INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(82482, 2019, 'bio', 'AQUAPONY: visualization and interpretation of phylogeographic information on phylogenetic trees', 'Cazaux, Bastien|Castel, Guillaume|Rivals, Eric', 'Department of Computer Science, LIRMM, UMR 5506, CNRS and Université Montpellier, Montpellier, France.|Institut de Biologie Computationelle, Montpellier, France.|Department of Computer Science, LIRMM, UMR 5506, CNRS and Université Montpellier, Montpellier, France.', 'France|France|France', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz011', '30649190', 'The visualization and interpretation of evolutionary spatiotemporal scenarios is broadly and increasingly used in infectious disease research, ecology or agronomy. Using probabilistic frameworks, well-known tools can infer from molecular data ancestral traits for internal nodes in a phylogeny, and numerous phylogenetic rendering tools can display such evolutionary trees. However, visualizing such ancestral information and its uncertainty on the tree remains tedious. For instance, ancestral nodes can be associated to several geographical annotations with close probabilities and thus, several migration or transmission scenarios exist.'),
(82483, 2019, 'bio', 'SMuRF: portable and accurate ensemble prediction of somatic mutations', 'Huang, Weitai|Guo, Yu Amanda|Muthukumar, Karthik|Baruah, Probhonjon|Chang, Mei Mei|Jacobsen Skanderup, Anders', 'Department of Computational and Systems Biology, Agency for Science Technology and Research, Genome Institute of Singapore, Singapore, Singapore.|Department of Computational and Systems Biology, Agency for Science Technology and Research, Genome Institute of Singapore, Singapore, Singapore.|Department of Computational and Systems Biology, Agency for Science Technology and Research, Genome Institute of Singapore, Singapore, Singapore.|Department of Computational and Systems Biology, Agency for Science Technology and Research, Genome Institute of Singapore, Singapore, Singapore.|Department of Computational and Systems Biology, Agency for Science Technology and Research, Genome Institute of Singapore, Singapore, Singapore.|Department of Computational and Systems Biology, Agency for Science Technology and Research, Genome Institute of Singapore, Singapore, Singapore.', 'Singapore|Singapore|Singapore|Singapore|Singapore|Singapore', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz018', '30649191', 'Somatic Mutation calling method using a Random Forest (SMuRF) integrates predictions and auxiliary features from multiple somatic mutation callers using a supervised machine learning approach. SMuRF is trained on community-curated matched tumor and normal whole genome sequencing data. SMuRF predicts both SNVs and indels with high accuracy in genome or exome-level sequencing data. Furthermore, the method is robust across multiple tested cancer types and predicts low allele frequency variants with high accuracy. In contrast to existing ensemble-based somatic mutation calling approaches, SMuRF works out-of-the-box and is orders of magnitudes faster.'),
(82484, 2019, 'bio', 'Identifying residues that determine palmitoylation using association rule mining', 'Kumari, Bandana|Kumar, Ravindra|Kumar, Manish', 'Department of Biophysics, University of Delhi South Campus, New Delhi, India.|Department of Biophysics, University of Delhi South Campus, New Delhi, India.|Department of Biophysics, University of Delhi South Campus, New Delhi, India.', 'India|India|India', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz003', '30649192', 'In eukaryotes, palmitoylation drives several essential cellular mechanisms like protein sorting, protein stability and protein-protein interaction. Several amino acids namely Cys, Gly, Ser, Thr and Lys undergo palmitoylation. But very little is known about the amino acid patterns that promote palmitoylation.'),
(82485, 2019, 'bio', 'KORP: knowledge-based 6D potential for fast protein and loop modeling', 'López-Blanco, José Ramón|Chacón, Pablo', 'Department of Biological Chemical Physics, Rocasolano Institute of Physical Chemistry C.S.I.C, Madrid, Spain.|Department of Biological Chemical Physics, Rocasolano Institute of Physical Chemistry C.S.I.C, Madrid, Spain.', 'Spain|Spain', 8, '-', 'Journal Article', '10.1093/bioinformatics/btz026', '30649193', 'Knowledge-based statistical potentials constitute a simpler and easier alternative to physics-based potentials in many applications, including folding, docking and protein modeling. Here, to improve the effectiveness of the current approximations, we attempt to capture the six-dimensional nature of residue-residue interactions from known protein structures using a simple backbone-based representation.'),
(82486, 2019, 'bio', 'Characterizing and ranking computed metabolic engineering strategies', 'Schneider, Philipp|Klamt, Steffen', 'Max Planck Institute for Dynamics of Complex Technical Systems, Analysis and Redesign of Biological Networks, Magdeburg, Germany.|Max Planck Institute for Dynamics of Complex Technical Systems, Analysis and Redesign of Biological Networks, Magdeburg, Germany.', 'Germany|Germany', 4, '-', 'Journal Article', '10.1093/bioinformatics/bty1065', '30649194', 'The computer-aided design of metabolic intervention strategies has become a key component of an integrated metabolic engineering approach and a broad range of methods and algorithms has been developed for this task. Many of these algorithms enforce coupling of growth with product synthesis and may return thousands of possible intervention strategies from which the most suitable strategy must then be selected.'),
(82487, 2019, 'bio', 'EBIC: an open source software for high-dimensional and big data analyses', 'Orzechowski, Patryk|Moore, Jason H', 'Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA.|Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA.', 'United States|United States', 4, '-', 'Journal Article', '10.1093/bioinformatics/btz027', '30649199', 'In this paper, we present an open source package with the latest release of Evolutionary-based BIClustering (EBIC), a next-generation biclustering algorithm for mining genetic data. The major contribution of this paper is adding a full support for multiple graphics processing units (GPUs) support, which makes it possible to run efficiently large genomic data mining analyses. Multiple enhancements to the first release of the algorithm include integration with R and Bioconductor, and an option to exclude missing values from the analysis.'),
