georgia|Georgia|Germany', 258, 'Eukaryota|Genome|RNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btv661', '26559507', 'Gene finding in eukaryotic genomes is notoriously difficult to automate. The task is to design a work flow with a minimal set of tools that would reach state-of-the-art performance across a wide range of species. GeneMark-ET is a gene prediction tool that incorporates RNA-Seq data into unsupervised training and subsequently generates ab initio gene predictions. AUGUSTUS is a gene finder that usually requires supervised training and uses information from RNA-Seq reads in the prediction step. Complementary strengths of GeneMark-ET and AUGUSTUS provided motivation for designing a new combined tool for automatic gene prediction.'),
(79632, 2016, 'bio', 'Annotation of rule-based models with formal semantics to enable creation, analysis, reuse and visualization', 'Misirli, Goksel|Cavaliere, Matteo|Waites, William|Pocock, Matthew|Madsen, Curtis|Gilfellon, Owen|Honorato-Zimmer, Ricardo|Zuliani, Paolo|Danos, Vincent|Wipat, Anil', 'Interdisciplinary Computing and Complex BioSystems Research Group, School of Computing Science and Centre for Synthetic Biology and the Bioeconomy, Newcastle University, Newcastle upon Tyne, UK.|School of Informatics, University of Edinburgh, Edinburgh, UK and.|School of Informatics, University of Edinburgh, Edinburgh, UK and.|Turing Ate My Hamster Ltd., Newcastle upon Tyne, UK.|Interdisciplinary Computing and Complex BioSystems Research Group, School of Computing Science and Centre for Synthetic Biology and the Bioeconomy, Newcastle University, Newcastle upon Tyne, UK.|Interdisciplinary Computing and Complex BioSystems Research Group, School of Computing Science and Centre for Synthetic Biology and the Bioeconomy, Newcastle University, Newcastle upon Tyne, UK.|School of Informatics, University of Edinburgh, Edinburgh, UK and.|Interdisciplinary Computing and Complex BioSystems Research Group, School of Computing Science and Centre for Synthetic Biology and the Bioeconomy, Newcastle University, Newcastle upon Tyne, UK.|School of Informatics, University of Edinburgh, Edinburgh, UK and.|Interdisciplinary Computing and Complex BioSystems Research Group, School of Computing Science and Centre for Synthetic Biology and the Bioeconomy, Newcastle University, Newcastle upon Tyne, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Models, Theoretical|Semantics', 'Journal Article', '10.1093/bioinformatics/btv660', '26559508', 'Biological systems are complex and challenging to model and therefore model reuse is highly desirable. To promote model reuse, models should include both information about the specifics of simulations and the underlying biology in the form of metadata. The availability of computationally tractable metadata is especially important for the effective automated interpretation and processing of models. Metadata are typically represented as machine-readable annotations which enhance programmatic access to information about models. Rule-based languages have emerged as a modelling framework to represent the complexity of biological systems. Annotation approaches have been widely used for reaction-based formalisms such as SBML. However, rule-based languages still lack a rich annotation framework to add semantic information, such as machine-readable descriptions, to the components of a model.'),
(79633, 2016, 'bio', 'Highly accurate sequence-based prediction of half-sphere exposures of amino acid residues in proteins', 'Heffernan, Rhys|Dehzangi, Abdollah|Lyons, James|Paliwal, Kuldip|Sharma, Alok|Wang, Jihua|Sattar, Abdul|Zhou, Yaoqi|Yang, Yuedong', 'Signal Processing Laboratory, School of Engineering, Griffith University, Brisbane, Australia.|Signal Processing Laboratory, School of Engineering, Griffith University, Brisbane, Australia, Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia, Medical Research Center (MRC), Department of Psychiatry, University of Iowa, Iowa City, USA.|Signal Processing Laboratory, School of Engineering, Griffith University, Brisbane, Australia.|Signal Processing Laboratory, School of Engineering, Griffith University, Brisbane, Australia.|Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia, School of Engineering and Physics, University of the South Pacific, Private Mail Bag, Laucala Campus, Suva, Fiji.|Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University, Dezhou, Shandong 253023, China.|Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia, National ICT Australia (NICTA), Brisbane, Australia and.|Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University, Dezhou, Shandong 253023, China, Institute for Glycomics and School of Information and Communication Technique, Griffith University, Parklands Dr. Southport, QLD 4222, Australia.|Institute for Glycomics and School of Information and Communication Technique, Griffith University, Parklands Dr. Southport, QLD 4222, Australia.', 'Australia|Australia|Australia|Australia|Australia|China|Australia|Australia|Australia', 0, 'Amino Acid Sequence|Amino Acids|Models, Molecular|Proteins|Solvents', 'Journal Article', '10.1093/bioinformatics/btv665', '26568622', 'Solvent exposure of amino acid residues of proteins plays an important role in understanding and predicting protein structure, function and interactions. Solvent exposure can be characterized by several measures including solvent accessible surface area (ASA), residue depth (RD) and contact numbers (CN). More recently, an orientation-dependent contact number called half-sphere exposure (HSE) was introduced by separating the contacts within upper and down half spheres defined according to the Cxcexb1-Cxcexb2 (HSExcexb2) vector or neighboring Cxcexb1-Cxcexb1 vectors (HSExcexb1). HSExcexb1 calculated from protein structures was found to better describe the solvent exposure over ASA, CN and RD in many applications. Thus, a sequence-based prediction is desirable, as most proteins do not have experimentally determined structures. To our best knowledge, there is no method to predict HSExcexb1 and only one method to predict HSExcexb2.'),
(79634, 2016, 'bio', 'MIEC-SVM: automated pipeline for protein peptide/ligand interaction prediction', 'Li, Nan|Ainsworth, Richard I|Wu, Meixin|Ding, Bo|Wang, Wei', 'Department of Chemistry and Biochemistry, UC, San Diego, La Jolla, CA 92093-0359 USA.|Department of Chemistry and Biochemistry, UC, San Diego, La Jolla, CA 92093-0359 USA.|Department of Chemistry and Biochemistry, UC, San Diego, La Jolla, CA 92093-0359 USA.|Department of Chemistry and Biochemistry, UC, San Diego, La Jolla, CA 92093-0359 USA.|Department of Chemistry and Biochemistry, UC, San Diego, La Jolla, CA 92093-0359 USA.', 'United States|United States|United States|United States|United States', 6, 'Ligands|Peptides|Protein Processing, Post-Translational|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btv666', '26568623', 'MIEC-SVM is a structure-based method for predicting protein recognition specificity. Here, we present an automated MIEC-SVM pipeline providing an integrated and user-friendly workflow for construction and application of the MIEC-SVM models. This pipeline can handle standard amino acids and those with post-translational modifications (PTMs) or small molecules. Moreover, multi-threading and support to Sun Grid Engine (SGE) are implemented to significantly boost the computational efficiency.'),
(79635, 2016, 'bio', 'Optimal seed solver: optimizing seed selection in read mapping', 'Xin, Hongyi|Nahar, Sunny|Zhu, Richard|Emmons, John|Pekhimenko, Gennady|Kingsford, Carl|Alkan, Can|Mutlu, Onur', 'Computer Science Department.|Computer Science Department.|Computer Science Department.|Department of Computer Science and Engineering, Washington University, St. Louis, MO 63130, USA.|Computer Science Department.|Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|Department of Computer Engineering, Bilkent University, Bilkent, Ankara 06800, Turkey and.|Computer Science Department, Department of Electrical and Computer Engineering.', 'United States|United States|United States|United States|United States|United States|Turkey|United States', 9, 'Algorithms', 'Journal Article', '10.1093/bioinformatics/btv670', '26568624', 'Optimizing seed selection is an important problem in read mapping. The number of non-overlapping seeds a mapper selects determines the sensitivity of the mapper while the total frequency of all selected seeds determines the speed of the mapper. Modern seed-and-extend mappers usually select seeds with either an equal and fixed-length scheme or with an inflexible placement scheme, both of which limit the ability of the mapper in selecting less frequent seeds to speed up the mapping process. Therefore, it is crucial to develop a new algorithm that can adjust both the individual seed length and the seed placement, as well as derive less frequent seeds.'),
(79636, 2016, 'bio', 'Using de novo protein structure predictions to measure the quality of very large multiple sequence alignments', 'Fox, Gearóid|Sievers, Fabian|Higgins, Desmond G', 'Conway Institute of Biomolecular and Biomedical Research, and UCD School of Medicine and Medical Science, University College Dublin, Dublin 4, Ireland.|Conway Institute of Biomolecular and Biomedical Research, and UCD School of Medicine and Medical Science, University College Dublin, Dublin 4, Ireland.|Conway Institute of Biomolecular and Biomedical Research, and UCD School of Medicine and Medical Science, University College Dublin, Dublin 4, Ireland.', 'Ireland|Ireland|Ireland', 0, 'Algorithms|Proteins|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btv592', '26568625', 'Multiple sequence alignments (MSAs) with large numbers of sequences are now commonplace. However, current multiple alignment benchmarks are ill-suited for testing these types of alignments, as test cases either contain a very small number of sequences or are based purely on simulation rather than empirical data.'),
(79637, 2016, 'bio', 'Metabolic network-guided binning of metagenomic sequence fragments', 'Biggs, Matthew B|Papin, Jason A', 'Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22903 USA.|Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22903 USA.', 'United States|United States', 0, 'Algorithms|Cluster Analysis|Humans|Metabolic Networks and Pathways|Metagenomics|Microbiota', 'Journal Article', '10.1093/bioinformatics/btv671', '26568626', 'Most microbes on Earth have never been grown in a laboratory, and can only be studied through DNA sequences. Environmental DNA sequence samples are complex mixtures of fragments from many different species, often unknown. There is a pressing need for methods that can reliably reconstruct genomes from complex metagenomic samples in order to address questions in ecology, bioremediation, and human health.'),
(79638, 2016, 'bio', 'Uncovering multiloci-ordering by algebraic property of Laplacian matrix and its Fiedler vector', 'Cheon, Mookyung|Kim, Choongrak|Chang, Iksoo', 'Creative Research Initiatives Center for Proteome Biophysics, Department of Brain and Cognitive Sciences, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 711-873, Korea and.|Department of Statistics, Pusan National University, Busan 609-735, Korea.|Creative Research Initiatives Center for Proteome Biophysics, Department of Brain and Cognitive Sciences, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 711-873, Korea and.', '-|-|-', 3, 'Software', 'Journal Article', '10.1093/bioinformatics/btv669', '26568627', 'The loci-ordering, based on two-point recombination fractions for a pair of loci, is the most important step in constructing a reliable and fine genetic map.'),
(79639, 2016, 'bio', 'rHAT: fast alignment of noisy long reads with regional hashing', 'Liu, Bo|Guan, Dengfeng|Teng, Mingxiang|Wang, Yadong', 'Center for Bioinformatics, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.|Center for Bioinformatics, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.|Center for Bioinformatics, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.|Center for Bioinformatics, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.', 'China|China|China|China', 0, 'Algorithms|Genomics|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btv662', '26568628', 'Single Molecule Real-Time (SMRT) sequencing has been widely applied in cutting-edge genomic studies. However, it is still an expensive task to align the noisy long SMRT reads to reference genome by state-of-the-art aligners, which is becoming a bottleneck in applications with SMRT sequencing. Novel approach is on demand for improving the efficiency and effectiveness of SMRT read alignment.'),
(79640, 2016, 'bio', 'Accurate estimation of isoelectric point of protein and peptide based on amino acid sequences', 'Audain, Enrique|Ramos, Yassel|Hermjakob, Henning|Flower, Darren R|Perez-Riverol, Yasset', 'Department of Proteomics, Center of Molecular Immunology.|Department of Proteomics, Center for Genetic Engineering and Biotechnology, Ciudad de la Habana, Cuba.|Department European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK and.|School of Life and Health Sciences, Aston University, Aston Triangle, Birmingham, B4 7ET, UK.|Department European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK and.', 'United Kingdom|Cuba|United Kingdom|United Kingdom|United Kingdom', 15, 'Amino Acid Sequence|Isoelectric Focusing|Isoelectric Point|Peptides|Proteomics|Tandem Mass Spectrometry', 'Journal Article', '10.1093/bioinformatics/btv674', '26568629', 'In any macromolecular polyprotic system-for example protein, DNA or RNA-the isoelectric point-commonly referred to as the pI-can be defined as the point of singularity in a titration curve, corresponding to the solution pH value at which the net overall surface charge-and thus the electrophoretic mobility-of the ampholyte sums to zero. Different modern analytical biochemistry and proteomics methods depend on the isoelectric point as a principal feature for protein and peptide characterization. Protein separation by isoelectric point is a critical part of 2-D gel electrophoresis, a key precursor of proteomics, where discrete spots can be digested in-gel, and proteins subsequently identified by analytical mass spectrometry. Peptide fractionation according to their pI is also widely used in current proteomics sample preparation procedures previous to the LC-MS/MS analysis. Therefore accurate theoretical prediction of pI would expedite such analysis. While such pI calculation is widely used, it remains largely untested, motivating our efforts to benchmark pI prediction methods.'),
(79641, 2016, 'bio', 'PAPA: a flexible tool for identifying pleiotropic pathways using genome-wide association study summaries', 'Wen, Yan|Wang, Wenyu|Guo, Xiong|Zhang, Feng', 'Key Laboratory of Trace Elements and Endemic Diseases of Ministry of Health, School of Public Health, Health Science Center, Xian Jiaotong University, Xian, Peoples Republic of China.|Key Laboratory of Trace Elements and Endemic Diseases of Ministry of Health, School of Public Health, Health Science Center, Xian Jiaotong University, Xian, Peoples Republic of China.|Key Laboratory of Trace Elements and Endemic Diseases of Ministry of Health, School of Public Health, Health Science Center, Xian Jiaotong University, Xian, Peoples Republic of China.|Key Laboratory of Trace Elements and Endemic Diseases of Ministry of Health, School of Public Health, Health Science Center, Xian Jiaotong University, Xian, Peoples Republic of China.', 'China|China|China|China', 0, 'Genome-Wide Association Study|Humans|Waist-Hip Ratio', 'Journal Article', '10.1093/bioinformatics/btv668', '26568630', ': Pleiotropy is common in the genetic architectures of complex diseases. To the best of our knowledge, no analysis tool has been developed for identifying pleiotropic pathways using multiple genome-wide association study (GWAS) summaries by now. Here, we present PAPA, a flexible tool for pleiotropic pathway analysis utilizing GWAS summary results. The performance of PAPA was validated using publicly available GWAS summaries of body mass index and waist-hip ratio of the GIANT datasets. PAPA identified a set of pleiotropic pathways, which have been demonstrated to be involved in the development of obesity.'),
(79642, 2016, 'bio', 'CHRONOS: a time-varying method for microRNA-mediated subpathway enrichment analysis', 'Vrahatis, Aristidis G|Dimitrakopoulou, Konstantina|Balomenos, Panos|Tsakalidis, Athanasios K|Bezerianos, Anastasios', 'Department of Computer Engineering and Informatics, Department of Medical Physics, School of Medicine, University of Patras, Patras 26500, Greece.|Centre for Cancer Biomarkers CCBIO and Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway and.|Department of Computer Engineering and Informatics, Department of Medical Physics, School of Medicine, University of Patras, Patras 26500, Greece.|Department of Computer Engineering and Informatics.|Department of Medical Physics, School of Medicine, University of Patras, Patras 26500, Greece, SINAPSE Institute, Center of Life Sciences, National University of Singapore, Singapore 117456.', 'Greece|Norway|Greece|-|Greece', 0, 'MicroRNAs|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btv673', '26568631', 'In the era of network medicine and the rapid growth of paired time series mRNA/microRNA expression experiments, there is an urgent need for pathway enrichment analysis methods able to capture the time- and condition-specific active parts of the biological circuitry as well as the microRNA impact. Current methods ignore the multiple dynamical themes-in the form of enriched biologically relevant microRNA-mediated subpathways-that determine the functionality of signaling networks across time.'),
(79643, 2016, 'bio', 'BioPartsBuilder: a synthetic biology tool for combinatorial assembly of biological parts', 'Yang, Kun|Stracquadanio, Giovanni|Luo, Jingchuan|Boeke, Jef D|Bader, Joel S', 'Department of Biomedical Engineering, Johns Hopkins University, 3400xe2x80x89N. Charles Street, Baltimore, MD 21218, USA and.|Department of Biomedical Engineering, Johns Hopkins University, 3400xe2x80x89N. Charles Street, Baltimore, MD 21218, USA and.|Institute for Systems Genetics and Department of Biochemistry and Molecular Pharmacology, NYU Langone Medical Center, New York, NY 10016, USA.|Institute for Systems Genetics and Department of Biochemistry and Molecular Pharmacology, NYU Langone Medical Center, New York, NY 10016, USA.|Department of Biomedical Engineering, Johns Hopkins University, 3400xe2x80x89N. Charles Street, Baltimore, MD 21218, USA and.', 'United States|United States|United States|United States|United States', 6, 'DNA|Programming Languages|Synthetic Biology', 'Journal Article', '10.1093/bioinformatics/btv664', '26568632', 'Combinatorial assembly of DNA elements is an efficient method for building large-scale synthetic pathways from standardized, reusable components. These methods are particularly useful because they enable assembly of multiple DNA fragments in one reaction, at the cost of requiring that each fragment satisfies design constraints. We developed BioPartsBuilder as a biologist-friendly web tool to design biological parts that are compatible with DNA combinatorial assembly methods, such as Golden Gate and related methods. It retrieves biological sequences, enforces compliance with assembly design standards and provides a fabrication plan for each fragment.'),
(79644, 2016, 'bio', 'Optimal design of gene knockout experiments for gene regulatory network inference', 'Ud-Dean, S M Minhaz|Gunawan, Rudiyanto', 'Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland and Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland and.|Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland and Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland and.', 'Switzerland|Switzerland', 14, 'Algorithms|Escherichia coli|Gene Expression|Gene Knockout Techniques|Gene Regulatory Networks', 'Journal Article', '10.1093/bioinformatics/btv672', '26568633', 'We addressed the problem of inferring gene regulatory network (GRN) from gene expression data of knockout (KO) experiments. This inference is known to be underdetermined and the GRN is not identifiable from data. Past studies have shown that suboptimal design of experiments (DOE) contributes significantly to the identifiability issue of biological networks, including GRNs. However, optimizing DOE has received much less attention than developing methods for GRN inference.'),
