INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(81934, 2019, 'bio', 'A network-based zoning for parallel whole-cell simulation', 'Das, Barnali|Patil, Abhijeet Rajendra|Mitra, Pralay', 'Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal, India.|Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal, India.|Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal, India.', 'India|India|India', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty530', '29955764', 'In Computational Cell Biology, whole-cell modeling and simulation is an absolute requirement to analyze and explore the cell of an organism. Despite few individual efforts on modeling, the prime obstacle hindering its development and progress is its compute-intensive nature. Towards this end, little knowledge is available on how to reduce the enormous computational overhead and which computational systems will be of use.'),
(81935, 2018, 'bio', 'Hybrid correction of highly noisy long reads using a variable-order de Bruijn graph', 'Morisse, Pierre|Lecroq, Thierry|Lefebvre, Arnaud', 'Normandie Univ, UNIROUEN, LITIS, Rouen, France.|Normandie Univ, UNIROUEN, LITIS, Rouen, France.|Normandie Univ, UNIROUEN, LITIS, Rouen, France.', 'France|France|France', 4, '-', 'Journal Article', '10.1093/bioinformatics/bty521', '29955770', 'The recent rise of long read sequencing technologies such as Pacific Biosciences and Oxford Nanopore allows to solve assembly problems for larger and more complex genomes than what allowed short reads technologies. However, these long reads are very noisy, reaching an error rate of around 10-15% for Pacific Biosciences, and up to 30% for Oxford Nanopore. The error correction problem has been tackled by either self-correcting the long reads, or using complementary short reads in a hybrid approach. However, even though sequencing technologies promise to lower the error rate of the long reads below 10%, it is still higher in practice, and correcting such noisy long reads remains an issue.'),
(81936, 2019, 'bio', 'SLIDE - a web-based tool for interactive visualization of large-scale - omics data', 'Ghosh, Soumita|Datta, Abhik|Tan, Kaisen|Choi, Hyungwon', 'Institute of Molecular and Cell Biology, A*STAR, Singapore, Singapore.|Centre for BioImaging Sciences and Department of Biological Sciences, National University of Singapore, Singapore, Singapore.|Department of Otolaryngology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore and.|Institute of Molecular and Cell Biology, A*STAR, Singapore, Singapore.', 'Singapore|Singapore|Singapore|Singapore', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty534', '29955804', 'Data visualization is often regarded as a post hoc step for verifying statistically significant results in the analysis of high-throughput datasets. This common practice leaves a large amount of raw data behind, from which more information can be extracted. However, existing solutions do not provide capabilities to explore large-scale raw datasets using biologically sensible queries, nor do they allow user interaction based real-time customization of graphics. To address these drawbacks, we have designed an open-source, web-based tool called Systems-Level Interactive Data Exploration, or SLIDE to visualize large-scale -omics data interactively. SLIDEs interface makes it easier for scientists to explore quantitative expression data in multiple resolutions in a single screen.'),
(81937, 2018, 'bio', 'LncmiRSRN: identification and analysis of long non-coding RNA related miRNA sponge regulatory network in human cancer', 'Zhang, Junpeng|Liu, Lin|Li, Jiuyong|Le, Thuc Duy', 'School of Engineering, Dali University, Dali, Yunnan, China.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, Australia.', 'China|Australia|Australia|Australia', 14, '-', 'Journal Article', '10.1093/bioinformatics/bty525', '29955818', 'MicroRNAs (miRNAs) are small non-coding RNAs with the length of xe2x88xbc22 nucleotides. miRNAs are involved in many biological processes including cancers. Recent studies show that long non-coding RNAs (lncRNAs) are emerging as miRNA sponges, playing important roles in cancer physiology and development. Despite accumulating appreciation of the importance of lncRNAs, the study of their complex functions is still in its preliminary stage. Based on the hypothesis of competing endogenous RNAs (ceRNAs), several computational methods have been proposed for investigating the competitive relationships between lncRNAs and miRNA target messenger RNAs (mRNAs). However, when the mRNAs are released from the control of miRNAs, it remains largely unknown as to how the sponge lncRNAs influence the expression levels of the endogenous miRNA targets.'),
(81938, 2018, 'bio', 'MetaboAnalystR: an R package for flexible and reproducible analysis of metabolomics data', 'Chong, Jasmine|Xia, Jianguo', 'School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, Australia.', 'Australia|Australia', 113, '-', 'Journal Article', '10.1093/bioinformatics/bty528', '29955821', 'The MetaboAnalyst web application has been widely used for metabolomics data analysis and interpretation. Despite its user-friendliness, the web interface has presented its inherent limitations (especially for advanced users) with regard to flexibility in creating customized workflow, support for reproducible analysis, and capacity in dealing with large data. To address these limitations, we have developed a companion R package (MetaboAnalystR) based on the R code base of the web server. The package has been thoroughly tested to ensure that the same R commands will produce identical results from both interfaces. MetaboAnalystR complements the MetaboAnalyst web server to facilitate transparent, flexible and reproducible analysis of metabolomics data.'),
(81939, 2018, 'bio', 'MrBait: universal identification and design of targeted-enrichment capture probes', 'Chafin, Tyler K|Douglas, Marlis R|Douglas, Michael E', 'Department of Biological Sciences, University of Arkansas, Fayetteville, AR, USA.|Department of Biological Sciences, University of Arkansas, Fayetteville, AR, USA.|Department of Biological Sciences, University of Arkansas, Fayetteville, AR, USA.', 'United States|United States|United States', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty548', '29961853', 'It is a non-trivial task to identify and design capture probes (baits) for the diverse array of targeted-enrichment methods now available (e.g. ultra-conserved elements, anchored hybrid enrichment, RAD-capture). This often involves parsing large genomic alignments, followed by multiple steps of curating candidate genomic regions to optimize targeted information content (e.g. genetic variation) and to minimize potential probe dimerization and non-target enrichment.'),
(81940, 2019, 'bio', 'Unraveling the role of low-frequency mutated genes in breast cancer', 'Lusito, Eleonora|Felice, Barbara|DArio, Giovanni|Ogier, Alessandro|Montani, Francesca|Di Fiore, Pier Paolo|Bianchi, Fabrizio', 'Molecular Medicine Program, European Institute of Oncology, Milan, Italy.|IFOM, The FIRC Institute for Molecular Oncology Foundation, Milan, Italy.|Molecular Medicine Program, European Institute of Oncology, Milan, Italy.|Department of Experimental Oncology, European Institute of Oncology, Milan, Italy.|Molecular Medicine Program, European Institute of Oncology, Milan, Italy.|Molecular Medicine Program, European Institute of Oncology, Milan, Italy.|ISBREMIT, Institute for Stem-cell Biology, Regenerative Medicine and Innovative Therapies, IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo (FG), Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty520', '29961866', 'Breast cancer is the most commonly diagnosed malignancy in women and the second cause of cancer death in developed countries. While advancements in early detection and therapeutic options have led to a significant decrease in mortality, response to treatment is affected by the genetic heterogeneity of the disease. Recent genome-wide DNA mutation analyses revealed the existence of hundreds of low-frequency mutated genes, in addition to known cancer drivers: a finding that is prompting research into the impact of these genes on the pathogenesis of the disease.'),
(81941, 2019, 'bio', 'MetaboRank: network-based recommendation system to interpret and enrich metabolomics results', 'Frainay, Clxc3xa9ment|Aros, Sandrine|Chazalviel, Maxime|Garcia, Thomas|Vinson, Florence|Weiss, Nicolas|Colsch, Benoit|Sedel, Frxc3xa9dxc3xa9ric|Thabut, Dominique|Junot, Christophe|Jourdan, Fabien', 'Toxalim, Universitxc3xa9 de Toulouse, INRA, Universitxc3xa9 de Toulouse 3 Paul Sabatier, Toulouse, France.|Medday Pharmaceuticals, Paris, France.|Medday Pharmaceuticals, Paris, France.|Toxalim, Universitxc3xa9 de Toulouse, INRA, Universitxc3xa9 de Toulouse 3 Paul Sabatier, Toulouse, France.|Toxalim, Universitxc3xa9 de Toulouse, INRA, Universitxc3xa9 de Toulouse 3 Paul Sabatier, Toulouse, France.|Unitxc3xa9 de Rxc3xa9animation Neurologique, Dxc3xa9partement de Neurologie, Pxc3xb4le des Maladies du Systxc3xa8me Nerveux Central, Groupement Hospitalier Pitixc3xa9-Salpxc3xaatrixc3xa8re Charles Foix, Assistance Publique - Hxc3xb4pitaux de Paris, Paris, France.|Service de Pharmacologie et Immunoanalyse (SPI), CEA, INRA, Universitxc3xa9 Paris-Saclay, MetaboHUB, Gif-sur-Yvette, France and.|Medday Pharmaceuticals, Paris, France.|Brain Liver Pitixc3xa9-Salpxc3xaatrixc3xa8re (BLIPS) Study Group, Groupement Hospitalier Pitixc3xa9-Salpxc3xaatrixc3xa8re-Charles Foix, Assistance Publique - Hxc3xb4pitaux de Paris & INSERM UMR_S 938, CDR Saint-Antoine Maladies Mxc3xa9taboliques, Biliaires et Fibro-inflammatoire du Foie & Institut de Cardiomxc3xa9tabolisme et Nutrition, ICAN, Paris, France.|Service de Pharmacologie et Immunoanalyse (SPI), CEA, INRA, Universitxc3xa9 Paris-Saclay, MetaboHUB, Gif-sur-Yvette, France and.|Toxalim, Universitxc3xa9 de Toulouse, INRA, Universitxc3xa9 de Toulouse 3 Paul Sabatier, Toulouse, France.', 'France|France|France|France|France|France|France|France|France|France|France', 4, '-', 'Journal Article', '10.1093/bioinformatics/bty577', '29982278', 'Metabolomics has shown great potential to improve the understanding of complex diseases, potentially leading to therapeutic target identification. However, no single analytical method allows monitoring all metabolites in a sample, resulting in incomplete metabolic fingerprints. This incompleteness constitutes a stumbling block to interpretation, raising the need for methods that can enrich those fingerprints. We propose MetaboRank, a new solution inspired by social network recommendation systems for the identification of metabolites potentially related to a metabolic fingerprint.'),
(81942, 2019, 'bio', 'PepBDB: a comprehensive structural database of biological peptide-protein interactions', 'Wen, Zeyu|He, Jiahua|Tao, Huanyu|Huang, Sheng-You', 'Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China.|Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China.|Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China.|Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China.', 'China|China|China|China', 4, '-', 'Journal Article', '10.1093/bioinformatics/bty579', '29982280', 'A structural database of peptide-protein interactions is important for drug discovery targeting peptide-mediated interactions. Although some peptide databases, especially for special types of peptides, have been developed, a comprehensive database of cleaned peptide-protein complex structures is still not available. Such cleaned structures are valuable for docking and scoring studies in structure-based drug design. Here, we have developed PepBDB-a curated Peptide Binding DataBase of biological complex structures from the Protein Data Bank (PDB). PepBDB presents not only cleaned structures but also extensive information about biological peptide-protein interactions, and allows users to search the database with a variety of options and interactively visualize the search results.'),
(81943, 2018, 'bio', 'GATK PathSeq: a customizable computational tool for the discovery and identification of microbial sequences in libraries from eukaryotic hosts', 'Walker, Mark A|Pedamallu, Chandra Sekhar|Ojesina, Akinyemi I|Bullman, Susan|Sharpe, Ted|Whelan, Christopher W|Meyerson, Matthew', 'Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA.|Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA.|University of Alabama at Birmingham (UAB), Birmingham, AL, USA.|Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA.|Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA.|Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA.|Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA.', 'United States|United States|United States|United States|United States|United States|United States', 7, '-', 'Journal Article', '10.1093/bioinformatics/bty501', '29982281', 'We present an updated version of our computational pipeline, PathSeq, for the discovery and identification of microbial sequences in genomic and transcriptomic libraries from eukaryotic hosts. This pipeline is available in the Genome Analysis Toolkit (GATK) as a suite of configurable tools that can report the microbial composition of DNA or RNA short-read sequencing samples and identify unknown sequences for downstream assembly of novel organisms. GATK PathSeq enables sample analysis in minutes at low cost. In addition, these tools are built with the GATK engine and Apache Spark framework, providing robust, rapid parallelization of read quality filtering, host subtraction and microbial alignment in workstation, cluster and cloud environments.'),
(81944, 2019, 'bio', 'SATurn: a modular bioinformatics framework for the design of robust maintainable web-based and standalone applications', 'Damerell, David R|Strain-Damerell, Claire|Garsot, Sefa|Joyce, Stephen P|Barrett, Paul|Marsden, Brian D', 'Structural Genomics Consortium, NDM, University of Oxford, Oxford, UK.|Structural Genomics Consortium, NDM, University of Oxford, Oxford, UK.|Structural Genomics Consortium, NDM, University of Oxford, Oxford, UK.|Structural Genomics Consortium, NDM, University of Oxford, Oxford, UK.|Structural Genomics Consortium, NDM, University of Oxford, Oxford, UK.|Structural Genomics Consortium, NDM, University of Oxford, Oxford, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty549', '29982288', 'SATurn is a modular, open-source, bioinformatics platform designed to specifically address the problems of maintenance and longevity commonly associated with the development of simple tools funded by academic research grants. Applications developed in SATurn can be deployed as web-based tools, standalone applications or hybrid tools which have the benefits of both. Within the Structural Genomics Consortium we have utilized SATurn to create a bioinformatics portal which routinely supports a diverse group of scientists including those interested in structural biology, cloning, glycobiology and chemical biology.'),
(81945, 2018, 'bio', 'On the power of epigenome-wide association studies using a disease-discordant twin design', 'Li, Weilong|Christiansen, Lene|Hjelmborg, Jacob|Baumbach, Jan|Tan, Qihua', 'Epidemiology and Biostatistics, Department of Public Health, University of Southern Denmark, Odense, Denmark.|Epidemiology and Biostatistics, Department of Public Health, University of Southern Denmark, Odense, Denmark.|Epidemiology and Biostatistics, Department of Public Health, University of Southern Denmark, Odense, Denmark.|Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark.|Epidemiology and Biostatistics, Department of Public Health, University of Southern Denmark, Odense, Denmark.', 'Denmark|Denmark|Denmark|Denmark|Denmark', 8, '-', 'Journal Article', '10.1093/bioinformatics/bty532', '29982314', 'Many studies have investigated the association between DNA methylation alterations and disease occurrences using two design paradigms, traditional case-control and disease-discordant twins. In the disease-discordant twin design, the affected twin serves as the case and the unaffected twin serves as the control. Theoretically the twin design takes advantage of controlling for the shared genetic make-up, but it is still highly debatable if and how much researchers may benefit from such a design over the traditional case-control design.'),
(81946, 2019, 'bio', 'Compound-protein interaction prediction with end-to-end learning of neural networks for graphs and sequences', 'Tsubaki, Masashi|Tomii, Kentaro|Sese, Jun', 'National Institute of Advanced Industrial Science and Technology, Artificial Intelligence Research Center, Tokyo, Japan.|National Institute of Advanced Industrial Science and Technology, Artificial Intelligence Research Center, Tokyo, Japan.|National Institute of Advanced Industrial Science and Technology, Artificial Intelligence Research Center, Tokyo, Japan.', 'Japan|Japan|Japan', 30, '-', 'Journal Article', '10.1093/bioinformatics/bty535', '29982330', 'In bioinformatics, machine learning-based methods that predict the compound-protein interactions (CPIs) play an important role in the virtual screening for drug discovery. Recently, end-to-end representation learning for discrete symbolic data (e.g. words in natural language processing) using deep neural networks has demonstrated excellent performance on various difficult problems. For the CPI problem, data are provided as discrete symbolic data, i.e. compounds are represented as graphs where the vertices are atoms, the edges are chemical bonds, and proteins are sequences in which the characters are amino acids. In this study, we investigate the use of end-to-end representation learning for compounds and proteins, integrate the representations, and develop a new CPI prediction approach by combining a graph neural network (GNN) for compounds and a convolutional neural network (CNN) for proteins.'),
(81947, 2018, 'bio', 'iS-CellR: a user-friendly tool for analyzing and visualizing single-cell RNA sequencing data', 'Patel, Mitulkumar V', 'Immunocore Ltd, Abingdon, Oxfordshire, UK.', 'United Kingdom', 6, '-', 'Journal Article', '10.1093/bioinformatics/bty517', '29982379', 'Interactive platform for single-cell RNA-sequencing (iS-CellR) is a web-based Shiny application that is designed to provide user-friendly, comprehensive analysis of single-cell RNA sequencing data. iS-CellR has the capability to run on any modern web browser and provides an accessible graphical user interface that enables the user to perform complex single-cell RNA-sequencing analysis without requiring programming skills.'),
(81948, 2019, 'bio', 'Differential coexpression in human tissues and the confounding effect of mean expression levels', 'Farahbod, Marjan|Pavlidis, Paul', 'Graduate program in Bioinformatics, University of British Columbia, Vancouver, Canada.|Department of Psychiatry, University of British Columbia, Vancouver, Canada.', 'Canada|Canada', 3, '-', 'Journal Article', '10.1093/bioinformatics/bty538', '29982380', 'Differential coexpression-the alteration of gene coexpression patterns observed in different biological conditions-has been proposed to be a mechanism for revealing rewiring of transcription regulatory networks. Despite wide use of methods for differential coexpression analysis, the phenomenon has not been well-studied. In particular, in many applications, differential coexpression is confounded with differential expression, that is, changes in average levels of expression across conditions. This confounding, despite affecting the interpretation of the differential coexpression, has rarely been studied.'),
(81949, 2019, 'bio', 'Statistical tests for detecting variance effects in quantitative trait studies', 'Dumitrascu, Bianca|Darnell, Gregory|Ayroles, Julien|Engelhardt, Barbara E', 'Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA.|Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA.|Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.|Department of Computer Science, Princeton University, Princeton, NJ, USA.', 'United States|United States|United States|United States', 5, '-', 'Journal Article', '10.1093/bioinformatics/bty565', '29982387', 'Identifying variants, both discrete and continuous, that are associated with quantitative traits, or QTs, is the primary focus of quantitative genetics. Most current methods are limited to identifying mean effects, or associations between genotype or covariates and the mean value of a quantitative trait. It is possible, however, that a variant may affect the variance of the quantitative trait in lieu of, or in addition to, affecting the trait mean. Here, we develop a general methodology to identify covariates with variance effects on a quantitative trait using a Bayesian heteroskedastic linear regression model (BTH). We compare BTH with existing methods to detect variance effects across a large range of simulations drawn from scenarios common to the analysis of quantitative traits.'),
(81950, 2019, 'bio', 'LigVoxel: inpainting binding pockets using 3D-convolutional neural networks', 'Skalic, Miha|Varela-Rial, Alejandro|Jimxc3xa9nez, Josxc3xa9|Martxc3xadnez-Rosell, Gerard|De Fabritiis, Gianni', 'Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB).|Acellera, Barcelona Biomedical Research Park (PRBB), Doctor Aiguader 88, Barcelona, Spain.|Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB).|Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB).|Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB).', '-|Spain|-|-|-', 11, '-', 'Journal Article', '10.1093/bioinformatics/bty583', '29982392', 'Structure-based drug discovery methods exploit protein structural information to design small molecules binding to given protein pockets. This work proposes a purely data driven, structure-based approach for imaging ligands as spatial fields in target protein pockets. We use an end-to-end deep learning framework trained on experimental protein-ligand complexes with the intention of mimicking a chemists intuition at manually placing atoms when designing a new compound. We show that these models can generate spatial images of ligand chemical properties like occupancy, aromaticity and donor-acceptor matching the protein pocket.'),
(81951, 2019, 'bio', 'BrainEXP: a database featuring with spatiotemporal expression variations and co-expression organizations in human brains', 'Jiao, Chuan|Yan, Pengpeng|Xia, Cuihua|Shen, Zhaoming|Tan, Zexi|Tan, Yanyan|Wang, Kangli|Jiang, Yi|Huang, Lingling|Dai, Rujia|Wei, Yu|Xia, Yan|Meng, Qingtuan|Ouyang, Yanmei|Yi, Liu|Duan, Fangyuan|Dai, Jiacheng|Zhao, Shunan|Liu, Chunyu|Chen, Chao', 'Center for Medical Genetics, School of Life Science, Central South University, Changsha, China.|Hunan Qingpeng International Information Technology, Changsha, China.|Center for Medical Genetics, School of Life Science, Central South University, Changsha, China.|Hunan Qingpeng International Information Technology, Changsha, China.|Hunan Qingpeng International Information Technology, Changsha, China.|Hunan Qingpeng International Information Technology, Changsha, China.|Center for Medical Genetics, School of Life Science, Central South University, Changsha, China.|Center for Medical Genetics, School of Life Science, Central South University, Changsha, China.|Center for Medical Genetics, School of Life Science, Central South University, Changsha, China.|Center for Medical Genetics, School of Life Science, Central South University, Changsha, China.|Center for Medical Genetics, School of Life Science, Central South University, Changsha, China.|Center for Medical Genetics, School of Life Science, Central South University, Changsha, China.|Center for Medical Genetics, School of Life Science, Central South University, Changsha, China.|Hunan Qingpeng International Information Technology, Changsha, China.|Center for Medical Genetics, School of Life Science, Central South University, Changsha, China.|Center for Medical Genetics, School of Life Science, Central South University, Changsha, China.|Center for Medical Genetics, School of Life Science, Central South University, Changsha, China.|Center for Medical Genetics, School of Life Science, Central South University, Changsha, China.|Center for Medical Genetics, School of Life Science, Central South University, Changsha, China.|Center for Medical Genetics, School of Life Science, Central South University, Changsha, China.', 'China|China|China|China|China|China|China|China|China|China|China|China|China|China|China|China|Chin', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty576', '29985970', 'Gene expression changes over the lifespan and varies among different tissues or cell types. Gene co-expression also changes by sex, age, different tissues or cell types. However, gene expression under the normal state and gene co-expression in the human brain has not been fully defined and quantified. Here we present a database named Brain EXPression Database (BrainEXP) which provides spatiotemporal expression of individual genes and co-expression in normal human brains. BrainEXP consists of 4567 samples from 2863 healthy individuals gathered from existing public databases and our own data, in either microarray or RNA-Seq library types. We mainly provide two analysis results based on the large dataset: (i) basic gene expression across specific brain regions, age ranges and sexes, (ii) co-expression analysis from different platforms.'),
(81952, 2019, 'bio', 'Computational analysis of kinase inhibitor selectivity using structural knowledge', 'Lo, Yu-Chen|Liu, Tianyun|Morrissey, Kari M|Kakiuchi-Kiyota, Satoko|Johnson, Adam R|Broccatelli, Fabio|Zhong, Yu|Joshi, Amita|Altman, Russ B', 'Department of Bioengineering, Stanford, CA, USA.|Department of Bioengineering, Stanford, CA, USA.|Department of Clinical Pharmacology, South San Francisco, CA, USA.|Department of Safety Assessment, South San Francisco, CA, USA.|Biochemical and Cellular Pharmacology, South San Francisco, CA, USA.|Department of Drug Metabolism and Pharmacokinetic, Genentech Inc., South San Francisco, CA, USA.|Department of Safety Assessment, South San Francisco, CA, USA.|Department of Clinical Pharmacology, South San Francisco, CA, USA.|Department of Bioengineering, Stanford, CA, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 3, '-', 'Journal Article', '10.1093/bioinformatics/bty582', '29985971', 'Kinases play a significant role in diverse disease signaling pathways and understanding kinase inhibitor selectivity, the tendency of drugs to bind to off-targets, remains a top priority for kinase inhibitor design and clinical safety assessment. Traditional approaches for kinase selectivity analysis using biochemical activity and binding assays are useful but can be costly and are often limited by the kinases that are available. On the other hand, current computational kinase selectivity prediction methods are computational intensive and can rarely achieve sufficient accuracy for large-scale kinome wide inhibitor selectivity profiling.'),
(81953, 2018, 'bio', 'Noise peak filtering in multi-dimensional NMR spectra using convolutional neural networks', 'Kobayashi, Naohiro|Hattori, Yoshikazu|Nagata, Takashi|Shinya, Shoko|Güntert, Peter|Kojima, Chojiro|Fujiwara, Toshimichi', 'Institute for Protein Research, Osaka University, Osaka, Japan.|Faculty of Pharmaceutical Sciences, Tokushima Bunri University, Tokushima, Japan.|Institute of Advanced Energy, Kyoto University, Kyoto, Japan.|Institute for Protein Research, Osaka University, Osaka, Japan.|Institute of Biophysical Chemistry, Goethe-University, Frankfurt am Main, Germany.|College of Engineering Science, Yokohama National University, Yokohama, Tokyo, Japan.|Institute for Protein Research, Osaka University, Osaka, Japan.', 'Japan|Japan|Japan|Japan|Germany|Japan|Japan', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty581', '29985974', 'Multi-dimensional NMR spectra are generally used for NMR signal assignment and structure analysis. There are several programs that can achieve highly automated NMR signal assignments and structure analysis. On the other hand, NMR spectra tend to have a large number of noise peaks even for data acquired with good sample and machine conditions, and it is still difficult to eliminate these noise peaks.'),
(81954, 2019, 'bio', 'RNAvista: a webserver to assess RNA secondary structures with non-canonical base pairs', 'Antczak, Maciej|Zablocki, Marcin|Zok, Tomasz|Rybarczyk, Agnieszka|Blazewicz, Jacek|Szachniuk, Marta', 'Institute of Computing Science, Poznan University of Technology, Poznan, Poland.|Institute of Computing Science, Poznan University of Technology, Poznan, Poland.|Institute of Computing Science, Poznan University of Technology, Poznan, Poland.|Institute of Computing Science, Poznan University of Technology, Poznan, Poland.|Institute of Computing Science, Poznan University of Technology, Poznan, Poland.|Institute of Computing Science, Poznan University of Technology, Poznan, Poland.', 'Poland|Poland|Poland|Poland|Poland|Poland', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty609', '29985979', 'In the study of 3D RNA structure, information about non-canonical interactions between nucleobases is increasingly important. Specialized databases support investigation of this issue based on experimental data, and several programs can annotate non-canonical base pairs in the RNA 3D structure. However, predicting the extended RNA secondary structure which describes both canonical and non-canonical interactions remains difficult.'),
(81955, 2019, 'bio', 'PingPongPro: a tool for the detection of piRNA-mediated transposon-silencing in small RNA-Seq data', 'Uhrig, Sebastian|Klein, Holger', 'Bioinformatics Core Facility, Institute of Molecular Biology, Mainz, Germany.|Bioinformatics Core Facility, Institute of Molecular Biology, Mainz, Germany.', 'Germany|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty578', '29985981', 'Piwi-interacting RNAs (piRNAs) are a class of small non-coding RNAs which guide endonucleases to mRNAs of actively transcribed transposons in order to prevent their translation. The resulting mRNA fragments induce a positive feedback loop (the ping-pong cycle), which reinforces piRNA production and hence the transposon-silencing effect. PingPongPro is a command-line tool to scan small RNA-Seq data for signs of ping-pong cycle activity. It implements a novel algorithm that combines empirical probabilities in a multi-factor model to accurately identify transposons which are suppressed through the ping-pong cycle.'),
(81956, 2019, 'bio', 'Kmer-db: instant evolutionary distance estimation', 'Deorowicz, Sebastian|Gudys, Adam|Dlugosz, Maciej|Kokot, Marek|Danek, Agnieszka', 'Institute of Informatics, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Gliwice, Poland.|Institute of Informatics, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Gliwice, Poland.|Institute of Informatics, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Gliwice, Poland.|Institute of Informatics, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Gliwice, Poland.|Institute of Informatics, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Gliwice, Poland.', 'Poland|Poland|Poland|Poland|Poland', 3, '-', 'Journal Article', '10.1093/bioinformatics/bty610', '29986074', 'Kmer-db is a new tool for estimating evolutionary relationship on the basis of k-mers extracted from genomes or sequencing reads. Thanks to an efficient data structure and parallel implementation, our software estimates distances between 40 715 pathogens in <7 min (on a modern workstation), 26 times faster than Mash, its main competitor.'),
(81957, 2018, 'bio', 'Integrative DNA copy number detection and genotyping from sequencing and array-based platforms', 'Zhou, Zilu|Wang, Weixin|Wang, Li-San|Zhang, Nancy Ruonan', 'Graduate Group in Genomics and Computational Biology.|Department of Pathology and Laboratory Medicine, Perelman School of Medicine.|Department of Pathology and Laboratory Medicine, Perelman School of Medicine.|Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Alleles|DNA Copy Number Variations|Genomics|Humans|Polymorphism, Single Nucleotide|Software|Whole Genome Sequencing', 'Journal Article', '10.1093/bioinformatics/bty104', '29992253', 'Copy number variations (CNVs) are gains and losses of DNA segments and have been associated with disease. Many large-scale genetic association studies are performing CNV analysis using whole exome sequencing (WES) and whole genome sequencing (WGS). In many of these studies, previous single-nucleotide polymorphism (SNP)-array data are available. An integrated cross-platform analysis is expected to improve resolution and accuracy, yet there is no tool for effectively combining data from sequencing and array platforms. The detection of CNVs using sequencing data alone can also be further improved by the utilization of allele-specific reads.'),
(81958, 2019, 'bio', 'Accurate multiple alignment of distantly related genome sequences using filtered spaced word matches as anchor points', 'Leimeister, Chris-André|Dencker, Thomas|Morgenstern, Burkhard', 'Department of Bioinformatics, Institute of Microbiology and Genetics.|Department of Bioinformatics, Institute of Microbiology and Genetics.|Department of Bioinformatics, Institute of Microbiology and Genetics.', '-|-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty592', '29992260', 'Most methods for pairwise and multiple genome alignment use fast local homology search tools to identify anchor points, i.e. high-scoring local alignments of the input sequences. Sequence segments between those anchor points are then aligned with slower, more sensitive methods. Finding suitable anchor points is therefore crucial for genome sequence comparison, speed and sensitivity of genome alignment depend on the underlying anchoring methods.'),
(81959, 2019, 'bio', 'Crumble: reference free lossy compression of sequence quality values', 'Bonfield, James K|McCarthy, Shane A|Durbin, Richard', 'DNA Pipelines, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.|DNA Pipelines, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.|DNA Pipelines, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.', 'United Kingdom|United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty608', '29992288', 'The bulk of space taken up by NGS sequencing CRAM files consists of per-base quality values. Most of these are unnecessary for variant calling, offering an opportunity for space saving.'),
(81960, 2019, 'bio', 'Uncertainty quantification, propagation and characterization by Bayesian analysis combined with global sensitivity analysis applied to dynamical intracellular pathway models', 'Eriksson, Olivia|Jauhiainen, Alexandra|Maad Sasane, Sara|Kramer, Andrei|Nair, Anu G|Sartorius, Carolina|Hellgren Kotaleski, Jeanette', 'Science for Life Laboratory, Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden.|Biometrics, Early Clinical Development, IMED Biotech Unit, AstraZeneca, Gothenburg, Sweden.|Centre for Mathematical Sciences, Lund University, Lund, Sweden.|Science for Life Laboratory, Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden.|Science for Life Laboratory, Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden.|Centre for Mathematical Sciences, Lund University, Lund, Sweden.|Science for Life Laboratory, Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden.', 'Sweden|Sweden|Sweden|Sweden|Sweden|Sweden|Sweden', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty607', '30010712', 'Dynamical models describing intracellular phenomena are increasing in size and complexity as more information is obtained from experiments. These models are often over-parameterized with respect to the quantitative data used for parameter estimation, resulting in uncertainty in the individual parameter estimates as well as in the predictions made from the model. Here we combine Bayesian analysis with global sensitivity analysis (GSA) in order to give better informed predictions, to point out weaker parts of the model that are important targets for further experiments, as well as to give guidance on parameters that are essential in distinguishing different qualitative output behaviours.'),
(81961, 2018, 'bio', 'Hierarchical optimization for the efficient parametrization of ODE models', 'Loos, Carolin|Krause, Sabrina|Hasenauer, Jan', 'Helmholtz Zentrum München-German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany.|Helmholtz Zentrum München-German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany.|Helmholtz Zentrum München-German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany.', 'Germany|Germany|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty514', '30010716', 'Mathematical models are nowadays important tools for analyzing dynamics of cellular processes. The unknown model parameters are usually estimated from experimental data. These data often only provide information about the relative changes between conditions, hence, the observables contain scaling parameters. The unknown scaling parameters and corresponding noise parameters have to be inferred along with the dynamic parameters. The nuisance parameters often increase the dimensionality of the estimation problem substantially and cause convergence problems.'),
(81962, 2019, 'bio', 'A parallel computational framework for ultra-large-scale sequence clustering analysis', 'Zheng, Wei|Mao, Qi|Genco, Robert J|Wactawski-Wende, Jean|Buck, Michael|Cai, Yunpeng|Sun, Yijun', 'Department of Computer Science and Engineering, The State University of New York, NY, Buffalo, NY, USA.|Department of Microbiology and Immunology, The State University of New York, NY, Buffalo, NY, USA.|Department of Oral Biology, The State University of New York, NY, Buffalo, NY, USA.|Department of Epidemiology and Environmental Health, The State University of New York, NY, Buffalo, NY, USA.|Department of Biochemistry, University at Buffalo, The State University of New York, NY, Buffalo, NY, USA.|Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.|Department of Computer Science and Engineering, The State University of New York, NY, Buffalo, NY, USA.', 'United States|United States|United States|United States|United States|China|United States', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty617', '30010718', 'The rapid development of sequencing technology has led to an explosive accumulation of genomic data. Clustering is often the first step to be performed in sequence analysis. However, existing methods scale poorly with respect to the unprecedented growth of input data size. As high-performance computing systems are becoming widely accessible, it is highly desired that a clustering method can easily scale to handle large-scale sequence datasets by leveraging the power of parallel computing.'),
(81963, 2019, 'bio', 'BrainImageR: spatiotemporal gene set analysis referencing the human brain', 'Linker, Sara B|Hsu, Jonathan Y|Pfaff, Adela|Amatya, Debha|Ko, Shu-Meng|Voter, Sarah|Wong, Quinn|Gage, Fred H', 'Laboratory of Genetics, Salk Institute for Biological Studies, La Jolla, CA, USA.|Laboratory of Genetics, Salk Institute for Biological Studies, La Jolla, CA, USA.|Laboratory of Genetics, Salk Institute for Biological Studies, La Jolla, CA, USA.|Laboratory of Genetics, Salk Institute for Biological Studies, La Jolla, CA, USA.|Laboratory of Genetics, Salk Institute for Biological Studies, La Jolla, CA, USA.|Laboratory of Genetics, Salk Institute for Biological Studies, La Jolla, CA, USA.|Laboratory of Genetics, Salk Institute for Biological Studies, La Jolla, CA, USA.|Laboratory of Genetics, Salk Institute for Biological Studies, La Jolla, CA, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty618', '30010719', 'Neuronal analyses such as transcriptomics, epigenetics and genome-wide association studies must be assessed in the context of the human brain to generate biologically meaningful inferences. It is often difficult to access primary human brain tissue, therefore, approximations are made using alternative sources such as peripheral tissues or in vitro-derived neurons. Gene sets from these studies are then assessed for their association with the post-mortem human brain. However, most analyses of post-mortem datasets are achieved by building new computational tools each time in-house, which can cause discrepancies from study to study. The field is in need of a user-friendly tool to examine spatiotemporal expression with respect to the postmortem brain. Such a tool will be of use in the molecular interrogation of neurological and psychiatric disorders, with direct advantages for the disease-modeling and human genetics communities.'),
(81964, 2019, 'bio', 'CoMSA: compression of protein multiple sequence alignment files', 'Deorowicz, Sebastian|Walczyszyn, Joanna|Debudaj-Grabysz, Agnieszka', 'Institute of Informatics, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Gliwice, Poland.|Institute of Informatics, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Gliwice, Poland.|Institute of Informatics, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Gliwice, Poland.', 'Poland|Poland|Poland', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty619', '30010777', 'Bioinformatics databases grow rapidly and achieve values hardly to imagine a decade ago. Among numerous bioinformatics processes generating hundreds of GB is multiple sequence alignments of protein families. Its largest database, i.e. Pfam, consumes 40-230 GB, depending of the variant. Storage and transfer of such massive data has become a challenge.'),
(81965, 2019, 'bio', 'Matt: Unix tools for alternative splicing analysis', 'Gohr, André|Irimia, Manuel', 'Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Spain.|Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Spain.', 'Spain|Spain', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty606', '30010778', 'Tracking thousands of alternative splicing (AS) events genome-wide makes their downstream analysis computationally challenging and laborious. Here, we present Matt, the first UNIX command-line toolkit with focus on high-level AS analyses. With 50 commands it facilitates computational AS analyses by (i) expediting repetitive data-preparation tasks, (ii) offering routine high-level analyses, including the extraction of exon/intron features, discriminative feature detection, motif enrichment analysis, and the generation of motif RNA-maps, (iii) improving reproducibility by documenting all analysis steps and (iv) accelerating the implementation of own analysis pipelines by offering users to exploit its modular functionality.'),
(81966, 2019, 'bio', 'SPUTNIK: an R package for filtering of spatially related peaks in mass spectrometry imaging data', 'Inglese, Paolo|Correia, Gonçalo|Takats, Zoltan|Nicholson, Jeremy K|Glen, Robert C', 'Computational and System Medicine, Department of Surgery and Cancer, Imperial College London, London, UK.|Computational and System Medicine, Department of Surgery and Cancer, Imperial College London, London, UK.|Computational and System Medicine, Department of Surgery and Cancer, Imperial College London, London, UK.|Computational and System Medicine, Department of Surgery and Cancer, Imperial College London, London, UK.|Computational and System Medicine, Department of Surgery and Cancer, Imperial College London, London, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty622', '30010780', 'SPUTNIK is an R package consisting of a series of tools to filter mass spectrometry imaging peaks characterized by a noisy or unlikely spatial distribution. SPUTNIK can produce mass spectrometry imaging datasets characterized by a smaller but more informative set of peaks, reduce the complexity of subsequent multi-variate analysis and increase the interpretability of the statistical results.'),
(81967, 2019, 'bio', 'DMCM: a Data-adaptive Mutation Clustering Method to identify cancer-related mutation clusters', 'Lu, Xinguo|Qian, Xin|Li, Xing|Miao, Qiumai|Peng, Shaoliang', 'College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.|College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.|College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.|College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.|College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.', 'China|China|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty624', '30010784', 'Functional somatic mutations within coding amino acid sequences confer growth advantage in pathogenic process. Most existing methods for identifying cancer-related mutations focus on the single amino acid or the entire gene level. However, gain-of-function mutations often cluster in specific protein regions instead of existing independently in the amino acid sequences. Some approaches for identifying mutation clusters with mutation density on amino acid chain have been proposed recently. But their performance in identification of mutation clusters remains to be improved.'),