(82488, 2019, 'bio', 'Characterization and identification of long non-coding RNAs based on feature relationship', 'Wang, Guangyu|Yin, Hongyan|Li, Boyang|Yu, Chunlei|Wang, Fan|Xu, Xingjian|Cao, Jiabao|Bao, Yiming|Wang, Liguo|Abbasi, Amir A|Bajic, Vladimir B|Ma, Lina|Zhang, Zhang', 'CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.|CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.|Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.|CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.|CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.|CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.|CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.|CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.|Division of Biomedical Statistics and Informatics, Mayo Clinic College of Medicine, Rochester, MN, USA.|National Center for Bioinformatics, Programme of Comparative and Evolutionary Genomics, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad, Pakistan.|King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Thuwal, Kingdom of Saudi Arabia.|CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.|CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.', 'China|China|United States|China|China|China|China|China|United States|Pakistan|Saudi Arabia|China|China', 6, '-', 'Journal Article', '10.1093/bioinformatics/btz008', '30649200', 'The significance of long non-coding RNAs (lncRNAs) in many biological processes and diseases has gained intense interests over the past several years. However, computational identification of lncRNAs in a wide range of species remains challenging, it requires prior knowledge of well-established sequences and annotations or species-specific training data, but the reality is that only a limited number of species have high-quality sequences and annotations.'),
(82489, 2019, 'bio', 'Large-scale analysis of human gene expression variability associates highly variable drug targets with lower drug effectiveness and safety', 'Simonovsky, Eyal|Schuster, Ronen|Yeger-Lotem, Esti', 'Department of Clinical Biochemistry & Pharmacology, Ben-Gurion University of the Negev, Beer-Sheva, Israel.|Department of Clinical Biochemistry & Pharmacology, Ben-Gurion University of the Negev, Beer-Sheva, Israel.|Department of Clinical Biochemistry & Pharmacology, Ben-Gurion University of the Negev, Beer-Sheva, Israel.', 'Israel|Israel|Israel', 4, '-', 'Journal Article', '10.1093/bioinformatics/btz023', '30649201', 'The effectiveness of drugs tends to vary between patients. One of the well-known reasons for this phenomenon is genetic polymorphisms in drug target genes among patients. Here, we propose that differences in expression levels of drug target genes across individuals can also contribute to this phenomenon.'),
(82490, 2019, 'bio', 'ClusterMap: compare multiple single cell RNA-Seq datasets across different experimental conditions', 'Gao, Xin|Hu, Deqing|Gogol, Madelaine|Li, Hua', 'Computational Biology, Stowers Institute for Medical Research, Kansas City, MO, USA.|Department of Cell Biology, 2011 Collaborative Innovation Center of Tianjin for Medical Epigenetics, Tianjin Key Laboratory of Medical Epigenetics, Tianjin Medical University, Tianjin, China.|Computational Biology, Stowers Institute for Medical Research, Kansas City, MO, USA.|Computational Biology, Stowers Institute for Medical Research, Kansas City, MO, USA.', 'United States|China|United States|United States', 5, '-', 'Journal Article', '10.1093/bioinformatics/btz024', '30649203', 'Single cell RNA-Seq (scRNA-Seq) facilitates the characterization of cell type heterogeneity and developmental processes. Further study of single cell profiles across different conditions enables the understanding of biological processes and underlying mechanisms at the sub-population level. However, developing proper methodology to compare multiple scRNA-Seq datasets remains challenging.'),
(82491, 2019, 'bio', 'MSC: a metagenomic sequence classification algorithm', 'Saha, Subrata|Johnson, Jethro|Pal, Soumitra|Weinstock, George M|Rajasekaran, Sanguthevar', 'Healthcare and Life Sciences Division, IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA.|The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.|National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD, USA.|The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.|Computer Science and Engineering Department, University of Connecticut, Storrs, CT, USA.', 'United States|United States|United States|United States|United States', 3, '-', 'Journal Article', '10.1093/bioinformatics/bty1071', '30649204', 'Metagenomics is the study of genetic materials directly sampled from natural habitats. It has the potential to reveal previously hidden diversity of microscopic life largely due to the existence of highly parallel and low-cost next-generation sequencing technology. Conventional approaches align metagenomic reads onto known reference genomes to identify microbes in the sample. Since such a collection of reference genomes is very large, the approach often needs high-end computing machines with large memory which is not often available to researchers. Alternative approaches follow an alignment-free methodology where the presence of a microbe is predicted using the information about the unique k-mers present in the microbial genomes. However, such approaches suffer from high false positives due to trading off the value of k with the computational resources. In this article, we propose a highly efficient metagenomic sequence classification (MSC) algorithm that is a hybrid of both approaches. Instead of aligning reads to the full genomes, MSC aligns reads onto a set of carefully chosen, shorter and highly discriminating model sequences built from the unique k-mers of each of the reference sequences.'),
(82492, 2019, 'bio', 'A web application and service for imputing and visualizing missense variant effect maps', 'Wu, Yingzhou|Weile, Jochen|Cote, Atina G|Sun, Song|Knapp, Jennifer|Verby, Marta|Roth, Frederick P', 'Donnelly Centre, University of Toronto, Toronto, ON, Canada.|Donnelly Centre, University of Toronto, Toronto, ON, Canada.|Donnelly Centre, University of Toronto, Toronto, ON, Canada.|Donnelly Centre, University of Toronto, Toronto, ON, Canada.|Donnelly Centre, University of Toronto, Toronto, ON, Canada.|Donnelly Centre, University of Toronto, Toronto, ON, Canada.|Donnelly Centre, University of Toronto, Toronto, ON, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz012', '30649215', 'The promise of personalized genomic medicine depends on our ability to assess the functional impact of rare sequence variation. Multiplexed assays can experimentally measure the functional impact of missense variants on a massive scale. However, even after such assays, many missense variants remain poorly measured. Here we describe a software pipeline and application to impute missing information in experimentally determined variant effect maps.'),
(82493, 2019, 'bio', 'Sstack: an R package for stacking with applications to scenarios involving sequential addition of samples and features', 'Matlock, Kevin|Rahman, Raziur|Ghosh, Souparno|Pal, Ranadip', 'Department of Electrical and Computer Engineering.|Department of Electrical and Computer Engineering.|Department of Mathematics and Statistics, Texas Tech University, Lubbock, TX, USA.|Department of Electrical and Computer Engineering.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz010', '30649230', 'Biological processes are characterized by a variety of different genomic feature sets. However, often times when building models, portions of these features are missing for a subset of the dataset. We provide a modeling framework to effectively integrate this type of heterogeneous data to improve prediction accuracy. To test our methodology, we have stacked data from the Cancer Cell Line Encyclopedia to increase the accuracy of drug sensitivity prediction. The package addresses the dynamic regime of information integration involving sequential addition of features and samples.'),