(79645, 2016, 'bio', 'TCGA2STAT: simple TCGA data access for integrated statistical analysis in R', 'Wan, Ying-Wooi|Allen, Genevera I|Liu, Zhandong', 'Computational and Integrative Biomedical Research Center, Department of Obstetrics and Gynecology, Baylor College of Medicine, Houston, TX, USA.|Department of Statistics and Electrical & Computer Engineering, Rice University, Houston, TX, USA and Department of Pediatrics-Neurology, Jan and Dan Duncan Neurological Research Institute, Texas Childrens Hospital, Baylor College of Medicine, Houston, TX, USA.|Computational and Integrative Biomedical Research Center, Department of Pediatrics-Neurology, Jan and Dan Duncan Neurological Research Institute, Texas Childrens Hospital, Baylor College of Medicine, Houston, TX, USA.', 'United States|United States|United States', 0, 'Genomics|Humans|Neoplasms|Software', 'Journal Article', '10.1093/bioinformatics/btv677', '26568634', 'Massive amounts of high-throughput genomics data profiled from tumor samples were made publicly available by the Cancer Genome Atlas (TCGA).'),
(79646, 2016, 'bio', 'KMAD: knowledge-based multiple sequence alignment for intrinsically disordered proteins', 'Lange, Joanna|Wyrwicz, Lucjan S|Vriend, Gert', 'Laboratory of Bioinformatics and Biostatistics, M. Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Warsaw, Poland and Laboratory of Bioinformatics and Biostatistics, M. Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Warsaw, Poland and.|Laboratory of Bioinformatics and Biostatistics, M. Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Warsaw, Poland and.|CMBI Radboudumc, 6525 GA, Nijmegen, The Netherlands.', 'Poland|Poland|Netherlands', 9, 'Intrinsically Disordered Proteins|Knowledge Bases|Proteins|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btv663', '26568635', 'Intrinsically disordered proteins (IDPs) lack tertiary structure and thus differ from globular proteins in terms of their sequence-structure-function relations. IDPs have lower sequence conservation, different types of active sites and a different distribution of functionally important regions, which altogether make their multiple sequence alignment (MSA) difficult. The KMAD MSA software has been written specifically for the alignment and annotation of IDPs. It augments the substitution matrix with knowledge about post-translational modifications, functional domains and short linear motifs.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(79647, 2016, 'bio', 'The intrinsic combinatorial organization and information theoretic content of a sequence are correlated to the DNA encoded nucleosome organization of eukaryotic genomes', 'Utro, Filippo|Di Benedetto, Valeria|Corona, Davide F V|Giancarlo, Raffaele', 'Computational Genomics Group, IBM T.J. Watson Research Center, Yorktown Heights, NY, USA.|Dipartimento di Matematica ed Informatica, Università di Palermo and.|Dipartimento STEBICEF, Dulbecco Telethon Institute c/o Università di Palermo, Palermo, Italy.|Dipartimento di Matematica ed Informatica, Università di Palermo and.', 'United States|United States|Italy|United States', 11, 'Chromatin|DNA|Eukaryota|Genome|Nucleosomes', 'Journal Article', '10.1093/bioinformatics/btv679', '26576651', 'Thanks to research spanning nearly 30 years, two major models have emerged that account for nucleosome organization in chromatin: statistical and sequence specific. The first is based on elegant, easy to compute, closed-form mathematical formulas that make no assumptions of the physical and chemical properties of the underlying DNA sequence. Moreover, they need no training on the data for their computation. The latter is based on some sequence regularities but, as opposed to the statistical model, it lacks the same type of closed-form formulas that, in this case, should be based on the DNA sequence only.'),
(79648, 2016, 'bio', 'Global copy number profiling of cancer genomes', 'Wang, Xuefeng|Chen, Mengjie|Yu, Xiaoqing|Pornputtapong, Natapol|Chen, Hao|Zhang, Nancy R|Powers, R Scott|Krauthammer, Michael', 'Department of Family, Population & Preventive Medicine, Stony Brook University, Stony Brook, NY 11794, USA, Department of Pathology, Yale School of Medicine, New Haven, CT 06520, USA.|Departments of Biostatistics and Genetics, University of North Carolina, Chapel Hill, NC 27599, USA.|Department of Biostatistics.|Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, Department of Pathology, Yale School of Medicine, New Haven, CT 06520, USA.|Department of Statistics, University of California, Davis, CA 9516, USA.|Department of Statistics, The Wharton School, University of Pennsylvania, PA 19104, USA and.|Department of Pathology, Stony Brook University, Stony Brook, NY 11794, USA.|Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, Department of Pathology, Yale School of Medicine, New Haven, CT 06520, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|DNA Copy Number Variations|Gene Frequency|Genome, Human|Humans|Neoplasms|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btv676', '26576652', 'In this article, we introduce a robust and efficient strategy for deriving global and allele-specific copy number alternations (CNA) from cancer whole exome sequencing data based on Log R ratios and B-allele frequencies. Applying the approach to the analysis of over 200 skin cancer samples, we demonstrate its utility for discovering distinct CNA events and for deriving ancillary information such as tumor purity.'),
(79649, 2016, 'bio', 'JSpeciesWS: a web server for prokaryotic species circumscription based on pairwise genome comparison', 'Richter, Michael|Rossellxc3xb3-Mxc3xb3ra, Ramon|Oliver Glxc3xb6ckner, Frank|Peplies, Jxc3xb6rg', 'Ribocon GmbH, Fahrenheitstraxc3x9fe. 1, 28359 Bremen, Germany.|Marine Microbiology Group, Mediterranean Institute for Advanced Studies, C/ Miquel Marquxc3xa9s, 21, 07190, Esporles, Spain.|Max Planck Institute for Marine Microbiology, Microbial Genomics and Bioinformatics Research Group, Celsiusstraxc3x9fe 1, 28359 Bremen, Germany and Jacobs University gGmbH Bremen, Department of Life Sciences and Chemistry, Campus Ring 1, 28759 Bremen, Germany.|Ribocon GmbH, Fahrenheitstraxc3x9fe. 1, 28359 Bremen, Germany.', 'Germany|Spain|Germany|Germany', 0, 'Base Sequence|Computers|Genome|Genomics|Internet|Prokaryotic Cells', 'Comparative Study', '10.1093/bioinformatics/btv681', '26576653', 'JSpecies Web Server (JSpeciesWS) is a user-friendly online service for in silico calculating the extent of identity between two genomes, a parameter routinely used in the process of polyphasic microbial species circumscription. The service measures the average nucleotide identity (ANI) based on BLAST+ (ANIb) and MUMmer (ANIm), as well as correlation indexes of tetra-nucleotide signatures (Tetra). In addition, it provides a Tetra Correlation Search function, which allows to rapidly compare selected genomes against a continuously updated reference database with currently about 32xe2x80x89000 published whole and draft genome sequences. For comparison, own genomes can be uploaded and references can be selected from the JSpeciesWS reference database. The service indicates whether two genomes share genomic identities above or below the species embracing thresholds, and serves as a fast way to allocate unknown genomes in the frame of the hitherto sequenced species.'),
(79650, 2016, 'bio', 'Prioritizing hypothesis tests for high throughput data', 'Kim, Sangjin|Schliekelman, Paul', 'Department of Statistics, University of Georgia, Athens, GA 30602, USA.|Department of Statistics, University of Georgia, Athens, GA 30602, USA.', 'Georgia|Georgia', 1, 'Algorithms', 'Journal Article', '10.1093/bioinformatics/btv608', '26576654', 'The advent of high throughput data has led to a massive increase in the number of hypothesis tests conducted in many types of biological studies and a concomitant increase in stringency of significance thresholds. Filtering methods, which use independent information to eliminate less promising tests and thus reduce multiple testing, have been widely and successfully applied. However, key questions remain about how to best apply them: When is filtering beneficial and when is it detrimental? How good does the independent information need to be in order for filtering to be effective? How should one choose the filter cutoff that separates tests that pass the filter from those that dont?'),
(79651, 2016, 'bio', 'Analyzing synergistic and non-synergistic interactions in signalling pathways using Boolean Nested Effect Models', 'Pirkl, Martin|Hand, Elisabeth|Kube, Dieter|Spang, Rainer', 'Statistical Bioinformatics Department, Institute of Functional Genomics, University of Regensburg, 93053 Regensburg and.|Department of Haematology and Oncology, University Medical Centre of the Georg-August University of Göttingen, 37073 Göttingen.|Department of Haematology and Oncology, University Medical Centre of the Georg-August University of Göttingen, 37073 Göttingen.|Statistical Bioinformatics Department, Institute of Functional Genomics, University of Regensburg, 93053 Regensburg and.', '-|-|-|-', 10, 'Databases, Factual|Models, Theoretical|Phosphatidylinositol 3-Kinases|Proteins|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btv680', '26581413', 'Understanding the structure and interplay of cellular signalling pathways is one of the great challenges in molecular biology. Boolean Networks can infer signalling networks from observations of protein activation. In situations where it is difficult to assess protein activation directly, Nested Effect Models are an alternative. They derive the network structure indirectly from downstream effects of pathway perturbations. To date, Nested Effect Models cannot resolve signalling details like the formation of signalling complexes or the activation of proteins by multiple alternative input signals. Here we introduce Boolean Nested Effect Models (B-NEM). B-NEMs combine the use of downstream effects with the higher resolution of signalling pathway structures in Boolean Networks.'),
(79652, 2016, 'bio', 'AccessFold: predicting RNA-RNA interactions with consideration for competing self-structure', 'DiChiacchio, Laura|Sloma, Michael F|Mathews, David H', 'Department of Biochemistry and Biophysics and Center for RNA Biology and.|Department of Biochemistry and Biophysics and Center for RNA Biology and.|Department of Biochemistry and Biophysics and Center for RNA Biology and Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY 14642, USA.', 'United States|United States|United States', 0, 'Algorithms|Base Pairing|Base Sequence|Nucleic Acid Conformation|RNA|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btv682', '26589271', 'There are numerous examples of RNA-RNA complexes, including microRNA-mRNA and small RNA-mRNA duplexes for regulation of translation, guide RNA interactions with target RNA for post-transcriptional modification and small nuclear RNA duplexes for splicing. Predicting the base pairs formed between two interacting sequences remains difficult, at least in part because of the competition between unimolecular and bimolecular structure.'),
(79653, 2016, 'bio', 'iPQF: a new peptide-to-protein summarization method using peptide spectra characteristics to improve protein quantification', 'Fischer, Martina|Renard, Bernhard Y', 'Research Group Bioinformatics (NG 4), Robert Koch Institute, 13353 Berlin, Germany.|Research Group Bioinformatics (NG 4), Robert Koch Institute, 13353 Berlin, Germany.', 'Germany|Germany', 0, 'Algorithms|Computational Biology|Humans|Peptides|Proteins|Proteomics|Reproducibility of Results', 'Journal Article', '10.1093/bioinformatics/btv675', '26589272', 'Isobaric labelling techniques such as iTRAQ and TMT are popular methods for relative protein abundance estimation in proteomic studies. However, measurements are assessed at the peptide spectrum level and exhibit substantial heterogeneity per protein. Hence, clever summarization strategies are required to infer protein ratios. So far, current methods rely exclusively on quantitative values, while additional information on peptides is available, yet it is not considered in these methods.'),
(79654, 2016, 'bio', 'CellTracker (not only) for dummies', 'Piccinini, Filippo|Kiss, Alexa|Horvath, Peter', 'Advanced Research Center on Electronic Systems for Information and Communication Technologies E. De Castro (ARCES), University of Bologna, I-40125 Bologna, Italy.|Advanced Research Center on Electronic Systems for Information and Communication Technologies E. De Castro (ARCES), University of Bologna, I-40125 Bologna, Italy.|Synthetic and System Biology Unit, Hungarian Academia of Sciences, Biological Research Center (BRC), H-6726 Szeged, Hungary and Institute for Molecular Medicine Finland, University of Helsinki, FI-00014 Helsinki, Finland.', 'Italy|Italy|Finland', 37, 'Image Processing, Computer-Assisted|Software', 'Journal Article', '10.1093/bioinformatics/btv686', '26589273', 'Time-lapse experiments play a key role in studying the dynamic behavior of cells. Single-cell tracking is one of the fundamental tools for such analyses. The vast majority of the recently introduced cell tracking methods are limited to fluorescently labeled cells. An equally important limitation is that most software cannot be effectively used by biologists without reasonable expertise in image processing. Here we present CellTracker, a user-friendly open-source software tool for tracking cells imaged with various imaging modalities, including fluorescent, phase contrast and differential interference contrast (DIC) techniques.'),
(79655, 2016, 'bio', 'LocusExplorer: a user-friendly tool for integrated visualization of human genetic association data and biological annotations', 'Dadaev, Tokhir|Leongamornlert, Daniel A|Saunders, Edward J|Eeles, Rosalind|Kote-Jarai, Zsofia', 'The Institute of Cancer Research, London, UK and.|The Institute of Cancer Research, London, UK and.|The Institute of Cancer Research, London, UK and.|The Institute of Cancer Research, London, UK and Royal Marsden NHS Foundation Trust, London, UK.|The Institute of Cancer Research, London, UK and.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 9, 'Genetics, Medical|Humans|Programming Languages|Software|Web Browser', 'Journal Article', '10.1093/bioinformatics/btv690', '26589274', ': In this article, we present LocusExplorer, a data visualization and exploration tool for genetic association data. LocusExplorer is written in R using the Shiny library, providing access to powerful R-based functions through a simple user interface. LocusExplorer allows users to simultaneously display genetic, statistical and biological data for humans in a single image and allows dynamic zooming and customization of the plot features. Publication quality plots may then be produced in a variety of file formats.'),
(79656, 2016, 'bio', 'FoCuS-point: software for STED fluorescence correlation and time-gated single photon counting', 'Waithe, Dominic|Clausen, Mathias P|Sezgin, Erdinc|Eggeling, Christian', 'Wolfson Imaging Centre and.|MRC Human Immunology Unit, Weatherall Institute of Molecular Medicine, University of Oxford, Headley Way, Oxford, OX3 9DS, UK.|MRC Human Immunology Unit, Weatherall Institute of Molecular Medicine, University of Oxford, Headley Way, Oxford, OX3 9DS, UK.|Wolfson Imaging Centre and MRC Human Immunology Unit, Weatherall Institute of Molecular Medicine, University of Oxford, Headley Way, Oxford, OX3 9DS, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 23, 'Algorithms|Diffusion|Molecular Dynamics Simulation|Software|Spectrometry, Fluorescence', 'Journal Article', '10.1093/bioinformatics/btv687', '26589275', 'Fluorescence Correlation Spectroscopy (FCS) is a popular tool for measuring molecular mobility and how mobility relates to molecular interaction dynamics and bioactivity in living cells. The FCS technique has been significantly advanced by its combination with super-resolution STED microscopy (STED-FCS). Specifically, the use of gated detection has shown great potential for enhancing STED-FCS, but has also created a demand for software which is efficient and also implements the latest algorithms. Prior to this study, no open software has been available which would allow practical time-gating and correlation of point data derived from STED-FCS experiments.'),
(79657, 2016, 'bio', 'ParTIES: a toolbox for Paramecium interspersed DNA elimination studies', 'Denby Wilkes, Cyril|Arnaiz, Olivier|Sperling, Linda', 'Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Univ. Paris-Sud, Université Paris-Saclay, 91198, Gif-sur-Yvette cedex, France.|Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Univ. Paris-Sud, Université Paris-Saclay, 91198, Gif-sur-Yvette cedex, France.|Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Univ. Paris-Sud, Université Paris-Saclay, 91198, Gif-sur-Yvette cedex, France.', 'France|France|France', 10, 'Ciliophora Infections|DNA, Protozoan|Genome, Protozoan|Interspersed Repetitive Sequences|Paramecium|Protozoan Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btv691', '26589276', 'Developmental DNA elimination occurs in a wide variety of multicellular organisms, but ciliates are the only single-celled eukaryotes in which this phenomenon has been reported. Despite considerable interest in ciliates as models for DNA elimination, no standard methods for identification and characterization of the eliminated sequences are currently available.'),
(79658, 2016, 'bio', 'SomVarIUS: somatic variant identification from unpaired tissue samples', 'Smith, Kyle S|Yadav, Vinod K|Pei, Shanshan|Pollyea, Daniel A|Jordan, Craig T|De, Subhajyoti', 'Department of Medicine, Department of Pharmacology, Computational Biosciences Training Program, University of Colorado School of Medicine, Aurora, CO, USA.|Department of Medicine.|University of Colorado Cancer Center, Aurora, CO, USA and.|Department of Medicine, University of Colorado Cancer Center, Aurora, CO, USA and.|Department of Medicine, University of Colorado Cancer Center, Aurora, CO, USA and.|Department of Medicine, Department of Pharmacology, Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, USA.', 'United States|United States|United States|United States|United States|United States', 15, 'Exome|Genomics|High-Throughput Nucleotide Sequencing|Humans|Neoplasms|Software', 'Journal Article', '10.1093/bioinformatics/btv685', '26589277', 'Somatic variant calling typically requires paired tumor-normal tissue samples. Yet, paired normal tissues are not always available in clinical settings or for archival samples.'),
(79659, 2016, 'bio', 'Reliable ABC model choice via random forests', 'Pudlo, Pierre|Marin, Jean-Michel|Estoup, Arnaud|Cornuet, Jean-Marie|Gautier, Mathieu|Robert, Christian P', 'Université de Montpellier, IMAG, Montpellier, Institut de Biologie Computationnelle (IBC), Montpellier.|Université de Montpellier, IMAG, Montpellier, Institut de Biologie Computationnelle (IBC), Montpellier.|Institut de Biologie Computationnelle (IBC), Montpellier, CBGP, INRA, Montpellier.|CBGP, INRA, Montpellier.|Institut de Biologie Computationnelle (IBC), Montpellier, CBGP, INRA, Montpellier.|Université Paris Dauphine, CEREMADE, Paris, France and University of Warwick, Coventry, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|France', 83, 'Algorithms|Bayes Theorem|Computer Simulation|Genetics, Population|Models, Genetic', 'Journal Article', '10.1093/bioinformatics/btv684', '26589278', 'Approximate Bayesian computation (ABC) methods provide an elaborate approach to Bayesian inference on complex models, including model choice. Both theoretical arguments and simulation experiments indicate, however, that model posterior probabilities may be poorly evaluated by standard ABC techniques.'),