(81968, 2019, 'bio', 'Broom: application for non-redundant storage of high throughput sequencing data', 'Albayrak, Levent|Khanipov, Kamil|Golovko, George|Fofanov, Yuriy', 'Department of Pharmacology and Toxicology, University of Texas Medical Branch - Galveston, Galveston, TX, USA.|Department of Pharmacology and Toxicology, University of Texas Medical Branch - Galveston, Galveston, TX, USA.|Department of Pharmacology and Toxicology, University of Texas Medical Branch - Galveston, Galveston, TX, USA.|Department of Pharmacology and Toxicology, University of Texas Medical Branch - Galveston, Galveston, TX, USA.', 'United States|United States|United States|United States', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty580', '30010786', 'The data generation capabilities of high throughput sequencing (HTS) instruments have exponentially increased over the last few years, while the cost of sequencing has dramatically decreased allowing this technology to become widely used in biomedical studies. For small labs and individual researchers, however, storage and transfer of large amounts of HTS data present a significant challenge. The recent trends in increased sequencing quality and genome coverage can be used to reconsider HTS data storage strategies.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(81969, 2019, 'bio', 'Comparative assessment of different familial aggregation methods in the context of large and unstructured pedigrees', 'Weichenberger, Christian X|Rainer, Johannes|Pattaro, Cristian|Pramstaller, Peter P|Domingues, Francisco S', 'Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Bolzano, Italy.|Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Bolzano, Italy.|Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Bolzano, Italy.|Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Bolzano, Italy.|Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Bolzano, Italy.', 'Italy|Italy|Italy|Italy|Italy', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty541', '30010787', 'Familial aggregation analysis is an important early step for characterizing the genetic determinants of phenotypes in epidemiological studies. To facilitate this analysis, a collection of methods to detect familial aggregation in large pedigrees has been made available recently. However, efficacy of these methods in real world scenarios remains largely unknown. Here, we assess the performance of five aggregation methods to identify individuals or groups of related individuals affected by a Mendelian trait within a large set of decoys. We investigate method performance under a representative set of combinations of causal variant penetrance, trait prevalence and number of affected generations in the pedigree. These methods are then applied to assess familial aggregation of familial hypercholesterolemia and stroke, in the context of the Cooperative Health Research in South Tyrol (CHRIS) study.'),
(81970, 2019, 'bio', 'Determination of sets of covariating gene expression using graph analysis on pairwise expression ratios', 'Curis, Emmanuel|Courtin, Cindie|Geoffroy, Pierre Alexis|Laplanche, Jean-Louis|Saubamxc3xa9a, Bruno|Marie-Claire, Cynthia', 'Laboratoire de Biomathxc3xa9matiques, Facultxc3xa9 de Pharmacie de Paris, Universitxc3xa9 Paris Descartes, Sorbonne Paris Citxc3xa9, Paris F-75006, France.|UMR S-1144 INSERM-Universitxc3xa9 Paris Descartes-Universitxc3xa9 Paris Diderot, Paris F-75006, France.|UMR S-1144 INSERM-Universitxc3xa9 Paris Descartes-Universitxc3xa9 Paris Diderot, Paris F-75006, France.|UMR S-1144 INSERM-Universitxc3xa9 Paris Descartes-Universitxc3xa9 Paris Diderot, Paris F-75006, France.|UMR S-1144 INSERM-Universitxc3xa9 Paris Descartes-Universitxc3xa9 Paris Diderot, Paris F-75006, France.|UMR S-1144 INSERM-Universitxc3xa9 Paris Descartes-Universitxc3xa9 Paris Diderot, Paris F-75006, France.', 'France|France|France|France|France|France', 3, '-', 'Journal Article', '10.1093/bioinformatics/bty629', '30010788', 'RNA quantification experiments result in compositional data, however usual methods for compositional data analysis [additive log ratio (alr), centered log ratio (clr), isometric log ratio (ilr)] do not apply easily and give results difficult to interpret. To handle this, a method based on disjoint subgraphs in a graph whose nodes are the quantified RNAs is proposed. Edges in the graph are defined by lack of change in ratios of the corresponding RNAs between conditions.'),
(81971, 2019, 'bio', 'pLoc_bal-mAnimal: predict subcellular localization of animal proteins by balancing training dataset and PseAAC', 'Cheng, Xiang|Lin, Wei-Zhong|Xiao, Xuan|Chou, Kuo-Chen', 'Computer Science, Jingdezhen Ceramic Institute, Jingdezhen, China.|Computer Science, Jingdezhen Ceramic Institute, Jingdezhen, China.|Computer Science, Jingdezhen Ceramic Institute, Jingdezhen, China.|Computational Biology, Gordon Life Science Institute, Boston, MA, USA.', 'China|China|China|United States', 58, '-', 'Journal Article', '10.1093/bioinformatics/bty628', '30010789', 'A cell contains numerous protein molecules. One of the fundamental goals in cell biology is to determine their subcellular locations, which can provide useful clues about their functions. Knowledge of protein subcellular localization is also indispensable for prioritizing and selecting the right targets for drug development. With the avalanche of protein sequences emerging in the post-genomic age, it is highly desired to develop computational tools for timely and effectively identifying their subcellular localization based on the sequence information alone. Recently, a predictor called pLoc-mAnimal was developed for identifying the subcellular localization of animal proteins. Its performance is overwhelmingly better than that of the other predictors for the same purpose, particularly in dealing with the multi-label systems in which some proteins, called multiplex proteins, may simultaneously occur in two or more subcellular locations. Although it is indeed a very powerful predictor, more efforts are definitely needed to further improve it. This is because pLoc-mAnimal was trained by an extremely skewed dataset in which some subset (subcellular location) was about 128 times the size of the other subsets. Accordingly, such an uneven training dataset will inevitably cause a biased consequence.'),
(81972, 2019, 'bio', 'Metagenomic binning through low-density hashing', 'Luo, Yunan|Yu, Yun William|Zeng, Jianyang|Berger, Bonnie|Peng, Jian', 'Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL, USA.|Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.|Machine Learning and Computational Biology Group, Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China and.|Department of Mathematics and Computer Science and AI Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.|Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL, USA.', 'United States|United States|China|United States|United States', 6, '-', 'Journal Article', '10.1093/bioinformatics/bty611', '30010790', 'Vastly greater quantities of microbial genome data are being generated where environmental samples mix together the DNA from many different species. Here, we present Opal for metagenomic binning, the task of identifying the origin species of DNA sequencing reads. We introduce low-density locality sensitive hashing to bioinformatics, with the addition of Gallager codes for even coverage, enabling quick and accurate metagenomic binning.'),
(81973, 2019, 'bio', 'SMSSVD: SubMatrix Selection Singular Value Decomposition', 'Henningsson, Rasmus|Fontes, Magnus', 'The Centre for Mathematical Sciences, Lund University, Lund, Sweden.|The Centre for Mathematical Sciences, Lund University, Lund, Sweden.', 'Sweden|Sweden', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty566', '30010791', 'High throughput biomedical measurements normally capture multiple overlaid biologically relevant signals and often also signals representing different types of technical artefacts like e.g. batch effects. Signal identification and decomposition are accordingly main objectives in statistical biomedical modeling and data analysis. Existing methods, aimed at signal reconstruction and deconvolution, in general, are either supervised, contain parameters that need to be estimated or present other types of ad hoc features. We here introduce SubMatrix Selection Singular Value Decomposition (SMSSVD), a parameter-free unsupervised signal decomposition and dimension reduction method, designed to reduce noise, adaptively for each low-rank-signal in a given data matrix, and represent the signals in the data in a way that enable unbiased exploratory analysis and reconstruction of multiple overlaid signals, including identifying groups of variables that drive different signals.'),
(81974, 2019, 'bio', 'AuTom-dualx: a toolkit for fully automatic fiducial marker-based alignment of dual-axis tilt series with simultaneous reconstruction', 'Han, Renmin|Wan, Xiaohua|Li, Lun|Lawrence, Albert|Yang, Peng|Li, Yu|Wang, Sheng|Sun, Fei|Liu, Zhiyong|Gao, Xin|Zhang, Fa', 'King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal, Saudi Arabia.|High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.|High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.|National Center for Microscopy and Imaging Research, Center for Research in Biological Systems, University of California, San Diego, CA, USA and.|King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal, Saudi Arabia.|King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal, Saudi Arabia.|King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal, Saudi Arabia.|National Laboratory of Biomacromolecules, Institute of Biophysics Chinese Academy of Sciences, Beijing, China.|High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.|King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal, Saudi Arabia.|High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.', 'Saudi Arabia|China|China|-|Saudi Arabia|Saudi Arabia|Saudi Arabia|China|China|Saudi Arabia|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty620', '30010792', 'Dual-axis electron tomography is an important 3xe2x80x89D macro-molecular structure reconstruction technology, which can reduce artifacts and suppress the effect of missing wedge. However, the fully automatic data process for dual-axis electron tomography still remains a challenge due to three difficulties: (i) how to track the mass of fiducial markers automatically, (ii) how to integrate the information from the two different tilt series, and (iii) how to cope with the inconsistency between the two different tilt series.'),
(81975, 2018, 'bio', 'ProteomeVis: a web app for exploration of protein properties from structure to sequence evolution across organisms proteomes', 'Razban, Rostam M|Gilson, Amy I|Durfee, Niamh|Strobelt, Hendrik|Dinkla, Kasper|Choi, Jeong-Mo|Pfister, Hanspeter|Shakhnovich, Eugene I', 'High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.|High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.|High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.|High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.|High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.|High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.|High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.|High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.', 'China|China|China|China|China|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty516', '30010795', '-'),
(81976, 2019, 'bio', 'GECO: gene expression correlation analysis after genetic algorithm-driven deconvolution', 'Najafov, Jamil|Najafov, Ayaz', 'Department of Cell Biology, Harvard Medical School, Boston, MA, USA.|Department of Cell Biology, Harvard Medical School, Boston, MA, USA.', 'United States|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty623', '30010797', 'Large-scale gene expression analysis is a valuable asset for data-driven hypothesis generation. However, the convoluted nature of large expression datasets often hinders extraction of meaningful biological information.'),
(81977, 2019, 'bio', 'GIGSEA: genotype imputed gene set enrichment analysis using GWAS summary level data', 'Zhu, Shijia|Qian, Tongqi|Hoshida, Yujin|Shen, Yuan|Yu, Jing|Hao, Ke', 'Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.|Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.|Liver Tumor Translational Research Program, Simmons Comprehensive Cancer Center, Division of Digestive and Liver Diseases, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA.|Department of Psychiatry, Shanghai Tenth Peoples Hospital, Tongji University, Shanghai, China.|Department of Ophthalmology, Shanghai Tenth Peoples Hospital, Tongji University, Shanghai, China.|Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.', 'United States|United States|United States|China|China|United States', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty529', '30010968', 'level data of GWAS becomes increasingly important in post-GWAS data mining. Here, we present GIGSEA (Genotype Imputed Gene Set Enrichment Analysis), a novel method that uses GWAS summary statistics and eQTL to infer differential gene expression and interrogate gene set enrichment for the trait-associated SNPs. By incorporating empirical eQTL of the disease relevant tissue, GIGSEA naturally accounts for factors such as gene size, gene boundary, SNP distal regulation and multiple-marker regulation. The weighted linear regression model was used to perform the enrichment test, properly adjusting for imputation accuracy, model incompleteness and redundancy in different gene sets. The significance level of enrichment is assessed by the permutation test, where matrix operation was employed to dramatically improve computation speed. GIGSEA has appropriate type I error rates, and discovers the plausible biological findings on the real data set.'),
(81978, 2019, 'bio', 'ape 50: an environment for modern phylogenetics and evolutionary analyses in R', 'Paradis, Emmanuel|Schliep, Klaus', 'ISEM, IRD, Univ. Montpellier, CNRS, EPHE, Montpellier, France.|Department of Biology, University of Massachusetts Boston, Boston, MA, USA.', 'France|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty633', '30016406', 'After more than fifteen years of existence, the R package ape has continuously grown its contents, and has been used by a growing community of users. The release of version 5.0 has marked a leap towards a modern software for evolutionary analyses. Efforts have been put to improve efficiency, flexibility, support for big data (Rs long vectors), ease of use and quality check before a new release. These changes will hopefully make ape a useful software for the study of biodiversity and evolution in a context of increasing data quantity.'),
(81979, 2019, 'bio', 'Automatic recognition of ligands in electron density by machine learning', 'Kowiel, Marcin|Brzezinski, Dariusz|Porebski, Przemyslaw J|Shabalin, Ivan G|Jaskolski, Mariusz|Minor, Wladek', 'Center for Biocrystallographic Research, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland.|Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA, USA.|Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA, USA.|Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA, USA.|Center for Biocrystallographic Research, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland.|Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA, USA.', 'Poland|United States|United States|United States|Poland|United States', 6, '-', 'Journal Article', '10.1093/bioinformatics/bty626', '30016407', 'The correct identification of ligands in crystal structures of protein complexes is the cornerstone of structure-guided drug design. However, cognitive bias can sometimes mislead investigators into modeling fictitious compounds without solid support from the electron density maps. Ligand identification can be aided by automatic methods, but existing approaches are based on time-consuming iterative fitting.'),
(81980, 2019, 'bio', 'Reproducible and replicable comparisons using SummarizedBenchmark', 'Kimes, Patrick K|Reyes, Alejandro', 'Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA.|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA.', 'United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty627', '30016409', 'Benchmark studies are widely used to compare and evaluate tools developed for answering various biological questions. Despite the popularity of these comparisons, the implementation is often ad hoc, with little consistency across studies. To address this problem, we developed SummarizedBenchmark, an R package and framework for organizing and structuring benchmark comparisons. SummarizedBenchmark defines a general grammar for benchmarking and allows for easier setup and execution of benchmark comparisons, while improving the reproducibility and replicability of such comparisons. We demonstrate the wide applicability of our framework using four examples from different applications.'),
(81981, 2019, 'bio', 'Simulating Illumina metagenomic data with InSilicoSeq', 'Gourlé, Hadrien|Karlsson-Lindsjö, Oskar|Hayer, Juliette|Bongcam-Rudloff, Erik', 'Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, SLU-Global Bioinformatics Centre.|Department of Molecular Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden.|Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, SLU-Global Bioinformatics Centre.|Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, SLU-Global Bioinformatics Centre.', '-|Sweden|-|-', 9, '-', 'Journal Article', '10.1093/bioinformatics/bty630', '30016412', 'The accurate in silico simulation of metagenomic datasets is of great importance for benchmarking bioinformatics tools as well as for experimental design. Users are dependant on large-scale simulation to not only design experiments and new projects but also for accurate estimation of computational needs within a project. Unfortunately, most current read simulators are either not suited for metagenomics, out of date or relatively poorly documented. In this article, we describe InSilicoSeq, a software package to simulate metagenomic Illumina sequencing data. InsilicoSeq has a simple command-line interface and extensive documentation.'),
(81982, 2019, 'bio', 'MAVIS: merging, annotation, validation, and illustration of structural variants', 'Reisle, Caralyn|Mungall, Karen L|Choo, Caleb|Paulino, Daniel|Bleile, Dustin W|Muhammadzadeh, Amir|Mungall, Andrew J|Moore, Richard A|Shlafman, Inna|Coope, Robin|Pleasance, Stephen|Ma, Yussanne|Jones, Steven J M', 'Canadas Michael Smith Genome Sciences Centre, Vancouver, BC, Canada.|Canadas Michael Smith Genome Sciences Centre, Vancouver, BC, Canada.|Canadas Michael Smith Genome Sciences Centre, Vancouver, BC, Canada.|Canadas Michael Smith Genome Sciences Centre, Vancouver, BC, Canada.|Canadas Michael Smith Genome Sciences Centre, Vancouver, BC, Canada.|Canadas Michael Smith Genome Sciences Centre, Vancouver, BC, Canada.|Canadas Michael Smith Genome Sciences Centre, Vancouver, BC, Canada.|Canadas Michael Smith Genome Sciences Centre, Vancouver, BC, Canada.|Canadas Michael Smith Genome Sciences Centre, Vancouver, BC, Canada.|Canadas Michael Smith Genome Sciences Centre, Vancouver, BC, Canada.|Canadas Michael Smith Genome Sciences Centre, Vancouver, BC, Canada.|Canadas Michael Smith Genome Sciences Centre, Vancouver, BC, Canada.|Canadas Michael Smith Genome Sciences Centre, Vancouver, BC, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada', 5, '-', 'Journal Article', '10.1093/bioinformatics/bty621', '30016509', 'Reliably identifying genomic rearrangements and interpreting their impact is a key step in understanding their role in human cancers and inherited genetic diseases. Many short read algorithmic approaches exist but all have appreciable false negative rates. A common approach is to evaluate the union of multiple tools increasing sensitivity, followed by filtering to retain specificity. Here we describe an application framework for the rapid generation of structural variant consensus, unique in its ability to visualize the genetic impact and context as well as process both genome and transcriptome data.'),
(81983, 2019, 'bio', 'Kinome-wide identification of phosphorylation networks in eukaryotic proteomes', 'Parca, Luca|Ariano, Bruno|Cabibbo, Andrea|Paoletti, Marco|Tamburrini, Annalaura|Palmeri, Antonio|Ausiello, Gabriele|Helmer-Citterich, Manuela', 'Department of Biology, Centro di Bioinformatica Molecolare, University of Rome Tor Vergata, Rome, Italy.|Department of Biology, Centro di Bioinformatica Molecolare, University of Rome Tor Vergata, Rome, Italy.|Department of Biology, Centro di Bioinformatica Molecolare, University of Rome Tor Vergata, Rome, Italy.|Department of Biology, Centro di Bioinformatica Molecolare, University of Rome Tor Vergata, Rome, Italy.|Department of Biology, Centro di Bioinformatica Molecolare, University of Rome Tor Vergata, Rome, Italy.|Department of Biology, Centro di Bioinformatica Molecolare, University of Rome Tor Vergata, Rome, Italy.|Department of Biology, Centro di Bioinformatica Molecolare, University of Rome Tor Vergata, Rome, Italy.|Department of Biology, Centro di Bioinformatica Molecolare, University of Rome Tor Vergata, Rome, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty545', '30016513', 'Signaling and metabolic pathways are finely regulated by a network of protein phosphorylation events. Unraveling the nature of this intricate network, composed of kinases, target proteins and their interactions, is therefore of crucial importance. Although thousands of kinase-specific phosphorylations (KsP) have been annotated in model organisms their kinase-target network is far from being complete, with less studied organisms lagging behind.'),
(81984, 2019, 'bio', 'ACDtool: a web-server for the generic analysis of large data sets of counts', 'Claverie, Jean-Michel|Ta, Thi Ngan', 'Aix Marseille Univ, CNRS, IGS (UMR7256), IMM (FR3479), Marseille, France.|Aix Marseille Univ, CNRS, IGS (UMR7256), IMM (FR3479), Marseille, France.', 'France|France', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty640', '30020402', 'More than 20xe2x80x89years ago, our laboratory published an original statistical test [referred to as the Audic-Claverie (AC) test in the literature] to identify differentially expressed genes from the pairwise comparison of counts of expressed sequence tags determined in different conditions. Despite its antiquity and the publications of more sophisticated packages, this original publication continued to gather more than 200 citations per year, indicating the persistent usefulness of the simple AC test for the community. This prompted us to propose a fully revamped version of the AC test with a user interface adapted to the diverse and much larger datasets produced by contemporary omics techniques.'),
(81985, 2019, 'bio', 'Dynamic compression schemes for graph coloring', 'Mustafa, Harun|Schilken, Ingo|Karasikov, Mikhail|Eickhoff, Carsten|Rätsch, Gunnar|Kahles, André', 'Department of Computer Science, ETH Zurich, Zurich, Switzerland.|Department of Computer Science, ETH Zurich, Zurich, Switzerland.|Department of Computer Science, ETH Zurich, Zurich, Switzerland.|Brown Center for Biomedical Informatics, Brown University, Providence, RI, USA.|Department of Computer Science, ETH Zurich, Zurich, Switzerland.|Department of Computer Science, ETH Zurich, Zurich, Switzerland.', 'Switzerland|Switzerland|Switzerland|United States|Switzerland|Switzerland', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty632', '30020403', 'Technological advancements in high-throughput DNA sequencing have led to an exponential growth of sequencing data being produced and stored as a byproduct of biomedical research. Despite its public availability, a majority of this data remains hard to query for the research community due to a lack of efficient data representation and indexing solutions. One of the available techniques to represent read data is a condensed form as an assembly graph. Such a representation contains all sequence information but does not store contextual information and metadata.'),
(81986, 2019, 'bio', 'BIPSPI: a method for the prediction of partner-specific protein-protein interfaces', 'Sanchez-Garcia, Ruben|Sorzano, C O S|Carazo, J M|Segura, Joan', 'GN7 of the Spanish National Institute for Bioinformatics (INB), Biocomputing Unit, National Center of Biotechnology (CSIC), Instruct Image Processing Center, Madrid, Spain.|GN7 of the Spanish National Institute for Bioinformatics (INB), Biocomputing Unit, National Center of Biotechnology (CSIC), Instruct Image Processing Center, Madrid, Spain.|GN7 of the Spanish National Institute for Bioinformatics (INB), Biocomputing Unit, National Center of Biotechnology (CSIC), Instruct Image Processing Center, Madrid, Spain.|GN7 of the Spanish National Institute for Bioinformatics (INB), Biocomputing Unit, National Center of Biotechnology (CSIC), Instruct Image Processing Center, Madrid, Spain.', 'Spain|Spain|Spain|Spain', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty647', '30020406', 'Protein-Protein Interactions (PPI) are essentials for most cellular processes and thus, unveiling how proteins interact is a crucial question that can be better understood by identifying which residues are responsible for the interaction. Computational approaches are orders of magnitude cheaper and faster than experimental ones, leading to proliferation of multiple methods aimed to predict which residues belong to the interface of an interaction.'),
(81987, 2019, 'bio', 'Scaling read aligners to hundreds of threads on general-purpose processors', 'Langmead, Ben|Wilks, Christopher|Antonescu, Valentin|Charles, Rone', 'Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.|Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.|Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.|Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty648', '30020410', 'General-purpose processors can now contain many dozens of processor cores and support hundreds of simultaneous threads of execution. To make best use of these threads, genomics software must contend with new and subtle computer architecture issues. We discuss some of these and propose methods for improving thread scaling in tools that analyze each read independently, such as read aligners.'),
(81988, 2019, 'bio', 'Random walk with restart on multiplex and heterogeneous biological networks', 'Valdeolivas, Alberto|Tichit, Laurent|Navarro, Claire|Perrin, Sophie|Odelin, Gaëlle|Levy, Nicolas|Cau, Pierre|Remy, Elisabeth|Baudot, Anaïs', 'Aix Marseille Univ, CNRS, Centrale Marseille, I2M, Marseille, France.|Aix Marseille Univ, CNRS, Centrale Marseille, I2M, Marseille, France.|ProGeLife, Marseille.|ProGeLife, Marseille.|ProGeLife, Marseille.|Aix Marseille Univ, INSERM, MMG, Marseille, France.|ProGeLife, Marseille.|Aix Marseille Univ, CNRS, Centrale Marseille, I2M, Marseille, France.|Aix Marseille Univ, CNRS, Centrale Marseille, I2M, Marseille, France.', 'France|France|-|-|-|France|-|France|France', 24, '-', 'Journal Article', '10.1093/bioinformatics/bty637', '30020411', 'Recent years have witnessed an exponential growth in the number of identified interactions between biological molecules. These interactions are usually represented as large and complex networks, calling for the development of appropriated tools to exploit the functional information they contain. Random walk with restart (RWR) is the state-of-the-art guilt-by-association approach. It explores the network vicinity of gene/protein seeds to study their functions, based on the premise that nodes related to similar functions tend to lie close to each other in the networks.'),
(81989, 2019, 'bio', 'Heritability estimation and differential analysis of count data with generalized linear mixed models in genomic sequencing studies', 'Sun, Shiquan|Zhu, Jiaqiang|Mozaffari, Sahar|Ober, Carole|Chen, Mengjie|Zhou, Xiang', 'Department of Computer Science, Northwestern Polytechnical University, Xian, Shaanxi, China.|Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.|Department of Human Genetics, University of Chicago, Chicago, IL, USA.|Department of Human Genetics, University of Chicago, Chicago, IL, USA.|Department of Human Genetics, University of Chicago, Chicago, IL, USA.|Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.', 'China|United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty644', '30020412', 'Genomic sequencing studies, including RNA sequencing and bisulfite sequencing studies, are becoming increasingly common and increasingly large. Large genomic sequencing studies open doors for accurate molecular trait heritability estimation and powerful differential analysis. Heritability estimation and differential analysis in sequencing studies requires the development of statistical methods that can properly account for the count nature of the sequencing data and that are computationally efficient for large datasets.'),
(81990, 2019, 'bio', 'SKEMPI 20: an updated benchmark of changes in protein-protein binding energy, kinetics and thermodynamics upon mutation', 'Jankauskaite, Justina|Jiménez-García, Brian|Dapkunas, Justas|Fernández-Recio, Juan|Moal, Iain H', 'Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania.|Barcelona Supercomputing Center (BSC), Barcelona, Spain.|Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania.|Barcelona Supercomputing Center (BSC), Barcelona, Spain.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, UK.', 'Lithuania|Spain|Lithuania|Spain|United Kingdom', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty635', '30020414', 'Understanding the relationship between the sequence, structure, binding energy, binding kinetics and binding thermodynamics of protein-protein interactions is crucial to understanding cellular signaling, the assembly and regulation of molecular complexes, the mechanisms through which mutations lead to disease, and protein engineering.'),
(81991, 2019, 'bio', 'Cryfa: a secure encryption tool for genomic data', 'Hosseini, Morteza|Pratas, Diogo|Pinho, Armando J', 'European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, UK.', 'United Kingdom|United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty645', '30020420', 'The ever-increasing growth of high-throughput sequencing technologies has led to a great acceleration of medical and biological research and discovery. As these platforms advance, the amount of information for diverse genomes increases at unprecedented rates. Confidentiality, integrity and authenticity of such genomic information should be ensured due to its extremely sensitive nature. In this paper, we propose Cryfa, a fast secure encryption tool for genomic data, namely in Fasta, Fastq, VCF, SAM and BAM formats, which is also capable of reducing the storage size of Fasta and Fastq files. Cryfa uses advanced encryption standard (AES) encryption combined with a shuffling mechanism, which leads to a substantial enhancement of the security against low data complexity attacks. Compared to AES Crypt, a general-purpose encryption tool, Cryfa is an industry-oriented tool, which is able to provide confidentiality, integrity and authenticity of data at four times more speed, in addition, it can reduce the file sizes to 1/3. Due to the absence of a method similar to Cryfa, we have simulated its behavior with a combination of encryption and compression tools, for comparison purpose. For instance, our tool is nine times faster than its fastest competitor in Fasta files. Also, Cryfa has a very low memory usage (only a few megabytes), which makes it feasible to run on any computer.'),
(81992, 2019, 'bio', 'Systematic discovery of novel and valuable plant gene modules by large-scale RNA-seq samples', 'Yu, Hua|Lu, Lu|Jiao, Bingke|Liang, Chengzhi', 'State Key Laboratory of Plant Genomics, Institute of Genetic and Developmental Biology, Chinese Academy of Sciences, Beijing, China.|School of Computer and Information Engineering, NanTong Institute of Technology, Nantong, China.|State Key Laboratory of Plant Genomics, Institute of Genetic and Developmental Biology, Chinese Academy of Sciences, Beijing, China.|State Key Laboratory of Plant Genomics, Institute of Genetic and Developmental Biology, Chinese Academy of Sciences, Beijing, China.', 'China|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty642', '30032165', 'The complex cellular networks underlying phenotypes are formed by the interacting gene modules. Building and analyzing genome-wide and high-quality Gene Co-expression Networks (GCNs) is useful for uncovering these modules and understanding the phenotypes of an organism.'),
(81993, 2019, 'bio', 'Toward fast and accurate SNP genotyping from whole genome sequencing data for bedside diagnostics', 'Sun, Chen|Medvedev, Paul', 'Department of Computer Science and Engineering, Pennsylvania State University, USA.|Department of Computer Science and Engineering, Pennsylvania State University, USA.', 'United States|United States', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty641', '30032192', 'Genotyping a set of variants from a database is an important step for identifying known genetic traits and disease-related variants within an individual. The growing size of variant databases as well as the high depth of sequencing data poses an efficiency challenge. In clinical applications, where time is crucial, alignment-based methods are often not fast enough. To fill the gap, Shajii et al. propose LAVA, an alignment-free genotyping method which is able to more quickly genotype single nucleotide polymorphisms (SNPs), however, there remains large room for improvements in running time and accuracy.'),
(81994, 2019, 'bio', 'TreeGrafter: phylogenetic tree-based annotation of proteins with Gene Ontology terms and other annotations', 'Tang, Haiming|Finn, Robert D|Thomas, Paul D', 'Division of Bioinformatics, Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.|Division of Bioinformatics, Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA.', 'United Kingdom|United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty625', '30032202', 'TreeGrafter is a new software tool for annotating protein sequences using pre-annotated phylogenetic trees. Currently, the tool provides annotations to Gene Ontology (GO) terms, and PANTHER family and subfamily. The approach is generalizable to any annotations that have been made to internal nodes of a reference phylogenetic tree. TreeGrafter takes each input query protein sequence, finds the best matching homologous family in a library of pre-calculated, pre-annotated gene trees, and then grafts it to the best location in the tree. It then annotates the sequence by propagating annotations from ancestral nodes in the reference tree. We show that TreeGrafter outperforms subfamily HMM scoring for correctly assigning subfamily membership, and that it produces highly specific annotations of GO terms based on annotated reference phylogenetic trees. This method will be further integrated into InterProScan, enabling an even broader user community.'),
(81995, 2019, 'bio', 'StackDPPred: a stacking based prediction of DNA-binding protein from sequence', 'Mishra, Avdesh|Pokhrel, Pujan|Hoque, Md Tamjidul', 'Department of Computer Science, University of New Orleans, New Orleans, LA, USA.|Department of Computer Science, University of New Orleans, New Orleans, LA, USA.|Department of Computer Science, University of New Orleans, New Orleans, LA, USA.', 'United States|United States|United States', 12, '-', 'Journal Article', '10.1093/bioinformatics/bty653', '30032213', 'Identification of DNA-binding proteins from only sequence information is one of the most challenging problems in the field of genome annotation. DNA-binding proteins play an important role in various biological processes such as DNA replication, repair, transcription and splicing. Existing experimental techniques for identifying DNA-binding proteins are time-consuming and expensive. Thus, prediction of DNA-binding proteins from sequences alone using computational methods can be useful to quickly annotate and guide the experimental process. Most of the methods developed for predicting DNA-binding proteins use the information from the evolutionary profile, called the position-specific scoring matrix (PSSM) profile, alone and the accuracies of such methods have been limited. Here, we propose a method, called StackDPPred, which utilizes features extracted from PSSM and residue specific contact-energy to help train a stacking based machine learning method for the effective prediction of DNA-binding proteins.'),
(81996, 2019, 'bio', 'MendelProb: probability and sample size calculations for Mendelian studies of exome and whole genome sequence data', 'He, Zongxiao|Wang, Lu|DeWan, Andrew T|Leal, Suzanne M', 'Department of Molecular and Human Genetics, Baylor College of Medicine, Center for Statistical Genetics, Houston, TX, USA.|Department of Statistics, Rice University, Houston, TX, USA.|Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT, USA.|Department of Molecular and Human Genetics, Baylor College of Medicine, Center for Statistical Genetics, Houston, TX, USA.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty542', '30032240', 'For the design of genetic studies, it is necessary to perform power calculations. Although for Mendelian traits the power of detecting linkage for pedigree(s) can be determined, it is also of great interest to determine the probability of identifying multiple pedigrees or unrelated cases with variants in the same gene. For many diseases, due to extreme locus heterogeneity this probability can be small. If only one family is observed segregating a variant classified as likely pathogenic or of unknown significance, the gene cannot be implicated in disease etiology. The probability of identifying several disease families or cases is dependent on the gene-specific disease prevalence and the sample size. The observation of multiple disease families or cases with variants in the same gene as well as evidence of pathogenicity from other sources, e.g. in silico prediction, expression and functional studies, can aid in implicating a gene in disease etiology. MendelProb can determine the probability of detecting a minimum number of families or cases with variants in the same gene. It can also calculate the probability of detecting genes with variants in different data types, e.g. identifying a variant in at least one family that can establish linkage and more the two additional families regardless of their size. Additionally, for a specified probability MendelProb can determine the number of probands which need to be screened to detect a minimum number of individuals with variants within the same gene.'),
(81997, 2019, 'bio', 'CIRCOAST: a statistical hypothesis test for cellular colocalization with network structures', 'Corliss, Bruce A|Ray, H Clifton|Patrie, James T|Mansour, Jennifer|Kesting, Sam|Park, Janice H|Rohde, Gustavo|Yates, Paul A|Janes, Kevin A|Peirce, Shayn M', 'Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.|Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.|Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA.|Department of Biology, University of Virginia, Charlottesville, VA, USA.|Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.|Department of Biology, University of Virginia, Charlottesville, VA, USA.|Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.|Department of Ophthalmology, University of Virginia, Charlottesville, VA, USA.|Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.|Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty638', '30032263', 'Colocalization of structures in biomedical images can lead to insights into biological behaviors. One class of colocalization problems is examining an annular structure (disk-shaped such as a cell, vesicle or molecule) interacting with a network structure (vascular, neuronal, cytoskeletal, organellar). Examining colocalization events across conditions is often complicated by changes in density of both structure types, confounding traditional statistical approaches since colocalization cannot be normalized to the density of both structure types simultaneously. We have developed a technique to measure colocalization independent of structure density and applied it to characterizing intercellular colocation with blood vessel networks. This technique could be used to analyze colocalization of any annular structure with an arbitrarily shaped network structure.'),
(81998, 2019, 'bio', 'MoDentify: phenotype-driven module identification in metabolomics networks at different resolutions', 'Do, Kieu Trinh|Rasp, David J N-P|Kastenmüller, Gabi|Suhre, Karsten|Krumsiek, Jan', 'Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.|Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.|German Center for Diabetes Research (DZD), Neuherberg, Germany.|Department of Physiology and Biophysics, Weill Cornell Medical College-Qatar Education City, Doha, Qatar.|Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.', 'Germany|Germany|Germany|Qatar|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty650', '30032270', 'Associations of metabolomics data with phenotypic outcomes are expected to span functional modules, which are defined as sets of correlating metabolites that are coordinately regulated. Moreover, these associations occur at different scales, from entire pathways to only a few metabolites, an aspect that has not been addressed by previous methods. Here, we present MoDentify, a free R package to identify regulated modules in metabolomics networks at different layers of resolution. Importantly, MoDentify shows higher statistical power than classical association analysis. Moreover, the package offers direct interactive visualization of the results in Cytoscape. We present an application example using complex, multifluid metabolomics data. Due to its generic character, the method is widely applicable to other types of data.'),
(81999, 2019, 'bio', 'SonicParanoid: fast, accurate and easy orthology inference', 'Cosentino, Salvatore|Iwasaki, Wataru', 'Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, Japan.|Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, Japan.', 'Japan|Japan', 10, '-', 'Journal Article', '10.1093/bioinformatics/bty631', '30032301', 'Orthology inference constitutes a common base of many genome-based studies, as a pre-requisite for annotating new genomes, finding target genes for biotechnological applications and revealing the evolutionary history of life. Although its importance keeps rising with the ever-growing number of sequenced genomes, existing tools are computationally demanding and difficult to employ.'),
(82000, 2019, 'bio', 'Re-identification of individuals in genomic data-sharing beacons via allele inference', 'von Thenen, Nora|Ayday, Erman|Cicek, A Ercument', 'Computer Engineering Department, Bilkent University, Ankara, Turkey.|Computer Engineering Department, Bilkent University, Ankara, Turkey.|Computer Engineering Department, Bilkent University, Ankara, Turkey.', 'Turkey|Turkey|Turkey', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty643', '30052749', 'Genomic data-sharing beacons aim to provide a secure, easy to implement and standardized interface for data-sharing by only allowing yes/no queries on the presence of specific alleles in the dataset. Previously deemed secure against re-identification attacks, beacons were shown to be vulnerable despite their stringent policy. Recent studies have demonstrated that it is possible to determine whether the victim is in the dataset, by repeatedly querying the beacon for his/her single-nucleotide polymorphisms (SNPs). Here, we propose a novel re-identification attack and show that the privacy risk is more serious than previously thought.'),
(82001, 2019, 'bio', 'MinIONQC: fast and simple quality control for MinION sequencing data', 'Lanfear, R|Schalamun, M|Kainer, D|Wang, W|Schwessinger, B', 'The Research School of Biology, Australian National University, Canberra ACT 2601, Australia.|The Research School of Biology, Australian National University, Canberra ACT 2601, Australia.|The Research School of Biology, Australian National University, Canberra ACT 2601, Australia.|The Research School of Biology, Australian National University, Canberra ACT 2601, Australia.|The Research School of Biology, Australian National University, Canberra ACT 2601, Australia.', 'Australia|Australia|Australia|Australia|Australia', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty654', '30052755', 'MinIONQC provides rapid diagnostic plots and quality control data from one or more flowcells of sequencing data from Oxford Nanopore Technologies MinION instrument. It can be used to assist with the optimisation of extraction, library preparation, and sequencing protocols, to quickly and directly compare the data from many flowcells, and to provide publication-ready figures summarising sequencing data.'),
