INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(82746, 2019, 'bio', 'Graph-Based data integration from bioactive peptide databases of pharmaceutical interest: towards an organized collection enabling visual network analysis', 'Aguilera-Mendoza, Longendri|Marrero-Ponce, Yovani|Beltran, Jesus A|Tellez Ibarra, Roberto|Guillen-Ramirez, Hugo A|Brizuela, Carlos A', 'Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), Baja California, Mexico.|Universidad San Francisco de Quito (USFQ), Grupo de Medicina Molecular y Traslacional (MeM&T), Colegio de Ciencias de la Salud (COCSA), Escuela de Medicina, Edificio de Especialidades Médicas, Av. Interoceánica Km 12 1/2 - Cumbayá, Quito, Ecuador.|Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), Baja California, Mexico.|Grupo de Investigación de Bioinformática, Universidad de las Ciencias Informáticas (UCI), La Habana, Cuba.|Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), Baja California, Mexico.|Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), Baja California, Mexico.', 'Mexico|Ecuador|Mexico|Cuba|Mexico|Mexico', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz260', '30994884', 'Bioactive peptides have gained great attention in the academy and pharmaceutical industry since they play an important role in human health. However, the increasing number of bioactive peptide databases is causing the problem of data redundancy and duplicated efforts. Even worse is the fact that the available data is non-standardized and often dirty with data entry errors. Therefore, there is a need for a unified view that enables a more comprehensive analysis of the information on this topic residing at different sites.'),
(82747, 2019, 'bio', 'Computational identification of prion-like RNA-binding proteins that form liquid phase-separated condensates', 'Orlando, Gabriele|Raimondi, Daniele|Tabaro, Francesco|Codicxc3xa9, Francesco|Moreau, Yves|Vranken, Wim', 'Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, Belgium.|ESAT-STADIUS, KU Leuven, Kasteelpark Arenberg 10, Leuven, Belgium.|Institute of Biosciences and Medical Technology, Arvo Ylpxc5x91n katu 34, Tampere, Finland.|Department of Computer Science and Engineering, University of Bologna, Bologna, Italy.|ESAT-STADIUS, KU Leuven, Kasteelpark Arenberg 10, Leuven, Belgium.|Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, Belgium.', 'Belgium|Belgium|Finland|Italy|Belgium|Belgium', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz274', '30994888', 'Eukaryotic cells contain different membrane-delimited compartments, which are crucial for the biochemical reactions necessary to sustain cell life. Recent studies showed that cells can also trigger the formation of membraneless organelles composed by phase-separated proteins to respond to various stimuli. These condensates provide new ways to control the reactions and phase-separation proteins (PSPs) are thus revolutionising how cellular organization is conceived. The small number of experimentally validated proteins, and the difficulty in discovering them, remain bottlenecks in PSPs research.'),
(82748, 2019, 'bio', 'Fast detection of maximal exact matches via fixed sampling of query k-mers and Bloom filtering of index k-mers', 'Liu, Yuansheng|Yu Zhang, Leo|Li, Jinyan', 'Advanced Analytics Institute, Faculty of Engineering and IT, University of Technology Sydney, Australia.|School of Information Technology, Deakin University, Victoria, Australia.|Advanced Analytics Institute, Faculty of Engineering and IT, University of Technology Sydney, Australia.', 'Australia|Australia|Australia', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz273', '30994891', 'Detection of maximal exact matches (MEMs) between two long sequences is a fundamental problem in pairwise reference-query genome comparisons. To efficiently compare larger and larger genomes, reducing the number of indexed k-mers as well as the number of query k-mers has been adopted as a mainstream approach which saves the computational resources by avoiding a significant number of unnecessary matches.'),
(82749, 2019, 'bio', 'Hybrid analysis of gene dynamics predicts context specific expression and offers regulatory insights', 'Finkle, Justin D|Bagheri, Neda', 'Interdisciplinary Biological Sciences, Northwestern University, Evanston, IL.|Interdisciplinary Biological Sciences, Northwestern University, Evanston, IL.', '-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz256', '30994899', 'To understand the regulatory pathways underlying diseases, studies often investigate the differential gene expression between genetically or chemically differing cell populations. Differential expression analysis identifies global changes in transcription and enables the inference of functional roles of applied perturbations. This approach has transformed the discovery of genetic drivers of disease and possible therapies. However, differential expression analysis does not provide quantitative predictions of gene expression in untested conditions. We present a hybrid approach, termed DiffExPy, that uniquely combines discrete, differential expression analysis with in silico differential equation simulations to yield accurate, quantitative predictions of gene expression from time-series data.'),
(82750, 2019, 'bio', 'Overlap graph-based generation of haplotigs for diploids and polyploids', 'Baaijens, Jasmijn A|Schxc3xb6nhuth, Alexander', 'Centrum Wiskunde & Informatica, Amsterdam, Netherlands.|Centrum Wiskunde & Informatica, Amsterdam, Netherlands.', 'Netherlands|Netherlands', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz255', '30994902', 'Haplotype aware genome assembly plays an important role in genetics, medicine, and various other disciplines, yet generation of haplotype-resolved de novo assemblies remains a major challenge. Beyond distinguishing between errors and true sequential variants, one needs to assign the true variants to the different genome copies. Recent work has pointed out that the enormous quantities of traditional NGS read data have been greatly underexploited in terms of haplotig computation so far, which reflects that methodology for reference independent haplotig computation has not yet reached maturity.'),
(82751, 2019, 'bio', 'DeepSignal: detecting DNA methylation state from Nanopore sequencing reads using deep-learning', 'Ni, Peng|Huang, Neng|Zhang, Zhi|Wang, De-Peng|Liang, Fan|Miao, Yu|Xiao, Chuan-Le|Luo, Feng|Wang, Jianxin', 'School of Information Science and Engineering, Central South University, Changsha, China.|School of Information Science and Engineering, Central South University, Changsha, China.|School of Information Science and Engineering, Central South University, Changsha, China.|GrandOmics Biosciences, Beijing, China.|GrandOmics Biosciences, Beijing, China.|GrandOmics Biosciences, Beijing, China.|State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.|School of Computing, Clemson University, Clemson, SC, USA.|School of Information Science and Engineering, Central South University, Changsha, China.', 'China|China|China|China|China|China|China|United States|China', 17, '-', 'Journal Article', '10.1093/bioinformatics/btz276', '30994904', 'The Oxford Nanopore sequencing enables to directly detect methylation states of bases in DNA from reads without extra laboratory techniques. Novel computational methods are required to improve the accuracy and robustness of DNA methylation state prediction using Nanopore reads.'),
(82752, 2019, 'bio', 'Evolution of biosequence search algorithms: a brief survey', 'Kucherov, Gregory', 'CNRS and LIGM/University of Paris-Est, Marne-la-Vallée, France.', 'France', 4, '-', 'Journal Article', '10.1093/bioinformatics/btz272', '30994912', 'Although modern high-throughput biomolecular technologies produce various types of data, biosequence data remain at the core of bioinformatic analyses. However, computational techniques for dealing with this data evolved dramatically.'),
(82753, 2019, 'bio', 'VCPA: genomic variant calling pipeline and data management tool for Alzheimers Disease Sequencing Project', 'Leung, Yuk Yee|Valladares, Otto|Chou, Yi-Fan|Lin, Han-Jen|Kuzma, Amanda B|Cantwell, Laura|Qu, Liming|Gangadharan, Prabhakaran|Salerno, William J|Schellenberg, Gerard D|Wang, Li-San', 'CNRS and LIGM/University of Paris-Est, Marne-la-Vallée, France.|CNRS and LIGM/University of Paris-Est, Marne-la-Vallée, France.|CNRS and LIGM/University of Paris-Est, Marne-la-Vallée, France.|CNRS and LIGM/University of Paris-Est, Marne-la-Vallée, France.|CNRS and LIGM/University of Paris-Est, Marne-la-Vallée, France.|CNRS and LIGM/University of Paris-Est, Marne-la-Vallée, France.|CNRS and LIGM/University of Paris-Est, Marne-la-Vallée, France.|CNRS and LIGM/University of Paris-Est, Marne-la-Vallée, France.|CNRS and LIGM/University of Paris-Est, Marne-la-Vallée, France.|CNRS and LIGM/University of Paris-Est, Marne-la-Vallée, France.|CNRS and LIGM/University of Paris-Est, Marne-la-Vallée, France.', 'France|France|France|France|France|France|France|France|France|France|France', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz216', '31004159', '-'),
(82754, 2019, 'bio', 'A framework for space-efficient variable-order Markov models', 'Cunial, Fabio|Alanko, Jarno|Belazzougui, Djamal', 'Max Planck Institute for Molecular Cell Biology and Genetics (MPI-CBG), and Center for Systems Biology Dresden (CSBD), Dresden, Germany.|Department of Computer Science, University of Helsinki, Helsinki, Finland.|CAPA, DTISI, Centre de Recherche sur lInformation Scientifique et Technique, Algiers, Algeria.', 'Germany|Finland|Algeria', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz268', '31004473', 'Markov models with contexts of variable length are widely used in bioinformatics for representing sets of sequences with similar biological properties. When models contain many long contexts, existing implementations are either unable to handle genome-scale training datasets within typical memory budgets, or they are optimized for specific model variants and are thus inflexible.'),
(82755, 2019, 'bio', 'ORCA: A Comprehensive Bioinformatics Container Environment for Education and Research', 'Jackman, Shaun D|Mozgacheva, Tatyana|Chen, Susie|OHuiginn, Brendan|Bailey, Lance|Birol, Inanc|Jones, Steven J M', 'Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada.|Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada.|Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada.|Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada.|Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada.|Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada.|Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz278', '31004474', 'The ORCA bioinformatics environment is a Docker image that contains hundreds of bioinformatics tools and their dependencies. The ORCA image and accompanying server infrastructure provide a comprehensive bioinformatics environment for education and research. The ORCA environment on a server is implemented using Docker containers, but without requiring users to interact directly with Docker, suitable for novices who may not yet have familiarity with managing containers. ORCA has been used successfully to provide a private bioinformatics environment to external collaborators at a large genome institute, for teaching an undergraduate class on bioinformatics targeted at biologists, and to provide a ready-to-go bioinformatics suite for a hackathon. Using ORCA eliminates time that would be spent debugging software installation issues, so that time may be better spent on education and research.'),
(82756, 2019, 'bio', 'FuncTree2: an interactive radial tree for functional hierarchies and omics data visualization', 'Darzi, Youssef|Yamate, Yuta|Yamada, Takuji', 'Department of Life Science and Technology, Tokyo Institute of Technology, Tokyo, Japan.|Department of Life Science and Technology, Tokyo Institute of Technology, Tokyo, Japan.|Department of Life Science and Technology, Tokyo Institute of Technology, Tokyo, Japan.', 'Japan|Japan|Japan', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz245', '31004476', 'Functional annotations and their hierarchical classification are widely used in omics workflows to build novel insight upon existing biological knowledge. Currently, a plethora of tools is available to explore omics datasets at the level of functional annotations, but there is a lack of feature rich and user-friendly tools that help scientists take advantage of their hierarchical classification for additional and often invaluable insights. Here we present FuncTree2, a user-friendly web application that turns hierarchical classifications into interactive and highly customizable radial trees, and enables researchers to visualize their data simultaneously on all its levels. FuncTree2 features mapping of data from multiple samples and several navigation features like zooming, panning, re-rooting, and collapsing of nodes or levels.'),
(82757, 2019, 'bio', 'The EntOptLayout Cytoscape plug-in for the efficient visualization of major protein complexes in protein-protein interaction and signalling networks', 'xc3x81gg, Bence|Csxc3xa1szxc3xa1r, Andrea|Szalay-Bekxc5x91, Mxc3xa1txc3xa9|Veres, Dxc3xa1niel V|Mizsei, Rxc3xa9ka|Ferdinandy, Pxc3xa9ter|Csermely, Pxc3xa9ter|Kovxc3xa1cs, Istvxc3xa1n A', 'Department of Pharmacology and Pharmacotherapy, Semmelweis University, Budapest, Hungary.|Department of Medical Chemistry, Semmelweis University, Budapest, Hungary.|Department of Medical Chemistry, Semmelweis University, Budapest, Hungary.|Department of Medical Chemistry, Semmelweis University, Budapest, Hungary.|Laboratory of Immunobiology, Department of Medical Oncology, Dana Farber Cancer Institute Boston, MA, USA.|Department of Pharmacology and Pharmacotherapy, Semmelweis University, Budapest, Hungary.|Department of Medical Chemistry, Semmelweis University, Budapest, Hungary.|Network Science Institute, Northeastern University, Boston MA, USA.', 'Hungary|Hungary|Hungary|Hungary|United States|Hungary|Hungary|United States', 4, '-', 'Journal Article', '10.1093/bioinformatics/btz257', '31004478', 'Network visualizations of complex biological datasets usually result in hairball images, which do not discriminate network modules.'),
(82758, 2019, 'bio', 'BHap: a novel approach for bacterial haplotype reconstruction', 'Li, Xin|Saadat, Samaneh|Hu, Haiyan|Li, Xiaoman', 'Department of Computer Science, University of Central Florida, Orlando, FL, USA.|Department of Computer Science, University of Central Florida, Orlando, FL, USA.|Department of Computer Science, University of Central Florida, Orlando, FL, USA.|Burnett School of Biomedical Science, College of Medicine, University of Central Florida, Orlando, FL, USA.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz280', '31004480', 'The bacterial haplotype reconstruction is critical for selecting proper treatments for diseases caused by unknown haplotypes. Existing methods and tools do not work well on this task, because they are usually developed for viral instead of bacterial populations.'),
(82759, 2019, 'bio', 'scMatch: a single-cell gene expression profile annotation tool using reference datasets', 'Hou, Rui|Denisenko, Elena|Forrest, Alistair R R', 'Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, the University of Western Australia, PO Box 7214, 6 Verdun Street, Nedlands, Perth, Western Australia, Australia.|Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, the University of Western Australia, PO Box 7214, 6 Verdun Street, Nedlands, Perth, Western Australia, Australia.|Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, the University of Western Australia, PO Box 7214, 6 Verdun Street, Nedlands, Perth, Western Australia, Australia.', 'Australia|Australia|Australia', 10, '-', 'Journal Article', '10.1093/bioinformatics/btz292', '31028376', 'Single-cell RNA sequencing (scRNA-seq) measures gene expression at the resolution of individual cells. Massively multiplexed single-cell profiling has enabled large-scale transcriptional analyses of thousands of cells in complex tissues. In most cases, the true identity of individual cells is unknown and needs to be inferred from the transcriptomic data. Existing methods typically cluster (group) cells based on similarities of their gene expression profiles and assign the same identity to all cells within each cluster using the averaged expression levels. However, scRNA-seq experiments typically produce low-coverage sequencing data for each cell, which hinders the clustering process.'),
(82760, 2019, 'bio', 'Cell-level somatic mutation detection from single-cell RNA-sequencing', 'Vu, Trung Nghia|Nguyen, Ha-Nam|Calza, Stefano|Kalari, Krishna R|Wang, Liewei|Pawitan, Yudi', 'Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.|Information Technology Institute, Vietnam National University in Hanoi, 144 Xuan Thuy, Hanoi, Vietnam.|Department of Molecular and Translational Medicine, University of Brescia, Viale Europa 11, Brescia, Italy.|Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN, USA.|Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, 200 First Street SW, Rochester, MN, USA.|Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.', 'Sweden|United States|Italy|United States|United States|Sweden', 4, '-', 'Journal Article', '10.1093/bioinformatics/btz288', '31028395', 'Both single-cell RNA-sequencing (scRNA-seq) and DNA-sequencing have been applied for cell-level genomic profiling. For mutation profiling, the latter seems more natural. However, the task is highly challenging due to the limited input materials from only two copies of DNA molecules, while whole-genome amplification generates biases and other technical noises. ScRNA-seq starts with a higher input amount, so generally has better data quality. There exists various methods for mutation detection from DNA sequencing, it is not clear whether these methods work for scRNA-seq data.'),
(82761, 2019, 'bio', 'Adaptive boosting-based computational model for predicting potential miRNA-disease associations', 'Zhao, Yan|Chen, Xing|Yin, Jun', 'School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China.|School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China.|School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China.', 'China|China|China', 17, '-', 'Journal Article', '10.1093/bioinformatics/btz297', '31038664', 'Recent studies have shown that microRNAs (miRNAs) play a critical part in several biological processes and dysregulation of miRNAs is related with numerous complex human diseases. Thus, in-depth research of miRNAs and their association with human diseases can help us solve many problems.'),
(82762, 2019, 'bio', 'kmcEx: memory-frugal and retrieval-efficient encoding of counted k-mers', 'Jiang, Peng|Luo, Jie|Wang, Yiqi|Deng, Pingji|Schmidt, Bertil|Tang, Xiangjun|Chen, Ningjiang|Wong, Limsoon|Zhao, Liang', 'Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, China.|Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, China.|Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, China.|Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, China.|Institute of Computer Science, Johannes Gutenberg University Mainz, Staudingerweg 9, Mainz, Germany.|Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, China.|School of Computing and Electronic Information, Guangxi University, Nanning, Guangxi, China.|School of Computing, National University of Singapore, Singapore, Singapore.|Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, China.', 'China|China|China|China|Germany|China|China|Singapore|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz299', '31038666', 'K-mers along with their frequency have served as an elementary building block for error correction, repeat detection, multiple sequence alignment, genome assembly, etc., attracting intensive studies in k-mer counting. However, the output of k-mer counters itself is large, very often, it is too large to fit into main memory, leading to highly narrowed usability.'),
(82763, 2019, 'bio', 'icHET: Interactive Visualization of Cytoplasmic Heteroplasmy', 'Phan, Vinhthuy|Pham, Diem-Trang|Melton, Caroline|Ramsey, Adam|Daigle, Bernie J|Mandel, Jennifer R', 'Department of Computer Science, University of Memphis, Memphis, USA.|Department of Computer Science, University of Memphis, Memphis, USA.|Department of Biological Sciences, University of Memphis, Memphis, USA.|Department of Biological Sciences, University of Memphis, Memphis, USA.|Department of Computer Science, University of Memphis, Memphis, USA.|Department of Biological Sciences, University of Memphis, Memphis, USA.', 'United States|United States|United States|United States|United States|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz300', '31038667', 'Although heteroplasmy has been studied extensively in animal systems, there is a lack of tools for analyzing, exploring and visualizing heteroplasmy at the genome-wide level in other taxonomic systems. We introduce icHET, which is a computational workflow that produces an interactive visualization that facilitates the exploration, analysis, and discovery of heteroplasmy across multiple genomic samples. icHET works on short reads from multiple samples from any organism with an organellar reference genome (mitochondrial or plastid) and a nuclear reference genome.'),
(82764, 2019, 'bio', 'ClinOmicsTrailbc: a visual analytics tool for breast cancer treatment stratification', 'Schneider, Lara|Kehl, Tim|Thedinga, Kristina|Grammes, Nadja Liddy|Backes, Christina|Mohr, Christopher|Schubert, Benjamin|Lenhof, Kerstin|Gerstner, Nico|Daniel Hartkopf, Andreas|Wallwiener, Markus|Kohlbacher, Oliver|Keller, Andreas|Meese, Eckart|Graf, Norbert|Lenhof, Hans-Peter', 'Center for Bioinformatics, Saarland Informatics Campus, Saarland University, Saarbrücken, Germany.|Center for Bioinformatics, Saarland Informatics Campus, Saarland University, Saarbrücken, Germany.|Center for Bioinformatics, Saarland Informatics Campus, Saarland University, Saarbrücken, Germany.|Center for Bioinformatics, Saarland Informatics Campus, Saarland University, Saarbrücken, Germany.|Center for Bioinformatics, Saarland Informatics Campus, Saarland University, Saarbrücken, Germany.|Quantitative Biology Center (QBiC), Tübingen, Germany.|Department of Systems Biology, Harvard Medical School, Boston, MA, USA.|Center for Bioinformatics, Saarland Informatics Campus, Saarland University, Saarbrücken, Germany.|Center for Bioinformatics, Saarland Informatics Campus, Saarland University, Saarbrücken, Germany.|Department of Obstetrics and Gynecology, University of Tübingen, Tübingen, Germany.|Department of Obstetrics and Gynecology, University of Heidelberg, Heidelberg, Germany.|Quantitative Biology Center (QBiC), Tübingen, Germany.|Center for Bioinformatics, Saarland Informatics Campus, Saarland University, Saarbrücken, Germany.|Center for Bioinformatics, Saarland Informatics Campus, Saarland University, Saarbrücken, Germany.|Center for Bioinformatics, Saarland Informatics Campus, Saarland University, Saarbrücken, Germany.|Center for Bioinformatics, Saarland Informatics Campus, Saarland University, Saarbrücken, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|United States|Germany|Germany|Germany|Germany|Germany|Germany|Ge', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz302', '31038669', 'Breast cancer is the second leading cause of cancer death among women. Tumors, even of the same histopathological subtype, exhibit a high genotypic diversity that impedes therapy stratification and that hence must be accounted for in the treatment decision-making process.'),
(82765, 2019, 'bio', 'Continuous State HMMs for Modeling Time Series Single Cell RNA-Seq Data', 'Lin, Chieh|Bar-Joseph, Ziv', 'Machine Learning Dept. School of Computer Science, Carnegie Mellon University.|Machine Learning Dept. School of Computer Science, Carnegie Mellon University.', '-|-', 6, '-', 'Journal Article', '10.1093/bioinformatics/btz296', '31038684', 'Methods for reconstructing developmental trajectories from time series single cell RNA-Seq (scRNA-Seq) data can be largely divided into two categories. The first, often referred to as pseudotime ordering methods, are deterministic and rely on dimensionality reduction followed by an ordering step. The second learns a probabilistic branching model to represent the developmental process. While both types have been successful, each suffers from shortcomings that can impact their accuracy.'),
(82766, 2019, 'bio', 'Develop machine learning based regression predictive models for engineering protein solubility', 'Han, Xi|Wang, Xiaonan|Zhou, Kang', 'Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore.|Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore.|Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore.', 'Singapore|Singapore|Singapore', 6, '-', 'Journal Article', '10.1093/bioinformatics/btz294', '31038685', 'Protein activity is a significant characteristic for recombinant proteins which can be used as biocatalysts. High activity of proteins reduces the cost of biocatalysts. A model that can predict protein activity from amino acid sequence is highly desired, as it aids experimental improvement of proteins. However, only limited data for protein activity are currently available, which prevents the development of such models. Since protein activity and solubility are correlated for some proteins, the publicly available solubility dataset may be adopted to develop models that can predict protein solubility from sequence. The models could serve as a tool to indirectly predict protein activity from sequence. In literature, predicting protein solubility from sequence has been intensively explored, but the predicted solubility represented in binary values from all the developed models was not suitable for guiding experimental designs to improve protein solubility. Here we propose new machine learning models for improving protein solubility in vivo.'),
(82767, 2019, 'bio', 'LAmbDA: Label Ambiguous Domain Adaptation Dataset Integration Reduces Batch Effects and Improves Subtype Detection', 'Johnson, Travis S|Wang, Tongxin|Huang, Zhi|Yu, Christina Y|Wu, Yi|Han, Yatong|Zhang, Yan|Huang, Kun|Zhang, Jie', 'Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA.|School of Medicine,Indiana University, Indianapolis, IN, USA.|School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA.|Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA.|School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA.|School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA.|Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA.|School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA.|School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA.', 'United States|India|United States|United States|United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz295', '31038689', 'Rapid advances in single cell RNA sequencing (scRNA-seq) have produced higher-resolution cellular subtypes in multiple tissues and species. Methods are increasingly needed across datasets and species to i) remove systematic biases, ii) model multiple datasets with ambiguous labels, and iii) classify cells and map cell type labels. However, most methods only address one of these problems on broad cell types or simulated data using a single model type. It is also important to address higher-resolution cellular subtypes, subtype labels from multiple datasets, models trained on multiple datasets simultaneously, and generalizability beyond a single model type.'),
(82768, 2019, 'bio', 'Dynamical Important Residue Network (DIRN): Network Inference via Conformational Change', 'Li, Quan|Luo, Ray|Chen, Hai-Feng', 'State Key Laboratory of Microbial Metabolism, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.|Departments of Molecular Biology and Biochemistry, Chemical and Biomolecular Engineering, Materials Science and Engineering, and Biomedical Engineering, University of California, Irvine, CA 92697, USA and Department of Chemistry, Southern University of Science and Technology, Shenzhen, Guangdong, China.|State Key Laboratory of Microbial Metabolism, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.', 'China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz298', '31038692', 'Protein residue interaction network has emerged as a useful strategy to understand the complex relationship between protein structures and functions and how functions are regulated. In a residue interaction network, every residue is used to define a network node, adding noises in network post-analysis and increasing computational burden. In addition, dynamical information is often necessary in deciphering biological functions.'),
(82769, 2019, 'bio', 'Predicted dynamical couplings of protein residues characterize catalysis, transport and allostery', 'Alfayate, Alvaro|Caceres, Carlos Rodriguez|Gomes Dos Santos, Helena|Bastolla, Ugo', 'Centro de Biologia Molecular ``Severo Ochoa CSIC-UAM Cantoblanco, Madrid, Spain.|Centro de Biologia Molecular ``Severo Ochoa CSIC-UAM Cantoblanco, Madrid, Spain.|Centro de Biologia Molecular ``Severo Ochoa CSIC-UAM Cantoblanco, Madrid, Spain.|Centro de Biologia Molecular ``Severo Ochoa CSIC-UAM Cantoblanco, Madrid, Spain.', 'Spain|Spain|Spain|Spain', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz301', '31038697', 'Protein function is intrinsically linked to native dynamics, but the systematic characterization of functionally relevant dynamics remains elusive besides specific examples. Here we exhaustively characterize three types of dynamical couplings between protein residues: co-directionality (moving along collinear directions), coordination (small fluctuations of the interatomic distance) and deformation (the extent by which perturbations applied at one residue modify the local structure of the other one), which we analytically compute through the torsional network model.'),
(82770, 2019, 'bio', 'Large-scale prediction of binding affinity in protein-small ligand complexes: the PRODIGY-LIG web server', 'Vangone, Anna|Schaarschmidt, Joerg|Koukos, Panagiotis|Geng, Cunliang|Citro, Nevia|Trellet, Mikael E|Xue, Li C|Bonvin, Alexandre M J J', 'Department of Chemistry, Faculty of Science, Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, Utrecht, The Netherlands.|Department of Chemistry, Faculty of Science, Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, Utrecht, The Netherlands.|Department of Chemistry, Faculty of Science, Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, Utrecht, The Netherlands.|Department of Chemistry, Faculty of Science, Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, Utrecht, The Netherlands.|Department of Chemistry, Faculty of Science, Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, Utrecht, The Netherlands.|Department of Chemistry, Faculty of Science, Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, Utrecht, The Netherlands.|Department of Chemistry, Faculty of Science, Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, Utrecht, The Netherlands.|Department of Chemistry, Faculty of Science, Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, Utrecht, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 10, '-', 'Journal Article', '10.1093/bioinformatics/bty816', '31051038', 'Recently we published PROtein binDIng enerGY (PRODIGY), a web-server for the prediction of binding affinity in protein-protein complexes. By using a combination of simple structural properties, such as the residue-contacts made at the interface, PRODIGY has demonstrated a top performance compared with other state-of-the-art predictors in the literature. Here we present an extension of it, named PRODIGY-LIG, aimed at the prediction of affinity in protein-small ligand complexes. The predictive method, properly readapted for small ligand by making use of atomic instead of residue contacts, has been successfully applied for the blind prediction of 102 protein-ligand complexes during the D3R Grand Challenge 2. PRODIGY-LIG has the advantage of being simple, generic and applicable to any kind of protein-ligand complex. It provides an automatic, fast and user-friendly tool ensuring broad accessibility.'),
(82771, 2019, 'bio', 'High precision protein functional site detection using 3D convolutional neural networks', 'Torng, Wen|Altman, Russ B', 'Department of Bioengineering, Stanford University, Stanford, CA, USA.|Department of Bioengineering, Stanford University, Stanford, CA, USA.', 'United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty813', '31051039', 'Accurate annotation of protein functions is fundamental for understanding molecular and cellular physiology. Data-driven methods hold promise for systematically deriving rules underlying the relationship between protein structure and function. However, the choice of protein structural representation is critical. Pre-defined biochemical features emphasize certain aspects of protein properties while ignoring others, and therefore may fail to capture critical information in complex protein sites.'),
(82772, 2019, 'bio', 'RETRACTED: LFQC: a lossless compression algorithm for FASTQ files', 'Pathak, Sudipta|Rajasekaran, Sanguthevar', 'Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269-4155, USA.|Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269-4155, USA.', 'United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btu701', '31051040', 'Next-generation sequencing (NGS) technologies have revolutionized genomic research by reducing the cost of whole-genome sequencing. One of the biggest challenges posed by modern sequencing technology is economic storage of NGS data. Storing raw data is infeasible because of its enormous size and high redundancy. In this article, we address the problem of storage and transmission of large Fastq files using innovative compression techniques.'),
(82773, 2019, 'bio', 'HiCNN: a very deep convolutional neural network to better enhance the resolution of Hi-C data', 'Liu, Tong|Wang, Zheng', 'Department of Computer Science, University of Miami, Coral Gables, FL, USA.|Department of Computer Science, University of Miami, Coral Gables, FL, USA.', 'United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz251', '31056636', 'High-resolution Hi-C data are indispensable for the studies of three-dimensional (3D) genome organization at kilobase level. However, generating high-resolution Hi-C data (e.g. 5xe2x80x89kb) by conducting Hi-C experiments needs millions of mammalian cells, which may eventually generate billions of paired-end reads with a high sequencing cost. Therefore, it will be important and helpful if we can enhance the resolutions of Hi-C data by computational methods.'),
(82774, 2019, 'bio', 'Identification of differentially expressed gene sets using the Generalized Berk-Jones statistica', 'Gaynor, Sheila M|Sun, Ryan|Lin, Xihong|Quackenbush, John', 'Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.|Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.|Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.|Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz277', '31062858', 'Cancer genomics studies frequently aim to identify genes that are differentially expressed between clinically distinct patient subgroups, generally by testing single genes one at a time. However, the results of any individual transcriptomic study are often not fully reproducible. A particular challenge impeding statistical analysis is the difficulty of distinguishing between differential expression comprising part of the genomic disease etiology and that induced by downstream effects. More robust analytical approaches that are well-powered to detect potentially causative genes, are less prone to discovering spurious associations, and can deliver reproducible findings across different studies are needed.'),
(82775, 2019, 'bio', 'gpart: human genome partitioning and visualization of high-density SNP data by identifying haplotype blocks', 'Ah Kim, Sun|Brossard, Myriam|Roshandel, Delnaz|Paterson, Andrew D|Bull, Shelley B|Yoo, Yun Joo', 'The Research Institute of Basic Sciences, Seoul National University, Seoul, South Korea.|Prosserman Centre for Health Research, The Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Canada.|Hospital for Sick Children Research Institute, Toronto, Canada.|Hospital for Sick Children Research Institute, Toronto, Canada.|Prosserman Centre for Health Research, The Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Canada.|Department of Mathematics Education, Seoul National University, Seoul.', '-|Canada|Canada|Canada|Canada|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz308', '31070701', 'For the analysis of high-throughput genomic data produced by next-generation sequencing (NGS) technologies, researchers need to identify linkage disequilibrium (LD) structure in the genome. In this work, we developed an R package gpart which provides clustering algorithms to define LD blocks or analysis units consisting of SNPs. The visualization tool in gpart can display the LD structure and gene positions for up to 20,000 SNPs in one image. The gpart functions facilitate construction of LD blocks and SNP partitions for vast amounts of genome sequencing data within a reasonable time and memory limits in personal computing environments.'),
(82776, 2019, 'bio', 'A decision-theoretic approach to the evaluation of machine learning algorithms in computational drug discovery', 'Watson, Oliver P|Cortes-Ciriano, Isidro|Taylor, Aimee R|Watson, James A', 'Evariste Technologies Ltd, Goring on Thames, United Kingdom.|Evariste Technologies Ltd, Goring on Thames, United Kingdom.|Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.|Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, United Kingdom.', 'United Kingdom|United Kingdom|United States|United Kingdom', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz293', '31070704', 'Artificial intelligence, trained via machine learning (e.g. neural nets, random forests) or computational statistical algorithms (e.g. support vector machines, ridge regression), holds much promise for the improvement of small molecule drug discovery. However, small molecule structure-activity data are high dimensional with low signal-to-noise ratios and proper validation of predictive methods is difficult. It is poorly understood which, if any, of the currently available machine learning algorithms will best predict new candidate drugs.'),
(82777, 2019, 'bio', 'Identification of disease-associated loci using machine learning for genotype and network data integration', 'Leal, Luis G|David, Alessia|Jarvelin, Marjo-Riita|Sebert, Sylvain|Ruddock, Minna|Karhunen, Ville|Seaby, Eleanor|Hoggart, Clive|Sternberg, Michael J E', 'Department of Life Sciences, Centre for Integrative Systems Biology and Bioinformatics, Imperial College London, London, SW7 2AZ, United Kingdom.|Department of Life Sciences, Centre for Integrative Systems Biology and Bioinformatics, Imperial College London, London, SW7 2AZ, United Kingdom.|Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, FI-90014, Finland.|Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, FI-90014, Finland.|Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, FI-90014, Finland.|Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, FI-90014, Finland.|Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.|Department of Medicine, Imperial College London, London, W2 1PG, United Kingdom.|Department of Life Sciences, Centre for Integrative Systems Biology and Bioinformatics, Imperial College London, London, SW7 2AZ, United Kingdom.', 'United Kingdom|United Kingdom|Finland|Finland|Finland|Finland|United States|United Kingdom|United Kingdom', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz310', '31070705', 'Integration of different omics data could markedly help to identify biological signatures, understand the missing heritability of complex diseases and ultimately achieve personalised medicine. Standard regression models used in Genome Wide Association Studies (GWAS) identify loci with a strong effect size, whereas GWAS meta-analyses are often needed to capture weak loci contributing to the missing heritability. Development of novel machine learning algorithms for merging genotype data with other omics data is highly needed as it could enhance the prioritisation of weak loci.'),
(82778, 2019, 'bio', 'G-OnRamp: A Galaxy-based platform for collaborative annotation of eukaryotic genomes', 'Liu, Yating|Sargent, Luke|Leung, Wilson|Elgin, Sarah C R|Goecks, Jeremy', 'Department of Biology, Washington University in St. Louis, St. Louis, MO, USA.|Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA.|Department of Biology, Washington University in St. Louis, St. Louis, MO, USA.|Department of Biology, Washington University in St. Louis, St. Louis, MO, USA.|Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA.', 'United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz309', '31070714', 'G-OnRamp provides a user-friendly, web-based platform for collaborative, end-to-end annotation of eukaryotic genomes using UCSC Assembly Hubs and JBrowse/Apollo genome browsers with evidence tracks derived from sequence alignments, ab initio gene predictors, RNA-Seq data, and repeat finders. G-OnRamp can be used to visualize large genomics datasets and to perform collaborative genome annotation projects in both research and educational settings.'),
(82779, 2019, 'bio', 'ResPRE: high-accuracy protein contact prediction by coupling precision matrix with deep residual neural networks', 'Li, Yang|Hu, Jun|Zhang, Chengxin|Yu, Dong-Jun|Zhang, Yang', 'School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing, China.|School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing, China.|Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw, Ann Arbor, MI, USA.|School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing, China.|Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw, Ann Arbor, MI, USA.', 'China|China|United States|China|United States', 22, '-', 'Journal Article', '10.1093/bioinformatics/btz291', '31070716', 'Contact-map of a protein sequence dictates the global topology of structural fold. Accurate prediction of the contact-map is thus essential to protein 3D structure prediction, which is particularly useful for the protein sequences that do not have close homology templates in the PDB.'),
(82780, 2019, 'bio', 'RAxML-NG: A fast, scalable, and user-friendly tool for maximum likelihood phylogenetic inference', 'Kozlov, Alexey M|Darriba, Diego|Flouri, Tomáš|Morel, Benoit|Stamatakis, Alexandros', 'Computational Molecular Evolution Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany.|Computational Molecular Evolution Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany.|Computational Molecular Evolution Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany.|Computational Molecular Evolution Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany.|Computational Molecular Evolution Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany.', 'Germany|Germany|Germany|Germany|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz305', '31070718', 'Phylogenies are important for fundamental biological research, but also have numerous applications in biotechnology, agriculture, and medicine. Finding the optimal tree under the popular maximum likelihood (ML) criterion is known to be NP-hard. Thus, highly optimized and scalable codes are needed to analyze constantly growing empirical datasets.'),
(82781, 2019, 'bio', 'sbgntikz - a TikZ library to draw SBGN maps', 'Rougny, Adrien', 'Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science and Technology (AIST), Aomi, Tokyo, Japan.', 'Japan', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz287', '31070720', 'The Systems Biology Graphical Notation (SBGN) has emerged as the main standard to represent biological maps graphically. It comprises three complementary languages: Process Description, for detailed biomolecular processes, Activity Flow, for influences of biological activities, and Entity Relationship, for independent relations shared among biological entities. On the other hand, TikZ is one of the most commonly used package to program graphics within TEX/LATEX. Here, we present sbgntikz, a TikZ library that allows drawing and customizing SBGN maps directly into TEX/LATEX documents, using the TikZ language. sbgntikz supports all glyphs of the three SBGN languages, and offers options that facilitate the drawing of complex glyph assembly within TikZ. Furthermore, sbgntikz is provided together with a converter that allows transforming any SBGN map stored under the SBGN Markup Language (SBGN-ML) into a TikZ picture, or rendering it directly into a PDF file.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(82782, 2019, 'bio', 'TractaViewer: a genome-wide tool for preliminary assessment of therapeutic target druggability', 'Pearson, Neil|Malki, Karim|Evans, David|Vidler, Lewis|Ruble, Cara|Scherschel, James|Eastwood, Brian|Collier, David A', 'Eli Lilly & Co. Ltd., Erl Wood Manor, Windlesham, UK.|Eli Lilly & Co. Ltd., Erl Wood Manor, Windlesham, UK.|Eli Lilly & Co. Ltd., Erl Wood Manor, Windlesham, UK.|Eli Lilly & Co. Ltd., Erl Wood Manor, Windlesham, UK.|Eli Lilly & Co., Corporate Centre, Indianapolis, USA.|Eli Lilly & Co., Corporate Centre, Indianapolis, USA.|Eli Lilly & Co. Ltd., Erl Wood Manor, Windlesham, UK.|Eli Lilly & Co. Ltd., Erl Wood Manor, Windlesham, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|India|India|United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz270', '31070721', 'We present software to characterise and rank potential therapeutic (drug) targets with data from public databases and present it in a user-friendly format. By understanding potential obstacles to drug development through the gathering and understanding of this information, combined with robust approaches to target validation to generate therapeutic hypotheses, this approach may provide high quality targets, leading the process of drug development to become more efficient and cost-effective.'),
(82783, 2019, 'bio', 'Graphical data mining of cancer mechanisms with SEMA', 'Solmaz, Mustafa|Lane, Adam|Gonen, Bilal|Akmamedova, Ogulsheker|Gunes, Mehmet H|Komurov, Kakajan', 'Department of Computer Science and Engineering, University of Nevada, Reno.|Division of Bone Marrow Transplantation, Cincinnati Childrens Hospital Medical Center.|School of Information Technology, University of Cincinnati.|School of Information Technology, University of Cincinnati.|Department of Computer Science and Engineering, University of Nevada, Reno.|Divisions of Experimental Hematology and Cancer Biology, Human Genetics and Biomedical Informatics, Cincinnati Childrens Hospital Medical Center.', '-|-|-|-|-|-', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz303', '31070723', 'An important goal of cancer genomics initiatives is to provide the research community with the resources for the unbiased query of cancer mechanisms. Several excellent web platforms have been developed to enable the visual analyses of molecular alterations in cancers from these datasets. However, there are few tools to allow the researchers to mine these resources for mechanisms of cancer processes and their functional interactions in an intuitive unbiased manner.'),
(82784, 2019, 'bio', 'FP2VEC: a new molecular featurizer for learning molecular properties', 'Jeon, Woosung|Kim, Dongsup', 'Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.|Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.', '-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz307', '31070725', 'One of the most successful methods for predicting the properties of chemical compounds is the quantitative structure-activity relationship (QSAR) methods. The prediction accuracy of QSAR models has recently been greatly improved by employing deep learning technology. Especially, newly developed molecular featurizers based on graph convolution operations on molecular graphs significantly outperform the conventional extended connectivity fingerprints (ECFP) feature in both classification and regression tasks, indicating that it is critical to develop more effective new featurizers to fully realize the power of deep learning techniques. Motivated by the fact that there is a clear analogy between chemical compounds and natural languages, this work develops a new molecular featurizer, FP2VEC, which represents a chemical compound as a set of trainable embedding vectors.'),
(82785, 2019, 'bio', 'INDRA-IPM: interactive pathway modeling using natural language with automated assembly', 'Todorov, Petar V|Gyori, Benjamin M|Bachman, John A|Sorger, Peter K', 'Laboratory of Systems Pharmacology, Harvard Medical School, 200 Longwood Ave, Boston, MA, 02115, USA.|Laboratory of Systems Pharmacology, Harvard Medical School, 200 Longwood Ave, Boston, MA, 02115, USA.|Laboratory of Systems Pharmacology, Harvard Medical School, 200 Longwood Ave, Boston, MA, 02115, USA.|Laboratory of Systems Pharmacology, Harvard Medical School, 200 Longwood Ave, Boston, MA, 02115, USA.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz289', '31070726', 'INDRA-IPM (Interactive Pathway Map) is a web-based pathway map modeling tool that combines natural language processing with automated model assembly and visualization. INDRA-IPM contextualizes models with expression data and exports them to standard formats.'),
(82786, 2019, 'bio', 'Efficient Multivariate Analysis Algorithms for Longitudinal Genome-wide Association Studies', 'Ning, Chao|Wang, Dan|Zhou, Lei|Wei, Julong|Liu, Yuanxin|Kang, Huimin|Zhang, Shengli|Zhou, Xiang|Xu, Shizhong|Liu, Jian-Feng', 'National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China.|National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China.|National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China.|Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.|School of English, Beijing International Studies University, Beijing, China.|National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China.|National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China.|Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.|Department of Botany and Plant Science, University of California, Riverside, USA.|National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China.', 'China|China|China|United States|China|China|China|United States|United States|China', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz304', '31070732', 'Current dynamic phenotyping system introduces time as an extra dimension to genome-wide association studies (GWAS), which helps to explore the mechanism of dynamical genetic control for complex longitudinal traits. However, existing methods for longitudinal GWAS either ignore the covariance among observations of different time points or encounter computational efficiency issues.'),