(82494, 2019, 'bio', 'A network of networks approach for modeling interconnected brain tissue-specific networks', 'Kawakubo, Hideko|Matsui, Yusuke|Kushima, Itaru|Ozaki, Norio|Shimamura, Teppei', 'Division of Systems of Biology, Nagoya University Graduate School of Medicine, Nagoya, Japan.|Laboratory of Intelligence Healthcare, Nagoya University Graduate School of Medicine, Nagoya, Japan.|Institute for Advanced Research, Nagoya University Graduate School of Medicine, Nagoya, Japan.|Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan.|Division of Systems of Biology, Nagoya University Graduate School of Medicine, Nagoya, Japan.', 'Japan|Japan|Japan|Japan|Japan', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz032', '30649245', 'Recent sequence-based analyses have identified a lot of gene variants that may contribute to neurogenetic disorders such as autism spectrum disorder and schizophrenia. Several state-of-the-art network-based analyses have been proposed for mechanical understanding of genetic variants in neurogenetic disorders. However, these methods were mainly designed for modeling and analyzing single networks that do not interact with or depend on other networks, and thus cannot capture the properties between interdependent systems in brain-specific tissues, circuits and regions which are connected each other and affect behavior and cognitive processes.'),
(82495, 2019, 'bio', 'Improved indel detection in DNA and RNA via realignment with ABRA2', 'Mose, Lisle E|Perou, Charles M|Parker, Joel S', 'Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.|Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.|Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.', 'United States|United States|United States', 7, '-', 'Journal Article', '10.1093/bioinformatics/btz033', '30649250', 'Genomic variant detection from next-generation sequencing has become established as an extremely important component of research and clinical diagnoses in both cancer and Mendelian disorders. Insertions and deletions (indels) are a common source of variation and can frequently impact functionality, thus making their detection vitally important. While substantial effort has gone into detecting indels from DNA, there is still opportunity for improvement. Further, detection of indels from RNA-Seq data has largely been an afterthought and offers another critical area for variant detection.'),
(82496, 2019, 'bio', 'An accurate and powerful method for copy number variation detection', 'Xiao, Feifei|Luo, Xizhi|Hao, Ning|Niu, Yue S|Xiao, Xiangjun|Cai, Guoshuai|Amos, Christopher I|Zhang, Heping', 'Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, USA.|Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, USA.|Department of Mathematics, University of Arizona, Tucson, AZ, USA.|Department of Mathematics, University of Arizona, Tucson, AZ, USA.|Department of Quantitative Sciences, Baylor College of Medicine, Houston, TX, USA.|Department of Environmental Health Science, University of South Carolina, Columbia, SC, USA.|Department of Quantitative Sciences, Baylor College of Medicine, Houston, TX, USA.|Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty1041', '30649252', 'Integration of multiple genetic sources for copy number variation detection (CNV) is a powerful approach to improve the identification of variants associated with complex traits. Although it has been shown that the widely used change point based methods can increase statistical power to identify variants, it remains challenging to effectively detect CNVs with weak signals due to the noisy nature of genotyping intensity data. We previously developed modSaRa, a normal mean-based model on a screening and ranking algorithm for copy number variation identification which presented desirable sensitivity with high computational efficiency. To boost statistical power for the identification of variants, here we present a novel improvement that integrates the relative allelic intensity with external information from empirical statistics with modeling, which we called modSaRa2.'),
(82497, 2019, 'bio', 'PSiTE: a Phylogeny guided Simulator for Tumor Evolution', 'Yang, Hechuan|Lu, Bingxin|Lai, Lan Huong|Lim, Abner Herbert|Alvarez, Jacob Josiah Santiago|Zhai, Weiwei', 'Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, P.R.China.|Human Genetics, Genome Institute of Singapore, A*STAR, Singapore, Singapore.|Human Genetics, Genome Institute of Singapore, A*STAR, Singapore, Singapore.|Human Genetics, Genome Institute of Singapore, A*STAR, Singapore, Singapore.|Human Genetics, Genome Institute of Singapore, A*STAR, Singapore, Singapore.|Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, P.R.China.', 'China|Singapore|Singapore|Singapore|Singapore|China', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz028', '30649258', 'Simulating realistic clonal dynamics of tumors is an important topic in cancer genomics. Here, we present Phylogeny guided Simulator for Tumor Evolution, a tool that can simulate different types of tumor samples including single sector, multi-sector bulk tumor as well as single-cell tumor data under a wide range of evolutionary trajectories. Phylogeny guided Simulator for Tumor Evolution provides an efficient tool for understanding clonal evolution of cancer.'),
(82498, 2019, 'bio', 'Exosomal lncRNAs and cancer: connecting the missing links', 'Naderi-Meshkin, Hojjat|Lai, Xin|Amirkhah, Raheleh|Vera, Julio|Rasko, John E J|Schmitz, Ulf', 'Stem Cells & Regenerative Medicine Research Group, Academic Center for Education, Culture Research (ACECR), Khorasan Razavi Branch, Mashhad, Iran.|Laboratory of Systems Tumour Immunology, Department of Dermatology, Friedrich-Alexander-University of Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany.|Nastaran Center for Cancer Prevention, Mashhad, Iran.|Laboratory of Systems Tumour Immunology, Department of Dermatology, Friedrich-Alexander-University of Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany.|Gene and Stem Cell Therapy Program, Centenary Institute, University of Sydney, Camperdown, Australia.|Gene and Stem Cell Therapy Program, Centenary Institute, University of Sydney, Camperdown, Australia.', '-|Germany|-|Germany|Australia|Australia', 16, '-', 'Journal Article', '10.1093/bioinformatics/bty527', '30649349', 'Extracellular vesicles (EVs), including exosomes and microvesicles, are potent and clinically valuable tools for early diagnosis, prognosis and potentially the targeted treatment of cancer. The content of EVs is closely related to the type and status of the EV-secreting cell. Circulating exosomes are a source of stable RNAs including mRNAs, microRNAs and long non-coding RNAs (lncRNAs).'),