(79660, 2016, 'bio', 'CDSfold: an algorithm for designing a protein-coding sequence with the most stable secondary structure', 'Terai, Goro|Kamegai, Satoshi|Asai, Kiyoshi', 'National Institute of Advanced Industrial Science and Technology (AIST), Koto-ku, Tokyo 135-0064, Japan, INTEC Inc., Koto-ku, Tokyo 136-8637, Japan and.|National Institute of Advanced Industrial Science and Technology (AIST), Koto-ku, Tokyo 135-0064, Japan, INTEC Inc., Koto-ku, Tokyo 136-8637, Japan and.|National Institute of Advanced Industrial Science and Technology (AIST), Koto-ku, Tokyo 135-0064, Japan, Graduate School of Frontier Sciences, University of Tokyo, Kashiwa 277-8562, Japan.', 'Japan|Japan|Japan', 1, 'Algorithms|Amino Acid Sequence|Base Sequence|Open Reading Frames|Protein Structure, Secondary|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btv678', '26589279', 'An important problem in synthetic biology is to design a nucleotide sequence of an mRNA that confers a desirable expression level of a target protein. The secondary structure of protein-coding sequences (CDSs) is one potential factor that could have both positive and negative effects on protein production. To elucidate the role of secondary structure in CDSs, algorithms for manipulating secondary structure should be developed.'),
(79661, 2016, 'bio', 'hybridSPAdes: an algorithm for hybrid assembly of short and long reads', 'Antipov, Dmitry|Korobeynikov, Anton|McLean, Jeffrey S|Pevzner, Pavel A', 'Center for Algorithmic Biotechnology, Institute for Translational Biomedicine.|Center for Algorithmic Biotechnology, Institute for Translational Biomedicine, Department of Statistical Modelling, St. Petersburg State University, St. Petersburg, Russia.|Department of Periodontics, University of Washington, Seattle, WA 98195, USA.|Center for Algorithmic Biotechnology, Institute for Translational Biomedicine, Department of Computer Science and Engineering, University of California, San Diego, USA and.', 'United States|United States|United States|United States', 133, 'Algorithms|Base Sequence|Chromosome Mapping|Genome|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btv688', '26589280', 'Recent advances in single molecule real-time (SMRT) and nanopore sequencing technologies have enabled high-quality assemblies from long and inaccurate reads. However, these approaches require high coverage by long reads and remain expensive. On the other hand, the inexpensive short reads technologies produce accurate but fragmented assemblies. Thus, a hybrid approach that assembles long reads (with low coverage) and short reads has a potential to generate high-quality assemblies at reduced cost.'),
(79662, 2016, 'bio', 'Large-scale machine learning for metagenomics sequence classification', 'Vervier, Kévin|Mahé, Pierre|Tournoud, Maud|Veyrieras, Jean-Baptiste|Vert, Jean-Philippe', 'Bioinformatics Research Departement, bioMérieux, 69280 Marcy-lÉtoile, MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, 77300 Fontainebleau, Institut Curie, 75248 Paris Cedex and INSERM U900, 75248 Paris Cedex, France.|Bioinformatics Research Departement, bioMérieux, 69280 Marcy-lÉtoile.|Bioinformatics Research Departement, bioMérieux, 69280 Marcy-lÉtoile.|Bioinformatics Research Departement, bioMérieux, 69280 Marcy-lÉtoile.|MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, 77300 Fontainebleau, Institut Curie, 75248 Paris Cedex and INSERM U900, 75248 Paris Cedex, France.', 'France|-|-|-|France', 24, 'Algorithms|Machine Learning|Metagenome|Metagenomics|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btv683', '26589281', 'Metagenomics characterizes the taxonomic diversity of microbial communities by sequencing DNA directly from an environmental sample. One of the main challenges in metagenomics data analysis is the binning step, where each sequenced read is assigned to a taxonomic clade. Because of the large volume of metagenomics datasets, binning methods need fast and accurate algorithms that can operate with reasonable computing requirements. While standard alignment-based methods provide state-of-the-art performance, compositional approaches that assign a taxonomic class to a DNA read based on the k-mers it contains have the potential to provide faster solutions.'),
(79663, 2016, 'bio', 'Genefu: an R/Bioconductor package for computation of gene expression-based signatures in breast cancer', 'Gendoo, Deena M A|Ratanasirigulchai, Natchar|Schröder, Markus S|Paré, Laia|Parker, Joel S|Prat, Aleix|Haibe-Kains, Benjamin', 'Bioinformatics and Computational Laboratory, Princess Margaret Cancer Centre, University Health Network and Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.|Bioinformatics and Computational Laboratory, Princess Margaret Cancer Centre, University Health Network and.|UCD School of Biomolecular and Biomedical Science, Conway Institute, University College Dublin, Dublin, UK.|Translational Genomics and Targeted Therapeutics in Solid Tumors, August Pi i Sunyer Biomedical Research Institute (IDIBAPS), 08036 Barcelona, Spain.|Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC 27599, USA.|Translational Genomics and Targeted Therapeutics in Solid Tumors, August Pi i Sunyer Biomedical Research Institute (IDIBAPS), 08036 Barcelona, Spain, Translational Genomics Group, Vall dHebron Institute of Oncology (VHIO), 08035 Barcelona, Spain and Department of Medical Oncology, Hospital Clínic of Barcelona, 08036 Barcelona, Spain.|Bioinformatics and Computational Laboratory, Princess Margaret Cancer Centre, University Health Network and Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.', 'Canada|United Kingdom|United Kingdom|Spain|United Kingdom|Spain|Canada', 97, 'Algorithms|Breast Neoplasms|Female|Humans|Programming Languages|Software|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btv693', '26607490', 'Breast cancer is one of the most frequent cancers among women. Extensive studies into the molecular heterogeneity of breast cancer have produced a plethora of molecular subtype classification and prognosis prediction algorithms, as well as numerous gene expression signatures. However, reimplementation of these algorithms is a tedious but important task to enable comparison of existing signatures and classification models between each other and with new models. Here, we present the genefu R/Bioconductor package, a multi-tiered compendium of bioinformatics algorithms and gene signatures for molecular subtyping and prognostication in breast cancer.'),
(79664, 2016, 'bio', 'Synchronized navigation and comparative analyses across Ensembl complete bacterial genomes with INSYGHT', 'Lacroix, Thomas|Thérond, Sylvie|Rugeri, Marc|Nicolas, Pierre|Gendrault, Annie|Loux, Valentin|Gibrat, Jean-François', 'INRA, UR1404, Unité Mathématiques et Informatique Appliquées du Génome à lEnvironnement, 78350 Jouy-en-Josas, France and.|IDRIS-CNRS, UPS851, 91400 Orsay, France.|IDRIS-CNRS, UPS851, 91400 Orsay, France.|INRA, UR1404, Unité Mathématiques et Informatique Appliquées du Génome à lEnvironnement, 78350 Jouy-en-Josas, France and.|INRA, UR1404, Unité Mathématiques et Informatique Appliquées du Génome à lEnvironnement, 78350 Jouy-en-Josas, France and.|INRA, UR1404, Unité Mathématiques et Informatique Appliquées du Génome à lEnvironnement, 78350 Jouy-en-Josas, France and.|INRA, UR1404, Unité Mathématiques et Informatique Appliquées du Génome à lEnvironnement, 78350 Jouy-en-Josas, France and.', 'France|France|France|France|France|France|France', 1, 'Computer Graphics|Genome, Bacterial|Genomics|High-Throughput Nucleotide Sequencing|Internet|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/btv689', '26607491', 'High-throughput sequencing technologies provide access to an increasing number of bacterial genomes. Today, many analyses involve the comparison of biological properties among many strains of a given species, or among species of a particular genus. Tools that can help the microbiologist with these tasks become increasingly important.'),
(79665, 2016, 'bio', 'RNA-Enrich: a cut-off free functional enrichment testing method for RNA-seq with improved detection power', 'Lee, Chee|Patil, Snehal|Sartor, Maureen A', 'Department of Computational Medicine and Bioinformatics and.|Department of Computational Medicine and Bioinformatics and.|Department of Computational Medicine and Bioinformatics and Biostatistics Department, University of Michigan, Ann Arbor, MI 48109, USA.', 'United States|United States|United States', 17, 'Gene Expression Profiling|Molecular Sequence Annotation|RNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btv694', '26607492', 'Tests for differential gene expression with RNA-seq data have a tendency to identify certain types of transcripts as significant, e.g. longer and highly-expressed transcripts. This tendency has been shown to bias gene set enrichment (GSE) testing, which is used to find over- or under-represented biological functions in the data. Yet, there remains a surprising lack of tools for GSE testing specific for RNA-seq. We present a new GSE method for RNA-seq data, RNA-Enrich, that accounts for the above tendency empirically by adjusting for average read count per gene. RNA-Enrich is a quick, flexible method and web-based tool, with 16 available gene annotation databases. It does not require a P-value cut-off to define differential expression, and works well even with small sample-sized experiments. We show that adjusting for read counts per gene improves both the type I error rate and detection power of the test.'),
(79666, 2016, 'bio', 'SVD-phy: improved prediction of protein functional associations through singular value decomposition of phylogenetic profiles', 'Franceschini, Andrea|Lin, Jianyi|von Mering, Christian|Jensen, Lars Juhl', 'Institute of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, Zurich, 8057, Switzerland, Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Génopode, Lausanne, 1015, Switzerland.|Department of Computer Science, University of Milan, via Comelico 39, Milan, 20135, Italy and.|Institute of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, Zurich, 8057, Switzerland, Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Génopode, Lausanne, 1015, Switzerland.|Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen N, 2200, Denmark.', 'Switzerland|Italy|Switzerland|Denmark', 34, 'Algorithms|Evolution, Molecular|Phylogeny|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btv696', '26614125', 'A successful approach for predicting functional associations between non-homologous genes is to compare their phylogenetic distributions. We have devised a phylogenetic profiling algorithm, SVD-Phy, which uses truncated singular value decomposition to address the problem of uninformative profiles giving rise to false positive predictions. Benchmarking the algorithm against the KEGG pathway database, we found that it has substantially improved performance over existing phylogenetic profiling methods.'),
(79667, 2016, 'bio', 'Identification of drug-target interaction from interactome network with guilt-by-association principle and topology features', 'Li, Zhan-Chao|Huang, Meng-Hua|Zhong, Wen-Qian|Liu, Zhi-Qing|Xie, Yun|Dai, Zong|Zou, Xiao-Yong', 'School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, Peoples Republic of China.|School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, Peoples Republic of China.|School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, Peoples Republic of China.|School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, Peoples Republic of China.|School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, Peoples Republic of China.|School of Chemistry and Chemical Engineering, Sun Yat-Sen University, Guangzhou 510275, Peoples Republic of China.|SYSU-CMU Shunde International Joint Research Institute, Shunde 528300, Peoples Republic of China and School of Chemistry and Chemical Engineering, Sun Yat-Sen University, Guangzhou 510275, Peoples Republic of China.', 'China|China|China|China|China|China|China', 21, 'Algorithms|Drug Delivery Systems|Drug Discovery|Humans|Protein Conformation|Protein Interaction Maps|Proteins', 'Journal Article', '10.1093/bioinformatics/btv695', '26614126', 'Identifying drug-target protein interaction is a crucial step in the process of drug research and development. Wet-lab experiment are laborious, time-consuming and expensive. Hence, there is a strong demand for the development of a novel theoretical method to identify potential interaction between drug and target protein.'),
(79668, 2016, 'bio', 'MetaQUAST: evaluation of metagenome assemblies', 'Mikheenko, Alla|Saveliev, Vladislav|Gurevich, Alexey', 'Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg 199034, Russia.|Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg 199034, Russia.|Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg 199034, Russia.', '-|-|-', 108, 'Algorithms|Genomic Structural Variation|Metagenome|Metagenomics|Software', 'Journal Article', '10.1093/bioinformatics/btv697', '26614127', 'During the past years we have witnessed the rapid development of new metagenome assembly methods. Although there are many benchmark utilities designed for single-genome assemblies, there is no well-recognized evaluation and comparison tool for metagenomic-specific analogues. In this article, we present MetaQUAST, a modification of QUAST, the state-of-the-art tool for genome assembly evaluation based on alignment of contigs to a reference. MetaQUAST addresses such metagenome datasets features as (i) unknown species content by detecting and downloading reference sequences, (ii) huge diversity by giving comprehensive reports for multiple genomes and (iii) presence of highly relative species by detecting chimeric contigs. We demonstrate MetaQUAST performance by comparing several leading assemblers on one simulated and two real datasets.'),
(79669, 2016, 'bio', 'Admixem: a flexible framework for forward-time simulations of hybrid populations with selection and mate choice', 'Cui, Rongfeng|Schumer, Molly|Rosenthal, Gil G', 'Department of Biology, Texas A&M University, College Station, TX 77843, USA, Centro De Investigaciones Cientxc3xadficas De Las Huastecas Aguazarca, Calnali, Mexico and Max Planck Institute for the Biology of Aging, Cologne, Germany.|Centro De Investigaciones Cientxc3xadficas De Las Huastecas Aguazarca, Calnali, Mexico and.|Department of Biology, Texas A&M University, College Station, TX 77843, USA, Centro De Investigaciones Cientxc3xadficas De Las Huastecas Aguazarca, Calnali, Mexico and.', 'Germany|Mexico|Mexico', 3, 'Epistasis, Genetic|Genetics, Population|Genome|Genomics|Polymorphism, Single Nucleotide|Selection, Genetic|Software', 'Journal Article', '10.1093/bioinformatics/btv700', '26615212', 'We introduce a new forward-time simulator, Admixem, that allows for rapid and realistic simulations of admixed populations with selection. Complex selection can be achieved through user-defined fitness and mating-preference probability functions. Users can specify realistic genomic landscapes and model neutral SNPs in addition to sites under selection. Admixem is designed to simulate selection in admixed populations but can also be used as a general population simulator. Usage and examples are in the supplement.'),
(79670, 2016, 'bio', 'Comment on: ERGC: an efficient referential genome compression algorithm', 'Deorowicz, Sebastian|Grabowski, Szymon|Ochoa, Idoia|Hernaez, Mikel|Weissman, Tsachy', 'Institute of Informatics, Silesian University of Technology, Akademicka 16, Gliwice, 44-100 Poland.|Institute of Applied Computer Science, Lodz University of Technology, Al. Politechniki 11, 90-924 xc5x81xc3xb3dxc5xba, Poland and.|Department of Electrical Engineering, Stanford University, 350 Serra Mall, Stanford, CA, USA.|Department of Electrical Engineering, Stanford University, 350 Serra Mall, Stanford, CA, USA.|Department of Electrical Engineering, Stanford University, 350 Serra Mall, Stanford, CA, USA.', 'Poland|Poland|United States|United States|United States', 0, 'Algorithms|Data Compression|Genome|Genome, Human|Genomics|Humans|Sequence Analysis, DNA', 'Letter', '10.1093/bioinformatics/btv704', '26615213', 'Data compression is crucial in effective handling of genomic data. Among several recently published algorithms, ERGC seems to be surprisingly good, easily beating all of the competitors.'),
(79671, 2016, 'bio', 'Accurate continuous geographic assignment from low- to high-density SNP data', 'Guillot, Gilles|Jxc3xb3nsson, Hxc3xa1kon|Hinge, Antoine|Manchih, Nabil|Orlando, Ludovic', 'Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark and Centre for GeoGenetics, Natural History Museum of Denmark and University of Copenhagen, Copenhagen, Denmark.|Centre for GeoGenetics, Natural History Museum of Denmark and University of Copenhagen, Copenhagen, Denmark.|Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark and.|Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark and.|Centre for GeoGenetics, Natural History Museum of Denmark and University of Copenhagen, Copenhagen, Denmark.', 'Denmark|Denmark|Denmark|Denmark|Denmark', 6, 'Data Interpretation, Statistical|Genotype|High-Throughput Nucleotide Sequencing|Humans|Models, Theoretical|Monte Carlo Method|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btv703', '26615214', 'Large-scale genotype datasets can help track the dispersal patterns of epidemiological outbreaks and predict the geographic origins of individuals. Such genetically-based geographic assignments also show a range of possible applications in forensics for profiling both victims and criminals, and in wildlife management, where poaching hotspot areas can be located. They, however, require fast and accurate statistical methods to handle the growing amount of genetic information made available from genotype arrays and next-generation sequencing technologies.'),
(79672, 2016, 'bio', 'An automated workflow for parallel processing of large multiview SPIM recordings', 'Schmied, Christopher|Steinbach, Peter|Pietzsch, Tobias|Preibisch, Stephan|Tomancak, Pavel', 'Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.|Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.|Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.|Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany, HHMI Janelia Research Campus, Ashburn, VA, USA and Max Delbrück Center for Molecular Medicine, Berlin Institute for Medical Systems Biology, Berlin, Germany.|Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.', 'Germany|Germany|Germany|Germany|Germany', 16, 'Computing Methodologies|Microscopy|Programming Languages|Software|Workflow', 'Journal Article', '10.1093/bioinformatics/btv706', '26628585', 'Selective Plane Illumination Microscopy (SPIM) allows to image developing organisms in 3D at unprecedented temporal resolution over long periods of time. The resulting massive amounts of raw image data requires extensive processing interactively via dedicated graphical user interface (GUI) applications. The consecutive processing steps can be easily automated and the individual time points can be processed independently, which lends itself to trivial parallelization on a high performance computing (HPC) cluster. Here, we introduce an automated workflow for processing large multiview, multichannel, multiillumination time-lapse SPIM data on a single workstation or in parallel on a HPC cluster. The pipeline relies on snakemake to resolve dependencies among consecutive processing steps and can be easily adapted to any cluster environment for processing SPIM data in a fraction of the time required to collect it.'),
(79673, 2016, 'bio', 'LedPred: an R/bioconductor package to predict regulatory sequences using support vector machines', 'Seyres, Denis|Darbo, Elodie|Perrin, Laurent|Herrmann, Carl|González, Aitor', 'INSERM, UMR1090 TAGC, Marseille, F-13288 France, Aix-Marseille Université, UMR1090 TAGC, Marseille, F-13288 France.|Cancer Research UK, London Research Institute, London WC2A 3LY, UK.|INSERM, UMR1090 TAGC, Marseille, F-13288 France, Aix-Marseille Université, UMR1090 TAGC, Marseille, F-13288 France, CNRS, Marseille, France and.|IPMB, Universität Heidelberg and Department of Theoretical Bioinformatics, DKFZ, Heidelberg 69120, Germany.|INSERM, UMR1090 TAGC, Marseille, F-13288 France, Aix-Marseille Université, UMR1090 TAGC, Marseille, F-13288 France.', 'France|United Kingdom|France|Germany|France', 2, 'Animals|Computer Graphics|Drosophila|Gene Expression Regulation|Metabolic Networks and Pathways|Mice|Molecular Sequence Annotation|Software|Support Vector Machine|Systems Integration', 'Journal Article', '10.1093/bioinformatics/btv705', '26628586', 'Supervised classification based on support vector machines (SVMs) has successfully been used for the prediction of cis-regulatory modules (CRMs). However, no integrated tool using such heterogeneous data as position-specific scoring matrices, ChIP-seq data or conservation scores is currently available. Here, we present LedPred, a flexible SVM workflow that predicts new regulatory sequences based on the annotation of known CRMs, which are associated to a large variety of feature types. LedPred is provided as an R/Bioconductor package connected to an online server to avoid installation of non-R software. Due to the heterogeneous CRM feature integration, LedPred excels at the prediction of regulatory sequences in Drosophila and mouse datasets compared with similar SVM-based software.'),