(82787, 2019, 'bio', 'mirTime: Identifying Condition-Specific Targets of MicroRNA in Time-series Transcript Data using Gaussian Process Model and Spherical Vector Clustering', 'Kang, Hyejin|Ahn, Hongryul|Jo, Kyuri|Oh, Minsik|Kim, Sun', 'Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea.|Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea.|Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea.|Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea.|Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea.', '-|-|-|-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz306', '31070735', 'MicroRNAs, small non-coding RNAs, are conserved in many species and they are key regulators that mediate post-transcriptional gene silencing. Since biologists cannot perform experiments for each of target genes of thousands of microRNAs in numerous specific conditions, prediction on microRNA target genes has been extensively investigated. A general framework is a two-step process of selecting target candidates based on sequence and binding energy features and then predicting targets based on negative correlation of microRNAs and their targets. However, there are few methods that are designed for target predictions using time-series gene expression data.'),
(82788, 2019, 'bio', 'ProSampler: an ultra-fast and accurate motif finder in large ChIP-seq datasets for combinatory motif discovery', 'Li, Yang|Ni, Pengyu|Zhang, Shaoqiang|Li, Guojun|Su, Zhengchang', 'School of Mathematics, Shandong University, Jinan, China.|Department of Bioinformatics and Genomics, College of Computing and Informatics, The University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, USA.|College of Computer and Information Engineering, Tianjin Normal University, Tianjin, China.|School of Mathematics, Shandong University, Jinan, China.|Department of Bioinformatics and Genomics, College of Computing and Informatics, The University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, USA.', 'China|United States|China|China|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz290', '31070745', 'The availability of numerous ChIP-seq datasets for transcription factors (TF) has provided an unprecedented opportunity to identify all TF binding sites in genomes. However, the progress has been hindered by the lack of a highly efficient and accurate tool to find not only the target motifs, but also cooperative motifs in very big datasets.'),
(82789, 2019, 'bio', 'The Barcode, UMI, Set format and BUStools', 'Melsted, Páll|Ntranos, Vasilis|Pachter, Lior', 'Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland, Reykjavik, Iceland.|Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, United States.|Department of Computing & Mathematical Sciences. California Institute of Technology, Pasadena, CA, USA.', 'Iceland|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz279', '31073610', 'We introduce the Barcode-UMI-Set format (BUS) for representing pseudoalignments of reads from single-cell RNA-seq experiments. The format can be used with all single-cell RNA-seq technologies, and we show that BUS files can be efficiently generated. BUStools is a suite of tools for working with BUS files and facilitates rapid quantification and analysis of single-cell RNA-seq data. The BUS format therefore makes possible the development of modular, technology-specific, and robust workflows for single-cell RNA-seq analysis.'),
(82790, 2019, 'bio', 'MINERVA API and plugins: opening molecular network analysis and visualization to the community', 'Hoksza, David|Gawron, Piotr|Ostaszewski, Marek|Smula, Ewa|Schneider, Reinhard', 'Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, Luxembourg.|Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, Luxembourg.|Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, Luxembourg.|Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, Luxembourg.|Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, Luxembourg.', 'Luxembourg|Luxembourg|Luxembourg|Luxembourg|Luxembourg', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz286', '31074494', 'The complexity of molecular networks makes them difficult to navigate and interpret, creating a need for specialized software. MINERVA is a web platform for visualization, exploration and management of molecular networks. Here, we introduce an extension to MINERVA architecture that greatly facilitates the access and use of the stored molecular network data. It allows to incorporate such data in analytical pipelines via a programmatic access interface, and to extend the platforms visual exploration and analytics functionality via plugin architecture. This is possible for any molecular network hosted by the MINERVA platform encoded in well-recognized systems biology formats. To showcase the possibilities of the plugin architecture, we have developed several plugins extending the MINERVA core functionalities. In the article, we demonstrate the plugins for interactive tree traversal of molecular networks, for enrichment analysis and for mapping and visualization of known disease variants or known adverse drug reactions to molecules in the network.'),
(82791, 2019, 'bio', 'Exact hypothesis testing for shrinkage based Gaussian Graphical Models', 'Bernal, Victor|Bischoff, Rainer|Guryev, Victor|Grzegorczyk, Marco|Horvatovich, Peter', 'Bernoulli Institute, University of Groningen, Groningen, AG, The Netherlands.|Department of Analytical Biochemistry, Groningen Research Institute of Pharmacy, University of Groningen, Groningen, AV, The Netherlands.|European Research Institute for the Biology of Ageing, University Medical Center Groningen, University of Groningen, Groningen, AV, The Netherlands.|Bernoulli Institute, University of Groningen, Groningen, AG, The Netherlands.|Department of Analytical Biochemistry, Groningen Research Institute of Pharmacy, University of Groningen, Groningen, AV, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz357', '31077287', 'One of the main goals in systems biology is to learn molecular regulatory networks from quantitative profile data. In particular, Gaussian Graphical Models (GGMs) are widely used network models in bioinformatics where variables (e.g. transcripts, metabolites or proteins) are represented by nodes, and pairs of nodes are connected with an edge according to their partial correlation. Reconstructing a GGM from data is a challenging task when the sample size is smaller than the number of variables. The main challenge consists in finding the inverse of the covariance estimator which is ill-conditioned in this case. Shrinkage based covariance estimators are a popular approach, producing an invertible shrunk covariance. However, a proper significance test for the shrunk partial correlation (i.e. the GGM edges) is an open challenge as a probability density including the shrinkage is unknown. In this article we present (i) a geometric reformulation of the shrinkage based GGM, and (ii) a probability density that naturally includes the shrinkage parameter.'),
(82792, 2019, 'bio', 'SPLATCHE3: simulation of serial genetic data under spatially explicit evolutionary scenarios including long-distance dispersal', 'Currat, Mathias|Arenas, Miguel|Quilodran, Claudio S|Excoffier, Laurent|Ray, Nicolas', 'Laboratory of anthropology, genetics and peopling history, Department of Genetics and Evolution - Anthropology Unit, University of Geneva, Geneva, Switzerland.|Department of Biochemistry, Genetics and Immunology, University of Vigo, Vigo, Spain.|Laboratory of anthropology, genetics and peopling history, Department of Genetics and Evolution - Anthropology Unit, University of Geneva, Geneva, Switzerland.|Computational and Molecular Population Genetics Lab, Institute of Ecology and Evolution, University of Bern, Bern, Switzerland.|Institute of Global Health, GeoHealth group, University of Geneva, Geneva, Switzerland.', 'Switzerland|Spain|Switzerland|Switzerland|Switzerland', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz311', '31077292', 'SPLATCHE3 simulates genetic data under a variety of spatially explicit evolutionary scenarios, extending previous versions of the framework. The new capabilities include long-distance migration, spatially and temporally heterogeneous short-scale migrations, alternative hybridization models, simulation of serial samples of genetic data and a large variety of DNA mutation models. These implementations have been applied independently to various studies, but grouped together in the current version.'),
(82793, 2019, 'bio', 'Tibanna: software for scalable execution of portable pipelines on the cloud', 'Lee, Soohyun|Johnson, Jeremy|Vitzthum, Carl|Kxc4xb1rlxc4xb1, Koray|Alver, Burak H|Park, Peter J', 'Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.|Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.|Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.|Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.|Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.|Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.', 'United States|United States|United States|United States|United States|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz379', '31077294', 'We introduce Tibanna, an open-source software tool for automated execution of bioinformatics pipelines on Amazon Web Services (AWS). Tibanna accepts reproducible and portable pipeline standards including Common Workflow Language (CWL), Workflow Description Language (WDL) and Docker. It adopts a strategy of isolation and optimization of individual executions, combined with a serverless scheduling approach. Pipelines are executed and monitored using local commands or the Python Application Programming Interface (API) and cloud configuration is automatically handled. Tibanna is well suited for projects with a range of computational requirements, including those with large and widely fluctuating loads. Notably, it has been used to process terabytes of data for the 4D Nucleome (4DN) Network.'),
(82794, 2019, 'bio', 'iRNAD: a computational tool for identifying D modification sites in RNA sequence', 'Xu, Zhao-Chun|Feng, Peng-Mian|Yang, Hui|Qiu, Wang-Ren|Chen, Wei|Lin, Hao', 'Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China.|Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China.|Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu.|Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China.|Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China.|Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu.', 'China|China|China|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz358', '31077296', 'Dihydrouridine (D) is a common RNA posttranscriptional modification found in eukaryotes, bacteria and a few archaea. The modification can promote the conformational flexibility of individual nucleotide bases. And its levels are increased in cancerous tissues. Therefore, it is necessary to detect D in RNA for further understanding its functional roles. Since wet-experimental techniques for the aim are time-consuming and laborious, it is urgent to develop computational models to identify D modification sites in RNA.'),
(82795, 2019, 'bio', 'CaverDock: A Molecular Docking-Based Tool to Analyse Ligand Transport through Protein Tunnels and Channels', 'Vavra, O|Filipovic, J|Plhak, J|Bednar, D|Marques, S M|Brezovsky, J|Stourac, J|Matyska, L|Damborsky, J', 'Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Masaryk University, Kamenice 5, Brno, Czech Republic.|Institute of Computer Science, Masaryk University, Botanickxc3xa1 68a, Brno, Czech Republic.|Institute of Computer Science, Masaryk University, Botanickxc3xa1 68a, Brno, Czech Republic.|Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Masaryk University, Kamenice 5, Brno, Czech Republic.|Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Masaryk University, Kamenice 5, Brno, Czech Republic.|Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Masaryk University, Kamenice 5, Brno, Czech Republic.|Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Masaryk University, Kamenice 5, Brno, Czech Republic.|Institute of Computer Science, Masaryk University, Botanickxc3xa1 68a, Brno, Czech Republic.|Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Masaryk University, Kamenice 5, Brno, Czech Republic.', '-|-|-|-|-|-|-|-|-', 9, '-', 'Journal Article', '10.1093/bioinformatics/btz386', '31077297', 'Protein tunnels and channels are key transport pathways that allow ligands to pass between proteins external and internal environments. These functionally important structural features warrant detailed attention. It is difficult to study the ligand binding and unbinding process experimentally, while molecular dynamics simulations can be time-consuming and computationally demanding.'),
(82796, 2019, 'bio', 'A Bayesian Two-Way Latent Structure Model for Genomic Data Integration Reveals Few Pan-Genomic Cluster Subtypes in a Breast Cancer Cohort', 'Swanson, David M|Lien, Tonje|Bergholtz, Helga|Sørlie, Therese|Frigessi, Arnoldo', 'Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital.|Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital.|Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital.|Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital.|Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital.', '-|-|-|-|-', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz381', '31077301', 'Unsupervised clustering is important in disease subtyping, among having other genomic applications. As genomic data has become more multifaceted, how to cluster across data sources for more precise subtyping is an ever more important area of research. Many of the methods proposed so far, including iCluster and Cluster of Cluster Assignments, make an unreasonble assumption of a common clustering across all data sources, and those that do not are fewer and tend to be computationally intensive.'),
(82797, 2019, 'bio', 'Deep Learning of the Back-splicing Code for Circular RNA Formation', 'Wang, Jun|Wang, Liangjiang', 'Department of Genetics and Biochemistry, Clemson University, Clemson, South Carolina, 29634, USA.|Department of Genetics and Biochemistry, Clemson University, Clemson, South Carolina, 29634, USA.', 'United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz382', '31077303', 'Circular RNAs (circRNAs) are a new class of endogenous RNAs in animals and plants. During pre-RNA splicing, the 5 and 3 termini of exon(s) can be covalently ligated to form circRNAs through back-splicing (head-to-tail splicing). CircRNAs can be conserved across species, show tissue- and developmental stage-specific expression patterns, and may be associated with human disease. However, the mechanism of circRNA formation is still unclear although some sequence features have been shown to affect back-splicing.'),
(82798, 2019, 'bio', 'Entrezpy: A Python library to dynamically interact with the NCBI Entrez databases', 'Buchmann, Jan P|Holmes, Edward C', 'Marie Bashir Institute for Infectious Diseases and Biosecurity, Charles Perkins Centre, School of Life and Environmental Sciences and Sydney Medical School, The University of Sydney, Sydney, NSW, Australia.|Marie Bashir Institute for Infectious Diseases and Biosecurity, Charles Perkins Centre, School of Life and Environmental Sciences and Sydney Medical School, The University of Sydney, Sydney, NSW, Australia.', 'Australia|Australia', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz385', '31077305', 'Entrezpy is a Python library that automates the querying and downloading of data from the Entrez databases at NCBI by interacting with E-Utilities. Entrezpy implements complex queries by automatically creating E-Utility parameters from the results obtained that can then be used directly in subsequent queries. Entrezpy also allows the user to cache and retrieve results locally, implements interactions with all Entrez databases as part of an analysis pipeline, and adjusts parameters within an ongoing query or using prior results. Entrezpys modular design enables it to easily extend and adjust existing E-Utility functions.'),
(82799, 2019, 'bio', 'Accurate peptide fragmentation predictions allow data driven approaches to replace and improve upon proteomics search engine scoring functions', 'Silva, Ana S C|Bouwmeester, Robbin|Martens, Lennart|Degroeve, Sven', 'VIB-UGent Center for Medical Biotechnology, Ghent, Belgium.|VIB-UGent Center for Medical Biotechnology, Ghent, Belgium.|VIB-UGent Center for Medical Biotechnology, Ghent, Belgium.|VIB-UGent Center for Medical Biotechnology, Ghent, Belgium.', 'Belgium|Belgium|Belgium|Belgium', 4, '-', 'Journal Article', '10.1093/bioinformatics/btz383', '31077310', 'The use of post-processing tools to maximize the information gained from a proteomics search engine is widely accepted and used by the community, with the most notable example being Percolator - a semi-supervised machine learning model which learns a new scoring function for a given dataset. The usage of such tools is however bound to the search engines scoring scheme, which doesnt always make full use of the intensity information present in a spectrum. We aim to show how this tool can be applied in such a way that maximizes the use of spectrum intensity information by leveraging another machine learning-based tool, MS2PIP. MS2PIP predicts fragment ion peak intensities.'),
(82800, 2019, 'bio', 'Another look at matrix correlations', 'Borzou, Ahmad|Yousefi, Razie|Sadygov, Rovshan G', 'Department of Biochemistry and Molecular Biology, Sealy Center for Molecular Medicine, The University of Texas Medical Branch, Galveston, TX, USA.|Department of Biochemistry and Molecular Biology, Sealy Center for Molecular Medicine, The University of Texas Medical Branch, Galveston, TX, USA.|Department of Biochemistry and Molecular Biology, Sealy Center for Molecular Medicine, The University of Texas Medical Branch, Galveston, TX, USA.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz281', '31081021', 'High throughput technologies are widely employed in modern biomedical research. They yield measurements of a large number of biomolecules in a single experiment. The number of experiments usually is much smaller than the number of measurements in each experiment. The simultaneous measurements of biomolecules provide a basis for a comprehensive, systems view for describing relevant biological processes. Often it is necessary to determine correlations between the data matrices under different conditions or pathways. However, the techniques for analyzing the data with a low number of samples for possible correlations within or between conditions are still in development. Earlier developed correlative measures, such as the RV coefficient, use the trace of the product of data matrices as the most relevant characteristic. However, a recent study has shown that the RV coefficient consistently overestimates the correlations in the case of low sample numbers. To correct for this bias, it was suggested to discard the diagonal elements of the outer products of each data matrix. In this work, a principled approach based on the matrix decomposition generates three trace-independent parts for every matrix. These components are unique, and they are used to determine different aspects of correlations between the original datasets.'),
(82801, 2019, 'bio', 'A Coordinate Descent Approach for Sparse Bayesian Learning in High Dimensional QTL Mapping and Genome-wide Association Studies', 'Wang, Meiyue|Xu, Shizhong', 'Department of Botany and Plant Sciences, University of California, Riverside, CA, USA.|Department of Botany and Plant Sciences, University of California, Riverside, CA, USA.', 'United States|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz244', '31081037', 'Genomic scanning approaches that detect one locus at a time are subject to many problems in genome-wide association studies (GWAS) and quantitative trait locus (QTL) mapping. The problems include large matrix inversion, over-conservativeness for tests after Bonferroni correction and difficulty in evaluation of the total genetic contribution to a traits variance. Targeting these problems, we take a further step and investigate a multiple locus model that detects all markers simultaneously in a single model.'),
(82802, 2019, 'bio', 'DeepPASTA: deep neural network based polyadenylation site analysis', 'Arefeen, Ashraful|Xiao, Xinshu|Jiang, Tao', 'Department of Computer Science and Engineering, University of California, Riverside, CA, USA.|Department of Integrative Biology and Physiology, University of California, Los Angeles, CA, USA.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz283', '31081512', 'Alternative polyadenylation (polyA) sites near the 3 end of a pre-mRNA create multiple mRNA transcripts with different 3 untranslated regions (3 UTRs). The sequence elements of a 3 UTR are essential for many biological activities such as mRNA stability, sub-cellular localization, protein translation, protein binding and translation efficiency. Moreover, numerous studies in the literature have reported the correlation between diseases and the shortening (or lengthening) of 3 UTRs. As alternative polyA sites are common in mammalian genes, several machine learning tools have been published for predicting polyA sites from sequence data. These tools either consider limited sequence features or use relatively old algorithms for polyA site prediction. Moreover, none of the previous tools consider RNA secondary structures as a feature to predict polyA sites.'),
(82803, 2019, 'bio', 'Variant Score Ranker-a web application for intuitive missense variant prioritization', 'Du, Juanjiangmeng|Sudarsanam, Monica|Pérez-Palma, Eduardo|Ganna, Andrea|Francioli, Laurent|Iqbal, Sumaiya|Niestroj, Lisa-Marie|Leu, Costin|Weisburd, Ben|Poterba, Tim|Nürnberg, Peter|Daly, Mark J|Palotie, Aarno|May, Patrick|Lal, Dennis', 'Cologne Center for Genomics, University of Cologne, University Hospital Cologne, Cologne 50931, Germany.|Cologne Center for Genomics, University of Cologne, University Hospital Cologne, Cologne 50931, Germany.|Cologne Center for Genomics, University of Cologne, University Hospital Cologne, Cologne 50931, Germany.|Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.|Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.|Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.|Cologne Center for Genomics, University of Cologne, University Hospital Cologne, Cologne 50931, Germany.|Genomic Medicine Institute, Lerner Research Institute Cleveland Clinic, Cleveland, OH 44106, USA.|Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.|Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.|Cologne Center for Genomics, University of Cologne, University Hospital Cologne, Cologne 50931, Germany.|Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.|Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.|Luxembourg Centre for Systems Biomedicine, University Luxembourg, Esch-sur-Alzette 4365, Luxembourg.|Cologne Center for Genomics, University of Cologne, University Hospital Cologne, Cologne 50931, Germany.', 'Germany|Germany|Germany|United States|United States|United States|Germany|United States|United States|United States|Germany|United States|United States|Luxembourg|Germany', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz252', '31086968', 'The correct classification of missense variants as benign or pathogenic remains challenging. Pathogenic variants are expected to have higher deleterious prediction scores than benign variants in the same gene. However, most of the existing variant annotation tools do not reference the score range of benign population variants on gene level.'),
(82804, 2019, 'bio', 'multiPhATE: bioinformatics pipeline for functional annotation of phage isolates', 'Ecale Zhou, Carol L|Malfatti, Stephanie|Kimbrel, Jeffrey|Philipson, Casandra|McNair, Katelyn|Hamilton, Theron|Edwards, Robert|Souza, Brian', 'Lawrence Livermore National Laboratory, Livermore, CA, USA.|Lawrence Livermore National Laboratory, Livermore, CA, USA.|Lawrence Livermore National Laboratory, Livermore, CA, USA.|Naval Medical Research Center, Fort Detrick, MD, USA.|California State University, San Diego, CA, USA.|Naval Medical Research Center, Fort Detrick, MD, USA.|California State University, San Diego, CA, USA.|Lawrence Livermore National Laboratory, Livermore, CA, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz258', '31086982', 'To address the need for improved phage annotation tools that scale, we created an automated throughput annotation pipeline: multiple-genome Phage Annotation Toolkit and Evaluator (multiPhATE). multiPhATE is a throughput pipeline driver that invokes an annotation pipeline (PhATE) across a user-specified set of phage genomes. This tool incorporates a de novo phage gene-calling algorithm and assigns putative functions to gene calls using protein-, virus-, and phage-centric databases. multiPhATEs modular construction allows the user to implement all or any portion of the analyses by acquiring local instances of the desired databases and specifying the desired analyses in a configuration file. We demonstrate multiPhATE by annotating two newly sequenced Yersinia pestis phage genomes. Within multiPhATE, the PhATE processing pipeline can be readily implemented across multiple processors, making it adaptable for throughput sequencing projects. Software documentation assists the user in configuring the system.'),
(82805, 2019, 'bio', 'Protein-ensemble-RNA docking by efficient consideration of protein flexibility through homology models', 'He, Jiahua|Tao, Huanyu|Huang, Sheng-You', 'Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, P. R. China.|Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, P. R. China.|Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, P. R. China.', 'China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz388', '31086984', 'Given the importance of protein-RNA interactions in many biological processes, a variety of docking algorithms have been developed to predict the complex structure from individual protein and RNA partners in the past decade. However, due to the impact of molecular flexibility, the performance of current methods has hit a bottleneck in realistic unbound docking. Pushing the limit, we have proposed a protein-ensemble-RNA docking strategy to explicitly consider the protein flexibility in protein-RNA docking through an ensemble of multiple protein structures, which is referred to as MPRDock. Instead of taking conformations from MD simulations or experimental structures, we obtained the multiple structures of a protein by building models from its homologous templates in the Protein Data Bank (PDB).'),
(82806, 2019, 'bio', 'Is the Bombali virus pathogenic in humans?', 'Martell, Henry J|Masterson, Stuart G|McGreig, Jake E|Michaelis, Martin|Wass, Mark N', 'Industrial Biotechnology Centre and School of Biosciences, University of Kent, Canterbury, Kent, UK.|Industrial Biotechnology Centre and School of Biosciences, University of Kent, Canterbury, Kent, UK.|Industrial Biotechnology Centre and School of Biosciences, University of Kent, Canterbury, Kent, UK.|Industrial Biotechnology Centre and School of Biosciences, University of Kent, Canterbury, Kent, UK.|Industrial Biotechnology Centre and School of Biosciences, University of Kent, Canterbury, Kent, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz267', '31093647', 'The potential of the Bombali virus, a novel Ebolavirus, to cause disease in humans remains unknown. We have previously identified potential determinants of Ebolavirus pathogenicity in humans by analysing the amino acid positions that are differentially conserved (specificity determining positions, SDPs) between human pathogenic Ebolaviruses and the non-pathogenic Reston virus. Here, we include the many Ebolavirus genome sequences that have since become available into our analysis and investigate the amino acid sequence of the Bombali virus proteins at the SDPs that discriminate between human pathogenic and non-human pathogenic Ebolaviruses.'),
(82807, 2019, 'bio', 'Hayai-Annotation Plants: an ultra-fast and comprehensive functional gene annotation system in plants', 'Ghelfi, Andrea|Shirasawa, Kenta|Hirakawa, Hideki|Isobe, Sachiko', 'Laboratory of Plant Genetics and Genomics, Department of Frontier Research and Development, Kazusa DNA Research Institute, 2-6-7 Kazusa-kamatari, Chiba, Japan.|Laboratory of Plant Genetics and Genomics, Department of Frontier Research and Development, Kazusa DNA Research Institute, 2-6-7 Kazusa-kamatari, Chiba, Japan.|Laboratory of Plant Genetics and Genomics, Department of Frontier Research and Development, Kazusa DNA Research Institute, 2-6-7 Kazusa-kamatari, Chiba, Japan.|Laboratory of Plant Genetics and Genomics, Department of Frontier Research and Development, Kazusa DNA Research Institute, 2-6-7 Kazusa-kamatari, Chiba, Japan.', 'Japan|Japan|Japan|Japan', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz380', '31093654', 'Hayai-Annotation Plants is a browser-based interface for an ultra-fast and accurate functional gene annotation system for plant species using R. The pipeline combines the sequence-similarity searches, using USEARCH against UniProtKB (taxonomy Embryophyta), with a functional annotation step. Hayai-Annotation Plants provides five layers of annotation: 1) protein name, 2) gene ontology terms consisting of its three main domains (Biological Process, Molecular Function, and Cellular Component), 3) enzyme commission number, 4) protein existence level, and 5) evidence type. It implements a new algorithm that gives priority to protein existence level to propagate GO and EC information and annotated Arabidopsis thaliana representative peptide sequences (Araport11) within five minutes at the PC level.'),
(82808, 2019, 'bio', 'YeastSpotter: Accurate and parameter-free web segmentation for microscopy images of yeast cells', 'Lu, Alex X|Zarin, Taraneh|Hsu, Ian S|Moses, Alan M', 'Department of Computer Science, University of Toronto, Toronto, Canada.|Department of Cells and Systems Biology, University of Toronto, Toronto, Canada.|Department of Cells and Systems Biology, University of Toronto, Toronto, Canada.|Department of Computer Science, University of Toronto, Toronto, Canada.', 'Canada|Canada|Canada|Canada', 5, '-', 'Journal Article', '10.1093/bioinformatics/btz402', '31095270', 'We introduce YeastSpotter, a web application for the segmentation of yeast microscopy images into single cells. YeastSpotter is user-friendly and generalizable, reducing the computational expertise required for this critical preprocessing step in many image analysis pipelines.'),
(82809, 2019, 'bio', 'Prediction of survival risks with adjusted gene expression through risk-gene networks', 'Lee, Minhyeok|Han, Sung Won|Seok, Junhee', 'School of Electrical Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, South Korea.|School of Industrial Management Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, South Korea.|School of Electrical Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, South Korea.', '-|-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz399', '31095279', 'Network-based analysis of biomedical data has been extensively studied over the last decades. As a successful application, gene networks have been used to illustrate interactions among genes and explain the associated phenotypes. However, the gene network approaches have not been actively applied for survival analysis, which is one of the main interests of biomedical research. In addition, a few previous studies using gene networks for survival analysis construct networks mainly from prior knowledge, such as pathways, regulations and gene sets, while the performance considerably depends on the selection of prior knowledge.'),
(82810, 2019, 'bio', 'ntEdit: scalable genome sequence polishing', 'Warren, Renxc3xa9 L|Coombe, Lauren|Mohamadi, Hamid|Zhang, Jessica|Jaquish, Barry|Isabel, Nathalie|Jones, Steven J M|Bousquet, Jean|Bohlmann, Joerg|Birol, Inanxc3xa7', 'Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada.|Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada.|Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada.|Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada.|BC Ministry of Forests, Lands, and Natural Resource Operations, Victoria, BC, Canada.|Natural Resources Canada, Laurentian Forestry Centre, Quxc3xa9bec, QC, Canada.|Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada.|Canada Research Chair in Forest Genomics, Universitxc3xa9 Laval, Quxc3xa9bec, QC, Canada.|Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada.|Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada', 4, '-', 'Journal Article', '10.1093/bioinformatics/btz400', '31095290', 'In the modern genomics era, genome sequence assemblies are routine practice. However, depending on the methodology, resulting drafts may contain considerable base errors. Although utilities exist for genome base polishing, they work best with high read coverage and do not scale well. We developed ntEdit, a Bloom filter-based genome sequence editing utility that scales to large mammalian and conifer genomes.'),
(82811, 2019, 'bio', 'MyelinJ: An ImageJ macro for high throughput analysis of myelinating cultures', 'Whitehead, Michael J|McCanney, George|Willison, Hugh J|Barnett, Susan C', 'Institute of Infection, Immunity and Inflammation, College of Medical, Veterinary and Life Sciences, University of Glasgow, 120 University Place, Glasgow, UK.|Institute of Infection, Immunity and Inflammation, College of Medical, Veterinary and Life Sciences, University of Glasgow, 120 University Place, Glasgow, UK.|Institute of Infection, Immunity and Inflammation, College of Medical, Veterinary and Life Sciences, University of Glasgow, 120 University Place, Glasgow, UK.|Institute of Infection, Immunity and Inflammation, College of Medical, Veterinary and Life Sciences, University of Glasgow, 120 University Place, Glasgow, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz403', '31095292', 'MyelinJ is a free user friendly ImageJ macro for high throughput analysis of fluorescent micrographs such as 2D-myelinating cultures and statistical analysis using R. MyelinJ can analyse single images or complex experiments with multiple conditions, where the ggpubr package in R is automatically used for statistical analysis and the production of publication quality graphs. The main outputs are percentage (%) neurite density and % myelination. % neurite density is calculated using the normalise local contrast (NLC) algorithm, followed by thresholding, to adjust for differences in intensity. For % myelination the myelin sheaths are selected using the Frangi vesselness algorithm, in conjunction with a grey scale morphology filter and the removal of cell bodies using a high intensity mask. MyelinJ uses a simple graphical user interface and user name system for reproducibility and sharing that will be useful to the wider scientific community that study 2D-myelination in vitro.'),
(82812, 2019, 'bio', 'Incorporating spatial-anatomical similarity into the VGWAS framework for AD biomarker detection', 'Huang, Meiyan|Yu, Yuwei|Yang, Wei|Feng, Qianjin|Alzheimer’s Disease Neuroimaging Initiative', 'Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China.|Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China.|Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China.|Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China.|Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China.', 'China|China|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz401', '31095298', 'The detection of potential biomarkers of Alzheimers disease (AD) is crucial for its early prediction, diagnosis, and treatment. Voxelwise genome-wide association study (VGWAS) is a commonly used method in imaging genomics and usually applied to detect AD biomarkers in imaging and genetic data. However, existing VGWAS methods entail large computational cost and disregard spatial correlations within imaging data. A novel method is proposed to solve these issues.'),
(82813, 2019, 'bio', 'Iterative feature representations improve N4-methylcytosine site prediction', 'Wei, Leyi|Su, Ran|Luan, Shasha|Liao, Zhijun|Manavalan, Balachandran|Zou, Quan|Shi, Xiaolong', 'College of Intelligence and Computing, Tianjin University, Tianjin, China.|College of Intelligence and Computing, Tianjin University, Tianjin, China.|College of Intelligence and Computing, Tianjin University, Tianjin, China.|Department of Physiology, Ajou University School of Medicine, Suwon, Republic of Korea.|Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fujian Medical University, Fujian, China.|Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.|Institute of Computing Science & Technology, Guangzhou University, Guangzhou, China.', 'China|China|China|-|China|China|China', 37, '-', 'Journal Article', '10.1093/bioinformatics/btz408', '31099381', 'Accurate identification of 4mC (N4-methylcytosine) modifications in a genome wide can provide insights into their biological functions and mechanisms. Machine learning recently have become effective approaches for computational identification of 4mC sites in genome. Unfortunately, existing methods cannot achieve satisfactory performance, owing to the lack of effective DNA feature representations that are capable to capture the characteristics of 4mC modifications.'),
(82814, 2019, 'bio', 'Cram-JS: Reference-Based Decompression in Node and the Browser', 'Buels, Robert|Dider, Shihab|Diesh, Colin|Robinson, James|Holmes, Ian', 'Department of Bioengineering, University of California, Berkeley, USA.|Department of Bioengineering, University of California, Berkeley, USA.|Department of Bioengineering, University of California, Berkeley, USA.|Broad Institute, 415 Main St, Cambridge, USA.|Department of Bioengineering, University of California, Berkeley, USA.', 'United States|United States|United States|United States|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz384', '31099383', 'The CRAM format addresses rising DNA storage costs for short-read sequencing by aligning reads to a reference genome and encoding the resulting alignment with Huffman, subexponential, Elias gamma, rANS, gzip, and other codes. The CRAM codec is complex, and until now, there has been no JavaScript implementation.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(82815, 2019, 'bio', 'NGSEP3: accurate variant calling across species and sequencing protocols', 'Tello, Daniel|Gil, Juanita|Loaiza, Cristian D|Riascos, John J|Cardozo, Nicolás|Duitama, Jorge', 'Systems and Computing Engineering Department, Universidad de los Andes, Bogotá 111711, Colombia.|Systems and Computing Engineering Department, Universidad de los Andes, Bogotá 111711, Colombia.|Biotechnology lab, Centro de Investigación de la caña de azúcar de Colombia, CENICAÑA, Cali 760046, Colombia.|Biotechnology lab, Centro de Investigación de la caña de azúcar de Colombia, CENICAÑA, Cali 760046, Colombia.|Systems and Computing Engineering Department, Universidad de los Andes, Bogotá 111711, Colombia.|Systems and Computing Engineering Department, Universidad de los Andes, Bogotá 111711, Colombia.', 'Colombia|Colombia|Colombia|Colombia|Colombia|Colombia', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz275', '31099384', 'Accurate detection, genotyping and downstream analysis of genomic variants from high-throughput sequencing data are fundamental features in modern production pipelines for genetic-based diagnosis in medicine or genomic selection in plant and animal breeding. Our research group maintains the Next-Generation Sequencing Experience Platform (NGSEP) as a precise, efficient and easy-to-use software solution for these features.'),
(82816, 2019, 'bio', 'Multi-SNP mediation intersection-union test', 'Zhong, Wujuan|Spracklen, Cassandra N|Mohlke, Karen L|Zheng, Xiaojing|Fine, Jason|Li, Yun', 'Department of Biostatistics.|Department of Genetics.|Department of Genetics.|Department of Pediatrics.|Department of Biostatistics.|Department of Biostatistics.', '-|-|-|-|-|-', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz285', '31099385', 'Tens of thousands of reproducibly identified GWAS (Genome-Wide Association Studies) variants, with the vast majority falling in non-coding regions resulting in no eventual protein products, call urgently for mechanistic interpretations. Although numerous methods exist, there are few, if any methods, for simultaneously testing the mediation effects of multiple correlated SNPs via some mediator (e.g. the expression of a gene in the neighborhood) on phenotypic outcome. We propose multi-SNP mediation intersection-union test (SMUT) to fill in this methodological gap. Our extensive simulations demonstrate the validity of SMUT as well as substantial, up to 92%, power gains over alternative methods. In addition, SMUT confirmed known mediators in a real dataset of Finns for plasma adiponectin level, which were missed by many alternative methods. We believe SMUT will become a useful tool to generate mechanistic hypotheses underlying GWAS variants, facilitating functional follow-up.'),
(82817, 2019, 'bio', 'TPES: Tumor Purity Estimation from SNVs', 'Locallo, Alessio|Prandi, Davide|Fedrizzi, Tarcisio|Demichelis, Francesca', 'Laboratory of Computational and Functional Oncology, CIBIO Department, University of Trento.|Laboratory of Computational and Functional Oncology, CIBIO Department, University of Trento.|Laboratory of Computational and Functional Oncology, CIBIO Department, University of Trento.|Laboratory of Computational and Functional Oncology, CIBIO Department, University of Trento.', '-|-|-|-', 4, '-', 'Journal Article', '10.1093/bioinformatics/btz406', '31099386', 'Tumor purity (TP) is the proportion of cancer cells in a tumor sample. TP impacts on the accurate assessment of molecular and genomics features as assayed with NGS approaches. State-of-the-art tools mainly rely on somatic copy-number alterations (SCNA) to quantify TP and therefore fail when a tumor genome is nearly euploid, i.e. non-aberrant in terms of identifiable SCNAs.'),
(82818, 2019, 'bio', 'Accurate differential analysis of transcription factor activity from gene expression', 'Amin, Viren|Axc4x9faxc3xa7, Didem|Barnes, Spencer D|xc3x87obanoxc4x9flu, Murat Can', 'Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, Texas, USA.|Department of Immunology, University of Texas MD Anderson Cancer Center, Houston, Texas, USA.|Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, Texas, USA.|Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, Texas, USA.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz398', '31099391', 'Activity of transcriptional regulators are crucial in elucidating the mechanism of phenotypes. However regulatory activity hypotheses are difficult to experimentally test. Therefore we need accurate and reliable computational methods for regulator activity inference. There is extensive work in this area, however, current methods have difficulty with one or more of the following: resolving activity of TFs with overlapping regulons, reflecting known regulatory relationships, or flexible modeling of TF activity over the regulon.'),
(82819, 2019, 'bio', 'epiTAD: a web application for visualizing chromosome conformation capture data in the context of genetic epidemiology', 'Creed, Jordan H|Aden-Buie, Garrick|Monteiro, Alvaro N|Gerke, Travis A', 'Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, USA.|Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, USA.|Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, USA.|Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, USA.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz387', '31099399', 'Complementary advances in genomic technology and public data resources have created opportunities for researchers to conduct multifaceted examination of the genome on a large scale. To meet the need for integrative genome wide exploration, we present epiTAD. This web-based tool enables researchers to compare genomic 3D organization and annotations across multiple databases in an interactive manner to facilitate in silico discovery.'),
(82820, 2019, 'bio', 'Corrigendum to: A new cis-acting regulatory element driving gene expression in the zebrafish pineal gland', 'Alon, Shahar|Eisenberg, Eli|Jacob-Hirsch, Jasmine|Rechavi, Gideon|Vatine, Gad|Toyama, Reiko|Coon, Steven L|Klein, David C|Gothilf, Yoav', 'Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, USA.|Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, USA.|Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, USA.|Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, USA.|Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, USA.|Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, USA.|Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, USA.|Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, USA.|Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz410', '31102508', '-'),
(82821, 2019, 'bio', 'PathFXweb: a web application for identifying drug safety and efficacy phenotypes', 'Wilson, Jennifer L|Wong, Mike|Chalke, Ajinkya|Stepanov, Nicholas|Petkovic, Dragutin|Altman, Russ B', 'Department of Bioengineering, Stanford University, Palo Alto CA.|CoSE Computing for Life Sciences, San Francisco State University, San Francisco CA.|Department of Computer Science, San Francisco State University, San Francisco CA.|Department of Computer Science, San Francisco State University, San Francisco CA.|CoSE Computing for Life Sciences, San Francisco State University, San Francisco CA.|Department of Bioengineering, Stanford University, Palo Alto CA.', '-|-|-|-|-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz419', '31114840', 'Limited efficacy and intolerable safety limit therapeutic development and identification of potential liabilities earlier in development could significantly improve this process. Computational approaches which aggregate data from multiple sources and consider the drugs pathways effects could add to identification of these liabilities earlier. Such computational methods must be accessible to a variety of users beyond computational scientists, especially regulators and industry scientists, in order to impact the therapeutic development process. We have previously developed and published PathFX, an algorithm for identifying drug networks and phenotypes for understanding drug associations to safety and efficacy. Here we present a streamlined and easy-to-use PathFX web application that allows users to search for drug networks and associated phenotypes. We have also added visualization, and phenotype clustering to improve functionality and interpretability of PathFXweb.'),
(82822, 2019, 'bio', 'ARMBIS: Accurate and Robust Matching of Brain Image Sequences from Multiple Modal Imaging Techniques', 'Shen, Qi|Xiao, Goayu|Zheng, Yingwei|Wang, Jie|Liu, Yue|Zhu, Xutao|Jia, Fan|Su, Peng|Nie, Binbin|Xu, Fuqiang|Zhang, Bin', 'Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY.|Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY.|University of Chinese Academy of Sciences, Beijing, P.R. China.|State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Key Laboratory of Magnetic Resonance in Biological Systems, Wuhan Center for Magnetic Resonance, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan, P.R. China.|State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Key Laboratory of Magnetic Resonance in Biological Systems, Wuhan Center for Magnetic Resonance, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan, P.R. China.|University of Chinese Academy of Sciences, Beijing, P.R. China.|State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Key Laboratory of Magnetic Resonance in Biological Systems, Wuhan Center for Magnetic Resonance, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan, P.R. China.|Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China.|Division of Nuclear Technology and Applications, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China.|University of Chinese Academy of Sciences, Beijing, P.R. China.|Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY.', '-|-|China|China|China|China|China|China|China|China|-', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz404', '31114841', 'Study of brain images of rodent animals is the most straightforward way to understand brain functions and neural basis of physiological functions. An important step in brain image analysis is to precisely assign signal labels to specified brain regions through matching brain images to standardized brain reference atlases. However, no significant effort has been made to match different types of brain images to atlas images due to influence of artifact operation during slice preparation, relatively low resolution of images and large structural variations in individual brains.'),
(82823, 2019, 'bio', 'CytoPacq: A web-interface for simulating multi-dimensional cell imaging', 'Wiesner, David|Svoboda, David|Maška, Martin|Kozubek, Michal', 'Centre for Biomedical Image Analysis, Masaryk University, Botanická 68a, CZ Brno, Czech Republic.|Centre for Biomedical Image Analysis, Masaryk University, Botanická 68a, CZ Brno, Czech Republic.|Centre for Biomedical Image Analysis, Masaryk University, Botanická 68a, CZ Brno, Czech Republic.|Centre for Biomedical Image Analysis, Masaryk University, Botanická 68a, CZ Brno, Czech Republic.', '-|-|-|-', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz417', '31114843', 'Objective assessment of bioimage analysis methods is an essential step towards understanding their robustness and parameter sensitivity, calling for the availability of heterogeneous bioimage datasets accompanied by their reference annotations. Because manual annotations are known to be arduous, highly subjective, and barely reproducible, numerous simulators have emerged over past decades, generating synthetic bioimage datasets complemented with inherent reference annotations. However, the installation and configuration of these tools generally constitutes a barrier to their widespread use.'),