(82499, 2019, 'bio', 'Xolik: finding cross-linked peptides with maximum paired scores in linear time', 'Dai, Jiaan|Jiang, Wei|Yu, Fengchao|Yu, Weichuan', 'Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.|Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.|Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.|Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.', 'China|China|China|China', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty526', '30649350', 'Cross-linking technique coupled with mass spectrometry (MS) is widely used in the analysis of protein structures and protein-protein interactions. In order to identify cross-linked peptides from MS data, we need to consider all pairwise combinations of peptides, which is computationally prohibitive when the sequence database is large. To alleviate this problem, some heuristic screening strategies are used to reduce the number of peptide pairs during the identification. However, heuristic screening strategies may miss some true cross-linked peptides.'),
(82500, 2019, 'bio', 'Flux tope analysis: studying the coordination of reaction directions in metabolic networks', 'Gerstl, Matthias P|Müller, Stefan|Regensburger, Georg|Zanghellini, Jürgen', 'Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria, EU.|Faculty of Mathematics, University of Vienna, Vienna, Austria, EU.|Institute for Algebra, Johannes Kepler University Linz, Linz, Austria, EU.|Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria, EU.', 'Austria|Austria|Austria|Austria', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty550', '30649351', 'Elementary flux mode (EFM) analysis allows an unbiased description of metabolic networks in terms of minimal pathways (involving a minimal set of reactions). To date, the enumeration of EFMs is impracticable in genome-scale metabolic models. In a complementary approach, we introduce the concept of a flux tope (FT), involving a maximal set of reactions (with fixed directions), which allows one to study the coordination of reaction directions in metabolic networks and opens a new way for EFM enumeration.'),
(82501, 2019, 'bio', 'Benchmarking time-series data discretization on inference methods', 'Li, Yuezhe|Jann, Tiffany|Vera-Licona, Paola', 'R.D. Berlin Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, Farmington, CT, USA.|Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA.|Center for Quantitative Medicine, University of Connecticut School of Medicine, Farmington, CT, USA.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz036', '30657860', 'The rapid development in quantitatively measuring DNA, RNA and protein has generated a great interest in the development of reverse-engineering methods, that is, data-driven approaches to infer the network structure or dynamical model of the system. Many reverse-engineering methods require discrete quantitative data as input, while many experimental data are continuous. Some studies have started to reveal the impact that the choice of data discretization has on the performance of reverse-engineering methods. However, more comprehensive studies are still greatly needed to systematically and quantitatively understand the impact that discretization methods have on inference methods. Furthermore, there is an urgent need for systematic comparative methods that can help select between discretization methods. In this work, we consider four published intracellular networks inferred with their respective time-series datasets. We discretized the data using different discretization methods. Across all datasets, changing the data discretization to a more appropriate one improved the reverse-engineering methods performance. We observed no universal best discretization method across different time-series datasets. Thus, we propose DiscreeTest, a two-step evaluation metric for ranking discretization methods for time-series data. The underlying assumption of DiscreeTest is that an optimal discretization method should preserve the dynamic patterns observed in the original data across all variables. We used the same datasets and networks to show that DiscreeTest is able to identify an appropriate discretization among several candidate methods. To our knowledge, this is the first time that a method for benchmarking and selecting an appropriate discretization method for time-series data has been proposed.'),
(82502, 2019, 'bio', 'DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays', 'Singh, Amrit|Shannon, Casey P|Gautier, Benoît|Rohart, Florian|Vacher, Michaël|Tebbutt, Scott J|Lê Cao, Kim-Anh', 'Prevention of Organ Failure (PROOF) Centre of Excellence, University of British Columbia, Vancouver, BC, Canada.|Prevention of Organ Failure (PROOF) Centre of Excellence, University of British Columbia, Vancouver, BC, Canada.|The University of Queensland Diamantina Institute, Translational Research Institute, Woolloongabba, Queensland, Australia.|Institute for Molecular Bioscience, The University of Queensland, St Lucia, Queensland, Australia.|Australian eHealth Research Centre, Commonwealth Scientific and Industrial Research Organisation, Brisbane, Queensland, Australia.|Prevention of Organ Failure (PROOF) Centre of Excellence, University of British Columbia, Vancouver, BC, Canada.|Melbourne Integrative Genomics, School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia.', 'Canada|Canada|Australia|Australia|Australia|Canada|Australia', 36, '-', 'Journal Article', '10.1093/bioinformatics/bty1054', '30657866', 'In the continuously expanding omics era, novel computational and statistical strategies are needed for data integration and identification of biomarkers and molecular signatures. We present Data Integration Analysis for Biomarker discovery using Latent cOmponents (DIABLO), a multi-omics integrative method that seeks for common information across different data types through the selection of a subset of molecular features, while discriminating between multiple phenotypic groups.'),
(82503, 2019, 'bio', 'queryMed: Semantic Web functions for linking pharmacological and medical knowledge to data', 'Rivault, Y|Dameron, O|Le Meur, N', 'Univ Rennes, CNRS, Inria, IRISA - UMR 6074, Rennes, F-35000, France.|Univ Rennes, CNRS, Inria, IRISA - UMR 6074, Rennes, F-35000, France.|Univ Rennes, EHESP, REPERES (Pharmacology and Health Services Research) - EA 7449, Rennes, F-35000, France.', 'France|France|France', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz034', '30657867', 'In public health research and more precisely in the reuse of electronic health data, selecting patients, identifying specific events and interpreting results typically requires biomedical knowledge. The queryMed R package aims to facilitate the integration of medical and pharmacological knowledge stored in formats compliant with the Linked Data paradigm (e.g. OWL ontologies and RDF datasets) into the R statistical programming environment. We show how it allowed us to identify all the drugs prescribed for critical limb ischemia (CLI) and also to detect one contraindicated prescription for one patient by linking a medical database of 1003 CLI patients to ontologies.'),
(82504, 2019, 'bio', 'METACLUSTER-an R package for context-specific expression analysis of metabolic gene clusters', 'Banf, Michael|Zhao, Kangmei|Rhee, Seung Y', 'Department of Plant Biology, Carnegie Institution for Science, Stanford, CA, USA.|Department of Plant Biology, Carnegie Institution for Science, Stanford, CA, USA.|Department of Plant Biology, Carnegie Institution for Science, Stanford, CA, USA.', 'United States|United States|United States', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz021', '30657869', 'Plants and microbes produce numerous compounds to cope with their environments but the biosynthetic pathways for most of these compounds have yet to be elucidated. Some biosynthetic pathways are encoded by enzymes collocated in the chromosome. To facilitate a more comprehensive condition and tissue-specific expression analysis of metabolic gene clusters, we developed METACLUSTER, a probabilistic framework for characterizing metabolic gene clusters using context-specific gene expression information.'),