(82002, 2019, 'bio', 'WAVES: a web application for versatile enhanced bioinformatic services', 'Chakiachvili, Marc|Milanesi, Sylvain|Arigon Chifolleau, Anne-Muriel|Lefort, Vincent', 'LIRMM UMR 5506 - CNRS & Université de Montpellier, Montpellier, France.|LIRMM UMR 5506 - CNRS & Université de Montpellier, Montpellier, France.|LIRMM UMR 5506 - CNRS & Université de Montpellier, Montpellier, France.|LIRMM UMR 5506 - CNRS & Université de Montpellier, Montpellier, France.', 'France|France|France|France', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty639', '30052762', 'WAVES is a web application dedicated to bioinformatic tool integration. It provides an efficient way to implement a service for any bioinformatic software. Such services are automatically made available in three ways: web pages, web forms to include in remote websites and a RESTful web services single application programing interface to access remotely from applications. In order to fulfill the services computational needs, WAVES can perform computation on various resources and environments, such as Galaxy instances.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(82003, 2019, 'bio', 'BinDash, software for fast genome distance estimation on a typical personal laptop', 'Zhao, XiaoFei', 'Independent researcher, Waterloo, ON, Canada.', 'Canada', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty651', '30052763', 'The number of genomes (including meta-genomes) is increasing at an accelerating pace. In the near future, we may need to estimate pairwise distances between millions of genomes. Even with the use of cloud computing, very few softwares can perform such estimation.'),
(82004, 2018, 'bio', 'Distinguishing prognostic and predictive biomarkers: an information theoretic approach', 'Sechidis, Konstantinos|Papangelou, Konstantinos|Metcalfe, Paul D|Svensson, David|Weatherall, James|Brown, Gavin', 'Independent researcher, Waterloo, ON, Canada.|Independent researcher, Waterloo, ON, Canada.|Independent researcher, Waterloo, ON, Canada.|Independent researcher, Waterloo, ON, Canada.|Independent researcher, Waterloo, ON, Canada.|Independent researcher, Waterloo, ON, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty515', '30052765', '-'),
(82005, 2019, 'bio', '4mCPred: machine learning methods for DNA N4-methylcytosine sites prediction', 'He, Wenying|Jia, Cangzhi|Zou, Quan', 'School of Computer Science and Technology, Tianjin University, Tianjin, China.|Department of Mathematics, Dalian Maritime University, Dalian, China.|School of Computer Science and Technology, Tianjin University, Tianjin, China.', 'China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty668', '30052767', 'N4-methylcytosine (4mC), an important epigenetic modification formed by the action of specific methyltransferases, plays an essential role in DNA repair, expression and replication. The accurate identification of 4mC sites aids in-depth research to biological functions and mechanisms. Because, experimental identification of 4mC sites is time-consuming and costly, especially given the rapid accumulation of gene sequences. Supplementation with efficient computational methods is urgently needed.'),
(82006, 2019, 'bio', 'gMCS: fast computation of genetic minimal cut sets in large networks', 'Apaolaza, Iñigo|Valcarcel, Luis Vitores|Planes, Francisco J', 'Department of Biomedical Engineering and Sciences, Tecnun School of Engineering, University of Navarra, San Sebastián, Spain.|Department of Biomedical Engineering and Sciences, Tecnun School of Engineering, University of Navarra, San Sebastián, Spain.|Department of Biomedical Engineering and Sciences, Tecnun School of Engineering, University of Navarra, San Sebastián, Spain.', 'Spain|Spain|Spain', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty656', '30052768', 'The identification of minimal gene knockout strategies to engineer metabolic systems constitutes one of the most relevant applications of the COnstraint-Based Reconstruction and Analysis (COBRA) framework. In the last years, the minimal cut sets (MCSs) approach has emerged as a promising tool to carry out this task. However, MCSs define reaction knockout strategies, which are not necessarily transformed into feasible strategies at the gene level.'),
(82007, 2019, 'bio', 'MR4Cancer: a web server prioritizing master regulators for cancer', 'Ru, Beibei|Tong, Yin|Zhang, Jiangwen', 'School of Biological Sciences, The University of Hong Kong, Hong Kong, China.|School of Biological Sciences, The University of Hong Kong, Hong Kong, China.|School of Biological Sciences, The University of Hong Kong, Hong Kong, China.', 'China|China|China', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty658', '30052770', 'During cancer stage transition, a master regulator (MR) refers to the key gene controlling cancer initiation and progression by orchestrating the associated target genes (termed as its regulon). Due to their inherent importance, MRs can serve as critical biomarkers for cancer diagnosis and prognosis, and therapeutic targets. However, it is challenging to infer key MRs that might explain gene expression profile changes between two groups due to lack of context-specific regulons, whose expression level can collectively reflect the activity of likely MRs. There is also a need to design an easy-to-use tool of MR identification for research community.'),
(82008, 2019, 'bio', 'Quantifying the notions of canalizing and master genes in a gene regulatory network-a Boolean network modeling perspective', 'Kim, Eunji|Ivanov, Ivan|Dougherty, Edward R', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA.|Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, TX, USA.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty665', '30052771', 'Canalizing genes enforce broad corrective actions on cellular processes for the purpose of biological robustness maintaining a constant phenotype to remain unchanged in spite of genetic mutations or environmental perturbations. Despite their central role in biological systems, the observation/detection of canalizing genes is often impeded because the behavior of affected genes is highly varied relative to the inactive canalizer. Therefore, the activity of canalizing genes is difficult to predict to any significant degree by their subject genes under normal cell conditions.'),
(82009, 2019, 'bio', 'The Terabase Search Engine: a large-scale relational database of short-read sequences', 'Wilton, Richard|Wheelan, Sarah J|Szalay, Alexander S|Salzberg, Steven L', 'Department of Physics and Astronomy, Johns Hopkins University, Baltimore, MD, USA.|Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.|Department of Physics and Astronomy, Johns Hopkins University, Baltimore, MD, USA.|Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty657', '30052772', 'DNA sequencing archives have grown to enormous scales in recent years, and thousands of human genomes have already been sequenced. The size of these data sets has made searching the raw read data infeasible without high-performance data-query technology. Additionally, it is challenging to search a repository of short-read data using relational logic and to apply that logic across samples from multiple whole-genome sequencing samples.'),
(82010, 2019, 'bio', 'HOGMMNC: a higher order graph matching with multiple network constraints model for gene-drug regulatory modules identification', 'Chen, Jiazhou|Peng, Hong|Han, Guoqiang|Cai, Hongmin|Cai, Jiulun', 'School of Computer Science and Engineering, South China University of Technology, Guangzhou, China.|School of Computer Science and Engineering, South China University of Technology, Guangzhou, China.|School of Computer Science and Engineering, South China University of Technology, Guangzhou, China.|School of Computer Science and Engineering, South China University of Technology, Guangzhou, China.|School of Computer Science and Engineering, South China University of Technology, Guangzhou, China.', 'China|China|China|China|China', 13, '-', 'Journal Article', '10.1093/bioinformatics/bty662', '30052773', 'The emergence of large amounts of genomic, chemical, and pharmacological data provides new opportunities and challenges. Identifying gene-drug associations is not only crucial in providing a comprehensive understanding of the molecular mechanisms of drug action, but is also important in the development of effective treatments for patients. However, accurately determining the complex associations among pharmacogenomic data remains challenging. We propose a higher order graph matching with multiple network constraints (HOGMMNC) model to accurately identify gene-drug modules. The HOGMMNC model aims to capture the inherent structural relations within data drawn from multiple sources by hypergraph matching. The proposed technique seamlessly integrates prior constraints to enhance the accuracy and reliability of the identified relations. An effective numerical solution is combined with a novel sampling strategy to solve the problem efficiently.'),
(82011, 2019, 'bio', 'iSwathX: an interactive web-based application for extension of DIA peptide reference libraries', 'Noor, Zainab|Wu, Jemma X|Pascovici, Dana|Mohamedali, Abidali|Molloy, Mark P|Baker, Mark S|Ranganathan, Shoba', 'Department of Molecular Sciences, Macquarie University, Sydney, NSW, Australia.|Australian Proteome Analysis Facility (APAF), Macquarie University, Sydney, NSW, Australia.|Australian Proteome Analysis Facility (APAF), Macquarie University, Sydney, NSW, Australia.|Department of Molecular Sciences, Macquarie University, Sydney, NSW, Australia.|Department of Molecular Sciences, Macquarie University, Sydney, NSW, Australia.|Department of Biomedical Sciences, Macquarie University, Sydney, NSW, Australia.|Department of Molecular Sciences, Macquarie University, Sydney, NSW, Australia.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty660', '30052774', 'Large-scale peptide mass spectrometry (MS)/MS reference libraries are essential for the comprehensive analysis of data-independent acquisition (DIA) MS datasets, providing a comprehensive set of spectra for identification and quantification of proteins. We have developed a novel web-based R-package (iSwathX) for combining reference libraries that is compatible with different DIA analysis software. This open-source web GUI automates the process of normalization and combination of spectral libraries and provides a user-friendly method for performing library format conversions, analysis and visualizations, with no need for programing familiarity.'),
(82012, 2019, 'bio', 'WebMetabase: cleavage sites analysis tool for natural and unnatural substrates from diverse data source', 'Radchenko, Tatiana|Fontaine, Fabien|Morettoni, Luca|Zamora, Ismael', 'Pompeu Fabra University, Barcelona, Spain.|Lead Molecular Design, S.L., Sant Cugat del Vallés, Spain.|Molecular Discovery Ltd, London, UK.|Pompeu Fabra University, Barcelona, Spain.', 'Spain|Spain|United Kingdom|Spain', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty667', '30052776', 'More than 150 peptide therapeutics are globally in clinical development. Many enzymatic barriers should be crossed by a successful drug to be prosperous in such a process. Therefore, the new peptide drugs must be designed preventing the potential protease cleavage to make the compound less susceptible to protease reaction. We present a new data analysis tool developed in WebMetabase, an approach that stores the information from liquid chromatography mass spectrometry-based experimental data or from external sources such as the MEROPS database. The tool is a chemically aware system where each peptide substrate is presented as a sequence of structural blocks (SBs) connected by amide bonds and not being limited to the natural amino acids. Each SB is characterized by its pharmacophoric and physicochemical properties including a similarity score that describes likelihood between a SB and each one of the other SBs in the database. This methodology can be used to perform a frequency analysis to discover the most frequent cleavage sites for similar amide bonds, defined based on the similarity of the SB that participate in such a bond within the experimentally derived and/or public database.'),
(82013, 2019, 'bio', 'GPseudoRank: a permutation sampler for single cell orderings', 'Strauß, Magdalena E|Reid, John E|Wernisch, Lorenz', 'MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK.|MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK.|MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK.', 'United Kingdom|United Kingdom|United Kingdom', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty664', '30052778', 'A number of pseudotime methods have provided point estimates of the ordering of cells for scRNA-seq data. A still limited number of methods also model the uncertainty of the pseudotime estimate. However, there is still a need for a method to sample from complicated and multi-modal distributions of orders, and to estimate changes in the amount of the uncertainty of the order during the course of a biological development, as this can support the selection of suitable cells for the clustering of genes or for network inference.'),
(82014, 2019, 'bio', 'LipidIMMS Analyzer: integrating multi-dimensional information to support lipid identification in ion mobility-mass spectrometry based lipidomics', 'Zhou, Zhiwei|Shen, Xiaotao|Chen, Xi|Tu, Jia|Xiong, Xin|Zhu, Zheng-Jiang', 'Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China.|Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China.|Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China.|Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China.|Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China.|Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China.', 'China|China|China|China|China|China', 16, '-', 'Journal Article', '10.1093/bioinformatics/bty661', '30052780', 'Ion mobility-mass spectrometry (IM-MS) has showed great application potential for lipidomics. However, IM-MS based lipidomics is significantly restricted by the available software for lipid structural identification. Here, we developed a software tool, namely, LipidIMMS Analyzer, to support the accurate identification of lipids in IM-MS. For the first time, the software incorporates a large-scale database covering over 260 000 lipids and four-dimensional structural information for each lipid [i.e. m/z, retention time (RT), collision cross-section (CCS) and MS/MS spectra]. Therefore, multi-dimensional information can be readily integrated to support lipid identifications, and significantly improve the coverage and confidence of identification. Currently, the software supports different IM-MS instruments and data acquisition approaches.'),
(82015, 2019, 'bio', 'zfRegeneration: a database for gene expression profiling during regeneration', 'Nieto-Arellano, Rocío|Sánchez-Iranzo, Héctor', 'Independent Researcher, Boxbergring 107, Heidelberg, Germany.|Independent Researcher, Boxbergring 107, Heidelberg, Germany.', 'Germany|Germany', 7, '-', 'Journal Article', '10.1093/bioinformatics/bty659', '30052798', 'Zebrafish is a model organism with the ability to regenerate many different organs. Although RNA-Seq has been used extensively to study this process, there are no databases that allow easy access to data.'),
(82016, 2019, 'bio', 'The epiGenomic Efficient Correlator (epiGeEC) tool allows fast comparison of user datasets with thousands of public epigenomic datasets', 'Laperle, Jonathan|Hébert-Deschamps, Simon|Raby, Joanny|de Lima Morais, David A|Barrette, Michel|Bujold, David|Bastin, Charlotte|Robert, Marc-Antoine|Nadeau, Jean-François|Harel, Marie|Nordell-Markovits, Alexei|Veilleux, Alain|Bourque, Guillaume|Jacques, Pierre-Étienne', 'Département dInformatique, Université de Sherbrooke, Sherbrooke, QC, Canada.|Département dInformatique, Université de Sherbrooke, Sherbrooke, QC, Canada.|Département de Biologie, Université de Sherbrooke, Sherbrooke, QC, Canada.|Centre de Calcul Scientifique, Faculté des Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada.|Centre de Calcul Scientifique, Faculté des Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada.|McGill University and Génome Québec Innovation Center, Montréal, Canada.|Département de Biologie, Université de Sherbrooke, Sherbrooke, QC, Canada.|Département de Biologie, Université de Sherbrooke, Sherbrooke, QC, Canada.|Département de Biologie, Université de Sherbrooke, Sherbrooke, QC, Canada.|Département de Biologie, Université de Sherbrooke, Sherbrooke, QC, Canada.|Département de Biologie, Université de Sherbrooke, Sherbrooke, QC, Canada.|Centre de Calcul Scientifique, Faculté des Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada.|Department of Human Genetics, McGill University, Montréal, Canada.|Département dInformatique, Université de Sherbrooke, Sherbrooke, QC, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada', 3, '-', 'Journal Article', '10.1093/bioinformatics/bty655', '30052804', 'In recent years, major initiatives such as the International Human Epigenome Consortium have generated thousands of high-quality genome-wide datasets for a large variety of assays and cell types. This data can be used as a reference to assess whether the signal from a user-provided dataset corresponds to its expected experiment, as well as to help reveal unexpected biological associations. We have developed the epiGenomic Efficient Correlator (epiGeEC) tool to enable genome-wide comparisons of very large numbers of datasets. A public Galaxy implementation of epiGeEC allows comparison of user datasets with thousands of public datasets in a few minutes.'),
(82017, 2019, 'bio', 'DoRes within CellMissy: dose-response analysis on cell migration and related data', 'Sergeant, Gwendolien|Martens, Lennart|Van Troys, Marleen|Masuzzo, Paola', 'VIB-UGent Center for Medical Biotechnology, Ghent, Belgium.|VIB-UGent Center for Medical Biotechnology, Ghent, Belgium.|Department of Biochemistry, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium.|VIB-UGent Center for Medical Biotechnology, Ghent, Belgium.', 'Belgium|Belgium|Belgium|Belgium', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty634', '30052834', 'In cancer research, cell-based assays are used to assess cell migration and invasion. The major bottleneck is the lack of automated tools to visualize and analyse the large amounts of biological dose-response data produced. To address this challenge, we have developed an automated and free software package for dose-response analyses, DoRes, which is released as an add-on of the freely available and open-source tool CellMissy, dedicated to the management and analysis of cell migration data. DoRes implements non-linear curve fitting functionality into a robust, user-friendly and flexible software package with the possibility of importing a tabular file or starting from a cell migration experiment. We demonstrate the ability of the software by analysing public dose-response data and a typical cell migration experiment, and show that the extracted dose-response parameters and the calculated statistical values are consistently comparable to those of the widely used, commercial software GraphPad Prism.'),
(82018, 2018, 'bio', 'TAPAS: tool for alternative polyadenylation site analysis', 'Arefeen, Ashraful|Liu, Juntao|Xiao, Xinshu|Jiang, Tao', 'Department of Computer Science and Engineering, University of California, Riverside, CA, USA.|School of Mathematics, Shandong University, Jinan, Shandong, China.|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|China|United States|United States', 7, '3 Untranslated Regions|Animals|Eukaryota|Humans|Polyadenylation|RNA, Messenger|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/bty110', '30052912', 'The length of the 3 untranslated region (3 UTR) of an mRNA is essential for many biological activities such as mRNA stability, sub-cellular localization, protein translation, protein binding and translation efficiency. Moreover, correlation between diseases and the shortening (or lengthening) of 3 UTRs has been reported in the literature. This length is largely determined by the polyadenylation cleavage site in the mRNA. As alternative polyadenylation (APA) sites are common in mammalian genes, several tools have been published recently for detecting APA sites from RNA-Seq data or performing shortening/lengthening analysis. These tools consider either up to only two APA sites in a gene or only APA sites that occur in the last exon of a gene, although a gene may generally have more than two APA sites and an APA site may sometimes occur before the last exon. Furthermore, the tools are unable to integrate the analysis of shortening/lengthening events with APA site detection.'),
(82019, 2018, 'bio', 'Design tools for MPRA experiments', 'Ghazi, Andrew R|Chen, Edward S|Henke, David M|Madan, Namrata|Edelstein, Leonard C|Shaw, Chad A', 'Department of Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX, USA.|Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.|Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.|Cardeza Foundation for Hematologic Research and Department of Medicine, Thomas Jefferson University, Philadelphia, PA, USA.|Cardeza Foundation for Hematologic Research and Department of Medicine, Thomas Jefferson University, Philadelphia, PA, USA.|Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.', 'United States|United States|United States|United States|United States|United States', 3, 'Biological Assay|Computational Biology|Gene Expression Regulation|Genes, Reporter|Genetic Techniques|Software', 'Journal Article', '10.1093/bioinformatics/bty150', '30052913', 'Genetic reporter assays are a convenient, relatively inexpensive method for studying the regulation of gene expression. Massively Parallel Reporter Assays (MPRA) are high-throughput functionalization assays that interrogate the transcriptional activity of many genetic variants at once using a library of synthetic barcoded constructs. Despite growing interest in this area, there are few computational tools to design and execute MPRA studies.'),
(82020, 2019, 'bio', 'DiscoverSL: an R package for multi-omic data driven prediction of synthetic lethality in cancers', 'Das, Shaoli|Deng, Xiang|Camphausen, Kevin|Shankavaram, Uma', 'Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.|Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.|Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.|Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.', 'United States|United States|United States|United States', 4, '-', 'Journal Article', '10.1093/bioinformatics/bty673', '30059974', 'Synthetic lethality is a state when simultaneous loss of two genes is lethal to a cancer cell, while the loss of the individual genes is not. We developed an R package DiscoverSL to predict and visualize synthetic lethality in cancers using multi-omic cancer data. Mutation, copy number alteration and gene expression data from The Cancer Genome Atlas project were combined to develop a multi-parametric Random Forest classifier. The effects of selectively targeting the predicted synthetic lethal genes is tested in silico using shRNA and drug screening data from cancer cell line databases. The clinical outcome in patients with mutation in primary gene and over/under-expression in the synthetic lethal gene is evaluated using Kaplan-Meier analysis. The method helps to identify new therapeutic approaches by exploiting the concept of synthetic lethality.'),
(82021, 2019, 'bio', 'Quantifying heterogeneity of expression data based on principal components', 'Yang, Zi|Michailidis, George', 'Department of Statistics, University of Michigan, Ann Arbor, MI, USA.|Department of Statistics, University of Florida, Gainesville, FL, USA.', 'United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty671', '30060088', 'The diversity of biological omics data provides richness of information, but also presents an analytic challenge. While there has been much methodological and theoretical development on the statistical handling of large volumes of biological data, far less attention has been devoted to characterizing their veracity and variability.'),
(82022, 2019, 'bio', 'Automated workflow composition in mass spectrometry-based proteomics', 'Palmblad, Magnus|Lamprecht, Anna-Lena|Ison, Jon|Schwämmle, Veit', 'Center for Proteomics and Metabolomics, Leiden University Medical Center, RC Leiden, The Netherlands.|Department of Information and Computing Sciences, Utrecht University, CC Utrecht, The Netherlands.|National Life Science Supercomputing Center, Technical University of Denmark, Kongens Lyngby, Denmark.|Department of Biochemistry and Molecular Biology and VILLUM Center for Bioanalytical Sciences, University of Southern Denmark, Odense, Denmark.', 'Netherlands|Netherlands|Denmark|Denmark', 11, '-', 'Journal Article', '10.1093/bioinformatics/bty646', '30060113', 'Numerous software utilities operating on mass spectrometry (MS) data are described in the literature and provide specific operations as building blocks for the assembly of on-purpose workflows. Working out which tools and combinations are applicable or optimal in practice is often hard. Thus researchers face difficulties in selecting practical and effective data analysis pipelines for a specific experimental design.'),
(82023, 2019, 'bio', 'copMEM: finding maximal exact matches via sampling both genomes', 'Grabowski, Szymon|Bieniecki, Wojciech', 'Department of Biochemistry and Molecular Biology and VILLUM Center for Bioanalytical Sciences, University of Southern Denmark, Odense, Denmark.|Department of Biochemistry and Molecular Biology and VILLUM Center for Bioanalytical Sciences, University of Southern Denmark, Odense, Denmark.', 'Denmark|Denmark', 3, '-', 'Journal Article', '10.1093/bioinformatics/bty670', '30060142', 'Genome-to-genome comparisons require designating anchor points, which are given by Maximum Exact Matches (MEMs) between their sequences. For large genomes this is a challenging problem and the performance of existing solutions, even in parallel regimes, is not quite satisfactory.'),
(82024, 2019, 'bio', 'Effusion: prediction of protein function from sequence similarity networks', 'Yunes, Jeffrey M|Babbitt, Patricia C', 'UC Berkeley - UCSF Graduate Program in Bioengineering, University of California, San Francisco, CA, USA.|Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA.', 'United States|United States', 3, '-', 'Journal Article', '10.1093/bioinformatics/bty672', '30084920', 'Critical evaluation of methods for protein function prediction shows that data integration improves the performance of methods that predict protein function, but a basic BLAST-based method is still a top contender. We sought to engineer a method that modernizes the classical approach while avoiding pitfalls common to state-of-the-art methods.'),
(82025, 2019, 'bio', 'Evaluation of cross-platform and interlaboratory concordance via consensus modelling of genomic measurements', 'Peters, Timothy J|French, Hugh J|Bradford, Stephen T|Pidsley, Ruth|Stirzaker, Clare|Varinli, Hilal|Nair, Shalima|Qu, Wenjia|Song, Jenny|Giles, Katherine A|Statham, Aaron L|Speirs, Helen|Speed, Terence P|Clark, Susan J', 'Epigenetics Laboratory, Genomics and Epigenetics Division, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia.|Epigenetics Laboratory, Genomics and Epigenetics Division, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia.|Epigenetics Laboratory, Genomics and Epigenetics Division, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia.|Epigenetics Laboratory, Genomics and Epigenetics Division, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia.|Epigenetics Laboratory, Genomics and Epigenetics Division, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia.|Epigenetics Laboratory, Genomics and Epigenetics Division, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia.|Epigenetics Laboratory, Genomics and Epigenetics Division, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia.|Epigenetics Laboratory, Genomics and Epigenetics Division, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia.|Epigenetics Laboratory, Genomics and Epigenetics Division, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia.|Epigenetics Laboratory, Genomics and Epigenetics Division, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia.|Epigenetics Laboratory, Genomics and Epigenetics Division, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia.|Ramaciotti Centre for Genomics, University of New South Wales, Randwick, NSW, Australia.|Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.|Epigenetics Laboratory, Genomics and Epigenetics Division, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty675', '30084929', 'A synoptic view of the human genome benefits chiefly from the application of nucleic acid sequencing and microarray technologies. These platforms allow interrogation of patterns such as gene expression and DNA methylation at the vast majority of canonical loci, allowing granular insights and opportunities for validation of original findings. However, problems arise when validating against a gold standard measurement, since this immediately biases all subsequent measurements towards that particular technology or protocol. Since all genomic measurements are estimates, in the absence of a gold standard we instead empirically assess the measurement precision and sensitivity of a large suite of genomic technologies via a consensus modelling method called the row-linear model. This method is an application of the American Society for Testing and Materials Standard E691 for assessing interlaboratory precision and sources of variability across multiple testing sites. Both cross-platform and cross-locus comparisons can be made across all common loci, allowing identification of technology- and locus-specific tendencies.'),
(82026, 2019, 'bio', 'JustOrthologs: a fast, accurate and user-friendly ortholog identification algorithm', 'Miller, Justin B|Pickett, Brandon D|Ridge, Perry G', 'Department of Biology, Brigham Young University, Provo, UT, USA.|Department of Biology, Brigham Young University, Provo, UT, USA.|Department of Biology, Brigham Young University, Provo, UT, USA.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty669', '30084941', 'Orthologous gene identification is fundamental to all aspects of biology. For example, ortholog identification between species can provide functional insights for genes of unknown function and is a necessary step in phylogenetic inference. Currently, most ortholog identification algorithms require all-versus-all BLAST comparisons, which are time-consuming and memory intensive.'),
(82027, 2019, 'bio', 'TIMING 20: high-throughput single-cell profiling of dynamic cell-cell interactions by time-lapse imaging microscopy in nanowell grids', 'Lu, Hengyang|Li, Jiabing|Martinez-Paniagua, Melisa A|Bandey, Irfan N|Amritkar, Amit|Singh, Harjeet|Mayerich, David|Varadarajan, Navin|Roysam, Badrinath', 'Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA.|Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA.|Department of Chemical and Biomolecular Engineering, University of Houston, Houston, TX, USA.|Department of Chemical and Biomolecular Engineering, University of Houston, Houston, TX, USA.|Center for Advanced Computing and Data Science and Department of Mechanical Engineering, University of Houston, Houston, TX, USA.|Division of Pediatrics, University of Texas MD Anderson Cancer Center, Houston, TX, USA.|Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA.|Department of Chemical and Biomolecular Engineering, University of Houston, Houston, TX, USA.|Department of Electrical and Computer Engineering, University of Houston, Houston, TX, 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/bty676', '30084956', 'Automated profiling of cell-cell interactions from high-throughput time-lapse imaging microscopy data of cells in nanowell grids (TIMING) has led to fundamental insights into cell-cell interactions in immunotherapy. This application note aims to enable widespread adoption of TIMING by (i) enabling the computations to occur on a desktop computer with a graphical processing unit instead of a server, (ii) enabling image acquisition and analysis to occur in the laboratory avoiding network data transfers to/from a server and (iii) providing a comprehensive graphical user interface.'),
(82028, 2019, 'bio', 'VisioProt-MS: interactive 2D maps from intact protein mass spectrometry', 'Locard-Paulet, Marie|Parra, Julien|Albigot, Renaud|Mouton-Barbosa, Emmanuelle|Bardi, Laurent|Burlet-Schiltz, Odile|Marcoux, Julien', 'Institut de Pharmacologie et de Biologie Structurale, Universitxc3xa9 de Toulouse, CNRS, UPS, Toulouse, France.|Institut de Pharmacologie et de Biologie Structurale, Universitxc3xa9 de Toulouse, CNRS, UPS, Toulouse, France.|Institut de Pharmacologie et de Biologie Structurale, Universitxc3xa9 de Toulouse, CNRS, UPS, Toulouse, France.|Institut de Pharmacologie et de Biologie Structurale, Universitxc3xa9 de Toulouse, CNRS, UPS, Toulouse, France.|Institut de Pharmacologie et de Biologie Structurale, Universitxc3xa9 de Toulouse, CNRS, UPS, Toulouse, France.|Institut de Pharmacologie et de Biologie Structurale, Universitxc3xa9 de Toulouse, CNRS, UPS, Toulouse, France.|Institut de Pharmacologie et de Biologie Structurale, Universitxc3xa9 de Toulouse, CNRS, UPS, Toulouse, France.', 'France|France|France|France|France|France|France', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty680', '30084957', 'VisioProt-MS is designed to summarize and analyze intact protein and top-down proteomics data. It plots the molecular weights of eluting proteins as a function of their retention time, thereby allowing inspection of runs from liquid chromatography coupled to mass spectrometry (LC-MS). It also overlays MS/MS identification results. VisioProt-MS is compatible with outputs from many different top-down dedicated software. To our knowledge, this is the only open source standalone application that allows the dynamic comparison of several MS files, a prerequisite for comparative analysis of different biological conditions. With its dynamic rendering, this user-friendly web application facilitates inspection, comparison and export of publication quality 2xe2x80x89D maps from deconvoluted LC-MS run(s) and top-down proteomics data.'),
(82029, 2019, 'bio', 'PredMP: a web server for de novo prediction and visualization of membrane proteins', 'Wang, Sheng|Fei, Shiyang|Wang, Zongan|Li, Yu|Xu, Jinbo|Zhao, Feng|Gao, Xin', 'Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.|COMPASS, New York, NY, USA.|Department of Chemistry, James Franck Institute, University of Chicago, Chicago, IL, USA.|Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.|Toyota Technological Institute at Chicago, Chicago, IL, USA.|Prospect Institute of Fatty Acids and Health, Qingdao University, Ningxia, China.|Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.', 'Saudi Arabia|United States|United States|Saudi Arabia|United States|China|Saudi Arabia', 7, '-', 'Journal Article', '10.1093/bioinformatics/bty684', '30084960', 'PredMP is the first web service, to our knowledge, that aims at de novo prediction of the membrane protein (MP) 3D structure followed by the embedding of the MP into the lipid bilayer for visualization. Our approach is based on a high-throughput Deep Transfer Learning (DTL) method that first predicts MP contacts by learning from non-MPs and then predicts the 3D model of the MP using the predicted contacts as distance restraints. This algorithm is derived from our previous Deep Learning (DL) method originally developed for soluble protein contact prediction, which has been officially ranked No. 1 in CASP12. The DTL framework in our approach overcomes the challenge that there are only a limited number of solved MP structures for training the deep learning model. There are three modules in the PredMP server: (i) The DTL framework followed by the contact-assisted folding protocol has already been implemented in RaptorX-Contact, which serves as the key module for 3D model generation, (ii) The 1D annotation module, implemented in RaptorX-Property, is used to predict the secondary structure and disordered regions, and (iii) the visualization module to display the predicted MPs embedded in the lipid bilayer guided by the predicted transmembrane topology.'),
(82030, 2019, 'bio', 'On the problem of confounders in modeling gene expression', 'Schmidt, Florian|Schulz, Marcel H', 'High-througput Genomics and Systems Biology, Cluster of Excellence on Multimodal Computing and Interaction, Saarland Informatics Campus, Saarbrücken, Germany.|High-througput Genomics and Systems Biology, Cluster of Excellence on Multimodal Computing and Interaction, Saarland Informatics Campus, Saarbrücken, Germany.', 'Germany|Germany', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty674', '30084962', 'Modeling of Transcription Factor (TF) binding from both ChIP-seq and chromatin accessibility data has become prevalent in computational biology. Several models have been proposed to generate new hypotheses on transcriptional regulation. However, there is no distinct approach to derive TF binding scores from ChIP-seq and open chromatin experiments. Here, we review biases of various scoring approaches and their effects on the interpretation and reliability of predictive gene expression models.'),
(82031, 2019, 'bio', 'Detection of de novo copy number deletions from targeted sequencing of trios', 'Fu, Jack M|Leslie, Elizabeth J|Scott, Alan F|Murray, Jeffrey C|Marazita, Mary L|Beaty, Terri H|Scharpf, Robert B|Ruczinski, Ingo', 'Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.|Department of Human Genetics, Emory University, Atlanta, GA, USA.|Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA.|Department of Pediatrics, Carver College of Medicine, University of Iowa, Iowa City, IA, USA.|Department of Oral Biology, University of Pittsburgh, Pittsburgh, PA, USA.|Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.|Department of Oncology, Johns Hopkins School of Medicine, Baltimore, MD, USA.|Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty677', '30084993', 'De novo copy number deletions have been implicated in many diseases, but there is no formal method to date that identifies de novo deletions in parent-offspring trios from capture-based sequencing platforms.'),
(82032, 2019, 'bio', 'MicroPheno: predicting environments and host phenotypes from 16S rRNA gene sequencing using a k-mer based representation of shallow sub-samples', 'Asgari, Ehsaneddin|Garakani, Kiavash|McHardy, Alice C|Mofrad, Mohammad R K', 'Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, Berkeley, CA, USA.|Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, Berkeley, CA, USA.|Computational Biology of Infection Research, Helmholtz Center for Infection Research, Braunschweig, Germany and.|Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, Berkeley, CA, USA.', 'United States|United States|Germany|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty652', '30099528', '-'),
(82033, 2019, 'bio', 'KEDDY: a knowledge-based statistical gene set test method to detect differential functional protein-protein interactions', 'Jung, Sungwon', 'Department of Genome Medicine and Science, Gachon University College of Medicine, Incheon, Republic of Korea.', '-', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty686', '30101275', 'Identifying differential patterns between conditions is a popular approach to understanding the discrepancy between different biological contexts. Although many statistical tests were proposed for identifying gene sets with differential patterns based on different definitions of differentiality, few methods were suggested to identify gene sets with differential functional protein networks due to computational complexity.'),
(82034, 2019, 'bio', 'CABS-flex standalone: a simulation environment for fast modeling of protein flexibility', 'Kurcinski, Mateusz|Oleniecki, Tymoteusz|Ciemny, Maciej Pawel|Kuriata, Aleksander|Kolinski, Andrzej|Kmiecik, Sebastian', 'Biological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, Warsaw, Poland.|Biological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, Warsaw, Poland.|Biological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, Warsaw, Poland.|Biological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, Warsaw, Poland.|Biological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, Warsaw, Poland.|Biological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, Warsaw, Poland.', 'Poland|Poland|Poland|Poland|Poland|Poland', 14, '-', 'Journal Article', '10.1093/bioinformatics/bty685', '30101282', 'CABS-flex standalone is a Python package for fast simulations of protein structure flexibility. The package combines simulations of protein dynamics using CABS coarse-grained protein model with the reconstruction of selected models to all-atom representation and analysis of modeling results. CABS-flex standalone is designed to allow for command-line access to the CABS computations and complete control over simulation process. CABS-flex standalone is equipped with features such as: modeling of multimeric and large-size protein systems, contact map visualizations, analysis of similarities to the reference structure and configurable modeling protocol. For instance, the user may modify the simulation parameters, distance restraints, structural clustering scheme or all-atom reconstruction parameters. With these features CABS-flex standalone can be easily incorporated into other methodologies of structural biology.'),
(82035, 2019, 'bio', 'Genetic Simulation Resources and the GSR Certification Program', 'Peng, Bo|Leong, Man Chong|Chen, Huann-Sheng|Rotunno, Melissa|Brignole, Katy R|Clarke, John|Mechanic, Leah E', 'Department of Bioinformatics and Computational Biology, MD Anderson Cancer Center, Houston, TX, USA.|Childrens Environmental Health Initiative, Rice University, Houston, TX, USA.|Division of Cancer Control and Population Sciences, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA.|Division of Cancer Control and Population Sciences, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA.|Division of Cancer Control and Population Sciences, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA.|Cornerstone Systems, Lynden, WA, USA.|Division of Cancer Control and Population Sciences, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA.', 'United States|United States|United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty666', '30101297', '-'),
(82036, 2018, 'bio', 'ASGART: fast and parallel genome scale segmental duplications mapping', 'Delehelle, Franklin|Cussat-Blanc, Sylvain|Alliot, Jean-Marc|Luga, Hervé|Balaresque, Patricia', 'UMR5505 - CNRS-Université de Toulouse, Institut de Recherche en Informatique de Toulouse (IRIT), Toulouse, France.|UMR5505 - CNRS-Université de Toulouse, Institut de Recherche en Informatique de Toulouse (IRIT), Toulouse, France.|UMR5505 - CNRS-Université de Toulouse, Institut de Recherche en Informatique de Toulouse (IRIT), Toulouse, France.|UMR5505 - CNRS-Université de Toulouse, Institut de Recherche en Informatique de Toulouse (IRIT), Toulouse, France.|UMR 5288 - AMIS Université Paul Sabatier/CNRS, Faculté de Médecine Purpan, Toulouse, France.', 'France|France|France|France|France', 1, 'Animals|Chromosome Mapping|Eukaryota|Genomics|Humans|Segmental Duplications, Genomic|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bty172', '30101303', 'Segmental Duplications (SDs) are DNA fragments longer than 1 kbp, distributed within and between chromosomes and sharing more than 90% identity. Although they hold a significant role in genomic fluidity and adaptability, many key questions about their intrinsic characteristics and mutability remain unsolved due to the persistent difficulty of sequencing highly duplicated genomic regions. The recent development of long and linked-read NGS technologies will increase the need to search for SDs in genomes newly sequenced with these technics. The main limitation of SD analysis will soon be the availability of efficient detection software, to retrieve and compare SD genomic component between species or lineages.'),