(82824, 2019, 'bio', 'Estimating the Strength of Expression Conservation from High Throughput RNA-seq Data', 'Gu, Xun|Ruan, Hang|Yang, Jingwen', 'Department of Genetics, Development, and Cell Biology, Iowa State University, Ames, IA, USA.|MOE Key Laboratory of Contemporary Anthropology, Fudan University, Shanghai, China.|MOE Key Laboratory of Contemporary Anthropology, Fudan University, Shanghai, China.', 'United States|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz405', '31114853', 'Evolution of gene across species is usually subject to the stabilizing selection to maintain the optimal expression level. While it is generally accepted that the resulting expression conservation may vary considerably among genes, statistically reliable estimation remains challenging, due to few species included in current comparative RNA-seq data with high number of unknown parameters.'),
(82825, 2019, 'bio', 'HyAsP, a greedy tool for plasmids identification', 'Müller, Robert|Chauve, Cedric', 'Computational Methods for the Analysis of the Diversity and Dynamics of Genomes, Bielefeld University, Bielefeld, Germany.|Department of Mathematics, Simon Fraser University, Vancouver BC, Canada.', 'Germany|Canada', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz413', '31116364', 'Plasmids are ubiquituous in bacterial genomes, and have been shown to be involved in important evolutionary processes, in particular the acquisition of antimicrobial resistance. However separating chromosomal contigs from plasmid contigs and assembling the later is a challenging problem.'),
(82826, 2019, 'bio', 'SPar-K : a method to partition NGS signal data', 'Groux, Romain|Bucher, Philipp', 'The Swiss Institute for Experimental Cancer Research (ISREC), Swiss Federal Institute of Technology Lausanne (EPFL) and Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.|The Swiss Institute for Experimental Cancer Research (ISREC), Swiss Federal Institute of Technology Lausanne (EPFL) and Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.', 'Switzerland|Switzerland', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz416', '31116370', 'We present SPar-K (Signal Partitioning with K-means), a method to search for archetypical chromatin architectures by partitioning a set of genomic regions characterized by chromatin signal profiles around ChIP-seq peaks and others kinds of of functional sites. This method efficiently deals with problems of data heterogeneity, limited misalignment of anchor points and unknown orientation of asymmetric patterns.'),
(82827, 2019, 'bio', 'AlphaFold at CASP13', 'AlQuraishi, Mohammed', 'Department of Systems Biology, Harvard Medical School, 200 Longwood Ave., Boston, MA, USA.', 'United States', 23, '-', 'Journal Article', '10.1093/bioinformatics/btz422', '31116374', 'Computational prediction of protein structure from sequence is broadly viewed as a foundational problem of biochemistry and one of the most difficult challenges in bioinformatics. Once every two years the Critical Assessment of protein Structure Prediction (CASP) experiments are held to assess the state of the art in the field in a blind fashion, by presenting predictor groups with protein sequences whose structures have been solved but have not yet been made publicly available. The first CASP was organized in 1994, and the latest, CASP13, took place last December, when for the first time the industrial laboratory DeepMind entered the competition. DeepMinds entry, AlphaFold, placed first in the Free Modeling (FM) category, which assesses methods on their ability to predict novel protein folds (the Zhang group placed first in the Template-Based Modeling (TBM) category, which assess methods on predicting proteins whose folds are related to ones already in the Protein Data Bank.) DeepMinds success generated significant public interest. Their approach builds on two ideas developed in the academic community during the preceding decade: (i) the use of co-evolutionary analysis to map residue co-variation in protein sequence to physical contact in protein structure, and (ii) the application of deep neural networks to robustly identify patterns in protein sequence and co-evolutionary couplings and convert them into contact maps. In this Letter, we contextualize the significance of DeepMinds entry within the broader history of CASP, relate AlphaFolds methodological advances to prior work, and speculate on the future of this important problem.'),
(82828, 2019, 'bio', 'MetaQUBIC: a computational pipeline for gene-level functional profiling of metagenome and metatranscriptome', 'Ma, Anjun|Sun, Minxuan|McDermaid, Adam|Liu, Bingqiang|Ma, Qin', 'Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA.|Department of Computer Science, South Dakota State University, Brookings, SD, USA.|Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA.|School of Mathematics, Shandong University, Jinan, China.|Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA.', 'United States|United States|United States|China|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz414', '31116375', 'Metagenomic and metatranscriptomic analyses can provide an abundance of information related to microbial communities. However, straightforward analysis of this data does not provide optimal results, with a required integration of data types being needed to thoroughly investigate these microbiomes and their environmental interactions.'),
(82829, 2019, 'bio', 'simuG: a general-purpose genome simulator', 'Yue, Jia-Xing|Liti, Gianni', 'Université Côte dAzur, CNRS, INSERM, IRCAN, Nice, France.|Université Côte dAzur, CNRS, INSERM, IRCAN, Nice, France.', 'France|France', 4, '-', 'Journal Article', '10.1093/bioinformatics/btz424', '31116378', 'Simulated genomes with pre-defined and random genomic variants can be very useful for benchmarking genomic and bioinformatics analyses. Here we introduce simuG, a lightweight tool for simulating the full-spectrum of genomic variants (SNPs, INDELs, CNVs, inversions, and translocations) for any organisms (including human). The simplicity and versatility of simuG makes it a unique general- purpose genome simulator for a wide-range of simulation-based applications.'),
(82830, 2019, 'bio', 'SeRenDIP: SEquential REmasteriNg to DerIve Profiles for fast and accurate predictions of PPI interface positions', 'Hou, Qingzhen|De Geest, Paul|Griffioen, Christian J|Abeln, Sanne|Heringa, Jaap|Feenstra, K Anton', 'Department of BioModeling, BioInformatics & BioProcesses, Université Libre de Bruxelles, Brussels, Belgium.|IBIVU - Center for Integrative Bioinformatics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.|IBIVU - Center for Integrative Bioinformatics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.|IBIVU - Center for Integrative Bioinformatics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.|IBIVU - Center for Integrative Bioinformatics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.|IBIVU - Center for Integrative Bioinformatics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.', 'Belgium|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz428', '31116381', 'Interpretation of ubiquitous protein sequence data has become a bottleneck in biomolecular research, due to a lack of structural and other experimental annotation data for these proteins. Prediction of protein interaction sites from sequence may be a viable substitute. We therefore recently developed a sequence-based random-forest method for protein-protein interface prediction, which yielded a significantly increased performance than other methods on both homomeric and heteromeric protein-protein interactions. Here we present a webserver that implements this method efficiently.'),
(82831, 2019, 'bio', 'NRStitcher: Non-rigid stitching of terapixel-scale volumetric images', 'Miettinen, Arttu|Oikonomidis, Ioannis Vogiatzis|Bonnin, Anne|Stampanoni, Marco', 'Centre dimagerie biomxc3xa9dicale, xc3x89cole polytechnique fxc3xa9dxc3xa9rale de Lausanne, Lausanne, Switzerland.|Swiss Light Source, Paul Scherrer Institute, Villigen, Switzerland.|Swiss Light Source, Paul Scherrer Institute, Villigen, Switzerland.|Swiss Light Source, Paul Scherrer Institute, Villigen, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland', 5, '-', 'Journal Article', '10.1093/bioinformatics/btz423', '31116382', 'In modern microscopy the field of view is often increased by obtaining an image mosaic, where multiple sub-images are taken side by side and combined post-acquisition. Mosaic imaging often leads to long imaging times that can increase the probability of sample deformation during the acquisition due to, e.g., changes in the environment, damage caused by the radiation used to probe the sample, or biologically induced deterioration. Here we propose a technique, based on local phase correlation, to detect the deformations and construct an artifact-free image mosaic from deformed sub-images. The implementation of the method supports distributed computing and can be used to generate teravoxel-size mosaics. We demonstrate its capabilities by assembling a 5.6 teravoxel tomographic image mosaic of microvasculature in whole mouse brain. The method is compared to existing rigid stitching implementations designed for very large datasets, and observed to create artifact-free image mosaics in comparable runtime with the same hardware resources.'),
(82832, 2019, 'bio', 'C-InterSecture - a computational tool for interspecies comparison of genome architecture', 'Nuriddinov, M|Fishman, V', 'Institute of Cytology and Genetics, Novosibirsk, Russia.|Institute of Cytology and Genetics, Novosibirsk, Russia.', '-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz415', '31116383', 'Recent development of Hi-C technique, a biochemical method to study 3-dimensional genome architecture, provided large amount of information describing spatial organization of chromosomes in different cell types and species. While multiple tools are available for analysis and comparison of Hi-C data of different cell types, there are almost no resources for systematic interspecies comparison.'),
(82833, 2019, 'bio', 'ReSimNet: Drug Response Similarity Prediction using Siamese Neural Networks', 'Jeon, Minji|Park, Donghyeon|Lee, Jinhyuk|Jeon, Hwisang|Ko, Miyoung|Kim, Sunkyu|Choi, Yonghwa|Tan, Aik-Choon|Kang, Jaewoo', 'Department of Computer Science and Engineering, Korea University, Seoul, Korea.|Department of Computer Science and Engineering, Korea University, Seoul, Korea.|Department of Computer Science and Engineering, Korea University, Seoul, Korea.|Interdisciplinary Graduate Program in Bioinformatics, Korea University, Seoul, Korea.|Department of Computer Science and Engineering, Korea University, Seoul, Korea.|Department of Computer Science and Engineering, Korea University, Seoul, Korea.|Department of Computer Science and Engineering, Korea University, Seoul, Korea.|Division of Medical Oncology, Department of Medicine, Translational Bioinformatics and Cancer Systems Biology Laboratory, University of Colorado Anschutz Medical Campus, Aurora, USA.|Department of Computer Science and Engineering, Korea University, Seoul, Korea.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz411', '31116384', 'Traditional drug discovery approaches identify a target for a disease and find a compound that binds to the target. In this approach, structures of compounds are considered as the most important features because it is assumed that similar structures will bind to the same target. Therefore, structural analogs of the drugs that bind to the target are selected as drug candidates. However, even though compounds are not structural analogs, they may achieve the desired response. A new drug discovery method based on drug response, which can complement the structure-based methods, is needed.'),
(82834, 2019, 'bio', 'SureTypeSC - A Random Forest and Gaussian Mixture predictor of high confidence genotypes in single cell data', 'Vogel, Ivan|Blanshard, Robert C|Hoffmann, Eva R', 'DNRF Center for Chromosome Stability, Department of Cellular and Molecular Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.|Illumina Inc., Capital Park, Fulbourn, Cambridge, UK.|DNRF Center for Chromosome Stability, Department of Cellular and Molecular Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.', 'Denmark|United Kingdom|Denmark', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz412', '31116387', 'Accurate genotyping of DNA from a single cell is required for applications such as de novo mutation detection, linkage analysis and lineage tracing. However, achieving high precision genotyping in the single cell environment is challenging due to the errors caused by whole genome amplification. Two factors make genotyping from single cells using single nucleotide polymorphism (SNP) arrays challenging. The lack of a comprehensive single cell dataset with a reference genotype and the absence of genotyping tools specifically designed to detect noise from the whole genome amplification step. Algorithms designed for bulk DNA genotyping cause significant data loss when used for single cell applications.'),
(82835, 2019, 'bio', 'deepDR: A network-based deep learning approach to in silico drug repositioning', 'Zeng, Xiangxiang|Zhu, Siyi|Liu, Xiangrong|Zhou, Yadi|Nussinov, Ruth|Cheng, Feixiong', 'Department of Computer Science, Xiamen University, Xiamen, China.|Department of Computer Science, Xiamen University, Xiamen, China.|Department of Computer Science, Xiamen University, Xiamen, China.|Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.|Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, MD, USA.|Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.', 'China|China|China|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz418', '31116390', 'Traditional drug discovery and development are often time-consuming and high-risk. Repurposing/repositioning of approved drugs offers a relatively low-cost and high-efficiency approach towards rapid development of efficacious treatments. The emergence of large-scale, heterogeneous biological networks has offered unprecedented opportunities for developing in silico drug repositioning approaches. However, capturing highly non-linear, heterogenous network structures by most existing approaches for drug repositioning has been challenging.'),
(82836, 2019, 'bio', 'Improving data splitting for classification applications in spectrochemical analyses employing a random-mutation Kennard-Stone algorithm approach', 'Morais, Camilo L M|Santos, Marfran C D|Lima, Kássio M G|Martin, Francis L', 'School of Pharmacy and Biomedical Sciences, University of Central Lancashire, Preston, United Kingdom.|Institute of Chemistry, Biological Chemistry and Chemometrics, Federal University of Rio Grande do Norte, Natal, Brazil.|Institute of Chemistry, Biological Chemistry and Chemometrics, Federal University of Rio Grande do Norte, Natal, Brazil.|School of Pharmacy and Biomedical Sciences, University of Central Lancashire, Preston, United Kingdom.', 'United Kingdom|Brazil|Brazil|United Kingdom', 7, '-', 'Journal Article', '10.1093/bioinformatics/btz421', '31116391', 'Data splitting is a fundamental step for building classification models with spectral data, especially in biomedical applications. This approach is performed following pre-processing and prior to model construction, and consists of dividing the samples into at least training and test sets, herein, the training set is used for model construction and the test set for model validation. Some of the most-used methodologies for data splitting are the random selection (RS) and the Kennard-Stone (KS) algorithms, here, the former works based on a random splitting process and the latter is based on the calculation of the Euclidian distance between the samples. We propose an algorithm called the Morais-Lima-Martin (MLM) algorithm, as an alternative method to improve data splitting in classification models. MLM is a modification of KS algorithm by adding a random-mutation factor.'),
(82837, 2019, 'bio', 'Statistical test of structured continuous trees based on discordance matrix', 'Bai, Xiangqi|Ma, Liang|Wan, Lin', 'NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China.|Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.|NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China.', 'China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz425', '31116393', 'Cell fate determination is a continuous process in which one cell type diversifies to other cell types following a hierarchical path. Advancements in single-cell technologies provide the opportunity to reveal the continuum of cell progression which forms a structured continuous tree. Computational algorithms, which are usually based on a priori assumptions on the hidden structures, have previously been proposed as a means of recovering pseudo-trajectory along cell differentiation process. However, there still lack of statistical framework on the assessments of intrinsic structure embedded in high-dimensional gene expression profile. Inherit noise and cell-to-cell variation underlie the single-cell data, on the other hand, pose grand challenges to testing even basic structures, such as linear versus bifurcation.'),
(82838, 2019, 'bio', 'ACME: Pan-specific peptide-MHC class I binding prediction through attention-based deep neural networks', 'Hu, Yan|Wang, Ziqiang|Hu, Hailin|Wan, Fangping|Chen, Lin|Yuanpeng, Xiong|Wang, Xiaoxia|Zhao, Dan|Huang, Weiren|Zeng, Jianyang', 'School of Life Sciences, Tsinghua University, Beijing, China.|Department of Urology, Shenzhen Second Peoples Hospital, The First Affiliated Hospital of Shenzhen University, International Cancer Center, Shenzhen University School of Medicine, Shenzhen, China.|School of Medicine, Tsinghua University, Beijing, China.|Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China.|Turing AI Institute of Nanjing, Nanjing, China.|Department of Computer Science and Technology, Tsinghua University, Beijing, China.|Department of Urology, Shenzhen Second Peoples Hospital, The First Affiliated Hospital of Shenzhen University, International Cancer Center, Shenzhen University School of Medicine, Shenzhen, China.|Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China.|Department of Urology, Shenzhen Second Peoples Hospital, The First Affiliated Hospital of Shenzhen University, International Cancer Center, Shenzhen University School of Medicine, Shenzhen, China.|Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China.', 'China|China|China|China|China|China|China|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz427', '31120490', 'Prediction of peptide binding to the major histocompatibility complex (MHC) plays a vital role in the development of therapeutic vaccines for the treatment of cancer. Algorithms with improved correlations between predicted and actual binding affinities are needed to increase precision and reduce the number of false positive predictions.'),
(82839, 2019, 'bio', 'EnImpute: imputing dropout events in single cell RNA sequencing data via ensemble learning', 'Zhang, Xiao-Fei|Ou-Yang, Le|Yang, Shuo|Zhao, Xing-Ming|Hu, Xiaohua|Yan, Hong', 'Department of Statistics, School of Mathematics and Statistics, Central China Normal University, Wuhan, China.|Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security, Shenzhen University, Shenzhen, China.|Department of Respiratory Medicine, Wuhan Number 1 Hospital, Wuhan, China.|Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.|Department of Computer Science, College of Computing and Informatics, Drexel University, Philadelphia, USA.|Department of Electrical Engineering, City University of Hong Kong, Hong Kong, China.', 'China|China|China|China|United States|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz435', '31125056', 'Imputation of dropout events that may mislead downstream analyses is a key step in analyzing single-cell RNA-sequencing (scRNA-seq) data. We develop EnImpute, an R package that introduces an ensemble learning method for imputing dropout events in scRNA-seq data. EnImpute combines the results obtained from multiple imputation methods to generate a more accurate result. A Shiny application is developed to provide easier implementation and visualization. Experiment results show that EnImpute outperforms the individual state-of-the-art methods in almost all situations. EnImpute is useful for correcting the noisy scRNA-seq data before performing downstream analysis.'),
(82840, 2019, 'bio', 'Joint detection of germline and somatic copy number events in matched tumor-normal sample pairs', 'Liu, Yongzhuang|Liu, Jian|Wang, Yadong', 'School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.|School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.|School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.', 'China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz429', '31125057', 'Whole genome sequencing (WGS) of tumor-normal sample pairs is a powerful approach for comprehensively characterizing germline copy number variations and somatic copy number alterations in cancer research and clinical practice. Existing computational approaches for detecting copy number events cannot detect germline copy number variations and somatic copy number alterations simultaneously, and yield low accuracy for somatic copy number alterations.'),
(82841, 2019, 'bio', 'EROS-DOCK: Protein-Protein Docking Using Exhaustive Branch-and-Bound Rotational Search', 'Ruiz, Maria Elisa Echartea|de Beauchxc3xaane, Isaure Chauvot|Ritchie, David W', 'University of Lorraine, CNRS, Inria, LORIA, Nancy, France.|University of Lorraine, CNRS, Inria, LORIA, Nancy, France.|University of Lorraine, CNRS, Inria, LORIA, Nancy, France.', 'France|France|France', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz434', '31125060', 'Protein-protein docking algorithms aim to predict the 3D structure of a binary complex using the structures of the individual proteins. This typically involves searching and scoring in a six-dimensional space. Many docking algorithms use FFT techniques to exhaustively cover the search space and to accelerate the scoring calculation. However, FFT docking results often depend on the initial protein orientations with respect to the Fourier sampling grid. Furthermore, Fourier-transforming a physics-base force field can involve a serious loss of precision.'),
(82842, 2019, 'bio', 'snakePipes: facilitating flexible, scalable and integrative epigenomic analysis', 'Bhardwaj, Vivek|Heyne, Steffen|Sikora, Katarzyna|Rabbani, Leily|Rauer, Michael|Kilpert, Fabian|Richter, Andreas S|Ryan, Devon P|Manke, Thomas', 'Max Planck Institute of Immunobiology and Epigenetics. Stübeweg 51, Freiburg Germany.|Max Planck Institute of Immunobiology and Epigenetics. Stübeweg 51, Freiburg Germany.|Max Planck Institute of Immunobiology and Epigenetics. Stübeweg 51, Freiburg Germany.|Max Planck Institute of Immunobiology and Epigenetics. Stübeweg 51, Freiburg Germany.|Max Planck Institute of Immunobiology and Epigenetics. Stübeweg 51, Freiburg Germany.|Institutes of Neurogenetics & Cardiogenetics, University of Lübeck, Maria-Goeppert-Str. 1, Lübeck, Germany.|Genedata AG, Margarethenstrasse 38, Basel, Switzerland.|Max Planck Institute of Immunobiology and Epigenetics. Stübeweg 51, Freiburg Germany.|Max Planck Institute of Immunobiology and Epigenetics. Stübeweg 51, Freiburg Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Switzerland|Germany|Germany', 12, '-', 'Journal Article', '10.1093/bioinformatics/btz436', '31134269', 'Due to the rapidly increasing scale and diversity of epigenomic data, modular and scalable analysis workflows are of wide interest. Here we present snakePipes, a workflow package for processing and downstream analysis of data from common epigenomic assays: ChIP-seq, RNA-seq, Bisulfite-seq, ATAC-seq, Hi-C and single-cell RNA-seq. snakePipes enables users to assemble variants of each workflow and to easily install and upgrade the underlying tools, via its simple command line wrappers and yaml files.'),
(82843, 2019, 'bio', 'clustermq enables efficient parallelisation of genomic analyses', 'Schubert, Michael', 'European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Cambridge, UK.', 'United Kingdom', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz284', '31134271', 'High performance computing (HPC) clusters play a pivotal role in large-scale bioinformatics analysis and modeling. For the statistical computing language R, packages exist to enable a user to submit their analyses as jobs on HPC schedulers. However, these packages do not scale well to high numbers of tasks, and their processing overhead quickly becomes a prohibitive bottleneck.'),
(82844, 2019, 'bio', 'Benchmarking of 4C-seq pipelines based on real and simulated data', 'Walter, Carolin|Schuetzmann, Daniel|Rosenbauer, Frank|Dugas, Martin', 'Institute of Medical Informatics, University of Münster, Münster, Germany.|Institute of Molecular Tumorbiology, University of Münster, Münster, Germany.|Institute of Molecular Tumorbiology, University of Münster, Münster, Germany.|Institute of Medical Informatics, University of Münster, Münster, Germany.', 'Germany|Germany|Germany|Germany', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz426', '31134276', 'With its capacity for high-resolution data output in one region of interest, chromosome conformation capture combined with high-throughput sequencing (4C-seq) is a state-of-the-art next-generation sequencing technique that provides epigenetic insights, and regularly advances current medical research. However, 4C-seq data is complex and prone to biases, and while specialized programs exist, an unbiased, extensive benchmarking is still lacking. Furthermore, neither substantial datasets with fully characterized ground truth, nor simulation programs for realistic 4C-seq data have been published.'),
(82845, 2019, 'bio', 'ExpansionHunter: A sequence-graph based tool to analyze variation in short tandem repeat regions', 'Dolzhenko, Egor|Deshpande, Viraj|Schlesinger, Felix|Krusche, Peter|Petrovski, Roman|Chen, Sai|Emig-Agius, Dorothea|Gross, Andrew|Narzisi, Giuseppe|Bowman, Brett|Scheffler, Konrad|van Vugt, Joke J F A|French, Courtney|Sanchis-Juan, Alba|Ibáñez, Kristina|Tucci, Arianna|Lajoie, Bryan|Veldink, Jan H|Raymond, F Lucy|Taft, Ryan J|Bentley, David R|Eberle, Michael A', 'Illumina Inc., 5200 Illumina Way, San Diego, CA, USA.|Illumina Inc., 5200 Illumina Way, San Diego, CA, USA.|Illumina Inc., 5200 Illumina Way, San Diego, CA, USA.|Illumina Cambridge Ltd., Chesterford Research Park, Little Chesterford, UK.|Illumina Cambridge Ltd., Chesterford Research Park, Little Chesterford, UK.|Illumina Inc., 5200 Illumina Way, San Diego, CA, USA.|Illumina Inc., 5200 Illumina Way, San Diego, CA, USA.|Illumina Inc., 5200 Illumina Way, San Diego, CA, USA.|New York Genome Center, 101 Avenue of the Americas, New York, NY, USA.|Illumina Inc., 5200 Illumina Way, San Diego, CA, USA.|Illumina Inc., 5200 Illumina Way, San Diego, CA, USA.|Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands.|Department of Medical Genetics, University of Cambridge, Cambridge, UK.|Department of Haematology, University of Cambridge, Cambridge, UK.|Genomics England, Queen Mary University London, Dawson Hall, London, EC1M 6BQ.|Genomics England, Queen Mary University London, Dawson Hall, London, EC1M 6BQ.|Illumina Inc., 5200 Illumina Way, San Diego, CA, USA.|Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands.|Department of Medical Genetics, University of Cambridge, Cambridge, UK.|Illumina Inc., 5200 Illumina Way, San Diego, CA, USA.|Illumina Cambridge Ltd., Chesterford Research Park, Little Chesterford, UK.|Illumina Inc., 5200 Illumina Way, San Diego, CA, USA.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|Netherlands|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|Net', 11, '-', 'Journal Article', '10.1093/bioinformatics/btz431', '31134279', 'We describe a novel computational method for genotyping repeats using sequence graphs. This method addresses the long-standing need to accurately genotype medically important loci containing repeats adjacent to other variants or imperfect DNA repeats such as polyalanine repeats. Here we introduce a new version of our repeat genotyping software, ExpansionHunter, that uses this method to perform targeted genotyping of a broad class of such loci.'),
(82846, 2019, 'bio', 'Deep Fusion of Contextual and Object-based Representations for Delineation of Multiple Nuclear Phenotypes', 'Khoshdeli, Mina|Winkelmaier, Garrett|Parvin, Bahram', 'Department of Electrical and Biomedical Engineering, University of Nevada, Reno, 1664 N. Virginia Street, Reno, NV, USA.|Department of Electrical and Biomedical Engineering, University of Nevada, Reno, 1664 N. Virginia Street, Reno, NV, USA.|Department of Electrical and Biomedical Engineering, University of Nevada, Reno, 1664 N. Virginia Street, Reno, NV, USA.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz430', '31135022', 'Nuclear delineation and phenotypic profiling are important steps in the automated analysis of histology sections. However, these are challenging problems due to (i) technical variations (e.g., fixation, staining) that originate as a result of sample preparation, (ii) biological heterogeneity (e.g., vesicular versus high chromatin phenotypes, nuclear atypia), and (iii) overlapping nuclei. This Application-Note couples contextual information about the cellular organization with the individual signature of nuclei to improve performance. As a result, routine delineation of nuclei in H&E stained histology sections is enabled for either computer-aided pathology (CAP) or integration with genome-wide molecular data.'),
(82847, 2019, 'bio', 'PSI-Sigma: a comprehensive splicing-detection method for short-read and long-read RNA-seq analysis', 'Lin, Kuan-Ting|Krainer, Adrian R', 'Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.|Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.', 'United States|United States', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz438', '31135034', 'Percent Spliced-In (PSI) values are commonly used to report alternative pre-mRNA splicing (AS) changes. Previous PSI-detection tools were limited to specific AS events and were evaluated by in silico RNA-seq data. We developed PSI-Sigma, which uses a new PSI index, and we employed actual (non-simulated) RNA-seq data from spliced synthetic genes (RNA Sequins) to benchmark its performance (i.e., precision, recall, false positive rate, and correlation) in comparison with three leading tools (rMATS, SUPPA2, and Whippet).'),
(82848, 2019, 'bio', 'Seq2Feature: a comprehensive web-based feature extraction tool', 'Nikam, Rahul|Gromiha, M Michael', 'Department of Biotechnology Bhupat and Jyoti Mehta School of BioSciences Indian Institute of Technology Madras, Chennai, Tamilnadu, India.|Department of Biotechnology Bhupat and Jyoti Mehta School of BioSciences Indian Institute of Technology Madras, Chennai, Tamilnadu, India.', 'India|India', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz432', '31135038', 'Machine learning techniques require various descriptors from protein and nucleic acid sequences to understand/predict their structure and function as well as distinguishing between disease and neutral mutations. Hence, availability of a feature extraction tool is necessary to bridge the gap.'),
(82849, 2019, 'bio', 'Smart computational exploration of stochastic gene regulatory network models using human-in-the-loop semi-supervised learning', 'Wrede, Fredrik|Hellander, Andreas', 'Uppsala University, Department of Information Technology, Uppsala, Sweden.|Uppsala University, Department of Information Technology, Uppsala, Sweden.', 'Sweden|Sweden', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz420', '31141124', 'Discrete stochastic models of gene regulatory network models are indispensable tools for biological inquiry since they allow the modeler to predict how molecular interactions give rise to nonlinear system output. Model exploration with the objective of generating qualitative hypotheses about the workings of a pathway is usually the first step in the modeling process. It involves simulating the gene network model under a very large range of conditions, due to the the large uncertainty in interactions and kinetic parameters. This makes model exploration highly computational demanding. Furthermore, with no prior information about the model behavior, labour-intensive manual inspection of very large amounts of simulation results becomes necessary. This limits systematic computational exploration to simplistic models.'),
(82850, 2019, 'bio', 'CNet: A Multi-omics Approach to Detecting Clinically Associated, Combinatory Genomic Signatures', 'Jia, Peilin|Pei, Guangsheng|Zhao, Zhongming', 'Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.|Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.|Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz441', '31141125', 'Genome-wide multi-omics profiling of complex diseases provides valuable resources and opportunities to discover associations between various measures of genes and diseases. Currently, a pressing challenge is how to effectively detect functional genes associated with or causing phenotypic outcomes. We developed CNet to identify groups of genomic signatures whose combinatory effect is significantly associated with clinical and phenotypical outcomes.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(82851, 2019, 'bio', 'PRODIGY-crystal: a web-tool for classification of biological interfaces in protein complexes', 'Jiménez-García, Brian|Elez, Katarina|Koukos, Panagiotis I|Bonvin, Alexandre M J J|Vangone, Anna', 'Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Utrecht, The Netherlands.|Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Utrecht, The Netherlands.|Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Utrecht, The Netherlands.|Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Utrecht, The Netherlands.|Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Utrecht, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz437', '31141126', 'Distinguishing biologically-relevant interfaces from crystallographic ones in biological complexes is fundamental in order to associate cellular functions to the correct macromolecular assemblies. Recently, we described a detailed study reporting the differences in the type of intermolecular residue-residue contacts between biological and crystallographic interfaces. Our findings allowed us to develop a fast predictor of biological interfaces reaching an accuracy of 0.92 and competitive to the current state of the art. Here we present its web-server implementation, PRODIGY-CRYSTAL, aimed at the classification of biological and crystallographic interfaces. PRODIGY-CRYSTAL has the advantage of being fast, accurate and simple. This, together with its user-friendly interface and user support forum, ensures its broad accessibility.'),
(82852, 2019, 'bio', 'Assessing concordance among human, in silico predictions and functional assays on genetic variant classification', 'Luo, Jiaqi|Zhou, Tianliangwen|You, Xiaobin|Zi, Yi|Li, Xiaoting|Wu, Yangming|Lan, Zhaoji|Zhi, Qihuan|Yi, Dandan|Xu, Lei|Li, Ang|Zhong, Zaixuan|Zhu, Mei|Sun, Gang|Zhu, Tao|Rao, Jianmei|Lin, Luhua|Sang, Jianfeng|Shi, Yujian', 'Top Gene Tech (Guangzhou) Co., Ltd. Guangzhou, Guangdong, China.|Top Gene Tech (Guangzhou) Co., Ltd. Guangzhou, Guangdong, China.|Top Gene Tech (Guangzhou) Co., Ltd. Guangzhou, Guangdong, China.|Top Gene Tech (Guangzhou) Co., Ltd. Guangzhou, Guangdong, China.|Top Gene Tech (Guangzhou) Co., Ltd. Guangzhou, Guangdong, China.|Top Gene Tech (Guangzhou) Co., Ltd. Guangzhou, Guangdong, China.|Top Gene Tech (Guangzhou) Co., Ltd. Guangzhou, Guangdong, China.|Top Gene Tech (Guangzhou) Co., Ltd. Guangzhou, Guangdong, China.|Department of General Surgery, Nanjing Drum Tower Hospital, Nanjing, Jiangsu, China.|Department of General Surgery, Drum Tower Clinical Medical College of Nanjing Medical University, Nanjing, Jiangsu, China.|Top Gene Tech (Guangzhou) Co., Ltd. Guangzhou, Guangdong, China.|Top Gene Tech (Guangzhou) Co., Ltd. Guangzhou, Guangdong, China.|Top Gene Tech (Guangzhou) Co., Ltd. Guangzhou, Guangdong, China.|Top Gene Tech (Guangzhou) Co., Ltd. Guangzhou, Guangdong, China.|Top Gene Tech (Guangzhou) Co., Ltd. Guangzhou, Guangdong, China.|Top Gene Tech (Guangzhou) Co., Ltd. Guangzhou, Guangdong, China.|Top Gene Tech (Guangzhou) Co., Ltd. Guangzhou, Guangdong, China.|Department of General Surgery, Nanjing Drum Tower Hospital, Nanjing, Jiangsu, China.|Top Gene Tech (Guangzhou) Co., Ltd. Guangzhou, Guangdong, China.', 'China|China|China|China|China|China|China|China|China|China|China|China|China|China|China|China|Chin', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz442', '31141141', 'A variety of in silico tools have been developed and frequently used to aid high-throughput rapid variant classification, but their performances vary, and their ability to classify variants of uncertain significance (VUS) were not systemically assessed previously due to lack of validation data. This has been changed recently by advances of functional assays, where functional impact of genetic changes can be measured in single-nucleotide resolution using saturation genome editing (SGE) assay.'),
(82853, 2019, 'bio', 'CoCo: RNA-seq Read Assignment Correction for Nested Genes and Multimapped Reads', 'Deschamps-Francoeur, Gabrielle|Boivin, Vincent|Elela, Sherif Abou|Scott, Michelle S', 'Department of biochemistry and RNA group, Faculty of Medicine and Health Sciences, Université de Sherbrooke.|Department of biochemistry and RNA group, Faculty of Medicine and Health Sciences, Université de Sherbrooke.|Department of microbiology and infectiology, Faculty of Medicine and Health Sciences, Université de Sherbrooke.|Department of biochemistry and RNA group, Faculty of Medicine and Health Sciences, Université de Sherbrooke.', '-|-|-|-', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz433', '31141144', 'Next generation sequencing techniques revolutionized the study of RNA expression by permitting whole transcriptome analysis. However, sequencing reads generated from nested and multi-copy genes are often either misassigned or discarded, which greatly reduces both quantification accuracy and gene coverage.'),
(82854, 2019, 'bio', 'deconvSeq: Deconvolution of Cell Mixture Distribution in Sequencing Data', 'Du, Rose|Carey, Vince|Weiss, Scott', 'Department of Neurosurgery, Brigham and Womens Hospital, Boston, USA.|Channing Division of Network Medicine, Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, Boston, USA.|Channing Division of Network Medicine, Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, Boston, USA.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz444', '31147676', 'Although single-cell sequencing is becoming more widely available, many tissue samples such as intracranial aneurysms are both fibrous and minute, and therefore not easily dissociated into single cells. To account for the cell type heterogeneity in such tissues therefore requires a computational method. We present a computational deconvolution method, deconvSeq, for sequencing data (RNA and bisulfite) obtained from bulk tissue. This method can also be applied to single-cell RNA sequencing data.'),
(82855, 2019, 'bio', 'Path2Surv: Pathway/gene set-based survival analysis using multiple kernel learning', 'Dereli, Onur|Oxc4x9fuz, Ceyda|Gxc3xb6nen, Mehmet', 'Graduate School of Sciences and Engineering.|Department of Industrial Engineering, College of Engineering.|Department of Industrial Engineering, College of Engineering.', '-|-|-', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz446', '31147687', 'Survival analysis methods that integrate pathways/gene sets into their learning model could identify molecular mechanisms that determine survival characteristics of patients. Rather than first picking the predictive pathways/gene sets from a given collection and then training a predictive model on the subset of genomic features mapped to these selected pathways/gene sets, we developed a novel machine learning algorithm (Path2Surv) that conjointly performs these two steps using multiple kernel learning.'),
(82856, 2019, 'bio', 'Full-length de novo viral quasispecies assembly through variation graph construction', 'Baaijens, Jasmijn A|Van der Roest, Bastiaan|Köster, Johannes|Stougie, Leen|Schönhuth, Alexander', 'Centrum Wiskunde & Informatica, Amsterdam, Netherlands.|University Medical Center Utrecht, Utrecht, Netherlands.|University of Duisburg-Essen, Essen, Germany.|Centrum Wiskunde & Informatica, Amsterdam, Netherlands.|Centrum Wiskunde & Informatica, Amsterdam, Netherlands.', 'Netherlands|Netherlands|Germany|Netherlands|Netherlands', 4, '-', 'Journal Article', '10.1093/bioinformatics/btz443', '31147688', 'Viruses populate their hosts as a viral quasispecies: a collection of genetically related mutant strains. Viral quasispecies assembly is the reconstruction of strain-specific haplotypes from read data, and predicting their relative abundances within the mix of strains is an important step for various treatment-related reasons. Reference-genome-independent (de novo) approaches have yielded benefits over reference-guided approaches, because reference-induced biases can become overwhelming when dealing with divergent strains. While being very accurate, extant de novo methods only yield rather short contigs. The remaining challenge is to reconstruct full-length haplotypes together with their abundances from such contigs.'),
(82857, 2019, 'bio', 'RBPSponge: genome-wide identification of lncRNAs that sponge RBPs', 'HafezQorani, Saber|Houdjedj, Aissa|Arici, Mehmet|Said, Abdesselam|Kazan, Hilal', 'Graduate School of Informatics, Department of Health Informatics, Middle East Technical University, Ankara, Turkey.|Graduate School of Electrical and Computer Engineering, Antalya Bilim University, Antalya, Turkey.|Department of Computer Engineering, Antalya Bilim University, Antalya, Turkey.|Department of Computer Engineering, Antalya Bilim University, Antalya, Turkey.|Graduate School of Electrical and Computer Engineering, Antalya Bilim University, Antalya, Turkey.', 'Turkey|Turkey|Turkey|Turkey|Turkey', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz448', '31150052', 'Long noncoding RNAs (lncRNAs) can act as molecular sponge or decoys for an RNA-binding protein (RBP) through their RBP binding sites, thereby modulating the expression of all target genes of the corresponding RBP of interest. Here, we present a web tool named RBPSponge to explore lncRNAs based on their potential to act as a sponge for an RBP of interest. RBPSponge identifies the occurrences of RBP binding sites and CLIP peaks on lncRNAs, and enables users to run statistical analyses to investigate the regulatory network between lncRNAs, RBPs and targets of RBPs.'),
(82858, 2019, 'bio', 'Augmented Interval List: a novel data structure for efficient genomic interval search', 'Feng, Jianglin|Ratan, Aakrosh|Sheffield, Nathan C', 'Center for Public Health Genomics, University of Virginia.|Center for Public Health Genomics, University of Virginia.|Center for Public Health Genomics, University of Virginia.', '-|-|-', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz407', '31150060', 'Genomic data is frequently stored as segments or intervals. Because this data type is so common, interval-based comparisons are fundamental to genomic analysis. As the volume of available genomic data grows, developing efficient and scalable methods for searching interval data is necessary.'),
(82859, 2019, 'bio', 'relax: the analysis of biomolecular kinetics and thermodynamics using NMR relaxation dispersion data', 'Morin, Sébastien|Linnet, Troels E|Lescanne, Mathilde|Schanda, Paul|Thompson, Gary S|Tollinger, Martin|Teilum, Kaare|Gagné, Stéphane|Marion, Dominique|Griesinger, Christian|Blackledge, Martin|dAuvergne, Edward J', 'Center for Public Health Genomics, University of Virginia.|Center for Public Health Genomics, University of Virginia.|Center for Public Health Genomics, University of Virginia.|Center for Public Health Genomics, University of Virginia.|Center for Public Health Genomics, University of Virginia.|Center for Public Health Genomics, University of Virginia.|Center for Public Health Genomics, University of Virginia.|Center for Public Health Genomics, University of Virginia.|Center for Public Health Genomics, University of Virginia.|Center for Public Health Genomics, University of Virginia.|Center for Public Health Genomics, University of Virginia.|Center for Public Health Genomics, University of Virginia.', '-|-|-|-|-|-|-|-|-|-|-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz397', '31161193', '-'),
(82860, 2019, 'bio', 'MTTFsite: Cross-cell-type TF Binding Site Prediction by using Multi-task Learning', 'Zhou, Jiyun|Lu, Qin|Gui, Lin|Xu, Ruifeng|Long, Yunfei|Wang, Hongpeng', 'Schoolof Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China.|Department of Computing, the Hong Kong Polytechnic University, Hung Hom, Hong Kong.|Department of Computer Science, University of Warwick, Coventry, cv4 4al, UK.|Schoolof Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China.|Department of Computing, the Hong Kong Polytechnic University, Hung Hom, Hong Kong.|Schoolof Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China.', 'China|Hong Kong|United Kingdom|China|Hong Kong|China', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz451', '31161194', 'The prediction of transcription factor binding sites (TFBSs) is crucial for gene expression analysis. Supervised learning approaches for TFBS predictions require large amounts of labeled data. However, many TFs of certain cell-types either do not have sufficient labeled data or do not have any labeled data.'),
(82861, 2019, 'bio', 'VCF/Plotein: Visualisation and prioritisation of genomic variants from human exome sequencing projects', 'Ossio, Raul|Garcia-Salinas, O Isaac|Anaya-Mancilla, Diego Said|Garcia-Sotelo, Jair S|Aguilar, Luis A|Adams, David J|Robles-Espinoza, Carla Daniela', 'Laboratorio Internacional de Investigación sobre el Genoma Humano, Universidad Nacional Autónoma de México, Querétaro, Qro., Mexico.|Laboratorio Internacional de Investigación sobre el Genoma Humano, Universidad Nacional Autónoma de México, Querétaro, Qro., Mexico.|Laboratorio Internacional de Investigación sobre el Genoma Humano, Universidad Nacional Autónoma de México, Querétaro, Qro., Mexico.|Laboratorio Internacional de Investigación sobre el Genoma Humano, Universidad Nacional Autónoma de México, Querétaro, Qro., Mexico.|Laboratorio Internacional de Investigación sobre el Genoma Humano, Universidad Nacional Autónoma de México, Querétaro, Qro., Mexico.|Experimental Cancer Genetics, Wellcome Sanger Institute, Hinxton Cambridge, UK.|Laboratorio Internacional de Investigación sobre el Genoma Humano, Universidad Nacional Autónoma de México, Querétaro, Qro., Mexico.', 'Mexico|Mexico|Mexico|Mexico|Mexico|United Kingdom|Mexico', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz458', '31161195', 'Identifying disease-causing variants from exome sequencing projects remains a challenging task that often requires bioinformatics expertise. Here we describe a user-friendly graphical application that allows medical professionals and bench biologists to prioritise and visualise genetic variants from human exome sequencing data.'),
(82862, 2019, 'bio', 'DeepSymmetry: Using 3D convolutional networks for identification of tandem repeats and internal symmetries in protein structures', 'Pagxc3xa8s, Guillaume|Grudinin, Sergei', 'Inria, Univ. Grenoble Alpes, LJK-CNRS, F-38000 Grenoble, France.|Inria, Univ. Grenoble Alpes, LJK-CNRS, F-38000 Grenoble, France.', 'France|France', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz454', '31161198', 'Thanks to the recent advances in structural biology, nowadays three-dimensional structures of various proteins are solved on a routine basis. A large portion of these structures contain structural repetitions or internal symmetries. To understand the evolution mechanisms of these proteins and how structural repetitions affect the protein function, we need to be able to detect such proteins very robustly. As deep learning is particularly suited to deal with spatially organized data, we applied it to the detection of proteins with structural repetitions.'),
(82863, 2019, 'bio', 'Inferring clonal heterogeneity in cancer using SNP arrays and whole genome sequencing', 'Zucker, Mark R|Abruzzo, Lynne V|Herling, Carmen D|Barron, Lynn L|Keating, Michael J|Abrams, Zachary B|Heerema, Nyla|Coombes, Kevin R', 'Inria, Univ. Grenoble Alpes, LJK-CNRS, F-38000 Grenoble, France.|Inria, Univ. Grenoble Alpes, LJK-CNRS, F-38000 Grenoble, France.|Inria, Univ. Grenoble Alpes, LJK-CNRS, F-38000 Grenoble, France.|Inria, Univ. Grenoble Alpes, LJK-CNRS, F-38000 Grenoble, France.|Inria, Univ. Grenoble Alpes, LJK-CNRS, F-38000 Grenoble, France.|Inria, Univ. Grenoble Alpes, LJK-CNRS, F-38000 Grenoble, France.|Inria, Univ. Grenoble Alpes, LJK-CNRS, F-38000 Grenoble, France.|Inria, Univ. Grenoble Alpes, LJK-CNRS, F-38000 Grenoble, France.', 'France|France|France|France|France|France|France|France', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz243', '31161200', '-'),
(82864, 2019, 'bio', 'eFORGE v20: updated analysis of cell type-specific signal in epigenomic data', 'Breeze, Charles E|Reynolds, Alex P|van Dongen, Jenny|Dunham, Ian|Lazar, John|Neph, Shane|Vierstra, Jeff|Bourque, Guillaume|Teschendorff, Andrew E|Stamatoyannopoulos, John A|Beck, Stephan', 'Medical Genomics Group, Paul OGorman Building, UCL Cancer Institute, University College London, 72 Huntley Street, London, United Kingdom.|Altius Institute for Biomedical Sciences, 2211 Elliott Avenue, Seattle.|Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK.|Altius Institute for Biomedical Sciences, 2211 Elliott Avenue, Seattle.|Altius Institute for Biomedical Sciences, 2211 Elliott Avenue, Seattle.|Altius Institute for Biomedical Sciences, 2211 Elliott Avenue, Seattle.|Department of Human Genetics, McGill University, Montréal, Québec, Canada, McGill University and Génome Québec Innovation Center, Montréal, Québec, Canada.|CAS Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, China.|Altius Institute for Biomedical Sciences, 2211 Elliott Avenue, Seattle.|Medical Genomics Group, Paul OGorman Building, UCL Cancer Institute, University College London, 72 Huntley Street, London, United Kingdom.', 'United Kingdom|United Kingdom|Netherlands|United Kingdom|United Kingdom|United Kingdom|United Kingdom|Canada|China|United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz456', '31161210', 'The Illumina Infinium EPIC BeadChip is a new high-throughput array for DNA methylation analysis, extending the earlier 450k array by over 400,000 new sites. Previously, a method named eFORGE was developed to provide insights into cell type-specific and cell composition effects for 450k data. Here, we present a significantly updated and improved version of eFORGE that can analyse both EPIC and 450k array data. New features include analysis of chromatin states, TF motifs and DNase I footprints, providing tools for EWAS interpretation and epigenome editing.'),