(82505, 2019, 'bio', 'OLGA: fast computation of generation probabilities of B- and T-cell receptor amino acid sequences and motifs', 'Sethna, Zachary|Elhanati, Yuval|Callan, Curtis G|Walczak, Aleksandra M|Mora, Thierry', 'Joseph Henry Laboratories, Princeton University, Princeton, NJ, USA.|Joseph Henry Laboratories, Princeton University, Princeton, NJ, USA.|Joseph Henry Laboratories, Princeton University, Princeton, NJ, USA.|Laboratoire de physique de lEcole normale supérieure (PSL University), Centre national de la recherche scientifique, Sorbonne University, University Paris-Diderot, Paris, France.|Laboratoire de physique de lEcole normale supérieure (PSL University), Centre national de la recherche scientifique, Sorbonne University, University Paris-Diderot, Paris, France.', 'United States|United States|United States|France|France', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz035', '30657870', 'High-throughput sequencing of large immune repertoires has enabled the development of methods to predict the probability of generation by V(D)J recombination of T- and B-cell receptors of any specific nucleotide sequence. These generation probabilities are very non-homogeneous, ranging over 20 orders of magnitude in real repertoires. Since the function of a receptor really depends on its protein sequence, it is important to be able to predict this probability of generation at the amino acid level. However, brute-force summation over all the nucleotide sequences with the correct amino acid translation is computationally intractable. The purpose of this paper is to present a solution to this problem.'),
(82506, 2019, 'bio', 'Ordino: a visual cancer analysis tool for ranking and exploring genes, cell lines and tissue samples', 'Streit, Marc|Gratzl, Samuel|Stitz, Holger|Wernitznig, Andreas|Zichner, Thomas|Haslinger, Christian', 'Institute of Computer Graphics, Johannes Kepler University Linz, Linz A-4040, Austria.|Institute of Computer Graphics, Johannes Kepler University Linz, Linz A-4040, Austria.|Institute of Computer Graphics, Johannes Kepler University Linz, Linz A-4040, Austria.|Department of Pharmacology and Translational Research, Boehringer Ingelheim RCV GmbH & Co KG, Vienna A-1121, Austria.|Department of Pharmacology and Translational Research, Boehringer Ingelheim RCV GmbH & Co KG, Vienna A-1121, Austria.|Department of Pharmacology and Translational Research, Boehringer Ingelheim RCV GmbH & Co KG, Vienna A-1121, Austria.', 'Austria|Austria|Austria|Austria|Austria|Austria', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz009', '30657871', 'Ordino is a web-based analysis tool for cancer genomics that allows users to flexibly rank, filter and explore genes, cell lines and tissue samples based on pre-loaded data, including The Cancer Genome Atlas, the Cancer Cell Line Encyclopedia and manually uploaded information. Interactive tabular data visualization that facilitates the user-driven prioritization process forms a core component of Ordino. Detail views of selected items complement the exploration. Findings can be stored, shared and reproduced via the integrated session management.'),
(82507, 2019, 'bio', 'AllerCatPro-prediction of protein allergenicity potential from the protein sequence', 'Maurer-Stroh, Sebastian|Krutz, Nora L|Kern, Petra S|Gunalan, Vithiagaran|Nguyen, Minh N|Limviphuvadh, Vachiranee|Eisenhaber, Frank|Gerberick, G Frank', 'Biomolecular Function Discovery Division, Bioinformatics Institute, Agency for Science, Technology and Research, Singapore.|The Procter & Gamble Services Company, Strombeek-Bever, Belgium.|The Procter & Gamble Services Company, Strombeek-Bever, Belgium.|Biomolecular Function Discovery Division, Bioinformatics Institute, Agency for Science, Technology and Research, Singapore.|Biomolecular Function Discovery Division, Bioinformatics Institute, Agency for Science, Technology and Research, Singapore.|Biomolecular Function Discovery Division, Bioinformatics Institute, Agency for Science, Technology and Research, Singapore.|Biomolecular Function Discovery Division, Bioinformatics Institute, Agency for Science, Technology and Research, Singapore.|The Procter and Gamble Company, Mason, OH, USA.', 'Singapore|Belgium|Belgium|Singapore|Singapore|Singapore|Singapore|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz029', '30657872', 'Due to the risk of inducing an immediate Type I (IgE-mediated) allergic response, proteins intended for use in consumer products must be investigated for their allergenic potential before introduction into the marketplace. The FAO/WHO guidelines for computational assessment of allergenic potential of proteins based on short peptide hits and linear sequence window identity thresholds misclassify many proteins as allergens.'),
(82508, 2019, 'bio', 'SMARTCyp 30: enhanced cytochrome P450 site-of-metabolism prediction server', 'Olsen, Lars|Montefiori, Marco|Tran, Khanhvi Phuc|Jørgensen, Flemming Steen', 'Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, DK-2100, Denmark.|Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, DK-2100, Denmark.|Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, DK-2100, Denmark.|Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, DK-2100, Denmark.', 'Denmark|Denmark|Denmark|Denmark', 8, '-', 'Journal Article', '10.1093/bioinformatics/btz037', '30657882', 'Cytochromes P450 are the most important class of drug metabolizing enzymes. Prediction of drug metabolism is important in development of new drugs, to understand and reduce adverse drug reactions and to reduce animal testing.'),
(82509, 2019, 'bio', 'admixr-R package for reproducible analyses using ADMIXTOOLS', 'Petr, Martin|Vernot, Benjamin|Kelso, Janet', 'Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany.|Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany.|Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany.', 'Germany|Germany|Germany', 7, '-', 'Journal Article', '10.1093/bioinformatics/btz030', '30668635', 'We present a new R package admixr, which provides a convenient interface for performing reproducible population genetic analyses (f3, D, f4, f4-ratio, qpWave and qpAdm), as implemented by command-line programs in the ADMIXTOOLS software suite. In a traditional ADMIXTOOLS workflow, the user must first generate a set of text configuration files tailored to each individual analysis, often using a combination of shell scripting and manual text editing. The non-tabular output files then need to be parsed to extract values of interest prior to further analyses. Our package simplifies this process by automating all low-level configuration and parsing steps, making analyses as simple as running a single R command. Furthermore, we provide a set of R functions for processing, filtering and manipulating datasets in the EIGENSTRAT format. By unifying all steps of the workflow under a single R framework, this package enables the automation of analytic pipelines, significantly improving the reproducibility of population genetic studies.'),