(82037, 2019, 'bio', 'Global importance of RNA secondary structures in protein-coding sequences', 'Fricke, Markus|Gerst, Ruman|Ibrahim, Bashar|Niepmann, Michael|Marz, Manja', 'RNA Bioinformatics and High Throughput Analysis, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Jena, Germany.|European Virus Bioinformatics Center (EVBC), Jena, Germany.|RNA Bioinformatics and High Throughput Analysis, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Jena, Germany.|Institute of Biochemistry, Faculty of Medicine, Justus-Liebig-University, Giessen, Germany.|RNA Bioinformatics and High Throughput Analysis, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Jena, Germany.', 'Germany|Germany|Germany|Germany|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty678', '30101307', 'The protein-coding sequences of messenger RNAs are the linear template for translation of the gene sequence into protein. Nevertheless, the RNA can also form secondary structures by intramolecular base-pairing.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(82038, 2019, 'bio', 'Container-based bioinformatics with Pachyderm', 'Novella, Jon Ander|Emami Khoonsari, Payam|Herman, Stephanie|Whitenack, Daniel|Capuccini, Marco|Burman, Joachim|Kultima, Kim|Spjuth, Ola', 'Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden.|Department of Medical Sciences, Clinical Chemistry, Uppsala University, Uppsala, Sweden.|Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden.|Pachyderm, Inc., San Francisco, CA, USA.|Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden.|Department of Neuroscience, Uppsala University, Uppsala, Sweden.|Department of Medical Sciences, Clinical Chemistry, Uppsala University, Uppsala, Sweden.|Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden.', 'Sweden|Sweden|Sweden|United States|Sweden|Sweden|Sweden|Sweden', 12, '-', 'Journal Article', '10.1093/bioinformatics/bty699', '30101309', 'Computational biologists face many challenges related to data size, and they need to manage complicated analyses often including multiple stages and multiple tools, all of which must be deployed to modern infrastructures. To address these challenges and maintain reproducibility of results, researchers need (i) a reliable way to run processing stages in any computational environment, (ii) a well-defined way to orchestrate those processing stages and (iii) a data management layer that tracks data as it moves through the processing pipeline.'),
(82039, 2019, 'bio', 'Linkage disequilibrium maps to guide contig ordering for genome assembly', 'Pengelly, Reuben J|Collins, Andrew', 'Genetic Epidemiology & Bioinformatics, Faculty of Medicine, University of Southampton, Southampton, UK.|Genetic Epidemiology & Bioinformatics, Faculty of Medicine, University of Southampton, Southampton, UK.', 'United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty687', '30101310', 'Efforts to establish reference genome sequences by de novo sequence assembly have to address the difficulty of linking relatively short sequence contigs to form much larger chromosome assemblies. Efficient strategies are required to span gaps and establish contig order and relative orientation. We consider here the use of linkage disequilibrium (LD) maps of sequenced contigs and the utility of LD for ordering, orienting and positioning linked sequences. LD maps are readily constructed from population data and have at least an order of magnitude higher resolution than linkage maps providing the potential to resolve difficult areas in assemblies. We empirically evaluate a linkage disequilibrium map-based method using single nucleotide polymorphism genotype data in a 216 kilobase region of human 6p21.3 from which three shorter contigs are formed.'),
(82040, 2019, 'bio', 'Processing of big heterogeneous genomic datasets for tertiary analysis of Next Generation Sequencing data', 'Masseroli, Marco|Canakoglu, Arif|Pinoli, Pietro|Kaitoua, Abdulrahman|Gulino, Andrea|Horlova, Olha|Nanni, Luca|Bernasconi, Anna|Perna, Stefano|Stamoulakatou, Eirini|Ceri, Stefano', 'Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy.|Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy.|Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy.|The German Research Center for Artificial Intelligence (DFKI), Berlin, Germany.|Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy.|Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy.|Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy.|Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy.|Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy.|Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy.|Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy.', 'Italy|Italy|Italy|Germany|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 9, '-', 'Journal Article', '10.1093/bioinformatics/bty688', '30101316', 'We previously proposed a paradigm shift in genomic data management, based on the Genomic Data Model (GDM) for mediating existing data formats and on the GenoMetric Query Language (GMQL) for supporting, at a high level of abstraction, data extraction and the most common data-driven computations required by tertiary data analysis of Next Generation Sequencing datasets. Here, we present a new GMQL-based system with enhanced accessibility, portability, scalability and performance.'),
(82041, 2019, 'bio', 'GlycanAnalyzer: software for automated interpretation of N-glycan profiles after exoglycosidase digestions', 'Walsh, Ian|Nguyen-Khuong, Terry|Wongtrakul-Kish, Katherine|Tay, Shi Jie|Chew, Daniel|José, Tasha|Taron, Christopher H|Rudd, Pauline M', 'Bioprocessing Technology Institute Agency for Science, Technology and Research (A*STAR), Singapore.|Bioprocessing Technology Institute Agency for Science, Technology and Research (A*STAR), Singapore.|Bioprocessing Technology Institute Agency for Science, Technology and Research (A*STAR), Singapore.|Bioprocessing Technology Institute Agency for Science, Technology and Research (A*STAR), Singapore.|Bioprocessing Technology Institute Agency for Science, Technology and Research (A*STAR), Singapore.|New England Biolabs, 240 County Road, Ipswich, MA, USA.|New England Biolabs, 240 County Road, Ipswich, MA, USA.|Bioprocessing Technology Institute Agency for Science, Technology and Research (A*STAR), Singapore.', 'Singapore|Singapore|Singapore|Singapore|Singapore|United States|United States|Singapore', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty681', '30101321', 'Many eukaryotic proteins are modified by N-glycans. Liquid chromatography (ultra-performance -UPLC and high-performance-HPLC) coupled with mass spectrometry (MS) is conventionally used to characterize N-glycan structures. Software can automatically assign glycan structures by matching their observed retention times and masses with standardized values in reference databases. However, more precise confirmation of N-glycan structures can be derived using exoglycosidases, enzymes that remove specific monosaccharides from glycans. Exoglycosidase removal of monosaccharides results in signature peak shifts, in both UPLC and MS1, yielding an effective way to verify N-glycan structure with high detail (down to the position and isomeric linkage of each monosaccharide). Because manual interpretation of exoglycosidase data is complex and time consuming, we developed GlycanAnalyzer, a web application that pattern matches N-glycan peak shifts following exoglycosidase digestion and automates structure assignments. GlycanAnalyzer significantly improves assignment accuracy over other auto-assignment methods on tests with a monoclonal antibody and four glycan standards (100% versus 82% for the next best software). By automating data interpretation, GlycanAnalyzer enables the easier use of exoglycosidases to precisely define N-glycan structure.'),
(82042, 2019, 'bio', 'MIPUP: minimum perfect unmixed phylogenies for multi-sampled tumors via branchings and ILP', 'Husixc4x87, Edin|Li, Xinyue|Hujdurovixc4x87, Ademir|Mehine, Miika|Rizzi, Romeo|Mxc3xa4kinen, Veli|Milanixc4x8d, Martin|Tomescu, Alexandru I', 'Department of Mathematics, London School of Economics and Political Science, London, UK.|Department of Computer Science, Helsinki Institute for Information Technology HIIT, University of Helsinki, Finland.|University of Primorska, UP IAM, Koper, Slovenia.|Genome-Scale Biology Research Program, Research Programs Unit, Department of Medical and Clinical Genetics, Faculty of Medicine, University of Helsinki, Medicum, Helsinki, Finland.|Department of Computer Science, University of Verona, Verona, Italy.|Department of Computer Science, Helsinki Institute for Information Technology HIIT, University of Helsinki, Finland.|University of Primorska, UP IAM, Koper, Slovenia.|Department of Computer Science, Helsinki Institute for Information Technology HIIT, University of Helsinki, Finland.', 'United Kingdom|Finland|Slovenia|Finland|Italy|Finland|Slovenia|Finland', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty683', '30101335', 'Discovering the evolution of a tumor may help identify driver mutations and provide a more comprehensive view on the history of the tumor. Recent studies have tackled this problem using multiple samples sequenced from a tumor, and due to clinical implications, this has attracted great interest. However, such samples usually mix several distinct tumor subclones, which confounds the discovery of the tumor phylogeny.'),
(82043, 2019, 'bio', 'LipidFinder on LIPID MAPS: peak filtering, MS searching and statistical analysis for lipidomics', 'Fahy, Eoin|Alvarez-Jarreta, Jorge|Brasher, Christopher J|Nguyen, An|Hawksworth, Jade I|Rodrigues, Patricia|Meckelmann, Sven|Allen, Stuart M|ODonnell, Valerie B', 'Department of Bioengineering, University of California San Diego, San Diego, CA, USA.|Institute of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK.|Institute of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK.|Babraham Institute, Babraham Research Campus, Cambridge, UK.|Institute of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK.|Institute of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK.|Institute of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK.|School of Computer Science and Informatics, Cardiff University, Cardiff, UK.|Institute of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 12, '-', 'Journal Article', '10.1093/bioinformatics/bty679', '30101336', 'We present LipidFinder online, hosted on the LIPID MAPS website, as a liquid chromatography/mass spectrometry (LC/MS) workflow comprising peak filtering, MS searching and statistical analysis components, highly customized for interrogating lipidomic data. The online interface of LipidFinder includes several innovations such as comprehensive parameter tuning, a MS search engine employing in-house customized, curated and computationally generated databases and multiple reporting/display options. A set of integrated statistical analysis tools which enable users to identify those features which are significantly-altered under the selected experimental conditions, thereby greatly reducing the complexity of the peaklist prior to MS searching is included. LipidFinder is presented as a highly flexible, extensible user-friendly online workflow which leverages the lipidomics knowledge base and resources of the LIPID MAPS website, long recognized as a leading global lipidomics portal.'),
(82044, 2019, 'bio', 'A powerful conditional gene-based association approach implicated functionally important genes for schizophrenia', 'Li, Miaoxin|Jiang, Lin|Mak, Timothy Shin Heng|Kwan, Johnny Sheung Him|Xue, Chao|Chen, Peikai|Leung, Henry Chi-Ming|Cui, Liqian|Li, Tao|Sham, Pak Chung', 'Zhongshan School of Medicine, First Affiliated Hospital, Center for Genome Research, Center for Precision Medicine, Sun Yat-sen University, Guangzhou, China.|Zhongshan School of Medicine, First Affiliated Hospital, Center for Genome Research, Center for Precision Medicine, Sun Yat-sen University, Guangzhou, China.|The Centre for Genomic Sciences, The University of Hong Kong, Pokfulam, Hong Kong, China.|Department of Psychiatry, The University of Hong Kong, Pokfulam, Hong Kong, China.|Zhongshan School of Medicine, First Affiliated Hospital, Center for Genome Research, Center for Precision Medicine, Sun Yat-sen University, Guangzhou, China.|The Centre for Genomic Sciences, The University of Hong Kong, Pokfulam, Hong Kong, China.|Department of Computer Science, The University of Hong Kong, Pokfulam, Hong Kong, China.|The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.|The Mental Health Center and the Psychiatric Laboratory, West China Hospital, Sichuan University, Chengdu, China.|The Centre for Genomic Sciences, The University of Hong Kong, Pokfulam, Hong Kong, China.', 'China|China|China|China|China|China|China|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty682', '30101339', 'It remains challenging to unravel new susceptibility genes of complex diseases and the mechanisms in genome-wide association studies. There are at least two difficulties, isolation of the genuine susceptibility genes from many indirectly associated genes and functional validation of these genes.'),
(82045, 2019, 'bio', 'PrimedRPA: primer design for recombinase polymerase amplification assays', 'Higgins, Matthew|Ravenhall, Matt|Ward, Daniel|Phelan, Jody|Ibrahim, Amy|Forrest, Matthew S|Clark, Taane G|Campino, Susana', 'Pathogen Molecular Biology Department, London School of Hygiene and Tropical Medicine (LSHTM), London, UK.|Pathogen Molecular Biology Department, London School of Hygiene and Tropical Medicine (LSHTM), London, UK.|Pathogen Molecular Biology Department, London School of Hygiene and Tropical Medicine (LSHTM), London, UK.|Pathogen Molecular Biology Department, London School of Hygiene and Tropical Medicine (LSHTM), London, UK.|Pathogen Molecular Biology Department, London School of Hygiene and Tropical Medicine (LSHTM), London, UK.|TwistDx, Coldhams Business Park, Cambridge, UK.|Pathogen Molecular Biology Department, London School of Hygiene and Tropical Medicine (LSHTM), London, UK.|Pathogen Molecular Biology Department, London School of Hygiene and Tropical Medicine (LSHTM), London, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty701', '30101342', 'Recombinase polymerase amplification (RPA), an isothermal nucleic acid amplification method, is enhancing our ability to detect a diverse array of pathogens, thereby assisting the diagnosis of infectious diseases and the detection of microorganisms in food and water. However, new bioinformatics tools are needed to automate and improve the design of the primers and probes sets to be used in RPA, particularly to account for the high genetic diversity of circulating pathogens and cross detection of genetically similar organisms. PrimedRPA is a python-based package that automates the creation and filtering of RPA primers and probe sets. It aligns several sequences to identify conserved targets, and filters regions that cross react with possible background organisms.'),
(82046, 2019, 'bio', 'Clustering of temporal gene expression data with mixtures of mixed effects models with a penalized likelihood', 'Lu, Darlene|Tripodis, Yorghos|Gerstenfeld, Louis C|Demissie, Serkalem', 'Biostatistics, Boston University, Boston, MA, USA.|Biostatistics, Boston University, Boston, MA, USA.|Orthopaedic Surgery, Boston University, Boston, MA, USA.|Biostatistics, Boston University, Boston, MA, USA.', 'United States|United States|United States|United States', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty696', '30101356', 'Clustering algorithms like K-Means and standard Gaussian mixture models (GMM) fail to account for the structure of variability of replicated data or repeated measures over time. Additionally, a priori cluster number assumptions add an additional complexity to the process. Current methods to optimize cluster labels and number can be inaccurate or computationally intensive for temporal gene expression data with this additional variability.'),
(82047, 2019, 'bio', 'Cell membrane proteins with high N-glycosylation, high expression and multiple interaction partners are preferred by mammalian viruses as receptors', 'Zhang, Zheng|Zhu, Zhaozhong|Chen, Wenjun|Cai, Zena|Xu, Beibei|Tan, Zhiying|Wu, Aiping|Ge, Xingyi|Guo, Xinhong|Tan, Zhongyang|Xia, Zanxian|Zhu, Haizhen|Jiang, Taijiao|Peng, Yousong', 'College of Biology, Hunan University, Changsha, China.|College of Biology, Hunan University, Changsha, China.|College of Biology, Hunan University, Changsha, China.|College of Biology, Hunan University, Changsha, China.|College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.|College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.|Center of System Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.|College of Biology, Hunan University, Changsha, China.|College of Biology, Hunan University, Changsha, China.|College of Biology, Hunan University, Changsha, China.|School of Life Sciences, Central South University, Changsha, China.|College of Biology, Hunan University, Changsha, China.|Center of System Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.|College of Biology, Hunan University, Changsha, China.', 'China|China|China|China|China|China|China|China|China|China|China|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty694', '30102334', 'Receptor mediated entry is the first step for viral infection. However, the question of how viruses select receptors remains unanswered.'),
(82048, 2019, 'bio', 'CharGer: clinical Characterization of Germline variants', 'Scott, Adam D|Huang, Kuan-Lin|Weerasinghe, Amila|Mashl, R Jay|Gao, Qingsong|Martins Rodrigues, Fernanda|Wyczalkowski, Matthew A|Ding, Li', 'Oncology Division, Washington University School of Medicine, St. Louis, MO, USA.|Oncology Division, Washington University School of Medicine, St. Louis, MO, USA.|Oncology Division, Washington University School of Medicine, St. Louis, MO, USA.|Oncology Division, Washington University School of Medicine, St. Louis, MO, USA.|Oncology Division, Washington University School of Medicine, St. Louis, MO, USA.|Oncology Division, Washington University School of Medicine, St. Louis, MO, USA.|Oncology Division, Washington University School of Medicine, St. Louis, MO, USA.|Oncology Division, 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', 3, '-', 'Journal Article', '10.1093/bioinformatics/bty649', '30102335', 'CharGer (Characterization of Germline variants) is a software tool for interpreting and predicting clinical pathogenicity of germline variants. CharGer gathers evidence from databases and annotations, provided by local tools and files or via ReST APIs, and classifies variants according to ACMG guidelines for assessing variant pathogenicity. User-designed pathogenicity criteria can be incorporated into CharGers flexible framework, thereby allowing users to create a customized classification protocol.'),
(82049, 2019, 'bio', 'A quantitative map of protein sequence space for the cis-defensin superfamily', 'Shafee, Thomas|Anderson, Marilyn A', 'Department of biochemistry and genetics, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Australia.|Department of biochemistry and genetics, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Australia.', 'Australia|Australia', 10, '-', 'Journal Article', '10.1093/bioinformatics/bty697', '30102339', 'The cis-defensins are a superfamily of small, cationic, cysteine-rich proteins, sharing a common scaffold, but highly divergent sequences and varied functions from host-defence to signalling. Superfamily members are most abundant in plants (with some genomes containing hundreds of members), but are also found across fungi and invertebrates. However, of the thousands of cis-defensin sequences in databases, only have a handful have solved structures or assigned activities. Non-phylogenetic sequence-analysis methods are therefore necessary to use the relationships within the superfamily to classify members, and to predict and engineer functions.'),
(82050, 2019, 'bio', 'isiKnock: in silico knockouts in signaling pathways', 'Hannig, Jennifer|Giese, Heiko|Schweizer, Börje|Amstein, Leonie|Ackermann, Jörg|Koch, Ina', 'Department of Molecular Bioinformatics, Institute of Computer Science, Johann Wolfgang Goethe-University, Frankfurt am Main, Germany.|Department of Molecular Bioinformatics, Institute of Computer Science, Johann Wolfgang Goethe-University, Frankfurt am Main, Germany.|Department of Molecular Bioinformatics, Institute of Computer Science, Johann Wolfgang Goethe-University, Frankfurt am Main, Germany.|Department of Molecular Bioinformatics, Institute of Computer Science, Johann Wolfgang Goethe-University, Frankfurt am Main, Germany.|Department of Molecular Bioinformatics, Institute of Computer Science, Johann Wolfgang Goethe-University, Frankfurt am Main, Germany.|Department of Molecular Bioinformatics, Institute of Computer Science, Johann Wolfgang Goethe-University, Frankfurt am Main, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty700', '30102342', 'isiKnock is a new software that automatically conducts in silico knockouts for mathematical models of signaling pathways. The software allows for the prediction of the behavior of biological systems after single or multiple knockout. The implemented algorithm applies transition invariants and the novel concept of Manatee invariants. A knockout matrix visualizes the results. The tool enables the analysis of dependencies, for example, in signal flows from the receptor activation to the cell response at steady state.'),
(82051, 2019, 'bio', 'On entropy and information in gene interaction networks', 'Wallace, Z S|Rosenthal, S B|Fisch, K M|Ideker, T|Sasik, R', 'Department of Mathematics, Tufts University School of Arts and Sciences, Medford, MA, USA.|Department of Medicine, Center for Computational Biology and Bioinformatics, University of California San Diego, La Jolla, CA, USA.|Department of Medicine, Center for Computational Biology and Bioinformatics, University of California San Diego, La Jolla, CA, USA.|Department of Medicine, University of California San Diego, La Jolla, CA, USA.|Department of Medicine, Center for Computational Biology and Bioinformatics, University of California San Diego, La Jolla, CA, USA.', 'United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty691', '30102349', 'Modern biological experiments often produce candidate lists of genes presumably related to the studied phenotype. One can ask if the gene list as a whole makes sense in the context of existing knowledge: Are the genes in the list reasonably related to each other or do they look like a random assembly? There are also situations when one wants to know if two or more gene sets are closely related. Gene enrichment tests based on counting the number of genes two sets have in common are adequate if we presume that two genes are related only when they are in fact identical. If by related we mean well connected in the interaction network space, we need a new measure of relatedness for gene sets.'),
(82052, 2019, 'bio', 'DCARS: differential correlation across ranked samples', 'Ghazanfar, Shila|Strbenac, Dario|Ormerod, John T|Yang, Jean Y H|Patrick, Ellis', 'The Judith and David Coffey Life Lab, Charles Perkins Centre, University of Sydney, Sydney, NSW, Australia.|School of Mathematics and Statistics, University of Sydney, Sydney, NSW, Australia.|School of Mathematics and Statistics, University of Sydney, Sydney, NSW, Australia.|The Judith and David Coffey Life Lab, Charles Perkins Centre, University of Sydney, Sydney, NSW, Australia.|School of Mathematics and Statistics, University of Sydney, Sydney, NSW, Australia.', 'Australia|Australia|Australia|Australia|Australia', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty698', '30102408', 'Genes act as a system and not in isolation. Thus, it is important to consider coordinated changes of gene expression rather than single genes when investigating biological phenomena such as the aetiology of cancer. We have developed an approach for quantifying how changes in the association between pairs of genes may inform the outcome of interest called Differential Correlation across Ranked Samples (DCARS). Modelling gene correlation across a continuous sample ranking does not require the dichotomisation of samples into two distinct classes and can identify differences in gene correlation across early, mid or late stages of the outcome of interest.'),
(82053, 2019, 'bio', 'PathwayConnector: finding complementary pathways to enhance functional analysis', 'Minadakis, George|Zachariou, Margarita|Oulas, Anastasis|Spyrou, George M', 'Bioinformatics Group, Bioinformatics ERA Chair, The Cyprus Institute of Neurology & Genetics, 6 International Airport Avenue, 2370 Nicosia, Cyprus, Nicosia, Cyprus.|Bioinformatics Group, Bioinformatics ERA Chair, The Cyprus Institute of Neurology & Genetics, 6 International Airport Avenue, 2370 Nicosia, Cyprus, Nicosia, Cyprus.|Bioinformatics Group, Bioinformatics ERA Chair, The Cyprus Institute of Neurology & Genetics, 6 International Airport Avenue, 2370 Nicosia, Cyprus, Nicosia, Cyprus.|Bioinformatics Group, Bioinformatics ERA Chair, The Cyprus Institute of Neurology & Genetics, 6 International Airport Avenue, 2370 Nicosia, Cyprus, Nicosia, Cyprus.', 'Cyprus|Cyprus|Cyprus|Cyprus', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty693', '30124768', 'PathwayConnector is a web-tool that facilitates the construction of complementary pathway-to-pathway networks and subnetworks of them, based on a reference pathway network derived from the rich information available either in KEGG or Reactome database for pathway mapping. Specifically, for a given set of pathways, PathwayConnector (i) finds all the direct connections between them, (ii) adds a minimum set of complementary pathways required to achieve connectivity between the pathways, leading to informative fully connected networks and (ii) provides a series of clustering methods for the further grouping of pathways in to sub-clusters. The proposed web-tool is a simple yet informative tool towards identifying connected groups of pathways that are significantly related to specific diseases.'),
(82054, 2019, 'bio', 'StructureProfiler: an all-in-one tool for 3D protein structure profiling', 'Meyder, Agnes|Kampen, Stefanie|Sieg, Jochen|Fährrolfes, Rainer|Friedrich, Nils-Ole|Flachsenberg, Florian|Rarey, Matthias', 'ZBH-Center for Bioinformatics, Universität Hamburg, Hamburg, Germany.|ZBH-Center for Bioinformatics, Universität Hamburg, Hamburg, Germany.|ZBH-Center for Bioinformatics, Universität Hamburg, Hamburg, Germany.|ZBH-Center for Bioinformatics, Universität Hamburg, Hamburg, Germany.|ZBH-Center for Bioinformatics, Universität Hamburg, Hamburg, Germany.|ZBH-Center for Bioinformatics, Universität Hamburg, Hamburg, Germany.|ZBH-Center for Bioinformatics, Universität Hamburg, Hamburg, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty692', '30124779', 'Three-dimensional protein structures are important starting points for elucidating protein function and applications like drug design. Computational methods in this area rely on high quality validation datasets which are usually manually assembled. Due to the increase in published structures as well as the increasing demand for specially tailored validation datasets, automatic procedures should be adopted.'),
(82055, 2019, 'bio', 'Genome Detective: an automated system for virus identification from high-throughput sequencing data', 'Vilsker, Michael|Moosa, Yumna|Nooij, Sam|Fonseca, Vagner|Ghysens, Yoika|Dumon, Korneel|Pauwels, Raf|Alcantara, Luiz Carlos|Vanden Eynden, Ewout|Vandamme, Anne-Mieke|Deforche, Koen|de Oliveira, Tulio', 'Emweb bvba, Herent, Belgium.|KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine and Medical Sciences, Nelson R Mandela School of Medicine, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa.|The Dutch National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands.|KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine and Medical Sciences, Nelson R Mandela School of Medicine, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa.|Emweb bvba, Herent, Belgium.|Emweb bvba, Herent, Belgium.|Emweb bvba, Herent, Belgium.|Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil.|KU Leuven, Department of Microbiology and Immunology, Rega Institute for Medical Research, Clinical and Epidemiological Virology, Leuven, Belgium.|KU Leuven, Department of Microbiology and Immunology, Rega Institute for Medical Research, Clinical and Epidemiological Virology, Leuven, Belgium.|Emweb bvba, Herent, Belgium.|KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine and Medical Sciences, Nelson R Mandela School of Medicine, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa.', 'Belgium|South Africa|Netherlands|South Africa|Belgium|Belgium|Belgium|Brazil|Belgium|Belgium|Belgium', 3, '-', 'Journal Article', '10.1093/bioinformatics/bty695', '30124794', 'Genome Detective is an easy to use web-based software application that assembles the genomes of viruses quickly and accurately. The application uses a novel alignment method that constructs genomes by reference-based linking of de novo contigs by combining amino-acids and nucleotide scores. The software was optimized using synthetic datasets to represent the great diversity of virus genomes. The application was then validated with next generation sequencing data of hundreds of viruses. User time is minimal and it is limited to the time required to upload the data.'),
(82056, 2019, 'bio', 'MCO: towards an ontology and unified vocabulary for a framework-based annotation of microbial growth conditions', 'Tierrafría, V H|Mejía-Almonte, C|Camacho-Zaragoza, J M|Salgado, H|Alquicira, K|Ishida, C|Gama-Castro, S|Collado-Vides, J', 'Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México.|Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México.|Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México.|Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México.|Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México.|Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México.|Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México.|Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México.', '-|-|-|-|-|-|-|-', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty689', '30137210', 'A major component in increasing our understanding of the biology of an organism is the mapping of its genotypic potential into its phenotypic expression profiles. This mapping is executed by the machinery of gene regulation, which is essentially studied by changes in growth conditions. Although many efforts have been made to systematize the annotation of experimental conditions in microbiology, the available annotations are not based on a consistent and controlled vocabulary, making difficult the identification of biologically meaningful comparisons of knowledge derived from different experiments or laboratories.'),
(82057, 2019, 'bio', 'gemBS: high throughput processing for DNA methylation data from bisulfite sequencing', 'Merkel, Angelika|Fernández-Callejo, Marcos|Casals, Eloi|Marco-Sola, Santiago|Schuyler, Ronald|Gut, Ivo G|Heath, Simon C', 'Centro Nacional de Análisis Genómico (CNAG-CRG), Centre de Regulacio Genómico (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain.|Centro Nacional de Análisis Genómico (CNAG-CRG), Centre de Regulacio Genómico (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain.|Centro Nacional de Análisis Genómico (CNAG-CRG), Centre de Regulacio Genómico (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain.|Universitat Autònoma de Barcelona, Bellaterra, Spain.|Department of Immunology and Microbiology, University of Colorado, Aurora, CO, USA.|Centro Nacional de Análisis Genómico (CNAG-CRG), Centre de Regulacio Genómico (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain.|Centro Nacional de Análisis Genómico (CNAG-CRG), Centre de Regulacio Genómico (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain.', 'Spain|Spain|Spain|Spain|United States|Spain|Spain', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty690', '30137223', 'DNA methylation is essential for normal embryogenesis and development in mammals and can be captured at single base pair resolution by whole genome bisulfite sequencing (WGBS). Current available analysis tools are becoming rapidly outdated as they lack sensible functionality and efficiency to handle large amounts of data now commonly created.'),
(82058, 2019, 'bio', 'ImaGEO: integrative gene expression meta-analysis from GEO database', 'Toro-Domínguez, Daniel|Martorell-Marugán, Jordi|López-Domínguez, Raúl|García-Moreno, Adrián|González-Rumayor, Víctor|Alarcón-Riquelme, Marta E|Carmona-Sáez, Pedro', 'Bioinformatics Unit, GENYO Centre for Genomics and Oncological Research: Pfizer/University of Granada/Andalusian Regional Government, PTS Granada, Granada, Spain.|Bioinformatics Unit, GENYO Centre for Genomics and Oncological Research: Pfizer/University of Granada/Andalusian Regional Government, PTS Granada, Granada, Spain.|Bioinformatics Unit, GENYO Centre for Genomics and Oncological Research: Pfizer/University of Granada/Andalusian Regional Government, PTS Granada, Granada, Spain.|Bioinformatics Unit, GENYO Centre for Genomics and Oncological Research: Pfizer/University of Granada/Andalusian Regional Government, PTS Granada, Granada, Spain.|Atrys Health, Barcelona, Spain.|Area of Medical Genomics, GENYO Centre for Genomics and Oncological Research: Pfizer/University of Granada/Andalusian Regional Government, PTS Granada, Granada, Spain.|Bioinformatics Unit, GENYO Centre for Genomics and Oncological Research: Pfizer/University of Granada/Andalusian Regional Government, PTS Granada, Granada, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty721', '30137226', 'The Gene Expression Omnibus (GEO) database provides an invaluable resource of publicly available gene expression data that can be integrated and analyzed to derive new hypothesis and knowledge. In this context, gene expression meta-analysis (geMAs) is increasingly used in several fields to improve study reproducibility and discovering robust biomarkers. Nevertheless, integrating data is not straightforward without bioinformatics expertise. Here, we present ImaGEO, a web tool for geMAs that implements a complete and comprehensive meta-analysis workflow starting from GEO dataset identifiers. The application integrates GEO datasets, applies different meta-analysis techniques and provides functional analysis results in an easy-to-use environment. ImaGEO is a powerful and useful resource that allows researchers to integrate and perform meta-analysis of GEO datasets to lead robust findings for biomarker discovery studies.'),
(82059, 2019, 'bio', 'Increased sensitivity with automated validation of XL-MS cleavable peptide crosslinks', 'Keller, Andrew|Chavez, Juan D|Bruce, James E', 'Department of Genome Sciences, University of Washington, Seattle, WA, USA.|Department of Genome Sciences, University of Washington, Seattle, WA, USA.|Department of Genome Sciences, University of Washington, Seattle, WA, USA.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty720', '30137231', 'Peptides crosslinked with cleavable chemical crosslinkers are identified with mass spectrometry by independent database search of spectra associated with the two linked peptides. A major challenge is to combine together the evidence of the two peptides into an overall assessment of the two-peptide crosslink.'),
(82060, 2019, 'bio', 'VARAN-GIE: curation of genomic interval sets', 'Popitsch, Niko', 'Childrens Cancer Research Institute, St. Anna Kinderkrebsforschung, Zimmermannplatz 10, Vienna, Austria.', 'Austria', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty723', '30137307', 'Genomic interval sets are fundamental elements of genome annotation and are the output of countless bioinformatics applications. Nevertheless, tool support for the manual curation of these data is currently limited. We developed VARAN-GIE, an extension of the popular Integrative Genomics Viewer (IGV) that adds functionality to edit, annotate and merge genomic interval sets. Data can easily be shared with other users and imported/exported from/to multiple common data formats.'),
(82061, 2019, 'bio', 'Characterizing protein-DNA binding event subtypes in ChIP-exo data', 'Yamada, Naomi|Lai, William K M|Farrell, Nina|Pugh, B Franklin|Mahony, Shaun', 'Department of Biochemistry & Molecular Biology and Center for Eukaryotic Gene Regulation, The Pennsylvania State University, University Park, PA, USA.|Department of Biochemistry & Molecular Biology and Center for Eukaryotic Gene Regulation, The Pennsylvania State University, University Park, PA, USA.|Department of Biochemistry & Molecular Biology and Center for Eukaryotic Gene Regulation, The Pennsylvania State University, University Park, PA, USA.|Department of Biochemistry & Molecular Biology and Center for Eukaryotic Gene Regulation, The Pennsylvania State University, University Park, PA, USA.|Department of Biochemistry & Molecular Biology and Center for Eukaryotic Gene Regulation, The Pennsylvania State University, University Park, PA, USA.', 'United States|United States|United States|United States|United States', 7, '-', 'Journal Article', '10.1093/bioinformatics/bty703', '30165373', 'Regulatory proteins associate with the genome either by directly binding cognate DNA motifs or via protein-protein interactions with other regulators. Each recruitment mechanism may be associated with distinct motifs and may also result in distinct characteristic patterns in high-resolution protein-DNA binding assays. For example, the ChIP-exo protocol precisely characterizes protein-DNA crosslinking patterns by combining chromatin immunoprecipitation (ChIP) with 5 xe2x86x92 3 exonuclease digestion. Since different regulatory complexes will result in different protein-DNA crosslinking signatures, analysis of ChIP-exo tag enrichment patterns should enable detection of multiple protein-DNA binding modes for a given regulatory protein. However, current ChIP-exo analysis methods either treat all binding events as being of a uniform type or rely on motifs to cluster binding events into subtypes.'),
(82062, 2019, 'bio', 'MOLGENIS research: advanced bioinformatics data software for non-bioinformaticians', 'van der Velde, K Joeri|Imhann, Floris|Charbon, Bart|Pang, Chao|van Enckevort, David|Slofstra, Mariska|Barbieri, Ruggero|Alberts, Rudi|Hendriksen, Dennis|Kelpin, Fleur|de Haan, Mark|de Boer, Tommy|Haakma, Sido|Stroomberg, Connor|Scholtens, Salome|van de Geijn, Gert-Jan|Festen, Eleonora A M|Weersma, Rinse K|Swertz, Morris A', 'Genomics Coordination Center, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands.|Department of Genetics, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands.|Genomics Coordination Center, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands.|Genomics Coordination Center, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands.|Genomics Coordination Center, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands.|Genomics Coordination Center, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands.|Department of Genetics, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands.|Department of Genetics, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands.|Genomics Coordination Center, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands.|Genomics Coordination Center, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands.|Genomics Coordination Center, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands.|Genomics Coordination Center, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands.|Genomics Coordination Center, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands.|Genomics Coordination Center, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands.|Genomics Coordination Center, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands.|Genomics Coordination Center, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands.|Department of Genetics, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands.|Department of Gastroenterology and Hepatology, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands.|Genomics Coordination Center, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Neth', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty742', '30165396', 'The volume and complexity of biological data increases rapidly. Many clinical professionals and biomedical researchers without a bioinformatics background are generating big -omics data, but do not always have the tools to manage, process or publicly share these data.'),
(82063, 2019, 'bio', 'Penalized co-inertia analysis with applications to -omics data', 'Min, Eun Jeong|Safo, Sandra E|Long, Qi', 'Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA.|Division of Biostatistics, University of Minnesota, Minneapolis, MN, USA.|Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA.', 'United States|United States|United States', 4, '-', 'Journal Article', '10.1093/bioinformatics/bty726', '30165424', 'Co-inertia analysis (CIA) is a multivariate statistical analysis method that can assess relationships and trends in two sets of data. Recently CIA has been used for an integrative analysis of multiple high-dimensional omics data. However, for classical CIA, all elements in the loading vectors are nonzero, presenting a challenge for the interpretation when analyzing omics data. For other multivariate statistical methods such as canonical correlation analysis (CCA), penalized least squares (PLS), various approaches have been proposed to produce sparse loading vectors via l1-penalization/constraint. We propose a novel CIA method that uses l1-penalization to induce sparsity in estimators of loading vectors. Our method simultaneously conducts model fitting and variable selection. Also, we propose another CIA method that incorporates structure/network information such as those from functional genomics, besides using sparsity penalty so that one can get biologically meaningful and interpretable results.'),
(82064, 2019, 'bio', 'ISDB: a database toolkit for storing and analyzing viral integration site data', 'Sibley, Thomas R|Silberman, Evan J|Mullins, James I', 'Department of Microbiology, Global Health, and Medicine, University of Washington, Seattle, WA, USA.|Department of Microbiology, Global Health, and Medicine, University of Washington, Seattle, WA, USA.|Department of Microbiology, Global Health, and Medicine, University of Washington, Seattle, WA, USA.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty712', '30165425', 'We introduce ISDB, a set of software tools for the creation and administration of relational databases of viral integration site (IS) data. Using ISDB, investigators can curate a private database from any heterogeneous set of data sources, including previously-published datasets and internal, work-in-progress data. To make data visible and accessible to collaborators with varying degrees of computational expertise, ISDB automatically generates web sites describing database contents and data exports in several common formats. Compared to a public depository database, the ability to build local, private databases makes ISDB suitable for use in testing hypotheses and developing analyses in the long pre-publication phase of most research.'),
(82065, 2019, 'bio', 'Inferring gene expression networks with hubs using a degree weighted Lasso approach', 'Sulaimanov, Nurgazy|Kumar, Sunil|Burdet, Frédéric|Ibberson, Mark|Pagni, Marco|Koeppl, Heinz', 'Department of Electrical Engineering and Information Technology, Technische Universität Darmstadt, Darmstadt, Germany.|Department of Electrical Engineering and Information Technology, Technische Universität Darmstadt, Darmstadt, Germany.|Swiss Institute of Bioinformatics, Lausanne, Switzerland.|Swiss Institute of Bioinformatics, Lausanne, Switzerland.|Swiss Institute of Bioinformatics, Lausanne, Switzerland.|Department of Electrical Engineering and Information Technology, Technische Universität Darmstadt, Darmstadt, Germany.', 'Germany|Germany|Switzerland|Switzerland|Switzerland|Germany', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty716', '30165436', 'Genome-scale gene networks contain regulatory genes called hubs that have many interaction partners. These genes usually play an essential role in gene regulation and cellular processes. Despite recent advancements in high-throughput technology, inferring gene networks with hub genes from high-dimensional data still remains a challenging problem. Novel statistical network inference methods are needed for efficient and accurate reconstruction of hub networks from high-dimensional data.'),