(82865, 2019, 'bio', 'RNA-align: quick and accurate alignment of RNA 3D structures based on size-independent TM-scoreRNA', 'Gong, Sha|Zhang, Chengxin|Zhang, Yang', 'School of Physics and Electronic Information, Huanggang Normal University, Huanggang 438000, China.|Department of Computational Medicine and Bioinformatics.|Department of Computational Medicine and Bioinformatics.', 'China|-|-', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz282', '31161212', 'Comparison of RNA 3D structures can be used to infer functional relationship of RNA molecules. Most of the current RNA structure alignment programs are built on size-dependent scales, which complicate the interpretation of structure and functional relations. Meanwhile, the low speed prevents the programs from being applied to large-scale RNA structural database search.'),
(82866, 2019, 'bio', 'AutoDock CrankPep: Combining folding and docking to predict protein-peptide complexes', 'Zhang, Yuqi|Sanner, Michel', 'Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, United States of America.|Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, United States of America.', 'United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz459', '31161213', 'Protein-peptide interactions mediate a wide variety of cellular and biological functions. Methods for predicting these interactions have garnered a lot of interest over the past few years, as witnessed by the rapidly growing number of peptide-based therapeutic molecules currently in clinical trials. The size and flexibility of peptides has shown to be challenging for existing automated docking software programs.'),
(82867, 2019, 'bio', 'WDSPdb: an updated resource for WD40 proteins', 'Ma, Jing|An, Ke|Zhou, Jing-Bo|Wu, Nuo-Si|Wang, Yang|Ye, Zhi-Qiang|Wu, Yun-Dong', 'Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen, P. R. China.|Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen, P. R. China.|Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen, P. R. China.|College of Information Engineering, Shenzhen University, Shenzhen, P. R. China.|Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA.|Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen, P. R. China.|Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen, P. R. China.', 'China|China|China|China|United States|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz460', '31161214', 'The WD40-repeat proteins are a large family of scaffold molecules that assemble complexes in various cellular processes. Obtaining their structures is the key to understanding their interaction details. We present WDSPdb 2.0, a significantly updated resource providing accurately predicted secondary and tertiary structures and featured sites annotations. Based on an optimized pipeline, WDSPdb 2.0 contains about 600 thousand entries, an increase of 10-fold, and integrates more than 37,000 variants from sources of ClinVar, Cosmic, 1000 Genomes, ExAC, IntOGen, cBioPotral, and IntAct. In addition, the web site is largely improved for visualization, exploring, and data downloading.'),
(82868, 2019, 'bio', 'NX4: A web-based visualization of large multiple sequence alignments', 'Solano-Roman, A|Cruz-Castillo, C|Offenhuber, D|Colubri, A', 'Broad Institute of MIT and Harvard, Cambridge, MA, United States.|Universidad Latina de Costa Rica, Department of Computer Science, San José, Costa Rica.|College of Arts, Media + Design, Northeastern University, Boston, MA, United States.|Broad Institute of MIT and Harvard, Cambridge, MA, United States.', 'United States|Costa Rica|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz457', '31161215', 'Multiple Sequence Alignments (MSAs) are a fundamental operation in genome analysis. However, MSA visualizations such as sequence logos and matrix representations have changed little since the nineties and are not well suited for displaying large-scale alignments. We propose a novel, web-based MSA visualization tool called NX4, which can handle genome alignments comprising thousands of sequences. NX4 calculates the frequency of each nucleotide along the alignment and visually summarizes the results using a color-blind friendly palette that helps identifying regions of high genetic diversity. NX4 also provides the user with additional assistance in finding these regions with a focus + context mechanism that uses a line chart of the Shannon entropy across the alignment. The tool offers geneticists an easy-to-use and scalable analysis for large MSA studies.'),
(82869, 2019, 'bio', 'Gene set enrichment for reproducible science: comparison of CERNO and eight other algorithms', 'Zyla, Joanna|Marczyk, Michal|Domaszewska, Teresa|Kaufmann, Stefan H E|Polanska, Joanna|Weiner, January', 'Data Mining Group, Institute of Automatic Control, Faculty of Automatic Control, Electronic and Computer Science, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland.|Data Mining Group, Institute of Automatic Control, Faculty of Automatic Control, Electronic and Computer Science, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland.|Department of Immunology, Max Planck Institute for Infection Biology, Charitéplatz 1, Berlin, Germany.|Department of Immunology, Max Planck Institute for Infection Biology, Charitéplatz 1, Berlin, Germany.|Data Mining Group, Institute of Automatic Control, Faculty of Automatic Control, Electronic and Computer Science, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland.|Department of Immunology, Max Planck Institute for Infection Biology, Charitéplatz 1, Berlin, Germany.', 'Poland|Poland|Germany|Germany|Poland|Germany', 11, '-', 'Journal Article', '10.1093/bioinformatics/btz447', '31165139', 'Analysis of gene set enrichment is an essential part of functional omics studies. Here, we complement the established evaluation metrics of gene set enrichment algorithms with a novel approach to assess the practical reproducibility of scientific results obtained from gene set enrichment tests when applied to related data from different studies.'),
(82870, 2019, 'bio', 'Scaling tree-based automated machine learning to biomedical big data with a feature set selector', 'Le, Trang T|Fu, Weixuan|Moore, Jason H', 'Department of Biostatistics, Epidemiology and Informatics, Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA.|Department of Biostatistics, Epidemiology and Informatics, Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA.|Department of Biostatistics, Epidemiology and Informatics, Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA.', '-|-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz470', '31165141', 'Automated machine learning (AutoML) systems are helpful data science assistants designed to scan data for novel features, select appropriate supervised learning models and optimize their parameters. For this purpose, Tree-based Pipeline Optimization Tool (TPOT) was developed using strongly typed genetic programming to recommend an optimized analysis pipeline for the data scientists prediction problem. However, like other AutoML systems, TPOT may reach computational resource limits when working on big data such as whole-genome expression data.'),
(82871, 2019, 'bio', 'New algorithms for detecting multi-effect and multi-way epistatic interactions', 'Ansarifar, Javad|Wang, Lizhi', 'Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, 50011, USA.|Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, 50011, USA.', 'United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz463', '31168598', 'Epistasis, which is the phenomenon of genetic interactions, plays a central role in many scientific discoveries. However, due to the combinatorial nature of the problem, it is extremely challenging to decipher the exact combinations of genes that trigger the epistatic effects. Many existing methods only focus on two-way interactions. Some of the most effective methods used machine learning techniques, but many were designed for special case-and-control studies or suffer from overfitting. We propose three new algorithms for multi-effect and multi-way epistases detection, with one guaranteeing global optimality and the other two being local optimization oriented heuristics.'),
(82872, 2019, 'bio', 'A Dynamic Programming Approach to Integrate Gene Expression Data and Network Information for Pathway Model Generation', 'Jiang, Yuexu|Liang, Yanchun|Wang, Duolin|Xu, Dong|Joshi, Trupti', 'Department of Computer Science and Technology, Jilin University, Changchun, Jilin, China.|Department of Computer Science and Technology, Jilin University, Changchun, Jilin, China.|Department of Computer Science and Technology, Jilin University, Changchun, Jilin, China.|Department of Computer Science and Technology, Jilin University, Changchun, Jilin, China.|Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA.', 'China|China|China|China|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz467', '31168616', 'As large amounts of biological data continue to be rapidly generated, a major focus of bioinformatics research has been aimed towards integrating these data to identify active pathways or modules under certain experimental conditions or phenotypes. Although biologically significant modules can often be detected globally by many existing methods, it is often hard to interpret or make use of the results towards pathway model generation and testing.'),
(82873, 2019, 'bio', 'P3DOCK: a protein-RNA docking webserver based on template-based and template-free docking', 'Zheng, Jinfang|Hong, Xu|Xie, Juan|Tong, Xiaoxue|Liu, Shiyong', 'School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.|School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.|School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.|School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.|School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.', 'China|China|China|China|China', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz478', '31173056', 'The main function of protein-RNA interaction is to regulate the expression of genes. Therefore, studying protein-RNA interactions is of great significance. The information of three-dimensional (3D) structures reveals that atomic interactions are particularly important. The calculation method for modeling a 3D structure of a complex mainly includes two strategies: free docking and template-based docking. And these two methods are complementary in protein-protein docking. Therefore, integrating these two methods may improve the prediction accuracy.'),
(82874, 2019, 'bio', 'DepthFinder: A Tool to Determine the Optimal Read Depth for Reduced-Representation Sequencing', 'Torkamneh, Davoud|Laroche, Jérôme|Boyle, Brian|Belzile, François', 'Département de Phytologie, Université Laval, Québec City, QC, Canada.|Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec City, QC, Canada.|Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec City, QC, Canada.|Département de Phytologie, Université Laval, Québec City, QC, Canada.', 'Canada|Canada|Canada|Canada', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz473', '31173057', 'Identification of DNA sequence variations such as single nucleotide polymorphisms (SNPs) is a fundamental step towards genetic studies. Reduced-representation sequencing methods have been developed as alternatives to whole genome sequencing to reduce costs and enable the analysis of many more individual. Amongst these methods, restriction site associated sequencing (RSAS) methodologies have been widely used for rapid and cost-effective discovery of SNPs and for high-throughput genotyping in a wide range of species. Despite the extensive improvements of the RSAS methods in the last decade, the estimation of the number of reads (i.e. read depth) required per sample for an efficient and effective genotyping remains mostly based on trial and error.'),
(82875, 2019, 'bio', 'capC-MAP: software for analysis of Capture-C data', 'Buckle, Adam|Gilbert, Nick|Marenduzzo, Davide|Brackley, Chris A', 'Medical Research Council Human Genetics Unit, Medical Research Council Institute of Genetics & Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, United Kingdom.|Medical Research Council Human Genetics Unit, Medical Research Council Institute of Genetics & Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, United Kingdom.|SUPA, School of Physics and Astronomy, University of Edinburgh, Peter Guthrie Tait Road, Edinburgh EH9 3FD, United Kingdom.|SUPA, School of Physics and Astronomy, University of Edinburgh, Peter Guthrie Tait Road, Edinburgh EH9 3FD, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz480', '31173058', 'Capture-C is a member of the chromosome-conformation-capture family of experimental methods which probes the 3-D organisation of chromosomes within the cell nucleus. It provides high-resolution information on the genome-wide chromatin interactions from a set of target genomic locations, and is growing in popularity as a tool for improving our understanding of cis-regulation and gene function. Yet, analysis of the data is complicated, and to date there has been no dedicated or easy-to-use software to automate the process. We present capC-MAP, a software package for the analysis of Capture-C data.'),
(82876, 2019, 'bio', 'arcasHLA: high resolution HLA typing from RNAseq', 'Orenbuch, R|Filip, I|Comito, D|Shaman, J|Peer, I|Rabadan, R', 'Department of Systems Biology, Columbia University, New York, NY, USA.|Department of Systems Biology, Columbia University, New York, NY, USA.|Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, USA.|Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, USA.|Department of Computer Science, Columbia University, New York, NY, USA.|Department of Systems Biology, Columbia University, New York, NY, USA.', 'United States|United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz474', '31173059', 'The human leukocyte antigen (HLA) locus plays a critical role in tissue compatibility and regulates the host response to many diseases, including cancers and autoimmune disorders. Recent improvements in the quality and accessibility of next-generation sequencing have made HLA typing from standard short-read data practical. However, this task remains challenging given the high level of polymorphism and homology between HLA genes. HLA typing from RNA sequencing is further complicated by post-transcriptional modifications and bias due to amplification.'),
(82877, 2019, 'bio', 'Protein contact prediction using metagenome sequence data and residual neural networks', 'Wu, Qi|Peng, Zhenling|Anishchenko, Ivan|Cong, Qian|Baker, David|Yang, Jianyi', 'School of Mathematical Sciences, Nankai University, Tianjin, China.|Center for Applied Mathematics, Tianjin University, Tianjin, China.|Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, WA, USA.|Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, WA, USA.|Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, WA, USA.|School of Mathematical Sciences, Nankai University, Tianjin, China.', 'China|China|United States|United States|United States|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz477', '31173061', 'Almost all protein residue contact prediction methods rely on the availability of deep multiple sequence alignments (MSAs). However, many proteins from the poorly populated families do not have sufficient number of homologs in the conventional UniProt database. Here we aim to solve this issue by exploring the rich sequence data from the metagenome sequencing projects.'),
(82878, 2019, 'bio', 'RAbHIT: R Antibody Haplotype Inference Tool', 'Peres, Ayelet|Gidoni, Moriah|Polak, Pazit|Yaari, Gur', 'Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel.|Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel.|Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel.|Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel.', 'Israel|Israel|Israel|Israel', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz481', '31173062', 'Antibody haplotype inference (chromosomal phasing) may have clinical implications for the identification of genetic predispositions to diseases. Yet, our knowledge of the genomic loci encoding for the variable regions of the antibody is only partial, mostly due to the challenge of aligning short reads from genome sequencing to these highly repetitive loci. A powerful approach to infer the content of these loci relies on analyzing repertoires of rearranged V(D)J sequences. We present here RAbHIT, an R Haplotype Antibody Inference Tool, that implements a novel algorithm to infer V(D)J haplotypes by adapting a Bayesian framework. RAbHIT offers inference of haplotype and gene deletions. It may be applied to sequences from naïve and non-naïve B-cells, sequenced by different library preparation protocols.'),
(82879, 2019, 'bio', 'RAISS: Robust and Accurate imputation from Summary Statistics', 'Julienne, Hanna|Shi, Huwenbo|Pasaniuc, Bogdan|Aschard, Hugues', 'Groupe de Génétique Statistique, Département de Génomes and Génétique, C3BI, Institut Pasteur, Paris, France.|Departments of Pathology and Lab Medicine, University of California, Los Angeles, Los Angeles, CA, USA.|Departments of Pathology and Lab Medicine, University of California, Los Angeles, Los Angeles, CA, USA.|Groupe de Génétique Statistique, Département de Génomes and Génétique, C3BI, Institut Pasteur, Paris, France.', 'France|United States|United States|France', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz466', '31173064', 'Multi-trait analyses using public summary statistics from genome-wide association studies (GWAS) are becoming increasingly popular. A constraint of multi-trait methods is that they require complete summary data for all traits. While methods for the imputation of summary statistics exist, they lack precision for genetic variants with small effect size. This is benign for univariate analyses where only variants with large effect size are selected a posteriori. However, it can lead to strong p-value inflation in multi-trait testing. Here we present a new approach that improve the existing imputation methods and reach a precision suitable for multi-trait analyses.'),
(82880, 2019, 'bio', 'vulcanSpot: a tool to prioritize therapeutic vulnerabilities in cancer', 'Perales-Patón, Javier|Domenico, Tomás Di|Fustero-Torre, Coral|Piñeiro-Yáñez, Elena|Carretero-Puche, Carlos|Tejero, Héctor|Valencia, Alfonso|Gómez-López, Gonzalo|Al-Shahrour, Fátima', 'Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, Spain.|Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, Spain.|Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, Spain.|Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, Spain.|Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, Spain.|Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, Spain.|Computational Biology Life Sciences Group, Barcelona Supercomputing Centre, Barcelona, Spain.|Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, Spain.|Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz465', '31173067', 'Genetic alterations lead to tumor progression and cell survival but also uncover cancer-specific vulnerabilities on gene dependencies that can be therapeutically exploited.'),
(82881, 2019, 'bio', 'Nanopype: A modular and scalable nanopore data processing pipeline', 'Giesselmann, Pay|Hetzel, Sara|Müller, Franz-Josef|Meissner, Alexander|Kretzmer, Helene', 'Department of Genome Regulation, Max Planck Institute for Molecular Genetics, Berlin, Germany.|Department of Genome Regulation, Max Planck Institute for Molecular Genetics, Berlin, Germany.|Department of Genome Regulation, Max Planck Institute for Molecular Genetics, Berlin, Germany.|Department of Genome Regulation, Max Planck Institute for Molecular Genetics, Berlin, Germany.|Department of Genome Regulation, Max Planck Institute for Molecular Genetics, Berlin, Germany.', 'Germany|Germany|Germany|Germany|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz461', '31192347', 'Long-read third-generation nanopore sequencing enables researchers to now address a range of questions that are difficult to tackle with short read approaches. The rapidly expanding user base and continuously increasing throughput have sparked the development of a growing number of specialized analysis tools. However, streamlined processing of nanopore datasets using reproducible and transparent workflows is still lacking. Here we present Nanopype, a nanopore data processing pipeline that integrates a diverse set of established bioinformatics software while maintaining consistent and standardized output formats. Seamless integration into compute cluster environments makes the framework suitable for high-throughput applications. As a result, Nanopype facilitates comparability of nanopore data analysis workflows and thereby should enhance the reproducibility of biological insights.'),
(82882, 2019, 'bio', 'RERconverge: an R package for associating evolutionary rates with convergent traits', 'Kowalczyk, Amanda|Meyer, Wynn K|Partha, Raghavendran|Mao, Weiguang|Clark, Nathan L|Chikina, Maria', 'Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.|Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.|Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.|Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.|Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.|Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.', 'United States|United States|United States|United States|United States|United States', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz468', '31192356', 'When different lineages of organisms independently adapt to similar environments, selection often acts repeatedly upon the same genes, leading to signatures of convergent evolutionary rate shifts at these genes. With the increasing availability of genome sequences for organisms displaying a variety of convergent traits, the ability to identify genes with such convergent rate signatures would enable new insights into the molecular basis of these traits.'),
(82883, 2019, 'bio', 'Graphlet Laplacians for topology-function and topology-disease relationships', 'Windels, Sam F L|Malod-Dognin, Noël|Pržulj, Nataša', 'Department of Computer Science, University College London, London, United Kingdom.|Barcelona Supercomputing Center, Barcelona, Spain.|Department of Computer Science, University College London, London, United Kingdom.', 'United Kingdom|Spain|United Kingdom', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz455', '31192358', 'Laplacian matrices capture the global structure of networks and are widely used to study biological networks. However, the local structure of the network around a node can also capture biological information. Local wiring patterns are typically quantified by counting how often a node touches different graphlets (small, connected, induced sub-graphs). Currently available graphlet-based methods do not consider whether nodes are in the same network neighbourhood.'),
(82884, 2019, 'bio', 'VarMap: a web tool for mapping genomic coordinates to protein sequence and structure and retrieving protein structural annotations', 'Stephenson, James D|Laskowski, Roman A|Nightingale, Andrew|Hurles, Matthew E|Thornton, Janet M', 'EMBL-EBI, Wellcome Genome Campus, Cambridge, UK.|EMBL-EBI, Wellcome Genome Campus, Cambridge, UK.|EMBL-EBI, Wellcome Genome Campus, Cambridge, UK.|Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK.|EMBL-EBI, Wellcome Genome Campus, Cambridge, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 4, '-', 'Journal Article', '10.1093/bioinformatics/btz482', '31192369', 'Understanding the protein structural context and patterning on proteins of genomic variants can help to separate benign from pathogenic variants and reveal molecular consequences. However, mapping genomic coordinates to protein structures is non-trivial, complicated by alternative splicing and transcript evidence.'),
(82885, 2019, 'bio', 'DNN-Dom: predicting protein domain boundary from sequence alone by deep neural network', 'Shi, Qiang|Chen, Weiya|Huang, Siqi|Jin, Fanglin|Dong, Yinghao|Wang, Yan|Xue, Zhidong', 'School of Software Engineering and College of Life Science & Technology, Huazhong University of Science and Technology, Wuhan, China.|School of Software Engineering and College of Life Science & Technology, Huazhong University of Science and Technology, Wuhan, China.|School of Software Engineering and College of Life Science & Technology, Huazhong University of Science and Technology, Wuhan, China.|School of Software Engineering and College of Life Science & Technology, Huazhong University of Science and Technology, Wuhan, China.|School of Software Engineering and College of Life Science & Technology, Huazhong University of Science and Technology, Wuhan, China.|School of Software Engineering and College of Life Science & Technology, Huazhong University of Science and Technology, Wuhan, China.|School of Software Engineering and College of Life Science & Technology, Huazhong University of Science and Technology, Wuhan, China.', 'China|China|China|China|China|China|China', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz464', '31197306', 'Accurate delineation of protein domain boundary plays an important role for protein engineering and structure prediction. Although machine learning methods are widely used to predict domain boundary, these approaches often ignore long-range interactions among residues, which have been proven to improve the prediction performance. However, how to simultaneously model the local and global interactions to further improve domain boundary prediction is still a challenging problem.'),
(82886, 2019, 'bio', 'DECENT: Differential Expression with Capture Efficiency adjustmeNT for single-cell RNA-seq data', 'Ye, Chengzhong|Speed, Terence P|Salim, Agus', 'Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Australia.|Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Australia.|Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Australia.', 'Australia|Australia|Australia', 4, '-', 'Journal Article', '10.1093/bioinformatics/btz453', '31197307', 'Dropout is a common phenomenon in single-cell RNA-seq (scRNA-seq) data, and when left unaddressed it affects the validity of the statistical analyses. Despite this, few current methods for differential expression (DE) analysis of scRNA-seq data explicitly model the process that gives rise to the dropout events. We develop DECENT, a method for differential expression (DE) analysis of scRNA-seq data that explicitly and accurately models the molecule capture process in scRNA-seq experiments.'),
(82887, 2019, 'bio', 'SCANVIS: a tool for SCoring, ANnotating and VISualizing splice junctions', 'Agius, Phaedra|Geiger, Heather|Robine, Nicolas', 'New York genome Center, New York, USA.|New York genome Center, New York, USA.|New York genome Center, New York, USA.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz452', '31197308', 'The association of splicing signatures with disease is a leading area of study for prognosis, diagnosis and therapy. We present a novel fast-performing annotation-dependent tool called SCANVIS for scoring and annotating splice junctions (SJs), with an efficient visualization tool that highlights SJ details such as frame-shifts and annotation support for individual samples or a sample cohort.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(82888, 2019, 'bio', 'iTUPA: An online automated application to perform Topographic-Unit Parsimony Analysis', 'Hammoud, Muhsen|Santos, Charles Morphy D|Gois, João Paulo', 'Center for Mathematics, Computing, and Cognition, Federal University of ABC, Brazil.|Center for Natural and Human Sciences, Federal University of ABC, Brazil.|Center for Mathematics, Computing, and Cognition, Federal University of ABC, Brazil.', 'Brazil|Brazil|Brazil', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz487', '31197309', 'iTUPA is a free online application for automatizing the Topographic-Unit Parsimony Analysis (TUPA), which identifies areas of endemism based on topography. iTUPA generates species-occurrences matrices based on user-defined topographic units (TUs) and provides a parsimony analysis of the generated matrix. We tested iTUPA after a proposal of regionalization for the Brazilian Atlantic Forest. iTUPA can handle millions of species registers simultaneously and uses Google Earth high-definition maps to visually explore the endemism data. We believe iTUPA is a useful tool for further discussions on biodiversity conservation.'),
(82889, 2019, 'bio', 'Cloud Bursting Galaxy: Federated Identity and Access Management', 'Jalili, Vahid|Afgan, Enis|Taylor, James|Goecks, Jeremy', 'Department of Biomedical Engineering, Oregon Health and Science University, OR, USA.|Department of Biology, Johns Hopkins University, Baltimore, MD, USA.|Department of Biology, Johns Hopkins University, Baltimore, MD, USA.|Department of Biomedical Engineering, Oregon Health and Science University, OR, USA.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz472', '31197310', 'Large biomedical datasets, such as those from genomics and imaging, are increasingly being stored on commercial and institutional cloud computing platforms. This is because cloud-scale computing resources, from robust backup to high-speed data transfer to scalable compute and storage, are needed to make these large datasets usable. However, one challenge for large-scale biomedical data on the cloud is providing secure access, especially when datasets are distributed across platforms. While there are open Web protocols for secure authentication and authorization, these protocols are not in wide use in bioinformatics and are difficult to use for even technologically sophisticated users.'),
(82890, 2019, 'bio', 'BAMixChecker: an automated checkup tool for matched sample pairs in NGS cohort', 'Chun, Hein|Kim, Sangwoo', 'Dept. of Biomedical Systems Informatics, Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, South Korea.|Dept. of Biomedical Systems Informatics, Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, South Korea.', '-|-', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz479', '31197312', 'Mislabeling in the process of Next Generation Sequencing (NGS) is a frequent problem that can cause an entire genomic analysis to fail, and a regular cohort-level checkup is needed to ensure that it has not occurred. We developed a new, automated tool (BAMixChecker) that accurately detects sample mismatches from a given BAM file cohort with minimal user intervention. BAMixChecker uses a flexible, data-specific set of single-nucleotide polymorphisms and detects orphan (unpaired) and swapped (mispaired) samples based on genotype-concordance score and entropy-based file name analysis. BAMixChecker shows xe2x88xbc100% accuracy in real WES, RNA-Seq, and targeted sequencing data cohorts, even for small panels (<50 genes). BAMixChecker provides an HTML-style report that graphically outlines the sample matching status in tables and heatmaps, with which users can quickly inspect any mismatch events.'),
(82891, 2019, 'bio', 'Chicdiff: a computational pipeline for detecting differential chromosomal interactions in Capture Hi-C data', 'Cairns, Jonathan|Orchard, William R|Malysheva, Valeriya|Spivakov, Mikhail', 'Regulatory Genomics Group, Nuclear Dynamics Programme, Babraham Institute, Cambridge, UK.|Regulatory Genomics Group, Nuclear Dynamics Programme, Babraham Institute, Cambridge, UK.|Regulatory Genomics Group, Nuclear Dynamics Programme, Babraham Institute, Cambridge, UK.|Regulatory Genomics Group, Nuclear Dynamics Programme, Babraham Institute, Cambridge, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz450', '31197313', 'Capture Hi-C is a powerful approach for detecting chromosomal interactions involving, at least on one end, DNA regions of interest, such as gene promoters. We present Chicdiff, an R package for robust detection of differential interactions in Capture Hi-C data. Chicdiff enhances a state-of-the-art differential testing approach for count data with bespoke normalisation and multiple testing procedures that account for specific statistical properties of Capture Hi-C. We validate Chicdiff on published Promoter Capture Hi-C data in human Monocytes and CD4+ T cells, identifying multitudes of cell type-specific interactions, and confirming the overall positive association between promoter interactions and gene expression.'),
(82892, 2019, 'bio', 'Mycorrhiza: Genotype Assignment using Phylogenetic Networks', 'Georges-Filteau, Jeremy|Hamelin, Richard C|Blanchette, Mathieu', 'School of Computer Science, McGill University, Montreal, Canada.|Department of Forest and Conservation Sciences, The University of British Columbia, Vancouver, Canada.|School of Computer Science, McGill University, Montreal, Canada.', 'Canada|Canada|Canada', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz476', '31197316', 'The genotype assignment problem consists of predicting, from the genotype of an individual, which of a known set of populations it originated from. The problem arises in a variety of contexts, including wildlife forensics, invasive species detection, and biodiversity monitoring. Existing approaches perform well under ideal conditions but are sensitive to a variety of common violations of the assumptions they rely on.'),
(82893, 2019, 'bio', 'A scalable method for parameter identification in kinetic models of metabolism using steady state data', 'Srinivasan, Shyam|Cluett, William R|Mahadevan, Radhakrishnan', 'Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada.|Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada.|Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada.', 'Canada|Canada|Canada', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz445', '31197317', 'In kinetic models of metabolism, the parameter values determine the dynamic behaviour predicted by these models. Estimating parameters from in vivo experimental data requires the parameters to be structurally identifiable, and the data to be informative enough to estimate these parameters. Existing methods to determine the structural identifiability of parameters in kinetic models of metabolism can only be applied to models of small metabolic networks due to their computational complexity. Additionally, a priori experimental design, a necessity to obtain informative data for parameter estimation, also does not account for using steady state data to estimate parameters in kinetic models.'),
(82894, 2019, 'bio', 'Sequence alignment using machine learning for accurate template-based protein structure prediction', 'Makigaki, Shuichiro|Ishida, Takashi', 'School of Computing, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo, 152-8550, Japan.|School of Computing, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo, 152-8550, Japan.', 'Japan|Japan', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz483', '31197318', 'Template-based modeling, the process of predicting the tertiary structure of a protein by using homologous protein structures, is useful if good templates can be found. Although modern homology detection methods can find remote homologs with high sensitivity, the accuracy of template-based models generated from homology-detection-based alignments is often lower than that from ideal alignments.'),
(82895, 2019, 'bio', 'MetOSite: an integrated resource for the study of methionine residues sulfoxidation', 'Valverde, Héctor|Cantón, Francisco R|Aledo, Juan Carlos', 'Departamento de Biología Molecular y Bioquímica, Universidad de Málaga, Málaga, Spain.|Departamento de Biología Molecular y Bioquímica, Universidad de Málaga, Málaga, Spain.|Departamento de Biología Molecular y Bioquímica, Universidad de Málaga, Málaga, Spain.', 'Spain|Spain|Spain', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz462', '31197322', 'The oxidation of protein-bound methionine to form methionine sulfoxide has traditionally been regarded as an oxidative damage. However, growing evidences support the view of this reversible reaction also as a regulatory post-translational modification. Thus, the oxidation of methionine residues has been reported to have multiple and varied implications for protein function. However, despite the importance of this modification and the abundance of reports, all these data are scattered in the literature. No database/resource on methionine sulfoxidation exists currently. Since this information is useful to gain further insights into the redox regulation of cellular proteins, we have created a primary database of experimentally confirmed sulfoxidation sites.'),
(82896, 2019, 'bio', 'An Empirical Bayesian ranking method, with applications to high throughput biology', 'Ferguson, John|Chang, Joseph', 'HRB Clinical Research Facility, National University of Ireland Galway, Ireland.|Department of Statistics and Data Science, Yale University, New Haven, CT, USA.', 'Ireland|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz471', '31197345', 'In bioinformatics, genome-wide experiments look for important biological differences between two groups at a large number of locations in the genome. Often, the final analysis focuses on a p-value based ranking of locations which might then be investigated further in follow up experiments. However, this strategy may result in small effect sizes, with low p-values, being ranked more favourably than larger more scientifically important effects. Bayesian ranking techniques may offer a solution to this problem provided a good prior distribution for the collective distribution of effect sizes is available.'),
(82897, 2019, 'bio', 'SIGN: Similarity identification in gene expression', 'Madani Tonekaboni, Seyed Ali|Kumar Manem, Venkata Satya|El-Hachem, Nehme|Haibe-Kains, Benjamin', 'Princess Margaret Cancer Centre, Toronto, Ontario, Canada.|Princess Margaret Cancer Centre, Toronto, Ontario, Canada.|Integrative systems biology, Institut de Recherches Cliniques de Montréal, Montreal, Quebec, Canada.|Princess Margaret Cancer Centre, Toronto, Ontario, Canada.', 'Canada|Canada|Canada|Canada', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz485', '31198954', 'High-throughput molecular profiles of human cells have been used in predictive computational approaches for stratification of healthy and malignant phenotypes and identification of their biological states. In this regard, pathway activities have been used as biological features in unsupervised and supervised learning schemes.'),
(82898, 2019, 'bio', 'Testing Clonal Relatedness of Two Tumors from the Same Patient based on their Mutational Profiles: update of the Clonality R Package', 'Mauguen, Audrey|Seshan, Venkatraman E|Begg, Colin B|Ostrovnaya, Irina', 'Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, 485 Lexington Ave, New York, NY.|Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, 485 Lexington Ave, New York, NY.|Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, 485 Lexington Ave, New York, NY.|Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, 485 Lexington Ave, New York, NY.', '-|-|-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz486', '31198957', 'The Clonality R package is a practical tool to assess the clonal relatedness of two tumors from the same patient. We have previously presented its functionality for testing tumors using loss of heterozygosity data or copy number arrays. Since then somatic mutation data have been more widely available through next generation sequencing and we have developed new methodology for comparing the tumors mutational profiles (Ostrovnaya et al 2015, Mauguen et al 2018). We thus extended the package to include these two new methods for comparing tumors as well as the mutational frequency estimation from external data required for their implementation. The first method is a likelihood ratio test that is readily available on a patient by patient basis. The second method employs a random-effects model to estimate both the population and individual probabilities of clonal relatedness from a group of patients with pairs of tumors. The package is available on Bioconductor.'),
(82899, 2019, 'bio', 'GMSimpute: a generalized two-step Lasso approach to impute missing values in label-free mass spectrum analysis', 'Li, Qian|Fisher, Kate|Meng, Wenjun|Fang, Bin|Welsh, Eric|Haura, Eric B|Koomen, John M|Eschrich, Steven A|Fridley, Brooke L|Chen, Y Ann', 'Health Informatics Institute, University of South Florida, Tampa, FL.|Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL.|Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL.|Proteomics and Metabolomics Core Facility, Moffitt Cancer Center, Tampa, FL.|Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL.|Department of Thoracic Oncology, Moffitt Cancer Center, Tampa, FL.|Department of Molecular Oncology, Moffitt Cancer Center, Tampa, FL.|Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL.|Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL.|Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL.', '-|-|-|-|-|-|-|-|-|-', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz488', '31199438', 'Missingness in label-free mass spectrometry (MS) is inherent to the technology. A computational approach to recover missing values in metabolomics and proteomics datasets is important. Most existing methods are designed under a particular assumption, either missing at random or under the detection limit. If the missing pattern deviates from the assumption, it may lead to biased results. Hence, we investigate the missing patterns in MS data and develop an omnibus approach GMSimpute, to allow effective imputation accommodating different missing patterns.'),
(82900, 2019, 'bio', 'iScore: A novel graph kernel-based function for scoring protein-protein docking models', 'Geng, Cunliang|Jung, Yong|Renaud, Nicolas|Honavar, Vasant|Bonvin, Alexandre M J J|Xue, Li C', 'Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584 CH Utrecht, the Netherlands.|Bioinformatics & Genomics Graduate Program, Pennsylvania State University, University Park, PA, USA.|Netherlands eScience Center, Science Park 140 1098 XG Amsterdam, the Netherlands.|Bioinformatics & Genomics Graduate Program, Pennsylvania State University, University Park, PA, USA.|Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584 CH Utrecht, the Netherlands.|Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584 CH Utrecht, the Netherlands.', 'Netherlands|United States|Netherlands|United States|Netherlands|Netherlands', 8, '-', 'Journal Article', '10.1093/bioinformatics/btz496', '31199455', 'Protein complexes play critical roles in many aspects of biological functions. Three-dimensional (3D) structures of protein complexes are critical for gaining insights into structural bases of interactions and their roles in the biomolecular pathways that orchestrate key cellular processes. Because of the expense and effort associated with experimental determinations of 3D protein complex structures, computational docking has evolved as a valuable tool to predict 3D structures of biomolecular complexes. Despite recent progress, reliably distinguishing near-native docking conformations from a large number of candidate conformations, the so-called scoring problem, remains a major challenge.'),
(82901, 2019, 'bio', 'GeneNoteBook, a collaborative notebook for comparative genomics', 'Holmer, Rens|van Velzen, Robin|Geurts, Rene|Bisseling, Ton|de Ridder, Dick|Smit, Sandra', 'Laboratory of Molecular Biology, Wageningen University, Wageningen, The Netherlands.|Laboratory of Molecular Biology, Wageningen University, Wageningen, The Netherlands.|Laboratory of Molecular Biology, Wageningen University, Wageningen, The Netherlands.|Laboratory of Molecular Biology, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz491', '31199463', 'Analysis and comparison of genomic and transcriptomic data sets have become standard procedures in biological research. However, for non-model organisms no efficient tools exist to visually work with multiple genomes and their metadata, and to annotate such data in a collaborative way. Here we present GeneNoteBook: a web based collaborative notebook for comparative genomics. GeneNoteBook allows experimental and computational researchers to query, browse, visualize and curate bioinformatic analysis results for multiple genomes. GeneNoteBook is particularly suitable for the analysis of non-model organisms, as it allows for comparing newly sequenced genomes to those of model organisms.'),
(82902, 2019, 'bio', 'CoCoScore: Context-aware co-occurrence scoring for text mining applications using distant supervision', 'Junge, Alexander|Jensen, Lars Juhl', 'Disease Systems Biology Program, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, Copenhagen N, Denmark.|Disease Systems Biology Program, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, Copenhagen N, Denmark.', 'Denmark|Denmark', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz490', '31199464', 'Information extraction by mining the scientific literature is key to uncovering relations between biomedical entities. Most existing approaches based on natural language processing extract relations from single sentence-level co-mentions, ignoring co-occurrence statistics over the whole corpus. Existing approaches counting entity co-occurrences ignore the textual context of each co-occurrence.'),
(82903, 2019, 'bio', 'POsitive Multistate Protein design', 'Vucinic, Jelena|Simoncini, David|Ruffini, Manon|Barbe, Sophie|Schiex, Thomas', 'LISBP, Université de Toulouse, CNRS, INRA, INSA, Toulouse, France.|LISBP, Université de Toulouse, CNRS, INRA, INSA, Toulouse, France.|LISBP, Université de Toulouse, CNRS, INRA, INSA, Toulouse, France.|LISBP, Université de Toulouse, CNRS, INRA, INSA, Toulouse, France.|MIAT, Université de Toulouse, INRA, Auzeville-Tolosane, France.', 'France|France|France|France|France', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz497', '31199465', 'Structure-based Computational Protein design (CPD) plays a critical role in advancing the field of protein engineering. Using an all-atom energy function, CPD tries to identify amino acid sequences that fold into a target structure and ultimately perform a desired function. The usual approach considers a single rigid backbone as a target, which ignores backbone flexibility. Multistate design (MSD) allows instead to consider several backbone states simultaneously, defining challenging computational problems.'),
(82904, 2019, 'bio', 'MRLR: unraveling high-resolution meiotic recombination by linked reads', 'Xu, Peng|Kennell, Timothy|Gao, Min|Human Genome Structural Variation Consortium|Kimberly, Robert|Chong, Zechen', 'Department of Genetics, the University of Alabama at Birmingham, Birmingham, Alabama, United States of America.|Informatics Institute, the University of Alabama at Birmingham, Birmingham, Alabama, United States of America.|Informatics Institute, the University of Alabama at Birmingham, Birmingham, Alabama, United States of America.|Informatics Institute, the University of Alabama at Birmingham, Birmingham, Alabama, United States of America.|Department of Medicine, the University of Alabama at Birmingham, Birmingham, Alabama, United States of America.|Department of Genetics, the University of Alabama at Birmingham, Birmingham, Alabama, United States of America.', 'United States|United States|United States|United States|United States|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz503', '31214684', 'Meiotic recombination facilitates the transmission of exchanged genetic material between homologous chromosomes and plays a crucial role in increasing the genetic variations in eukaryotic organisms. In humans, thousands of crossover events have been identified by genotyping related family members. However, most of these crossover regions span tens to hundreds of kb, which is not sufficient resolution to accurately identify the crossover breakpoints in a typical trio family.'),
(82905, 2019, 'bio', 'A Sequential Algorithm to Detect Diffusion Switching along Intracellular Particle Trajectories', 'Briane, V|Vimond, M|Valades Cruz, C A|Salomon, A|Wunder, C|Kervrann, C', 'INRIA, Centre de Rennes Bretagne Atlantique, Serpico Project-Team, Rennes, France.|CREST (Ensai, Université Bretagne Loire), Bruz, France.|Institut Curie, PLS Research University, Cellular and Chemical Biology, U1143 INSERM/UMR 3666 CNRS, 26 Rue dUlm, 75248 Paris Cedex 05, France.|INRIA, Centre de Rennes Bretagne Atlantique, Serpico Project-Team, Rennes, France.|Institut Curie, PLS Research University, Cellular and Chemical Biology, U1143 INSERM/UMR 3666 CNRS, 26 Rue dUlm, 75248 Paris Cedex 05, France.|INRIA, Centre de Rennes Bretagne Atlantique, Serpico Project-Team, Rennes, France.', 'France|France|France|France|France|France', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz489', '31214689', 'Recent advances in molecular biology and fluorescence microscopy imaging have made possible the inference of the dynamics of single molecules in living cells. Changes of dynamics can occur along a trajectory. Then, an issue is to estimate the temporal change-points that is the times at which a change of dynamics occurs. The number of points in the trajectory required to detect such changes will depend on both the magnitude and type of the motion changes. Here, the number of points per trajectory is of the order of 102, even if in practice dramatic motion changes can be detected with less points.'),