(79674, 2016, 'bio', 'IKAP: A heuristic framework for inference of kinase activities from Phosphoproteomics data', 'Mischnik, Marcel|Sacco, Francesca|Cox, Jürgen|Schneider, Hans-Christoph|Schäfer, Matthias|Hendlich, Manfred|Crowther, Daniel|Mann, Matthias|Klabunde, Thomas', 'Sanofi-Aventis Deutschland GmbH, Frankfurt, Germany and.|Department of Proteomics and Signal Transduction, Max-Planck-Institute for Biochemistry, Martinsried, Germany.|Department of Proteomics and Signal Transduction, Max-Planck-Institute for Biochemistry, Martinsried, Germany.|Sanofi-Aventis Deutschland GmbH, Frankfurt, Germany and.|Sanofi-Aventis Deutschland GmbH, Frankfurt, Germany and.|Sanofi-Aventis Deutschland GmbH, Frankfurt, Germany and.|Sanofi-Aventis Deutschland GmbH, Frankfurt, Germany and.|Department of Proteomics and Signal Transduction, Max-Planck-Institute for Biochemistry, Martinsried, Germany.|Sanofi-Aventis Deutschland GmbH, Frankfurt, Germany and.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 19, 'Algorithms|Animals|Cell Cycle|Cell Cycle Proteins|Cells, Cultured|Databases, Factual|HeLa Cells|Hepatocytes|Heuristics|Humans|Insulin|Mice|Phosphoproteins|Phosphorylation|Protein Kinases|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btv699', '26628587', 'Phosphoproteomics measurements are widely applied in cellular biology to detect changes in signalling dynamics. However, due to the inherent complexity of phosphorylation patterns and the lack of knowledge on how phosphorylations are related to functions, it is often not possible to directly deduce protein activities from those measurements. Here, we present a heuristic machine learning algorithm that infers the activities of kinases from Phosphoproteomics data using kinase-target information from the PhosphoSitePlus database. By comparing the estimated kinase activity profiles to the measured phosphosite profiles, it is furthermore possible to derive the kinases that are most likely to phosphorylate the respective phosphosite.'),
(79675, 2016, 'bio', 'Representation and inference of cellular architecture for metabolic reconstruction and modeling', 'Paley, Suzanne|Krummenacker, Markus|Karp, Peter D', 'Bioinformatics Research Group, SRI International, Menlo Park, CA 94025, USA.|Bioinformatics Research Group, SRI International, Menlo Park, CA 94025, USA.|Bioinformatics Research Group, SRI International, Menlo Park, CA 94025, USA.', 'United States|United States|United States', 0, 'Algorithms|Cell Biology|Cell Physiological Phenomena|Computer Graphics|Models, Theoretical|Software', 'Journal Article', '10.1093/bioinformatics/btv702', '26628588', 'Metabolic modeling depends on accurately representing the cellular locations of enzyme-catalyzed and transport reactions. We sought to develop a representation of cellular compartmentation that would accurately capture cellular location information. We further sought a representation that would support automated inference of the cellular compartments present in newly sequenced organisms to speed model development, and that would enable representing the cellular compartments present in multiple cell types within a multicellular organism.'),
(79676, 2016, 'bio', 'An integrative somatic mutation analysis to identify pathways linked with survival outcomes across 19 cancer types', 'Park, Sunho|Kim, Seung-Jun|Yu, Donghyeon|Peña-Llopis, Samuel|Gao, Jianjiong|Park, Jin Suk|Chen, Beibei|Norris, Jessie|Wang, Xinlei|Chen, Min|Kim, Minsoo|Yong, Jeongsik|Wardak, Zabi|Choe, Kevin|Story, Michael|Starr, Timothy|Cheong, Jae-Ho|Hwang, Tae Hyun', 'Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA.|Department of Computer Science and Electrical Engineering, University of Maryland at Baltimore County, Baltimore, MD, USA.|Department of Statistics, Keimyung University, Daegu, South Korea.|Internal Medicine and Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.|Center for Molecular Biology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.|Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA.|Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA.|Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA.|Department of Statistical Science, Southern Methodist University, Dallas, TX, USA.|Department of Mathematical Sciences, University of Texas at Dallas, Dallas, TX, USA.|Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA.|Department of Biochemistry, Molecular Biology and Biophysics, Obstetrics, Gynecology & Womens Health, University of Minnesota Twin Cities, Minneapolis, MN, USA.|Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA, Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA.|Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA, Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA.|Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA, Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA.|Genetics, Cell Biology, University of Minnesota Twin Cities, Minneapolis, MN, USA, Masonic Cancer Center, University of Minnesota Twin Cities, Minneapolis, MN, USA.|Department of Surgery, Yonsei University College of Medicine, Seoul, South Korea and Open NBI Convergence Technology Research Laboratory, Yonsei University College of Medicine, Seoul, South Korea.|Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA, Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'DNA Mutational Analysis|Gene Regulatory Networks|Genomics|Humans|Mutation|Neoplasms', 'Journal Article', '10.1093/bioinformatics/btv692', '26635139', 'Identification of altered pathways that are clinically relevant across human cancers is a key challenge in cancer genomics. Precise identification and understanding of these altered pathways may provide novel insights into patient stratification, therapeutic strategies and the development of new drugs. However, a challenge remains in accurately identifying pathways altered by somatic mutations across human cancers, due to the diverse mutation spectrum. We developed an innovative approach to integrate somatic mutation data with gene networks and pathways, in order to identify pathways altered by somatic mutations across cancers.'),
(79677, 2016, 'bio', 'qsubsec: a lightweight template system for defining sun grid engine workflows', 'Droop, Alastair P', 'MRC Medical Bioinformatics Centre, University of Leeds, Clarendon Way, Leeds LS2 9NL, UK.', 'United Kingdom', 1, 'Computational Biology|Computer Systems|Computing Methodologies|Software|User-Computer Interface|Workflow', 'Journal Article', '10.1093/bioinformatics/btv698', '26635140', 'The Sun Grid Engine (SGE) high-performance computing batch queueing system is commonly used in bioinformatics analysis. Creating re-usable scripts for the SGE is a common challenge. The qsubsec template language and interpreter described here allow researchers to easily create generic template definitions that encapsulate a particular computational job, effectively separating the process logic from the specific run details. At submission time, the generic template is filled in with specific values. This system provides an intermediate level between simple scripting and complete workflow management tools.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(79678, 2016, 'bio', 'rcellminer: exploring molecular profiles and drug response of the NCI-60 cell lines in R', 'Luna, Augustin|Rajapakse, Vinodh N|Sousa, Fabricio G|Gao, Jianjiong|Schultz, Nikolaus|Varma, Sudhir|Reinhold, William|Sander, Chris|Pommier, Yves', 'Computer Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY 10021, USA.|Developmental Therapeutic Branch, Center for Cancer Research, NCI, NIH, Bethesda, MD 20892, USA.|Centro De Estudos Em Cxc3xa9lulas Tronco, Terapia Celular E Genxc3xa9tica Toxicolxc3xb3gica, Programa De Pxc3xb3s-Graduaxc3xa7xc3xa3o Em Farmxc3xa1cia, Universidade Federal De Mato Grosso Do Sul, Campo Grande, MS 79070-900, Brazil and.|Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10021, USA.|Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10021, USA.|Developmental Therapeutic Branch, Center for Cancer Research, NCI, NIH, Bethesda, MD 20892, USA.|Developmental Therapeutic Branch, Center for Cancer Research, NCI, NIH, Bethesda, MD 20892, USA.|Computer Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY 10021, USA.|Developmental Therapeutic Branch, Center for Cancer Research, NCI, NIH, Bethesda, MD 20892, USA.', 'United States|United States|Brazil|United States|United States|United States|United States|United States|United States', 0, 'Cell Line|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btv701', '26635141', 'The rcellminer R package provides a wide range of functionality to help R users access and explore molecular profiling and drug response data for the NCI-60. The package enables flexible programmatic access to CellMiners unparalleled breadth of NCI-60 data, including gene and protein expression, copy number, whole exome mutations, as well as activity data for xe2x88xbc21K compounds, with information on their structure, mechanism of action and repeat screens. Functions are available to easily visualize compound structures, activity patterns and molecular feature profiles. Additionally, embedded R Shiny applications allow interactive data exploration.'),
(79679, 2016, 'bio', 'Maligner: a fast ordered restriction map aligner', 'Mendelowitz, Lee M|Schwartz, David C|Pop, Mihai', 'Center for Bioinformatics and Computational Biology, Applied Math & Statistics, and Scientific Computation.|Laboratory for Molecular and Computational Genomics, Department of Chemistry, Laboratory of Genetics, USA and the UW-Biotechnology Center, University of Wisconsin-Madison, WI 53706, USA.|Center for Bioinformatics and Computational Biology, Applied Math & Statistics, and Scientific Computation, Department of Computer Science, University of Maryland, College Park, MD 20742, USA and.', 'United States|United States|United States', 10, 'Algorithms|Computer Simulation|Genome|Restriction Mapping|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btv711', '26637292', 'The Optical Mapping System discovers structural variants and potentiates sequence assembly of genomes via scaffolding and comparisons that globally validate or correct sequence assemblies. Despite its utility, there are few publicly available tools for aligning optical mapping datasets.'),
(79680, 2016, 'bio', 'Extending gene ontology with gene association networks', 'Peng, Jiajie|Wang, Tao|Wang, Jixuan|Wang, Yadong|Chen, Jin', 'School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China, Department of Energy Plant Research Laboratory, Michigan State University, East Lansing, MI 48824, USA.|School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.|School of Software, Harbin Institute of Technology, Harbin, China and.|School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.|Department of Energy Plant Research Laboratory, Michigan State University, East Lansing, MI 48824, USA, Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA.', 'China|China|China|China|United States', 0, 'Algorithms|Computational Biology|Gene Ontology|Gene Regulatory Networks|Software', 'Journal Article', '10.1093/bioinformatics/btv712', '26644414', 'Gene ontology (GO) is a widely used resource to describe the attributes for gene products. However, automatic GO maintenance remains to be difficult because of the complex logical reasoning and the need of biological knowledge that are not explicitly represented in the GO. The existing studies either construct whole GO based on network data or only infer the relations between existing GO terms. None is purposed to add new terms automatically to the existing GO.'),
(79681, 2016, 'bio', 'Joint detection of copy number variations in parent-offspring trios', 'Liu, Yongzhuang|Liu, Jian|Lu, Jianguo|Peng, Jiajie|Juan, Liran|Zhu, Xiaolin|Li, Bingshan|Wang, Yadong', 'School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.|School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.|School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.|School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.|School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.|Institute for Genomic Medicine, Columbia University, New York, NY 10032, University Program in Genetics and Genomics, Duke University Medical School, Durham, NC 27708.|Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN 37235 and Center for Quantitative Sciences, Vanderbilt University, Nashville, TN 37235, USA.|School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.', 'China|China|China|China|China|United States|United States|China', 0, 'Algorithms|DNA Copy Number Variations|Genome|Humans|Parents|Software', 'Journal Article', '10.1093/bioinformatics/btv707', '26644415', 'Whole genome sequencing (WGS) of parent-offspring trios is a powerful approach for identifying disease-associated genes via detecting copy number variations (CNVs). Existing approaches, which detect CNVs for each individual in a trio independently, usually yield low-detection accuracy. Joint modeling approaches leveraging Mendelian transmission within the parent-offspring trio can be an efficient strategy to improve CNV detection accuracy.'),
(79682, 2016, 'bio', 'Improved topology prediction using the terminal hydrophobic helices rule', 'Peters, Christoph|Tsirigos, Konstantinos D|Shu, Nanjiang|Elofsson, Arne', 'Department of Biochemistry and Biophysics, Science for Life Laboratory and.|Department of Biochemistry and Biophysics, Science for Life Laboratory and.|Department of Biochemistry and Biophysics, Science for Life Laboratory and Sweden Bioinformatics Infrastructure for Life Sciences (BILS), Stockholm University, Solna 17121, Sweden.|Department of Biochemistry and Biophysics, Science for Life Laboratory and.', '-|-|Sweden|-', 17, 'Computational Biology|Forecasting|Hydrophobic and Hydrophilic Interactions|Membrane Proteins|Protein Sorting Signals|Protein Structure, Secondary', 'Journal Article', '10.1093/bioinformatics/btv709', '26644416', 'The translocon recognizes sufficiently hydrophobic regions of a protein and inserts them into the membrane. Computational methods try to determine what hydrophobic regions are recognized by the translocon. Although these predictions are quite accurate, many methods still fail to distinguish marginally hydrophobic transmembrane (TM) helices and equally hydrophobic regions in soluble protein domains. In vivo, this problem is most likely avoided by targeting of the TM-proteins, so that non-TM proteins never see the translocon. Proteins are targeted to the translocon by an N-terminal signal peptide. The targeting is also aided by the fact that the N-terminal helix is more hydrophobic than other TM-helices. In addition, we also recently found that the C-terminal helix is more hydrophobic than central helices. This information has not been used in earlier topology predictors.'),
(79683, 2016, 'bio', 'Sampling ARG of multiple populations under complex configurations of subdivision and admixture', 'Carrieri, Anna Paola|Utro, Filippo|Parida, Laxmi', 'Dipartimento Di Informatica Sistemistica E Comunicazione, Università Degli Studi Di Milano-Bicocca, Viale Sarca 336, Milano, Italy and.|Computational Genomics, IBM T. J. Watson Research, Yorktown Heights, NY 10598, USA.|Computational Genomics, IBM T. J. Watson Research, Yorktown Heights, NY 10598, USA.', 'Italy|United States|United States', 0, 'Algorithms|Genetics, Population|Genome, Human|Humans|Pedigree|Phylogeny|Population Groups|Recombination, Genetic', 'Journal Article', '10.1093/bioinformatics/btv716', '26644417', 'Simulating complex evolution scenarios of multiple populations is an important task for answering many basic questions relating to population genomics. Apart from the population samples, the underlying Ancestral Recombinations Graph (ARG) is an additional important means in hypothesis checking and reconstruction studies. Furthermore, complex simulations require a plethora of interdependent parameters making even the scenario-specification highly non-trivial.'),
(79684, 2016, 'bio', 'Manta: rapid detection of structural variants and indels for germline and cancer sequencing applications', 'Chen, Xiaoyu|Schulz-Trieglaff, Ole|Shaw, Richard|Barnes, Bret|Schlesinger, Felix|Kxc3xa4llberg, Morten|Cox, Anthony J|Kruglyak, Semyon|Saunders, Christopher T', 'Illumina, Inc, 5200 Illumina Way, San Diego, CA 92122, USA and.|Illumina Cambridge Ltd, Chesterford Research Park, Little Chesterford, Essex CB10 1XL, UK.|Illumina Cambridge Ltd, Chesterford Research Park, Little Chesterford, Essex CB10 1XL, UK.|Illumina, Inc, 5200 Illumina Way, San Diego, CA 92122, USA and.|Illumina, Inc, 5200 Illumina Way, San Diego, CA 92122, USA and.|Illumina Cambridge Ltd, Chesterford Research Park, Little Chesterford, Essex CB10 1XL, UK.|Illumina Cambridge Ltd, Chesterford Research Park, Little Chesterford, Essex CB10 1XL, UK.|Illumina, Inc, 5200 Illumina Way, San Diego, CA 92122, USA and.|Illumina, Inc, 5200 Illumina Way, San Diego, CA 92122, USA and.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 283, 'DNA, Neoplasm|Genome|Genomics|High-Throughput Nucleotide Sequencing|Humans|INDEL Mutation|Neoplasms|Software', 'Journal Article', '10.1093/bioinformatics/btv710', '26647377', ': We describe Manta, a method to discover structural variants and indels from next generation sequencing data. Manta is optimized for rapid germline and somatic analysis, calling structural variants, medium-sized indels and large insertions on standard compute hardware in less than a tenth of the time that comparable methods require to identify only subsets of these variant types: for example NA12878 at 50xc3x97 genomic coverage is analyzed in less than 20xe2x80x89min. Manta can discover and score variants based on supporting paired and split-read evidence, with scoring models optimized for germline analysis of diploid individuals and somatic analysis of tumor-normal sample pairs. Call quality is similar to or better than comparable methods, as determined by pedigree consistency of germline calls and comparison of somatic calls to COSMIC database variants. Manta consistently assembles a higher fraction of its calls to base-pair resolution, allowing for improved downstream annotation and analysis of clinical significance. We provide Manta as a community resource to facilitate practical and routine structural variant analysis in clinical and research sequencing scenarios.'),
(79685, 2016, 'bio', 'NBSPred: a support vector machine-based high-throughput pipeline for plant resistance protein NBSLRR prediction', 'Kushwaha, Sandeep K|Chauhan, Pallavi|Hedlund, Katarina|Ahrén, Dag', 'Department of Biology, Lund University, Ecology Building, Lund 22363, Sweden, PlantLink, Department of Plant Protection, Swedish University of Agricultural Sciences, Alnarp, Sweden and Bioinformatics Infrastructure for Life Sciences (BILS), Lund University, Lund, Sweden.|Department of Biology, Lund University, Ecology Building, Lund 22363, Sweden.|Department of Biology, Lund University, Ecology Building, Lund 22363, Sweden.|Department of Biology, Lund University, Ecology Building, Lund 22363, Sweden, Bioinformatics Infrastructure for Life Sciences (BILS), Lund University, Lund, Sweden.', 'Sweden|Sweden|Sweden|Sweden', 9, 'Amino Acid Sequence|Arabidopsis|Conserved Sequence|Lycopersicon esculentum|Nucleotides|Plant Diseases|Plant Proteins|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/btv714', '26656003', 'The nucleotide binding site leucine-rich repeats (NBSLRRs) belong to one of the largest known families of disease resistance genes that encode resistance proteins (R-protein) against the pathogens of plants. Various defence mechanisms have explained the regulation of plant immunity, but still, we have limited understanding about plant defence against different pathogens. Identification of R-proteins and proteins having R-protein-like features across the genome, transcriptome and proteome would be highly useful to develop the global understanding of plant defence mechanisms, but it is laborious and time-consuming task. Therefore, we have developed a support vector machine-based high-throughput pipeline called NBSPred to differentiate NBSLRR and NBSLRR-like protein from Non-NBSLRR proteins from genome, transcriptome and protein sequences. The pipeline was tested and validated with input sequences from three dicot and two monocot plants including Arabidopsis thaliana, Boechera stricta, Brachypodium distachyon Solanum lycopersicum and Zea mays.'),