(82066, 2019, 'bio', 'Variable selection and validation in multivariate modelling', 'Shi, Lin|Westerhuis, Johan A|Rosén, Johan|Landberg, Rikard|Brunius, Carl', 'Department of Molecular Sciences, Swedish University of Agricultural Sciences, Uppsala SE-750 07, Sweden.|Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam XH, The Netherlands.|Swedish National Food Agency, Uppsala, Sweden.|Department of Molecular Sciences, Swedish University of Agricultural Sciences, Uppsala SE-750 07, Sweden.|Department of Biology and Biological Engineering, Food and Nutrition Science, Chalmers University of Technology, Gothenburg SE-412 96, Sweden.', 'Sweden|Netherlands|Sweden|Sweden|Sweden', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty710', '30165467', 'Validation of variable selection and predictive performance is crucial in construction of robust multivariate models that generalize well, minimize overfitting and facilitate interpretation of results. Inappropriate variable selection leads instead to selection bias, thereby increasing the risk of model overfitting and false positive discoveries. Although several algorithms exist to identify a minimal set of most informative variables (i.e. the minimal-optimal problem), few can select all variables related to the research question (i.e. the all-relevant problem). Robust algorithms combining identification of both minimal-optimal and all-relevant variables with proper cross-validation are urgently needed.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(82067, 2019, 'bio', 'Parallel clustering of single cell transcriptomic data with split-merge sampling on Dirichlet process mixtures', 'Duan, Tiehang|Pinto, José P|Xie, Xiaohui', 'Department of Computer Science, University of California, Irvine, CA, USA.|SysBioLab, Centre for Biomedical Research (CBMR), University of Algarve, Faro, Algarve, Portugal.|Department of Computer Science, University of California, Irvine, CA, USA.', 'United States|Portugal|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty702', '30165477', 'With the development of droplet based systems, massive single cell transcriptome data has become available, which enables analysis of cellular and molecular processes at single cell resolution and is instrumental to understanding many biological processes. While state-of-the-art clustering methods have been applied to the data, they face challenges in the following aspects: (i) the clustering quality still needs to be improved, (ii) most models need prior knowledge on number of clusters, which is not always available, (iii) there is a demand for faster computational speed.'),
(82068, 2019, 'bio', 'NCycDB: a curated integrative database for fast and accurate metagenomic profiling of nitrogen cycling genes', 'Tu, Qichao|Lin, Lu|Cheng, Lei|Deng, Ye|He, Zhili', 'Institute of Marine Science and Technology, Shandong University, Qingdao, China.|Institute of Marine Science and Technology, Shandong University, Qingdao, China.|Department of Ecology, College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang, China.|Research Center for Eco-Environmental Science, Chinese Academy of Sciences, Beijing, China.|Department of Environmental Science, School of Environmental Science and Engineering, Sun Yat-Sen University, Guangzhou, Guangdong, China.', 'China|China|China|China|China', 7, '-', 'Journal Article', '10.1093/bioinformatics/bty741', '30165481', 'The nitrogen (N) cycle is a collection of important biogeochemical pathways in the Earth ecosystem and has gained extensive foci in ecology and environmental studies. Currently, shotgun metagenome sequencing has been widely applied to explore gene families responsible for N cycle processes. However, there are problems in applying publically available orthology databases to profile N cycle gene families in shotgun metagenomes, such as inefficient database searching, unspecific orthology groups and low coverage of N cycle genes and/or gene (sub)families.'),
(82069, 2019, 'bio', 'A cluster robustness score for identifying cell subpopulations in single cell gene expression datasets from heterogeneous tissues and tumors', 'Kanter, Itamar|Dalerba, Piero|Kalisky, Tomer', 'Department of Bioengineering and Bar-Ilan Institute of Nanotechnology and Advanced Materials (BINA), Bar-Ilan University, Ramat Gan, Israel.|Department of Pathology and Cell Biology, Department of Medicine (Division of Digestive and Liver Diseases), Herbert Irving Comprehensive Cancer Center (HICCC), and the Columbia Stem Cell Initiative (CSCI), Columbia University, New York, NY, USA.|Department of Bioengineering and Bar-Ilan Institute of Nanotechnology and Advanced Materials (BINA), Bar-Ilan University, Ramat Gan, Israel.', 'Israel|United States|Israel', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty708', '30165506', 'A major aim of single cell biology is to identify important cell types such as stem cells in heterogeneous tissues and tumors. This is typically done by isolating hundreds of individual cells and measuring expression levels of multiple genes simultaneously from each cell. Then, clustering algorithms are used to group together similar single-cell expression profiles into clusters, each representing a distinct cell type. However, many of these clusters result from overfitting, meaning that rather than representing biologically meaningful cell types, they describe the intrinsic noise in gene expression levels due to limitations in experimental precision or the intrinsic randomness of biochemical cellular processes. Consequentially, these non-meaningful clusters are most sensitive to noise: a slight shift in gene expression levels due to a repeated measurement will rearrange the grouping of data points such that these clusters break up.'),
(82070, 2019, 'bio', 'Dot2dot: accurate whole-genome tandem repeats discovery', 'Genovese, Loredana M|Mosca, Marco M|Pellegrini, Marco|Geraci, Filippo', 'Institute for Informatics and Telematics, CNR, Pisa, Italy.|Department of Computer Science, University of Liverpool, Liverpool, UK.|Institute for Informatics and Telematics, CNR, Pisa, Italy.|Institute for Informatics and Telematics, CNR, Pisa, Italy.', 'Italy|United Kingdom|Italy|Italy', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty747', '30165507', 'Large-scale sequencing projects have confirmed the hypothesis that eukaryotic DNA is rich in repetitions whose functional role needs to be elucidated. In particular, tandem repeats (TRs) (i.e. short, almost identical sequences that lie adjacent to each other) have been associated to many cellular processes and, indeed, are also involved in several genetic disorders. The need of comprehensive lists of TRs for association studies and the absence of a computational model able to capture their variability have revived research on discovery algorithms.'),
(82071, 2019, 'bio', 'The Distance Precision Matrix: computing networks from non-linear relationships', 'Ghanbari, Mahsa|Lasserre, Julia|Vingron, Martin', 'Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, D-14195 Berlin, Germany.|Zalando Research, Mühlenstr. 25, D-10243 Berlin, Germany.|Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, D-14195 Berlin, Germany.', 'Germany|Germany|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty724', '30165509', 'Full-order partial correlation, a fundamental approach for network reconstruction, e.g. in the context of gene regulation, relies on the precision matrix (the inverse of the covariance matrix) as an indicator of which variables are directly associated. The precision matrix assumes Gaussian linear data and its entries are zero for pairs of variables that are independent given all other variables. However, there is still very little theory on network reconstruction under the assumption of non-linear interactions among variables.'),
(82072, 2019, 'bio', 'SKmDB: an integrated database of next generation sequencing information in skeletal muscle', 'Yuan, Jie|Zhou, Jiajian|Wang, Huating|Sun, Hao', 'Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China.|Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China.|Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China.|Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China.', 'China|China|China|China', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty705', '30165538', 'Skeletal muscles have indispensable functions and also possess prominent regenerative ability. The rapid emergence of Next Generation Sequencing (NGS) data in recent years offers us an unprecedented perspective to understand gene regulatory networks governing skeletal muscle development and regeneration. However, the data from public NGS database are often in raw data format or processed with different procedures, causing obstacles to make full use of them.'),
(82073, 2019, 'bio', 'Haplotype matching in large cohorts using the Li and Stephens model', 'Lunter, Gerton', 'University of Oxford, Wellcome Centre for Human Genetics, Oxford, UK.', 'United Kingdom', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty735', '30165547', 'The Li and Stephens model, which approximates the coalescent describing the pattern of variation in a population, underpins a range of key tools and results in genetics. Although highly efficient compared to the coalescent, standard implementations of this model still cannot deal with the very large reference cohorts that are starting to become available, and practical implementations use heuristics to achieve reasonable runtimes.'),
(82074, 2019, 'bio', 'admetSAR 20: web-service for prediction and optimization of chemical ADMET properties', 'Yang, Hongbin|Lou, Chaofeng|Sun, Lixia|Li, Jie|Cai, Yingchun|Wang, Zhuang|Li, Weihua|Liu, Guixia|Tang, Yun', 'Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai, China.|Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai, China.|Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai, China.|Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai, China.|Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai, China.|Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai, China.|Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai, China.|Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai, China.|Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai, China.', 'China|China|China|China|China|China|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty707', '30165565', 'admetSAR was developed as a comprehensive source and free tool for the prediction of chemical ADMET properties. Since its first release in 2012 containing 27 predictive models, admetSAR has been widely used in chemical and pharmaceutical fields. This update, admetSAR 2.0, focuses on extension and optimization of existing models with significant quantity and quality improvement on training data. Now 47 models are available for either drug discovery or environmental risk assessment. In addition, we added a new module named ADMETopt for lead optimization based on predicted ADMET properties.'),
(82075, 2019, 'bio', 'Efficient computation of the kinship coefficients', 'Kirkpatrick, Brent|Ge, Shufei|Wang, Liangliang', 'Intrepid Net Computing, Dillon, MT, USA.|Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, Canada.|Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, Canada.', 'United States|Canada|Canada', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty725', '30165566', 'For families, kinship coefficients are quantifications of the amount of genetic sharing between a pair of individuals. These coefficients are critical for understanding the breeding habits and genetic diversity of diploid populations. Historically, computations of the inbreeding coefficient were used to prohibit inbred marriages and prohibit breeding of some pairs of pedigree animals. Such prohibitions foster genetic diversity and help prevent recessive Mendelian disease at a population level.'),
(82076, 2019, 'bio', 'AncestryView: data-driven visualization of whole-genome local-ancestry', 'Zhao, Yan|Ma, Li|Jiang, Sukun|Song, George D|He, Shaohua|Li, Hong|Song, Qing', 'Department of Single Cell Biology, First Affiliated Hospital, Xian Jiao tong University, Xian, China.|Department of R&D, 4DGenome Inc., Decatur, GA, USA.|Department of R&D, 4DGenome Inc., Decatur, GA, USA.|Cardiovascular Research Institute, Department of Medicine, Morehouse School of Medicine, Atlanta, GA, USA.|Department of R&D, 4DGenome Inc., Decatur, GA, USA.|Department of Allergy, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.|Department of Single Cell Biology, First Affiliated Hospital, Xian Jiao tong University, Xian, China.', 'China|United States|United States|United States|United States|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty732', '30165568', 'Data visualization is a crucial tool for data exploration, analysis and interpretation. To visualize the ancestry data, we developed a new software tool, called AncestryView. We demonstrate its functionality with the data from admixed individuals.'),
(82077, 2019, 'bio', 'Phylo-PFP: improved automated protein function prediction using phylogenetic distance of distantly related sequences', 'Jain, Aashish|Kihara, Daisuke', 'Department of Computer Science, Purdue University, West Lafayette, IN, USA.|Department of Computer Science, Purdue University, West Lafayette, IN, USA.', 'United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty704', '30165572', 'Function annotation of proteins is fundamental in contemporary biology across fields including genomics, molecular biology, biochemistry, systems biology and bioinformatics. Function prediction is indispensable in providing clues for interpreting omics-scale data as well as in assisting biologists to build hypotheses for designing experiments. As sequencing genomes is now routine due to the rapid advancement of sequencing technologies, computational protein function prediction methods have become increasingly important. A conventional method of annotating a protein sequence is to transfer functions from top hits of a homology search, however, this approach has substantial short comings including a low coverage in genome annotation.'),
(82078, 2019, 'bio', 'Large-scale comparative analysis of microbial pan-genomes using PanOCT', 'Inman, Jason M|Sutton, Granger G|Beck, Erin|Brinkac, Lauren M|Clarke, Thomas H|Fouts, Derrick E', 'Department of Informatics, J. Craig Venter Institute, Rockville, MD, USA.|Department of Informatics, J. Craig Venter Institute, Rockville, MD, USA.|Department of Informatics, J. Craig Venter Institute, Rockville, MD, USA.|Department of Informatics, J. Craig Venter Institute, Rockville, MD, USA.|Department of Informatics, J. Craig Venter Institute, Rockville, MD, USA.|Department of Informatics, J. Craig Venter Institute, Rockville, MD, USA.', 'United States|United States|United States|United States|United States|United States', 3, '-', 'Journal Article', '10.1093/bioinformatics/bty744', '30165579', 'The JCVI pan-genome pipeline is a collection of programs to run PanOCT and tools that support and extend the capabilities of PanOCT. PanOCT (pan-genome ortholog clustering tool) is a tool for pan-genome analysis of closely related prokaryotic species or strains. The JCVI Pan-Genome Pipeline wrapper invokes command-line utilities that prepare input genomes, invoke third-party tools such as NCBI Blast+, run PanOCT, generate a consensus pan-genome, annotate features of the pan-genome, detect sets of genes of interest such as antimicrobial resistance (AMR) genes and generate figures, tables and html pages to visualize the results. The pipeline can run in a hierarchical mode, lowering the RAM and compute resources used.'),
(82079, 2019, 'bio', 'Traitpedia: a collaborative effort to gather species traits', 'Mier, Pablo|Andrade-Navarro, Miguel A', 'Faculty of Biology, Institute of Organismic and Molecular Evolution, Johannes Gutenberg University, Mainz, Germany.|Faculty of Biology, Institute of Organismic and Molecular Evolution, Johannes Gutenberg University, Mainz, Germany.', 'Germany|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty743', '30165582', 'Traitpedia is a collaborative database aimed to collect binary traits in a tabular form for a growing number of species.'),
(82080, 2019, 'bio', 'PolyQTL: Bayesian multiple eQTL detection with control for population structure and sample relatedness', 'Zeng, Biao|Gibson, Greg', 'School of Biological Sciences and Center for Integrative Genomics, Georgia Institute of Technology, Atlanta, GA 30332, USA.|School of Biological Sciences and Center for Integrative Genomics, Georgia Institute of Technology, Atlanta, GA 30332, USA.', 'Georgia|Georgia', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty728', '30165584', 'Expression quantitative loci (eQTL) are being used widely to annotate and interpret GWAS hits. Recent studies have demonstrated that individual gene expression is often regulated by multiple independent cis-acting eQTL. Diverse methods, frequentist and Bayesian, have already been developed to simultaneously detect and fine-map such multiple eQTL, but most of these ignore sample relatedness and potential population structure. This can result in false positives and disrupt the accuracy of fine-mapping. Here we introduce PolyQTL software for identifying and estimating eQTL effects. The package incorporates a genetic relatedness matrix to remove the influence of population structure and sample relatedness, while utilizing a Bayesian multiple eQTL detection pipeline to identify the most plausible candidate causal variants at one or more independent loci influencing abundance of a transcript.'),
(82081, 2019, 'bio', 'checkMyIndex: a web-based R/Shiny interface for choosing compatible sequencing indexes', 'Varet, Hugo|Coppée, Jean-Yves', 'Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur - Bioinformatics and Biostatistics Hub - C3BI, USR 3756 IP CNRS, Paris, France.|Center for Technological Resources and Research, Institut Pasteur - Transcriptome and Epigenome Platform - Biomics Pole - C2RT, Paris, France.', 'France|France', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty706', '30165585', 'When sequencing several libraries simultaneously, the selection of compatible combinations of indexes is critical for ensuring that the sequencer will be able to decipher the short, sample-specific barcodes added to each fragment. However, researchers have few tools to help them choose optimal indexes. Here, we present checkMyIndex, an online R/Shiny application that facilitates the selection of the right indexes as a function of the experimental constraints.'),
(82082, 2019, 'bio', 'RepeatCraft: a meta-pipeline for repetitive element de-fragmentation and annotation', 'Wong, Wai Yee|Simakov, Oleg', 'Department of Molecular Evolution and Development, Faculty of Life Science, University of Vienna, A-1090 Vienna, Austria.|Department of Molecular Evolution and Development, Faculty of Life Science, University of Vienna, A-1090 Vienna, Austria.', 'Austria|Austria', 3, '-', 'Journal Article', '10.1093/bioinformatics/bty745', '30165587', 'Repetitive elements comprise large proportion of many genomes. They have impact on both genome evolution and regulation. Their classification and the study of evolutionary history is a major emerging field. Various software exist to-date to classify and map repeats across genomes. The major unresolved drawback, however, is the fragmented nature of many identified repeat loci. This ultimately makes the classification of novel repeats and their evolutionary analyses difficult. To improve on this, we developed a pipeline (RepeatCraft) that integrates results from several repeat element classification tools based on both sequence similarity and structural features. The pipeline de-fragments closely spaced repeat loci in the genomes, reconstructing longer copies, thus allowing for a better annotation and sequence comparisons. The pipeline also includes a user interface that can run in a web browser allowing for an easy access and exploration of the repeat data.'),
(82083, 2019, 'bio', 'miES: predicting the essentiality of miRNAs with machine learning and sequence features', 'Song, Fei|Cui, Chunmei|Gao, Lin|Cui, Qinghua', 'School of Computer Science and Technology, Xidian University, Xian, Shaanxi, China.|Department of Biomedical Informatics, Center for Noncoding RNA Medicine, School of Basic Medical Sciences, Peking University, Beijing, China.|School of Computer Science and Technology, Xidian University, Xian, Shaanxi, China.|Department of Biomedical Informatics, Center for Noncoding RNA Medicine, School of Basic Medical Sciences, Peking University, Beijing, China.', 'China|China|China|China', 5, '-', 'Journal Article', '10.1093/bioinformatics/bty738', '30165607', 'MicroRNAs (miRNAs) are one class of small noncoding RNA molecules, which regulate gene expression at the post-transcriptional level and play important roles in health and disease. To dissect the critical miRNAs in miRNAome, it is needed to predict the essentiality of miRNAs, however, bioinformatics methods for this purpose are limited.'),
(82084, 2019, 'bio', 'PlayMolecule BindScope: large scale CNN-based virtual screening on the web', 'Skalic, Miha|Martínez-Rosell, Gerard|Jiménez, José|De Fabritiis, Gianni', 'Computational Biophysics Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park, Barcelona, Spain.|Computational Biophysics Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park, Barcelona, Spain.|Computational Biophysics Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park, Barcelona, Spain.|Computational Biophysics Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park, Barcelona, Spain.', 'Spain|Spain|Spain|Spain', 9, '-', 'Journal Article', '10.1093/bioinformatics/bty758', '30169549', 'Virtual screening pipelines are one of the most popular used tools in structure-based drug discovery, since they can can reduce both time and cost associated with experimental assays. Recent advances in deep learning methodologies have shown that these outperform classical scoring functions at discriminating binder protein-ligand complexes. Here, we present BindScope, a web application for large-scale active-inactive classification of compounds based on deep convolutional neural networks. Performance is on a pair with current state-of-the-art pipelines. Users can screen on the order of hundreds of compounds at once and interactively visualize the results.'),
(82085, 2019, 'bio', 'A generalized framework for controlling FDR in gene regulatory network inference', 'Morgan, Daniel|Tjärnberg, Andreas|Nordling, Torbjörn E M|Sonnhammer, Erik L L', 'Department of Biochemistry and Biophysics, Stockholm Bioinformatics Center, Science for Life Laboratory, Stockholm University, Stockholm, Sweden.|Department of Physics, Chemistry and Biology/Bioinformatics, Linköping University, Linköping, Sweden.|Department of Mechanical Engineering, National Cheng Kung University, Tainan, Taiwan.|Department of Biochemistry and Biophysics, Stockholm Bioinformatics Center, Science for Life Laboratory, Stockholm University, Stockholm, Sweden.', 'Sweden|Sweden|-|Sweden', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty764', '30169550', 'Inference of gene regulatory networks (GRNs) from perturbation data can give detailed mechanistic insights of a biological system. Many inference methods exist, but the resulting GRN is generally sensitive to the choice of method-specific parameters. Even though the inferred GRN is optimal given the parameters, many links may be wrong or missing if the data is not informative. To make GRN inference reliable, a method is needed to estimate the support of each predicted link as the method parameters are varied.'),
(82086, 2019, 'bio', 'Resolution-exchanged structural modeling and simulations jointly unravel that subunit rolling underlies the mechanism of programmed ribosomal frameshifting', 'Chang, Kai-Chun|Salawu, Emmanuel Oluwatobi|Chang, Yuan-Yu|Wen, Jin-Der|Yang, Lee-Wei', 'Institute of Molecular and Cellular Biology, National Taiwan University, Taipei, 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 Molecular and Cellular Biology, National Taiwan University, Taipei, Taiwan.|Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, Taiwan.', '-|-|-|-|-', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty762', '30169551', 'Programmed ribosomal frameshifting (PRF) is widely used by viruses and bacteria to produce different proteins from a single mRNA template. How steric hindrance of a PRF-stimulatory mRNA structure transiently modifies the conformational dynamics of the ribosome, and thereby allows tRNA slippage, remains elusive.'),
(82087, 2019, 'bio', 'Synergizing CRISPR/Cas9 off-target predictions for ensemble insights and practical applications', 'Zhang, Shixiong|Li, Xiangtao|Lin, Qiuzhen|Wong, Ka-Chun', 'Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong.|Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong.|College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China.|Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong.', 'Hong Kong|Hong Kong|China|Hong Kong', 5, '-', 'Journal Article', '10.1093/bioinformatics/bty748', '30169558', 'The RNA-guided CRISPR/Cas9 system has been widely applied to genome editing. CRISPR/Cas9 system can effectively edit the on-target genes. Nonetheless, it has recently been demonstrated that many homologous off-target genomic sequences could be mutated, leading to unexpected gene-editing outcomes. Therefore, a plethora of tools were proposed for the prediction of off-target activities of CRISPR/Cas9. Nonetheless, each computational tool has its own advantages and drawbacks under diverse conditions. It is hardly believed that a single tool is optimal for all conditions. Hence, we would like to explore the ensemble learning potential on synergizing multiple tools with genomic annotations together to enhance its predictive abilities.'),
(82088, 2019, 'bio', 'FastSpar: rapid and scalable correlation estimation for compositional data', 'Watts, Stephen C|Ritchie, Scott C|Inouye, Michael|Holt, Kathryn E', 'Department of Biochemistry and Molecular Biology, Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Parkville, Australia.|Systems Genomics Lab, Baker Heart & Diabetes Institute, Melbourne, Australia.|Systems Genomics Lab, Baker Heart & Diabetes Institute, Melbourne, Australia.|Department of Biochemistry and Molecular Biology, Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Parkville, Australia.', 'Australia|Australia|Australia|Australia', 6, '-', 'Journal Article', '10.1093/bioinformatics/bty734', '30169561', 'A common goal of microbiome studies is the elucidation of community composition and member interactions using counts of taxonomic units extracted from sequence data. Inference of interaction networks from sparse and compositional data requires specialized statistical approaches. A popular solution is SparCC, however its performance limits the calculation of interaction networks for very high-dimensional datasets. Here we introduce FastSpar, an efficient and parallelizable implementation of the SparCC algorithm which rapidly infers correlation networks and calculates P-values using an unbiased estimator. We further demonstrate that FastSpar reduces network inference wall time by 2-3 orders of magnitude compared to SparCC.'),
(82089, 2019, 'bio', 'Improving protein function prediction using protein sequence and GO-term similarities', 'Makrodimitris, Stavros|van Ham, Roeland C H J|Reinders, Marcel J T', 'Department of Intelligent Systems, Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands.|Department of Intelligent Systems, Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands.|Department of Intelligent Systems, Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands.', 'Netherlands|Netherlands|Netherlands', 8, '-', 'Journal Article', '10.1093/bioinformatics/bty751', '30169569', 'Most automatic functional annotation methods assign Gene Ontology (GO) terms to proteins based on annotations of highly similar proteins. We advocate that proteins that are less similar are still informative. Also, despite their simplicity and structure, GO terms seem to be hard for computers to learn, in particular the Biological Process ontology, which has the most terms (>29 000). We propose to use Label-Space Dimensionality Reduction (LSDR) techniques to exploit the redundancy of GO terms and transform them into a more compact latent representation that is easier to predict.'),
(82090, 2019, 'bio', 'cpPredictor: a web server for template-based prediction of RNA secondary structure', 'Jelínek, Jan|Pánek, Josef', 'Laboratory of Bioinformatics, Institute of Microbiology, The Czech Academy of Sciences, Prague 1, Czech Republic.|Laboratory of Bioinformatics, Institute of Microbiology, The Czech Academy of Sciences, Prague 1, Czech Republic.', '-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty753', '30169571', 'We present the cpPredictor webserver that implements a novel template-based method for prediction of secondary structure of RNA. The method outperforms available prediction methods as it uses RNA structures of related molecules, either predicted or experimentally identified, as structural templates. The server aims at three major tasks: i) prediction of RNA secondary structures that are difficult to predict by available methods, ii) characterization of uncharacterized RNAs as compatible or incompatible with a chosen template structure and iii) an identification of the most relevant structure among different candidate structures of a single RNA ambiguously predicted by available methods. The web server is accompanied with a comprehensive documentation.'),
(82091, 2019, 'bio', 'Improving the prediction of protein-nucleic acids binding residues via multiple sequence profiles and the consensus of complementary methods', 'Su, Hong|Liu, Mengchen|Sun, Saisai|Peng, Zhenling|Yang, Jianyi', 'School of Mathematical Sciences, Nankai University, Tianjin, China.|School of Mathematical Sciences, Nankai University, Tianjin, China.|School of Mathematical Sciences, Nankai University, Tianjin, China.|Center for Applied Mathematics, Tianjin University, Tianjin, China.|School of Mathematical Sciences, Nankai University, Tianjin, China.', 'China|China|China|China|China', 4, '-', 'Journal Article', '10.1093/bioinformatics/bty756', '30169574', 'The interactions between protein and nucleic acids play a key role in various biological processes. Accurate recognition of the residues that bind nucleic acids can facilitate the study of uncharacterized protein-nucleic acids interactions. The accuracy of existing nucleic acids-binding residues prediction methods is relatively low.'),
(82092, 2019, 'bio', 'Protease target prediction via matrix factorization', 'Marini, Simone|Vitali, Francesca|Rampazzi, Sara|Demartini, Andrea|Akutsu, Tatsuya', 'Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.|Department of Medicine, Center for Biomedical Informatics and Biostatistics, BIO5 Institute), University of Arizona, Tucson, AZ, USA.|Department of Computer Science and Engineering, University of Michigan, Ann Arbor, MI, USA.|Department of Electrical Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto, Japan.', 'United States|United States|United States|Italy|Japan', 4, '-', 'Journal Article', '10.1093/bioinformatics/bty746', '30169576', 'Protein cleavage is an important cellular event, involved in a myriad of processes, from apoptosis to immune response. Bioinformatics provides in silico tools, such as machine learning-based models, to guide the discovery of targets for the proteases responsible for protein cleavage. State-of-the-art models have a scope limited to specific protease families (such as Caspases), and do not explicitly include biological or medical knowledge (such as the hierarchical protein domain similarity or gene-gene interactions). To fill this gap, we present a novel approach for protease target prediction based on data integration.'),
(82093, 2019, 'bio', 'HitPickV2: a web server to predict targets of chemical compounds', 'Hamad, Sabri|Adornetto, Gianluca|Naveja, J Jesús|Chavan Ravindranath, Aakash|Raffler, Johannes|Campillos, Mónica', 'Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany.|Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany.|Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany.|Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany.|Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany.|Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 4, '-', 'Journal Article', '10.1093/bioinformatics/bty759', '30169615', 'The identification of protein targets of novel compounds is essential to understand compounds mechanisms of action leading to biological effects. Experimental methods to determine these protein targets are usually slow, costly and time consuming. Computational tools have recently emerged as cheaper and faster alternatives that allow the prediction of targets for a large number of compounds.'),
(82094, 2019, 'bio', 'Comparative analysis of methods for evaluation of protein models against native structures', 'Olechnovixc4x8d, Kliment|Monastyrskyy, Bohdan|Kryshtafovych, Andriy|Venclovas, xc4x8ceslovas', 'Institute of Biotechnology Life Sciences Center Vilnius University, Saulxc4x97tekio 7, Vilnius, Lithuania.|Genome Center UC Davis, 451 Health Sciences Drive, Davis, CA, USA.|Genome Center UC Davis, 451 Health Sciences Drive, Davis, CA, USA.|Institute of Biotechnology Life Sciences Center Vilnius University, Saulxc4x97tekio 7, Vilnius, Lithuania.', 'Lithuania|United States|United States|Lithuania', 9, '-', 'Journal Article', '10.1093/bioinformatics/bty760', '30169622', 'Measuring discrepancies between protein models and native structures is at the heart of development of protein structure prediction methods and comparison of their performance. A number of different evaluation methods have been developed, however, their comprehensive and unbiased comparison has not been performed.'),
(82095, 2019, 'bio', 'PhysiBoSS: a multi-scale agent-based modelling framework integrating physical dimension and cell signalling', 'Letort, Gaelle|Montagud, Arnau|Stoll, Gautier|Heiland, Randy|Barillot, Emmanuel|Macklin, Paul|Zinovyev, Andrei|Calzone, Laurence', 'Institut Curie, PSL Research University, Paris, France.|Institut Curie, PSL Research University, Paris, France.|Université Paris Descartes/Paris V, Sorbonne Paris Cité, Paris, France.|Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA.|Institut Curie, PSL Research University, Paris, France.|Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA.|Institut Curie, PSL Research University, Paris, France.|Institut Curie, PSL Research University, Paris, France.', 'France|France|France|India|France|India|France|France', 12, '-', 'Journal Article', '10.1093/bioinformatics/bty766', '30169736', 'Due to the complexity and heterogeneity of multicellular biological systems, mathematical models that take into account cell signalling, cell population behaviour and the extracellular environment are particularly helpful. We present PhysiBoSS, an open source software which combines intracellular signalling using Boolean modelling (MaBoSS) and multicellular behaviour using agent-based modelling (PhysiCell).'),
(82096, 2019, 'bio', 'PDV: an integrative proteomics data viewer', 'Li, Kai|Vaudel, Marc|Zhang, Bing|Ren, Yan|Wen, Bo', 'BGI-Shenzhen, Shenzhen, China.|Department of Clinical Science, KG Jebsen Center for Diabetes Research, University of Bergen, Norway.|Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, USA.|BGI-Shenzhen, Shenzhen, China.|Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, USA.', 'China|Norway|United States|China|United States', 15, '-', 'Journal Article', '10.1093/bioinformatics/bty770', '30169737', 'Data visualization plays critical roles in proteomics studies, ranging from quality control of MS/MS data to validation of peptide identification results. Herein, we present PDV, an integrative proteomics data viewer that can be used to visualize a wide range of proteomics data, including database search results, de novo sequencing results, proteogenomics files, MS/MS data in mzML/mzXML format and data from public proteomics repositories. PDV is a lightweight visualization tool that enables intuitive and fast exploration of diverse, large-scale proteomics datasets on standard desktop computers in both graphical user interface and command line modes.'),
(82097, 2019, 'bio', 'Drug Gene Budger (DGB): an application for ranking drugs to modulate a specific gene based on transcriptomic signatures', 'Wang, Zichen|He, Edward|Sani, Kevin|Jagodnik, Kathleen M|Silverstein, Moshe C|Maayan, Avi', 'Department of Pharmacological Sciences, BD2K-LINCS Data Coordination and Integration Center, Knowledge Management Center for Illuminating the Druggable Genome, Mount Sinai Center for Bioinformatics, Icahn 10 School of Medicine at Mount Sinai, New York, NY, USA.|Department of Pharmacological Sciences, BD2K-LINCS Data Coordination and Integration Center, Knowledge Management Center for Illuminating the Druggable Genome, Mount Sinai Center for Bioinformatics, Icahn 10 School of Medicine at Mount Sinai, New York, NY, USA.|Department of Pharmacological Sciences, BD2K-LINCS Data Coordination and Integration Center, Knowledge Management Center for Illuminating the Druggable Genome, Mount Sinai Center for Bioinformatics, Icahn 10 School of Medicine at Mount Sinai, New York, NY, USA.|Department of Pharmacological Sciences, BD2K-LINCS Data Coordination and Integration Center, Knowledge Management Center for Illuminating the Druggable Genome, Mount Sinai Center for Bioinformatics, Icahn 10 School of Medicine at Mount Sinai, New York, NY, USA.|Department of Pharmacological Sciences, BD2K-LINCS Data Coordination and Integration Center, Knowledge Management Center for Illuminating the Druggable Genome, Mount Sinai Center for Bioinformatics, Icahn 10 School of Medicine at Mount Sinai, New York, NY, USA.|Department of Pharmacological Sciences, BD2K-LINCS Data Coordination and Integration Center, Knowledge Management Center for Illuminating the Druggable Genome, Mount Sinai Center for Bioinformatics, Icahn 10 School of Medicine at Mount Sinai, New York, NY, USA.', 'United States|United States|United States|United States|United States|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty763', '30169739', 'Mechanistic molecular studies in biomedical research often discover important genes that are aberrantly over- or under-expressed in disease. However, manipulating these genes in an attempt to improve the disease state is challenging. Herein, we reveal Drug Gene Budger (DGB), a web-based and mobile application developed to assist investigators in order to prioritize small molecules that are predicted to maximally influence the expression of their target gene of interest. With DGB, users can enter a gene symbol along with the wish to up-regulate or down-regulate its expression. The output of the application is a ranked list of small molecules that have been experimentally determined to produce the desired expression effect. The table includes log-transformed fold change, P-value and q-value for each small molecule, reporting the significance of differential expression as determined by the limma method. Relevant links are provided to further explore knowledge about the target gene, the small molecule and the source of evidence from which the relationship between the small molecule and the target gene was derived. The experimental data contained within DGB is compiled from signatures extracted from the LINCS L1000 dataset, the original Connectivity Map (CMap) dataset and the Gene Expression Omnibus (GEO). DGB also presents a specificity measure for a drug-gene connection based on the number of genes a drug modulates. DGB provides a useful preliminary technique for identifying small molecules that can target the expression of a single gene in human cells and tissues.'),
(82098, 2019, 'bio', 'HLA-IMPUTER: an easy to use web application for HLA imputation and association analysis using population-specific reference panels', 'Shen, Jiangshan J|Yang, Chao|Wang, Yong-Fei|Wang, Ting-You|Guo, Mengbiao|Lau, Yu Lung|Zhang, Xuejun|Sheng, Yujun|Yang, Wanling', 'Department of Paediatrics and Adolescent Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, SAR China.|Department of Dermatology, No. 1 Hospital and Key Laboratory of Dermatology, Ministry of Education, Anhui Medical University, Hefei, China.|Department of Paediatrics and Adolescent Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, SAR China.|Department of Paediatrics and Adolescent Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, SAR China.|Department of Paediatrics and Adolescent Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, SAR China.|Department of Paediatrics and Adolescent Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, SAR China.|Department of Dermatology, No. 1 Hospital and Key Laboratory of Dermatology, Ministry of Education, Anhui Medical University, Hefei, China.|Department of Dermatology, No. 1 Hospital and Key Laboratory of Dermatology, Ministry of Education, Anhui Medical University, Hefei, China.|Department of Paediatrics and Adolescent Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, SAR China.', 'China|China|China|China|China|China|China|China|China', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty730', '30169743', 'HLA allele imputation from SNP genotypes has become increasingly useful, but its accuracy is heavily dependent on the reference panels used. HLA-IMPUTER implements HIBAG algorithm for HLA imputation with different population specific reference panels, including a new Han Chinese reference panel derived from 10 689 samples. We provide a convenient platform for researchers to impute HLA alleles and perform association analysis.'),
(82099, 2019, 'bio', 'A generic deep convolutional neural network framework for prediction of receptor-ligand interactions-NetPhosPan: application to kinase phosphorylation prediction', 'Fenoy, Emilio|Izarzugaza, Jose M G|Jurtz, Vanessa|Brunak, Søren|Nielsen, Morten', 'Laboratorio de Bioinformatica, Instituto de Investigaciones Biotecnologicas, Universidad Nacional de San Martin, San Martin, B, HMP Buenos Aires, Argentina.|Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark.|Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark.|Translational Disease Systems Biology Group, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.|Laboratorio de Bioinformatica, Instituto de Investigaciones Biotecnologicas, Universidad Nacional de San Martin, San Martin, B, HMP Buenos Aires, Argentina.', 'Argentina|Denmark|Denmark|Denmark|Argentina', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty715', '30169744', 'Understanding the specificity of protein receptor-ligand interactions is pivotal for our comprehension of biological mechanisms and systems. Receptor protein families often have a certain level of sequence diversity that converges into fewer conserved protein structures, allowing the exertion of well-defined functions. T and B cell receptors of the immune system and protein kinases that control the dynamic behaviour and decision processes in eukaryotic cells by catalysing phosphorylation represent prime examples. Driven by the large sequence diversity, the receptors within such protein families are often found to share specificities although divergent at the sequence level. This observation has led to the notion that prediction models of such systems are most effectively handled in a receptor-specific manner.'),
(82100, 2019, 'bio', 'Robust prediction of clinical outcomes using cytometry data', 'Hu, Zicheng|Glicksberg, Benjamin S|Butte, Atul J', 'Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA.|Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA.|Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty768', '30169745', 'Flow cytometry and mass cytometry are widely used to diagnose diseases and to predict clinical outcomes. When associating clinical features with cytometry data, traditional analysis methods require cell gating as an intermediate step, leading to information loss and susceptibility to batch effects. Here, we wish to explore an alternative approach that predicts clinical features from cytometry data without the cell-gating step. We also wish to test if such a gating-free approach increases the accuracy and robustness of the prediction.'),
(82101, 2019, 'bio', 'Methods for automatic reference trees and multilevel phylogenetic placement', 'Czech, Lucas|Barbera, Pierre|Stamatakis, Alexandros', 'Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany.|Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany.|Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany.', 'Germany|Germany|Germany', 4, '-', 'Journal Article', '10.1093/bioinformatics/bty767', '30169747', 'In most metagenomic sequencing studies, the initial analysis step consists in assessing the evolutionary provenance of the sequences. Phylogenetic (or Evolutionary) Placement methods can be employed to determine the evolutionary position of sequences with respect to a given reference phylogeny. These placement methods do however face certain limitations: The manual selection of reference sequences is labor-intensive, the computational effort to infer reference phylogenies is substantially larger than for methods that rely on sequence similarity, the number of taxa in the reference phylogeny should be small enough to allow for visually inspecting the results.'),