(82906, 2019, 'bio', 'PatientExploreR: an extensible application for dynamic visualization of patient clinical history from Electronic Health Records in the OMOP Common Data Model Title', 'Glicksberg, Benjamin S|Oskotsky, Boris|Thangaraj, Phyllis M|Giangreco, Nicholas|Badgeley, Marcus A|Johnson, Kipp W|Datta, Debajyoti|Rudrapatna, Vivek|Rappoport, Nadav|Shervey, Mark M|Miotto, Riccardo|Goldstein, Theodore C|Rutenberg, Eugenia|Frazier, Remi|Lee, Nelson|Israni, Sharat|Larsen, Rick|Percha, Bethany|Li, Li|Dudley, Joel T|Tatonetti, Nicholas P|Butte, Atul J', 'Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA.|Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA.|Departments of Biomedical Informatics, Systems Biology, and Medicine, Columbia University, New York, New York, USA.|Departments of Biomedical Informatics, Systems Biology, and Medicine, Columbia University, New York, New York, USA.|Departments of Genomics and Data Science, Icahn Institute for Genomic Sciences and Multiscale Biology, Icahn School of Medicine at Mount Sinai, Institute of Next Generation Healthcare, New York, NY, USA.|Departments of Genomics and Data Science, Icahn Institute for Genomic Sciences and Multiscale Biology, Icahn School of Medicine at Mount Sinai, Institute of Next Generation Healthcare, New York, NY, USA.|Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA.|Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA.|Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA.|Departments of Genomics and Data Science, Icahn Institute for Genomic Sciences and Multiscale Biology, Icahn School of Medicine at Mount Sinai, Institute of Next Generation Healthcare, New York, NY, USA.|Departments of Genomics and Data Science, Icahn Institute for Genomic Sciences and Multiscale Biology, Icahn School of Medicine at Mount Sinai, Institute of Next Generation Healthcare, New York, NY, USA.|Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA.|Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA.|Enterprise Information and Analytics, University of California, San Francisco, San Francisco, CA, USA.|Enterprise Information and Analytics, University of California, San Francisco, San Francisco, CA, USA.|Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA.|Enterprise Information and Analytics, University of California, San Francisco, San Francisco, CA, USA.|Departments of Genomics and Data Science, Icahn Institute for Genomic Sciences and Multiscale Biology, Icahn School of Medicine at Mount Sinai, Institute of Next Generation Healthcare, New York, NY, USA.|Departments of Genomics and Data Science, Icahn Institute for Genomic Sciences and Multiscale Biology, Icahn School of Medicine at Mount Sinai, Institute of Next Generation Healthcare, New York, NY, USA.|Departments of Genomics and Data Science, Icahn Institute for Genomic Sciences and Multiscale Biology, Icahn School of Medicine at Mount Sinai, Institute of Next Generation Healthcare, New York, NY, USA.|Departments of Biomedical Informatics, Systems Biology, and Medicine, Columbia University, New York, New York, USA.|Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|Uni', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz409', '31214700', 'Electronic Health Records (EHR) are quickly becoming omnipresent in healthcare, but interoperability issues and technical demands limit their use for biomedical and clinical research. Interactive and flexible software that interfaces directly with EHR data structured around a common data model could accelerate more EHR-based research by making the data more accessible to researchers who lack computational expertise and/or domain knowledge.'),
(82907, 2019, 'bio', 'Reversible Jump MCMC for Multi-Model Inference in Metabolic Flux Analysis', 'Theorell, Axel|Nöh, Katharina', 'Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, Jülich, Germany.|Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, Jülich, Germany.', 'Germany|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz500', '31214716', 'The validity of model based inference, as used in systems biology, depends on the underlying model formulation. Often, a vast number of competing models is available, that are built on different assumptions, all consistent with the existing knowledge about the studied biological phenomenon. As a remedy for this, Bayesian Model Averaging (BMA) facilitates parameter and structural inferences based on multiple models simultaneously. However, in fields where a vast number of alternative, high-dimensional and non-linear models are involved, the BMA based inference task is computationally very challenging.'),
(82908, 2019, 'bio', 'iCn3D, a Web-based 3D Viewer for Sharing 1D/2D/3D Representations of Biomolecular Structures', 'Wang, Jiyao|Youkharibache, Philippe|Zhang, Dachuan|Lanczycki, Christopher J|Geer, Renata C|Madej, Thomas|Phan, Lon|Ward, Minghong|Lu, Shennan|Marchler, Gabriele H|Wang, Yanli|Bryant, Stephen H|Geer, Lewis Y|Marchler-Bauer, Aron', 'National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, 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', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz502', '31218344', 'Build a web-based 3D molecular structure viewer focusing on interactive structural analysis.'),
(82909, 2019, 'bio', 'svtools: population-scale analysis of structural variation', 'Larson, David E|Abel, Haley J|Chiang, Colby|Badve, Abhijit|Das, Indraniel|Eldred, James M|Layer, Ryan M|Hall, Ira M', 'McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA.|McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA.|McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA.|McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA.|McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA.|McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA.|Biofrontiers Institute, University of Colorado, Boulder, CO, USA.|McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 4, '-', 'Journal Article', '10.1093/bioinformatics/btz492', '31218349', 'Large-scale human genetics studies are now employing whole genome sequencing with the goal of conducting comprehensive trait mapping analyses of all forms of genome variation. However, methods for structural variation (SV) analysis have lagged far behind those for smaller scale variants, and there is an urgent need to develop more efficient tools that scale to the size of human populations. Here, we present a fast and highly scalable software toolkit (svtools) and cloud-based pipeline for assembling high quality SV maps - including deletions, duplications, mobile element insertions, inversions, and other rearrangements - in many thousands of human genomes. We show that this pipeline achieves similar variant detection performance to established per-sample methods (e.g., LUMPY), while providing fast and affordable joint analysis at the scale ofxe2x80x89xe2x89xa5xe2x80x89100,000 genomes. These tools will help enable the next generation of human genetics studies.'),
(82910, 2019, 'bio', 'DeepMito: accurate prediction of protein submitochondrial localization using convolutional neural networks', 'Savojardo, Castrense|Bruciaferri, Niccolò|Tartari, Giacomo|Martelli, Pier Luigi|Casadio, Rita', 'Biocomputing Group, Department of Pharmacy and Biotechnology (FaBiT), University of Bologna, Italy.|Biocomputing Group, Department of Pharmacy and Biotechnology (FaBiT), University of Bologna, Italy.|Biocomputing Group, Department of Pharmacy and Biotechnology (FaBiT), University of Bologna, Italy.|Biocomputing Group, Department of Pharmacy and Biotechnology (FaBiT), University of Bologna, Italy.|Biocomputing Group, Department of Pharmacy and Biotechnology (FaBiT), University of Bologna, Italy.', 'Italy|Italy|Italy|Italy|Italy', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz512', '31218353', 'The correct localization of proteins in cell compartments is a key issue for their functional annotation. Particularly, mitochondrial proteins are physiologically active in different compartments and their aberrant localization contributes to the pathogenesis of human mitochondrial pathologies. Many computational methods exist to assign protein sequences to subcellular compartments such as nucleus, cytoplasm and organelles. However, a substantial lack of experimental evidence in public sequence databases hampered so far a finer grain discrimination, including also intra-organelle compartments.'),
(82911, 2019, 'bio', 'sefOri: selecting the best-engineered squence features to predict DNA replication origins', 'Lou, Chenwei|Zhao, Jian|Shi, Ruoyao|Wang, Qian|Zhou, Wenyang|Wang, Yubo|Wang, Guoqing|Huang, Lan|Feng, Xin|Zhou, Fengfeng', 'BioKnow Health Informatics Lab, College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education,Jilin University, Changchun, Jilin, China.|BioKnow Health Informatics Lab, College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education,Jilin University, Changchun, Jilin, China.|BioKnow Health Informatics Lab, College of Life Sciences,Jilin University, Changchun, Jilin, China.|BioKnow Health Informatics Lab, College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education,Jilin University, Changchun, Jilin, China.|BioKnow Health Informatics Lab, College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education,Jilin University, Changchun, Jilin, China.|BioKnow Health Informatics Lab, College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education,Jilin University, Changchun, Jilin, China.|Department of Pathogenobiology, The Key Laboratory of Zoonosis, Chinese Ministry of Education, College of Basic Medicine,Jilin University, Changchun, China.|BioKnow Health Informatics Lab, College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education,Jilin University, Changchun, Jilin, China.|BioKnow Health Informatics Lab, College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education,Jilin University, Changchun, Jilin, China.|BioKnow Health Informatics Lab, College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education,Jilin University, Changchun, Jilin, China.', 'China|China|China|China|China|China|China|China|China|China', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz506', '31218360', 'Cell divisions start from replicating the double-stranded DNA, and the DNA replication process needs to be precisely regulated both spatially and temporally. The DNA is replicated starting from the DNA replication origins. A few successful prediction models were generated based on the assumption that the DNA replication origin regions have sequence level features like physicochemical properties significantly different from the other DNA regions.'),
(82912, 2019, 'bio', 'Towards reliable named entity recognition in the biomedical domain', 'Giorgi, John M|Bader, Gary D', 'Department of Computer Science, University of Toronto, 10 Kings College Road, Toronto, Canada.|Department of Computer Science, University of Toronto, 10 Kings College Road, Toronto, Canada.', 'Canada|Canada', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz504', '31218364', 'Automatic biomedical named entity recognition (BioNER) is a key task in biomedical information extraction (IE). For some time, state-of-the-art BioNER has been dominated by machine learning methods, particularly conditional random fields (CRFs), with a recent focus on deep learning. However, recent work has suggested that the high performance of CRFs for BioNER may not generalize to corpora other than the one it was trained on. In our analysis, we find that a popular deep learning-based approach to BioNER, known as bidirectional long short-term memory network-conditional random field (BiLSTM-CRF), is correspondingly poor at generalizing. To address this, we evaluate three modifications of BiLSTM-CRF for BioNER to improve generalization: improved regularization via variational dropout, transfer learning, and multi-task learning.'),
(82913, 2019, 'bio', 'Estimation of population genetic parameters using an EM algorithm and sequence data from experimental evolution populations', 'Kojima, Yasuhiro|Matsumoto, Hirotaka|Kiryu, Hisanori', 'Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, Japan.|Laboratory for Bioinformatics Research, RIKEN Center for Biosystems Dynamics Research, RIKEN, 2-1 Hirosawa, Wako, Saitama, Japan.|Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, Japan.', 'Japan|Japan|Japan', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz498', '31218366', 'Evolve and resequence (E&R) experiments shows promise in capturing real-time evolution at genome-wide scales, enabling the assessment of allele frequency changes SNPs in evolving populations and thus the estimation of population genetic parameters in the Wright-Fisher model (WF) that quantify the selection on SNPs. Currently, these analyses face two key difficulties: the numerous SNPs in E&R data and the frequent unreliability of estimates. Hence, a methodology for efficiently estimating WF parameters is needed to understand the evolutionary processes that shape genomes.'),
(82914, 2019, 'bio', 'Compositional data network analysis via lasso penalized D-trace loss', 'Yuan, Huili|He, Shun|Deng, Minghua', 'School of Mathematical Sciences, Peking University, Beijing, China.|School of Mathematical Sciences, Peking University, Beijing, China.|School of Mathematical Sciences, Peking University, Beijing, China.', 'China|China|China', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz098', '31220226', 'With the development of high-throughput sequencing techniques for 16S-rRNA gene profiling, the analysis of microbial communities is becoming more and more attractive and reliable. Inferring the direct interaction network among microbial communities helps in the identification of mechanisms underlying community structure. However, the analysis of compositional data remains challenging by the relative information conveyed by such data, as well as its high dimensionality.'),
(82915, 2019, 'bio', 'Deep representation learning for domain adaptable classification of infrared spectral imaging data', 'Raulf, Arne P|Butke, Joshua|Küpper, Claus|Großerueschkamp, Frederik|Gerwert, Klaus|Mosig, Axel', 'Department of Biology and Biotechnology, Ruhr-Universität Bochum, Universitätsstr. 150, Bochum, Germany.|Department of Biology and Biotechnology, Ruhr-Universität Bochum, Universitätsstr. 150, Bochum, Germany.|Department of Biology and Biotechnology, Ruhr-Universität Bochum, Universitätsstr. 150, Bochum, Germany.|Department of Biology and Biotechnology, Ruhr-Universität Bochum, Universitätsstr. 150, Bochum, Germany.|Department of Biology and Biotechnology, Ruhr-Universität Bochum, Universitätsstr. 150, Bochum, Germany.|Department of Biology and Biotechnology, Ruhr-Universität Bochum, Universitätsstr. 150, Bochum, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz505', '31225858', 'Applying infrared microscopy in the context of tissue diagnostics heavily relies on computationally preprocessing the infrared pixel spectra that constitute an infrared microscopic image. Existing approaches involve physical models, which are non-linear in nature and lead to classifiers that do not generalize well, e.g. across different types of tissue preparation. Furthermore, existing preprocessing approaches involve iterative procedures that are computationally demanding, so that computation time required for preprocessing does not keep pace with recent progress in infrared microscopes which can capture whole-slide images within minutes.'),
(82916, 2019, 'bio', 'GTShark: Genotype compression in large projects', 'Deorowicz, Sebastian|Danek, Agnieszka', 'Institute of Informatics, Silesian University of Technology, Gliwice, Poland.|Institute of Informatics, Silesian University of Technology, Gliwice, Poland.', 'Poland|Poland', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz508', '31225861', 'Nowadays large sequencing projects handle tens of thousands of individuals. The huge files summarizing the findings definitely require compression. We propose a tool able to compress large collections of genotypes almost 30% better than the best tool to date, i.e., squeezing human genotype to less than 62xe2x80x89KB. Moreover, it can also compress single samples in reference to the existing database achieving comparable results.'),
(82917, 2019, 'bio', 'Sequencing Data Discovery with MetaSeek', 'Hoarfrost, Adrienne|Brown, Nick|Brown, C Titus|Arnosti, Carol', 'Department of Marine Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC.|Independent Researcher, Durham, NC.|Department of Veterinary Medicine, University of California Davis.|Department of Marine Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC.', '-|-|-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz499', '31225863', 'Sequencing data resources have increased exponentially in recent years, as has interest in large-scale meta-analyses of integrated next-generation sequencing datasets. However, curation of integrated datasets that match a users particular research priorities is currently a time-intensive and imprecise task. MetaSeek is a sequencing data discovery tool that enables users to flexibly search and filter on any metadata field to quickly find the sequencing datasets that meet their needs. MetaSeek automatically scrapes metadata from all publicly available datasets in the Sequence Read Archive, cleans and parses messy, user-provided metadata into a structured, standard-compliant database, and predicts missing fields where possible. MetaSeek provides a web-based graphical user interface and interactive visualization dashboard, as well as a programmatic API to rapidly search, filter, visualize, save, share, and download matching sequencing metadata.'),
(82918, 2019, 'bio', 'DepLogo: Visualizing sequence dependencies in R', 'Grau, Jan|Nettling, Martin|Keilwagen, Jens', 'Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.|Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.|Institute for Biosafety in Plant Biotechnology, Julius Kühn-Institut (JKI), Quedlinburg, Germany.', 'Germany|Germany|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz507', '31225867', 'Statistical dependencies are present in a variety of sequence data, but are not discernible from traditional sequence logos. Here, we present the R package DepLogo for visualizing inter-position dependencies in aligned sequence data as dependency logos. Dependency logos make dependency structures, which correspond to regular co-occurrences of symbols at dependent positions, visually perceptible. To this end, sequences are partitioned based on their symbols at highly dependent positions as measured by mutual information, and each partition obtains its own visual representation. We illustrate the utility of the DepLogo package in several use cases generating dependency logos from DNA, RNA, and protein sequences.'),
(82919, 2019, 'bio', 'Deep Learning on Chaos Game Representation for Proteins', 'Löchel, Hannah F|Eger, Dominic|Sperlea, Theodor|Heider, Dominik', 'Department of Mathematics and Computer Science, Philipps-University of Marburg, Hans-Meerwein-Str. 6, Marburg, Germany.|Department of Mathematics and Computer Science, Philipps-University of Marburg, Hans-Meerwein-Str. 6, Marburg, Germany.|Department of Mathematics and Computer Science, Philipps-University of Marburg, Hans-Meerwein-Str. 6, Marburg, Germany.|Department of Mathematics and Computer Science, Philipps-University of Marburg, Hans-Meerwein-Str. 6, Marburg, Germany.', 'Germany|Germany|Germany|Germany', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz493', '31225868', 'Classification of protein sequences is one big task in bioinformatics and has many applications. Different machine learning methods exist and are applied on these problems, such as support vector machines (SVM), random forests (RF), and neural networks (NN). All of these methods have in common that protein sequences have to be made machine-readable and comparable in the first step, for which different encodings exist. These encodings are typically based on physical or chemical properties of the sequence. However, due to the outstanding performance of deep neural networks (DNN) on image recognition, we used frequency matrix chaos game representation (FCGR) for encoding of protein sequences into images. In this study, we compare the performance of SVMs, RFs, and DNNs, trained on FCGR encoded protein sequences. While the original chaos game representation (CGR) has been used mainly for genome sequence encoding and classification, we modified it to work also for protein sequences, resulting in n-flakes representation, an image with several icosagons.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(82920, 2019, 'bio', 'KinomeX: a web application for predicting kinome-wide polypharmacology effect of small molecules', 'Li, Zhaojun|Li, Xutong|Liu, Xiaohong|Fu, Zunyun|Xiong, Zhaoping|Wu, Xiaolong|Tan, Xiaoqin|Zhao, Jihui|Zhong, Feisheng|Wan, Xiaozhe|Luo, Xiaomin|Chen, Kaixian|Jiang, Hualiang|Zheng, Mingyue', 'School of Information Management, Dezhou University, Dezhou, China.|Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, China.|Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, China.|Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, China.|Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, China.|Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, China.|Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, China.|Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, China.|Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, China.|Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, China.|Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, China.|Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, China.|Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, China.|Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, China.', 'China|China|China|China|China|China|China|China|China|China|China|China|China|China', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz519', '31228181', 'The large-scale kinome-wide virtual profiling for small molecules is a daunting task by experimental and traditional in silico drug design approaches. Recent advances in deep learning algorithms have brought about new opportunities in promoting this process.'),
(82921, 2019, 'bio', 'OncodriveCLUSTL: a sequence-based clustering method to identify cancer drivers', 'Arnedo-Pac, Claudia|Mularoni, Loris|Muiños, Ferran|Gonzalez-Perez, Abel|Lopez-Bigas, Nuria', 'Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology (BIST), Barcelona, Spain.|Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology (BIST), Barcelona, Spain.|Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology (BIST), Barcelona, Spain.|Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology (BIST), Barcelona, Spain.|Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology (BIST), Barcelona, Spain.', 'Spain|Spain|Spain|Spain|Spain', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz501', '31228182', 'Identification of the genomic alterations driving tumorigenesis is one of the main goals in oncogenomics research. Given the evolutionary principles of cancer development, computational methods that detect signals of positive selection in the pattern of tumor mutations have been effectively applied in the search for cancer genes. One of these signals is the abnormal clustering of mutations, which has been shown to be complementary to other signals in the detection of driver genes.'),
(82922, 2019, 'bio', 'CaPSSA: Visual evaluation of cancer biomarker genes for patient stratification and survival analysis using mutation and expression data', 'Jang, Yeongjun|Seo, Jihae|Kim, Sun|Lee, Sanghyuk', 'Ewha Research Center for Systems Biology (ERCSB), Ewha Womans University, Seoul, Korea.|Ewha Research Center for Systems Biology (ERCSB), Ewha Womans University, Seoul, Korea.|Interdisciplinary Program in Bioinformatics, College of Natural Science, Seoul Natl University, Seoul, Korea.|Ewha Research Center for Systems Biology (ERCSB), Ewha Womans University, Seoul, Korea.', '-|-|-|-', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz516', '31228188', 'Predictive biomarkers for patient stratification play critical roles in realizing the paradigm of precision medicine. Molecular characteristics such as somatic mutations and expression signatures represent the primary source of putative biomarker genes for patient stratification. However, evaluation of such candidate biomarkers is still cumbersome and requires multistep procedures especially when using massive public omics data. Here, we present an interactive web application that divides patients from large cohorts (e.g. TCGA) dynamically into two groups according to the mutation, copy number variation, or gene expression of query genes. It further supports users to examine the prognostic value of resulting patient groups based on survival analysis and their association with the clinical features as well as the previously annotated molecular subtypes, facilitated with a rich and interactive visualization. Importantly, we also support custom omics data with clinical information.'),
(82923, 2019, 'bio', 'Genetic cooperativity in multi-layer networks implicates cell survival and senescence in the striatum of Huntingtons disease mice synchronous to symptoms', 'Bigan, Erwan|Sasidharan Nair, Satish|Lejeune, François-Xavier|Fragnaud, Hélissande|Parmentier, Frédéric|Mégret, Lucile|Verny, Marc|Aaronson, Jeff|Rosinski, Jim|Neri, Christian', 'Sorbonnes Université, Centre National de la Recherche Scientifique, Research Unit Biology of Adaptation and Aging (B2A), Team Compensation in Neurodegenerative Diseases and Aging (Brain-C), F-75252, Paris, France.|Sorbonnes Université, Centre National de la Recherche Scientifique, Research Unit Biology of Adaptation and Aging (B2A), Team Compensation in Neurodegenerative Diseases and Aging (Brain-C), F-75252, Paris, France.|Sorbonnes Université, Centre National de la Recherche Scientifique, Research Unit Biology of Adaptation and Aging (B2A), Team Compensation in Neurodegenerative Diseases and Aging (Brain-C), F-75252, Paris, France.|Sorbonnes Université, Centre National de la Recherche Scientifique, Research Unit Biology of Adaptation and Aging (B2A), Team Compensation in Neurodegenerative Diseases and Aging (Brain-C), F-75252, Paris, France.|Sorbonnes Université, Centre National de la Recherche Scientifique, Research Unit Biology of Adaptation and Aging (B2A), Team Compensation in Neurodegenerative Diseases and Aging (Brain-C), F-75252, Paris, France.|Sorbonnes Université, Centre National de la Recherche Scientifique, Research Unit Biology of Adaptation and Aging (B2A), Team Compensation in Neurodegenerative Diseases and Aging (Brain-C), F-75252, Paris, France.|Sorbonnes Université, Centre National de la Recherche Scientifique, Research Unit Biology of Adaptation and Aging (B2A), Team Compensation in Neurodegenerative Diseases and Aging (Brain-C), F-75252, Paris, France.|CHDI Foundation, Princeton, NJ, USA.|CHDI Foundation, Princeton, NJ, USA.|Sorbonnes Université, Centre National de la Recherche Scientifique, Research Unit Biology of Adaptation and Aging (B2A), Team Compensation in Neurodegenerative Diseases and Aging (Brain-C), F-75252, Paris, France.', 'France|France|France|France|France|France|France|United States|United States|France', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz514', '31228193', 'Huntingtons disease (HD) may evolve through gene deregulation. However, the impact of gene deregulation on the dynamics of genetic cooperativity in HD remains poorly-understood. Here, we built a multi-layer network model of temporal dynamics of genetic cooperativity in the brain of HD knock-in mice (allelic series of Hdh mice). To enhance biological precision and gene prioritization, we integrated three complementary families of source networks, all inferred from the same RNA-seq time series data in Hdh mice, into weighted-edge networks where an edge recapitulates path-length variation across source-networks and age-points.'),
(82924, 2019, 'bio', 'A fully computational and reasonable representation for karyotypes', 'Warrender, Jennifer D|Moorman, Anthony V|Lord, Phillip', 'School of Computing, Newcastle University, Newcastle-upon-Tyne, UK.|Northern Institute for Cancer Research, Newcastle University, Newcastle-upon-Tyne, NE1 7RU, UK.|School of Computing, Newcastle University, Newcastle-upon-Tyne, UK.', 'United Kingdom|United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz440', '31228194', 'The human karyotype has been used as a mechanism for describing and detecting gross abnormalities in the genome for many decades. It is used both for routine diagnostic purposes and for research to further our understanding of the causes of disease.Despite these important applications there has been no rigorous computational representation of the karyotype, rather an informal, string-based representation is used, making it hard to check, organise and search data of this form.In this paper, we describe our use of OWL, the Ontology Web Language, to generate a fully computational representation of the karyotype, the development of this ontology represents a significant advance from the traditional bioinformatics use for tagging and navigation and has necessitated the development of a new ontology development environment called Tawny-OWL.'),
(82925, 2019, 'bio', 'DEUS: an R package for accurate small RNA profiling based on differential expression of unique sequences', 'Jeske, Tim|Huypens, Peter|Stirm, Laura|Höckele, Selina|Wurmser, Christine M|Böhm, Anja|Weigert, Cora|Staiger, Harald|Klein, Christoph|Beckers, Johannes|Hastreiter, Maximilian', 'Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany.|Institute of Experimental Genetics, Helmholtz Zentrum München GmbH, Neuherberg, Germany.|German Center for Diabetes Research (DZD), Neuherberg, Germany.|Institute of Experimental Genetics, Helmholtz Zentrum München GmbH, Neuherberg, Germany.|Chair of Animal Breeding, Technische Universität München, Wissenschaftszentrum Weihenstephan, Freising, Germany.|German Center for Diabetes Research (DZD), Neuherberg, Germany.|German Center for Diabetes Research (DZD), Neuherberg, Germany.|Institute of Experimental Genetics, Helmholtz Zentrum München GmbH, Neuherberg, Germany.|Department of Pediatrics, Dr. von Hauner Childrens Hospital, University Hospital, LMU Munich, München, Germany.|Institute of Experimental Genetics, Helmholtz Zentrum München GmbH, Neuherberg, Germany.|Institute of Computational Biology, Helmholtz Zentrum München GmbH, Neuherberg, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz495', '31228198', 'Despite their fundamental role in various biological processes, the analysis of small RNA sequencing data remains a challenging task. Major obstacles arise when short RNA sequences map to multiple locations in the genome, align to regions that are not annotated or underwent post-transcriptional changes which hamper accurate mapping. In order to tackle these issues, we present a novel profiling strategy that circumvents the need for read mapping to a reference genome by utilizing the actual read sequences to determine expression intensities. After differential expression analysis of individual sequence counts, significant sequences are annotated against user defined feature databases and clustered by sequence similarity. This strategy enables a more comprehensive and concise representation of small RNA populations without any data loss or data distortion.'),
(82926, 2019, 'bio', 'PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations', 'Kamat, Mihir A|Blackshaw, James A|Young, Robin|Surendran, Praveen|Burgess, Stephen|Danesh, John|Butterworth, Adam S|Staley, James R', 'MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.|MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.|MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.|MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.|MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.|MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.|MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.|MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 35, '-', 'Journal Article', '10.1093/bioinformatics/btz469', '31233103', 'PhenoScanner is a curated database of publicly available results from large-scale genetic association studies in humans. This online tool facilitates phenome scans, where genetic variants are cross-referenced for association with many phenotypes of different types. Here we present a major update of PhenoScanner (PhenoScanner V2), including over 150 million genetic variants and more than 65 billion associations (compared to 350 million associations in PhenoScanner V1) with diseases and traits, gene expression, metabolite and protein levels, and epigenetic markers. The query options have been extended to include searches by genes, genomic regions and phenotypes, as well as for genetic variants. All variants are positionally annotated using the Variant Effect Predictor and the phenotypes are mapped to Experimental Factor Ontology terms. Linkage disequilibrium statistics from the 1000 Genomes project can be used to search for phenotype associations with proxy variants.'),
(82927, 2019, 'bio', 'MaNGA: a novel multi-objective multi-niche genetic algorithm for QSAR modelling', 'Serra, Angela|Önlü, Serli|Festa, Paola|Fortino, Vittorio|Greco, Dario', 'Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.|Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.|University of Napoli Federico II, Naples, Italy.|Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland.|Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.', 'Finland|Finland|Italy|Finland|Finland', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz521', '31233136', 'Quantitative structure-activity relationship (QSAR) modelling is currently used in multiple fields to relate structural properties of compounds to their biological activities. This technique is also used for drug design purposes with the aim of predicting parameters that determine drug behaviour. To this end, a sophisticated process, involving various analytical steps concatenated in series, is employed to identify and fine-tune the optimal set of predictors from a large dataset of molecular descriptors. The search of the optimal model requires to optimize multiple objectives at the same time, as the aim is to obtain the minimal set of features that maximizes the goodness of fit and the applicability domain. Hence, a multi-objective optimization strategy, improving multiple parameters in parallel, can be applied.'),
(82928, 2019, 'bio', 'Interactive exploration of heterogeneous biological networks with Biomine Explorer', 'Podpexc4x8dan, Vid|Ramxc5xa1ak, xc5xbdiva|Gruden, Kristina|Toivonen, Hannu|Lavraxc4x8d, Nada', 'Joxc5xbeef Stefan Institute, Ljubljana, Slovenia.|National Institute of Biology, Ljubljana, Slovenia.|National Institute of Biology, Ljubljana, Slovenia.|University of Helsinki, Finland.|Joxc5xbeef Stefan Institute, Ljubljana, Slovenia.', 'Slovenia|Slovenia|Slovenia|Finland|Slovenia', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz509', '31233141', 'Biomine Explorer is a web application that enables interactive exploration of large heterogeneous biological networks constructed from selected publicly available biological knowledge sources. It is built on top of Biomine, a system which integrates cross-references from several biological databases into a large heterogeneous probabilistic network. Biomine Explorer offers user-friendly interfaces for search, visualization, exploration and manipulation as well as public and private storage of discovered subnetworks with permanent links suitable for inclusion into scientific publications. A JSON-based web API for network search queries is also available for advanced users.'),
(82929, 2019, 'bio', 'Increasing the Efficiency and Accuracy of the ABACUS Protein Sequence Design Method', 'Xiong, Peng|Hu, Xiuhong|Huang, Bin|Zhang, Jiahai|Chen, Quan|Liu, Haiyan', 'School of Life Sciences, University of Sciences and Technology of China.|School of Life Sciences, University of Sciences and Technology of China.|School of Life Sciences, University of Sciences and Technology of China.|School of Life Sciences, University of Sciences and Technology of China.|School of Life Sciences, University of Sciences and Technology of China.|School of Life Sciences, University of Sciences and Technology of China.', 'China|China|China|China|China|China', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz515', '31240299', 'The ABACUS (a backbone-based amino acid usage survey) method uses unique statistical energy functions to carry out protein sequence design. Although some of its results have been experimentally verified, its accuracy remains improvable because several important components of the method have not been specifically optimized for sequence design or in contexts of other parts of the method. The computational efficiency also need to be improved to support interactive online applications or the consideration of a large number of alternative backbone structures.'),
(82930, 2019, 'bio', 'Hydra Image Processor: 5-D GPU Image Analysis Library with MATLAB and Python Wrappers', 'Wait, Eric|Winter, Mark|Cohen, Andrew R', 'Electrical and Computer Engineering, Drexel University, Philadelphia, PA, USA.|Electrical and Computer Engineering, Drexel University, Philadelphia, PA, USA.|Electrical and Computer Engineering, Drexel University, Philadelphia, PA, USA.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz523', '31240306', 'Light microscopes can now capture data in five dimensions at very high frame rates producing terabytes of data per experiment. Five dimensional data has three spatial dimensions (x,y,z), multiple channels (xcexbb) and time (t). Current tools are prohibitively time consuming and do not efficiently utilize available hardware. The Hydra Image Processor (HIP) is a new library providing hardware-accelerated image processing accessible from interpreted languages including MATLAB and Python. HIP automatically distributes data/computation across system and video RAM allowing hardware-accelerated processing of arbitrarily large images. HIP also partitions compute tasks optimally across multiple GPUs. HIP includes a new kernel renormalization reducing boundary effects associated with widely-used padding approaches.'),
(82931, 2019, 'bio', 'Pygenprop: a Python library for programmatic exploration and comparison of organism Genome Properties', 'Bergstrand, Lee H|Neufeld, Josh D|Doxey, Andrew C', 'Department of Biology, University of Waterloo, Waterloo, Canada.|Department of Biology, University of Waterloo, Waterloo, Canada.|Department of Biology, University of Waterloo, Waterloo, Canada.', 'Canada|Canada|Canada', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz522', '31240307', 'A critical step in comparative genomics is the identification of differences in the presence/absence of encoded biochemical pathways among organisms. Our library, Pygenprop, facilitates these comparisons using data from the Genome Properties database. Pygenprop is written in Python and, unlike existing libraries, it is compatible with a variety of tools in the Python data science ecosystem, such as Jupyter Notebooks for interactive analyses and scikit-learn for machine learning. Pygenprop assigns YES, NO, or PARTIAL support for each property based on InterProScan annotations of open reading frames from an organisms genome. The library contains classes for representing the Genome Properties database as a whole and methods for detecting differences in property assignments between organisms. As the Genome Properties database grows, we anticipate widespread adoption of Pygenprop for routine genome analyses and integration within third-party bioinformatics software.'),
(82932, 2019, 'bio', 'TCR3d: The T cell receptor structural repertoire database', 'Gowthaman, Ragul|Pierce, Brian G', 'University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD, USA.|University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD, USA.', 'United States|United States', 5, '-', 'Journal Article', '10.1093/bioinformatics/btz517', '31240309', 'T cell receptors (TCRs) are critical molecules of the adaptive immune system, capable of recognizing diverse antigens, including peptides, lipids, and small molecules, and represent a rapidly growing class of therapeutics. Determining the structural and mechanistic basis of TCR targeting of antigens is a major challenge, as each individual has a vast and diverse repertoire of TCRs. Despite shared general recognition modes, diversity in TCR sequence and recognition represents a challenge to predictive modeling and computational techniques being developed to predict antigen specificity and mechanistic basis of TCR targeting. To this end, we have developed the TCR3d database, a resource containing all known TCR structures, with a particular focus on antigen recognition. TCR3d provides key information on antigen binding mode, interface features, loop sequences and germline gene usage. Users can interactively view TCR complex structures, search sequences of interest against known structures and sequences, and download curated datasets of structurally characterized TCR complexes. This database is updated on a weekly basis, and can serve the community as a centralized resource for those studying T cell receptors and their recognition.'),
(82933, 2019, 'bio', 'MiTPeptideDB: a proteogenomic resource for the discovery of novel peptides', 'Guruceaga, Elizabeth|Garin-Muga, Alba|Segura, Victor', 'Bioinformatics Platform, Center for Applied Medical Research, University of Navarra, Pamplona, Spain.|eHealth and Biomedical Applications department, Vicomtech, San Sebastian, Spain.|Bioinformatics Platform, Center for Applied Medical Research, University of Navarra, Pamplona, Spain.', 'Spain|Spain|Spain', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz530', '31243428', 'The principal lines of research in MS/MS based Proteomics have been directed towards the molecular characterization of the proteins including their biological functions and their implications in human diseases. Recent advances in this field have also allowed the first attempts to apply these techniques to the clinical practice. Nowadays, the main progress in Computational Proteomics is based on the integration of genomic, transcriptomic and proteomic experimental data, what is known as Proteogenomics. This methodology is being especially useful for the discovery of new clinical biomarkers, smORFs and microproteins, although their validation is still challenging.'),
(82934, 2019, 'bio', 'Comprehensive study of the exposome and omic data using rexposome Bioconductor packages', 'Hernandez-Ferrer, Carles|Wellenius, Gregory A|Tamayo, Ibon|Basagaña, Xavier|Sunyer, Jordi|Vrijheid, Martine|Gonzalez, Juan R', 'ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain.|Department of Epidemiology, Brown University School of Public Health, Rhode Island, USA.|ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain.|ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain.|ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain.|ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain.|ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain.', 'Spain|United States|Spain|Spain|Spain|Spain|Spain', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz526', '31243429', 'Genomics has dramatically improved our understanding of the molecular origins of certain human diseases. Nonetheless, our health is also influenced by the cumulative impact of exposures experienced across the life course (termed exposome). The study of the high-dimensional exposome offers a new paradigm for investigating environmental contributions to disease etiology. However, there is a lack of bioinformatics tools for managing, visualizing and analyzing the exposome. The analysis data should include both association with health outcomes and integration with omic layers. We provide a generic framework called rexposome project, developed in the R/Bioconductor architecture that includes object-oriented classes and methods to leverage high-dimensional exposome data in disease association studies including its integration with a variety of high-throughput data types. The usefulness of the package is illustrated by analyzing a real dataset including exposome data, three health outcomes realted to respiratory diseases and its integration with the transcriptome and methylome.'),
(82935, 2019, 'bio', 'HUNER: Improving Biomedical NER with Pretraining', 'Weber, Leon|Mxc3xbcnchmeyer, Jannes|Rocktxc3xa4schel, Tim|Habibi, Maryam|Leser, Ulf', 'Computer Science Department, Humboldt-Universitxc3xa4t zu Berlin, Berlin, Germany.|Computer Science Department, Humboldt-Universitxc3xa4t zu Berlin, Berlin, Germany.|University College London, Department of Computer Science, Gower Street, London, UK.|Computer Science Department, Humboldt-Universitxc3xa4t zu Berlin, Berlin, Germany.|Computer Science Department, Humboldt-Universitxc3xa4t zu Berlin, Berlin, Germany.', 'Germany|Germany|United Kingdom|Germany|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz528', '31243432', 'Several recent studies showed that the application of deep neural networks advanced the state-of-the-art in named entity recognition (NER), including biomedical NER. However, the impact on performance and the robustness of improvements crucially depends on the availability of sufficiently large training corpora, which is a problem in the biomedical domain with its often rather small gold standard corpora.'),
(82936, 2019, 'bio', 'Modelling cancer progression using Mutual Hazard Networks', 'Schill, Rudolf|Solbrig, Stefan|Wettig, Tilo|Spang, Rainer', 'Department of Statistical Bioinformatics, Institute of Functional Genomics, University of Regensburg, Regensburg, Germany.|Department of Physics, University of Regensburg, Regensburg, Germany.|Department of Physics, University of Regensburg, Regensburg, Germany.|Department of Statistical Bioinformatics, Institute of Functional Genomics, University of Regensburg, Regensburg, Germany.', 'Germany|Germany|Germany|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz513', '31250881', 'Cancer progresses by accumulating genomic events, such as mutations and copy number alterations, whose chronological order is key to understanding the disease but difficult to observe. Instead, cancer progression models use co-occurence patterns in cross-sectional data to infer epistatic interactions between events and thereby uncover their most likely order of occurence. State-of-the-art progression models, however, are limited by mathematical tractability and only allow events to interact in directed acyclic graphs, to promote but not inhibit subsequent events, or to be mutually exclusive in distinct groups that cannot overlap.'),
(82937, 2019, 'bio', 'Isoform function prediction based on bi-random walks on a heterogeneous network', 'Yu, Guoxian|Wang, Keyao|Domeniconi, Carlotta|Guo, Maozu|Wang, Jun', 'College of Computer and Information Science, Southwest University, Chongqing, China.|College of Computer and Information Science, Southwest University, Chongqing, China.|Department of Computer Science, George Mason University, Fairfax 22030, USA.|School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China.|College of Computer and Information Science, Southwest University, Chongqing, China.', 'China|China|United States|China|China', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz535', '31250882', 'Alternative splicing contributes to the functional diversity of protein species and the proteoforms translated from alternatively spliced isoforms of a gene actually execute the biological functions. Computationally predicting the functions of genes has been studied for decades. However, how to distinguish the functional annotations of isoforms, whose annotations are essential for understanding developmental abnormalities and cancers, is rarely explored. The main bottleneck is that functional annotations of isoforms are generally unavailable and functional genomic databases universally store the functional annotations at the gene level.'),
(82938, 2019, 'bio', 'Sub-dominant principal components inform new vaccine targets for HIV Gag', 'Ahmed, Syed Faraz|Quadeer, Ahmed A|Morales-Jimenez, David|McKay, Matthew R', 'Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.|Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.|Institute of Electronics, Communications and Information Technology, Queens University Belfast, Belfast, United Kingdom.|Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.', 'China|China|United Kingdom|China', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz524', '31250884', 'Patterns of mutational correlations, learnt from patient-derived sequences of HIV proteins, are informative of biochemically-linked networks of interacting sites that may enable viral escape from the host immune system. Accurate identification of these networks is important for rationally designing vaccines which can effectively block immune escape pathways. Previous computational methods have partly identified such networks by examining the principal components (PCs) of the mutational correlation matrix of HIV Gag proteins. However, driven by a conservative approach, these methods analyze the few dominant (strongest) PCs, potentially missing information embedded within the sub-dominant (relatively weaker) ones that may be important for vaccine design.'),
(82939, 2019, 'bio', 'RTNduals: An R/Bioconductor package for analysis of co-regulation and inference of dual regulons', 'Chagas, Vinicius S|Groeneveld, Clarice S|Oliveira, Kelin G|Trefflich, Sheyla|de Almeida, Rodrigo C|Ponder, Bruce A J|Meyer, Kerstin B|Jones, Steven J M|Robertson, A Gordon|Castro, Mauro A A', 'Bioinformatics and Systems Biology Lab, Federal University of Paraná, Curitiba 81520-260, Brazil.|Bioinformatics and Systems Biology Lab, Federal University of Paraná, Curitiba 81520-260, Brazil.|Bioinformatics and Systems Biology Lab, Federal University of Paraná, Curitiba 81520-260, Brazil.|Graduate Program in Bioinformatics, Institute of Biological Sciences, Federal University of Minas Gerais, Belo Horizonte 31270-901, Brazil.|Department of Biomedical Data Sciences, Molecular Epidemiology, Leiden University Medical Center, Leiden 2300 RC, The Netherlands.|Department of Oncology and Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge CB2 ORE, United Kingdom.|Department of Oncology and Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge CB2 ORE, United Kingdom.|Canadas Michael Smith Genome Sciences Center, BC Cancer Agency, Vancouver V5Z 4S6, Canada.|Canadas Michael Smith Genome Sciences Center, BC Cancer Agency, Vancouver V5Z 4S6, Canada.|Bioinformatics and Systems Biology Lab, Federal University of Paraná, Curitiba 81520-260, Brazil.', 'Brazil|Brazil|Brazil|Brazil|Netherlands|United Kingdom|United Kingdom|Canada|Canada|Brazil', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz534', '31250887', 'Transcription factors (TFs) are key regulators of gene expression, and can activate or repress multiple target genes, forming regulatory units, or regulons. Understanding downstream effects of these regulators includes evaluating how TFs cooperate or compete within regulatory networks. Here we present RTNduals, an R/Bioconductor package that implements a general method for analysing pairs of regulons.'),