(79686, 2016, 'bio', 'PharmacoGx: an R package for analysis of large pharmacogenomic datasets', 'Smirnov, Petr|Safikhani, Zhaleh|El-Hachem, Nehme|Wang, Dong|She, Adrian|Olsen, Catharina|Freeman, Mark|Selby, Heather|Gendoo, Deena M A|Grossmann, Patrick|Beck, Andrew H|Aerts, Hugo J W L|Lupien, Mathieu|Goldenberg, Anna|Haibe-Kains, Benjamin', 'Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.|Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada, Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.|Institut De Recherches Cliniques De Montréal, Montreal, QC, Canada.|Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.|Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.|Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada, Interuniversity Institute of Bioinformatics in Brussels (IB)2, Brussels, Belgium, Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.|Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.|Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA, Department of Bioinformatics, Boston University, Boston, MA, USA.|Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada, Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.|Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.|Beth Israel Deaconess Medical Center, Boston, MA, USA.|Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.|Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada, Machine Learning Group (MLG), Department dInformatique, Université libre de Bruxelles (ULB), Brussels, Belgium, Ontario Institute of Cancer Research, Toronto, ON, Canada.|Hospital for Sick Children, Toronto, ON, Canada and Department of Computer Science, University of Toronto, Toronto, ON, Canada.|Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada, Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.', 'Canada|Canada|Canada|Canada|Canada|Belgium|Canada|United States|Canada|United States|Israel|United States|Belgium|Canada|Canada', 0, 'Genomics|Humans|Neoplasms|Pharmacogenetics|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btv723', '26656004', 'Pharmacogenomics holds great promise for the development of biomarkers of drug response and the design of new therapeutic options, which are key challenges in precision medicine. However, such data are scattered and lack standards for efficient access and analysis, consequently preventing the realization of the full potential of pharmacogenomics. To address these issues, we implemented PharmacoGx, an easy-to-use, open source package for integrative analysis of multiple pharmacogenomic datasets. We demonstrate the utility of our package in comparing large drug sensitivity datasets, such as the Genomics of Drug Sensitivity in Cancer and the Cancer Cell Line Encyclopedia. Moreover, we show how to use our package to easily perform Connectivity Map analysis. With increasing availability of drug-related data, our package will open new avenues of research for meta-analysis of pharmacogenomic data.'),
(79687, 2016, 'bio', 'CASSIS and SMIPS: promoter-based prediction of secondary metabolite gene clusters in eukaryotic genomes', 'Wolf, Thomas|Shelest, Vladimir|Nath, Neetika|Shelest, Ekaterina', 'Research Group Systems Biology/Bioinformatics, Leibniz Institute for Natural Product Research and Infection Biology - Hans-Knöll-Institute (HKI), Jena 07745, Germany.|Research Group Systems Biology/Bioinformatics, Leibniz Institute for Natural Product Research and Infection Biology - Hans-Knöll-Institute (HKI), Jena 07745, Germany.|Research Group Systems Biology/Bioinformatics, Leibniz Institute for Natural Product Research and Infection Biology - Hans-Knöll-Institute (HKI), Jena 07745, Germany.|Research Group Systems Biology/Bioinformatics, Leibniz Institute for Natural Product Research and Infection Biology - Hans-Knöll-Institute (HKI), Jena 07745, Germany.', 'Germany|Germany|Germany|Germany', 37, 'Eukaryota|Multigene Family|Peptide Synthases|Polyketide Synthases|Software', 'Journal Article', '10.1093/bioinformatics/btv713', '26656005', 'Secondary metabolites (SM) are structurally diverse natural products of high pharmaceutical importance. Genes involved in their biosynthesis are often organized in clusters, i.e., are co-localized and co-expressed. In silico cluster prediction in eukaryotic genomes remains problematic mainly due to the high variability of the clusters content and lack of other distinguishing sequence features.'),
(79688, 2016, 'bio', 'Fitchi: haplotype genealogy graphs based on the Fitch algorithm', 'Matschiner, Michael', 'Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, University of Oslo, Oslo, Norway and Zoological Institute, University of Basel, Vesalgasse 1, Basel 4051, Switzerland.', 'Switzerland', 15, 'Algorithms|Genealogy and Heraldry|Genetics, Population|Haplotypes|Phylogeography', 'Journal Article', '10.1093/bioinformatics/btv717', '26656006', ': In population genetics and phylogeography, haplotype genealogy graphs are important tools for the visualization of population structure based on sequence data. In this type of graph, node sizes are often drawn in proportion to haplotype frequencies and edge lengths represent the minimum number of mutations separating adjacent nodes. I here present Fitchi, a new program that produces publication-ready haplotype genealogy graphs based on the Fitch algorithm.'),
(79689, 2016, 'bio', 'FuMa: reporting overlap in RNA-seq detected fusion genes', 'Hoogstrate, Youri|Böttcher, René|Hiltemann, Saskia|van der Spek, Peter J|Jenster, Guido|Stubbs, Andrew P', 'Department of Urology and Department of Bioinformatics, Erasmus University Medical Center, Rotterdam, 3000 CA, The Netherlands.|Department of Urology and.|Department of Urology and Department of Bioinformatics, Erasmus University Medical Center, Rotterdam, 3000 CA, The Netherlands.|Department of Bioinformatics, Erasmus University Medical Center, Rotterdam, 3000 CA, The Netherlands.|Department of Urology and.|Department of Bioinformatics, Erasmus University Medical Center, Rotterdam, 3000 CA, The Netherlands.', 'Netherlands|-|Netherlands|Netherlands|-|Netherlands', 4, 'Genome|Genomics|Humans|RNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btv721', '26656567', 'A new generation of tools that identify fusion genes in RNA-seq data is limited in either sensitivity and or specificity. To allow further downstream analysis and to estimate performance, predicted fusion genes from different tools have to be compared. However, the transcriptomic context complicates genomic location-based matching. FusionMatcher (FuMa) is a program that reports identical fusion genes based on gene-name annotations. FuMa automatically compares and summarizes all combinations of two or more datasets in a single run, without additional programming necessary. FuMa uses one gene annotation, avoiding mismatches caused by tool-specific gene annotations. FuMa matches 10% more fusion genes compared with exact gene matching due to overlapping genes and accepts intermediate output files that allow a stepwise analysis of corresponding tools.'),
(79690, 2016, 'bio', 'Prediction of the permeability of neutral drugs inferred from their solvation properties', 'Milanetti, Edoardo|Raimondo, Domenico|Tramontano, Anna', 'Department of Physics, Sapienza Università di Roma, Rome 00185, Italy.|Department of Physics, Sapienza Università di Roma, Rome 00185, Italy.|Department of Physics, Sapienza Università di Roma, Rome 00185, Italy Institute Pasteur - Fondazione Cenci Bolognetti, Rome, 00161, Italy Center for Life Nano Science @Sapienza, Istituto Italiano di Tecnologia, Sapienza Università di Roma, Rome, 00185, Italy.', 'Italy|Italy|Italy', 9, 'Algorithms|Caco-2 Cells|Drug Discovery|Humans|Permeability|Structure-Activity Relationship|Water', 'Journal Article', '10.1093/bioinformatics/btv725', '26656568', 'Determination of drug absorption is an important component of the drug discovery and development process in that it plays a key role in the decision to promote drug candidates to clinical trials. We have developed a method that, on the basis of an analysis of the dynamic distribution of water molecules around a compound obtained by molecular dynamics simulations, can compute a parameter-free value that correlates very well with the compound permeability measured using the human colon adenocarcinoma (Caco-2) cell line assay.'),
(79691, 2016, 'bio', 'SpotCaliper: fast wavelet-based spot detection with accurate size estimation', 'Püspöki, Zsuzsanna|Sage, Daniel|Ward, John Paul|Unser, Michael', 'Biomedical Imaging Group, École polytechnique fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland.|Biomedical Imaging Group, École polytechnique fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland.|Biomedical Imaging Group, École polytechnique fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland.|Biomedical Imaging Group, École polytechnique fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland', 3, 'Algorithms|Image Processing, Computer-Assisted|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btv728', '26656569', 'SpotCaliper is a novel wavelet-based image-analysis software providing a fast automatic detection scheme for circular patterns (spots), combined with the precise estimation of their size. It is implemented as an ImageJ plugin with a friendly user interface. The user is allowed to edit the results by modifying the measurements (in a semi-automated way), extract data for further analysis. The fine tuning of the detections includes the possibility of adjusting or removing the original detections, as well as adding further spots.'),
(79692, 2016, 'bio', 'Adaptive gene- and pathway-trait association testing with GWAS summary statistics', 'Kwak, Il-Youp|Pan, Wei', 'Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA.|Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA.', 'United States|United States', 30, 'Genome-Wide Association Study|Genotype|Humans|Phenotype|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btv719', '26656570', 'Gene- and pathway-based analyses offer a useful alternative and complement to the usual single SNP-based analysis for GWAS. On the other hand, most existing gene- and pathway-based tests are not highly adaptive, and/or require the availability of individual-level genotype and phenotype data. It would be desirable to have highly adaptive tests applicable to summary statistics for single SNPs. This has become increasingly important given the popularity of large-scale meta-analyses of multiple GWASs and the practical availability of either single GWAS or meta-analyzed GWAS summary statistics for single SNPs.'),
(79693, 2016, 'bio', 'INSECT 20: a web-server for genome-wide cis-regulatory modules prediction', 'Parra, R Gonzalo|Rohr, Cristian O|Koile, Daniel|Perez-Castro, Carolina|Yankilevich, Patricio', 'Facultad De Ciencias Exactas Y Naturales, Universidad De Buenos Aires, Buenos Aires, Argentina and.|Facultad De Ciencias Exactas Y Naturales, Universidad De Buenos Aires, Buenos Aires, Argentina and.|Instituto De Investigacion En Biomedicina De Buenos Aires (IBioBA), CONICET, Partner Institute of the Max Planck Society, Godoy Cruz 2390, Buenos Aires C1425FQA, Argentina.|Instituto De Investigacion En Biomedicina De Buenos Aires (IBioBA), CONICET, Partner Institute of the Max Planck Society, Godoy Cruz 2390, Buenos Aires C1425FQA, Argentina.|Instituto De Investigacion En Biomedicina De Buenos Aires (IBioBA), CONICET, Partner Institute of the Max Planck Society, Godoy Cruz 2390, Buenos Aires C1425FQA, Argentina.', 'Argentina|Argentina|Argentina|Argentina|Argentina', 7, 'Algorithms|Binding Sites|Computer Simulation|Genome|Internet|Regulatory Elements, Transcriptional|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btv726', '26656931', 'INSECT is a user-friendly web server to predict the occurrence of Cis-Regulatory Modules (CRMs), which control gene expression. Here, we present a new release of INSECT which includes several new features, such as whole genome analysis, nucleosome occupancy predictions, and which provides additional links to third-party functional tools that complement user capabilities, CRM analysis and hypothesis construction. Improvements in the core implementation have led to a faster and more efficient tool. In addition, this new release introduces a new interface designed for a more integrative and dynamic user experience.'),
(79694, 2016, 'bio', 'Sequence database versioning for command line and Galaxy bioinformatics servers', 'Dooley, Damion M|Petkau, Aaron J|Van Domselaar, Gary|Hsiao, William W L', 'Department of Pathology, University of British Columbia, Vancouver, BC, Canada.|National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, MB, Canada.|National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, MB, Canada.|Department of Pathology, University of British Columbia, Vancouver, BC, Canada BC Public Health Microbiology and Reference Laboratory, Vancouver, BC, Canada.', 'Canada|Canada|Canada|Canada', 3, 'Computational Biology|Databases, Nucleic Acid|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btv724', '26656932', 'There are various reasons for rerunning bioinformatics tools and pipelines on sequencing data, including reproducing a past result, validation of a new tool or workflow using a known dataset, or tracking the impact of database changes. For identical results to be achieved, regularly updated reference sequence databases must be versioned and archived. Database administrators have tried to fill the requirements by supplying users with one-off versions of databases, but these are time consuming to set up and are inconsistent across resources. Disk storage and data backup performance has also discouraged maintaining multiple versions of databases since databases such as NCBI nr can consume 50xe2x80x89Gb or more disk space per version, with growth rates that parallel Moores law.'),
(79695, 2016, 'bio', 'BioFVM: an efficient, parallelized diffusive transport solver for 3-D biological simulations', 'Ghaffarizadeh, Ahmadreza|Friedman, Samuel H|Macklin, Paul', 'Center for Applied Molecular Medicine, University of Southern California, Los Angeles, CA 90033, USA.|Center for Applied Molecular Medicine, University of Southern California, Los Angeles, CA 90033, USA.|Center for Applied Molecular Medicine, University of Southern California, Los Angeles, CA 90033, USA.', 'United States|United States|United States', 16, 'Biological Transport|Computer Simulation|Imaging, Three-Dimensional|Models, Theoretical|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btv730', '26656933', 'Computational models of multicellular systems require solving systems of PDEs for release, uptake, decay and diffusion of multiple substrates in 3D, particularly when incorporating the impact of drugs, growth substrates and signaling factors on cell receptors and subcellular systems biology.'),
(79696, 2016, 'bio', 'destiny: diffusion maps for large-scale single-cell data in R', 'Angerer, Philipp|Haghverdi, Laleh|Büttner, Maren|Theis, Fabian J|Marr, Carsten|Buettner, Florian', 'Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany and.|Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany and.|Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany and.|Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany and Technische Universität München, Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, Boltzmannstr. 3, 85748 Garching, Germany.|Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany and.|Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany and.', 'Germany|Germany|Germany|Germany|Germany|Germany', 137, 'Algorithms|Cluster Analysis|Diffusion|Single-Cell Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btv715', '26668002', ': Diffusion maps are a spectral method for non-linear dimension reduction and have recently been adapted for the visualization of single-cell expression data. Here we present destiny, an efficient R implementation of the diffusion map algorithm. Our package includes a single-cell specific noise model allowing for missing and censored values. In contrast to previous implementations, we further present an efficient nearest-neighbour approximation that allows for the processing of hundreds of thousands of cells and a functionality for projecting new data on existing diffusion maps. We exemplarily apply destiny to a recent time-resolved mass cytometry dataset of cellular reprogramming.'),
(79697, 2016, 'bio', 'Fuse: multiple network alignment via data fusion', 'Gligorijevixc4x87, Vladimir|Malod-Dognin, Noxc3xabl|Prxc5xbeulj, Nataxc5xa1a', 'Department of Computing, Imperial College London, London SW7 2AZ, UK.|Department of Computing, Imperial College London, London SW7 2AZ, UK.|Department of Computing, Imperial College London, London SW7 2AZ, UK.', 'United Kingdom|United Kingdom|United Kingdom', 18, 'Algorithms|Computational Biology|Protein Interaction Mapping|Proteins|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btv731', '26668003', 'Discovering patterns in networks of protein-protein interactions (PPIs) is a central problem in systems biology. Alignments between these networks aid functional understanding as they uncover important information, such as evolutionary conserved pathways, protein complexes and functional orthologs. However, the complexity of the multiple network alignment problem grows exponentially with the number of networks being aligned and designing a multiple network aligner that is both scalable and that produces biologically relevant alignments is a challenging task that has not been fully addressed. The objective of multiple network alignment is to create clusters of nodes that are evolutionarily and functionally conserved across all networks. Unfortunately, the alignment methods proposed thus far do not meet this objective as they are guided by pairwise scores that do not utilize the entire functional and evolutionary information across all networks.'),
(79698, 2016, 'bio', 'Positive and negative forms of replicability in gene network analysis', 'Verleyen, W|Ballouz, S|Gillis, J', 'Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, 500 Sunnyside Boulevard Woodbury, NY 11797, USA.|Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, 500 Sunnyside Boulevard Woodbury, NY 11797, USA.|Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, 500 Sunnyside Boulevard Woodbury, NY 11797, USA.', 'United States|United States|United States', 5, 'Algorithms|Gene Regulatory Networks|Humans|Models, Theoretical', 'Journal Article', '10.1093/bioinformatics/btv734', '26668004', 'Gene networks have become a central tool in the analysis of genomic data but are widely regarded as hard to interpret. This has motivated a great deal of comparative evaluation and research into best practices. We explore the possibility that this may lead to overfitting in the field as a whole.'),