(82102, 2019, 'bio', 'NanoShaper-VMD interface: computing and visualizing surfaces, pockets and channels in molecular systems', 'Decherchi, Sergio|Spitaleri, Andrea|Stone, John|Rocchia, Walter', 'CONCEPT Lab, Istituto Italiano di Tecnologia, Genoa, Italy.|CONCEPT Lab, Istituto Italiano di Tecnologia, Genoa, Italy.|Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, USA.|CONCEPT Lab, Istituto Italiano di Tecnologia, Genoa, Italy.', 'Italy|Italy|United States|Italy', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty761', '30169777', 'NanoShaper is a program specifically aiming the construction and analysis of the molecular surface of nanoscopic systems. It uses ray-casting for parallelism and it performs analytical computations whenever possible to maximize robustness and accuracy of the approach. Among the other features, NanoShaper provides volume, surface area, including that of internal cavities, for any considered molecular system. It identifies pockets via a very intuitive definition based on the concept of probe radius, intrinsic to the definition of the solvent excluded surface. We show here that, with a suitable choice of the parameters, the same approach can also permit the visualisation of molecular channels. NanoShaper has now been interfaced with the widely used molecular visualization software VMD, further enriching its already well furnished toolset.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(82103, 2019, 'bio', 'novoCaller: a Bayesian network approach for de novo variant calling from pedigree and population sequence data', 'Mohanty, Anwoy Kumar|Vuzman, Dana|Francioli, Laurent|Cassa, Christopher|Brigham Genomic Medicine|Undiagnosed Diseases Network|Brigham and Women’s Hospital FaceBase Project|Toth-Petroczy, Agnes|Sunyaev, Shamil', 'Division of Genetics, Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, Boston, MA, USA.|Division of Genetics, Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, Boston, MA, USA.|Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.|Division of Genetics, Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, Boston, MA, USA.|Division of Genetics, Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, Boston, MA, USA.|Division of Genetics, Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, Boston, MA, USA.|Division of Genetics, Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, Boston, MA, USA.|Division of Genetics, Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, Boston, MA, USA.|Division of Genetics, Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, Boston, MA, 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/bty749', '30169785', 'De novo mutations (i.e. newly occurring mutations) are a pre-dominant cause of sporadic dominant monogenic diseases and play a significant role in the genetics of complex disorders. De novo mutation studies also inform population genetics models and shed light on the biology of DNA replication and repair. Despite the broad interest, there is room for improvement with regard to the accuracy of de novo mutation calling.'),
(82104, 2019, 'bio', 'RNAdetect: efficient computational detection of novel non-coding RNAs', 'Chen, Chun-Chi|Qian, Xiaoning|Yoon, Byung-Jun', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA.', 'United States|United States|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty765', '30169792', 'Non-coding RNAs (ncRNAs) are known to play crucial roles in various biological processes, and there is a pressing need for accurate computational detection methods that could be used to efficiently scan genomes to detect novel ncRNAs. However, unlike coding genes, ncRNAs often lack distinctive sequence features that could be used for recognizing them. Although many ncRNAs are known to have a well conserved secondary structure, which provides useful cues for computational prediction, it has been also shown that a structure-based approach alone may not be sufficient for detecting ncRNAs in a single sequence. Currently, the most effective ncRNA detection methods combine structure-based techniques with a comparative genome analysis approach to improve the prediction performance.'),
(82105, 2019, 'bio', 'Understanding and predicting disease relationships through similarity fusion', 'Oerton, Erin|Roberts, Ian|Lewis, Patrick S H|Guilliams, Tim|Bender, Andreas', 'Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK.|Healx Ltd, Park House, Castle Park, Cambridge, UK.|Healx Ltd, Park House, Castle Park, Cambridge, UK.|Healx Ltd, Park House, Castle Park, Cambridge, UK.|Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty754', '30169824', 'Combining disease relationships across multiple biological levels could aid our understanding of common processes taking place in disease, potentially indicating opportunities for drug sharing. Here, we propose a similarity fusion approach which accounts for differences in information content between different data types, allowing combination of each data type in a balanced manner.'),
(82106, 2019, 'bio', 'When a single lineage is not enough: Uncertainty-Aware Tracking for spatio-temporal live-cell image analysis', 'Theorell, Axel|Seiffarth, Johannes|Grünberger, Alexander|Nöh, Katharina', 'Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany.|Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany.|Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany.|Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany.', 'Germany|Germany|Germany|Germany', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty776', '30184044', 'Microfluidic platforms for live-cell analysis are in dire need of automated image analysis pipelines. In this context, producing reliable tracks of single cells in colonies has proven to be notoriously difficult without manual assistance, especially when image sequences experience low frame rates.'),
(82107, 2019, 'bio', 'Deconvoluting essential gene signatures for cancer growth from genomic expression in compound-treated cells', 'Jung, Jinmyung|Kang, Yeeok|Paik, Hyojung|Kwon, Mijin|Yu, Hasun|Lee, Doheon', 'Bio-Synergy Research Center, 291 Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea.|Department of Bio and Brain Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea.|Korea Institute of Science and Technology Information, Center for Applied Scientific Computing, Division of Supercomputing, Daejeon, Republic of Korea.|Department of Bio and Brain Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea.|Bio-Synergy Research Center, 291 Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea.|Bio-Synergy Research Center, 291 Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea.', '-|-|-|-|-|-', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty774', '30184045', 'Essential gene signatures for cancer growth have been typically identified via RNAi or CRISPR-Cas9. Here, we propose an alternative method that reveals the essential gene signatures by analysing genomic expression profiles in compound-treated cells. With a large amount of the existing compound-induced data, essential gene signatures at genomic scale are efficiently characterized without technical challenges in the previous techniques.'),
(82108, 2019, 'bio', 'SCOP: a novel scaffolding algorithm based on contig classification and optimization', 'Li, Min|Tang, Li|Wu, Fang-Xiang|Pan, Yi|Wang, Jianxin', 'Department of computer science, School of Information Science and Engineering, Central South University, Changsha, China.|Department of computer science, School of Information Science and Engineering, Central South University, Changsha, China.|Department of computer science, School of Information Science and Engineering, Central South University, Changsha, China.|Department of computer science, School of Information Science and Engineering, Central South University, Changsha, China.|Department of computer science, School of Information Science and Engineering, Central South University, Changsha, China.', 'China|China|China|China|China', 6, '-', 'Journal Article', '10.1093/bioinformatics/bty773', '30184046', 'Scaffolding is an essential step during the de novo sequence assembly process to infer the direction and order relationships between the contigs and make the sequence assembly results more continuous and complete. However, scaffolding still faces the challenges of repetitive regions in genome, sequencing errors and uneven sequencing depth. Moreover, the accuracy of scaffolding greatly depends on the quality of contigs. Generally, the existing scaffolding methods construct a scaffold graph, and then optimize the graph by deleting spurious edges. Nevertheless, due to the wrong joints between contigs, some correct edges connecting contigs may be deleted.'),
(82109, 2019, 'bio', 'metaGraphite-a new layer of pathway annotation to get metabolite networks', 'Sales, Gabriele|Calura, Enrica|Romualdi, Chiara', 'Department of Biology, University of Padova, Padova, Italy.|Department of Biology, University of Padova, Padova, Italy.|Department of Biology, University of Padova, Padova, Italy.', 'Italy|Italy|Italy', 6, '-', 'Journal Article', '10.1093/bioinformatics/bty719', '30184047', 'Metabolomics is an emerging omics science involving the characterization of metabolites and metabolism in biological systems. Few bioinformatic tools have been developed for the visualization, exploration and analysis of metabolomic data within the context of metabolic pathways: some of them became rapidly obsolete and are no longer supported, others are based on a single database. A systematic collection of existing annotations has the potential of considerably boosting the investigation and contextualization of metabolomic measurements.'),
(82110, 2019, 'bio', 'Precision Lasso: accounting for correlations and linear dependencies in high-dimensional genomic data', 'Wang, Haohan|Lengerich, Benjamin J|Aragam, Bryon|Xing, Eric P', 'Language Technologies Institute, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.|Department of Computer Science, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.|Department of Machine Learning, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.|Language Technologies Institute, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.', 'United States|United States|United States|United States', 13, '-', 'Journal Article', '10.1093/bioinformatics/bty750', '30184048', 'Association studies to discover links between genetic markers and phenotypes are central to bioinformatics. Methods of regularized regression, such as variants of the Lasso, are popular for this task. Despite the good predictive performance of these methods in the average case, they suffer from unstable selections of correlated variables and inconsistent selections of linearly dependent variables. Unfortunately, as we demonstrate empirically, such problematic situations of correlated and linearly dependent variables often exist in genomic datasets and lead to under-performance of classical methods of variable selection.'),
(82111, 2019, 'bio', 'AGILE: an assembled genome mining pipeline', 'Hughes, Graham M|Teeling, Emma C', 'School of Biology and Environmental Science, University College Dublin, Dublin 4, Ireland.|School of Biology and Environmental Science, University College Dublin, Dublin 4, Ireland.', 'Ireland|Ireland', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty781', '30184049', 'A number of limiting factors mean that traditional genome annotation tools either fail or perform sub-optimally when trying to detect coding sequences in poor quality genome assemblies/genome reports. This means that potentially useful data is accessible only to those with specific skills and expertise in assembly and annotation. We present an Assembled-Genome mIning pipeLinE (AGILE) written in Perl that combines bioinformatics tools with a number of steps to overcome the limitations imposed by such assemblies when applied to highly fragmented genomes. Our methodology uses user-specified query genes from a closely related species to mine and annotate coding sequences that would traditionally be missed by standard annotation packages. Despite a focus on mammalian genomes, the generalized implementation means that it may be applied to any genome assembly, providing a means for non-specialists to gather gene sequences for downstream analyses.'),
(82112, 2019, 'bio', 'Differential methylation values in differential methylation analysis', 'Xie, Changchun|Leung, Yuet-Kin|Chen, Aimin|Long, Ding-Xin|Hoyo, Catherine|Ho, Shuk-Mei', 'Department of Environmental Health, University of Cincinnati, Cincinnati, OH, USA.|Department of Environmental Health, University of Cincinnati, Cincinnati, OH, USA.|Department of Environmental Health, University of Cincinnati, Cincinnati, OH, USA.|School of Public Health, University of South China, Hengyang, Hunan, China.|Department of Biological Sciences, North Carolina State University, Raleigh, NC, USA.|Department of Environmental Health, University of Cincinnati, Cincinnati, OH, USA.', 'United States|United States|United States|China|United States|United States', 6, '-', 'Journal Article', '10.1093/bioinformatics/bty778', '30184051', 'Both xcexb2-value and M-value have been used as metrics to measure methylation levels. The M-value is more statistically valid for the differential analysis of methylation levels. However, the xcexb2-value is much more biologically interpretable and needs to be reported when M-value method is used for conducting differential methylation analysis. There is an urgent need to know how to interpret the degree of differential methylation from the M-value. In M-value linear regression model, differential methylation M-value xcex94M can be easily obtained from the coefficient estimate, but it is not straightforward to get the differential methylation xcexb2-value, xcex94xcexb2 since it cannot be obtained from the coefficient alone.'),
(82113, 2019, 'bio', 'DeepGSR: an optimized deep-learning structure for the recognition of genomic signals and regions', 'Kalkatawi, Manal|Magana-Mora, Arturo|Jankovic, Boris|Bajic, Vladimir B', 'Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.|Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.|Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.|Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.', 'Saudi Arabia|Saudi Arabia|Saudi Arabia|Saudi Arabia', 4, '-', 'Journal Article', '10.1093/bioinformatics/bty752', '30184052', 'Recognition of different genomic signals and regions (GSRs) in DNA is crucial for understanding genome organization, gene regulation, and gene function, which in turn generate better genome and gene annotations. Although many methods have been developed to recognize GSRs, their pure computational identification remains challenging. Moreover, various GSRs usually require a specialized set of features for developing robust recognition models. Recently, deep-learning (DL) methods have been shown to generate more accurate prediction models than shallow methods without the need to develop specialized features for the problems in question. Here, we explore the potential use of DL for the recognition of GSRs.'),
(82114, 2019, 'bio', 'Glutton: a tool for generating structural ensembles of partly disordered proteins from chemical shifts', 'He, Yi|Nagpal, Suhani|Sadqi, Mourad|de Alba, Eva|Muxc3xb1oz, Victor', 'Department of Bioengineering, University of California Merced, Merced, CA, USA.|Department of Bioengineering, University of California Merced, Merced, CA, USA.|Department of Bioengineering, University of California Merced, Merced, CA, USA.|Department of Bioengineering, University of California Merced, Merced, CA, USA.|Department of Bioengineering, University of California Merced, Merced, CA, USA.', 'United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty755', '30184055', 'Many proteins are partially disordered in physiological conditions and only fold, fully or partially, upon binding. Their structural analysis is challenging because the accessible information, typically chemical shifts (CS) from nuclear magnetic resonance experiments, are averages over broad ensembles of conformations. We aim to develop a database for the analysis of such data in terms of conformational distributions of the protein backbone rather than of individual high-resolution structures.'),
(82115, 2019, 'bio', 'Co-registration and analysis of multiple imaging mass spectrometry datasets targeting different analytes', 'Patterson, Nathan Heath|Yang, Ethan|Kranjec, Elizabeth-Ann|Chaurand, Pierre', 'Department of Chemistry, Université de Montréal, Montreal, Quebec, Canada.|Department of Chemistry, Université de Montréal, Montreal, Quebec, Canada.|Department of Chemistry, Université de Montréal, Montreal, Quebec, Canada.|Department of Chemistry, Université de Montréal, Montreal, Quebec, Canada.', 'Canada|Canada|Canada|Canada', 7, '-', 'Journal Article', '10.1093/bioinformatics/bty780', '30184057', 'MALDI imaging mass spectrometry (IMS) has been successfully used to image a variety of biomolecules. Imaging of the many classes of biomolecules is often achieved through several incompatible sample preparations. Thus, multiple datasets must be acquired from multiple tissue sections to obtain a total molecular overview of a single sample. Addressing the need for single datasets from multiple IMS analyses, we developed the R package RegCombIMS as an extension of R package Cardinal to co-register, combine and create single IMS datasets acquired from serial sections of tissue.'),
(82116, 2018, 'bio', 'Bayesian integrative model for multi-omics data with missingness', 'Fang, Zhou|Ma, Tianzhou|Tang, Gong|Zhu, Li|Yan, Qi|Wang, Ting|Celedón, Juan C|Chen, Wei|Tseng, George C', 'Department of Biostatistics, University of Pittsburgh, Pittsburgh, USA.|Department of Epidemiology and Biostatistics, University of Maryland, College Park, MD, USA.|Department of Biostatistics, University of Pittsburgh, Pittsburgh, USA.|Department of Biostatistics, University of Pittsburgh, Pittsburgh, USA.|Division of Pediatric Pulmonology, Allergy and Immunology, Childrens Hospital of Pittsburgh of UPMC, Pittsburgh, USA.|Division of Pediatric Pulmonology, Allergy and Immunology, Childrens Hospital of Pittsburgh of UPMC, Pittsburgh, USA.|Division of Pediatric Pulmonology, Allergy and Immunology, Childrens Hospital of Pittsburgh of UPMC, Pittsburgh, USA.|Department of Biostatistics, University of Pittsburgh, Pittsburgh, USA.|Department of Biostatistics, University of Pittsburgh, Pittsburgh, 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/bty775', '30184058', 'Integrative analysis of multi-omics data from different high-throughput experimental platforms provides valuable insight into regulatory mechanisms associated with complex diseases, and gains statistical power to detect markers that are otherwise overlooked by single-platform omics analysis. In practice, a significant portion of samples may not be measured completely due to insufficient tissues or restricted budget (e.g. gene expression profile are measured but not methylation). Current multi-omics integrative methods require complete data. A common practice is to ignore samples with any missing platform and perform complete case analysis, which leads to substantial loss of statistical power.'),
(82117, 2019, 'bio', 'De novo construction of polyploid linkage maps using discrete graphical models', 'Behrouzi, Pariya|Wit, Ernst C', 'Department of Plant Sciences, Wageningen University and Research, Wageningen, The Netherlands.|Institute of Computational Science, Università della Svizzera italiana, Lugano, Switzerland.', 'Netherlands|Switzerland', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty777', '30184062', 'Linkage maps are used to identify the location of genes responsible for traits and diseases. New sequencing techniques have created opportunities to substantially increase the density of genetic markers. Such revolutionary advances in technology have given rise to new challenges, such as creating high-density linkage maps. Current multiple testing approaches based on pairwise recombination fractions are underpowered in the high-dimensional setting and do not extend easily to polyploid species. To remedy these issues, we propose to construct linkage maps using graphical models either via a sparse Gaussian copula or a non-paranormal skeptic approach.'),
(82118, 2019, 'bio', 'Phylogenetic approaches to identifying fragments of the same gene, with application to the wheat genome', 'Piližota, Ivana|Train, Clément-Marie|Altenhoff, Adrian|Redestig, Henning|Dessimoz, Christophe', 'Department of Genetics Evolution & Environment, University College London, UK.|Department of Computational Biology, Lausanne, Switzerland.|Swiss Institute of Bioinformatics, Biophore Building, Lausanne, Switzerland.|Bayer CropScience NV, Ghent, Belgium.|Department of Genetics Evolution & Environment, University College London, UK.', 'United Kingdom|Switzerland|Switzerland|Belgium|United Kingdom', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty772', '30184069', 'As the time and cost of sequencing decrease, the number of available genomes and transcriptomes rapidly increases. Yet the quality of the assemblies and the gene annotations varies considerably and often remains poor, affecting downstream analyses. This is particularly true when fragments of the same gene are annotated as distinct genes, which may cause them to be mistaken as paralogs.'),
(82119, 2019, 'bio', 'Mixed graphical models for integrative causal analysis with application to chronic lung disease diagnosis and prognosis', 'Sedgewick, Andrew J|Buschur, Kristina|Shi, Ivy|Ramsey, Joseph D|Raghu, Vineet K|Manatakis, Dimitris V|Zhang, Yingze|Bon, Jessica|Chandra, Divay|Karoleski, Chad|Sciurba, Frank C|Spirtes, Peter|Glymour, Clark|Benos, Panayiotis V', 'Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.|Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.|Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.|Department of Philosophy, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.|Department of Computer Science, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.|Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.|Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.|Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.|Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.|Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.|Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.|Department of Philosophy, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.|Department of Philosophy, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.|Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, 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', 7, '-', 'Journal Article', '10.1093/bioinformatics/bty769', '30192904', 'Integration of data from different modalities is a necessary step for multi-scale data analysis in many fields, including biomedical research and systems biology. Directed graphical models offer an attractive tool for this problem because they can represent both the complex, multivariate probability distributions and the causal pathways influencing the system. Graphical models learned from biomedical data can be used for classification, biomarker selection and functional analysis, while revealing the underlying network structure and thus allowing for arbitrary likelihood queries over the data.'),
(82120, 2019, 'bio', 'Inference and visualization of DNA damage patterns using a grade of membership model', 'Al-Asadi, Hussein|Dey, Kushal K|Novembre, John|Stephens, Matthew', 'Committee on Evolutionary Biology, University of Chicago, Chicago, IL, USA.|Department of Statistics, University of Chicago, Chicago, IL, USA.|Committee on Evolutionary Biology, University of Chicago, Chicago, IL, USA.|Department of Statistics, University of Chicago, Chicago, IL, USA.', 'United States|United States|United States|United States', 3, '-', 'Journal Article', '10.1093/bioinformatics/bty779', '30192911', 'Quality control plays a major role in the analysis of ancient DNA (aDNA). One key step in this quality control is assessment of DNA damage: aDNA contains unique signatures of DNA damage that distinguish it from modern DNA, and so analyses of damage patterns can help confirm that DNA sequences obtained are from endogenous aDNA rather than from modern contamination. Predominant signatures of DNA damage include a high frequency of cytosine to thymine substitutions (C-to-T) at the ends of fragments, and elevated rates of purines (A & G) before the 5 strand-breaks. Existing QC procedures help assess damage by simply plotting for each sample, the C-to-T mismatch rate along the read and the composition of bases before the 5 strand-breaks. Here we present a more flexible and comprehensive model-based approach to infer and visualize damage patterns in aDNA, implemented in an R package aRchaic. This approach is based on a grade of membership model (also known as admixture or topic model) in which each sample has an estimated grade of membership in each of K damage profiles that are estimated from the data.'),
(82121, 2019, 'bio', 'Ambiguous splice sites distinguish circRNA and linear splicing in the human genome', 'Dehghannasiri, Roozbeh|Szabo, Linda|Salzman, Julia', 'Department of Biochemistry, Stanford University, Stanford, CA, USA.|Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.|Department of Biochemistry, Stanford University, Stanford, CA, USA.', 'United States|United States|United States', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty785', '30192918', 'Identification of splice sites is critical to gene annotation and to determine which sequences control circRNA biogenesis. Full-length RNA transcripts could in principle complete annotations of introns and exons in genomes without external ontologies, i.e., ab initio. However, whether it is possible to reconstruct genomic positions where splicing occurs from full-length transcripts, even if sampled in the absence of noise, depends on the genome sequence composition. If it is not, there exist provable limits on the use of RNA-Seq to define splice locations (linear or circular) in the genome.'),
(82122, 2019, 'bio', 'smCounter2: an accurate low-frequency variant caller for targeted sequencing data with unique molecular identifiers', 'Xu, Chang|Gu, Xiujing|Padmanabhan, Raghavendra|Wu, Zhong|Peng, Quan|DiCarlo, John|Wang, Yexun', 'Life Science Research and Foundation, QIAGEN Sciences Inc., Frederick, MD, USA.|Life Science Research and Foundation, QIAGEN Sciences Inc., Frederick, MD, USA.|Life Science Research and Foundation, QIAGEN Sciences Inc., Frederick, MD, USA.|Life Science Research and Foundation, QIAGEN Sciences Inc., Frederick, MD, USA.|Life Science Research and Foundation, QIAGEN Sciences Inc., Frederick, MD, USA.|Life Science Research and Foundation, QIAGEN Sciences Inc., Frederick, MD, USA.|Life Science Research and Foundation, QIAGEN Sciences Inc., Frederick, MD, USA.', 'United States|United States|United States|United States|United States|United States|United States', 11, '-', 'Journal Article', '10.1093/bioinformatics/bty790', '30192920', 'Low-frequency DNA mutations are often confounded with technical artifacts from sample preparation and sequencing. With unique molecular identifiers (UMIs), most of the sequencing errors can be corrected. However, errors before UMI tagging, such as DNA polymerase errors during end repair and the first PCR cycle, cannot be corrected with single-strand UMIs and impose fundamental limits to UMI-based variant calling.'),
(82123, 2019, 'bio', 'Prediction of protein group function by iterative classification on functional relevance network', 'Khan, Ishita K|Jain, Aashish|Rawi, Reda|Bensmail, Halima|Kihara, Daisuke', 'Department of Computer Science, Purdue University, West Lafayette, IN, USA.|Department of Computer Science, Purdue University, West Lafayette, IN, USA.|Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.|Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.|Department of Computer Science, Purdue University, West Lafayette, IN, USA.', 'United States|United States|Qatar|Qatar|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty787', '30192921', 'Biological experiments including proteomics and transcriptomics approaches often reveal sets of proteins that are most likely to be involved in a disease/disorder. To understand the functional nature of a set of proteins, it is important to capture the function of the proteins as a group, even in cases where function of individual proteins is not known. In this work, we propose a model that takes groups of proteins found to work together in a certain biological context, integrates them into functional relevance networks, and subsequently employs an iterative inference on graphical models to identify group functions of the proteins, which are then extended to predict function of individual proteins.'),
(82124, 2019, 'bio', 'VarSelLCM: an R/C++ package for variable selection in model-based clustering of mixed-data with missing values', 'Marbac, Matthieu|Sedki, Mohammed', 'CREST, Ensai, Bruz, France.|University of Paris-Sud and UMR Inserm-1181, Paris, France.', 'France|France', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty786', '30192923', 'VarSelLCM allows a full model selection (detection of the relevant features for clustering and selection of the number of clusters) in model-based clustering, according to classical information criteria. Data to be analyzed can be composed of continuous, integer and/or categorical features. Moreover, missing values are managed, without any pre-processing, by the model used to cluster with the assumption that values are missing completely at random. Thus, VarSelLCM also allows data imputation by using mixture models. A Shiny application is implemented to easily interpret the clustering results.'),
(82125, 2019, 'bio', 'Phylolink: phylogenetically-based profiling, visualisations and metrics for biodiversity', 'Miller, Joseph T|Pirzl, Rebecca|Rosauer, Dan|Jolley-Rogers, Garry|Varghese, Temi', 'National Research Collections Australia, CSIRO National Facilities and Collections, Canberra, ACT, Australia.|Atlas of Living Australia, CSIRO National Collections and Marine Infrastructure, Canberra, ACT, Australia and.|ANU College of Science, Australian National University, Canberra, ACT, Australia.|National Research Collections Australia, CSIRO National Facilities and Collections, Canberra, ACT, Australia.|Atlas of Living Australia, CSIRO National Collections and Marine Infrastructure, Canberra, ACT, Australia and.', 'Australia|Australia|Australia|Australia|Australia', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty792', '30202854', 'Phylolink is a research collaboration toolkit through which biodiversity can be explored from a phylogenetic perspective. It is an update of PhyloJiVE that has been integrated into the framework of the Atlas of Living Australia, including its spatial portal. Phylolink connects phylogenetic tree nodes with species occurrence records, environmental data, and species trait information. Features new to Phylolink allow users to upload and download spatial datasets, store files and link to the ALA spatial portal, improve graphics and provide the novel ability to analyze environmental attributes of species and clades distributions. Species richness and phylogenetic diversity comparisons can be made among geographic areas. The result is a powerful way of combining data to generate flexible and customizable visualizations, profiles and metrics for biodiversity.'),
(82126, 2019, 'bio', 'SequencEnG: an interactive knowledge base of sequencing techniques', 'Zhang, Yi|Manjunath, Mohith|Kim, Yeonsung|Heintz, Joerg|Song, Jun S', 'Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA.|Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.|Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.|Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.|Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.', 'United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty794', '30202870', 'Next-generation sequencing (NGS) techniques are revolutionizing biomedical research by providing powerful methods for generating genomic and epigenomic profiles. The rapid progress is posing an acute challenge to students and researchers to stay acquainted with the numerous available methods. We have developed an interactive online educational resource called Sequencing Techniques Engine for Genomics (SequencEnG) to provide a tree-structured knowledge base of 66 different sequencing techniques and step-by-step NGS data analysis pipelines comparing popular tools. SequencEnG is designed to facilitate barrier-free learning of current NGS techniques and provides a user-friendly interface for searching through experimental and analysis methods.'),
(82127, 2019, 'bio', 'TOBMI: trans-omics block missing data imputation using a k-nearest neighbor weighted approach', 'Dong, Xuesi|Lin, Lijuan|Zhang, Ruyang|Zhao, Yang|Christiani, David C|Wei, Yongyue|Chen, Feng', 'Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China.|Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China.|Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China.|Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China.|China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China.|Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China.|Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China.', 'China|China|China|China|China|China|China', 3, '-', 'Journal Article', '10.1093/bioinformatics/bty796', '30202885', 'Stitching together trans-omics data is a powerful approach to assess the complex mechanisms of cancer occurrence, progression and treatment. However, the integration process suffers from the block missing phenomena when part of individuals lacks some omics data.'),
(82128, 2019, 'bio', 'Development of a protein-ligand extended connectivity (PLEC) fingerprint and its application for binding affinity predictions', 'Wxc3xb3jcikowski, Maciej|Kukiexc5x82ka, Michaxc5x82|Stepniewska-Dziubinska, Marta M|Siedlecki, Pawel', 'Institute of Biochemistry and Biophysics PAS, Pawinskiego 5a, Warsaw, Poland.|Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Banacha 2, Warsaw, Poland.|Institute of Biochemistry and Biophysics PAS, Pawinskiego 5a, Warsaw, Poland.|Institute of Biochemistry and Biophysics PAS, Pawinskiego 5a, Warsaw, Poland.', 'Poland|Poland|Poland|Poland', 9, '-', 'Journal Article', '10.1093/bioinformatics/bty757', '30202917', 'Fingerprints (FPs) are the most common small molecule representation in cheminformatics. There are a wide variety of FPs, and the Extended Connectivity Fingerprint (ECFP) is one of the best-suited for general applications. Despite the overall FP abundance, only a few FPs represent the 3D structure of the molecule, and hardly any encode protein-ligand interactions.'),
(82129, 2019, 'bio', 'Filtering and inference for stochastic oscillators with distributed delays', 'Calderazzo, Silvia|Brancaccio, Marco|Finkenstxc3xa4dt, Bxc3xa4rbel', 'Department of Statistics, University of Warwick, Coventry, UK.|Division of Neurobiology, Medical Research Council Laboratory of Molecular Biology, Cambridge, UK.|Department of Statistics, University of Warwick, Coventry, UK.', 'United Kingdom|United Kingdom|United Kingdom', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty782', '30202930', 'The time evolution of molecular species involved in biochemical reaction networks often arises from complex stochastic processes involving many species and reaction events. Inference for such systems is profoundly challenged by the relative sparseness of experimental data, as measurements are often limited to a small subset of the participating species measured at discrete time points. The need for model reduction can be realistically achieved for oscillatory dynamics resulting from negative translational and transcriptional feedback loops by the introduction of probabilistic time-delays. Although this approach yields a simplified model, inference is challenging and subject to ongoing research. The linear noise approximation (LNA) has recently been proposed to address such systems in stochastic form and will be exploited here.'),
(82130, 2019, 'bio', 'SAFE-clustering: Single-cell Aggregated (from Ensemble) clustering for single-cell RNA-seq data', 'Yang, Yuchen|Huh, Ruth|Culpepper, Houston W|Lin, Yuan|Love, Michael I|Li, Yun', 'Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.|Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.|Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.|Center for Bioinformatics, School of Life Sciences, Peking University, Beijing, China.|Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.|Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.', 'United States|United States|United States|China|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty793', '30202935', 'Accurately clustering cell types from a mass of heterogeneous cells is a crucial first step for the analysis of single-cell RNA-seq (scRNA-Seq) data. Although several methods have been recently developed, they utilize different characteristics of data and yield varying results in terms of both the number of clusters and actual cluster assignments.'),
(82131, 2019, 'bio', 'CATANA: a tool for generating comprehensive annotations of alternative transcript events', 'Shiau, Cheng-Kai|Huang, Jia-Hsin|Tsai, Huai-Kuang', 'Institute of Information Science Academia Sinica, Taipei, Taiwan.|Institute of Information Science Academia Sinica, Taipei, Taiwan.|Institute of Information Science Academia Sinica, Taipei, Taiwan.', '-|-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty795', '30202999', 'In higher eukaryotes, the generation of transcript isoforms from a single gene through alternative splicing (AS) and alternative transcription (AT) mechanisms increases functional and regulatory diversities. Annotating these alternative transcript events is essential for genomic studies. However, there are no existing tools that generate comprehensive annotations of all these alternative transcript events including both AS and AT events. In the present study, we develop CATANA, with the encoded exon usage patterns based on the flattened gene model, to identify ten types of AS and AT events. We demonstrate the power and versatility of CATANA by showing greater depth of annotations of alternative transcript events according to either genome annotation or RNA-seq data.'),
(82132, 2019, 'bio', 'The GCTx format and cmap{Py, R, M, J} packages: resources for optimized storage and integrated traversal of annotated dense matrices', 'Enache, Oana M|Lahr, David L|Natoli, Ted E|Litichevskiy, Lev|Wadden, David|Flynn, Corey|Gould, Joshua|Asiedu, Jacob K|Narayan, Rajiv|Subramanian, Aravind', 'The Broad Institute, Cambridge, MA, USA.|The Broad Institute, Cambridge, MA, USA.|The Broad Institute, Cambridge, MA, USA.|The Broad Institute, Cambridge, MA, USA.|Department of Computer Science and Engineering, University of Washington, Seattle, WA, USA.|MediaSilo, Boston, MA, USA.|The Broad Institute, Cambridge, MA, USA.|The Broad Institute, Cambridge, MA, USA.|The Broad Institute, Cambridge, MA, USA.|The Broad Institute, Cambridge, MA, USA.', '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/bty784', '30203022', 'Facilitated by technological improvements, pharmacologic and genetic perturbational datasets have grown in recent years to include millions of experiments. Sharing and publicly distributing these diverse data creates many opportunities for discovery, but in recent years the unprecedented size of data generated and its complex associated metadata have also created data storage and integration challenges.'),
(82133, 2019, 'bio', 'Dante: genotyping of known complex and expanded short tandem repeats', 'Budixc5xa1, Jaroslav|Kucharxc3xadk, Marcel|xc4x8eurixc5xa1, Frantixc5xa1ek|Gazdarica, Juraj|Zrubcovxc3xa1, Michaela|Ficek, Andrej|Szemes, Tomxc3xa1xc5xa1|Brejovxc3xa1, Broxc5x88a|Radvanszky, Jan', 'Department of Computer Science, Faculty of Mathematics, Physics and Informatics, Comenius University in Bratislava, Bratislava, Slovakia.|Geneton Ltd., Bratislava, Slovakia.|Geneton Ltd., Bratislava, Slovakia.|Geneton Ltd., Bratislava, Slovakia.|Department of Molecular Biology, Faculty of Natural Sciences, Comenius University, Bratislava, Slovakia.|Department of Molecular Biology, Faculty of Natural Sciences, Comenius University, Bratislava, Slovakia.|Geneton Ltd., Bratislava, Slovakia.|Department of Computer Science, Faculty of Mathematics, Physics and Informatics, Comenius University in Bratislava, Bratislava, Slovakia.|Geneton Ltd., Bratislava, Slovakia.', 'Slovakia|Slovakia|Slovakia|Slovakia|Slovakia|Slovakia|Slovakia|Slovakia|Slovakia', 4, '-', 'Journal Article', '10.1093/bioinformatics/bty791', '30203023', 'Short tandem repeats (STRs) are stretches of repetitive DNA in which short sequences, typically made of 2-6 nucleotides, are repeated several times. Since STRs have many important biological roles and also belong to the most polymorphic parts of the human genome, they became utilized in several molecular-genetic applications. Precise genotyping of STR alleles, therefore, was of high relevance during the last decades. Despite this, massively parallel sequencing (MPS) still lacks the analysis methods to fully utilize the information value of STRs in genome scale assays.'),
(82134, 2018, 'bio', 'In vitro versus in vivo compositional landscapes of histone sequence preferences in eucaryotic genomes', 'Giancarlo, Raffaele|Rombo, Simona E|Utro, Filippo', 'Dipartimento di Matematica ed Informatica, Università degli Studi di Palermo, Palermo, Italy.|Dipartimento di Matematica ed Informatica, Università degli Studi di Palermo, Palermo, Italy.|Computational Biology Center, IBM T. J. Watson Research, Yorktown Heights, NY, USA.', 'Italy|Italy|United States', 4, '-', 'Journal Article', '10.1093/bioinformatics/bty799', '30204840', 'Although the nucleosome occupancy along a genome can be in part predicted by in vitro experiments, it has been recently observed that the chromatin organization presents important differences in vitro with respect to in vivo. Such differences mainly regard the hierarchical and regular structures of the nucleosome fiber, whose existence has long been assumed, and in part also observed in vitro, but that does not apparently occur in vivo. It is also well known that the DNA sequence has a role in determining the nucleosome occupancy. Therefore, an important issue is to understand if, and to what extent, the structural differences in the chromatin organization between in vitro and in vivo have a counterpart in terms of the underlying genomic sequences.'),
(82135, 2019, 'bio', 'KeggExp: a web server for visual integration of KEGG pathways and expression profile data', 'Liu, Xian|Han, Mingfei|Zhao, Chen|Chang, Cheng|Zhu, Yunping|Ge, Changhui|Yin, Ronghua|Zhan, Yiqun|Li, Changyan|Yu, Miao|He, Fuchu|Yang, Xiaoming', 'State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, P.R. China.|State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, P.R. China.|State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, P.R. China.|State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, P.R. China.|State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, P.R. China.|State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, P.R. China.|State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, P.R. China.|State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, P.R. China.|State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, P.R. China.|State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, P.R. China.|State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, P.R. China.|State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, P.R. China.', 'China|China|China|China|China|China|China|China|China|China|China|China', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty798', '30204847', 'Effective visualization is important for knowledge discovery when analysing expression profile data. However, existing tools for visually integrating expression profile data with KEGG pathway maps lack extensive interactive visualization operations. KeggExp simultaneously presents the pathway map of one pathway, dendrogram and heatmap of the genes in the pathway and scatter map of one gene, and also provides interactive operations for highlighting specific genes on the pathway map, including differentially-expressed genes, co-expressed genes selected from the heatmap and user-input genes. With KeggExp, researchers, including those without programming skills, can take advantage of its interactive operations to determine key genes or pathways when analysing expression profile data.'),