(82510, 2019, 'bio', 'CMEP: a database for circulating microRNA expression profiling', 'Li, Jian-Rong|Tong, Chun-Yip|Sung, Tsai-Jung|Kang, Ting-Yu|Zhou, Xianghong Jasmine|Liu, Chun-Chi', 'Institute of Genomics and Bioinformatics.|Institute of Genomics and Bioinformatics.|Institute of Genomics and Bioinformatics.|Institute of Genomics and Bioinformatics.|Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA.|Institute of Genomics and Bioinformatics.', 'United States|United States|United States|United States|United States|United States', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz042', '30668638', 'In recent years, several experimental studies have revealed that the microRNAs (miRNAs) in serum, plasma, exosome and whole blood are dysregulated in various types of diseases, indicating that the circulating miRNAs may serve as potential noninvasive biomarkers for disease diagnosis and prognosis. However, no database has been constructed to integrate the large-scale circulating miRNA profiles, explore the functional pathways involved and predict the potential biomarkers using feature selection between the disease conditions. Although there have been several studies attempting to generate a circulating miRNA database, they have not yet integrated the large-scale circulating miRNA profiles or provided the biomarker-selection function using machine learning methods.'),
(82511, 2019, 'bio', 'multiHiCcompare: joint normalization and comparative analysis of complex Hi-C experiments', 'Stansfield, John C|Cresswell, Kellen G|Dozmorov, Mikhail G', 'Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA.|Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA.|Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA.', 'United States|United States|United States', 8, '-', 'Journal Article', '10.1093/bioinformatics/btz048', '30668639', 'With the development of chromatin conformation capture technology and its high-throughput derivative Hi-C sequencing, studies of the three-dimensional interactome of the genome that involve multiple Hi-C datasets are becoming available. To account for the technology-driven biases unique to each dataset, there is a distinct need for methods to jointly normalize multiple Hi-C datasets. Previous attempts at removing biases from Hi-C data have made use of techniques which normalize individual Hi-C datasets, or, at best, jointly normalize two datasets.'),
(82512, 2019, 'bio', 'InterMineR: an R package for InterMine databases', 'Kyritsis, Konstantinos A|Wang, Bing|Sullivan, Julie|Lyne, Rachel|Micklem, Gos', 'Laboratory of Pharmacology, School of Pharmacy, Aristotle University of Thessaloniki, Thessaloniki GR-54124, Greece.|Department of Genetics, University of Cambridge, Cambridge, UK.|Department of Genetics, University of Cambridge, Cambridge, UK.|Department of Genetics, University of Cambridge, Cambridge, UK.|Department of Genetics, University of Cambridge, Cambridge, UK.', 'Greece|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz039', '30668641', 'InterMineR is a package designed to provide a flexible interface between the R programming environment and biological databases built using the InterMine platform. The package offers access to the flexible query builder and the library of term enrichment tools of the InterMine framework, as well as interoperability with other Bioconductor packages. This facilitates automation of data retrieval tasks as well as downstream analysis with existing statistical tools in the R environment.'),
(82513, 2019, 'bio', 'RIscoper: a tool for RNA-RNA interaction extraction from the literature', 'Zhang, Yang|Liu, Tianyuan|Chen, Liqun|Yang, Jinxurong|Yin, Jiayi|Zhang, Yuncong|Yun, Zhixi|Xu, Hao|Ning, Lin|Guo, Fengbiao|Jiang, Yongshuai|Lin, Hao|Wang, Dong|Huang, Yan|Huang, Jian', 'Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.|Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.|Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.|Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.|Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.', 'China|China|China|China|China|China|China|China|China|China|China|China|China|China|China', 10, '-', 'Journal Article', '10.1093/bioinformatics/btz044', '30668649', 'Numerous experimental and computational studies in the biomedical literature have provided considerable amounts of data on diverse RNA-RNA interactions (RRIs). However, few text mining systems for RRIs information extraction are available.'),
(82514, 2019, 'bio', 'Functional data analysis for computational biology', 'Cremona, Marzia A|Xu, Hongyan|Makova, Kateryna D|Reimherr, Matthew|Chiaromonte, Francesca|Madrigal, Pedro', 'Department of Statistics, The Pennsylvania State University, University Park, PA, USA.|Department of Population Health Sciences, Medical College of Georgia, Augusta University, Augusta, GA, USA.|Department of Biology, The Pennsylvania State University, University Park, PA, USA.|Department of Statistics, The Pennsylvania State University, University Park, PA, USA.|Department of Statistics, The Pennsylvania State University, University Park, PA, USA.|Wellcome Trust - MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK.', 'United Kingdom|Georgia|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz045', '30668667', 'Supplementary data are available at Bioinformatics online.'),
(82515, 2019, 'bio', 'The ReproGenomics Viewer: a multi-omics and cross-species resource compatible with single-cell studies for the reproductive science community', 'Darde, Thomas A|Lecluze, Estelle|Lardenois, Aurélie|Stévant, Isabelle|Alary, Nathan|Tüttelmann, Frank|Collin, Olivier|Nef, Serge|Jégou, Bernard|Rolland, Antoine D|Chalmel, Frédéric', 'Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, Rennes F-35000, France.|Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, Rennes F-35000, France.|Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, Rennes F-35000, France.|Department of Genetic Medicine and Development, University of Geneva, Geneva 1211, Switzerland.|Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, Rennes F-35000, France.|Institute of Human Genetics, University of Münster, Münster, Germany.|Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA/INRIA) - GenOuest platform, Université de Rennes 1, Rennes F-35042, France.|Department of Genetic Medicine and Development, University of Geneva, Geneva 1211, Switzerland.|Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, Rennes F-35000, France.|Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, Rennes F-35000, France.|Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, Rennes F-35000, France.', 'France|France|France|Switzerland|France|Germany|France|Switzerland|France|France|France', 13, '-', 'Journal Article', '10.1093/bioinformatics/btz047', '30668675', 'Recent advances in transcriptomics have enabled unprecedented insight into gene expression analysis at a single-cell resolution. While it is anticipated that the number of publications based on such technologies will increase in the next decade, there is currently no public resource to centralize and enable scientists to explore single-cell datasets published in the field of reproductive biology.'),