(82940, 2019, 'bio', 'JUCHMME: A Java Utility for Class Hidden Markov Models and Extensions for biological sequence analysis', 'Tamposis, Ioannis A|Tsirigos, Konstantinos D|Theodoropoulou, Margarita C|Kontou, Panagiota I|Tsaousis, Georgios N|Sarantopoulou, Dimitra|Litou, Zoi I|Bagos, Pantelis G', 'Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece.|Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, Kgs Lyngby, Denmark.|Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece.|Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece.|Genekor Medical SA, Athens, Greece.|Institute of Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia PA, USA.|Section of Cell Biology and Biophysics, Department of Biology, School of Science, National and Kapodistrian University of Athens, Athens, Greece.|Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece.', 'Greece|Denmark|Greece|Greece|Greece|United States|Greece|Greece', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz533', '31250907', 'JUCHMME is an open-source software package designed to fit arbitrary custom Hidden Markov Models (HMMs) with a discrete alphabet of symbols. We incorporate a large collection of standard algorithms for HMMs as well as a number of extensions and evaluate the software on various biological problems. Importantly, the JUCHMME toolkit includes several additional features that allow for easy building and evaluation of custom HMMs, which could be a useful resource for the research community.'),
(82941, 2019, 'bio', 'A helical lock and key model of polyproline II conformation with SH3', 'Meirson, Tomer|Bomze, David|Kahlon, Liron|Gil-Henn, Hava|Samson, Abraham O', 'Drug Discovery Laboratory, The Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel.|Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.|The Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel.|Laboratory of Cell Migration and Invasion, the Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel.|Drug Discovery Laboratory, The Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel.', 'Israel|Israel|Israel|Israel|Israel', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz527', '31250908', 'More than half of the human proteome contains the proline-rich motif, PxxP. This motif has a high propensity for adopting a left-handed polyproline II helix (PPII) and can potentially bind SH3 domains. SH3 domains are generally grouped into two classes, based on whether the PPII binds in a positive or negative orientation. Since the discovery of this structural motif, over six decades ago, a systematic understanding of its binding remains poor and the consensus amino acid sequence that binds SH3 domains is still ill defined.'),
(82942, 2019, 'bio', 'The NEW ESID Online Database Network', 'Scheible, Raphael|Rusch, Stephan|Guzman, David|Mahlaoui, Nizar|Ehl, Stephan|Kindle, Gerhard', 'Institute of Medical Biometry and Statistics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg.|Institute for Immunodeficiency, Center for Chronic Immunodeficiency (CCI), Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg.|Department of Computer Science, University College London, London, UK.|French National Reference Center for Primary Immune Deficiencies (CEREDIH), Necker Enfants Malades University Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France.|Institute for Immunodeficiency, Center for Chronic Immunodeficiency (CCI), Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg.|Institute for Immunodeficiency, Center for Chronic Immunodeficiency (CCI), Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg.', 'United Kingdom|United Kingdom|United Kingdom|France|United Kingdom|United Kingdom', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz525', '31263866', 'Primary Immunodeficiencies (PIDs) belong to the group of rare diseases. The European Society for Immunodeficiencies (ESID) operates an international research database application for continuous long-term documentation of patient data. The system is a web application which runs in a standard browser. Therefore, the system is easy to access from any location. Technically, the system is based on Gails backed by MariaDB with high standard security features to comply with the demands of a modern research platform.'),
(82943, 2019, 'bio', 'Benchmarking Fold Detection by DaliLite v5', 'Holm, Liisa', 'Institute of Biotechnology, Helsinki Institute of Life Sciences.', '-', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz536', '31263867', 'Protein structure comparison plays a fundamental role in understanding the evolutionary relationships between proteins. Here, we release a new version of the DaliLite standalone software. The novelties are hierarchical search of the structure database organized into sequence based clusters, and remote access to our knowledge base of structural neighbors. The detection of fold, superfamily and family level similarities by DaliLite and state-of-the-art competitors was benchmarked against a manually curated structural classification (SCOP).'),
(82944, 2019, 'bio', 'ValTrendsDB: bringing Protein Data Bank validation information closer to the user', 'Horskxc3xbd, Vladimxc3xadr|Bendovxc3xa1, Veronika|Touxc5xa1ek, Dominik|Koxc4x8da, Jaroslav|Svobodovxc3xa1, Radka', 'National Centre for Biomolecular Research, Faculty of Science.|National Centre for Biomolecular Research, Faculty of Science.|National Centre for Biomolecular Research, Faculty of Science.|National Centre for Biomolecular Research, Faculty of Science.|National Centre for Biomolecular Research, Faculty of Science.', '-|-|-|-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz532', '31263870', 'Structures in PDB tend to contain errors. This is a very serious issue for authors that rely on such potentially problematic data. The community of structural biologists develops validation methods as countermeasures, which are also included in the PDB deposition system. But how are these validation efforts influencing the structure quality of subsequently published data? Which quality aspects are improving, and which remain problematic? We developed ValTrendsDB, a database that provides the results of an extensive exploratory analysis of relationships between quality criteria, size, and metadata of biomacromolecules. Key input data are sourced from PDB. The discovered trends are presented via precomputed information-rich plots. ValTrendsDB also supports the visualization of a set of user-defined structures on top of general quality trends. Therefore, ValTrendsDB enables users to see the quality of structures published by selected author, laboratory or journal, discover quality outliers, etc. ValTrendsDB is updated weekly.'),
(82945, 2019, 'bio', 'Simultaneous prediction of multiple outcomes using revised stacking algorithms', 'Xing, Li|Lesperance, Mary|Zhang, Xuekui', 'Department of Mathematics and Statistics, University of Saskatchewan, Saskatoon, SK, Canada.|Department of Mathematics and Statistics, University of Victoria, Victoria, BC, Canada.|Department of Mathematics and Statistics, University of Victoria, Victoria, BC, Canada.', 'Canada|Canada|Canada', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz531', '31263871', 'HIV is difficult to treat because its virus mutates at a high rate and mutated viruses easily develop resistance to existing drugs. If the relationships between mutations and drug resistances can be determined from historical data, patients can be provided personalized treatment according to their own mutation information. The HIV Drug Resistance Database was built to investigate the relationships. Our goal is to build a model using data in this database, which simultaneously predicts the resistance of multiple drugs using mutation information from sequences of viruses for any new patient.'),
(82946, 2019, 'bio', 'Inference of Differential Gene Regulatory Networks Based on Gene Expression and Genetic Perturbation Data', 'Zhou, Xin|Cai, Xiaodong', 'Department of Electrical and Computer Engineering, University of Miami, Florida, USA.|Department of Electrical and Computer Engineering, University of Miami, Florida, USA.', 'United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz529', '31263873', 'Gene regulatory networks (GRNs) of the same organism can be different under different conditions, although the overall network structure may be similar. Understanding the difference in GRNs under different conditions is important to understand condition-specific gene regulation. When gene expression and other relevant data under two different conditions are available, they can be used by an existing network inference algorithm to estimate two GRNs separately, and then to identify the difference between the two GRNs. However, such an approach does not exploit the similarity in two GRNs, and may sacrifice inference accuracy.'),
(82947, 2019, 'bio', 'CytoGPS: A Web-Enabled Karyotype Analysis Tool for Cytogenetics', 'Abrams, Zachary B|Zhang, Lin|Abruzzo, Lynne V|Heerema, Nyla A|Li, Suli|Dillon, Tom|Rodriguez, Ricky|Coombes, Kevin R|Payne, Philip R O', 'Departments of Biomedical Informatics, Wexner Medical Center, The Ohio State University, Columbus, OH USA.|Institute for Informatics, Washington University School of Medicine in St. Louis, St. Louis, MO.|Departments of Pathology, Wexner Medical Center, The Ohio State University, Columbus, OH USA.|Departments of Pathology, Wexner Medical Center, The Ohio State University, Columbus, OH USA.|Departments of Biomedical Informatics, Wexner Medical Center, The Ohio State University, Columbus, OH USA.|Institute for Informatics, Washington University School of Medicine in St. Louis, St. Louis, MO.|Institute for Informatics, Washington University School of Medicine in St. Louis, St. Louis, MO.|Departments of Biomedical Informatics, Wexner Medical Center, The Ohio State University, Columbus, OH USA.|Institute for Informatics, Washington University School of Medicine in St. Louis, St. Louis, MO.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz520', '31263896', 'Karyotype data are the most common form of genetic data that is regularly used clinically. They are collected as part of the standard of care in many diseases, particularly in pediatric and cancer medicine contexts. Karyotypes are represented in a unique text-based format, with a syntax defined by the International System for human Cytogenetic Nomenclature (ISCN). While human-readable, ISCN is not intrinsically machine-readable. This limitation has prevented the full use of complex karyotype data in discovery science use cases. To enhance the utility and value of karyotype data, we developed a tool named CytoGPS. CytoGPS first parses ISCN karyotypes into a machine-readable format. It then converts the ISCN karyotype into a binary Loss-Gain-Fusion (LGF) model, which represents all cytogenetic abnormalities as combinations of loss, gain, or fusion events, in a format that is analyzable using modern computational methods. Such data is then made available for comprehensive downstream analyses that previously were not feasible.'),
(82948, 2019, 'bio', 'circDeep: Deep learning approach for circular RNA classification from other long non-coding RNA', 'Chaabane, Mohamed|Williams, Robert M|Stephens, Austin T|Park, Juw Won', 'Department of Computer Engineering and Computer Science, Louisville, KY, USA.|Department of Computer Engineering and Computer Science, Louisville, KY, USA.|Department of Computer Engineering and Computer Science, Louisville, KY, USA.|Department of Computer Engineering and Computer Science, Louisville, KY, USA.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz537', '31268128', 'Over the past two decades, a circular form of RNA (circular RNA), produced through alternative splicing, has become the focus of scientific studies due to its major role as a microRNA (miRNA) activity modulator and its association with various diseases including cancer. Therefore, the detection of circular RNAs is vital to understanding their biogenesis and purpose. Prediction of circular RNA can be achieved in three steps: distinguishing non-coding RNAs from protein coding gene transcripts, separating short and long non-coding RNAs, and predicting circular RNAs from other long non-coding RNAs (lncRNAs). However, the available tools are less than 80 percent accurate for distinguishing circular RNAs from other lncRNAs due to difficulty of classification. Therefore, the availability of a more accurate and fast machine learning method for the identification of circular RNAs, which considers the specific features of circular RNA, is essential to the development of systematic annotation.'),
(82949, 2019, 'bio', 'Artificial intelligence-based multi-objective optimization protocol for protein structure refinement', 'Wang, Di|Geng, Ling|Zhao, Yu-Jun|Yang, Yang|Huang, Yan|Zhang, Yang|Shen, Hong-Bin', 'Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China.|Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China.|Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China.|Department of Computer Science, Shanghai Jiao Tong University, Shanghai, China.|State Key Laboratory of Infrared Physics Shanghai Institute of Technical Physics Chinese Academy of Sciences, 500 Yutian Road, Shanghai China.|Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China.|Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China.', 'China|China|China|China|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz544', '31274151', 'Protein structure refinement is an important step of protein structure prediction. Existing approaches have generally used a single scoring function combined with Monte Carlo method or Molecular Dynamics method algorithm. The one-dimension optimization of a single energy function may take the structure too far away without a constraint. The basic motivation of our study is to reduce the bias problem caused by minimizing only a single energy function due to the very diversity of different protein structures.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(82950, 2019, 'bio', 'Haplotype assembly of autotetraploid potato using integer linear programing', 'Siragusa, Enrico|Haiminen, Niina|Finkers, Richard|Visser, Richard|Parida, Laxmi', 'Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China.|Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China.|Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China.|Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China.|Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China.', 'China|China|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz511', '31280288', '-'),
(82951, 2019, 'bio', 'VeriNA3d: An R Package for Nucleic Acids Data Mining', 'Gallego, Diego|Darré, Leonardo|Dans, Pablo D|Orozco, Modesto', 'Institute for Research in Biomedicine (IRB Barcelona), the Barcelona Institute of Science and Technology, Spain.|Institute for Research in Biomedicine (IRB Barcelona), the Barcelona Institute of Science and Technology, Spain.|Institute for Research in Biomedicine (IRB Barcelona), the Barcelona Institute of Science and Technology, Spain.|Institute for Research in Biomedicine (IRB Barcelona), the Barcelona Institute of Science and Technology, Spain.', 'Spain|Spain|Spain|Spain', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz553', '31286135', 'veriNA3d is an R package for the analysis of nucleic acids structural data, with an emphasis in complex RNA structures. In addition to single-structure analyses, veriNA3d also implements functions to handle whole datasets of mmCIF/PDB structures that could be retrieved from public/local repositories. Our package aims to fill a gap in the data mining of nucleic acids structures to produce flexible and high throughput analysis of structural databases.'),
(82952, 2019, 'bio', 'Comparison of single and module-based methods for modeling gene regulatory networks', 'Hernaez, Mikel|Blatti, Charles|Gevaert, Olivier', 'Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, IL, USA.|Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, IL, USA.|The Stanford Center of Biomedical Informatics Research (BMIR), Department of Medicine, and Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz549', '31287491', 'Gene regulatory networks describe the regulatory relationships among genes, and developing methods for reverse engineering these networks is an ongoing challenge in computational biology. The majority of the initially proposed methods for gene regulatory network discovery create a network of genes and then mine it in order to uncover previously unknown regulatory processes. More recent approaches have focused on inferring modules of co-regulated genes, linking these modules with regulatory genes and then mining them to discover new molecular biology.'),
(82953, 2019, 'bio', 'Look4TRs: A de-novo tool for detecting simple tandem repeats using self-supervised hidden Markov models', 'Velasco, Alfredo|James, Benjamin T|Wells, Vincent D|Girgis, Hani Z', 'Tandy School of Computer Science, University of Tulsa, 800 South Tucker Drive, Tulsa, OK, USA.|Tandy School of Computer Science, University of Tulsa, 800 South Tucker Drive, Tulsa, OK, USA.|Tandy School of Computer Science, University of Tulsa, 800 South Tucker Drive, Tulsa, OK, USA.|Tandy School of Computer Science, University of Tulsa, 800 South Tucker Drive, Tulsa, OK, USA.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz551', '31287494', 'Simple tandem repeats, microsatellites in particular, have regulatory functions, links to several diseases, and applications in biotechnology. There is an immediate need for an accurate tool for detecting microsatellites in newly sequenced genomes. The current available tools are either sensitive or specific but not both, some tools require adjusting parameters manually.'),
(82954, 2019, 'bio', 'HDX-Viewer: interactive 3D visualization of hydrogen-deuterium exchange data', 'Bouyssié, David|Lesne, Jean|Locard-Paulet, Marie|Albigot, Renaud|Burlet-Schiltz, Odile|Marcoux, Julien', 'Institut de Pharmacologie et de Biologie Structurale, Université de Toulouse, CNRS, UPS, France.|Institut de Pharmacologie et de Biologie Structurale, Université de Toulouse, CNRS, UPS, France.|Institut de Pharmacologie et de Biologie Structurale, Université de Toulouse, CNRS, UPS, France.|Institut de Pharmacologie et de Biologie Structurale, Université de Toulouse, CNRS, UPS, France.|Institut de Pharmacologie et de Biologie Structurale, Université de Toulouse, CNRS, UPS, France.|Institut de Pharmacologie et de Biologie Structurale, Université de Toulouse, CNRS, UPS, France.', 'France|France|France|France|France|France', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz550', '31287496', 'With the advent of fully automated sample preparation robots for Hydrogen-Deuterium eXchange coupled to Mass Spectrometry (HDX-MS), this method has become paramount for ligand binding or epitope mapping screening, both in academic research and biopharmaceutical industries. However, bridging the gap between commercial HDX-MS software (for raw data interpretation) and molecular viewers (to map experiment results onto a 3D structure for biological interpretation) remains laborious and requires simple but sometimes limiting coding skills. We solved this bottleneck by developing HDX-Viewer, an open-source web-based application that facilitates and quickens HDX-MS data analysis. This user-friendly application automatically incorporates HDX-MS data from a custom template or commercial HDX-MS software in PDB files, and uploads them to an online 3D molecular viewer, thereby facilitating their visualization and biological interpretation.'),
(82955, 2019, 'bio', 'MemBlob database and server for identifying transmembrane regions using cryo-EM maps', 'Farkas, Bianka|Csizmadia, Georgina|Katona, Eszter|Tusnxc3xa1dy, Gxc3xa1bor E|Hegedxc5xb1s, Tamxc3xa1s', 'Department of Biophysics and Radiation Biology, Semmelweis University, Budapest, HU.|Department of Biophysics and Radiation Biology, Semmelweis University, Budapest, HU.|Department of Biophysics and Radiation Biology, Semmelweis University, Budapest, HU.|Momentum Membrane Protein Bioinformatics Research Group, Institute of Enzymology, RCNS, Hungarian Academy of Sciences.|Department of Biophysics and Radiation Biology, Semmelweis University, Budapest, HU.', '-|-|-|-|-', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz539', '31290936', 'The identification of transmembrane helices in transmembrane proteins is crucial, not only to understand their mechanism of action, but also to develop new therapies. While experimental data on the boundaries of membrane-embedded regions is sparse, this information is present in cryo-electron microscopy (cryo-EM) density maps and it has not been utilized yet for determining membrane regions. We developed a computational pipeline, where the inputs of a cryo-EM map, the corresponding atomistic structure, and the potential bilayer orientation determined by TMDET algorithm of a given protein result in an output defining the residues assigned to the bulk water phase, lipid interface, and the lipid hydrophobic core. Based on this method, we built a database involving published cryo-EM protein structures and a server to be able to compute this data for newly obtained structures.'),
(82956, 2019, 'bio', 'Cooler: scalable storage for Hi-C data and other genomically-labeled arrays', 'Abdennur, Nezar|Mirny, Leonid', 'Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States.|Department of Physics, Massachusetts Institute of Technology, Cambridge,MA, United States.', 'United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz540', '31290943', 'Most existing coverage-based (epi)genomic datasets are one-dimensional, but newer technologies probing interactions (physical, genetic, etc.) produce quantitative maps with two-dimensional genomic coordinate systems. Storage and computational costs mount sharply with data resolution when such maps are stored in dense form. Hence, there is a pressing need to develop data storage strategies that handle the full range of useful resolutions in multidimensional genomic datasets by taking advantage of their sparse nature, while supporting efficient compression and providing fast random access to facilitate development of scalable algorithms for data analysis.'),
(82957, 2019, 'bio', 'Noise-Cancelling Repeat Finder: Uncovering tandem repeats in error-prone long-read sequencing data', 'Harris, Robert S|Cechova, Monika|Makova, Kateryna D', 'Department of Biology, The Pennsylvania State University, University Park, PA, USA.|Department of Biology, The Pennsylvania State University, University Park, PA, USA.|Department of Biology, The Pennsylvania State University, University Park, PA, USA.', 'United States|United States|United States', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz484', '31290946', 'Tandem DNA repeats can be sequenced with long-read technologies, but cannot be accurately deciphered due to the lack of computational tools taking high error rates of these technologies into account. Here we introduce Noise-Cancelling Repeat Finder (NCRF) to uncover putative tandem repeats of specified motifs in noisy long reads produced by Pacific Biosciences and Oxford Nanopore sequencers. Using simulations, we validated the use of NCRF to locate tandem repeats with motifs of various lengths and demonstrated its superior performance as compared to two alternative tools. Using real human whole-genome sequencing data, NCRF identified long arrays of the (AATGG)n repeat involved in heat shock stress response.'),
(82958, 2019, 'bio', 'MCMCtreeR: functions to prepare MCMCtree analyses and visualise posterior ages on trees', 'Puttick, Mark N', 'Milner Centre for Evolution, Department of Biology and Biochemistry, University of Bath, UK.', 'United Kingdom', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz554', '31292621', 'The fossil record is incomplete, so molecular divergence time analysis is a crucial tool in estimating evolutionary timescales. The software MCMCtree contained in the PAML software provides Bayesian methods to estimate divergence times of genomic-sized sequences. Here, I present MCMCtreeR, a flexible R package to prepare time priors for MCMCtree analysis and plot time-scaled phylogenies. The package provides functions to refine parameters and visualise time-calibrated node prior distributions so that these priors accurately reflect confidence in known, usually fossil, time information. After the parameters have been chosen, the package produces output files ready for MCMCtree analysis. Following analysis, the package has tools to compare prior and posterior calibrated node age distributions and produce plots of the time-scaled phylogenies. The plotting functions allow for the inclusion of age uncertainty on time-scaled phylogenies, including the display of full posterior distributions on nodes. Options also allow for the inclusion of the geological timescale, and these plotting functions are applicable with posterior age estimates from any Bayesian divergence-time estimation software.'),
(82959, 2019, 'bio', 'QuanTest2: Benchmarking Multiple Sequence Alignments using Secondary Structure Prediction', 'Sievers, Fabian|Higgins, Desmond G', 'School of Medicine and Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin 4, Ireland.|School of Medicine and Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin 4, Ireland.', 'Ireland|Ireland', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz552', '31292629', 'Secondary Structure Prediction Accuracy (SSPA) in the QuanTest benchmark can be used to measure accuracy of a Multiple Sequence Alignment (MSA). SSPA correlates well with the Sum-of-Pairs (SP) score, if results are averaged over many alignments but not on an alignment by alignment basis. This is due to a sub-optimal selection of reference and non-reference sequences in QuanTest.'),
(82960, 2019, 'bio', 'MEpurity: estimating tumor purity using DNA methylation data', 'Liu, Bowen|Yang, Xiaofei|Wang, Tingjie|Lin, Jiadong|Kang, Yongyong|Jia, Peng|Ye, Kai', 'MOE Key Lab for Intelligent Networks & Networks Security, School of Electronics and Information Engineering, Xian Jiaotong University, Xian, China.|The Department of Computer Science and Technology, School of Electronic and Information Engineering, Xian Jiaotong University, Xian, China.|MOE Key Lab for Intelligent Networks & Networks Security, School of Electronics and Information Engineering, Xian Jiaotong University, Xian, China.|MOE Key Lab for Intelligent Networks & Networks Security, School of Electronics and Information Engineering, Xian Jiaotong University, Xian, China.|MOE Key Lab for Intelligent Networks & Networks Security, School of Electronics and Information Engineering, Xian Jiaotong University, Xian, China.|MOE Key Lab for Intelligent Networks & Networks Security, School of Electronics and Information Engineering, Xian Jiaotong University, Xian, China.|MOE Key Lab for Intelligent Networks & Networks Security, School of Electronics and Information Engineering, Xian Jiaotong University, Xian, China.', 'China|China|China|China|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz555', '31297508', 'Tumor purity is a fundamental property of each cancer sample and affects downstream investigations. Current tumor purity estimation methods either require matched normal sample or report moderately high tumor purity even on normal samples. It is critical to develop a novel computational approach to estimate tumor purity with sufficient precision based on tumor-only sample.'),
(82961, 2019, 'bio', 'Identification of Expression Signatures For Non-Small-Cell Lung Carcinoma Subtype Classification', 'Su, Ran|Zhang, Jiahang|Liu, Xiaofeng|Wei, Leyi', 'School of Computer Software, College of Intelligence and Computing, Tianjin University, Tianjin, China.|School of Computer Software, College of Intelligence and Computing, Tianjin University, Tianjin, China.|Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.|School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China.', 'China|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz557', '31297509', 'Non-Small-Cell Lung Carcinoma (NSCLC) mainly consists of two subtypes: lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD). It has been reported that the genetic and epigenetic profiles vary strikingly between LUAD and LUSC in the process of tumorigenesis and development. Efficient and precise treatment can be made if subtypes can be identified correctly. Identification of discriminative expression signatures has been explored recently to aid the classification of NSCLC subtypes.'),
(82962, 2019, 'bio', 'MM-6mAPred: Identifying DNA N6-methyladenine sites based on Markov Model', 'Pian, Cong|Zhang, Guangle|Li, Fei|Fan, Xiaodan', 'Department of Statistics, The Chinese University of Hong Kong, Shatin, New Territory, Hong Kong SAR, China.|Binjiang College, Nanjing University of Information Science and Technology, Jiangsu, China.|Ministry of Agriculture Key Lab of Agricultural Entomology, Institute of Insect Sciences, College of Agriculture and Biotechnology, Zhejiang University, 866 Yuhangtang Road, Hangzhou, China.|Department of Statistics, The Chinese University of Hong Kong, Shatin, New Territory, Hong Kong SAR, China.', 'China|China|China|China', 8, '-', 'Journal Article', '10.1093/bioinformatics/btz556', '31297537', 'Recent studies have shown that DNA N6-methyladenine (6mA) plays an important role in epigenetic modification of eukaryotic organisms. It has been found that 6mA is closely related to embryonic development, stress response, and so on. Developing a new algorithm to quickly and accurately identify 6mA sites in genomes is important for explore their biological functions.'),
(82963, 2019, 'bio', 'DeePaC: Predicting pathogenic potential of novel DNA with reverse-complement neural networks', 'Bartoszewicz, Jakub M|Seidel, Anja|Rentzsch, Robert|Renard, Bernhard Y', 'Bioinformatics Unit (MF1), Department of Methodology and Research Infrastructure, Robert Koch Institute, 13353 Berlin, Germany.|Bioinformatics Unit (MF1), Department of Methodology and Research Infrastructure, Robert Koch Institute, 13353 Berlin, Germany.|Bioinformatics Unit (MF1), Department of Methodology and Research Infrastructure, Robert Koch Institute, 13353 Berlin, Germany.|Bioinformatics Unit (MF1), Department of Methodology and Research Infrastructure, Robert Koch Institute, 13353 Berlin, Germany.', 'Germany|Germany|Germany|Germany', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz541', '31298694', 'We expect novel pathogens to arise due to their fast-paced evolution, and new species to be discovered thanks to advances in DNA sequencing and metagenomics. Moreover, recent developments in synthetic biology raise concerns that some strains of bacteria could be modified for malicious purposes. Traditional approaches to open-view pathogen detection depend on databases of known organisms, which limits their performance on unknown, unrecognized, and unmapped sequences. In contrast, machine learning methods can infer pathogenic phenotypes from single NGS reads, even though the biological context is unavailable.'),
(82964, 2019, 'bio', 'Identifying Emerging Phenomenon in Long Temporal Phenotyping Experiments', 'Peng, Jiajie|Lu, Junya|Hoh, Donghee|Dina, Ayesha S|Shang, Xuequn|Kramer, David M|Chen, Jin', 'School of Computer Science, Northwestern Polytechnical University, Xian, China.|School of Computer Science, Northwestern Polytechnical University, Xian, China.|Department of Energy Plant Research Lab, Michigan State University, East Lansing, MI, USA.|Department of Computer Science, University of Kentucky, Lexington, KY, USA.|School of Computer Science, Northwestern Polytechnical University, Xian, China.|Department of Energy Plant Research Lab, Michigan State University, East Lansing, MI, USA.|Institute for Biomedical Informatics, University of Kentucky, Lexington, KY, USA.', 'China|China|United States|United States|China|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz559', '31304958', 'The rapid improvement of phenotyping capability, accuracy, and throughput have greatly increased the volume and diversity of phenomics data. A remaining challenge is an efficient way to identify phenotypic patterns to improve our understanding of the quantitative variation of complex phenotypes, and to attribute gene functions. To address this challenge, we developed a new algorithm to identify emerging phenomena from large-scale temporal plant phenotyping experiments. An emerging phenomenon is defined as a group of genotypes who exhibit a coherent phenotype pattern during a relatively short time. Emerging phenomena are highly transient and diverse, and are dependent in complex ways on both environmental conditions and development. Identifying emerging phenomena may help biologists to examine potential relationships among phenotypes and genotypes in a genetically diverse population and to associate such relationships with the change of environments or development.'),
(82965, 2019, 'bio', 'DDT - Drug Discovery Tool: a fast and intuitive graphics user interface for Docking and Molecular Dynamics analysis', 'Aureli, Simone|Di Marino, Daniele|Raniolo, Stefano|Limongelli, Vittorio', 'Università della Svizzera italiana (USI), Faculty of Biomedical Sciences, Institute of Computational Science - Center for Computational Medicine in Cardiology, via G. Buffi 13, CH-Lugano, Switzerland.|Università della Svizzera italiana (USI), Faculty of Biomedical Sciences, Institute of Computational Science - Center for Computational Medicine in Cardiology, via G. Buffi 13, CH-Lugano, Switzerland.|Università della Svizzera italiana (USI), Faculty of Biomedical Sciences, Institute of Computational Science - Center for Computational Medicine in Cardiology, via G. Buffi 13, CH-Lugano, Switzerland.|Università della Svizzera italiana (USI), Faculty of Biomedical Sciences, Institute of Computational Science - Center for Computational Medicine in Cardiology, via G. Buffi 13, CH-Lugano, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz543', '31304963', 'The ligand/protein binding interaction is typically investigated by docking and molecular dynamics (MD) simulations. In particular, docking-based virtual screening (VS) is used to select the best ligands from database of thousands of compounds, while MD calculations assess the energy stability of the ligand/protein binding complexes. Considering the broad use of these techniques, it is of great demand to have one single software that allows a combined and fast analysis of VS and MD results. With this in mind, we have developed the Drug Discovery Tool (DDT) that is an intuitive graphics user interface able to provide structural data and physico-chemical information on the ligand/protein interaction.'),
(82966, 2019, 'bio', 'The Influence of Heterogeneous Codon Frequencies along Sequences on the Estimation of Molecular Adaptation', 'Del Amparo, Roberto|Vicens, Alberto|Arenas, Miguel', 'Department of Biochemistry, Genetics and Immunology, University of Vigo, Vigo, Spain.|Department of Biochemistry, Genetics and Immunology, University of Vigo, Vigo, Spain.|Department of Biochemistry, Genetics and Immunology, University of Vigo, Vigo, Spain.', 'Spain|Spain|Spain', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz558', '31304972', 'The nonsynonymous/synonymous substitution rate ratio (dN/dS) is a commonly used parameter to quantify molecular adaptation in protein-coding data. It is known that the estimation of dN/dS can be biased if some evolutionary processes are ignored. In this concern, common ML methods to estimate dN/dS assume invariable codon frequencies among sites, despite this characteristic is rare in nature, and it could bias the estimation of this parameter.'),
(82967, 2019, 'bio', 'VariantQC: a visual quality control report for variant evaluation', 'Yan, Melissa Y|Ferguson, Betsy|Bimber, Benjamin N', 'Division of Genetics, Oregon National Primate Research Center, Oregon Health & Science University, Beaverton, OR, USA.|Division of Genetics, Oregon National Primate Research Center, Oregon Health & Science University, Beaverton, OR, USA.|Division of Genetics, Oregon National Primate Research Center, Oregon Health & Science University, Beaverton, OR, USA.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz560', '31309221', 'Large scale genomic studies produce millions of sequence variants, generating datasets far too massive for manual inspection. To ensure variant and genotype data are consistent and accurate, it is necessary to evaluate variants prior to downstream analysis using quality control (QC) reports. Variant call format (VCF) files are the standard format for representing variant data, however, generating summary statistics from these files is not always straightforward. While tools to summarize variant data exist, they generally produce simple text file tables, which still require additional processing and interpretation. VariantQC fills this gap as a user friendly, interactive visual QC report that generates and concisely summarizes statistics from VCF files. The report aggregates and summarizes variants by dataset, chromosome, sample, and filter type. The VariantQC report is useful for high-level dataset summary, quality control, and helps flag outliers. Furthermore, VariantQC operates on VCF files, so it can be easily integrated into many existing variant pipelines.'),
(82968, 2019, 'bio', 'Integrating distal and proximal information to predict gene expression via a densely connected convolutional neural network', 'Zeng, Wanwen|Wang, Yong|Jiang, Rui', 'MOE Key Laboratory of Bioinformatics, Beijing National Research Center for Information Science and Technology, Department of Automation, Tsinghua University, Beijing, China.|CEMS, NCMIS, MDIS, Academy of Mathematics and Systems Science, National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences, Beijing, China.|MOE Key Laboratory of Bioinformatics, Beijing National Research Center for Information Science and Technology, Department of Automation, Tsinghua University, Beijing, China.', 'China|China|China', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz562', '31318408', 'Interactions among cis-regulatory elements such as enhancers and promoters are main driving forces shaping context-specific chromatin structure and gene expression. Although there have been computational methods for predicting gene expression from genomic and epigenomic information, most of them neglect long-range enhancer-promoter interactions, due to the difficulty in precisely linking regulatory enhancers to target genes. Recently, HiChIP, a novel high-throughput experimental approach, has generated comprehensive data on high-resolution interactions between promoters and distal enhancers. Moreover, plenty of studies suggest that deep learning achieves state-of-the-art performance in epigenomic signal prediction, and thus promoting the understanding of regulatory elements. In consideration of these two factors, we integrate proximal promoter sequences and HiChIP distal enhancer-promoter interactions to accurately predict gene expression.'),
(82969, 2019, 'bio', 'PTM-Logo: a program for generation of sequence logos based on position-specific background amino-acid probabilities', 'Saethang, Thammakorn|Hodge, Kenneth|Yang, Chin-Rang|Zhao, Yue|Kimkong, Ingorn|Knepper, Mark A|Pisitkun, Trairak', 'Center of Excellence in Systems Biology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.|Center of Excellence in Systems Biology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.|Epithelial Systems Biology Laboratory, NHLBI, National Institutes of Health, Bethesda, MD, USA.|Epithelial Systems Biology Laboratory, NHLBI, National Institutes of Health, Bethesda, MD, USA.|Department of Microbiology, Faculty of Science, Kasetsart University, Bangkok, Thailand.|Epithelial Systems Biology Laboratory, NHLBI, National Institutes of Health, Bethesda, MD, USA.|Center of Excellence in Systems Biology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.', 'Thailand|Thailand|United States|United States|Thailand|United States|Thailand', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz568', '31318409', 'Identification of the amino-acid motifs in proteins that are targeted for post-translational modifications (PTMs) is of great importance in understanding regulatory networks. Information about targeted motifs can be derived from mass spectrometry data that identify peptides containing specific PTMs such as phosphorylation, ubiquitylation, and acetylation. Comparison of input data against a standardized background set allows identification of over- and under-represented amino acids surrounding the modified site. Conventionally, calculation of targeted motifs assumes a random background distribution of amino acids surrounding the modified position. However, we show that probabilities of amino acids depend on 1) the type of the modification and 2) their positions relative to the modified site. Thus, software that identifies such over- and under-represented amino acids should make appropriate adjustments for these effects. Here we present a new program, PTM-Logo, that generates representations of these amino acid preferences (logos) based on position-specific amino-acid probability backgrounds calculated either from user-input data or curated databases.'),
(82970, 2019, 'bio', 'PgpRules: a decision tree based prediction server for P-glycoprotein substrates and inhibitors', 'Wang, Pei-Hua|Tu, Yi-Shu|Tseng, Yufeng J', 'Center of Excellence in Systems Biology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.|Center of Excellence in Systems Biology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.|Center of Excellence in Systems Biology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.', 'Thailand|Thailand|Thailand', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz546', '31324914', '-'),
(82971, 2019, 'bio', 'QPARSE: searching for long-looped or multimeric G-quadruplexes potentially distinctive and druggable', 'Berselli, Michele|Lavezzo, Enrico|Toppo, Stefano', 'Department of Molecular Medicine, University of Padova, I-35131 Padova, Italy.|Department of Molecular Medicine, University of Padova, I-35131 Padova, Italy.|Department of Molecular Medicine, University of Padova, I-35131 Padova, Italy.', 'Italy|Italy|Italy', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz569', '31328780', 'G-quadruplexes (G4s) are non-canonical nucleic acid conformations that are widespread in all kingdoms of life and are emerging as important regulators both in RNA and DNA. Recently, two new higher-order architectures have been reported: adjacent interacting G4s, and G4s with stable long loops forming stem-loop structures. As there are no specialized tools to identify these conformations, we developed QPARSE.'),
(82972, 2019, 'bio', 'diSTruct v10: Generating Biomolecular Structures from Distance Constraints', 'Taubert, Oskar|Reinartz, Ines|Meyerhenke, Henning|Schug, Alexander', 'Steinbuch Centre for Computing, Karlsruhe Institute of Technology (KIT), Eggenstein-Leopoldshafen, 76344, Germany.|Steinbuch Centre for Computing, Karlsruhe Institute of Technology (KIT), Eggenstein-Leopoldshafen, 76344, Germany.|Department of Computer Science, Humbold-Universitxc3xa4t zu Berlin, 10099 Germany.|John von Neumann Institute for Computing, Jxc3xbclich Supercomputing Centre, Forschungszentrum Jxc3xbclich, 52425 Jxc3xbclich.', 'Germany|Germany|Germany|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz578', '31329240', 'The distance geometry problem is often encountered in molecular biology and the life sciences at large, as a host of experimental methods produce ambiguous and noisy distance data. In this note, we present diSTruct, an adaptation of the generic MaxEnt-Stress graph drawing algorithm to the domain of biological macromolecules. diSTruct is fast, provides reliable structural models even from incomplete or noisy distance data and integrates access to graph analysis tools.'),
(82973, 2019, 'bio', 'How sequence alignment scores correspond to probability models', 'Frith, Martin C', 'Artificial Intelligence Research Center, AIST, Tokyo, Japan.', 'Japan', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz576', '31329241', 'Sequence alignment remains fundamental in bioinformatics. Pair-wise alignment is traditionally based on ad hoc scores for substitutions, insertions, and deletions, but can also be based on probability models (pair hidden Markov models: PHMMs). PHMMs enable us to: fit the parameters to each kind of data, calculate the reliability of alignment parts, and measure sequence similarity integrated over possible alignments.'),
(82974, 2019, 'bio', 'Genetic association testing using the GENESIS R/Bioconductor package', 'Gogarten, Stephanie M|Sofer, Tamar|Chen, Han|Yu, Chaoyu|Brody, Jennifer A|Thornton, Timothy A|Rice, Kenneth M|Conomos, Matthew P', 'Department of Biostatistics, University of Washington, Seattle, WA, USA.|Department of Medicine, Harvard Medical School, Boston, MA, USA.|Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.|Department of Biostatistics, University of Washington, Seattle, WA, USA.|Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA.|Department of Biostatistics, University of Washington, Seattle, WA, USA.|Department of Biostatistics, University of Washington, Seattle, WA, USA.|Department of Biostatistics, University of Washington, Seattle, WA, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 11, '-', 'Journal Article', '10.1093/bioinformatics/btz567', '31329242', 'The Genomic Data Storage (GDS) format provides efficient storage and retrieval of genotypes measured by microarrays and sequencing. We developed GENESIS to perform various single- and aggregate-variant association tests using genotype data stored in GDS format. GENESIS implements highly flexible mixed models, allowing for different link functions, multiple variance components, and phenotypic heteroskedasticity. GENESIS integrates cohesively with other R/Bioconductor packages to build a complete genomic analysis workflow entirely within the R environment.'),
(82975, 2019, 'bio', 'Estimating and testing the microbial causal mediation effect with high-dimensional and compositional microbiome data', 'Wang, Chan|Hu, Jiyuan|Blaser, Martin J|Li, Huilin', 'Division of Biostatistics, Department of Population Health, New York University School of Medicine, New York, NY, U.S.A.|Division of Biostatistics, Department of Population Health, New York University School of Medicine, New York, NY, U.S.A.|Center for Advanced Biotechnology and Medicine, Rutgers University, Piscataway, NJ, U.S.A.|Division of Biostatistics, Department of Population Health, New York University School of Medicine, New York, NY, U.S.A.', '-|-|-|-', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz565', '31329243', 'Recent microbiome association studies have revealed important associations between microbiome and disease/health status. Such findings encourage scientists to dive deeper to uncover the causal role of microbiome in the underlying biological mechanism, and have led to applying statistical models to quantify causal microbiome effects and to identify the specific microbial agents. However, there are no existing causal mediation methods specifically designed to handle high dimensional and compositional microbiome data.'),
(82976, 2019, 'bio', 'GSMA: an approach to identify robust global and test Gene Signatures using Meta-Analysis', 'Shafi, Adib|Nguyen, Tin|Peyvandipour, Azam|Draghici, Sorin', 'Department of Computer Science, Wayne State University, Detroit, MI.|Department of Computer Science and Engineering, University of Nevada, Reno, NV.|Department of Computer Science, Wayne State University, Detroit, MI.|Department of Computer Science, Wayne State University, Detroit, MI.', '-|-|-|-', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz561', '31329248', 'Recent advances in biomedical research have made massive amount of transcriptomic data available in public repositories from different sources. Due to the heterogeneity present in the individual experiments, identifying reproducible biomarkers for a given disease from multiple independent studies has become a major challenge. The widely used meta-analysis approaches, such as Fishers method, Stouffers method, minP and maxP, have at least two major limitations: i) they are sensitive to outliers, and ii) they perform only one statistical test for each individual study, and hence do not fully utilize the potential sample size to gain statistical power.'),
(82977, 2019, 'bio', 'sAOP: Linking chemical stressors to Adverse Outcomes Pathway Networks', 'Aguayo-Orozco, Alejandro|Audouze, Karine|Siggaard, Troels|Barouki, Robert|Brunak, Søren|Taboureau, Olivier', 'Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.|Université de Paris, INSERM UMR-S 1124, Paris, France.|Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.|Université de Paris, INSERM UMR-S 1124, Paris, France.|Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.|Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.', 'Denmark|France|Denmark|France|Denmark|Denmark', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz570', '31329252', 'Adverse outcome pathway (AOP) is a toxicological concept proposed to provide a mechanistic representation of biological perturbation over different layers of biological organization. Although AOPs are by definition chemical-agnostic, many chemical stressors can putatively interfere with one or several AOPs and such information would be relevant for regulatory decision-making.'),