(79699, 2016, 'bio', 'DNAshapeR: an R/Bioconductor package for DNA shape prediction and feature encoding', 'Chiu, Tsu-Pei|Comoglio, Federico|Zhou, Tianyin|Yang, Lin|Paro, Renato|Rohs, Remo', 'Molecular and Computational Biology Program, Departments of Biological Sciences, Chemistry, Physics, and Computer Science, University of Southern California, Los Angeles, CA 90089, USA.|Department of Biosystems Science and Engineering, ETH Zürich, Mattenstrasse 26, 4058 Basel, Switzerland and.|Molecular and Computational Biology Program, Departments of Biological Sciences, Chemistry, Physics, and Computer Science, University of Southern California, Los Angeles, CA 90089, USA.|Molecular and Computational Biology Program, Departments of Biological Sciences, Chemistry, Physics, and Computer Science, University of Southern California, Los Angeles, CA 90089, USA.|Department of Biosystems Science and Engineering, ETH Zürich, Mattenstrasse 26, 4058 Basel, Switzerland and Faculty of Science, University of Basel, Klingelbergstrasse 50, 4056 Basel, Switzerland.|Molecular and Computational Biology Program, Departments of Biological Sciences, Chemistry, Physics, and Computer Science, University of Southern California, Los Angeles, CA 90089, USA.', 'United States|Switzerland|United States|United States|Switzerland|United States', 47, 'DNA|Genome|Genomics|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btv735', '26668005', 'DNAshapeR predicts DNA shape features in an ultra-fast, high-throughput manner from genomic sequencing data. The package takes either nucleotide sequence or genomic coordinates as input and generates various graphical representations for visualization and further analysis. DNAshapeR further encodes DNA sequence and shape features as user-defined combinations of k-mer and DNA shape features. The resulting feature matrices can be readily used as input of various machine learning software packages for further modeling studies.'),
(79700, 2016, 'bio', 'CytoGEDEVO-global alignment of biological networks with Cytoscape', 'Malek, Maximilian|Ibragimov, Rashid|Albrecht, Mario|Baumbach, Jan', 'Saarland University, 66123 Saarbrücken, Germany Institute for Knowledge Discovery, Graz University of Technology, 8010 Graz, Austria.|Saarland University, 66123 Saarbrücken, Germany Max Planck Institute for Informatics, 66123 Saarbrücken, Germany.|Institute for Knowledge Discovery, Graz University of Technology, 8010 Graz, Austria BioTechMed-Graz, Graz, Austria.|University of Southern Denmark, Odense, Denmark.', 'Austria|Germany|Austria|Denmark', 0, 'Computational Biology|Programming Languages|Protein Interaction Maps|Proteins|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btv732', '26669930', 'In the systems biology era, high-throughput omics technologies have enabled the unraveling of the interplay of some biological entities on a large scale (e.g. genes, proteins, metabolites or RNAs). Huge biological networks have emerged, where nodes correspond to these entities and edges between them model their relations. Protein-protein interaction networks, for instance, show the physical interactions of proteins in an organism. The comparison of such networks promises additional insights into protein and cell function as well as knowledge-transfer across species. Several computational approaches have been developed previously to solve the network alignment (NA) problem, but only a few concentrate on the usability of the implemented tools for the evaluation of protein-protein interactions by the end users (biologists and medical researchers).'),
(79701, 2016, 'bio', 'Integrating, summarizing and visualizing GWAS-hits and human diversity with DANCE (Disease-ANCEstry networks)', 'Araújo, Gilderlanio S|Lima, Lucas Henrique C|Schneider, Silvana|Leal, Thiago P|da Silva, Ana Paula C|Vaz de Melo, Pedro O S|Tarazona-Santos, Eduardo|Scliar, Marília O|Rodrigues, Maíra R', 'Department of General Biology.|Department of Computer Science and.|Department of Statistics, Federal University of Minas Gerais, Belo Horizonte, Brazil.|Department of General Biology.|Department of Computer Science and.|Department of Computer Science and.|Department of General Biology.|Department of General Biology.|Department of General Biology.', '-|-|Brazil|-|-|-|-|-|-', 2, 'Genetic Variation|Genome|Genome-Wide Association Study|Humans|Internet|Phenotype', 'Journal Article', '10.1093/bioinformatics/btv708', '26673785', 'The 1000 Genomes Project (1KGP) and thousands of Genome-Wide Association Studies (GWAS) performed during the last years have generated an enormous amount of information that needs to be integrated to better understand the genetic architecture of complex diseases in different populations. This integration is important in areas such as genetics, epidemiology, anthropology, as well as admixture mapping design and GWAS-replications. Network-based approaches that explore the genetic bases of human diseases and traits have not yet incorporated information on genetic diversity among human populations.'),
(79702, 2016, 'bio', 'CG2AA: backmapping protein coarse-grained structures', 'Lombardi, Leandro E|Martí, Marcelo A|Capece, Luciana', 'Dto. De Matemática - Instituto De Cálculo, Fac. de Ciencias Exactas y Naturales, Univ. De Buenos Aires..|Dto. De Química Biológica - IQUIBICEN-CONICET, Fac. De Ciencias Exactas Y Naturales, Univ. de Buenos Aires..|Dto. De Química Inorgánica, Analítica Y Química Física - INQUIMAE-CONICET, Fac. De Ciencias Exactas Y Naturales, Univ. De Buenos Aires, Cdad. Universitaria, CABA C1428EGA, Argentina.', '-|-|Argentina', 0, 'Algorithms|Molecular Dynamics Simulation|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btv740', '26677962', 'Coarse grain (CG) models allow long-scale simulations with a much lower computational cost than that of all-atom simulations. However, the absence of atomistic detail impedes the analysis of specific atomic interactions that are determinant in most interesting biomolecular processes. In order to study these phenomena, it is necessary to reconstruct the atomistic structure from the CG representation. This structure can be analyzed by itself or be used as an onset for atomistic molecular dynamics simulations. In this work, we present a computer program that accurately reconstructs the atomistic structure from a CG model for proteins, using a simple geometrical algorithm.'),
(79703, 2016, 'bio', 'OMPPM: online multiple palindrome pattern matching', 'Kim, Hwee|Han, Yo-Sub', 'Department of Computer Science, Yonsei University, Seoul 120-749, Republic of Korea.|Department of Computer Science, Yonsei University, Seoul 120-749, Republic of Korea.', '-|-', 1, 'Algorithms|Computational Biology|DNA|Inverted Repeat Sequences|Programming Languages|RNA|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btv738', '26677963', 'A palindrome is a string that reads the same forward and backward. Finding palindromic substructures is important in DNA, RNA or protein sequence analysis. We say that two strings of the same length are pal-equivalent if, for each possible centre, they have the same length of the maximal palindrome. Given a text T of length n and a pattern P of length m, we study the palindrome pattern matching problem that finds all indices i such that P and [Formula: see text] are pal-equivalent.'),
(79704, 2016, 'bio', 'Computational identification of piRNA targets on mouse mRNAs', 'Yuan, Jiao|Zhang, Peng|Cui, Ya|Wang, Jiajia|Skogerbø, Geir|Huang, Da-Wei|Chen, Runsheng|He, Shunmin', 'Key Laboratory of the Zoological Systematics and Evolution, Institute of Zoology and CAS Key Laboratory of Rna Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.|Key Laboratory of the Zoological Systematics and Evolution, Institute of Zoology and.|CAS Key Laboratory of Rna Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.|Key Laboratory of the Zoological Systematics and Evolution, Institute of Zoology and.|CAS Key Laboratory of Rna Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.|Key Laboratory of the Zoological Systematics and Evolution, Institute of Zoology and.|CAS Key Laboratory of Rna Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.|Key Laboratory of the Zoological Systematics and Evolution, Institute of Zoology and.', 'China|-|China|-|China|-|China|-', 0, 'Animals|Computational Biology|DNA Transposable Elements|Mice|RNA, Messenger|RNA, Small Interfering|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/btv729', '26677964', 'PIWI-interacting RNAs (piRNAs) are a class of small non-coding RNAs that are highly abundant in the germline. One important role of piRNAs is to defend genome integrity by guiding PIWI proteins to silence transposable elements (TEs), which have a high potential to cause deleterious effects on their host. The mechanism of piRNA-mediated post-transcriptional silencing was also observed to affect mRNAs, suggesting that piRNAs might play a broad role in gene expression regulation. However, there has been no systematic report with regard to how many protein-coding genes might be targeted and regulated by piRNAs.'),
(79705, 2016, 'bio', 'DeNovo: virus-host sequence-based protein-protein interaction prediction', 'Eid, Fatma-Elzahraa|ElHefnawi, Mahmoud|Heath, Lenwood S', 'Department of Computer Science, Virginia Tech, Blacksburg, VA, USA Department of Systems and Computer Engineering, Faculty of Engineering, Al-Azhar University, Cairo, Egypt.|Biomedical Informatics and Chemoinformatics Research Group, Department of Informatics and Systems, Center of Excellence for Advanced Sciences, National Research Center, Giza, Egypt.|Department of Computer Science, Virginia Tech, Blacksburg, VA, USA.', 'Egypt|Egypt|United States', 0, 'Forecasting|Humans|Protein Interaction Mapping|Sequence Analysis, DNA|Viral Proteins|Viruses', 'Journal Article', '10.1093/bioinformatics/btv737', '26677965', 'Can we predict protein-protein interactions (PPIs) of a novel virus with its host? Three major problems arise: the lack of known PPIs for that virus to learn from, the cost of learning about its proteins and the sequence dissimilarity among viral families that makes most methods inapplicable or inefficient. We develop DeNovo, a sequence-based negative sampling and machine learning framework that learns from PPIs of different viruses to predict for a novel one, exploiting the shared host proteins. We tested DeNovo on PPIs from different domains to assess generalization.'),
(79706, 2016, 'bio', 'FluxModeCalculator: an efficient tool for large-scale flux mode computation', 'van Klinken, Jan Bert|Willems van Dijk, Ko', 'Department of Human Genetics Einthoven Laboratory for Experimental Vascular Medicine.|Department of Human Genetics Einthoven Laboratory for Experimental Vascular Medicine Department of Medicine, Division of Endocrinology, LUMC, Leiden, The Netherlands.', '-|Netherlands', 13, 'Algorithms|Metabolic Networks and Pathways|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btv742', '26685305', 'Elementary flux mode (EFM) analysis is a powerful technique for determining the metabolic capacities and robustness of stoichiometric networks. Recently, several improvements have been made to the algorithm for enumerating the EFMs, making the study of large models possible. However, currently these tools require high performance workstations to perform large-scale EFM computations, thus limiting their applicability. We developed a more time and memory efficient implementation of the algorithm for EFM enumeration in MATLAB, called FluxModeCalculator, which enables large-scale EFM computation on ordinary desktop computers.'),
(79707, 2016, 'bio', 'PaxtoolsR: pathway analysis in R using Pathway Commons', 'Luna, Augustin|Babur, Özgün|Aksoy, Bülent Arman|Demir, Emek|Sander, Chris', 'Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.|Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.|Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.|Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.|Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.', 'United States|United States|United States|United States|United States', 11, 'Computational Biology|Documentation|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btv733', '26685306', 'PaxtoolsR package enables access to pathway data represented in the BioPAX format and made available through the Pathway Commons webservice for users of the R language to aid in advanced pathway analyses. Features include the extraction, merging and validation of pathway data represented in the BioPAX format. This package also provides novel pathway datasets and advanced querying features for R users through the Pathway Commons webservice allowing users to query, extract and retrieve data and integrate these data with local BioPAX datasets.'),
(79708, 2016, 'bio', 'traseR: an R package for performing trait-associated SNP enrichment analysis in genomic intervals', 'Chen, Li|Qin, Zhaohui S', 'Department of Mathematics and Computer Science.|Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322 USA and Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA 30322, USA.', 'United States|United States', 0, 'Genome-Wide Association Study|Genomics|Humans|Phenotype|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btv741', '26685307', 'Genome-wide association studies (GWASs) have successfully identified many sequence variants that are significantly associated with common diseases and traits. Tens of thousands of such trait-associated SNPs have already been cataloged, which we believe form a great resource for genomic research. Recent studies have demonstrated that the collection of trait-associated SNPs can be exploited to indicate whether a given genomic interval or intervals are likely to be functionally connected with certain phenotypes or diseases. Despite this importance, currently, there is no ready-to-use computational tool able to connect genomic intervals to phenotypes. Here, we present traseR, an easy-to-use R Bioconductor package that performs enrichment analyses of trait-associated SNPs in arbitrary genomic intervals with flexible options, including testing method, type of background and inclusion of SNPs in LD.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(79709, 2016, 'bio', 'MSAcquisitionSimulator: data-dependent acquisition simulator for LC-MS shotgun proteomics', 'Goldfarb, Dennis|Wang, Wei|Major, Michael B', 'Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.|Department of Computer Science, University of California, Los Angeles, CA, USA and.|Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA, Department of Cell Biology and Physiology, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.', 'United States|United States|United States', 4, 'Algorithms|Chromatography, Liquid|Humans|Mass Spectrometry|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btv745', '26685308', 'Data-dependent acquisition (DDA) is the most common method used to control the acquisition process of shotgun proteomics experiments. While novel DDA approaches have been proposed, their evaluation is made difficult by the need of programmatic control of a mass spectrometer. An alternative is in silico analysis, for which suitable software has been unavailable. To meet this need, we have developed MSAcquisitionSimulator-a collection of Cxe2x80x89++ programs for simulating ground truth LC-MS data and the subsequent application of custom DDA algorithms. It provides an opportunity for researchers to test, refine and evaluate novel DDA algorithms prior to implementation on a mass spectrometer.'),
(79710, 2016, 'bio', 'Fast integration-based prediction bands for ordinary differential equation models', 'Hass, Helge|Kreutz, Clemens|Timmer, Jens|Kaschek, Daniel', 'Institute of Physics, University of Freiburg and.|Institute of Physics, University of Freiburg and.|Institute of Physics, University of Freiburg and BIOSS Centre for Biological Signalling Studies, University of Freiburg, Freiburg im Breisgau, Germany.|Institute of Physics, University of Freiburg and.', '-|-|Germany|-', 8, 'Models, Biological|Nonlinear Dynamics|Probability|Research Design|Uncertainty', 'Journal Article', '10.1093/bioinformatics/btv743', '26685309', 'To gain a deeper understanding of biological processes and their relevance in disease, mathematical models are built upon experimental data. Uncertainty in the data leads to uncertainties of the models parameters and in turn to uncertainties of predictions. Mechanistic dynamic models of biochemical networks are frequently based on nonlinear differential equation systems and feature a large number of parameters, sparse observations of the model components and lack of information in the available data. Due to the curse of dimensionality, classical and sampling approaches propagating parameter uncertainties to predictions are hardly feasible and insufficient. However, for experimental design and to discriminate between competing models, prediction and confidence bands are essential. To circumvent the hurdles of the former methods, an approach to calculate a profile likelihood on arbitrary observations for a specific time point has been introduced, which provides accurate confidence and prediction intervals for nonlinear models and is computationally feasible for high-dimensional models.'),
(79711, 2016, 'bio', 'Hierarchical block matrices as efficient representations of chromosome topologies and their application for 3C data integration', 'Shavit, Yoli|Walker, Barnabas James|Lio, Pietro', 'Computer Laboratory, University of Cambridge, Cambridge CB3 0FD, UK.|University of Cambridge, Cambridge CB3 0FD, UK and Department of Life Sciences, Imperial College London, London SW7 2AZ, UK.|Computer Laboratory, University of Cambridge, Cambridge CB3 0FD, UK.', 'United Kingdom|United Kingdom|United Kingdom', 4, 'Algorithms|Chromatin|Chromosomes|Genome|Molecular Conformation', 'Journal Article', '10.1093/bioinformatics/btv736', '26685310', 'Recent advancements in molecular methods have made it possible to capture physical contacts between multiple chromatin fragments. The resulting association matrices provide a noisy estimate for average spatial proximity that can be used to gain insights into the genome organization inside the nucleus. However, extracting topological information from these data is challenging and their integration across resolutions is still poorly addressed. Recent findings suggest that a hierarchical approach could be advantageous for addressing these challenges.'),
(79712, 2016, 'bio', 'Foldalign 25: multithreaded implementation for pairwise structural RNA alignment', 'Sundfeld, Daniel|Havgaard, Jakob H|de Melo, Alba C M A|Gorodkin, Jan', 'Center for Non-Coding RNA in Technology and Health, IKVH, University of Copenhagen, Frederiksberg, Denmark and Department of Computer Science, University of Brasilia, Brasília, DF, Brazil.|Center for Non-Coding RNA in Technology and Health, IKVH, University of Copenhagen, Frederiksberg, Denmark and.|Department of Computer Science, University of Brasilia, Brasília, DF, Brazil.|Center for Non-Coding RNA in Technology and Health, IKVH, University of Copenhagen, Frederiksberg, Denmark and.', 'Brazil|Denmark|Brazil|Denmark', 12, 'RNA|Sequence Alignment|Sequence Analysis, RNA|Software|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btv748', '26704597', 'Structured RNAs can be hard to search for as they often are not well conserved in their primary structure and are local in their genomic or transcriptomic context. Thus, the need for tools which in particular can make local structural alignments of RNAs is only increasing.'),
(79713, 2016, 'bio', 'OfftargetFinder: a web tool for species-specific RNAi design', 'Good, R T|Varghese, T|Golz, J F|Russell, D A|Papanicolaou, A|Edwards, O|Robin, C', 'The Bio21 Institute School of Biosciences, The University of Melbourne, Melbourne 3010, Australia.|CSIRO National Facilities and Collections, Canberra, ACT 2601, Australia.|School of Biosciences, The University of Melbourne, Melbourne 3010, Australia.|Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Melbourne 3010, Australia.|CSIRO Land and Water Flagship, Canberra, ACT 2601, Australia.|CSIRO Land and Water Flagship, Canberra, ACT 2601, Australia.|The Bio21 Institute School of Biosciences, The University of Melbourne, Melbourne 3010, Australia.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia', 4, 'Animals|Computational Biology|Databases, Genetic|Insecta|Internet|RNA Interference|Risk Assessment|Species Specificity|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btv747', '26704598', 'RNA interference (RNAi) technology is being developed as a weapon for pest insect control. To maximize the specificity that such an approach affords we have developed a bioinformatic web tool that searches the ever-growing arthropod transcriptome databases so that pest-specific RNAi sequences can be identified. This will help technology developers finesse the design of RNAi sequences and suggests which non-target species should be assessed in the risk assessment process.'),