(82516, 2019, 'bio', 'SRG extractor: a skinny reference genome approach for reduced-representation sequencing', 'Torkamaneh, Davoud|Laroche, Jérôme|Rajcan, Istvan|Belzile, François', 'Département de Phytologie, Université Laval, Québec City, Canada.|Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec City, Canada.|Department of Plant Agriculture, University of Guelph, Guelph, ON, Canada.|Département de Phytologie, Université Laval, Québec City, Canada.', 'Canada|Canada|Canada|Canada', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz043', '30668828', 'Reduced-representation sequencing is a genome-wide scanning method for simultaneous discovery and genotyping of thousands to millions of single nucleotide polymorphisms that is used across a wide range of species. However, in this method a reproducible but very small fraction of the genome is captured for sequencing, while the resulting reads are typically aligned against the entire reference genome.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(82517, 2019, 'bio', 'SVIM: structural variant identification using mapped long reads', 'Heller, David|Vingron, Martin', 'Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany.|Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany.', 'Germany|Germany', 14, '-', 'Journal Article', '10.1093/bioinformatics/btz041', '30668829', 'Structural variants are defined as genomic variants larger than 50 bp. They have been shown to affect more bases in any given genome than single-nucleotide polymorphisms or small insertions and deletions. Additionally, they have great impact on human phenotype and diversity and have been linked to numerous diseases. Due to their size and association with repeats, they are difficult to detect by shotgun sequencing, especially when based on short reads. Long read, single-molecule sequencing technologies like those offered by Pacific Biosciences or Oxford Nanopore Technologies produce reads with a length of several thousand base pairs. Despite the higher error rate and sequencing cost, long-read sequencing offers many advantages for the detection of structural variants. Yet, available software tools still do not fully exploit the possibilities.'),
(82518, 2019, 'bio', 'DASHR 20: integrated database of human small non-coding RNA genes and mature products', 'Kuksa, Pavel P|Amlie-Wolf, Alexandre|Katanixc4x87, xc5xbdivadin|Valladares, Otto|Wang, Li-San|Leung, Yuk Yee', 'Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine.|Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine.|Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine.|Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine.|Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine.|Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine.', '-|-|-|-|-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty709', '30668832', 'Small non-coding RNAs (sncRNAs, <100 nts) are highly abundant RNAs that regulate diverse and often tissue-specific cellular processes by associating with transcription factor complexes or binding to mRNAs. While thousands of sncRNA genes exist in the human genome, no single resource provides searchable, unified annotation, expression and processing information for full sncRNA transcripts and mature RNA products derived from these larger RNAs.'),
(82519, 2019, 'bio', 'Protein fold recognition based on multi-view modeling', 'Yan, Ke|Fang, Xiaozhao|Xu, Yong|Liu, Bin', 'School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Guangdong, China.|School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China.|School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Guangdong, China.|School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Guangdong, China.', 'China|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz040', '30668845', 'Protein fold recognition has attracted increasing attention because it is critical for studies of the 3D structures of proteins and drug design. Researchers have been extensively studying this important task, and several features with high discriminative power have been proposed. However, the development of methods that efficiently combine these features to improve the predictive performance remains a challenging problem.'),
(82520, 2019, 'bio', 'Best-first search guided multistage mass spectrometry-based glycan identification', 'Wang, Yaojun|Bu, Dongbo|Huang, Chuncui|Wang, Hui|Zhou, Jinyu|Dong, Junchuan|Pan, Weiyi|Zhang, Jingwei|Zhang, Qi|Li, Yan|Sun, Shiwei', 'Key Lab of Intelligent Information Processing, Institute of Computing and Technology, Chinese Academy of Sciences, Beijing, China.|Key Lab of Intelligent Information Processing, Institute of Computing and Technology, Chinese Academy of Sciences, Beijing, China.|Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.|Key Lab of Intelligent Information Processing, Institute of Computing and Technology, Chinese Academy of Sciences, Beijing, China.|Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.|Key Lab of Intelligent Information Processing, Institute of Computing and Technology, Chinese Academy of Sciences, Beijing, China.|Key Lab of Intelligent Information Processing, Institute of Computing and Technology, Chinese Academy of Sciences, Beijing, China.|Key Lab of Intelligent Information Processing, Institute of Computing and Technology, Chinese Academy of Sciences, Beijing, China.|Key Lab of Intelligent Information Processing, Institute of Computing and Technology, Chinese Academy of Sciences, Beijing, China.|Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.|Key Lab of Intelligent Information Processing, Institute of Computing and Technology, Chinese Academy of Sciences, Beijing, China.', 'China|China|China|China|China|China|China|China|China|China|China', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz056', '30689704', 'Glycan identification has long been hampered by complicated branching patterns and various isomeric structures of glycans. Multistage mass spectrometry (MSn) is a promising glycan identification technique as it generates multiple-level fragments of a glycan, which can be explored to deduce branching pattern of the glycan and further distinguish it from other candidates with identical mass. However, the automatic glycan identification still remains a challenge since it mainly relies on expertise to guide a MSn instrument to generate spectra.'),