(82978, 2019, 'bio', 'PHANOTATE: a novel approach to gene identification in phage genomes', 'McNair, Katelyn|Zhou, Carol|Dinsdale, Elizabeth A|Souza, Brian|Edwards, Robert A', 'Computational Sciences Research Center, San Diego State University, San Diego, CA 92182, USA.|Lawrence Livermore National Laboratory, Livermore, CA 94550, USA.|Department of Biology.|Lawrence Livermore National Laboratory, Livermore, CA 94550, USA.|Computational Sciences Research Center, San Diego State University, San Diego, CA 92182, USA.', 'United States|United States|United States|United States|United States', 15, '-', 'Journal Article', '10.1093/bioinformatics/btz265', '31329826', 'Currently there are no tools specifically designed for annotating genes in phages. Several tools are available that have been adapted to run on phage genomes, but due to their underlying design, they are unable to capture the full complexity of phage genomes. Phages have adapted their genomes to be extremely compact, having adjacent genes that overlap and genes completely inside of other longer genes. This non-delineated genome structure makes it difficult for gene prediction using the currently available gene annotators. Here we present PHANOTATE, a novel method for gene calling specifically designed for phage genomes. Although the compact nature of genes in phages is a problem for current gene annotators, we exploit this property by treating a phage genome as a network of paths: where open reading frames are favorable, and overlaps and gaps are less favorable, but still possible. We represent this network of connections as a weighted graph, and use dynamic programing to find the optimal path.'),
(82979, 2019, 'bio', 'SquiggleKit: A toolkit for manipulating nanopore signal data', 'Ferguson, James M|Smith, Martin A', 'Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Darlinghurst, NSW Australia.|Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Darlinghurst, NSW Australia.', 'Australia|Australia', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz586', '31332428', 'The management of raw nanopore sequencing data poses a challenge that must be overcome to facilitate the creation of new bioinformatics algorithms predicated on signal analysis. SquiggleKit is a toolkit for manipulating and interrogating nanopore data that simplifies file handling, data extraction, visualisation, and signal processing.'),
(82980, 2019, 'bio', 'Efficient parameterization of large-scale dynamic models based on relative measurements', 'Schmiester, Leonard|Schälte, Yannik|Fröhlich, Fabian|Hasenauer, Jan|Weindl, Daniel', 'Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.|Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.|Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.|Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.|Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.', 'Germany|Germany|Germany|Germany|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz581', '31347657', 'Mechanistic models of biochemical reaction networks facilitate the quantitative understanding of biological processes and the integration of heterogeneous datasets. However, some biological processes require the consideration of comprehensive reaction networks and therefore large-scale models. Parameter estimation for such models poses great challenges, in particular when the data are on a relative scale.'),
(82981, 2019, 'bio', 'DR-SIP: Protocols for Higher Order Structure Modeling with Distance Restraints- and Cyclic Symmetry-Imposed Packing', 'Chan, Justin|Zou, Jinhao|Ortiz, Christopher Llynard|Chien, Chi-Hong Chang|Pan, Rong-Long|Yang, Lee-Wei', 'Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, Taiwan.|Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, Taiwan.|Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, Taiwan.|Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, Taiwan.|Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, Taiwan.|Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, Taiwan.', '-|-|-|-|-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz579', '31347658', 'Quaternary structure determination for transmembrane/soluble proteins requires a reliable computational protocol that leverages observed distance restraints and/or cyclic symmetry (Cn symmetry) found in most homo-oligomeric transmembrane proteins.'),
(82982, 2019, 'bio', 'MetaBMF: A Scalable Binning Algorithm for Large-scale Reference-free Metagenomic Studies', 'Ma, Terry|Xiao, Di|Xing, Xin', 'Department of Statistics, University of Georgia, Athens, USA.|Department of Statistics, University of Georgia, Athens, USA.|Department of Statistics, Harvard University, Cambridge, USA.', 'Georgia|Georgia|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz577', '31347687', 'Metagenomics studies microbial genomes in an ecosystem such as the gastrointestinal tract of a human. Identification of novel microbial species and quantification of their distributional variations among different samples that are sequenced using next-generation-sequencing technology hold the key to the success of most metagenomic studies. To achieve these goals, we propose a simple yet powerful metagenomic binning method, MetaBMF. The method does not require prior knowledge of reference genomes and produces highly accurate results, even at a strain level. Thus, it can be broadly used to identify disease-related microbial organisms that are not well-studied.'),
(82983, 2019, 'bio', 'Bayesian inference of distributed time delay in transcriptional and translational regulation', 'Choi, Boseung|Cheng, Yu-Yu|Cinar, Selahattin|Ott, William|Bennett, Matthew R|Josixc4x87, Krexc5xa1imir|Kim, Jae Kyoung', 'Department of National Statistics, Korea University Sejong Campus, Sejong, Korea.|Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA.|Department of Mathematics, University of Houston, Houston, TX, USA.|Department of Mathematics, University of Houston, Houston, TX, USA.|Department of Biosciences, Rice University, Houston, TX, USA.|Department of Mathematics, University of Houston, Houston, TX, USA.|Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Korea.', 'United States|United States|United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz574', '31347688', 'Advances in experimental and imaging techniques have allowed for unprecedented insights into the dynamical processes within individual cells. However, many facets of intracellular dynamics remain hidden, or can be measured only indirectly. This makes it challenging to reconstruct the regulatory networks that govern the biochemical processes underlying various cell functions. Current estimation techniques for inferring reaction rates frequently rely on marginalization over unobserved processes and states. Even in simple systems this approach can be computationally challenging, and can lead to large uncertainties and lack of robustness in parameter estimates. Therefore we will require alternative approaches to efficiently uncover the interactions in complex biochemical networks.'),
(82984, 2019, 'bio', 'TFEAChIP: A tool kit for transcription factor binding site enrichment analysis capitalizing on ChIP-seq datasets', 'Puente-Santamaria, Laura|Wasserman, Wyeth W|Del Peso, Luis', 'Departamento de Bioquímica, Universidad Autónoma de Madrid (UAM) and Instituto de Investigaciones Biomédicas Alberto Sols (CSIC-UAM), Madrid, Spain.|Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Department of Medical Genetics, University of British Columbia Vancouver, British Columbia, Canada.|Departamento de Bioquímica, Universidad Autónoma de Madrid (UAM) and Instituto de Investigaciones Biomédicas Alberto Sols (CSIC-UAM), Madrid, Spain.', 'Spain|Canada|Spain', 4, '-', 'Journal Article', '10.1093/bioinformatics/btz573', '31347689', 'The computational identification of the transcription factors (more generally, transcription regulators, TR) responsible for the co-regulation of a specific set of genes is a common problem found in genomic analysis. Herein, we describe TFEA.ChIP, a tool that makes use of ChIP-seq datasets to estimate and visualize TR enrichment in gene lists representing transcriptional profiles. We validated TFEA.ChIP using a wide variety of gene sets representing signatures of genetic and chemical perturbations as input and found that the relevant TR was correctly identified in 126 of a total of 174 analyzed. Comparison with other TR enrichment tools demonstrates that TFEA.ChIP is an highly customizable package with an outstanding performance.'),
(82985, 2019, 'bio', 'ISoLDE: a data-driven statistical method for the inference of allelic imbalance in datasets with reciprocal crosses', 'Reynès, Christelle|Kister, Guilhem|Rohmer, Marine|Bouschet, Tristan|Varrault, Annie|Dubois, Emeric|Rialle, Stéphanie|Journot, Laurent|Sabatier, Robert', 'Institut de Génomique Fonctionnelle, IGF, Univ Montpellier, CNRS, INSERM, Montpellier, France.|Faculté de Pharmacie, Univ Montpellier, Montpellier, France.|Montpellier GenomiX, MGX, Univ Montpellier, CNRS, INSERM, Montpellier, France.|Institut de Génomique Fonctionnelle, IGF, Univ Montpellier, CNRS, INSERM, Montpellier, France.|Institut de Génomique Fonctionnelle, IGF, Univ Montpellier, CNRS, INSERM, Montpellier, France.|Montpellier GenomiX, MGX, Univ Montpellier, CNRS, INSERM, Montpellier, France.|Montpellier GenomiX, MGX, Univ Montpellier, CNRS, INSERM, Montpellier, France.|Institut de Génomique Fonctionnelle, IGF, Univ Montpellier, CNRS, INSERM, Montpellier, France.|Institut de Génomique Fonctionnelle, IGF, Univ Montpellier, CNRS, INSERM, Montpellier, France.', 'France|France|France|France|France|France|France|France|France', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz564', '31350542', 'Allelic imbalance (AI), i.e. the unequal expression of the alleles of the same gene in a single cell, affects a subset of genes in diploid organisms. One prominent example of AI is parental genomic imprinting, which results in parent-of-origin-dependent, mono-allelic expression of a limited number of genes in metatherian and eutherian mammals and in angiosperms. Currently available methods for identifying allelic imbalance rely on data modeling and come with the associated limitations.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(82986, 2019, 'bio', 'The nPYc-Toolbox, a Python module for the pre-processing, quality-control, and analysis of metabolic profiling datasets', 'Sands, Caroline J|Wolfer, Arnaud M|Correia, Gonçalo Ds|Sadawi, Noureddin|Ahmed, Arfan|Jiménez, Beatriz|Lewis, Matthew R|Glen, Robert C|Nicholson, Jeremy K|Pearce, Jake Tm', 'National Phenome Centre and Imperial Clinical Phenotyping Centre, Department of Surgery & Cancer, Imperial College London, UK.|National Phenome Centre and Imperial Clinical Phenotyping Centre, Department of Surgery & Cancer, Imperial College London, UK.|National Phenome Centre and Imperial Clinical Phenotyping Centre, Department of Surgery & Cancer, Imperial College London, UK.|Division of Integrative Systems Medicine and Digestive Diseases, Department of Surgery & Cancer, Imperial College London, UK.|National Phenome Centre and Imperial Clinical Phenotyping Centre, Department of Surgery & Cancer, Imperial College London, UK.|National Phenome Centre and Imperial Clinical Phenotyping Centre, Department of Surgery & Cancer, Imperial College London, UK.|National Phenome Centre and Imperial Clinical Phenotyping Centre, Department of Surgery & Cancer, Imperial College London, UK.|Division of Integrative Systems Medicine and Digestive Diseases, Department of Surgery & Cancer, Imperial College London, UK.|National Phenome Centre and Imperial Clinical Phenotyping Centre, Department of Surgery & Cancer, Imperial College London, UK.|National Phenome Centre and Imperial Clinical Phenotyping Centre, Department of Surgery & Cancer, Imperial College London, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz566', '31350543', 'As large-scale metabolic phenotyping studies become increasingly common, the need for systemic methods for pre-processing and quality control (QC) of analytical data prior to statistical analysis has become increasingly important, both within a study, and to allow meaningful inter-study comparisons. The nPYc-Toolbox provides software for the import, pre-processing, QC, and visualisation of metabolic phenotyping datasets, either interactively, or in automated pipelines.'),
(82987, 2019, 'bio', 'amplimap: a versatile tool to process and analyze targeted NGS data', 'Koelling, Nils|Bernkopf, Marie|Calpena, Eduardo|Maher, Geoffrey J|Miller, Kerry A|Ralph, Hannah K|Goriely, Anne|Wilkie, Andrew O M', 'MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.|MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.|MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.|MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.|MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.|MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.|MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.|MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz582', '31350555', 'amplimap is a command-line tool to automate the processing and analysis of data from targeted next-generation sequencing (NGS) experiments with PCR-based amplicons or capture-based enrichment systems. From raw sequencing reads, amplimap generates output such as read alignments, annotated variant calls, target coverage statistics, and variant allele counts and frequencies for each target base pair. In addition to its focus on user-friendliness and reproducibility, amplimap supports advanced features such as consensus base calling for read families based on unique molecular identifiers (UMIs) and filtering false positive variant calls caused by amplification of off-target loci.'),
(82988, 2019, 'bio', 'Towards accurate high-throughput ligand affinity prediction by exploiting structural ensembles, docking metrics and ligand similarity', 'Schneider, Melanie|Pons, Jean-Luc|Bourguet, William|Labesse, Gilles', 'Centre de Biochimie Structurale, CNRS, INSERM, Univ Montpellier, Montpellier, France.|Centre de Biochimie Structurale, CNRS, INSERM, Univ Montpellier, Montpellier, France.|Centre de Biochimie Structurale, CNRS, INSERM, Univ Montpellier, Montpellier, France.|Centre de Biochimie Structurale, CNRS, INSERM, Univ Montpellier, Montpellier, France.', 'France|France|France|France', 5, '-', 'Journal Article', '10.1093/bioinformatics/btz538', '31350558', 'Nowadays, virtual screening (VS) plays a major role in the process of drug development. Nonetheless, an accurate estimation of binding affinities, which is crucial at all stages, is not trivial and may require target-specific fine-tuning. Furthermore, drug design also requires improved predictions for putative secondary targets among which is Estrogen Receptor alpha (ERxcexb1).'),
(82989, 2019, 'bio', 'THETA: A new genotypic approach for predicting HIV-1 CRF02-AG coreceptor usage', 'Dimeglio, Chloé|Raymond, Stéphanie|Jeanne, Nicolas|Reynes, Christelle|Carcenac, Romain|Lefebvre, Caroline|Cazabat, Michelle|Nicot, Florence|Delobel, Pierre|Izopet, Jacques', 'CHU de Toulouse, Hôpital Purpan, Laboratoire de Virologie, Toulouse, F-31300 France.|CHU de Toulouse, Hôpital Purpan, Laboratoire de Virologie, Toulouse, F-31300 France.|CHU de Toulouse, Hôpital Purpan, Laboratoire de Virologie, Toulouse, F-31300 France.|Institut de Génomique Fonctionnelle.|CHU de Toulouse, Hôpital Purpan, Laboratoire de Virologie, Toulouse, F-31300 France.|CHU de Toulouse, Hôpital Purpan, Laboratoire de Virologie, Toulouse, F-31300 France.|CHU de Toulouse, Hôpital Purpan, Laboratoire de Virologie, Toulouse, F-31300 France.|CHU de Toulouse, Hôpital Purpan, Laboratoire de Virologie, Toulouse, F-31300 France.|CHU de Toulouse, Service de Maladies Infectieuses et Tropicales, Toulouse, France.|CHU de Toulouse, Hôpital Purpan, Laboratoire de Virologie, Toulouse, F-31300 France.', 'France|France|France|-|France|France|France|France|France|France', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz585', '31350559', 'The circulating recombinant form of HIV-1 CRF02-AG is the most frequent non-B subtype in Europe. Anti-HIV therapy and pathophysiological studies on the impact of HIV-1 tropism require genotypic determination of HIV-1 tropism for non-B subtypes. But genotypic approaches based on analysis of the V3 envelope region perform poorly when used to determine the tropism of CRF02-AG. We therefore designed an algorithm based on information from the gp120 and gp41 ectodomain that better predicts the tropism of HIV-1 subtype CRF02-AG.'),
(82990, 2019, 'bio', 'BioNorm: Deep learning based event normalization for the curation of reaction databases', 'Lou, Peiliang|Yepes, Antonio Jimeno|Zhang, Zai|Zheng, Qinghua|Zhang, Xiangrong|Li, Chen', 'School of Electronic and Information Engineering, Xian Jiaotong University, Xian, Shaanxi China.|IBM Research Australia, 60 City Road, Southbank, VIC, Australia.|School of Electronic and Information Engineering, Xian Jiaotong University, Xian, Shaanxi China.|School of Electronic and Information Engineering, Xian Jiaotong University, Xian, Shaanxi China.|Institute of Intelligent Information Processing, Xidian University, Xian, Shaanxi, China.|School of Electronic and Information Engineering, Xian Jiaotong University, Xian, Shaanxi China.', 'China|Australia|China|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz571', '31350561', 'A biochemical reaction, bio-event, depicts the relationships between participating entities. Current text mining research has been focusing on identifying bio-events from scientific literature. However, rare efforts have been dedicated to normalize bio-events extracted from scientific literature with the entries in the curated reaction databases, which could disambiguate the events and further support interconnecting events into biologically meaningful and complete networks.'),
(82991, 2019, 'bio', 'Revealing dynamic regulations and the related key proteins of myeloma-initiating cells by integrating experimental data into a systems biological model', 'Zhang, Le|Liu, Guangdi|Kong, Meijing|Li, Tingting|Wu, Dan|Zhou, Xiaobo|Yang, Chuanwei|Xia, Lei|Yang, Zhenzhou|Chen, Luonan', 'College of Computer Science, Sichuan University, Chengdu, China.|College of computer and Information Science, Southwest University, Chongqing, China.|College of computer and Information Science, Southwest University, Chongqing, China.|College of Mathematics and Statistics, Southwest University, Chongqing, China.|Department of Radiology, Wake Forest University School of Medicine Winston-Salem, NC, USA.|Department of Radiology, Wake Forest University School of Medicine Winston-Salem, NC, USA.|Systems Biology, the University of Texas MD Anderson Cancer Center, Houston, USA.|Cancer Center, Research Institute of Surgery, Daping Hospital, Third Military Medical University, Chongqing, China.|Cancer Center, Research Institute of Surgery, Daping Hospital, Third Military Medical University, Chongqing, China.|Key Laboratory of Systems Biology, CAS Center for Excellence in Molecular Cell Science, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.', 'China|China|China|China|United States|United States|United States|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz542', '31350562', 'The growth and survival of myeloma cells are greatly affected by their surrounding microenvironment. To understand the molecular mechanism and the impact of stiffness on the fate of myeloma initiating cells (MICs), we develop a systems biological model to reveal the dynamic regulations by integrating reverse phase protein array data and the stiffness associated pathway.'),
(82992, 2019, 'bio', 'DISPOT: A simple knowledge-based protein domain interaction statistical potential', 'Narykov, Oleksandr|Bogatov, Dmytro|Korkin, Dmitry', 'Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA, USA.|Department of Computer Science, Boston University, Boston, MA, USA.|Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA, USA.', 'United States|United States|United States', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz587', '31350874', 'The complexity of protein-protein interactions (PPIs) is further compounded by the fact that an average protein consists of two or more domains, structurally and evolutionary independent subunits. Experimental studies have demonstrated that an interaction between a pair of proteins is not carried out by all domains constituting each protein, but rather by a select subset. However, finding which domains from each protein mediate the corresponding PPI is a challenging task.'),
(82993, 2019, 'bio', 'DeepGOPlus: Improved protein function prediction from sequence', 'Kulmanov, Maxat|Hoehndorf, Robert', 'Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, 4700 King Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia.|Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, 4700 King Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia.', 'Saudi Arabia|Saudi Arabia', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz595', '31350877', 'Protein function prediction is one of the major tasks of bioinformatics that can help in wide range of biological problems such as understanding disease mechanisms or finding drug targets. Many methods are available for predicting protein functions from sequence based features, protein-protein interaction networks, protein structure or literature. However, other than sequence, most of the features are difficult to obtain or not available for many proteins thereby limiting their scope. Furthermore, the performance of sequence-based function prediction methods is often lower than methods that incorporate multiple features and predicting protein functions may require a lot of time.'),
(82994, 2019, 'bio', 'geneCo: A visualized comparative genomic method to analyze multiple genome structures', 'Jung, Jaehee|Kim, Jong Im|Yi, Gangman', 'Department of Information and Communication Engineering, Myongji University, Yongin, Gyeonggi-do, Korea.|Department of Biology, Chungnam National University, Daejeon, Korea.|Department of Multimedia Engineering, Dongguk University, Seoul, Korea.', '-|-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz596', '31350879', 'In comparative and evolutionary genomics, a detailed comparison of common features between organisms is essential to evaluate genetic distance. However, identifying differences in matched and mismatched genes among multiple genomes is difficult using current comparative genomic approaches due to complicated methodologies or the generation of meager information from obtained results. This study describes a visualized software tool, geneCo (gene Comparison), for comparing genome structure and gene arrangements between various organisms. User data are aligned, gene information is recognized, and genome structures are compared based on user-defined GenBank files. Information regarding inversion, gain, loss, duplication, and gene rearrangement among multiple organisms being compared is provided by geneCo, which uses a web-based interface that users can easily access without any need to consider the computational environment.'),
(82995, 2019, 'bio', 'pourRNA - a time- and memory-efficient approach for the guided exploration of RNA energy landscapes', 'Entzian, Gregor|Raden, Martin', 'University of Vienna, Faculty of Chemistry, Department of Theoretical Chemistry, Wxc3xa4hringerstraxc3x9fe 17, Vienna, Austria.|Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany.', 'Austria|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz583', '31350881', 'The folding dynamics of RNAs are typically studied via coarse-grained models of the underlying energy landscape to face the exponential growths of the RNA secondary structure space. Still, studies of exact folding kinetics based on gradient basin abstractions are currently limited to short sequence lengths due to vast memory requirements. In order to compute exact transition rates between gradient basins, state-of-the-art approaches apply global flooding schemes that require to memorize the whole structure space at once. pourRNA tackles this problem via local flooding techniques where memorization is limited to the structure ensembles of individual gradient basins.'),
(82996, 2019, 'bio', 'Identifying small-effect genetic associations overlooked by the conventional fixed-effect model in a large-scale meta-analysis of coronary artery disease', 'Magosi, Lerato E|Goel, Anuj|Hopewell, Jemma C|Farrall, Martin', 'Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.|Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.|Nuffield Department of Population Health, University of Oxford, Oxford, UK.|Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz590', '31350884', 'Common small-effect genetic variants that contribute to human complex traits and disease are typically identified using traditional fixed-effect meta-analysis methods. However, the power to detect genetic associations under fixed-effect models deteriorates with increasing heterogeneity, so that some small-effect heterogeneous loci might go undetected. Han and Eskin developed a modified random-effects meta-analysis approach (RE2) that is more powerful than traditional fixed and random-effects methods at detecting small-effect heterogeneous genetic associations, updating the method (RE2C) to identify small-effect heterogeneous variants overlooked by traditional fixed-effect meta-analysis. Here we re-appraise a large-scale meta-analysis of coronary disease with RE2C to search for small-effect genetic signals potentially masked by heterogeneity in a fixed-effect meta-analysis.'),
(82997, 2019, 'bio', 'MPRAscore: robust and non-parametric analysis of massively parallel reporter assays', 'Niroula, Abhishek|Ajore, Ram|Nilsson, Bjxc3xb6rn', 'Department of Laboratory Medicine, Lund University, Lund, Sweden.|Department of Laboratory Medicine, Lund University, Lund, Sweden.|Department of Laboratory Medicine, Lund University, Lund, Sweden.', 'Sweden|Sweden|Sweden', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz591', '31359027', 'Massively parallel reporter assays (MPRA) enable systematic screening of DNA sequence variants for effects on transcriptional activity. However, convenient analysis tools are still needed.'),
(82998, 2019, 'bio', 'ACTINN: Automated Identification of Cell Types in Single Cell RNA Sequencing', 'Ma, Feiyang|Pellegrini, Matteo', 'Molecular Biology Institute, University of California, Los Angeles, CA, USA.|Molecular Biology Institute, University of California, Los Angeles, CA, USA.', 'United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz592', '31359028', 'Cell type identification is one of the major goals in single cell RNA sequencing (scRNA-seq). Current methods for assigning cell types typically involve the use of unsupervised clustering, the identification of signature genes in each cluster, followed by a manual lookup of these genes in the literature and databases to assign cell types. However, there are several limitations associated with these approaches, such as unwanted sources of variation that influence clustering and a lack of canonical markers for certain cell types. Here, we present ACTINN (Automated Cell Type Identification using Neural Networks), which employs a neural network with 3 hidden layers, trains on datasets with predefined cell types, and predicts cell types for other datasets based on the trained parameters.'),
(82999, 2019, 'bio', 'Predicting promoters in phage genomes using PhagePromoter', 'Sampaio, Marta|Rocha, Miguel|Oliveira, Hugo|Dias, Oscar', 'Centre of Biological Engineering, University of Minho, Braga, Portugal.|Centre of Biological Engineering, University of Minho, Braga, Portugal.|Centre of Biological Engineering, University of Minho, Braga, Portugal.|Centre of Biological Engineering, University of Minho, Braga, Portugal.', 'Portugal|Portugal|Portugal|Portugal', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz580', '31359029', 'The growing interest in phages as antibacterial agents has led to an increase in the number of sequenced phage genomes, increasing the need for intuitive bioinformatics tools for performing genome annotation. The identification of phage promoters is indeed the most difficult step of this process. Due to the lack of online tools for phage promoter prediction, we developed PhagePromoter, a tool for locating promoters in phage genomes, using machine learning methods. This is the first online tool for predicting promoters that uses phage promoter data and the first to identify both host and phage promoters with different motifs.'),
(83000, 2019, 'bio', 'CoBAMP: A Python framework for metabolic pathway analysis in constraint-based models', 'Vieira, Vítor|Rocha, Miguel', 'Centre of Biological Engineering, University of Minho, Braga, Portugal.|Centre of Biological Engineering, University of Minho, Braga, Portugal.', 'Portugal|Portugal', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz598', '31359031', 'CoBAMP is a modular framework for the enumeration of pathway analysis concepts, such as elementary flux modes (EFM) and minimal cut sets in genome-scale constraint-based models of metabolism. It currently includes the K-shortest EFM algorithm and facilitates integration with other frameworks involving reading, manipulation and analysis of constraint-based models.'),
(83001, 2019, 'bio', 'DEEPCON: Protein Contact Prediction using Dilated Convolutional Neural Networks with Dropout', 'Adhikari, Badri', 'Department of Mathematics and Computer Science, University of Missouri-St. Louis, St. Louis, MO, USA.', 'United States', 4, '-', 'Journal Article', '10.1093/bioinformatics/btz593', '31359036', 'Exciting new opportunities have arisen to solve the protein contact prediction problem from the progress in neural networks and the availability of a large number of homologous sequences through high-throughput sequencing. In this work, we study how deep convolutional neural networks (ConvNets) may be best designed and developed to solve this long-standing problem.'),
(83002, 2019, 'bio', 'P-value evaluation, variability index and biomarker categorization for adaptively weighted Fishers meta-analysis method in omics applications', 'Huo, Zhiguang|Tang, Shaowu|Park, Yongseok|Tseng, George', 'Department of Biostatistics, University of Florida, FL, USA.|Roche Molecular Solutions, Inc, CA, USA.|Department of Biostatistics, University of Pittsburgh, PA, USA.|Department of Biostatistics, University of Pittsburgh, PA, USA.', 'United States|United States|United States|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz589', '31359040', 'Meta-analysis methods have been widely used to combine results from multiple clinical or genomic studies to increase statistical powers and ensure robust and accurate conclusions. The adaptively weighted Fishers method (AW-Fisher), initially developed for omics applications but applicable for general meta-analysis, is an effective approach to combine p-values from K independent studies and to provide better biological interpretability by characterizing which studies contribute to the meta-analysis. Currently, AW-Fisher suffers from the lack of fast p-value computation and variability estimate of AW weights. When the number of studies K is large, the 3K- 1 possible differential expression pattern categories generated by AW-Fisher can become intractable. In this paper, we develop an importance sampling scheme with spline interpolation to increase the accuracy and speed of the p-value calculation. We also apply bootstrapping to construct a variability index for the AW-Fisher weight estimator and a co-membership matrix to categorize (cluster) differentially expressed genes based on their meta-patterns for intuitive biological investigations.'),
(83003, 2019, 'bio', 'PyMethylProcess - convenient high-throughput preprocessing workflow for DNA methylation data', 'Levy, Joshua J|Titus, Alexander J|Salas, Lucas A|Christensen, Brock C', 'Program in Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth,Lebanon, NH.|Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH.|Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH.|Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH.', 'Lebanon|Lebanon|Lebanon|Lebanon', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz594', '31368477', 'Performing highly parallelized preprocessing of methylation array data using Python can accelerate data preparation for downstream methylation analyses, including large scale production-ready machine learning pipelines. We present a highly reproducible, scalable pipeline (PyMethylProcess) that can be quickly set-up and deployed through Docker and PIP.'),
(83004, 2019, 'bio', 'Bayesian Estimation of Genetic Regulatory Effects in High-throughput Reporter Assays', 'Majoros, William H|Kim, Young-Sook|Barrera, Alejandro|Li, Fan|Wang, Xingyan|Cunningham, Sarah J|Johnson, Graham D|Guo, Cong|Lowe, William L|Scholtens, Denise M|Hayes, M Geoffrey|Reddy, Timothy E|Allen, Andrew S', 'Duke Center for Statistical Genetics and Genomics, Duke University, Durham, NC, USA.|Center for Genomic and Computational Biology, Duke University Medical School, Durham, NC, USA.|Center for Genomic and Computational Biology, Duke University Medical School, Durham, NC, USA.|Department of Biostatistics, Yale University, New Haven, CT, USA.|Masters Program in Biostatistics, Department of Biostatistics and Bioinformatics, Duke University Medical School, Durham, NC, USA.|University Program in Genetics and Genomics, Duke University, Durham, NC, USA.|Center for Genomic and Computational Biology, Duke University Medical School, Durham, NC, USA.|University Program in Genetics and Genomics, Duke University, Durham, NC, USA.|Division of Endocrinology, Metabolism & Molecular Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.|Division of Biostatistics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine,Chicago, Illinois, USA.|Division of Endocrinology, Metabolism & Molecular Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.|Duke Center for Statistical Genetics and Genomics, Duke University, Durham, NC, USA.|Duke Center for Statistical Genetics and Genomics, Duke University, Durham, NC, 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', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz545', '31368479', 'High-throughput reporter assays dramatically improve our ability to assign function to noncoding genetic variants, by measuring allelic effects on gene expression in the controlled setting of a reporter gene. Unlike genetic association tests, such assays are not confounded by linkage disequilibrium when loci are independently assayed. These methods can thus improve the identification of causal disease mutations. While work continues on improving experimental aspects of these assays, less effort has gone into developing methods for assessing the statistical significance of assay results, particularly in the case of rare variants captured from patient DNA.'),
(83005, 2019, 'bio', 'Gaussian Mixture Copulas for High-Dimensional Clustering and Dependency-based Subtyping', 'Kasa, Siva Rajesh|Bhattacharya, Sakyajit|Rajan, Vaibhav', 'Department of Information Systems and Analytics, School of Computing, National University of Singapore, Singapore.|TCS Innovation Labs, Kolkata, India.|Department of Information Systems and Analytics, School of Computing, National University of Singapore, Singapore.', 'Singapore|India|Singapore', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz599', '31368480', 'The identification of sub-populations of patients with similar characteristics, called patient subtyping, is important for realizing the goals of Precision Medicine. Accurate subtyping is crucial for tailoring therapeutic strategies that can potentially lead to reduced mortality and morbidity. Model-based clustering, such as Gaussian mixture models, provides a principled and interpretable methodology that is widely used to identify subtypes. However, they impose identical marginal distributions on each variable, such assumptions restrict their modeling flexibility and deteriorates clustering performance.'),
(83006, 2019, 'bio', 'ATEN: And/Or Tree Ensemble for inferring accurate Boolean network topology and dynamics', 'Shi, Ning|Zhu, Zexuan|Tang, Ke|Parker, David|He, Shan', 'School of Computer Science, University of Birmingham, Birmingham, UK.|College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China.|Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China.|School of Computer Science, University of Birmingham, Birmingham, UK.|School of Computer Science, University of Birmingham, Birmingham, UK.', 'United Kingdom|China|China|United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz563', '31368481', 'Inferring gene regulatory networks from gene expression time series data is important for gaining insights into the complex processes of cell life. A popular approach is to infer Boolean networks. However, it is still a pressing open problem to infer accurate Boolean networks from experimental data that are typically short and noisy.'),
(83007, 2019, 'bio', 'Discovering Protein Drug Targets Using Knowledge Graph Embeddings', 'Mohamed, Sameh K|Novxc3xa1xc4x8dek, Vxc3xadt|Nounu, Aayah', 'Data Science Institute, National University of Ireland Galway.|Data Science Institute, National University of Ireland Galway.|MRC Integrative Epidemiology Unit, University of Bristol.', 'Ireland|Ireland|-', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz600', '31368482', 'Computational approaches for predicting drug-target interactions (DTIs) can provide valuable insights into the drug mechanism of action. DTI predictions can help to quickly identify new promising (on-target) or unintended (off-target) effects of drugs. However, existing models face several challenges. Many can only process a limited number of drugs and/or have poor proteome coverage. The current approaches also often suffer from high false positive prediction rates.'),
(83008, 2019, 'bio', 'Sequence tube maps: making graph genomes intuitive to commuters', 'Beyer, Wolfgang|Novak, Adam M|Hickey, Glenn|Chan, Jeffrey|Tan, Vanessa|Paten, Benedict|Zerbino, Daniel R', 'UC Santa Cruz Genomics Institute, Santa Cruz, California, USA. Department of Biomolecular Engineering, University of California, Santa Cruz, California, USA.|UC Santa Cruz Genomics Institute, Santa Cruz, California, USA. Department of Biomolecular Engineering, University of California, Santa Cruz, California, USA.|UC Santa Cruz Genomics Institute, Santa Cruz, California, USA. Department of Biomolecular Engineering, University of California, Santa Cruz, California, USA.|UC Santa Cruz Genomics Institute, Santa Cruz, California, USA. Department of Biomolecular Engineering, University of California, Santa Cruz, California, USA.|UC Santa Cruz Genomics Institute, Santa Cruz, California, USA. Department of Biomolecular Engineering, University of California, Santa Cruz, California, USA.|UC Santa Cruz Genomics Institute, Santa Cruz, California, USA. Department of Biomolecular Engineering, University of California, Santa Cruz, California, USA.|European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 5, '-', 'Journal Article', '10.1093/bioinformatics/btz597', '31368484', 'Compared to traditional haploid reference genomes, graph genomes are an efficient and compact data structure for storing multiple genomic sequences, for storing polymorphisms or for mapping sequencing reads with greater sensitivity. Further, graphs are well-studied computer science objects that can be efficiently analysed. However, their adoption in genomic research is slow, in part because of the cognitive difficulty in interpreting graphs.'),
(83009, 2019, 'bio', 'Tailor-made multiple sequence alignments using the PRALINE 2 alignment toolkit', 'Dijkstra, Maurits J J|van der Ploeg, Atze J|Feenstra, K Anton|Fokkink, Wan J|Abeln, Sanne|Heringa, Jaap', 'Department of Computer Science, Vrije Universiteit, Amsterdam, The Netherlands.|Department of Computer Science, Vrije Universiteit, Amsterdam, The Netherlands.|Department of Computer Science, Vrije Universiteit, Amsterdam, The Netherlands.|Department of Computer Science, Vrije Universiteit, Amsterdam, The Netherlands.|Department of Computer Science, Vrije Universiteit, Amsterdam, The Netherlands.|Department of Computer Science, Vrije Universiteit, Amsterdam, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz572', '31368486', 'PRALINE 2 is a toolkit for custom multiple sequence alignment workflows. It can be used to incorporate sequence annotations, such as secondary structure or (DNA) motifs, into the alignment scoring, as well as to customize many other aspects of a progressive multiple alignment workflow.'),
(83010, 2019, 'bio', 'OncodriveCLUSTL: a sequence-based clustering method to identify cancer drivers', 'Arnedo-Pac, Claudia|Mularoni, Loris|Muiños, Ferran|Gonzalez-Perez, Abel|Lopez-Bigas, Nuria', 'Department of Computer Science, Vrije Universiteit, Amsterdam, The Netherlands.|Department of Computer Science, Vrije Universiteit, Amsterdam, The Netherlands.|Department of Computer Science, Vrije Universiteit, Amsterdam, The Netherlands.|Department of Computer Science, Vrije Universiteit, Amsterdam, The Netherlands.|Department of Computer Science, Vrije Universiteit, Amsterdam, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz588', '31369060', '-'),
(83011, 2019, 'bio', 'scRNABatchQC: Multi-samples quality control for single cell RNA-seq data', 'Liu, Qi|Sheng, Quanhu|Ping, Jie|Ramirez, Marisol Adelina|Lau, Ken S|Coffey, Robert|Shyr, Yu', 'Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA.|Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA.|Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA.|Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA.|Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.|Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA.|Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA.', 'United States|United States|United States|United States|United States|United States|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz601', '31373345', 'Single cell RNA sequencing is a revolutionary technique to characterize inter-cellular transcriptomics heterogeneity. However, the data is noise-prone because gene expression is often driven by both technical artifacts and genuine biological variations. Proper disentanglement of these two effects is critical to prevent spurious results. While several tools exist to detect and remove low-quality cells in one single cell RNA-seq dataset, there is lack of approach to examining consistency between sample sets and detecting systematic biases, batch effects, and outliers. We present scRNABatchQC, an R package to compare multiple sample sets simultaneously over numerous technical and biological features, which gives valuable hints to distinguish technical artifact from biological variations. scRNABatchQC helps identify and systematically characterize sources of variability in single cell transcriptome data. The examination of consistency across datasets allows visual detection of biases and outliers.'),
(83012, 2019, 'bio', 'BLANT - Fast Graphlet Sampling Tool', 'Maharaj, Sridevi|Tracy, Brennan|Hayes, Wayne B', 'Department of Computer Science, University of California Irvine.|Department of Computer Science, University of California Irvine.|Department of Computer Science, University of California Irvine.', '-|-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz603', '31373347', 'BLAST creates local sequence alignments by first building a database of small k-letter sub-sequences called k-mers. Identical k-mers from different regions provide seeds for longer local alignments. This seed-and-extend heuristic makes BLAST extremely fast and has led to its almost exclusive use despite the existence of more accurate, but slower, algorithms. In this paper, we introduce BLANT, the Basic Local Alignment for Networks Tool. BLANT is the analog of BLAST, but for networks: given an input graph, it samples small, induced, k-node subgraphs called k-graphlets. Graphlets have been used to classify networks, quantify structure, align networks both locally and globally, identify topology-function relationships, and build taxonomic trees without the use of sequences. Given an input network, BLANT produces millions of graphlet samples in seconds-orders of magnitude faster than existing methods. BLANT offers sampled graphlets in various forms: distributions of graphlets or their orbits, graphlet degree or graphlet orbit degree vectors, the latter being compatible with ORCA, or an index to be used as the basis for seed-and-extend local alignments. We demonstrate BLANTs usefelness by using its indexing mode to find functional similarity between yeast and human PPI networks.'),
(83013, 2019, 'bio', 'Non-Parametric Individual Treatment Effect Estimation for Survival Data with Random Forests', 'Tabib, Sami|Larocque, Denis', 'Department of Decision Sciences, HEC Montréal, Montréal (Québec), Canada.|Department of Decision Sciences, HEC Montréal, Montréal (Québec), Canada.', 'Canada|Canada', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz602', '31373350', 'Personalized medicine often relies on accurate estimation of a treatment effect for specific subjects. This estimation can be based on the subjects baseline covariates but additional complications arise for a time-to-event response subject to censoring. In this paper, the treatment effect is measured as the difference between the mean survival time of a treated subject and the mean survival time of a control subject. We propose a new random forest method for estimating the individual treatment effect with survival data. The random forest is formed by individual trees built with a splitting rule specifically designed to partition the data according to the individual treatment effect. For a new subject, the forest provides a set of similar subjects from the training data set that can be used to compute an estimation of the individual treatment effect with any adequate method.'),
(83014, 2019, 'bio', 'RAG-Web: RNA Structure Prediction/Design using RNA-As-Graphs', 'Meng, Grace|Tariq, Marva|Jain, Swati|Elmetwaly, Shereef|Schlick, Tamar', 'Department of Chemistry, New York University, 1021 Silver, 100 Washington Square East, New York, NY, USA.|Department of Chemistry, Smith College, Northampton, MA, USA.|Department of Chemistry, New York University, 1021 Silver, 100 Washington Square East, New York, NY, USA.|Department of Chemistry, New York University, 1021 Silver, 100 Washington Square East, New York, NY, USA.|Department of Chemistry, New York University, 1021 Silver, 100 Washington Square East, New York, NY, USA.', 'United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz611', '31373604', 'We launch a webserver for RNA structure prediction and design corresponding to tools developed using our RNA-As-Graphs (RAG) approach. RAG uses coarse-grained tree graphs to represent RNA secondary structure, allowing the application of graph theory to analyze and advance RNA structure discovery. Our webserver consists of three modules: (a) RAG Sampler: samples tree graph topologies from an RNA secondary structure to predict corresponding tertiary topologies, (b) RAG Builder: builds three-dimensional atomic models from candidate graphs generated by RAG Sampler, and (c) RAG Designer: designs sequences that fold onto novel RNA motifs (described by tree graph topologies). Results analyses are performed for further assessment/selection. The Results page provides links to download results and indicates possible errors encountered. RAG-Web offers a user-friendly interface to utilize our RAG software suite to predict and design RNA structures and sequences.'),
(83015, 2019, 'bio', 'Predicting the effects of SNPs on transcription factor binding affinity', 'Nishizaki, Sierra S|Ng, Natalie|Dong, Shengcheng|Porter, Robert S|Morterud, Cody|Williams, Colten|Asman, Courtney|Switzenberg, Jessica A|Boyle, Alan P', 'Department of Human Genetics, University of Michigan, Ann Arbor Michigan USA.|Department of Human Genetics, Stanford University, Stanford California USA.|Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor Michigan USA.|Department of Human Genetics, University of Michigan, Ann Arbor Michigan USA.|Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor Michigan USA.|Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor Michigan USA.|Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor Michigan USA.|Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor Michigan USA.|Department of Human Genetics, University of Michigan, Ann Arbor Michigan USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz612', '31373606', 'GWAS have revealed that 88% of disease associated SNPs reside in noncoding regions. However, noncoding SNPs remain understudied, partly because they are challenging to prioritize for experimental validation. To address this deficiency, we developed the SNP effect matrix pipeline (SEMpl).'),
(83016, 2019, 'bio', 'PIRD: Pan immune repertoire database', 'Zhang, Wei|Wang, Longlong|Liu, Ke|Wei, Xiaofeng|Yang, Kai|Du, Wensi|Wang, Shiyu|Guo, Nannan|Ma, Chuanchuan|Luo, Lihua|Wu, Jinghua|Lin, Liya|Yang, Fan|Gao, Fei|Wang, Xie|Li, Tao|Zhang, Ruifang|Saksena, Nitin K|Yang, Huanming|Wang, Jian|Fang, Lin|Hou, Yong|Xu, Xun|Liu, Xiao', 'BGI-Shenzhen, Shenzhen, China.|BGI-Shenzhen, Shenzhen, China.|BGI-Shenzhen, Shenzhen, China.|BGI-Shenzhen, Shenzhen, China.|BGI-Shenzhen, Shenzhen, China.|BGI-Shenzhen, Shenzhen, China.|BGI-Shenzhen, Shenzhen, China.|BGI-Shenzhen, Shenzhen, China.|BGI-Shenzhen, Shenzhen, China.|BGI-Shenzhen, Shenzhen, China.|BGI-Shenzhen, Shenzhen, China.|BGI-Shenzhen, Shenzhen, China.|BGI-Shenzhen, Shenzhen, China.|BGI-Shenzhen, Shenzhen, China.|BGI-Shenzhen, Shenzhen, China.|BGI-Shenzhen, Shenzhen, China.|BGI-Shenzhen, Shenzhen, China.|BGI-Shenzhen, Shenzhen, China.|BGI-Shenzhen, Shenzhen, China.|BGI-Shenzhen, Shenzhen, China.|BGI-Shenzhen, Shenzhen, China.|BGI-Shenzhen, Shenzhen, China.|BGI-Shenzhen, Shenzhen, China.|BGI-Shenzhen, Shenzhen, China.', 'China|China|China|China|China|China|China|China|China|China|China|China|China|China|China|China|Chin', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz614', '31373607', 'T and B cell receptors (TCRs and BCRs) play a pivotal role in the adaptive immune system by recognizing an enormous variety of external and internal antigens. Understanding these receptors is critical for exploring the process of immunoreaction and exploiting potential applications in immunotherapy and antibody drug design. Although a large number of samples have had their TCR and BCR repertoires sequenced using high-throughput sequencing in recent years, very few databases have been constructed to store these kinds of data. To resolve this issue, we developed a database.'),