(82136, 2019, 'bio', 'emeraLD: rapid linkage disequilibrium estimation with massive datasets', 'Quick, Corbin|Fuchsberger, Christian|Taliun, Daniel|Abecasis, Gonçalo|Boehnke, Michael|Kang, Hyun Min', 'Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.|Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.|Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.|Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.|Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.|Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.', 'United States|United States|United States|United States|United States|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty547', '30204848', 'Estimating linkage disequilibrium (LD) is essential for a wide range of summary statistics-based association methods for genome-wide association studies. Large genetic datasets, e.g. the TOPMed WGS project and UK Biobank, enable more accurate and comprehensive LD estimates, but increase the computational burden of LD estimation. Here, we describe emeraLD (Efficient Methods for Estimation and Random Access of LD), a computational tool that leverages sparsity and haplotype structure to estimate LD up to 2 orders of magnitude faster than current tools.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(82137, 2019, 'bio', 'bcGST-an interactive bias-correction method to identify over-represented gene-sets in boutique arrays', 'Wang, Kevin Y X|Menzies, Alexander M|Silva, Ines P|Wilmott, James S|Yan, Yibing|Wongchenko, Matthew|Kefford, Richard F|Scolyer, Richard A|Long, Georgina V|Tarr, Garth|Mueller, Samuel|Yang, Jean Y H', 'School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, Australia.|Melanoma Institute of Australia, Wollstonecraft, NSW, Australia.|Melanoma Institute of Australia, Wollstonecraft, NSW, Australia.|Melanoma Institute of Australia, Wollstonecraft, NSW, Australia.|Genentech Inc, South San Francisco, CA, USA.|Genentech Inc, South San Francisco, CA, USA.|Melanoma Institute of Australia, Wollstonecraft, NSW, Australia.|Melanoma Institute of Australia, Wollstonecraft, NSW, Australia.|Melanoma Institute of Australia, Wollstonecraft, NSW, Australia.|School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, Australia.|School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, Australia.|School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, Australia.', 'Australia|Australia|Australia|Australia|United States|United States|Australia|Australia|Australia|Australia|Australia|Austra', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty783', '30215668', 'Gene annotation and pathway databases such as Gene Ontology and Kyoto Encyclopaedia of Genes and Genomes are important tools in Gene-Set Test (GST) that describe gene biological functions and associated pathways. GST aims to establish an association relationship between a gene-set of interest and an annotation. Importantly, GST tests for over-representation of genes in an annotation term. One implicit assumption of GST is that the gene expression platform captures the complete or a very large proportion of the genome. However, this assumption is neither satisfied for the increasingly popular boutique array nor the custom designed gene expression profiling platform. Specifically, conventional GST is no longer appropriate due to the gene-set selection bias induced during the construction of these platforms.'),
(82138, 2019, 'bio', 'GenomeFlow: a comprehensive graphical tool for modeling and analyzing 3D genome structure', 'Trieu, Tuan|Oluwadare, Oluwatosin|Wopata, Julia|Cheng, Jianlin', 'Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA.|Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA.|Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA.|Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA.', 'United States|United States|United States|United States', 4, '-', 'Journal Article', '10.1093/bioinformatics/bty802', '30215673', 'Three-dimensional (3D) genome organization plays important functional roles in cells. User-friendly tools for reconstructing 3D genome models from chromosomal conformation capturing data and analyzing them are needed for the study of 3D genome organization.'),
(82139, 2019, 'bio', 'A Bayesian framework for high-throughput T cell receptor pairing', 'Holec, Patrick V|Berleant, Joseph|Bathe, Mark|Birnbaum, Michael E', 'Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.|Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.|Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.|Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.', 'United States|United States|United States|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty801', '30215679', 'The study of T cell receptor (TCR) repertoires has generated new insights into immune system recognition. However, the ability to robustly characterize these populations has been limited by technical barriers and an inability to reliably infer heterodimeric chain pairings for TCRs.'),
(82140, 2019, 'bio', 'TCellXTalk facilitates the detection of co-modified peptides for the study of protein post-translational modification cross-talk in T cells', 'Casanovas, Albert|Gallardo, Óscar|Carrascal, Montserrat|Abian, Joaquin', 'Proteomics Laboratory CSIC/UAB, Institute of Biomedical Research of Barcelona, Spanish National Research Council (IIBB-CSIC/IDIBAPS), Barcelona, Spain.|Proteomics Laboratory CSIC/UAB, Institute of Biomedical Research of Barcelona, Spanish National Research Council (IIBB-CSIC/IDIBAPS), Barcelona, Spain.|Proteomics Laboratory CSIC/UAB, Institute of Biomedical Research of Barcelona, Spanish National Research Council (IIBB-CSIC/IDIBAPS), Barcelona, Spain.|Proteomics Laboratory CSIC/UAB, Institute of Biomedical Research of Barcelona, Spanish National Research Council (IIBB-CSIC/IDIBAPS), Barcelona, Spain.', 'Spain|Spain|Spain|Spain', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty805', '30219844', 'Protein function is regulated by post-translational modifications (PTMs) that may act individually or interact with others in a phenomenon termed PTM cross-talk. Multiple databases have been dedicated to PTMs, including recent initiatives oriented towards the in silico prediction of PTM interactions. The study of PTM cross-talk ultimately requires experimental evidence about whether certain PTMs coexist in a single protein molecule. However, available resources do not assist researchers in the experimental detection of co-modified peptides.'),
(82141, 2019, 'bio', 'BhGLM: Bayesian hierarchical GLMs and survival models, with applications to genomics and epidemiology', 'Yi, Nengjun|Tang, Zaixiang|Zhang, Xinyan|Guo, Boyi', 'Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA.|Department of Biostatistics, School of Public Health, Medical College of Soochow University, Suzhou, China.|Department of Biostatistics, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA, USA.|Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA.', 'United States|China|Georgia|United States', 5, '-', 'Journal Article', '10.1093/bioinformatics/bty803', '30219850', 'BhGLM is a freely available R package that implements Bayesian hierarchical modeling for high-dimensional clinical and genomic data. It consists of functions for setting up various Bayesian hierarchical models, including generalized linear models (GLMs) and Cox survival models, with four types of prior distributions for coefficients, i.e. double-exponential, Student-t, mixture double-exponential and mixture Student-t. These functions adapt fast and stable algorithms to estimate parameters. BhGLM also provides functions for summarizing results numerically and graphically and for evaluating predictive values. The package is particularly useful for analyzing large-scale molecular data, i.e. detecting disease-associated variables and predicting disease outcomes. We here describe the models, algorithms and associated features implemented in BhGLM.'),
(82142, 2019, 'bio', 'Combining count- and length-based z-scores leads to improved predictions in non-invasive prenatal testing', 'Budis, Jaroslav|Gazdarica, Juraj|Radvanszky, Jan|Szucs, Gabor|Kucharik, Marcel|Strieskova, Lucia|Gazdaricova, Iveta|Harsanyova, Maria|Duris, Frantisek|Minarik, Gabriel|Sekelska, Martina|Nagy, Balint|Turna, Jan|Szemes, Tomas', 'Department of Computer Science, Faculty of Mathematics, Physics and Informatics, Comenius University, Bratislava, Slovakia.|Bratislava, Geneton s.r.o, Bratislava, Slovakia.|Bratislava, Geneton s.r.o, Bratislava, Slovakia.|Department of Applied Mathematics and Statistics, Faculty of Mathematics, Physics and Informatics, Comenius University, Bratislava, Slovakia.|Medirex a.s, Bratislava, Slovakia.|Bratislava, Geneton s.r.o, Bratislava, Slovakia.|Department of Molecular Biology, Faculty of Natural Sciences, Comenius University, Bratislava, Slovakia.|Bratislava, Geneton s.r.o, Bratislava, Slovakia.|Bratislava, Geneton s.r.o, Bratislava, Slovakia.|Medirex a.s, Bratislava, Slovakia.|Medirex a.s, Bratislava, Slovakia.|Department of Human Genetics, University of Debrecen, Debrecen, Hungary.|Slovak Centre of Scientific and Technical Information, Bratislava, Slovakia.|Bratislava, Geneton s.r.o, Bratislava, Slovakia.', 'Slovakia|Slovakia|Slovakia|Slovakia|Slovakia|Slovakia|Slovakia|Slovakia|Slovakia|Slovakia|Slovakia|H', 3, '-', 'Journal Article', '10.1093/bioinformatics/bty806', '30219853', 'Non-invasive prenatal testing or NIPT is currently among the top researched topic in obstetric care. While the performance of the current state-of-the-art NIPT solutions achieve high sensitivity and specificity, they still struggle with a considerable number of samples that cannot be concluded with certainty. Such uninformative results are often subject to repeated blood sampling and re-analysis, usually after two weeks, and this period may cause a stress to the future mothers as well as increase the overall cost of the test.'),
(82143, 2019, 'bio', 'RNApuzzler: efficient outerplanar drawing of RNA-secondary structures', 'Wiegreffe, Daniel|Alexander, Daniel|Stadler, Peter F|Zeckzer, Dirk', 'Image and Signal Processing Group, Department of Computer Science, Leipzig University, Leipzig, Germany.|Image and Signal Processing Group, Department of Computer Science, Leipzig University, Leipzig, Germany.|Bioinformatics Group, Department of Computer Science, Leipzig University, Leipzig, Germany.|Image and Signal Processing Group, Department of Computer Science, Leipzig University, Leipzig, Germany.', 'Germany|Germany|Germany|Germany', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty817', '30239566', 'RNA secondary structure is a useful representation for studying the function of RNA, which captures most of the free energy of RNA folding. Using empirically determined energy parameters, secondary structures of nucleic acids can be efficiently computed by recursive algorithms. Several software packages supporting this task are readily available. As RNA secondary structures are outerplanar graphs, they can be drawn without intersection in the plane. Interpretation by the practitioner is eased when these drawings conform to a series of additional constraints beyond outerplanarity. These constraints are the reason why RNA drawing is difficult. Many RNA drawing algorithms therefore do not always produce intersection-free (outerplanar) drawings.'),
(82144, 2019, 'bio', 'The Encyclopedia of Proteome Dynamics: the KinoViewer', 'Brenes, Alejandro|Lamond, Angus I', 'Centre for Gene Regulation and Expression, School of Life Sciences, University of Dundee, Dundee, UK.|Centre for Gene Regulation and Expression, School of Life Sciences, University of Dundee, Dundee, UK.', 'United Kingdom|United Kingdom', 3, '-', 'Journal Article', '10.1093/bioinformatics/bty823', '30239567', 'The Encyclopedia of Proteome Dynamics (EPD) KinoViewer is an interactive data visualization tool designed for analysis and exploration of both protein and transcript data, showing expression of kinase genes in either human or mouse cells and tissues. The KinoViewer provides a comprehensive, updated graphical display of all human/mouse kinases and an open access analysis tool for the community with a user-friendly graphical interface.'),
(82145, 2019, 'bio', 'A clustering linear combination approach to jointly analyze multiple phenotypes for GWAS', 'Sha, Qiuying|Wang, Zhenchuan|Zhang, Xiao|Zhang, Shuanglin', 'Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, USA.|Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, USA.|Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, USA.|Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, USA.', 'United States|United States|United States|United States', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty810', '30239574', 'There is an increasing interest in joint analysis of multiple phenotypes for genome-wide association studies (GWASs) based on the following reasons. First, cohorts usually collect multiple phenotypes and complex diseases are usually measured by multiple correlated intermediate phenotypes. Second, jointly analyzing multiple phenotypes may increase statistical power for detecting genetic variants associated with complex diseases. Third, there is increasing evidence showing that pleiotropy is a widespread phenomenon in complex diseases. In this paper, we develop a clustering linear combination (CLC) method to jointly analyze multiple phenotypes for GWASs. In the CLC method, we first cluster individual statistics into positively correlated clusters and then, combine the individual statistics linearly within each cluster and combine the between-cluster terms in a quadratic form. CLC is not only robust to different signs of the means of individual statistics, but also reduce the degrees of freedom of the test statistic. We also theoretically prove that if we can cluster the individual statistics correctly, CLC is the most powerful test among all tests with certain quadratic forms. Our simulation results show that CLC is either the most powerful test or has similar power to the most powerful test among the tests we compared, and CLC is much more powerful than other tests when effect sizes align with inferred clusters. We also evaluate the performance of CLC through a real case study.'),
(82146, 2019, 'bio', 'Integrating hypertension phenotype and genotype with hybrid non-negative matrix factorization', 'Luo, Yuan|Mao, Chengsheng|Yang, Yiben|Wang, Fei|Ahmad, Faraz S|Arnett, Donna|Irvin, Marguerite R|Shah, Sanjiv J', 'Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.|Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.|Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.|Department of Healthcare Policy & Research, Weill Cornell Medicine, Cornell University New York, NY, USA.|Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.|Department of Epidemiology, College of Public Health, University of Kentucky, Lexington, KY, USA.|Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA.|Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty804', '30239588', 'Hypertension is a heterogeneous syndrome in need of improved subtyping using phenotypic and genetic measurements with the goal of identifying subtypes of patients who share similar pathophysiologic mechanisms and may respond more uniformly to targeted treatments. Existing machine learning approaches often face challenges in integrating phenotype and genotype information and presenting to clinicians an interpretable model. We aim to provide informed patient stratification based on phenotype and genotype features.'),
(82147, 2019, 'bio', 'Gimpute: an efficient genetic data imputation pipeline', 'Chen, Junfang|Lippold, Dietmar|Frank, Josef|Rayner, William|Meyer-Lindenberg, Andreas|Schwarz, Emanuel', 'Department of Psychiatry and Psychotherapy, Heidelberg University, Mannheim, Germany.|Department of Psychiatry and Psychotherapy, Heidelberg University, Mannheim, Germany.|Department of Genetic Epidemiology in Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.|Radcliffe Department of Medicine, Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Headington, Oxford, UK.|Department of Psychiatry and Psychotherapy, Heidelberg University, Mannheim, Germany.|Department of Psychiatry and Psychotherapy, Heidelberg University, Mannheim, Germany.', 'Germany|Germany|Germany|United Kingdom|Germany|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty814', '30239591', 'Genotype imputation is essential for genome-wide association studies (GWAS) to retrieve information of untyped variants and facilitate comparability across studies. However, there is a lack of automated pipelines that perform all required processing steps prior to and following imputation.'),
(82148, 2019, 'bio', 'STatistical Inference Relief (STIR) feature selection', 'Le, Trang T|Urbanowicz, Ryan J|Moore, Jason H|McKinney, Brett A', 'Department of Biostatistics, Epidemiology and Informatics, Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA.|Department of Biostatistics, Epidemiology and Informatics, Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA.|Department of Biostatistics, Epidemiology and Informatics, Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA.|Department of Mathematics, University of Tulsa, Tulsa, OK, USA.', 'United States|United States|United States|United States', 9, '-', 'Journal Article', '10.1093/bioinformatics/bty788', '30239600', 'Relief is a family of machine learning algorithms that uses nearest-neighbors to select features whose association with an outcome may be due to epistasis or statistical interactions with other features in high-dimensional data. Relief-based estimators are non-parametric in the statistical sense that they do not have a parameterized model with an underlying probability distribution for the estimator, making it difficult to determine the statistical significance of Relief-based attribute estimates. Thus, a statistical inferential formalism is needed to avoid imposing arbitrary thresholds to select the most important features. We reconceptualize the Relief-based feature selection algorithm to create a new family of STatistical Inference Relief (STIR) estimators that retains the ability to identify interactions while incorporating sample variance of the nearest neighbor distances into the attribute importance estimation. This variance permits the calculation of statistical significance of features and adjustment for multiple testing of Relief-based scores. Specifically, we develop a pseudo t-test version of Relief-based algorithms for case-control data.'),
(82149, 2019, 'bio', 'Parkour LIMS: high-quality sample preparation in next generation sequencing', 'Anatskiy, E|Ryan, D P|Grüning, B A|Arrigoni, L|Manke, T|Bönisch, U', 'Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany.|Bioinformatics Facility, Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany.|Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany.|Deep Sequencing Facility, Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany.|Bioinformatics Facility, Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany.|Deep Sequencing Facility, Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty820', '30239601', 'This paper presents Parkour, a software package for sample processing and quality management of next generation sequencing data and samples.'),
(82150, 2019, 'bio', 'Powerful and efficient SNP-set association tests across multiple phenotypes using GWAS summary data', 'Guo, Bin|Wu, Baolin', 'Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA.|Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA.', 'United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty811', '30239606', 'Many GWAS conducted in the past decade have identified tens of thousands of disease related variants, which in total explained only part of the heritability for most traits. There remain many more genetics variants with small effect sizes to be discovered. This has motivated the development of sequencing studies with larger sample sizes and increased resolution of genotyped variants, e.g., the ongoing NHLBI Trans-Omics for Precision Medicine (TOPMed) whole genome sequencing project. An alternative approach is the development of novel and more powerful statistical methods. The current dominating approach in the field of GWAS analysis is the single trait single variant association test, despite the fact that most GWAS are conducted in deeply-phenotyped cohorts with many correlated traits measured. In this paper, we aim to develop rigorous methods that integrate multiple correlated traits and multiple variants to improve the power to detect novel variants. In recognition of the difficulty of accessing raw genotype and phenotype data due to privacy and logistic concerns, we develop methods that are applicable to publicly available GWAS summary data.'),
(82151, 2019, 'bio', 'CINNA: an R/CRAN package to decipher Central Informative Nodes in Network Analysis', 'Ashtiani, Minoo|Mirzaie, Mehdi|Jafari, Mohieddin', 'Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran.|Department of Applied Mathematics, Faculty of Mathematical Sciences, Tarbiat Modares University, Tehran, Iran.|Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran.', '-|-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty819', '30239607', 'Centrality analysis involves a series of ambiguities in that there are numerous well-known centrality measures with differing algorithms for establishing which nodes in a network are essential. There is no clearly preferred measure or means of deciding which measure is most germane to a given network with respect to node essentiality vis-à-vis topological features. Our aim here was to develop an instrument that enables comparisons among potentially appropriate centrality measures to be made with respect to network structure and thereby to support the identification of the most informative measure according to dimensional reduction methods.'),
(82152, 2019, 'bio', 'Exploring sequence-based features for the improved prediction of DNA N4-methylcytosine sites in multiple species', 'Wei, Leyi|Luan, Shasha|Nagai, Luis Augusto Eijy|Su, Ran|Zou, Quan', 'School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China.|School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China.|Lab of Functional Analysis In Silico, Institute of Medical Science, University of Tokyo, Tokyo, Japan.|School of Computer Software, College of Intelligence and Computing, Tianjin University, Tianjin, China.|School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China.', 'China|China|Japan|China|China', 34, '-', 'Journal Article', '10.1093/bioinformatics/bty824', '30239627', 'As one of important epigenetic modifications, DNA N4-methylcytosine (4mC) is recently shown to play crucial roles in restriction-modification systems. For better understanding of their functional mechanisms, it is fundamentally important to identify 4mC modification. Machine learning methods have recently emerged as an effective and efficient approach for the high-throughput identification of 4mC sites, although high predictive error rates are still challenging for existing methods. Therefore, it is highly desirable to develop a computational method to more accurately identify m4C sites.'),
(82153, 2019, 'bio', 'Learning and Imputation for Mass-spec Bias Reduction (LIMBR)', 'Crowell, Alexander M|Greene, Casey S|Loros, Jennifer J|Dunlap, Jay C', 'Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA.|Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.|Department of Biochemistry and Cell Biology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA.|Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty828', '30247517', 'Decreasing costs are making it feasible to perform time series proteomics and genomics experiments with more replicates and higher resolution than ever before. With more replicates and time points, proteome and genome-wide patterns of expression are more readily discernible. These larger experiments require more batches exacerbating batch effects and increasing the number of bias trends. In the case of proteomics, where methods frequently result in missing data this increasing scale is also decreasing the number of peptides observed in all samples. The sources of batch effects and missing data are incompletely understood necessitating novel techniques.'),
(82154, 2019, 'bio', 'Misunderstood parameter of NCBI BLAST impacts the correctness of bioinformatics workflows', 'Shah, Nidhi|Nute, Michael G|Warnow, Tandy|Pop, Mihai', 'Department of Computer Science, University of Maryland College Park, MD, USA.|Department of Statistics, University of Illinois Urbana-Champaign, Champaign, IL, USA.|Department of Computer Science, University of Illinois Urbana-Champaign, Champaign, IL, USA.|Department of Computer Science, University of Maryland College Park, MD, USA.', 'United States|United States|United States|United States', 15, '-', 'Journal Article', '10.1093/bioinformatics/bty833', '30247621', '-'),
(82155, 2019, 'bio', 'On the viability of unsupervised T-cell receptor sequence clustering for epitope preference', 'Meysman, Pieter|De Neuter, Nicolas|Gielis, Sofie|Bui Thi, Danh|Ogunjimi, Benson|Laukens, Kris', 'Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS).|Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS).|Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS).|Department of Computer Science and Mathematics, ADREM Data Lab.|Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS).|Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS).', '-|-|-|-|-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty821', '30247624', 'The T-cell receptor (TCR) is responsible for recognizing epitopes presented on cell surfaces. Linking TCR sequences to their ability to target specific epitopes is currently an unsolved problem, yet one of great interest. Indeed, it is currently unknown how dissimilar TCR sequences can be before they no longer bind the same epitope. This question is confounded by the fact that there are many ways to define the similarity between two TCR sequences. Here we investigate both issues in the context of TCR sequence unsupervised clustering.'),
(82156, 2019, 'bio', 'iTerm-PseKNC: a sequence-based tool for predicting bacterial transcriptional terminators', 'Feng, Chao-Qin|Zhang, Zhao-Yue|Zhu, Xiao-Juan|Lin, Yan|Chen, Wei|Tang, Hua|Lin, Hao', 'Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.|Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.|Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.|Key Laboratory for Animal Disease Resistance Nutrition of the Ministry of Education, Animal Nutrition Institute, Sichuan Agricultural University, Chengdu, China.|Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.|Department of Pathophysiology, Southwest Medical University, Luzhou, China.|Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.', 'China|China|China|China|China|China|China', 88, '-', 'Journal Article', '10.1093/bioinformatics/bty827', '30247625', 'Transcription termination is an important regulatory step of gene expression. If there is no terminator in gene, transcription could not stop, which will result in abnormal gene expression. Detecting such terminators can determine the operon structure in bacterial organisms and improve genome annotation. Thus, accurate identification of transcriptional terminators is essential and extremely important in the research of transcription regulations.'),
(82157, 2019, 'bio', 'PySFD: comprehensive molecular insights from significant feature differences detected among many simulated ensembles', 'Stolzenberg, Sebastian', 'Department of Mathematics and Computer Science, Computational Molecular Biology Group, Arnimallee 6, 14195 Berlin, Germany.', 'Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty818', '30247628', 'Many modeling analyses of molecular dynamics (MD) simulations are based on a definition of states that can be (groups of) clusters of simulation frames in a feature space composed of molecular coordinates. With increasing dimension of this feature space (due to the increasing size or complexity of a simulated molecule), it becomes very difficult to cluster the underlying MD data and estimate a statistically robust model. To mitigate this curse of dimensionality, one can reduce the feature space, e.g., with principal component or time-lagged independent component analysis transformations, focusing the analysis on the most important modes of transitions. In practice, however, all these reduction strategies may neglect important molecular details that are susceptible to experimental verification.'),
(82158, 2019, 'bio', 'Squiggle: a user-friendly two-dimensional DNA sequence visualization tool', 'Lee, Benjamin D', 'Department of Computer Science, School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.', 'United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty807', '30247632', 'Squiggle is a software tool that automatically generates interactive web-based two-dimensional graphical representations of raw DNA sequences. Built with ease of use in mind, Squiggle implements several prior sequence visualization algorithms and introduces novel visualization methods designed to maximize human usability.'),
(82159, 2019, 'bio', 'PRESM: personalized reference editor for somatic mutation discovery in cancer genomics', 'Cao, Chen|Mak, Lauren|Jin, Guangxu|Gordon, Paul|Ye, Kai|Long, Quan', 'Departments of Biochemistry & Molecular Biology and Medical Genetics, Alberta Childrens Hospital Research Institute, University of Calgary, Calgary, Canada.|Departments of Biochemistry & Molecular Biology and Medical Genetics, Alberta Childrens Hospital Research Institute, University of Calgary, Calgary, Canada.|Department of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, NC, USA.|Departments of Biochemistry & Molecular Biology and Medical Genetics, Alberta Childrens Hospital Research Institute, University of Calgary, Calgary, Canada.|Department of Bioinformatics, Electronic and Information Engineering School, Xian Jiaotong University, Xian, China.|Departments of Biochemistry & Molecular Biology and Medical Genetics, Alberta Childrens Hospital Research Institute, University of Calgary, Calgary, Canada.', 'Canada|Canada|United States|Canada|China|Canada', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty812', '30247633', 'Accurate detection of somatic mutations is a crucial step toward understanding cancer. Various tools have been developed to detect somatic mutations from cancer genome sequencing data by mapping reads to a universal reference genome and inferring likelihoods from complex statistical models. However, read mapping is frequently obstructed by mismatches between germline and somatic mutations on a read and the reference genome. Previous attempts to develop personalized genome tools are not compatible with downstream statistical models for somatic mutation detection.'),
(82160, 2019, 'bio', 'A framework and an algorithm to detect low-abundance DNA by a handy sequencer and a palm-sized computer', 'Masutani, Bansho|Morishita, Shinichi', 'Departments of Biochemistry & Molecular Biology and Medical Genetics, Alberta Childrens Hospital Research Institute, University of Calgary, Calgary, Canada.|Departments of Biochemistry & Molecular Biology and Medical Genetics, Alberta Childrens Hospital Research Institute, University of Calgary, Calgary, Canada.', 'Canada|Canada', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty771', '30252019', '-'),
(82161, 2019, 'bio', 'MSPminer: abundance-based reconstitution of microbial pan-genomes from shotgun metagenomic data', 'Plaza Oñate, Florian|Le Chatelier, Emmanuelle|Almeida, Mathieu|Cervino, Alessandra C L|Gauthier, Franck|Magoulès, Frédéric|Ehrlich, S Dusko|Pichaud, Matthieu', 'Enterome, 94-96 Avenue Ledru Rollin, Paris, France.|MGP MetaGénoPolis, INRA, Université Paris-Saclay, Jouy en Josas, France.|MGP MetaGénoPolis, INRA, Université Paris-Saclay, Jouy en Josas, France.|Enterome, 94-96 Avenue Ledru Rollin, Paris, France.|MGP MetaGénoPolis, INRA, Université Paris-Saclay, Jouy en Josas, France.|CentraleSupélec, Université Paris Saclay, 9 rue Joliot Curie, Gif-sur-Yvette, France.|MGP MetaGénoPolis, INRA, Université Paris-Saclay, Jouy en Josas, France.|Enterome, 94-96 Avenue Ledru Rollin, Paris, France.', 'France|France|France|France|France|France|France|France', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty830', '30252023', 'Analysis toolkits for shotgun metagenomic data achieve strain-level characterization of complex microbial communities by capturing intra-species gene content variation. Yet, these tools are hampered by the extent of reference genomes that are far from covering all microbial variability, as many species are still not sequenced or have only few strains available. Binning co-abundant genes obtained from de novo assembly is a powerful reference-free technique to discover and reconstitute gene repertoire of microbial species. While current methods accurately identify species core parts, they miss many accessory genes or split them into small gene groups that remain unassociated to core clusters.'),
(82162, 2019, 'bio', 'SanXoT: a modular and versatile package for the quantitative analysis of high-throughput proteomics experiments', 'Trevisan-Herraz, Marco|Bagwan, Navratan|García-Marqués, Fernando|Rodriguez, Jose Manuel|Jorge, Inmaculada|Ezkurdia, Iakes|Bonzon-Kulichenko, Elena|Vázquez, Jesús', 'Vascular Pathophysiology Area, Cardiovascular Proteomics Laboratory, Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid, Spain.|Vascular Pathophysiology Area, Cardiovascular Proteomics Laboratory, Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid, Spain.|Vascular Pathophysiology Area, Cardiovascular Proteomics Laboratory, Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid, Spain.|Vascular Pathophysiology Area, Cardiovascular Proteomics Laboratory, Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid, Spain.|Vascular Pathophysiology Area, Cardiovascular Proteomics Laboratory, Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid, Spain.|Vascular Pathophysiology Area, Cardiovascular Proteomics Laboratory, Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid, Spain.|Vascular Pathophysiology Area, Cardiovascular Proteomics Laboratory, Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid, Spain.|Vascular Pathophysiology Area, Cardiovascular Proteomics Laboratory, Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty815', '30252043', 'Mass spectrometry-based proteomics has had a formidable development in recent years, increasing the amount of data handled and the complexity of the statistical resources needed. Here we present SanXoT, an open-source, standalone software package for the statistical analysis of high-throughput, quantitative proteomics experiments. SanXoT is based on our previously developed weighted spectrum, peptide and protein statistical model and has been specifically designed to be modular, scalable and user-configurable. SanXoT allows limitless workflows that adapt to most experimental setups, including quantitative protein analysis in multiple experiments, systems biology, quantification of post-translational modifications and comparison and merging of experimental data from technical or biological replicates.'),
(82163, 2019, 'bio', 'A systematic method for surveying data visualizations and a resulting genomic epidemiology visualization typology: GEViT', 'Crisan, Anamaria|Gardy, Jennifer L|Munzner, Tamara', 'Department of Computer Science, University of British Columbia, Vancouver, Canada.|School of Population and Public Health, University of British Columbia, Vancouver, Canada.|Department of Computer Science, University of British Columbia, Vancouver, Canada.', 'Canada|Canada|Canada', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty832', '30256887', 'Data visualization is an important tool for exploring and communicating findings from genomic and healthcare datasets. Yet, without a systematic way of organizing and describing the design space of data visualizations, researchers may not be aware of the breadth of possible visualization design choices or how to distinguish between good and bad options.'),
(82164, 2019, 'bio', 'De novo pattern discovery enables robust assessment of functional consequences of non-coding variants', 'Yang, Hai|Chen, Rui|Wang, Quan|Wei, Qiang|Ji, Ying|Zheng, Guangze|Zhong, Xue|Cox, Nancy J|Li, Bingshan', 'Department of Molecular Physiology & Biophysics, Nashville, TN, USA.|Department of Molecular Physiology & Biophysics, Nashville, TN, USA.|Department of Molecular Physiology & Biophysics, Nashville, TN, USA.|Department of Molecular Physiology & Biophysics, Nashville, TN, USA.|Department of Molecular Physiology & Biophysics, Nashville, TN, USA.|Department of Molecular Physiology & Biophysics, Nashville, TN, USA.|Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA.|Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA.|Department of Molecular Physiology & Biophysics, Nashville, TN, 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/bty826', '30256891', 'Given the complexity of genome regions, prioritize the functional effects of non-coding variants remains a challenge. Although several frameworks have been proposed for the evaluation of the functionality of non-coding variants, most of them used black boxes methods that simplify the task as the pathogenicity/benign classification problem, which ignores the distinct regulatory mechanisms of variants and leads to less desirable performance. In this study, we developed DVAR, an unsupervised framework that leverage various biochemical and evolutionary evidence to distinguish the gene regulatory categories of variants and assess their comprehensive functional impact simultaneously.'),
(82165, 2019, 'bio', 'RMTL: an R library for multi-task learning', 'Cao, Han|Zhou, Jiayu|Schwarz, Emanuel', 'Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.|Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA.|Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.', 'Germany|United States|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty831', '30256897', 'Multi-task learning (MTL) is a machine learning technique for simultaneous learning of multiple related classification or regression tasks. Despite its increasing popularity, MTL algorithms are currently not available in the widely used software environment R, creating a bottleneck for their application in biomedical research.'),
(82166, 2019, 'bio', 'SemGen: a tool for semantics-based annotation and composition of biosimulation models', 'Neal, Maxwell L|Thompson, Christopher T|Kim, Karam G|James, Ryan C|Cook, Daniel L|Carlson, Brian E|Gennari, John H', 'Seattle Childrens Research Institute, Center for Global Infectious Disease Research, Seattle, WA, USA.|Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI, USA.|Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA.|Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA.|Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA.|Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI, USA.|Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA.', 'United States|United States|United States|United States|United States|United States|United States', 4, '-', 'Journal Article', '10.1093/bioinformatics/bty829', '30256901', 'As the number and complexity of biosimulation models grows, so do demands for tools that can help users understand models and compose more comprehensive and accurate systems from existing models. SemGen is a tool for semantics-based annotation and composition of biosimulation models designed to address this demand. A key SemGen capability is to decompose and then integrate models across existing model exchange formats including SBML and CellML. To support this capability, we use semantic annotations to explicitly capture the underlying biological and physical meanings of the entities and processes that are modeled. SemGen leverages annotations to expose a models biological and computational architecture and to help automate model composition.'),
(82167, 2019, 'bio', 'Presenting and sharing clinical data using the eTRIKS Standards Master Tree for tranSMART', 'Barbosa-Silva, Adriano|Bratfalean, Dorina|Gu, Wei|Satagopam, Venkata|Houston, Paul|Becnel, Lauren B|Eifes, Serge|Richard, Fabien|Tielmann, Andreas|Herzinger, Sascha|Rege, Kavita|Balling, Rudi|Peeters, Paul|Schneider, Reinhard', 'University of Luxembourg, Luxembourg Centre for Systems Biomedicine, L-4371 Belvaux, Luxembourg.|CDISC, Clinical Data Interchange Standards Consortium and CDISC EU Foundation, Saint-Louis, Alsace, France.|University of Luxembourg, Luxembourg Centre for Systems Biomedicine, L-4371 Belvaux, Luxembourg.|University of Luxembourg, Luxembourg Centre for Systems Biomedicine, L-4371 Belvaux, Luxembourg.|CDISC, Clinical Data Interchange Standards Consortium and CDISC EU Foundation, Saint-Louis, Alsace, France.|CDISC, Clinical Data Interchange Standards Consortium and CDISC EU Foundation, Saint-Louis, Alsace, France.|University of Luxembourg, Luxembourg Centre for Systems Biomedicine, L-4371 Belvaux, Luxembourg.|Centre National de la Recherche Scientifique (CNRS), Lyon, Auvergne-Rhône-Alpes, France.|Department of Research and Bioinformatics, Merck KGaA, Darmstadt, Germany.|University of Luxembourg, Luxembourg Centre for Systems Biomedicine, L-4371 Belvaux, Luxembourg.|University of Luxembourg, Luxembourg Centre for Systems Biomedicine, L-4371 Belvaux, Luxembourg.|University of Luxembourg, Luxembourg Centre for Systems Biomedicine, L-4371 Belvaux, Luxembourg.|BioSci Consulting, Maasmechelen, Belgium.|University of Luxembourg, Luxembourg Centre for Systems Biomedicine, L-4371 Belvaux, Luxembourg.', 'Luxembourg|France|Luxembourg|Luxembourg|France|France|Luxembourg|France|Germany|Luxembourg|Luxembour', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty809', '30256906', 'Standardization and semantic alignment have been considered one of the major challenges for data integration in clinical research. The inclusion of the CDISC SDTM clinical data standard into the tranSMART i2b2 via a guiding master ontology tree positively impacts and supports the efficacy of data sharing, visualization and exploration across datasets.'),
(82168, 2019, 'bio', 'CIRCOAST: a statistical hypothesis test for cellular colocalization with network structures', 'Corliss, Bruce A|Ray, H Clifton|Patrie, James T|Mansour, Jennifer|Kesting, Sam|Park, Janice H|Rohde, Gustavo|Yates, Paul A|Janes, Kevin A|Peirce, Shayn M', 'University of Luxembourg, Luxembourg Centre for Systems Biomedicine, L-4371 Belvaux, Luxembourg.|University of Luxembourg, Luxembourg Centre for Systems Biomedicine, L-4371 Belvaux, Luxembourg.|University of Luxembourg, Luxembourg Centre for Systems Biomedicine, L-4371 Belvaux, Luxembourg.|University of Luxembourg, Luxembourg Centre for Systems Biomedicine, L-4371 Belvaux, Luxembourg.|University of Luxembourg, Luxembourg Centre for Systems Biomedicine, L-4371 Belvaux, Luxembourg.|University of Luxembourg, Luxembourg Centre for Systems Biomedicine, L-4371 Belvaux, Luxembourg.|University of Luxembourg, Luxembourg Centre for Systems Biomedicine, L-4371 Belvaux, Luxembourg.|University of Luxembourg, Luxembourg Centre for Systems Biomedicine, L-4371 Belvaux, Luxembourg.|University of Luxembourg, Luxembourg Centre for Systems Biomedicine, L-4371 Belvaux, Luxembourg.|University of Luxembourg, Luxembourg Centre for Systems Biomedicine, L-4371 Belvaux, Luxembourg.', 'Luxembourg|Luxembourg|Luxembourg|Luxembourg|Luxembourg|Luxembourg|Luxembourg|Luxembourg|Luxembourg|L', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty797', '30289437', '-'),
(82169, 2019, 'bio', 'Strain-GeMS: optimized subspecies identification from microbiome data based on accurate variant modeling', 'Tan, Chongyang|Cui, Wei|Cui, Xinping|Ning, Kang', 'Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China.|School of Mathematical Sciences and LPMC, Nankai University, Tianjin, China.|Department of Statistics, University of California, Riverside, CA, USA.|Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China.', 'China|China|United States|China', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty844', '30295697', 'Subspecies identification is one of the most critical issues in microbiome studies, as it is directly related to their functions in response to the environmental stress and their feedbacks. However, identification of subspecies remains a challenge largely due to the small variation between different strains within the same species. Accurate identification of subspecies primarily relies on variant identification and categorization through microbiome data. However, current SNP calling and subspecies identification for microbiome data remain underdeveloped.'),