(79714, 2016, 'bio', 'MCAST: scanning for cis-regulatory motif clusters', 'Grant, Charles E|Johnson, James|Bailey, Timothy L|Noble, William Stafford', 'Department of Genome Sciences, University of Washington, Seattle, WA, USA.|Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia and.|Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia and.|Department of Genome Sciences, University of Washington, Seattle, WA, USA, Department of Computer Science and Engineering, University of Washington, Seattle, WA, USA.', 'United States|Australia|Australia|United States', 3, 'Algorithms|Binding Sites|Genome|Humans|Regulatory Elements, Transcriptional|Sequence Analysis, DNA|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btv750', '26704599', 'Precise regulatory control of genes, particularly in eukaryotes, frequently requires the joint action of multiple sequence-specific transcription factors. A cis-regulatory module (CRM) is a genomic locus that is responsible for gene regulation and that contains multiple transcription factor binding sites in close proximity. Given a collection of known transcription factor binding motifs, many bioinformatics methods have been proposed over the past 15 years for identifying within a genomic sequence candidate CRMs consisting of clusters of those motifs.'),
(79715, 2016, 'bio', 'simDEF: definition-based semantic similarity measure of gene ontology terms for functional similarity analysis of genes', 'Pesaranghader, Ahmad|Matwin, Stan|Sokolova, Marina|Beiko, Robert G', 'Faculty of Computer Science, Dalhousie University, Halifax, NS B3H 4R2, Canada, Institute for Big Data Analytics, Halifax, NS B3H 4R2, Canada.|Faculty of Computer Science, Dalhousie University, Halifax, NS B3H 4R2, Canada, Institute for Big Data Analytics, Halifax, NS B3H 4R2, Canada, Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland and.|Institute for Big Data Analytics, Halifax, NS B3H 4R2, Canada, Faculty of Medicine and Faculty of Engineering, University of Ottawa, Ottawa, ON K1H 8M5, Canada.|Faculty of Computer Science, Dalhousie University, Halifax, NS B3H 4R2, Canada.', 'Canada|Canada|Canada|Canada', 13, 'Algorithms|Animals|Computational Biology|Gene Ontology|Humans|Proteins|Semantics', 'Journal Article', '10.1093/bioinformatics/btv755', '26708333', 'Measures of protein functional similarity are essential tools for function prediction, evaluation of protein-protein interactions (PPIs) and other applications. Several existing methods perform comparisons between proteins based on the semantic similarity of their GO terms, however, these measures are highly sensitive to modifications in the topological structure of GO, tend to be focused on specific analytical tasks and concentrate on the GO terms themselves rather than considering their textual definitions.'),
(79716, 2016, 'bio', 'HitWalker2: visual analytics for precision medicine and beyond', 'Bottomly, Daniel|McWeeney, Shannon K|Wilmot, Beth', 'Knight Cancer Institute, Oregon Clinical and Translational Research Institute and.|Knight Cancer Institute, Oregon Clinical and Translational Research Institute and Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland OR 97239, USA.|Knight Cancer Institute, Oregon Clinical and Translational Research Institute and Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland OR 97239, USA.', 'United States|United States|United States', 9, 'Humans|Precision Medicine|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btv739', '26708334', 'The lack of visualization frameworks to guide interpretation and facilitate discovery is a potential bottleneck for precision medicine, systems genetics and other studies. To address this we have developed an interactive, reproducible, web-based prioritization approach that builds on our earlier work. HitWalker2 is highly flexible and can utilize many data types and prioritization methods based upon available data and desired questions, allowing it to be utilized in a diverse range of studies such as cancer, infectious disease and psychiatric disorders.'),
(79717, 2016, 'bio', 'Fast and efficient QTL mapper for thousands of molecular phenotypes', 'Ongen, Halit|Buil, Alfonso|Brown, Andrew Anand|Dermitzakis, Emmanouil T|Delaneau, Olivier', 'Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, 1211, Switzerland Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland and.|Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, 1211, Switzerland Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland and.|Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, 1211, Switzerland Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland and NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Norway.|Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, 1211, Switzerland Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland and.|Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, 1211, Switzerland Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland and.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 94, 'Genomics|Phenotype|Quantitative Trait Loci|Software|Statistical Distributions', 'Journal Article', '10.1093/bioinformatics/btv722', '26708335', 'In order to discover quantitative trait loci, multi-dimensional genomic datasets combining DNA-seq and ChiP-/RNA-seq require methods that rapidly correlate tens of thousands of molecular phenotypes with millions of genetic variants while appropriately controlling for multiple testing.'),
(79718, 2016, 'bio', 'rCGH: a comprehensive array-based genomic profile platform for precision medicine', 'Commo, Frederic|Guinney, Justin|Ferté, Charles|Bot, Brian|Lefebvre, Celine|Soria, Jean-Charles|André, Fabrice', 'INSERM U981, Gustave Roussy, University Paris-Sud, Villejuif 94805, France, Sage Bionetworks, Seattle, WA 98109, USA and.|Sage Bionetworks, Seattle, WA 98109, USA and.|INSERM U981, Gustave Roussy, University Paris-Sud, Villejuif 94805, France, Sage Bionetworks, Seattle, WA 98109, USA and.|Sage Bionetworks, Seattle, WA 98109, USA and.|INSERM U981, Gustave Roussy, University Paris-Sud, Villejuif 94805, France.|INSERM U981, Gustave Roussy, University Paris-Sud, Villejuif 94805, France, Department of Medical Oncology, Gustave Roussy, Villejuif 94805, France.|INSERM U981, Gustave Roussy, University Paris-Sud, Villejuif 94805, France, Department of Medical Oncology, Gustave Roussy, Villejuif 94805, France.', 'France|-|France|-|France|France|France', 8, 'Comparative Genomic Hybridization|Genomics|Humans|Precision Medicine|Software', 'Journal Article', '10.1093/bioinformatics/btv718', '26708336', 'We present rCGH, a comprehensive array-based comparative genomic hybridization analysis workflow, integrating computational improvements and functionalities specifically designed for precision medicine. rCGH supports the major microarray platforms, ensures a full traceability and facilitates profiles interpretation and decision-making through sharable interactive visualizations.'),
(79719, 2016, 'bio', 'Authors response to Comment on: ERGC: An efficient Referential Genome Compression Algorithm', 'Saha, Subrata|Rajasekaran, Sanguthevar', 'Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA.|Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA.', 'United States|United States', 1, 'Algorithms|Data Compression|Genome|Humans', 'Letter', '10.1093/bioinformatics/btv720', '26722114', 'subrata.saha@engr.uconn.edu or rajasek@engr.uconn.edu.'),
(79720, 2016, 'bio', 'MOGEN: a tool for reconstructing 3D models of genomes from chromosomal conformation capturing data', 'Trieu, Tuan|Cheng, Jianlin', 'Computer Science Department, University of Missouri, Columbia, MO 65201, USA.|Computer Science Department, University of Missouri, Columbia, MO 65201, USA.', 'United States|United States', 20, 'B-Lymphocytes|Chromosomes|Genome|Humans|Models, Theoretical|Molecular Conformation', 'Journal Article', '10.1093/bioinformatics/btv754', '26722115', 'The three-dimensional (3D) conformation of chromosomes and genomes play an important role in cellular processes such as gene regulation, DNA replication and genome methylation. Several methods have been developed to reconstruct 3D structures of individual chromosomes from chromosomal conformation capturing data such as Hi-C data. However, few methods can effectively reconstruct the 3D structures of an entire genome due to the difficulty of handling noisy and inconsistent inter-chromosomal contact data.'),
(79721, 2016, 'bio', 'ChromothripsisDB: a curated database of chromothripsis', 'Yang, Jian|Deng, Gaofeng|Cai, Haoyang', 'Center of Growth, Metabolism, and Aging, Key Laboratory of Bio-Resources and Eco-Environment, College of Life Sciences, Sichuan University, Chengdu, Sichuan 610064, China.|Center of Growth, Metabolism, and Aging, Key Laboratory of Bio-Resources and Eco-Environment, College of Life Sciences, Sichuan University, Chengdu, Sichuan 610064, China.|Center of Growth, Metabolism, and Aging, Key Laboratory of Bio-Resources and Eco-Environment, College of Life Sciences, Sichuan University, Chengdu, Sichuan 610064, China.', 'China|China|China', 0, 'Cell Transformation, Neoplastic|Chromosome Aberrations|Chromothripsis|Databases, Factual|Humans', 'Journal Article', '10.1093/bioinformatics/btv757', '26722116', 'Chromothripsis is a single catastrophic event that can lead to massive genomic rearrangements confined to one or a few chromosomes. It provides an alternative paradigm in cancer development and changes the conventional view that cancer develops in a stepwise progression. The mechanisms underlying chromothripsis and their specific impact on tumorigenesis are still poorly understood, and further examination of a large number of identified chromothripsis samples is needed. Unfortunately, this data are difficult to access, as they are scattered across multiple publications, come in different formats and descriptions, or are hidden in figures and supplementary materials. To improve access to this data and promote meta-analysis, we developed ChromothripsisDB, a manually curated database containing a unified description of all published chromothripsis cases and relevant genomic aberrations. Currently, 423 chromothripsis samples representing 107 research articles are included in our database. ChromothripsisDB represents an extraordinary resource for mining the existing knowledge of chromothripsis, and will facilitate the identification of mechanisms involved in this phenomenon.'),
(79722, 2016, 'bio', 'SPMM: estimating infection duration of multivariant HIV-1 infections', 'Love, Tanzy M T|Park, Sung Yong|Giorgi, Elena E|Mack, Wendy J|Perelson, Alan S|Lee, Ha Youn', 'Department of Biostatistics and Computational Biology, University of Rochester, Rochester, New York, 14642, USA.|Department of Molecular Microbiology and Immunology, Keck School of Medicine, University of Southern California, Los Angeles, 90089, USA.|Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM 87545, USA and.|Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, 90089, USA.|Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM 87545, USA and.|Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM 87545, USA and.', 'United States|United States|United States|United States|United States|United States', 9, 'Genetic Variation|HIV Infections|HIV-1|Humans|Models, Theoretical|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btv749', '26722117', 'Illustrating how HIV-1 is transmitted and how it evolves in the following weeks is an important step for developing effective vaccination and prevention strategies. It is currently possible through DNA sequencing to account for the diverse array of viral strains within an infected individual. This provides an unprecedented opportunity to pinpoint when each patient was infected and which viruses were transmitted.'),
(79723, 2016, 'bio', 'Utilizing the Jaccard index to reveal population stratification in sequencing data: a simulation study and an application to the 1000 Genomes Project', 'Prokopenko, Dmitry|Hecker, Julian|Silverman, Edwin K|Pagano, Marcello|Nöthen, Markus M|Dina, Christian|Lange, Christoph|Fier, Heide Loehlein', 'Institute of Genomic Mathematics, University of Bonn, Bonn, Germany.|Institute of Genomic Mathematics, University of Bonn, Bonn, Germany.|Channing Division of Network Medicine, Brigham and Womens Hospital.|Department of Biostatistics, Harvard School of Public Health, Boston, USA.|Institute of Human Genetics, University of Bonn, Bonn, Germany.|Institut National de la Santé et de la Recherche Médicale (INSERM) Unité Mixte de Recherche (UMR) 1087, linstitut du thorax, Nantes, France, Centre National de la Recherche Scientifique (CNRS) UMR 6291, linstitut du thorax, Nantes, France, Université de Nantes, linstitut du thorax, Nantes, France and Centre Hospitalier Universitaire (CHU) de Nantes, linstitut du thorax, Service de Cardiologie, Nantes, France.|Channing Division of Network Medicine, Brigham and Womens Hospital, Department of Biostatistics, Harvard School of Public Health, Boston, USA.|Institute of Genomic Mathematics, University of Bonn, Bonn, Germany, Department of Biostatistics, Harvard School of Public Health, Boston, USA.', 'Germany|Germany|United States|United States|Germany|France|United States|Germany', 0, 'Animals|Computer Simulation|Gene Frequency|Genetic Association Studies|Genetic Variation|Genome|Genotype|High-Throughput Nucleotide Sequencing|Humans', 'Journal Article', '10.1093/bioinformatics/btv752', '26722118', 'Population stratification is one of the major sources of confounding in genetic association studies, potentially causing false-positive and false-negative results. Here, we present a novel approach for the identification of population substructure in high-density genotyping data/next generation sequencing data. The approach exploits the co-appearances of rare genetic variants in individuals. The method can be applied to all available genetic loci and is computationally fast. Using sequencing data from the 1000 Genomes Project, the features of the approach are illustrated and compared to existing methodology (i.e. EIGENSTRAT). We examine the effects of different cutoffs for the minor allele frequency on the performance of the approach. We find that our approach works particularly well for genetic loci with very small minor allele frequencies. The results suggest that the inclusion of rare-variant data/sequencing data in our approach provides a much higher resolution picture of population substructure than it can be obtained with existing methodology. Furthermore, in simulation studies, we find scenarios where our method was able to control the type 1 error more precisely and showed higher power.'),
(79724, 2016, 'bio', 'Robust quantitative scratch assay', 'Vargas, Andrea|Angeli, Marc|Pastrello, Chiara|McQuaid, Rosanne|Li, Han|Jurisicova, Andrea|Jurisica, Igor', 'Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.|Princess Margaret Cancer Centre and Techna, University Health Network, Toronto, ON, Canada.|Princess Margaret Cancer Centre and Techna, University Health Network, Toronto, ON, Canada.|Department of Physiology, University of Toronto, Toronto, ON, Canada, Lunenfeld Tanenbaum Research Institute, Toronto, ON, Canada.|Lunenfeld Tanenbaum Research Institute, Toronto, ON, Canada.|Department of Physiology, University of Toronto, Toronto, ON, Canada, Lunenfeld Tanenbaum Research Institute, Toronto, ON, Canada, Department of Obstetrics and Gynecology and.|Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada, Princess Margaret Cancer Centre and Techna, University Health Network, Toronto, ON, Canada, Department of Computer Science, University of Toronto, Toronto, ON, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada', 4, 'Algorithms|Animals|Cell Migration Assays|Humans|Wound Healing', 'Journal Article', '10.1093/bioinformatics/btv746', '26722119', 'The wound healing assay (or scratch assay) is a technique frequently used to quantify the dependence of cell motility-a central process in tissue repair and evolution of disease-subject to various treatments conditions. However processing the resulting data is a laborious task due its high throughput and variability across images. This Robust Quantitative Scratch Assay algorithm introduced statistical outputs where migration rates are estimated, cellular behaviour is distinguished and outliers are identified among groups of unique experimental conditions. Furthermore, the RQSA decreased measurement errors and increased accuracy in the wound boundary at comparable processing times compared to previously developed method (TScratch).'),
(79725, 2016, 'bio', 'Characterizing rate limiting steps in transcription from RNA production times in live cells', 'Häkkinen, Antti|Ribeiro, Andre S', 'Laboratory of Biosystem Dynamics, Department of Signal Processing, Tampere University of Technology, P.O. box 553, 33101, Tampere, Finland.|Laboratory of Biosystem Dynamics, Department of Signal Processing, Tampere University of Technology, P.O. box 553, 33101, Tampere, Finland.', 'Finland|Finland', 0, 'Escherichia coli|Kinetics|Models, Genetic|Promoter Regions, Genetic|RNA, Bacterial|Transcriptional Activation', 'Journal Article', '10.1093/bioinformatics/btv744', '26722120', 'Single-molecule measurements of live Escherichia coli transcription dynamics suggest that this process ranges from sub- to super-Poissonian, depending on the conditions and on the promoter. For its accurate quantification, we propose a model that accommodates all these settings, and statistical methods to estimate the model parameters and to select the relevant components.'),
(79726, 2016, 'bio', 'VIRALpro: a tool to identify viral capsid and tail sequences', 'Galiez, Clovis|Magnan, Christophe N|Coste, Francois|Baldi, Pierre', 'INRIA, Campus De Beaulieu, Rennes Cedex, 35042, France.|Department of Computer Science and Institute for Genomics and Bioinformatics, University of California, Irvine, Irvine, CA 92697, USA.|INRIA, Campus De Beaulieu, Rennes Cedex, 35042, France.|Department of Computer Science and Institute for Genomics and Bioinformatics, University of California, Irvine, Irvine, CA 92697, USA.', 'France|United States|France|United States', 0, 'Amino Acid Sequence|Capsid|Genome, Viral|Humans|Software', 'Journal Article', '10.1093/bioinformatics/btv727', '26733451', 'Not only sequence data continue to outpace annotation information, but also the problem is further exacerbated when organisms are underrepresented in the annotation databases. This is the case with non-human-pathogenic viruses which occur frequently in metagenomic projects. Thus, there is a need for tools capable of detecting and classifying viral sequences.'),
(79727, 2016, 'bio', 'Path2PPI: an R package to predict protein-protein interaction networks for a set of proteins', 'Philipp, Oliver|Osiewacz, Heinz D|Koch, Ina', 'Molecular Bioinformatics, Institute of Computer Science, Faculty of Computer Science and Mathematics & Cluster of Excellence Macromolecular Complexes, Goethe-University, Frankfurt am Main, Germany and Molecular Developmental Biology, Institute of Molecular Biosciences, Faculty for Biosciences & Cluster of Excellence Macromolecular Complexes, Goethe-University, Frankfurt am Main, Germany.|Molecular Developmental Biology, Institute of Molecular Biosciences, Faculty for Biosciences & Cluster of Excellence Macromolecular Complexes, Goethe-University, Frankfurt am Main, Germany.|Molecular Bioinformatics, Institute of Computer Science, Faculty of Computer Science and Mathematics & Cluster of Excellence Macromolecular Complexes, Goethe-University, Frankfurt am Main, Germany and.', 'Germany|Germany|Germany', 5, 'Animals|Humans|Protein Interaction Maps|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btv765', '26733452', ': We introduce Path2PPI, a new R package to identify protein-protein interaction (PPI) networks for fully sequenced organisms for which nearly none PPI are known. Path2PPI predicts PPI networks based on sets of proteins from well-established model organisms, providing an intuitive visualization and usability. It can be used to combine and transfer information of a certain pathway or biological process from several reference organisms to one target organism.'),