(83017, 2019, 'bio', 'DNA Rchitect: An R based visualizer for network analysis of chromatin interaction data', 'Ramirez, R N|Bedirian, K|Gray, S M|Diallo, A', 'Department of Immunology, Harvard Medical School, Boston, Massachusetts, USA.|Department of Immunology, Harvard Medical School, Boston, Massachusetts, USA.|Department of Immunology, Harvard Medical School, Boston, Massachusetts, USA.|Department of Immunology, Harvard Medical School, Boston, Massachusetts, USA.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz608', '31373608', 'Visualization of multiple genomic data generally requires the use of public or commercially hosted browsers. Flexible visualization of chromatin interaction data as genomic features and network components offer informative insights to gene expression. An open source application for visualizing HiC and chromatin conformation-based data as 2D- arcs accompanied by interactive network analyses is valuable.'),
(83018, 2019, 'bio', 'gwasrapidd: an R package to query, download and wrangle GWAS Catalog data', 'Magno, Ramiro|Maia, Ana-Teresa', 'Centre for Biomedical Research (CBMR), University of Algarve, Gambelas Campus, Faro, Portugal.|Centre for Biomedical Research (CBMR), University of Algarve, Gambelas Campus, Faro, Portugal.', 'Portugal|Portugal', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz605', '31373609', 'The NHGRI Catalog of Published Genome-Wide Association Studies (GWAS) Catalog has collected, curated, and made available data from over 7,100 studies. The recently developed GWAS Catalog REST API is the only method allowing programmatic access to this resource.'),
(83019, 2019, 'bio', 'circMeta: a unified computational framework for genomic feature annotation and differential expression analysis of circular RNAs', 'Chen, Li|Wang, Feng|Bruggeman, Emily C|Li, Chao|Yao, Bing', 'Department of Health Outcomes Research and Policy, Auburn University, Auburn, AL 36849 USA.|Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322 USA.|Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322 USA.|Department of Health Outcomes Research and Policy, Auburn University, Auburn, AL 36849 USA.|Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322 USA.', 'United States|United States|United States|United States|United States', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz606', '31373611', 'Circular RNAs (circRNAs), a class of non-coding RNAs generated from non-canonical back-splicing events, have emerged to play key roles in many biological processes. Though numerous tools have been developed to detect circRNAs from rRNA-depleted RNA-seq data based on back-splicing junction-spanning reads, computational tools to identify critical genomic features regulating circRNA biogenesis are still lacking. In addition, rigorous statistical methods to perform differential expression (DE) analysis of circRNAs remain under-developed.'),
(83020, 2019, 'bio', 'ArtiFuse - Computational validation of fusion gene detection tools without relying on simulated reads', 'Sorn, Patrick|Hohlsträter, Christoph|Löwer, Martin|Sahin, Ugur|Weber, David', 'TRON - Translational Oncology at the University Medical Center of Johannes Gutenberg University Mainz gGmbH, 55131 Mainz, Germany.|TRON - Translational Oncology at the University Medical Center of Johannes Gutenberg University Mainz gGmbH, 55131 Mainz, Germany.|TRON - Translational Oncology at the University Medical Center of Johannes Gutenberg University Mainz gGmbH, 55131 Mainz, Germany.|TRON - Translational Oncology at the University Medical Center of Johannes Gutenberg University Mainz gGmbH, 55131 Mainz, Germany.|TRON - Translational Oncology at the University Medical Center of Johannes Gutenberg University Mainz gGmbH, 55131 Mainz, Germany.', 'Germany|Germany|Germany|Germany|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz613', '31373612', 'Gene fusions are an important class of transcriptional variants that can influence cancer development and can be predicted from RNA sequencing (RNA-seq) data by multiple existing tools. However, real world performance of these tools is unclear due to the lack of known positive and negative events, especially with regard to fusion genes in individual samples. Often simulated reads are used, but these cannot account for all technical biases in RNA-seq data generated from real samples.'),
(83021, 2019, 'bio', 'VirtualCytometry: a webserver for evaluating immune cell differentiation using single-cell RNA sequencing data', 'Kim, Kyungsoo|Yang, Sunmo|Ha, Sang-Jun|Lee, Insuk', 'Department of Biotechnology, Yonsei University, Seoul 03722, Korea.|Department of Biotechnology, Yonsei University, Seoul 03722, Korea.|Department of Biochemistry, Yonsei University, Seoul 03722, Korea.|Department of Biotechnology, Yonsei University, Seoul 03722, Korea.', '-|-|-|-', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz610', '31373613', 'The immune system has diverse types of cells that are differentiated or activated via various signaling pathways and transcriptional regulation upon challenging conditions. Immunophenotyping by flow and mass cytometry are the major approaches for identifying key signaling molecules and transcription factors directing the transition between the functional states of immune cells. However, few proteins can be evaluated by flow cytometry in a single experiment, preventing researchers from obtaining a comprehensive picture of the molecular programs involved in immune cell differentiation. Recent advances in single-cell RNA sequencing (scRNA-seq) have enabled unbiased genome-wide quantification of gene expression in individual cells on a large scale, providing a new and versatile analytical pipeline for studying immune cell differentiation.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(83022, 2019, 'bio', 'PyRanges: efficient comparison of genomic intervals in Python', 'Stovner, Endre Bakken|Sætrom, Pål', 'Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway.|Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway.', 'Norway|Norway', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz615', '31373614', 'Complex genomic analyses often use sequences of simple set operations like intersection, overlap, and nearest on genomic intervals. These operations, coupled with some custom programming, allow a wide range of analyses to be performed. To this end, we have written PyRanges, a data structure for representing and manipulating genomic intervals and their associated data in Python. Run single-threaded on binary set operations, PyRanges is in median 2.3-9.6 times faster than the popular R GenomicRanges library and is equally memory efficient, run multi-threaded on 8 cores, our library is up to 123 times faster. PyRanges is therefore ideally suited both for individual analyses and as a foundation for future genomic libraries in Python.'),
(83023, 2019, 'bio', 'ECHO: an Application for Detection and Analysis of Oscillators Identifies Metabolic Regulation on Genome-Wide Circadian Output', 'De Los Santos, Hannah|Collins, Emily J|Mann, Catherine|Sagan, April W|Jankowski, Meaghan S|Bennett, Kristin P|Hurley, Jennifer M', 'Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY, U.S.A.|Department of Biological Sciences, Rensselaer Polytechnic Institute, Troy, NY, U.S.A.|Department of Biological Sciences, Rensselaer Polytechnic Institute, Troy, NY, U.S.A.|Department of Mathematical Sciences, Rensselaer Polytechnic Institute, Troy, NY, U.S.A.|Department of Biological Sciences, Rensselaer Polytechnic Institute, Troy, NY, U.S.A.|Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY, U.S.A.|Department of Biological Sciences, Rensselaer Polytechnic Institute, Troy, NY, U.S.A.', '-|-|-|-|-|-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz617', '31384918', 'Time courses utilizing genome scale data are a common approach to identifying the biological pathways that are controlled by the circadian clock, an important regulator of organismal fitness. However, the methods used to detect circadian oscillations in these datasets are not able to accommodate changes in the amplitude of the oscillations over time, leading to an underestimation of the impact of the clock on biological systems.'),
(83024, 2019, 'bio', 'Topology-independent and global protein structure alignment through an FFT-based algorithm', 'Wen, Zeyu|He, Jiahua|Huang, Sheng-You', 'School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, P. R. China.|School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, P. R. China.|School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, P. R. China.', 'China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz609', '31384919', 'Protein structure alignment is one of the fundamental problems in computational structure biology. A variety of algorithms have been developed to address this important issue in the past decade. However, due to their heuristic nature, current structure alignment methods all more or less suffer from suboptimal alignment and/or over-fragmentation and may lead to a biologically wrong alignment. To overcome these limitations, we have developed an accurate topology-independent and global structure alignment method through an FFT-based exhaustive search algorithm, which is referred to as FTAlign.'),
(83025, 2019, 'bio', 'MultiGuideScan: a multi-processing tool for designing CRISPR guide RNA libraries', 'Li, Tao|Wang, Shaokai|Luo, Feng|Wu, Fang-Xiang|Wang, Jianxin', 'School of Computer Science and Engineering, Central South University, Changsha, China.|School of Computer Science and Engineering, Central South University, Changsha, China.|School of Computing, Clemson University, Clemson, SC, USA.|Division of Biomedical Engineering and Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SKS7N5A9, Canada.|School of Computer Science and Engineering, Central South University, Changsha, China.', 'China|China|United States|Canada|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz616', '31386102', 'The recent advance in genome engineering technologies based on CRISPR/Cas9 system is enabling people to systematically understand genomic functions. A short RNA string (the CRISPR guide RNA) can guide the Cas9 endonuclease to specific locations in complex genomes to cut DNA double-strands. The CRISPR guide RNA is essential for gene editing systems. Recently, the GuideScan software is developed to design CRISPR guide RNA libraries, which can be used for genome editing of coding and noncoding genomic regions effectively. However, GuideScan is a serial program and computationally expensive for designing CRISPR guide RNA libraries from large genomes. Here, we present an efficient guide RNA library designing tool (MultiGuideScan) by implementing multiple processes of GuideScan. MultiGuideScan speeds up the guide RNA library designing about 9-12 times on a 32-process mode comparing to GuideScan. MultiGuideScan makes it possible to design guide RNA libraries from large genomes.'),
(83026, 2019, 'bio', 'Nanopype: a modular and scalable nanopore data processing pipeline', 'Giesselmann, Pay|Hetzel, Sara|Müller, Franz-Josef|Meissner, Alexander|Kretzmer, Helene', 'Department of Genome Regulation, Max Planck Institute for Molecular Genetics, Berlin, Germany.|Department of Genome Regulation, Max Planck Institute for Molecular Genetics, Berlin, Germany.|Department of Genome Regulation, Max Planck Institute for Molecular Genetics, Berlin, Germany.|Department of Genome Regulation, Max Planck Institute for Molecular Genetics, Berlin, Germany.|Department of Genome Regulation, Max Planck Institute for Molecular Genetics, Berlin, Germany.', 'Germany|Germany|Germany|Germany|Germany', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz547', '31386105', '-'),
(83027, 2019, 'bio', 'DUETT quantitatively identifies known and novel events in nascent RNA structural dynamics from chemical probing data', 'Xue, Albert Y|Yu, Angela M|Lucks, Julius B|Bagheri, Neda', 'Department of Chemical & Biological Engineering, Evanston, Illinois United States.|Center for Synthetic Biology, Evanston, Illinois United States.|Department of Chemical & Biological Engineering, Evanston, Illinois United States.|Department of Chemical & Biological Engineering, Evanston, Illinois United States.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz449', '31389563', 'RNA molecules can undergo complex structural dynamics, especially during transcription, which influence their biological functions. Recently developed high-throughput chemical probing experiments that study RNA cotranscriptional folding generate nucleotide-resolution reactivities for each length of a growing nascent RNA that reflect structural dynamics. However, the manual annotation and qualitative interpretation of reactivity across these large datasets can be nuanced, laborious, and difficult for new practitioners. We developed a quantitative and systematic approach to automatically detect RNA folding events from these datasets to reduce human bias/error, standardize event discovery, and generate hypotheses about RNA folding trajectories for further analysis and experimental validation.'),
(83028, 2019, 'bio', 'CNVRanger: association analysis of CNVs with gene expression and quantitative phenotypes', 'da Silva, Vinicius|Ramos, Marcel|Groenen, Martien|Crooijmans, Richard|Johansson, Anna|Regitano, Luciana|Coutinho, Luiz|Zimmer, Ralf|Waldron, Levi|Geistlinger, Ludwig', 'Animal Breeding and Genomics, Wageningen University & Research, Wageningen, The Netherlands.|Graduate School of Public Health and Health Policy, City University of New York, New York, NY, USA.|Animal Breeding and Genomics, Wageningen University & Research, Wageningen, The Netherlands.|Animal Breeding and Genomics, Wageningen University & Research, Wageningen, The Netherlands.|Swedish University of Agricultural Sciences, Uppsala, Sweden.|Embrapa Pecuaria Sudeste, São Carlos, Brazil.|Animal Science Department, University of São Paulo, Piracicaba, Brazil.|Institute of Bioinformatics, Ludwig-Maximilians-Universität München, München, Germany.|Graduate School of Public Health and Health Policy, City University of New York, New York, NY, USA.|Graduate School of Public Health and Health Policy, City University of New York, New York, NY, USA.', 'Netherlands|United States|Netherlands|Netherlands|Sweden|Brazil|Brazil|Germany|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz632', '31392308', 'Copy number variation (CNV) is a major type of structural genomic variation that is increasingly studied across different species for association with diseases and production traits. Established protocols for experimental detection and computational inference of CNVs from SNP array and next-generation sequencing data are available. We present the CNVRanger R/Bioconductor package which implements a comprehensive toolbox for structured downstream analysis of CNVs. This includes functionality for summarizing individual CNV calls across a population, assessing overlap with functional genomic regions, and genome-wide association analysis with gene expression and quantitative phenotypes.'),
(83029, 2019, 'bio', 'KRAB ZNF explorer - the online tool for the exploration of the transcriptomic profiles of KRAB-ZNF factors in The Cancer Genome Atlas', 'Cylwa, Rafaxc5x82|Kiexc5x82czewski, Kornel|Machnik, Marta|Oleksiewicz, Urszula|Biecek, Przemysxc5x82aw', 'Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw.|Faculty of Mathematics and Information Science, Warsaw University of Technology.|Department of Cancer Immunology, Poznan University of Medical Sciences, Poznan, Poland.|Department of Cancer Immunology, Poznan University of Medical Sciences, Poznan, Poland.|Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw.', '-|-|Poland|Poland|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz624', '31392309', 'KRAB ZNF explorer is a web-based tool for comprehensive characterization of the mRNA expression status of KRAB-ZNF transcription factors in the data from The Cancer Genome Atlas study. Key functionalities cover a comparative analysis of KRAB-ZNF expression between normal and cancer tissues, an association of KRAB-ZNF expression with various pathological features, including survival analysis, association with global DNA methylation status, analyses of KRAB-ZNF isoform expressions and analysis of KRAB-ZNF levels in normal tissues.'),
(83030, 2019, 'bio', 'scHinter: Imputing dropout events for single-cell RNA-seq data with limited sample size', 'Ye, Pengchao|Ye, Wenbin|Ye, Congting|Li, Shuchao|Ye, Lishan|Ji, Guoli|Wu, Xiaohui', 'Department of Automation, Xiamen University, Xiamen, Fujian, China.|Department of Automation, Xiamen University, Xiamen, Fujian, China.|Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Xiamen, Fujian, China.|Department of Automation, Xiamen University, Xiamen, Fujian, China.|Zhongshan Hospital of Xiamen University, Xiamen, Fujian, China.|Department of Automation, Xiamen University, Xiamen, Fujian, China.|Department of Automation, Xiamen University, Xiamen, Fujian, China.', 'China|China|China|China|China|China|China', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz627', '31392316', 'Single-cell RNA-sequencing (scRNA-seq) is fast becoming a powerful technique for studying dynamic gene regulation at unprecedented resolution. However, scRNA-seq data suffer from problems of extremely high dropout rate and cell-to-cell variability, demanding new methods to recover gene expression loss. Despite the availability of various dropout imputation approaches for scRNA-seq, most studies focus on data with a medium or large number of cells, while few studies have explicitly investigated the differential performance across different sample sizes or the applicability of the approach on small or imbalanced data. It is imperative to develop new imputation approaches with higher generalizability for data with various sample sizes.'),
(83031, 2019, 'bio', 'Context awareness and embedding for biomedical event extraction', 'Yan, Shankai|Wong, Ka-Chun', 'Department of Computer Science, City University of Hong Kong, Hong Kong SAR.|Department of Computer Science, City University of Hong Kong, Hong Kong SAR.', 'Hong Kong|Hong Kong', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz607', '31392318', 'Biomedical event extraction is fundamental for information extraction in molecular biology and biomedical research. The detected events form the central basis for comprehensive biomedical knowledge fusion, facilitating the digestion of massive information influx from literature. Limited by the event context, the existing event detection models are mostly applicable for a single task. A general and scalable computational model is desiderated for biomedical knowledge management.'),
(83032, 2019, 'bio', 'Protein Homeostasis Database: protein quality control in E coli', 'Ramakrishnan, Reshmi|Houben, Bert|Kreft, xc5x81ukasz|Botzki, Alexander|Schymkowitz, Joost|Rousseau, Frederic', 'Switch Laboratory, VIB-KU Leuven Center for Brain & Disease Research, KU Leuven, Leuven, Belgium.|Switch Laboratory, VIB-KU Leuven Center for Brain & Disease Research, KU Leuven, Leuven, Belgium.|VIB Bioinformatics Core, VIB, Rijvisschestraat 120, Gent, Belgium.|VIB Bioinformatics Core, VIB, Rijvisschestraat 120, Gent, Belgium.|Switch Laboratory, VIB-KU Leuven Center for Brain & Disease Research, KU Leuven, Leuven, Belgium.|Switch Laboratory, VIB-KU Leuven Center for Brain & Disease Research, KU Leuven, Leuven, Belgium.', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz628', '31392322', 'In vivo protein folding is governed by molecular chaperones, that escort proteins from their translational birth to their proteolytic degradation. In E. coli the main classes of chaperones that interact with the nascent chain are trigger factor, DnaK/J and GroEL/ES and several authors have performed whole-genome experiments to construct exhaustive client lists for each of these.'),
(83033, 2019, 'bio', 'CROSSMAPPER: estimating cross-mapping rates and optimizing experimental design in multi-species sequencing studies', 'Hovhannisyan, H|Hafez, A|Llorens, C|Gabaldón, T', 'Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Spain.|Biotechvana S.L., Parc Científic Universitat de València.|Biotechvana S.L., Parc Científic Universitat de València.|Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Spain.', 'Spain|-|-|Spain', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz626', '31392323', 'Numerous sequencing studies, including transcriptomics of host-pathogen systems, sequencing of hybrid genomes, xenografts, mixed species systems, metagenomics, and metatranscriptomics, involve samples containing genetic material from divergent organisms. A crucial step in these studies is identifying from which organism each sequencing read originated, and the experimental design should be directed to minimize biases caused by cross-mapping of reads to incorrect source genomes. Additionally, pooling of sufficiently different genetic material into a single sequencing library could significantly reduce experimental costs but requires careful planning and assessment of the impact of cross-mapping. Having these applications in mind we designed Crossmapper, the first to our knowledge tool able to assess cross-mapping prior to sequencing, therefore allowing optimization of experimental design.'),
(83034, 2019, 'bio', 'Tersect: a set theoretical utility for exploring sequence variant data', 'Kurowski, Tomasz J|Mohareb, Fady', 'The Bioinformatics Group, School of Water, Energy and Environment, Cranfield University, College Road, Bedford, UK.|The Bioinformatics Group, School of Water, Energy and Environment, Cranfield University, College Road, Bedford, UK.', 'United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz634', '31392327', 'Comparing genomic features among a large panel of individuals across the same species is considered nowadays a core part of the bioinformatics analyses. This typically involves a series of complex theoretical expressions to compare, intersect, extract symmetric differences between individuals within a large set of genotypes. Several publically available tools are capable of performing such tasks, however, due to the sheer size of variants being queried, such tasks can be computationally expensive with a runtime ranging from few minutes up to several hours depending on the dataset size. This makes existing tools unsuitable for interactive data query or as part of genomic data visualization platforms such as genome browsers. Tersect is a lightweight, high-performance command-line utility which interprets and applies flexible set theoretical expressions to sets of sequence variant data. It can be used both for interactive data exploration and as part of a larger pipeline thanks to its highly optimized storage and indexing algorithms for variant data.'),
(83035, 2019, 'bio', 'Genome-Phenome Explorer (GePhEx): A tool for the visualization and interpretation of phenotypic relationships supported by genetic evidence', 'Farré, Xavier|Spataro, Nino|Haziza, Frederic|Rambla, Jordi|Navarro, Arcadi', 'Institute of Evolutionary Biology (UPF-CSIC), Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain.|Institute of Evolutionary Biology (UPF-CSIC), Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain.|Institute of Evolutionary Biology (UPF-CSIC), Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain.|Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona, Spain.|Institute of Evolutionary Biology (UPF-CSIC), Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain.', 'Spain|Spain|Spain|Spain|Spain', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz622', '31393550', 'Association studies based on SNP arrays and Next Generation Sequencing (NGS) technologies have enabled the discovery of thousands of genetic loci related to human diseases. Nevertheless, their biological interpretation is still elusive, and their medical applications limited. Recently, various tools have been developed to help bridging the gap between genomes and phenomes. To our knowledge, however none of these tools allows users to retrieve the phenotype-wide list of genetic variants that may be linked to a given disease or to visually explore the joint genetic architecture of different pathologies.'),
(83036, 2019, 'bio', 'PeNGaRoo, a combined gradient boosting and ensemble learning framework for predicting non-classical secreted proteins', 'Zhang, Yanju|Yu, Sha|Xie, Ruopeng|Li, Jiahui|Leier, André|Marquez-Lago, Tatiana T|Akutsu, Tatsuya|Smith, A Ian|Ge, Zongyuan|Wang, Jiawei|Lithgow, Trevor|Song, Jiangning', 'Bioinformatics Group, School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China.|Bioinformatics Group, School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China.|Bioinformatics Group, School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China.|Bioinformatics Group, School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China.|Department of Genetics, School of Medicine, University of Alabama at Birmingham, AL, USA.|Department of Genetics, School of Medicine, University of Alabama at Birmingham, AL, USA.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto, Japan.|Infection and Immunity Program, Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC, Australia.|Monash e-Research Centre and Faculty of Engineering, Monash University, VIC, Australia.|Infection and Immunity Program, Biomedicine Discovery Institute and Department of Microbiology, Monash University, Melbourne, VIC, Australia.|Infection and Immunity Program, Biomedicine Discovery Institute and Department of Microbiology, Monash University, Melbourne, VIC, Australia.|Infection and Immunity Program, Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC, Australia.', 'China|China|China|China|United States|United States|Japan|Australia|Australia|Australia|Australia|Australia', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz629', '31393553', 'Gram-positive bacteria have developed secretion systems to transport proteins across their cell wall, a process that plays an important role during host infection. These secretion mechanisms have also been harnessed for therapeutic purposes in many biotechnology applications. Accordingly, the identification of features that select a protein for efficient secretion from these microorganisms has become an important task. Among all the secreted proteins, non-classical secreted proteins are difficult to identify as they lack discernable signal peptide sequences and can make use of diverse secretion pathways. Currently, several computational methods have been developed to facilitate the discovery of such non-classical secreted proteins, however, the existing methods are based on either simulated or limited experimental datasets. In addition, they often employ basic features to train the models in a simple and coarse-grained manner. The availability of more experimentally validated datasets, advanced feature engineering techniques and novel machine learning approaches creates new opportunities for the development of improved predictors of non-classical secreted proteins from sequence data.'),
(83037, 2019, 'bio', 'RICOPILI: Rapid Imputation for COnsortias PIpeLIne', 'Lam, Max|Awasthi, Swapnil|Watson, Hunna J|Goldstein, Jackie|Panagiotaropoulou, Georgia|Trubetskoy, Vassily|Karlsson, Robert|Frei, Oleksander|Fan, Chun-Chieh|De Witte, Ward|Mota, Nina R|Mullins, Niamh|Brügger, Kim|Lee, Hong|Wray, Naomi|Skarabis, Nora|Huang, Hailiang|Neale, Benjamin|Daly, Mark|Mattheissen, Manuel|Walters, Raymond|Ripke, Stephan', 'Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, 415 Main St, Cambridge, MA, USA.|Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, 415 Main St, Cambridge, MA, USA.|Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill,NC, USA.|Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, 415 Main St, Cambridge, MA, USA.|Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, 415 Main St, Cambridge, MA, USA.|Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, 415 Main St, Cambridge, MA, USA.|Department of Medical Epidemiology and Biostatistics (MEB), C8, Karolinska Institutet, Stockholm, Sweden.|NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Ullevål Hospital, building 49, 4956 Nydalen, N-0424 Oslo, Norway.|NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Ullevål Hospital, building 49, 4956 Nydalen, N-0424 Oslo, Norway.|Human Genetics, Radboud University Medical Center, Geert Grooteplein Zuid 10, GA Nijmegen, Netherlands.|Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Montessorilaan 3, HR Nijmegen, Netherlands.|Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, 1425 Madison Ave, New York, NY, USA.|Department of Informatics, University of Bergen, Postboks, Bergen, Norway.|Queensland Statistical Genetics Laboratory, Queensland Institute of Medical Research, 300 Herston Road, Brisbane, Queensland, Australia.|Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia.|Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin, Berlin, Germany.|Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, 415 Main St, Cambridge, MA, USA.|Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, 415 Main St, Cambridge, MA, USA.|Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, 415 Main St, Cambridge, MA, USA.|Department of Psychiatry, Psychosomatics, and Psychotherapy, University of Würzburg, Fuchsleinstr 15, Würzburg, Germany.|Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, 415 Main St, Cambridge, MA, USA.|Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, 415 Main St, Cambridge, MA, USA.', 'United States|United States|United States|United States|United States|United States|Sweden|Norway|Norway|Netherlands|Netherlands|United States|Norway|Australia|Australia|Germany|United States|United States|United States|', 14, '-', 'Journal Article', '10.1093/bioinformatics/btz633', '31393554', 'Genome-wide association study (GWAS) analyses, at sufficient sample sizes and power, have successfully revealed biological insights for several complex traits. RICOPILI, an open sourced Perl-based pipeline was developed to address the challenges of rapidly processing large scale multi-cohort GWAS studies including quality control, imputation and downstream analyses. The pipeline is computationally efficient with portability to a wide range of high-performance computing (HPC) environments.'),
(83038, 2019, 'bio', 'Integrating ab initio and template-based algorithms for protein-protein complex structure prediction', 'Vangaveti, Sweta|Vreven, Thom|Zhang, Yang|Weng, Zhiping', 'Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts.|Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts.|Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan.|Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts.', '-|-|-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz623', '31393558', 'Template-based and template-free methods have both been widely used in predicting the structures of protein-protein complexes. Template-based modeling is effective when a reliable template is available, while template-free methods are required for predicting the binding modes or interfaces that have not been previously observed. Our goal is to combine the two methods to improve computational protein-protein complex structure prediction.'),
(83039, 2019, 'bio', 'G3viz: an R package to interactively visualize genetic mutation data using a lollipop-diagram', 'Guo, Xin|Zhang, Bo|Zeng, Wenqi|Zhao, Shuting|Ge, Dongliang', 'School of Innovation and Entrepreneurship, Southern University of Science and Technology, 1088 Xueyuan Lu, Nanshan, Shenzhen, P.R. China.|School of Innovation and Entrepreneurship, Southern University of Science and Technology, 1088 Xueyuan Lu, Nanshan, Shenzhen, P.R. China.|Shenzhen Apostle-SUSTECH LTD, 6 Baoxing Lu, Baoan, Shenzhen, P.R. China.|Shenzhen Apostle-SUSTECH LTD, 6 Baoxing Lu, Baoan, Shenzhen, P.R. China.|School of Innovation and Entrepreneurship, Southern University of Science and Technology, 1088 Xueyuan Lu, Nanshan, Shenzhen, P.R. China.', 'China|China|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz631', '31393560', 'The lollipop-diagram is one of the widely used graphical representations to visualize and explore translational effects of genetic mutations in cancer genomics. However, an easy-to-use lollipop-diagram tool with full functionality is still lacking. Here, we introduce g3viz, an R package that enables researchers to explore genetic mutation data using a lollipop-diagram in a web browser. With a few lines of R code, users can interactively visualize data details, annotate findings, and export resultant diagrams in high-quality figures. Because of usefulness and usability, g3viz can be generally exploited by researchers with different levels of bioinformatics skills and programming experience.'),
(83040, 2019, 'bio', 'pWGBSSimla: a profile-based whole-genome bisulfite sequencing data simulator incorporating methylation QTLs, allele-specific methylations and differentially methylated regions', 'Chung, Ren-Hua|Kang, Chen-Yu', 'Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan.|Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan.', '-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz635', '31397839', 'DNA methylation plays an important role in regulating gene expression. DNA methylation is commonly analyzed using bisulfite sequencing (BS-seq)-based designs, such as whole-genome bisulfite sequencing (WGBS), reduced representation bisulfite sequencing (RRBS), and oxidative bisulfite sequencing (oxBS-seq). Furthermore, there has been growing interest in investigating the roles that genetic variants play in changing the methylation levels (i.e., methylation quantitative trait loci or meQTLs), how methylation regulates the imprinting of gene expression (i.e., allele-specific methylation or ASM), and the differentially methylated regions (DMRs) among different cell types. However, none of the current simulation tools can generate different BS-seq data types (e.g., WGBS, RRBS, and oxBS-seq) while modeling meQTLs, ASM, and DMRs.'),
(83041, 2019, 'bio', 'MsPAC: A tool for haplotype-phased structural variant detection', 'Rodriguez, Oscar L|Ritz, Anna|Sharp, Andrew J|Bashir, Ali', 'Department of Genetics and Data Science, Icahn School of Medicine at Mount Sinai, New York, USA.|Biology Department, Reed College, Portland, USA.|Department of Genetics and Data Science, Icahn School of Medicine at Mount Sinai, New York, USA.|Department of Genetics and Data Science, Icahn School of Medicine at Mount Sinai, New York, USA.', 'United States|United States|United States|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz618', '31397844', 'While next-generation sequencing (NGS) has dramatically increased the availability of genomic data, phased genome assembly and structural variant (SV) analyses are limited by NGS read lengths. Long-read sequencing from Pacific Biosciences (PacBio) and NGS barcoding from 10X Genomics (10X) hold the potential for far more comprehensive views of individual genomes. Here we present MsPAC, a tool that combines both technologies to partition reads, assemble haplotypes (via existing software), and convert assemblies into high-quality, phased SV predictions. MsPAC represents a framework for haplotype-resolved SV calls that moves one step closer to fully resolved, diploid genomes.'),
(83042, 2019, 'bio', 'Graph convolution for predicting associations between miRNA and drug resistance', 'Huang, Yu-An|Hu, Pengwei|Chan, Keith C C|You, Zhu-Hong', 'Department of Computing, Hong Kong Polytechnic University, Hong Kong.|Department of Computing, Hong Kong Polytechnic University, Hong Kong.|Department of Computing, Hong Kong Polytechnic University, Hong Kong.|Department of Computing, Hong Kong Polytechnic University, Hong Kong.', 'Hong Kong|Hong Kong|Hong Kong|Hong Kong', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz621', '31397851', 'MicroRNA (miRNA) therapeutics is becoming increasingly important. However, aberrant expression of miRNAs is known to cause drug resistance and can become an obstacle for miRNA-based therapeutics. At present, little is known about associations between miRNA and drug resistance and there is no computational tool available for predicting such association relationship. Since it is known that miRNAs can regulate genes that encode specific proteins that are keys for drug efficacy, we propose here a computational approach, called GCMDR, for finding a three-layer latent factor model that can be used to predict miRNA-drug resistance associations.'),
(83043, 2019, 'bio', 'Using multiple measurements of tissue to estimate subject- and cell-type-specific gene expression', 'Wang, Jiebiao|Devlin, Bernie|Roeder, Kathryn', 'Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA, USA.|Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.|Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA, USA.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz619', '31400192', 'Patterns of gene expression, quantified at the level of tissue or cells, can inform on etiology of disease. There are now rich resources for tissue-level (bulk) gene expression data, which have been collected from thousands of subjects, and resources involving single-cell RNA-sequencing (scRNA-seq) data are expanding rapidly. The latter yields cell type information, although the data can be noisy and typically are derived from a small number of subjects.'),
(83044, 2019, 'bio', 'Performance of gene expression analyses using de novo assembled transcripts in polyploid species', 'Chen, Ling-Yun|Morales-Briones, Diego F|Passow, Courtney N|Ya, Yang', 'Department of Plant and Microbial Biology, University of Minnesota, Twin Cities, 1445 Gortner Avenue, Saint Paul, MN.|Department of Plant and Microbial Biology, University of Minnesota, Twin Cities, 1445 Gortner Avenue, Saint Paul, MN.|Department of Ecology Evolution and Behavior, University of Minnesota, Twin Cities, 1500 Gortner Avenue, Saint Paul, MN.|Department of Plant and Microbial Biology, University of Minnesota, Twin Cities, 1445 Gortner Avenue, Saint Paul, MN.', '-|-|-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz620', '31400193', 'Quality of gene expression analyses using de novo assembled transcripts in species that experienced recent polyploidization remains unexplored.'),
(83045, 2019, 'bio', 'U-PASS: unified power analysis and forensics for qualitative traits in genetic association studies', 'Gao, Zheng|Terhorst, Jonathan|Van Hout, Cristopher|Stoev, Stilian', 'Department of Statistics, University of Michigan, Ann Arbor, MI, USA.|Department of Statistics, University of Michigan, Ann Arbor, MI, USA.|Regeneron Genetics Center, Tarrytown, NY USA.|Department of Statistics, University of Michigan, Ann Arbor, MI, USA.', 'United States|United States|United States|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz637', '31400194', 'Despite the availability of existing calculators for statistical power analysis in genetic association studies, there has not been a model-invariant and test-independent tool that allows for both planning of prospective studies and systematic review of reported findings. In this work, we develop a web-based application U-PASS (Unified Power analysis of ASsociation Studies), implementing a unified framework for the analysis of common association tests for binary qualitative traits. The application quantifies the shared asymptotic power limits of the common association tests, and visualizes the fundamental statistical trade-off between risk allele frequency (RAF) and odds ratio (OR). The application also addresses the applicability of asymptotics-based power calculations in finite samples, and provides guidelines for single-SNP-based association tests. In addition to designing prospective studies, U-PASS enables researchers to retrospectively assess the statistical validity of previously reported associations.'),
(83046, 2019, 'bio', 'BBKNN: Fast Batch Alignment of Single Cell Transcriptomes', 'Polaxc5x84ski, Krzysztof|Park, Jong-Eun|Young, Matthew D|Miao, Zhichao|Meyer, Kerstin B|Teichmann, Sarah A', 'Cellular Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.|Cellular Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.|Cellular Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.|Cellular Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.|Cellular Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.|Cellular Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 32, '-', 'Journal Article', '10.1093/bioinformatics/btz625', '31400197', 'Increasing numbers of large scale single cell RNA-Seq projects are leading to a data explosion, which can only be fully exploited through data integration. A number of methods have been developed to combine diverse datasets by removing technical batch effects, but most are computationally intensive. To overcome the challenge of enormous datasets, we have developed BBKNN, an extremely fast graph-based data integration algorithm. We illustrate the power of BBKNN on large scale mouse atlasing data, and favourably benchmark its run time against a number of competing methods.'),
(83047, 2019, 'bio', 'Alternating EM algorithm for a bilinear model in isoform quantification from RNA-seq data', 'Deng, Wenjiang|Mou, Tian|Niu, Nifang|Wang, Liewei|Pawitan, Yudi|Vu, Trung Nghia', 'Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.|Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN, USA.|Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, 200 First Street SW, Rochester, MN , USA.|Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, 200 First Street SW, Rochester, MN , USA.|Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.|Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.', 'Sweden|United States|United States|United States|Sweden|Sweden', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz640', '31400221', 'Estimation of isoform-level gene expression from RNA-seq data depends on simplifying assumptions, such as uniform read distribution, that are easily violated in real data. Such violations typically lead to biased estimates. Most existing methods provide bias correction step(s), which is based on biological considerations-such as GC content- and applied in single samples separately. The main problem is that not all biases are known.'),
(83048, 2019, 'bio', 'Haplotype-aware graph indexes', 'Sirén, Jouni|Garrison, Erik|Novak, Adam M|Paten, Benedict|Durbin, Richard', 'UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA 95064, USA.|Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, UK.|UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA 95064, USA.|UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA 95064, USA.|Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz575', '31406990', 'The variation graph toolkit (VG) represents genetic variation as a graph. Although each path in the graph is a potential haplotype, most paths are non-biological, unlikely recombinations of true haplotypes.'),
(83049, 2019, 'bio', 'ROBOKOP: An Abstraction Layer and User Interface for Knowledge Graphs to Support Question Answering', 'Morton, Kenneth|Wang, Patrick|Bizon, Chris|Cox, Steven|Balhoff, James|Kebede, Yaphet|Fecho, Karamarie|Tropsha, Alexander', 'CoVar Applied Technologies, Durham, North Carolina, USA.|CoVar Applied Technologies, Durham, North Carolina, USA.|Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.|Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.|Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.|Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.|Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.|School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz604', '31410449', 'Knowledge graphs are quickly becoming a commonplace tool for storing relationships between entities from which higher-level reasoning can be conducted. Knowledge graphs are typically stored in a graph-database format, and graph-database queries can be used to answer questions of interest posed by users such as biomedical researchers. For simple queries, the inclusion of direct connections in the knowledge graph and the storage and analysis of query results are straightforward, however, for complex queries, these capabilities become exponentially more challenging with each increase in complexity of the query. For instance, one relatively complex query can yield a knowledge graph with hundreds of thousands of query results. Thus, the ability to efficiently query, store, rank, and explore subgraphs of a complex knowledge graph represents a major challenge to any effort designed to exploit the use of knowledge graphs for applications in biomedical research and other domains. We present ROBOKOP (Reasoning Over Biomedical Objects linked in Knowledge Oriented Pathways) as an abstraction layer and user interface to more easily query knowledge graphs and store, rank, and explore query results.'),
(83050, 2019, 'bio', 'Machine Learning Empowers Phosphoproteome Prediction in Cancers', 'Li, Hongyang|Guan, Yuanfang', 'Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA.|Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA.', 'United States|United States', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz639', '31410451', 'Reversible protein phosphorylation is an essential post-translational modification regulating protein functions and signaling pathways in many cellular processes. Aberrant activation of signaling pathways often contributes to cancer development and progression. The mass spectrometry-based phosphoproteomics technique is a powerful tool to investigate the site-level phosphorylation of the proteome in a global fashion, paving the way for understanding the regulatory mechanisms underlying cancers. However, this approach is time-consuming and requires expensive instruments, specialized expertise, and a large amount of starting material. An alternative in silico approach is predicting the phosphoproteomic profiles of cancer patients from the available proteomic, transcriptomic, and genomic data.'),
(83051, 2019, 'bio', 'EPIP: A novel approach for condition-specific enhancer-promoter interaction prediction', 'Talukder, Amlan|Saadat, Samaneh|Li, Xiaoman|Hu, Haiyan', 'Department of Computer Science, University of Central Florida, Orlando, FL, USA.|Department of Computer Science, University of Central Florida, Orlando, FL, USA.|Burnett School of Biomedical Science, College of Medicine, University of Central Orlando, FL, USA.|Department of Computer Science, University of Central Florida, Orlando, FL, USA.', 'United States|United States|United States|United States', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz641', '31410461', 'The identification of enhancer-promoter interactions (EPIs) especially cell-specific ones is important for the study of gene transcriptional regulation. Existing experimental approaches for EPI identification are still expensive, and the available computational methods either do not consider or have low-performance in predicting cell-specific EPIs.'),
(83052, 2019, 'bio', 'AntiHIV-Pred: Web-resource for in silico prediction of anti-HIV/AIDS activity', 'Stolbov, Leonid|Druzhilovskiy, Dmitry|Rudik, Anastasia|Filimonov, Dmitry|Poroikov, Vladimir|Nicklaus, Marc', 'Institute of Biomedical Chemistry, 10/8 Pogodinskaya Str., Moscow, Russia.|Institute of Biomedical Chemistry, 10/8 Pogodinskaya Str., Moscow, Russia.|Institute of Biomedical Chemistry, 10/8 Pogodinskaya Str., Moscow, Russia.|Institute of Biomedical Chemistry, 10/8 Pogodinskaya Str., Moscow, Russia.|Institute of Biomedical Chemistry, 10/8 Pogodinskaya Str., Moscow, Russia.|Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, Maryland, United States.', '-|-|-|-|-|United States', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz638', '31418763', 'Identification of new molecules promising for treatment of HIV-infection and HIV-associated disorders remains an important task in order to provide safer and more effective therapies. Utilization of prior knowledge by application of computer-aided drug discovery approaches reduces time & financial expenses and increases the chances of positive results in anti-HIV R&D. To provide the scientific community with a tool that allows estimating of potential agents for treatment of HIV-infection and its comorbidities, we have created a freely-available web-resource for prediction of relevant biological activities based on the structural formulae of drug-like molecules.'),
(83053, 2019, 'bio', 'StrainHub: A phylogenetic tool to construct pathogen transmission networks', 'de Bernardi Schneider, Adriano|Ford, Colby T|Hostager, Reilly|Williams, John|Cioce, Michael|Çatalyürek, Ümit V|Wertheim, Joel O|Janies, Daniel', 'Department of Medicine, University of California San Diego, San Diego, United States of America.|Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, United States of America.|Department of Medicine, University of California San Diego, San Diego, United States of America.|Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, United States of America.|Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, United States of America.|School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, United States of America.|Department of Medicine, University of California San Diego, San Diego, United States of America.|Department of Medicine, University of California San Diego, San Diego, United States of America.', 'United States|United States|United States|United States|United States|Georgia|United States|United S', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz646', '31418766', 'In exploring the epidemiology of infectious diseases, networks have been used to reconstruct contacts among individuals and/or populations. Summarizing networks using pathogen metadata (e.g., host species and place of isolation) and a phylogenetic tree is a nascent, alternative approach. In this paper, we introduce a tool for reconstructing transmission networks in arbitrary space from phylogenetic information and metadata. Our goals are to provide a means of deriving new insights and infection control strategies based on the dynamics of the pathogen lineages derived from networks and centrality metrics. We created a web-based application, called StrainHub, in which a user can input a phylogenetic tree based on genetic or other data along with characters derived from metadata using their preferred tree search method. StrainHub generates a transmission network based on character state changes in metadata, such as place or source of isolation, mapped on the phylogenetic tree. The user has the option to calculate centrality metrics on the nodes including betweenness, closeness, degree, and a new metric, the source/hub ratio. The outputs include the network with values for metrics on its nodes and the tree with characters reconstructed. All of these results can be exported for further analysis.');