(82170, 2019, 'bio', 'Inferring disease-associated long non-coding RNAs using genome-wide tissue expression profiles', 'Pan, Xiaoyong|Jensen, Lars Juhl|Gorodkin, Jan', 'Department of Veterinary and Animal Sciences, Center for Non-coding RNA in Technology and Health, University of Copenhagen, Frederiksberg C, Denmark.|Disease Systems Biology Program, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen N, Denmark.|Department of Veterinary and Animal Sciences, Center for Non-coding RNA in Technology and Health, University of Copenhagen, Frederiksberg C, Denmark.', 'Denmark|Denmark|Denmark', 11, '-', 'Journal Article', '10.1093/bioinformatics/bty859', '30295698', 'Long non-coding RNAs (lncRNAs) are important regulators in wide variety of biological processes, which are linked to many diseases. Compared to protein-coding genes (PCGs), the association between diseases and lncRNAs is still not well studied. Thus, inferring disease-associated lncRNAs on a genome-wide scale has become imperative.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(82171, 2019, 'bio', 'sRNAPrimerDB: comprehensive primer design and search web service for small non-coding RNAs', 'Xie, Shengsong|Zhu, Qin|Qu, Wubin|Xu, Zhong|Liu, Xiangdong|Li, Xinyun|Li, Shijun|Ma, Wubin|Miao, Yiliang|Zhang, Lisheng|Du, Xiaoyong|Dong, Wuzi|Li, Haiwei|Zhao, Changzhi|Wang, Yunlong|Fang, Yaping|Zhao, Shuhong', 'Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, P.R. China.|Agricultural Bioinformatics Key Laboratory of Hubei Province, College of Informatics, Huazhong Agricultural University, Wuhan, P.R. China.|iGeneTech Bioscience Co., Ltd, Beijing, P.R. China.|Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, P.R. China.|Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, P.R. China.|Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, P.R. China.|Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, P.R. China.|Department of Medicine, School of Medicine, Howard Hughes Medical Institute, University of California, San Diego, La Jolla, CA, USA.|Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, P.R. China.|Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, P.R. China.|Agricultural Bioinformatics Key Laboratory of Hubei Province, College of Informatics, Huazhong Agricultural University, Wuhan, P.R. China.|College of Animal Science and Technology, Northwest A & F University, Yangling, P.R. China.|iGeneTech Bioscience Co., Ltd, Beijing, P.R. China.|Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, P.R. China.|Agricultural Bioinformatics Key Laboratory of Hubei Province, College of Informatics, Huazhong Agricultural University, Wuhan, P.R. China.|Agricultural Bioinformatics Key Laboratory of Hubei Province, College of Informatics, Huazhong Agricultural University, Wuhan, P.R. China.|Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, P.R. China.', 'China|China|China|China|China|China|China|United States|China|China|China|China|China|China|China|China|China', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty852', '30295699', 'Small non-coding RNAs (ncRNAs), especially microRNAs (miRNAs) and piwi-interacting RNAs (piRNAs), play key roles in many biological processes. However, only a few tools can be used to develop the optimal primer or probe design for the expression profile of small ncRNAs. Here, we developed sRNAPrimerDB, the first automated primer designing and query web service for small ncRNAs.'),
(82172, 2019, 'bio', 'Multi-insight visualization of multi-omics data via ensemble dimension reduction and tensor factorization', 'Fanaee-T, Hadi|Thoresen, Magne', 'Department of Biostatistics, University of Oslo, Oslo, Norway.|Department of Biostatistics, University of Oslo, Oslo, Norway.', 'Norway|Norway', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty847', '30295701', 'Visualization of high-dimensional data is an important step in exploratory data analysis and knowledge discovery. However, it is challenging, because the interpretation is highly subjective. If we see dimensionality reduction (DR) techniques as the main tool for data visualization, they are like multiple cameras that look into the data from different perspectives or angles. We can hardly prescribe one single perspective for all datasets and problems. One snapshot of data cannot reveal all the relevant aspects of the data in higher dimensions. The reason is that each of these methods has its own specific strategy, normally based on well-established mathematical theories to obtain a low-dimensional projection of the data, which sometimes is totally different from the others. Therefore, relying only on one single projection can be risky, because it can close our eyes to important parts of the full knowledge space.'),
(82173, 2019, 'bio', 'MetWork: a web server for natural products anticipation', 'Beauxis, Yann|Genta-Jouve, Grégory', 'C-TAC UMR CNRS 8638 COMETE, Faculté de Pharmacie de Paris, Université Paris Descartes, Paris, France.|C-TAC UMR CNRS 8638 COMETE, Faculté de Pharmacie de Paris, Université Paris Descartes, Paris, France.', 'France|France', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty864', '30295702', 'The annotation of natural products and more generally small molecules is one of the major drawbacks in untargeted mass spectrometry analysis. Molecular networking has emerged as a structured way to organize and mine data from untargeted tandem mass spectrometry (MS/MS) experiments. Despite the great potential of this tool, the annotation is usually performed manually by the expert as only few spectral libraries are available.'),
(82174, 2019, 'bio', 'DeepHINT: understanding HIV-1 integration via deep learning with attention', 'Hu, Hailin|Xiao, An|Zhang, Sai|Li, Yangyang|Shi, Xuanling|Jiang, Tao|Zhang, Linqi|Zhang, Lei|Zeng, Jianyang', 'School of Medicine, Tsinghua University, Beijing, China.|Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China.|Department of Genetics, Stanford Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA.|Comprehensive AIDS Research Center, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, School of Life Sciences and School of Medicine, Tsinghua University, Beijing, China.|Comprehensive AIDS Research Center, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, School of Life Sciences and School of Medicine, Tsinghua University, Beijing, China.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA.|Comprehensive AIDS Research Center, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, School of Life Sciences and School of Medicine, Tsinghua University, Beijing, China.|School of Medicine, Tsinghua University, Beijing, China.|Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China.', 'China|China|United States|China|China|United States|China|China|China', 6, '-', 'Journal Article', '10.1093/bioinformatics/bty842', '30295703', 'Human immunodeficiency virus type 1 (HIV-1) genome integration is closely related to clinical latency and viral rebound. In addition to human DNA sequences that directly interact with the integration machinery, the selection of HIV integration sites has also been shown to depend on the heterogeneous genomic context around a large region, which greatly hinders the prediction and mechanistic studies of HIV integration.'),
(82175, 2019, 'bio', 'Bayesian negative binomial regression for differential expression with confounding factors', 'Dadaneh, Siamak Zamani|Zhou, Mingyuan|Qian, Xiaoning', 'Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China.|Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China.|Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China.', 'China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty808', '30295704', '-'),
(82176, 2019, 'bio', 'Accurate detection of chimeric contigs via Bionano optical maps', 'Pan, Weihua|Lonardi, Stefano', 'Department of Computer Science and Engineering, University of California, Riverside, CA, USA.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA.', 'United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty850', '30295726', 'A chimeric contig is contig that has been incorrectly assembled, i.e. a contig that contains one or more mis-joins. The detection of chimeric contigs can be carried out either by aligning assembled contigs to genome-wide maps (e.g. genetic, physical or optical maps) or by mapping sequenced reads to the assembled contigs. Here, we introduce a software tool called Chimericognizer that takes advantage of one or more Bionano Genomics optical maps to accurately detect and correct chimeric contigs. Experimental results show that Chimericognizer is very accurate, and significantly better than the chimeric detection method offered by the Bionano Hybrid Scaffold pipeline. Chimericognizer can also detect and correct chimeric optical molecules.'),
(82177, 2019, 'bio', 'DIGREM: an integrated web-based platform for detecting effective multi-drug combinations', 'Zhang, Minzhe|Lee, Sangin|Yao, Bo|Xiao, Guanghua|Xu, Lin|Xie, Yang', 'Department of Clinical Science, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.|Department of Information and Statistics, Chungnam National University, Daejeon, Korea.|Department of Clinical Science, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.|Department of Clinical Science, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.|Department of Clinical Science, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.|Department of Clinical Science, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.', 'United States|United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty860', '30295728', 'Synergistic drug combinations are a promising approach to achieve a desirable therapeutic effect in complex diseases through the multi-target mechanism. However, in vivo screening of all possible multi-drug combinations remains cost-prohibitive. An effective and robust computational model to predict drug synergy in silico will greatly facilitate this process.'),
(82178, 2019, 'bio', 'VIRULIGN: fast codon-correct alignment and annotation of viral genomes', 'Libin, Pieter J K|Deforche, Koen|Abecasis, Ana B|Theys, Kristof', 'KU Leuven, Rega Institute for Medical, Laboratorium of Clinical and Evolutionary Virology, Leuven, Belgium.|Emweb, Herent, Belgium.|Center for Global Health and Tropical Medicine, Institute for Hygiene and Tropical Medicine, Lisboa, Portugal.|KU Leuven, Rega Institute for Medical, Laboratorium of Clinical and Evolutionary Virology, Leuven, Belgium.', 'Belgium|Belgium|Portugal|Belgium', 7, '-', 'Journal Article', '10.1093/bioinformatics/bty851', '30295730', 'Virus sequence data are an essential resource for reconstructing spatiotemporal dynamics of viral spread as well as to inform treatment and prevention strategies. However, the potential benefit of these applications critically depends on accurate and correctly annotated alignments of genetically heterogeneous data. VIRULIGN was built for fast codon-correct alignments of large datasets, with standardized and formalized genome annotation and various alignment export formats.'),
(82179, 2019, 'bio', 'CoMM: a collaborative mixed model to dissecting genetic contributions to complex traits by leveraging regulatory information', 'Yang, Can|Wan, Xiang|Lin, Xinyi|Chen, Mengjie|Zhou, Xiang|Liu, Jin', 'Department of Mathematics, Hong Kong University of Science and Technology, Hong Kong, China.|Shenzhen Research Institute of Big Data, Shenzhen, China.|Centre for Quantitative Medicine, Program in Health Services and Systems Research, Duke-NUS Medical School, Singapore.|Department of Medicine, University of Chicago, Chicago, IL, USA.|Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.|Centre for Quantitative Medicine, Program in Health Services and Systems Research, Duke-NUS Medical School, Singapore.', 'China|China|Singapore|United States|United States|Singapore', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty865', '30295737', 'Genome-wide association studies (GWASs) have been successful in identifying many genetic variants associated with complex traits. However, the mechanistic links between these variants and complex traits remain elusive. A scientific hypothesis is that genetic variants influence complex traits at the organismal level via affecting cellular traits, such as regulating gene expression and altering protein abundance. Although earlier works have already presented some scientific insights about this hypothesis and their findings are very promising, statistical methods that effectively harness multilayered data (e.g. genetic variants, cellular traits and organismal traits) on a large scale for functional and mechanistic exploration are highly demanding.'),
(82180, 2019, 'bio', 'QuagmiR: a cloud-based application for isomiR big data analytics', 'Bofill-De Ros, Xavier|Chen, Kevin|Chen, Susanna|Tesic, Nikola|Randjelovic, Dusan|Skundric, Nikola|Nesic, Svetozar|Varjacic, Vojislav|Williams, Elizabeth H|Malhotra, Raunaq|Jiang, Minjie|Gu, Shuo', 'RNA Mediated Gene Regulation Section, RNA Biology Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD, USA.|RNA Mediated Gene Regulation Section, RNA Biology Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD, USA.|RNA Mediated Gene Regulation Section, RNA Biology Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD, USA.|Seven Bridges Genomics Inc., Cambridge, MA, USA.|Seven Bridges Genomics Inc., Cambridge, MA, USA.|Seven Bridges Genomics Inc., Cambridge, MA, USA.|Seven Bridges Genomics Inc., Cambridge, MA, USA.|Seven Bridges Genomics Inc., Cambridge, MA, USA.|Seven Bridges Genomics Inc., Cambridge, MA, USA.|Seven Bridges Genomics Inc., Cambridge, MA, USA.|RNA Mediated Gene Regulation Section, RNA Biology Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD, USA.|RNA Mediated Gene Regulation Section, RNA Biology Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, 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', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty843', '30295744', 'MicroRNAs (miRNAs) function as master regulators of gene expression. Recent studies demonstrate that miRNA isoforms (isomiRs) play a unique role in cancer development. Here, we present QuagmiR, the first cloud-based tool to analyze isomiRs from next generation sequencing data. Using a novel and flexible searching algorithm designed for the detection and annotation of heterogeneous isomiRs, it permits extensive customization of the query process and reference databases to meet the userxe2x80x89s diverse research needs.'),
(82181, 2019, 'bio', 'cath-resolve-hits: a new tool that resolves domain matches suspiciously quickly', 'Lewis, T E|Sillitoe, I|Lees, J G', 'Department of Structural and Molecular Biology, UCL, Darwin Building, London, UK.|Department of Structural and Molecular Biology, UCL, Darwin Building, London, UK.|Department of Biological and Medical Sciences, Faculty of Health and Life Sciences, Oxford Brookes University, Oxford, Oxfordshire, UK.', 'United Kingdom|United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty863', '30295745', 'Many bioinformatics areas require us to assign domain matches onto stretches of a query protein. Starting with a set of candidate matches, we want to identify the optimal subset that has limited/no overlap between matches. This may be further complicated by discontinuous domains in the input data. Existing tools are increasingly facing very large data-sets for which they require prohibitive amounts of CPU-time and memory.'),
(82182, 2019, 'bio', 'A new method bridging graph theory and residue co-evolutionary networks for specificity determinant positions detection', 'da Fonseca, Néli José|Afonso, Marcelo Querino Lima|de Oliveira, Lucas Carrijo|Bleicher, Lucas', 'Departmento de Bioquímica e Imunologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Pampulha, Belo Horizonte - MG, Brazil.|Departmento de Bioquímica e Imunologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Pampulha, Belo Horizonte - MG, Brazil.|Departmento de Bioquímica e Imunologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Pampulha, Belo Horizonte - MG, Brazil.|Departmento de Bioquímica e Imunologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Pampulha, Belo Horizonte - MG, Brazil.', 'Brazil|Brazil|Brazil|Brazil', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty846', '30295749', 'Computational studies of molecular evolution are usually performed from a multiple alignment of homologous sequences, on which sequences resulting from a common ancestor are aligned so that equivalent residues are placed in the same position. Residues frequency patterns of a full alignment or from a subset of its sequences can be highly useful for suggesting positions under selection. Most methods mapping co-evolving or specificity determinant sites are focused on positions, however, they do not consider the case for residues that are specificity determinants in one subclass, but variable in others. In addition, many methods are impractical for very large alignments, such as those obtained from Pfam, or require a priori information of the subclasses to be analyzed.'),
(82183, 2019, 'bio', 'TIVAN: tissue-specific cis-eQTL single nucleotide variant annotation and prediction', 'Chen, Li|Wang, Ye|Yao, Bing|Mitra, Amit|Wang, Xu|Qin, Xiao', 'Department of Health Outcomes Research and Policy, Harrison School of Pharmacy, Auburn University, Auburn, AL, USA.|Department of Computer Science and Software Engineering, Samuel Ginn College of Engineering, Auburn University, Auburn, AL, USA.|Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA.|Department of Drug Discovery and Development, Harrison School of Pharmacy, Auburn University, Auburn, AL, USA.|Department of Pathobiology, College of Veterinary Medicine, Auburn University, Auburn, AL, USA.|Department of Computer Science and Software Engineering, Samuel Ginn College of Engineering, Auburn University, Auburn, AL, USA.', 'United States|United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty872', '30304335', 'Predicting genetic regulatory variants, most of which locate in non-coding genomic regions, still remain a challenge in genetic research. Among all non-coding regulatory variants, cis-eQTL single nucleotide variants (SNVs) are of particular interest for their crucial role in regulating gene expression. Since different gene expression patterns are believed to contribute to the etiologies of different phenotypes, it is desirable to characterize the impact of cis-eQTL SNVs in a context-specific manner. Though computational methods for predicting the potential of variants being pathogenic or deleterious are well-established, methods for annotating and predicting cis-eQTL SNVs are under-developed. Here, we present TIVAN (TIssue-specific Variant ANnotation and prediction), an ensemble method of decision trees, to predict tissue-specific cis-eQTL SNVs. TIVAN is trained based on a comprehensive collection of features, including genome-wide genomic and epigenomic profiling data. As a result, TIVAN has been shown to accurately discriminate cis-eQTL SNVs from non-eQTL SNVs and perform favorably to other methods by obtaining higher five-fold cross-validation AUC values (CV-AUC) and Leave-One-Chromosome-Out predicted AUC values (LOCO-AUC) across 44 different tissues belonging to 27 different tissue classes. Finally, TIVAN consistently maintains top performance on an independent testing dataset, which includes 7 tissues in 11 studies.'),
(82184, 2019, 'bio', 'LION LBD: a literature-based discovery system for cancer biology', 'Pyysalo, Sampo|Baker, Simon|Ali, Imran|Haselwimmer, Stefan|Shah, Tejas|Young, Andrew|Guo, Yufan|Högberg, Johan|Stenius, Ulla|Narita, Masashi|Korhonen, Anna', 'Language Technology Lab, Department of Theoretical and Applied Linguistics, University of Cambridge, Cambridge, UK.|Language Technology Lab, Department of Theoretical and Applied Linguistics, University of Cambridge, Cambridge, UK.|Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.|Language Technology Lab, Department of Theoretical and Applied Linguistics, University of Cambridge, Cambridge, UK.|Language Technology Lab, Department of Theoretical and Applied Linguistics, University of Cambridge, Cambridge, UK.|Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK.|Language Technology Lab, Department of Theoretical and Applied Linguistics, University of Cambridge, Cambridge, UK.|Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.|Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.|Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK.|Language Technology Lab, Department of Theoretical and Applied Linguistics, University of Cambridge, Cambridge, UK.', 'United Kingdom|United Kingdom|Sweden|United Kingdom|United Kingdom|United Kingdom|United Kingdom|Sweden|Sweden|United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty845', '30304355', 'The overwhelming size and rapid growth of the biomedical literature make it impossible for scientists to read all studies related to their work, potentially leading to missed connections and wasted time and resources. Literature-based discovery (LBD) aims to alleviate these issues by identifying implicit links between disjoint parts of the literature. While LBD has been studied in depth since its introduction three decades ago, there has been limited work making use of recent advances in biomedical text processing methods in LBD.'),
(82185, 2019, 'bio', 'MetaOmics: analysis pipeline and browser-based software suite for transcriptomic meta-analysis', 'Ma, Tianzhou|Huo, Zhiguang|Kuo, Anche|Zhu, Li|Fang, Zhou|Zeng, Xiangrui|Lin, Chien-Wei|Liu, Silvia|Wang, Lin|Liu, Peng|Rahman, Tanbin|Chang, Lun-Ching|Kim, Sunghwan|Li, Jia|Park, Yongseok|Song, Chi|Oesterreich, Steffi|Sibille, Etienne|Tseng, George C', 'Department of Epidemiology and Biostatistics, University of Maryland, College Park, MD, USA.|Department of Biostatistics, University of Florida, Gainesville, FL, USA.|Department of Biostatistics, Graduate School of Public Health University of Pittsburgh, Pittsburgh, PA, USA.|Department of Biostatistics, Graduate School of Public Health University of Pittsburgh, Pittsburgh, PA, USA.|Department of Biostatistics, Graduate School of Public Health University of Pittsburgh, Pittsburgh, PA, USA.|School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.|Division of Biostatistics, Medical College of Wisconsin, Milwaukee, WI, USA.|Department of Pathology, University of Pittsburgh, Pittsburgh, PA, USA.|School of Statistics, Capital University of Economics and Business, China.|Department of Biostatistics, Graduate School of Public Health University of Pittsburgh, Pittsburgh, PA, USA.|Department of Biostatistics, Graduate School of Public Health University of Pittsburgh, Pittsburgh, PA, USA.|Department of Mathematical Sciences, Florida Atlantic University, Boca Raton, FL, USA.|Department of Statistics, Keimyung University, Korea.|Henry Ford Health System, USA.|Department of Biostatistics, Graduate School of Public Health University of Pittsburgh, Pittsburgh, PA, USA.|Division of Biostatistics, Ohio State University, Columbus, OH, USA.|Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, PA, USA.|Centre for Addiction and Mental Health, University of Toronto, Toronto, Canada.|Department of Biostatistics, Graduate School of Public Health University of Pittsburgh, Pittsburgh, PA, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|China|United States|United States|United States|United States|United States|United States|United States|United States|Canada|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty825', '30304367', 'The rapid advances of omics technologies have generated abundant genomic data in public repositories and effective analytical approaches are critical to fully decipher biological knowledge inside these data. Meta-analysis combines multiple studies of a related hypothesis to improve statistical power, accuracy and reproducibility beyond individual study analysis. To date, many transcriptomic meta-analysis methods have been developed, yet few thoughtful guidelines exist. Here, we introduce a comprehensive analytical pipeline and browser-based software suite, called MetaOmics, to meta-analyze multiple transcriptomic studies for various biological purposes, including quality control, differential expression analysis, pathway enrichment analysis, differential co-expression network analysis, prediction, clustering and dimension reduction. The pipeline includes many public as well as >10 in-house transcriptomic meta-analytic methods with data-driven and biological-aim-driven strategies, hands-on protocols, an intuitive user interface and step-by-step instructions.'),
(82186, 2019, 'bio', 'RiboProP: a probabilistic ribosome positioning algorithm for ribosome profiling', 'Zhao, Dengke|Baez, William D|Fredrick, Kurt|Bundschuh, Ralf', 'Interdisciplinary Biophysics Graduate Program, Division of Hematology, The Ohio State University, Columbus, OH, USA.|Department of Physics, Division of Hematology, The Ohio State University, Columbus, OH, USA.|Department of Microbiology, Division of Hematology, The Ohio State University, Columbus, OH, USA.|Interdisciplinary Biophysics Graduate Program, Division of Hematology, The Ohio State University, Columbus, OH, USA.', 'United States|United States|United States|United States', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty854', '30304369', 'Ribosome profiling has been widely used to study translation in a genome-wide fashion. It requires deep sequencing of ribosome protected mRNA fragments followed by mapping of fragments to the reference genome. For applications such as identification of ribosome pausing sites, it is not enough to map a fragment to a given gene, but the exact position of the ribosome represented by the fragment must be identified for each mRNA fragment. The assignment of the correct ribosome position is complicated by the broad length distribution of the ribosome protected fragments caused by the known sequence bias of micrococcal nuclease (MNase), the most widely used nuclease for digesting mRNAs in bacteria. Available mapping algorithms suffer from either MNase bias or low accuracy in characterizing the ribosome pausing kinetics.'),
(82187, 2019, 'bio', 'Pisces: an accurate and versatile variant caller for somatic and germline next-generation sequencing data', 'Dunn, Tamsen|Berry, Gwenn|Emig-Agius, Dorothea|Jiang, Yu|Lei, Serena|Iyer, Anita|Udar, Nitin|Chuang, Han-Yu|Hegarty, Jeff|Dickover, Michael|Klotzle, Brandy|Robbins, Justin|Bibikova, Marina|Peeters, Marc|Strömberg, Michael', 'Departments of Bioinformatics and Clinical Genomics, Illumina Inc., San Diego, CA, USA.|Departments of Bioinformatics and Clinical Genomics, Illumina Inc., San Diego, CA, USA.|Departments of Bioinformatics and Clinical Genomics, Illumina Inc., San Diego, CA, USA.|Departments of Bioinformatics and Clinical Genomics, Illumina Inc., San Diego, CA, USA.|Departments of Bioinformatics and Clinical Genomics, Illumina Inc., San Diego, CA, USA.|Departments of Bioinformatics and Clinical Genomics, Illumina Inc., San Diego, CA, USA.|Departments of Bioinformatics and Clinical Genomics, Illumina Inc., San Diego, CA, USA.|Departments of Bioinformatics and Clinical Genomics, Illumina Inc., San Diego, CA, USA.|Departments of Bioinformatics and Clinical Genomics, Illumina Inc., San Diego, CA, USA.|Departments of Bioinformatics and Clinical Genomics, Illumina Inc., San Diego, CA, USA.|Departments of Bioinformatics and Clinical Genomics, Illumina Inc., San Diego, CA, USA.|Departments of Bioinformatics and Clinical Genomics, Illumina Inc., San Diego, CA, USA.|Departments of Bioinformatics and Clinical Genomics, Illumina Inc., San Diego, CA, USA.|Department of Oncology, Antwerp University Hospital, Edegem, Belgium.|Departments of Bioinformatics and Clinical Genomics, Illumina Inc., San Diego, 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|Belgium|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty849', '30304370', 'Next-generation sequencing technology is transitioning quickly from research labs to clinical settings. The diagnosis and treatment selection for many acquired and autosomal conditions necessitate a method for accurately detecting somatic and germline variants.'),
(82188, 2019, 'bio', 'TEPIC 2-an extended framework for transcription factor binding prediction and integrative epigenomic analysis', 'Schmidt, Florian|Kern, Fabian|Ebert, Peter|Baumgarten, Nina|Schulz, Marcel H', 'High throughput Genomics and Systems Biology, Cluster of Excellence on Multimodal Computing and Interaction, Saarland Informatics Campus, Saarbrücken, Germany.|High throughput Genomics and Systems Biology, Cluster of Excellence on Multimodal Computing and Interaction, Saarland Informatics Campus, Saarbrücken, Germany.|Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany.|High throughput Genomics and Systems Biology, Cluster of Excellence on Multimodal Computing and Interaction, Saarland Informatics Campus, Saarbrücken, Germany.|High throughput Genomics and Systems Biology, Cluster of Excellence on Multimodal Computing and Interaction, Saarland Informatics Campus, Saarbrücken, Germany.', 'Germany|Germany|Germany|Germany|Germany', 4, '-', 'Journal Article', '10.1093/bioinformatics/bty856', '30304373', 'Prediction of transcription factor (TF) binding from epigenetics data and integrative analysis thereof are challenging. Here, we present TEPIC 2 a framework allowing for fast, accurate and versatile prediction, and analysis of TF binding from epigenetics data: it supports 30 species with binding motifs, computes TF gene and scores up to two orders of magnitude faster than before due to improved implementation, and offers easy-to-use machine learning pipelines for integrated analysis of TF binding predictions with gene expression data allowing the identification of important TFs.'),
(82189, 2019, 'bio', 'A novel approach for drug response prediction in cancer cell lines via network representation learning', 'Yang, Jianghong|Li, Ao|Li, Yongqiang|Guo, Xiangqian|Wang, Minghui', 'School of Information Science and Technology, University of Science and Technology of China, Hefei AH230037, China.|School of Information Science and Technology, University of Science and Technology of China, Hefei AH230037, China.|Department of Preventive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Kaifeng, China.|Department of Preventive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Kaifeng, China.|School of Information Science and Technology, University of Science and Technology of China, Hefei AH230037, China.', 'China|China|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty848', '30304378', 'Prediction of cancer patients response to therapeutic agent is important for personalized treatment. Because experimental verification of reactions between large cohort of patients and drugs is time-intensive, expensive and impractical, preclinical prediction model based on large-scale pharmacogenomic of cancer cell line is highly expected. However, most of the existing computational studies are primarily based on genomic profiles of cancer cell lines while ignoring relationships among genes and failing to capture functional similarity of cell lines.'),
(82190, 2019, 'bio', 'PrecursorFinder: a customized biosynthetic precursor explorer', 'Yuan, Le|Tian, Yu|Ding, Shaozhen|Liu, Yanfang|Chen, Fu|Zhang, Tong|Tu, Weizhong|Chen, Junni|Hu, Qian-Nan', 'Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, P. R. China.|Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, P. R. China.|CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences Chinese Academy of Sciences, Shanghai, P. R. China.|Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, P. R. China.|Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, P. R. China.|Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, P. R. China.|Wuhan LifeSynther Science and Technology Co. Limited, Wuhan, P. R. China.|Wuhan LifeSynther Science and Technology Co. Limited, Wuhan, P. R. China.|CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences Chinese Academy of Sciences, Shanghai, P. R. China.', 'China|China|China|China|China|China|China|China|China', 8, '-', 'Journal Article', '10.1093/bioinformatics/bty838', '30304379', 'Synthetic biology has a great potential to produce high value pharmaceuticals, commodities or bulk chemicals. However, many biosynthetic target molecules have no defined or predicted biosynthetic pathways. Biosynthetic precursors are crucial to create biosynthetic pathways. Thus computer-assisted tools for precursor identification are urgently needed to develop novel metabolic pathways. To this end, we present PrecursorFinder, a computational tool that explores biosynthetic precursors for the query target molecules using chemical structure, similarity as well as MCS (maximum common substructure). This platform comprises more than 60 000 compounds biosynthesized for being promising precursors, which are extracted from >500 000 scientific literatures and manually curated by more than 100 people over the past 8xe2x80x89years. The PrecursorFinder could speed up the process of biosynthesis research and make synthetic biology or metabolic engineering more efficient.'),
(82191, 2019, 'bio', 'CANDI: an R package and Shiny app for annotating radiographs and evaluating computer-aided diagnosis', 'Badgeley, Marcus A|Liu, Manway|Glicksberg, Benjamin S|Shervey, Mark|Zech, John|Shameer, Khader|Lehar, Joseph|Oermann, Eric K|McConnell, Michael V|Snyder, Thomas M|Dudley, Joel T', 'Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.|Verily Life Sciences LLC, South San Francisco, CA, USA.|Institute for Computational Health Sciences, University of California, San Francisco, CA, USA.|Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.|Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.|Department of Medical Informatics, Northwell Health, Centre for Research Informatics and Innovation, New Hyde Park, NY, USA.|Department of Bioinformatics, Boston University, Boston, MA, USA.|Department of Neurological Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA.|Verily Life Sciences LLC, South San Francisco, CA, USA.|Verily Life Sciences LLC, South San Francisco, CA, USA.|Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty855', '30304439', 'Radiologists have used algorithms for Computer-Aided Diagnosis (CAD) for decades. These algorithms use machine learning with engineered features, and there have been mixed findings on whether they improve radiologists interpretations. Deep learning offers superior performance but requires more training data and has not been evaluated in joint algorithm-radiologist decision systems.'),
(82192, 2019, 'bio', 'PlateDesigner: a web-based application for the design of microplate experiments', 'Suprun, Maria|Suárez-Fariñas, Mayte', 'Department of Pediatrics, Allergy and Immunology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.|Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA.', 'United States|United States', 4, '-', 'Journal Article', '10.1093/bioinformatics/bty853', '30304481', 'In biological assays, systematic variability, known as a batch effect, can often confound the effects of true biological conditions and has been well documented for a variety of high-throughput technologies. In microplate-based multiplex experiments, such as Luminex or OLINK assays, researchers need to consider both position and plate effects. Those effects can be easily accounted for if the experiments are properly designed, which includes randomization of the samples across multiple experimental runs. However, doing the ad hoc randomization becomes challenging when handling multiple samples. PlateDesigner is the first web-based application that provides randomization for microplate experiments, ensuring that the main principles of the experimental design, such as grouping samples from the same biological units and balancing the distribution of experimental conditions, are applied. Creating randomizations with PlateDesigner is simple and the results can be exported in a variety of formats, and easily integrated with microplate readers and statistical analysis software.'),
(82193, 2019, 'bio', 'Towards region-specific propagation of protein functions', 'Koo, Da Chen Emily|Bonneau, Richard', 'Department of Biology, Center for Genomics and Systems Biology, New York University, New York, NY, USA.|Department of Biology, Center for Genomics and Systems Biology, New York University, New York, NY, USA.', 'United States|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty834', '30304483', 'Due to the nature of experimental annotation, most protein function prediction methods operate at the protein-level, where functions are assigned to full-length proteins based on overall similarities. However, most proteins function by interacting with other proteins or molecules, and many functional associations should be limited to specific regions rather than the entire protein length. Most domain-centric function prediction methods depend on accurate domain family assignments to infer relationships between domains and functions, with regions that are unassigned to a known domain-family left out of functional evaluation. Given the abundance of residue-level annotations currently available, we present a function prediction methodology that automatically infers function labels of specific protein regions using protein-level annotations and multiple types of region-specific features.'),
(82194, 2019, 'bio', 'The EVcouplings Python framework for coevolutionary sequence analysis', 'Hopf, Thomas A|Green, Anna G|Schubert, Benjamin|Mersmann, Sophia|Schärfe, Charlotta P I|Ingraham, John B|Toth-Petroczy, Agnes|Brock, Kelly|Riesselman, Adam J|Palmedo, Perry|Kang, Chan|Sheridan, Robert|Draizen, Eli J|Dallago, Christian|Sander, Chris|Marks, Debora S', 'Department of Systems Biology, Harvard Medical School, Boston, MA, USA.|Department of Systems Biology, Harvard Medical School, Boston, MA, USA.|Department of Systems Biology, Harvard Medical School, Boston, MA, USA.|Department of Systems Biology, Harvard Medical School, Boston, MA, USA.|Department of Systems Biology, Harvard Medical School, Boston, MA, USA.|Department of Systems Biology, Harvard Medical School, Boston, MA, USA.|Department of Systems Biology, Harvard Medical School, Boston, MA, USA.|Department of Systems Biology, Harvard Medical School, Boston, MA, USA.|Department of Systems Biology, Harvard Medical School, Boston, MA, USA.|Department of Systems Biology, Harvard Medical School, Boston, MA, USA.|Department of Systems Biology, Harvard Medical School, Boston, MA, USA.|Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, NY, USA.|Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.|Department of Systems Biology, Harvard Medical School, Boston, MA, USA.|Department of Cell Biology, Harvard Medical School, Boston, MA, USA.|Department of Systems Biology, Harvard Medical School, Boston, MA, 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', 9, '-', 'Journal Article', '10.1093/bioinformatics/bty862', '30304492', 'Coevolutionary sequence analysis has become a commonly used technique for de novo prediction of the structure and function of proteins, RNA, and protein complexes. We present the EVcouplings framework, a fully integrated open-source application and Python package for coevolutionary analysis. The framework enables generation of sequence alignments, calculation and evaluation of evolutionary couplings (ECs), and de novo prediction of structure and mutation effects. The combination of an easy to use, flexible command line interface and an underlying modular Python package makes the full power of coevolutionary analyses available to entry-level and advanced users.'),
(82195, 2019, 'bio', 'Multimodal network diffusion predicts future disease-gene-chemical associations', 'Lin, Chih-Hsu|Konecki, Daniel M|Liu, Meng|Wilson, Stephen J|Nassar, Huda|Wilkins, Angela D|Gleich, David F|Lichtarge, Olivier', 'Graduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX, USA.|Graduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX, USA.|Department of Computer Science, Purdue University, West Lafayette, IN, USA.|Department of Biochemistry and Molecular Biology, Houston, TX, USA.|Department of Computer Science, Purdue University, West Lafayette, IN, USA.|Departments of Molecular and Human Genetics, and Pharmacology, Houston, TX, USA.|Department of Computer Science, Purdue University, West Lafayette, IN, USA.|Graduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 9, '-', 'Journal Article', '10.1093/bioinformatics/bty858', '30304494', 'Precision medicine is an emerging field with hopes to improve patient treatment and reduce morbidity and mortality. To these ends, computational approaches have predicted associations among genes, chemicals and diseases. Such efforts, however, were often limited to using just some available association types. This lowers prediction coverage and, since prior evidence shows that integrating heterogeneous data is likely beneficial, it may limit accuracy. Therefore, we systematically tested whether using more association types improves prediction.'),
(82196, 2019, 'bio', 'A global transcriptomic pipeline decoding core network of genes involved in stages leading to acquisition of drug-resistance to cisplatin in osteosarcoma cells', 'Niveditha, Divya|Mukherjee, Sudeshna|Majumder, Syamantak|Chowdhury, Rajdeep|Chowdhury, Shibasish', 'Department of Biological Sciences, Birla Institute of Technology and Science (BITS), Pilani, India.|Department of Biological Sciences, Birla Institute of Technology and Science (BITS), Pilani, India.|Department of Biological Sciences, Birla Institute of Technology and Science (BITS), Pilani, India.|Department of Biological Sciences, Birla Institute of Technology and Science (BITS), Pilani, India.|Department of Biological Sciences, Birla Institute of Technology and Science (BITS), Pilani, India.', 'India|India|India|India|India', 4, '-', 'Journal Article', '10.1093/bioinformatics/bty868', '30307528', 'Traditional cancer therapy is focused on eradicating fast proliferating population of tumor cells. However, existing evidences suggest survival of sub-population of cancer cells that can resist chemotherapy by entering a persister state of minimal growth. These cells eventually survive to produce cells resistant to drugs. The identifying of appropriate targets that can eliminate the drug-tolerant persisters remains a challenge. Hence, a deeper understanding of the distinctive genetic signatures that lead to resistance is of utmost importance to design an appropriate therapy.'),
(82197, 2019, 'bio', 'Colocalization analyses of genomic elements: approaches, recommendations and challenges', 'Kanduri, Chakravarthi|Bock, Christoph|Gundersen, Sveinung|Hovig, Eivind|Sandve, Geir Kjetil', 'Department of Informatics, University of Oslo, Oslo, Norway.|CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria.|Department of Informatics, University of Oslo, Oslo, Norway.|Department of Informatics, University of Oslo, Oslo, Norway.|Department of Informatics, University of Oslo, Oslo, Norway.', 'Norway|Austria|Norway|Norway|Norway', 8, '-', 'Journal Article', '10.1093/bioinformatics/bty835', '30307532', 'Many high-throughput methods produce sets of genomic regions as one of their main outputs. Scientists often use genomic colocalization analysis to interpret such region sets, for example to identify interesting enrichments and to understand the interplay between the underlying biological processes. Although widely used, there is little standardization in how these analyses are performed. Different practices can substantially affect the conclusions of colocalization analyses.'),
(82198, 2019, 'bio', 'Cross-type biomedical named entity recognition with deep multi-task learning', 'Wang, Xuan|Zhang, Yu|Ren, Xiang|Zhang, Yuhao|Zitnik, Marinka|Shang, Jingbo|Langlotz, Curtis|Han, Jiawei', 'Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA.|Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA.|Department of Computer Science, University of Southern California, Los Angeles, CA, USA.|Department of Radiology, School of Medicine, Stanford University, Stanford, CA, USA.|Department of Computer Science, Stanford University, Stanford, CA, USA.|Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA.|Department of Radiology, School of Medicine, Stanford University, Stanford, CA, USA.|Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 25, '-', 'Journal Article', '10.1093/bioinformatics/bty869', '30307536', 'State-of-the-art biomedical named entity recognition (BioNER) systems often require handcrafted features specific to each entity type, such as genes, chemicals and diseases. Although recent studies explored using neural network models for BioNER to free experts from manual feature engineering, the performance remains limited by the available training data for each entity type.');