(79728, 2016, 'bio', 'ProQ2: estimation of model accuracy implemented in Rosetta', 'Uziela, Karolis|Wallner, Björn', 'Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden.|Division of Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, SE-581 83, Linköping, Sweden and Swedish e-Science Research Center, Linköping, Sweden.', 'Sweden|Sweden', 32, 'Models, Molecular|Protein Conformation|Proteins', 'Journal Article', '10.1093/bioinformatics/btv767', '26733453', 'Model quality assessment programs are used to predict the quality of modeled protein structures. They can be divided into two groups depending on the information they are using: ensemble methods using consensus of many alternative models and methods only using a single model to do its prediction. The consensus methods excel in achieving high correlations between prediction and true quality measures. However, they frequently fail to pick out the best possible model, nor can they be used to generate and score new structures. Single-model methods on the other hand do not have these inherent shortcomings and can be used both to sample new structures and to improve existing consensus methods.'),
(79729, 2016, 'bio', 'Prediction of missing sequences and branch lengths in phylogenomic data', 'Darriba, Diego|Weiß, Michael|Stamatakis, Alexandros', 'Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, Heidelberg 69118, Germany.|Department of Biology, University of Tübingen, Auf Der Morgenstelle 1, Tübingen 72076, Germany, Steinbeis Innovation Center, Organismal Mycology and Microbiology, Vor dem Kreuzberg 17, 72070 Tübingen, Germany and.|Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, Heidelberg 69118, Germany, Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Karlsruhe 76131, Germany.', 'Germany|Germany|Germany', 0, 'Algorithms|Phylogeny|Probability|Sequence Alignment', 'Journal Article', '10.1093/bioinformatics/btv768', '26733454', 'The presence of missing data in large-scale phylogenomic datasets has negative effects on the phylogenetic inference process. One effect that is caused by alignments with missing per-gene or per-partition sequences is that the inferred phylogenies may exhibit extremely long branch lengths. We investigate if statistically predicting missing sequences for organisms by using information from genes/partitions that have data for these organisms alleviates the problem and improves phylogenetic accuracy.'),
(79730, 2016, 'bio', 'SV-Bay: structural variant detection in cancer genomes using a Bayesian approach with correction for GC-content and read mappability', 'Iakovishina, Daria|Janoueix-Lerosey, Isabelle|Barillot, Emmanuel|Regnier, Mireille|Boeva, Valentina', 'INRIA Projet AMIB, Ecole Polytechnique, Palaiseau, France.|Institut Curie, Centre De Recherche, Paris Inserm, U830, Department Genetics and Biology of Cancers, Paris, France.|Institut Curie, Centre De Recherche, Paris Inserm, Department of Bioinformatics, Biostatistics, Epidemiology and Computational Systems Biology of Cancer, U900, Paris, France Mines ParisTech, Centre for Computational Biology, Fontainebleau, France PSL Research University, Paris, France.|INRIA Projet AMIB, Ecole Polytechnique, Palaiseau, France.|Institut Curie, Centre De Recherche, Paris Inserm, Department of Bioinformatics, Biostatistics, Epidemiology and Computational Systems Biology of Cancer, U900, Paris, France Mines ParisTech, Centre for Computational Biology, Fontainebleau, France PSL Research University, Paris, France.', 'France|France|France|France|France', 0, 'Base Composition|Bayes Theorem|Genome|Genome-Wide Association Study|Genomic Structural Variation|High-Throughput Nucleotide Sequencing|Humans|Metagenomics|Neoplasms', 'Journal Article', '10.1093/bioinformatics/btv751', '26740523', 'Whole genome sequencing of paired-end reads can be applied to characterize the landscape of large somatic rearrangements of cancer genomes. Several methods for detecting structural variants with whole genome sequencing data have been developed. So far, none of these methods has combined information about abnormally mapped read pairs connecting rearranged regions and associated global copy number changes automatically inferred from the same sequencing data file. Our aim was to create a computational method that could use both types of information, i.e. normal and abnormal reads, and demonstrate that by doing so we can highly improve both sensitivity and specificity rates of structural variant prediction.'),
(79731, 2016, 'bio', 'Bayesian variable selection for binary outcomes in high-dimensional genomic studies using non-local priors', 'Nikooienejad, Amir|Wang, Wenyi|Johnson, Valen E', 'Department of Statistics, Texas A&M University, College Station, TX 77843, USA and.|Department of Bioinformatics and Computational Biology, M. D. Anderson Cancer Center, Houston, TX 77030, USA.|Department of Statistics, Texas A&M University, College Station, TX 77843, USA and.', 'United States|United States|United States', 15, 'Algorithms|Animals|Bayes Theorem|Genomics|Humans|Likelihood Functions|Software', 'Journal Article', '10.1093/bioinformatics/btv764', '26740524', 'The advent of new genomic technologies has resulted in the production of massive data sets. Analyses of these data require new statistical and computational methods. In this article, we propose one such method that is useful in selecting explanatory variables for prediction of a binary response. Although this problem has recently been addressed using penalized likelihood methods, we adopt a Bayesian approach that utilizes a mixture of non-local prior densities and point masses on the binary regression coefficient vectors.'),
(79732, 2016, 'bio', 'PINCAGE: probabilistic integration of cancer genomics data for perturbed gene identification and sample classification', 'xc5x9awitnicki, Michaxc5x82 P|Juul, Malene|Madsen, Tobias|Sxc3xb8rensen, Karina D|Pedersen, Jakob S', 'Department of Molecular Medicine (MOMA).|Department of Molecular Medicine (MOMA).|Department of Molecular Medicine (MOMA).|Department of Molecular Medicine (MOMA).|Department of Molecular Medicine (MOMA) Bioinformatics Research Centre (BiRC), Aarhus University, Aarhus, 8000, Denmark.', '-|-|-|-|Denmark', 6, 'Breast Neoplasms|DNA Methylation|Epigenomics|Gene Expression Regulation, Neoplastic|Genomics|Humans', 'Journal Article', '10.1093/bioinformatics/btv758', '26740525', 'Cancer development and progression is driven by a complex pattern of genomic and epigenomic perturbations. Both types of perturbations can affect gene expression levels and disease outcome. Integrative analysis of cancer genomics data may therefore improve detection of perturbed genes and prediction of disease state. As different data types are usually dependent, analysis based on independence assumptions will make inefficient use of the data and potentially lead to false conclusions.'),
(79733, 2016, 'bio', 'Modelling-based experiment retrieval: a case study with gene expression clustering', 'Blomstedt, Paul|Dutta, Ritabrata|Seth, Sohan|Brazma, Alvis|Kaski, Samuel', 'Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Espoo, Finland and.|Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Espoo, Finland and.|Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Espoo, Finland and.|European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, UK.|Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Espoo, Finland and.', 'Finland|Finland|Finland|United Kingdom|Finland', 6, 'Algorithms|Cluster Analysis|Gene Expression|Gene Expression Profiling|Models, Genetic', 'Journal Article', '10.1093/bioinformatics/btv762', '26740526', 'Public and private repositories of experimental data are growing to sizes that require dedicated methods for finding relevant data. To improve on the state of the art of keyword searches from annotations, methods for content-based retrieval have been proposed. In the context of gene expression experiments, most methods retrieve gene expression profiles, requiring each experiment to be expressed as a single profile, typically of case versus control. A more general, recently suggested alternative is to retrieve experiments whose models are good for modelling the query dataset. However, for very noisy and high-dimensional query data, this retrieval criterion turns out to be very noisy as well.'),
(79734, 2016, 'bio', 'UCSC Data Integrator and Variant Annotation Integrator', 'Hinrichs, Angie S|Raney, Brian J|Speir, Matthew L|Rhead, Brooke|Casper, Jonathan|Karolchik, Donna|Kuhn, Robert M|Rosenbloom, Kate R|Zweig, Ann S|Haussler, David|Kent, W James', 'Genomics Institute, University of California, Santa Cruz, CA, USA.|Genomics Institute, University of California, Santa Cruz, CA, USA.|Genomics Institute, University of California, Santa Cruz, CA, USA.|Computational Biology Graduate Group, University of California, Berkeley, CA, USA and.|Genomics Institute, University of California, Santa Cruz, CA, USA.|Genomics Institute, University of California, Santa Cruz, CA, USA.|Genomics Institute, University of California, Santa Cruz, CA, USA.|Genomics Institute, University of California, Santa Cruz, CA, USA.|Genomics Institute, University of California, Santa Cruz, CA, USA.|Genomics Institute, University of California, Santa Cruz, CA, USA, Howard Hughes Medical Institute, University of California, Santa Cruz, CA, USA.|Genomics Institute, University of California, Santa Cruz, CA, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 23, 'Animals|Databases, Genetic|Genome|Genomics|Humans|Internet|Software', 'Journal Article', '10.1093/bioinformatics/btv766', '26740527', 'Two new tools on the UCSC Genome Browser web site provide improved ways of combining information from multiple datasets, optionally including the users own custom track data and/or data from track hubs. The Data Integrator combines columns from multiple data tracks, showing all items from the first track along with overlapping items from the other tracks. The Variant Annotation Integrator is tailored to adding functional annotations to variant calls, it offers a more restricted set of underlying data tracks but adds predictions of each variants consequences for any overlapping or nearby gene transcript. When available, it optionally adds additional annotations including effect prediction scores from dbNSFP for missense mutations, ENCODE regulatory summary tracks and conservation scores.'),
(79735, 2016, 'bio', 'OEFinder: a user interface to identify and visualize ordering effects in single-cell RNA-seq data', 'Leng, Ning|Choi, Jeea|Chu, Li-Fang|Thomson, James A|Kendziorski, Christina|Stewart, Ron', 'Morgridge Institute for Research.|Department of Statistics, University of Wisconsin and.|Morgridge Institute for Research.|Morgridge Institute for Research.|Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI, USA.|Morgridge Institute for Research.', 'United States|United States|United States|United States|United States|United States', 10, 'Animals|RNA|Sequence Analysis, RNA|Single-Cell Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btw004', '26743507', 'A recent article identified an artifact in multiple single-cell RNA-seq (scRNA-seq) datasets generated by the Fluidigm C1 platform. Specifically, Leng et al. showed significantly increased gene expression in cells captured from sites with small or large plate output IDs. We refer to this artifact as an ordering effect (OE). Including OE genes in downstream analyses could lead to biased results. To address this problem, we developed a statistical method and software called OEFinder to identify a sorted list of OE genes. OEFinder is available as an R package along with user-friendly graphical interface implementations which allows users to check for potential artifacts in scRNA-seq data generated by the Fluidigm C1 platform.'),
(79736, 2016, 'bio', 'MAESTROweb: a web server for structure-based protein stability prediction', 'Laimer, Josef|Hiebl-Flach, Julia|Lengauer, Daniel|Lackner, Peter', 'Department of Molecular Biology, University of Salzburg, Salzburg 5020 School of Informatics, Communications and Media, University of Applied Sciences Upper Austria, Hagenberg 4232, Austria.|School of Informatics, Communications and Media, University of Applied Sciences Upper Austria, Hagenberg 4232, Austria.|School of Informatics, Communications and Media, University of Applied Sciences Upper Austria, Hagenberg 4232, Austria.|Department of Molecular Biology, University of Salzburg, Salzburg 5020.', 'Austria|Austria|Austria|-', 33, 'Computers|Protein Stability|Proteins|Software|Web Browser', 'Journal Article', '10.1093/bioinformatics/btv769', '26743508', 'The prediction of change in stability upon point mutations in proteins has many applications in protein analysis and engineering. We recently adjoined a new structure-based method called MAESTRO, which is distributed as command line program. We now provide access to the most important features of MAESTRO by an easy to use web service. MAESTROweb allows the prediction of change in stability for user-defined mutations, provides a scan functionality for the most (de)stabilizing n-point mutations for a maximum of n = 5, creates mutation sensitivity profiles and evaluates potential disulfide bonds. MAESTROweb operates on monomers, multimers and biological assemblies as defined by PDB.'),
(79737, 2016, 'bio', 'MMseqs software suite for fast and deep clustering and searching of large protein sequence sets', 'Hauser, Maria|Steinegger, Martin|Sxc3xb6ding, Johannes', 'Gene Center, Ludwig-Maximilians-Universitxc3xa4t Mxc3xbcnchen, Munich 81377, Germany.|Gene Center, Ludwig-Maximilians-Universitxc3xa4t Mxc3xbcnchen, Munich 81377, Germany, Computational Biology, Max Planck Institute for Biophysical Chemistry, Gxc3xb6ttingen 37077, Germany and TUM, Department of Informatics, Bioinformatics & Computational Biology-I12, Garching 85748, Germany.|Gene Center, Ludwig-Maximilians-Universitxc3xa4t Mxc3xbcnchen, Munich 81377, Germany, Computational Biology, Max Planck Institute for Biophysical Chemistry, Gxc3xb6ttingen 37077, Germany and.', 'Germany|Germany|Germany', 18, 'Algorithms|Amino Acid Sequence|Animals|Cluster Analysis|Databases, Nucleic Acid|Humans|Metagenomics|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btw006', '26743509', 'Sequence databases are growing fast, challenging existing analysis pipelines. Reducing the redundancy of sequence databases by similarity clustering improves speed and sensitivity of iterative searches. But existing tools cannot efficiently cluster databases of the size of UniProt to 50% maximum pairwise sequence identity or below. Furthermore, in metagenomics experiments typically large fractions of reads cannot be matched to any known sequence anymore because searching with sensitive but relatively slow tools (e.g. BLAST or HMMER3) through comprehensive databases such as UniProt is becoming too costly.'),
(79738, 2016, 'bio', 'Improving microRNA target prediction by modeling with unambiguously identified microRNA-target pairs from CLIP-ligation studies', 'Wang, Xiaowei', 'Department of Radiation Oncology, Washington University School of Medicine, St Louis, MO 63108, USA.', 'United States', 123, 'Algorithms|Animals|Computational Biology|Humans|MicroRNAs', 'Journal Article', '10.1093/bioinformatics/btw002', '26743510', 'MicroRNAs (miRNAs) are small non-coding RNAs that are extensively involved in many physiological and disease processes. One major challenge in miRNA studies is the identification of genes targeted by miRNAs. Currently, most researchers rely on computational programs to initially identify target candidates for subsequent validation. Although considerable progress has been made in recent years for computational target prediction, there is still significant room for algorithmic improvement.'),
(79739, 2016, 'bio', 'Homology-driven assembly of NOn-redundant protEin sequence sets (NOmESS) for mass spectrometry', 'Temu, Tikira|Mann, Matthias|Räschle, Markus|Cox, Jürgen', 'Computational Systems Biochemistry and Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried 82152, Germany.|Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried 82152, Germany.|Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried 82152, Germany.|Computational Systems Biochemistry and.', 'Germany|Germany|Germany|-', 0, 'Amino Acid Sequence|Animals|Base Sequence|High-Throughput Nucleotide Sequencing|Humans|Mass Spectrometry|Proteomics', 'Journal Article', '10.1093/bioinformatics/btv756', '26743511', 'To enable mass spectrometry (MS)-based proteomic studies with poorly characterized organisms, we developed a computational workflow for the homology-driven assembly of a non-redundant reference sequence dataset. In the automated pipeline, translated DNA sequences (e.g. ESTs, RNA deep-sequencing data) are aligned to those of a closely related and fully sequenced organism. Representative sequences are derived from each cluster and joined, resulting in a non-redundant reference set representing the maximal available amino acid sequence information for each protein. We here applied NOmESS to assemble a reference database for the widely used model organism Xenopus laevis and demonstrate its use in proteomic applications.'),
(79740, 2016, 'bio', 'Estimating the number and assignment of clock models in analyses of multigene datasets', 'Duchêne, Sebastián|Foster, Charles S P|Ho, Simon Y W', 'School of Life and Environmental Sciences Sydney Medical School, University of Sydney, Sydney, NSW 2006, Australia.|School of Life and Environmental Sciences.|School of Life and Environmental Sciences.', 'Australia|-|-', 0, 'Algorithms|Animals|Cluster Analysis|Genome|Genomics|Humans|Models, Genetic', 'Journal Article', '10.1093/bioinformatics/btw005', '26743512', 'Molecular-clock methods can be used to estimate evolutionary rates and timescales from DNA sequence data. However, different genes can display different patterns of rate variation across lineages, calling for the employment of multiple clock models. Selecting the optimal clock-partitioning scheme for a multigene dataset can be computationally demanding, but clustering methods provide a feasible alternative. We investigated the performance of different clustering methods using data from chloroplast genomes and data generated by simulation.'),
(79741, 2016, 'bio', 'EpimiRBase: a comprehensive database of microRNA-epilepsy associations', 'Mooney, Catherine|Becker, Brett A|Raoof, Rana|Henshall, David C', 'Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland.|School of Computer Science, University College Dublin, Dublin, Ireland.|Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland Department of Anatomy, Mosul Medical College, Mosul, Iraq.|Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland.', 'Ireland|Ireland|Ireland|Ireland', 0, '3 Untranslated Regions|Databases, Genetic|Epilepsy|Gene Expression Regulation|Humans|MicroRNAs|RNA, Messenger', 'Journal Article', '10.1093/bioinformatics/btw008', '26748106', 'MicroRNAs are short non-coding RNA which function to fine-tune protein levels in all cells. This is achieved mainly by sequence-specific binding to 3 untranslated regions of target mRNA. The result is post-transcriptional interference in gene expression which reduces protein levels either by promoting destabilisation of mRNA or translational repression. Research published since 2010 shows that microRNAs are important regulators of gene expression in epilepsy. A series of microRNA profiling studies in rodent and human tissue has revealed that epilepsy is associated with wide ranging changes to microRNA levels in the brain. These are thought to influence processes including cell death, inflammation and re-wiring of neuronal networks. MicroRNAs have also been identified in the blood after injury to the brain and therefore may serve as biomarkers of epilepsy. EpimiRBase is a manually curated database for researchers interested in the role of microRNAs in epilepsy. The fully searchable database includes information on up- and down-regulated microRNAs in the brain and blood, as well as functional studies, and covers both rodent models and human epilepsy.');