(82521, 2019, 'bio', 'Efficient implementation of convolutional neural networks in the data processing of two-photon in vivo imaging', 'Wang, Yangzhen|Su, Feng|Wang, Shanshan|Yang, Chaojuan|Tian, Yonglu|Yuan, Peijiang|Liu, Xiaorong|Xiong, Wei|Zhang, Chen', 'School of Basic Medical Sciences, Beijing Key Laboratory of Neural Regeneration and Repair, Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.|School of Basic Medical Sciences, Beijing Key Laboratory of Neural Regeneration and Repair, Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.|School of Basic Medical Sciences, Beijing Key Laboratory of Neural Regeneration and Repair, Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.|School of Basic Medical Sciences, Beijing Key Laboratory of Neural Regeneration and Repair, Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.|School of Basic Medical Sciences, Beijing Key Laboratory of Neural Regeneration and Repair, Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.|Robotics Institute, Beihang University, Beijing, China.|School of Basic Medical Sciences, Beijing Key Laboratory of Neural Regeneration and Repair, Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.|School of Life Sciences, Tsinghua University, Beijing, China.|School of Basic Medical Sciences, Beijing Key Laboratory of Neural Regeneration and Repair, Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.', 'China|China|China|China|China|China|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz055', '30689714', 'Functional imaging at single-neuron resolution offers a highly efficient tool for studying the functional connectomics in the brain. However, mainstream neuron-detection methods focus on either the morphologies or activities of neurons, which may lead to the extraction of incomplete information and which may heavily rely on the experience of the experimenters.'),
(82522, 2019, 'bio', 'Inferring clonal heterogeneity in cancer using SNP arrays and whole genome sequencing', 'Zucker, Mark R|Abruzzo, Lynne V|Herling, Carmen D|Barron, Lynn L|Keating, Michael J|Abrams, Zachary B|Heerema, Nyla|Coombes, Kevin R', 'Department of Biomedical Informatics, Wexner Medical Center, The Ohio State University, Columbus, OH, USA.|Department of Pathology, Wexner Medical Center, The Ohio State University, Columbus, OH, USA.|Department I of Internal Medicine, CIO Köln-Bonn, and CECAD, University of Cologne, Cologne, Germany.|Department of Hematopathology, University of Texas MD Anderson Cancer Center, Texas, MD, USA.|Department of Leukemia, University of Texas MD Anderson Cancer Center, Texas, MD, USA.|Department of Biomedical Informatics, Wexner Medical Center, The Ohio State University, Columbus, OH, USA.|Department of Pathology, Wexner Medical Center, The Ohio State University, Columbus, OH, USA.|Department of Biomedical Informatics, Wexner Medical Center, The Ohio State University, Columbus, OH, USA.', 'United States|United States|Germany|United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz057', '30689715', 'Clonal heterogeneity is common in many types of cancer, including chronic lymphocytic leukemia (CLL). Previous research suggests that the presence of multiple distinct cancer clones is associated with clinical outcome. Detection of clonal heterogeneity from high throughput data, such as sequencing or single nucleotide polymorphism (SNP) array data, is important for gaining a better understanding of cancer and may improve prediction of clinical outcome or response to treatment. Here, we present a new method, CloneSeeker, for inferring clinical heterogeneity from sequencing data, SNP array data, or both.'),
(82523, 2019, 'bio', 'Varanto: variant enrichment analysis and annotation', 'Timonen, Oskari|Särkkä, Mikko|Fülöp, Tibor|Mattsson, Anton|Kekäläinen, Juha|Paananen, Jussi', 'Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland.|Institute of Biomedicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland.|Institute of Biomedicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland.|Institute of Biomedicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland.|Institute of Biomedicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland.|Institute of Biomedicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland.', 'Finland|Finland|Finland|Finland|Finland|Finland', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz046', '30689722', 'Genome-wide association studies (GWAS) aim to identify associations of genetic variations such as single-nucleotide polymorphisms (SNPs) to a specific trait or a disease. Identifying common themes such as pathways, biological processes and diseases associations is needed to further explore and interpret these results. Varanto is a novel web tool for annotating, visualizing and analyzing human genetic variations using diverse data sources. Varanto can be used to query a set of input variations, retrieve their associated variation and gene level annotations, perform annotation enrichment analysis and visualize the results.'),
(82524, 2019, 'bio', 'The interplay between microRNA and alternative splicing of linear and circular RNAs in eleven plant species', 'Wang, Huiyuan|Wang, Huihui|Zhang, Hangxiao|Liu, Sheng|Wang, Yongsheng|Gao, Yubang|Xi, Feihu|Zhao, Liangzhen|Liu, Bo|Reddy, Anireddy S N|Lin, Chentao|Gu, Lianfeng', 'Basic Forestry and Proteomics Research Center, College of Forestry, Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology.|Basic Forestry and Proteomics Research Center, College of Forestry, Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology.|Basic Forestry and Proteomics Research Center, College of Forestry, Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology.|College of Life Science, Fujian Agriculture and Forestry University, Fuzhou, China.|Basic Forestry and Proteomics Research Center, College of Forestry, Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology.|Basic Forestry and Proteomics Research Center, College of Forestry, Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology.|Basic Forestry and Proteomics Research Center, College of Forestry, Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology.|Basic Forestry and Proteomics Research Center, College of Forestry, Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology.|College of Forestry, Fujian Agriculture and Forestry University, Fuzhou, China.|Department of Biology, Program in Molecular Plant Biology, Program in Cell and Molecular Biology, Colorado State University, Fort Collins, CO, USA.|Basic Forestry and Proteomics Research Center, College of Forestry, Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology.|Basic Forestry and Proteomics Research Center, College of Forestry, Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology.', 'United States|United States|United States|China|United States|United States|United States|United States|China|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz038', '30689723', 'MicroRNA (miRNA) and alternative splicing (AS)-mediated post-transcriptional regulation has been extensively studied in most eukaryotes. However, the interplay between AS and miRNAs has not been explored in plants. To our knowledge, the overall profile of miRNA target sites in circular RNAs (circRNA) generated by alternative back splicing has never been reported previously. To address the challenge, we identified miRNA target sites located in alternatively spliced regions of the linear and circular splice isoforms using the up-to-date single-molecule real-time (SMRT) isoform sequencing (Iso-Seq) and Illumina sequencing data in eleven plant species.'),
(82525, 2019, 'bio', 'ensembldb: an R package to create and use Ensembl-based annotation resources', 'Rainer, Johannes|Gatto, Laurent|Weichenberger, Christian X', 'Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Bolzano, Italy.|de Duve Institute, UCLouvain, Brussels, Belgium.|Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Bolzano, Italy.', 'Italy|Belgium|Italy', 7, '-', 'Journal Article', '10.1093/bioinformatics/btz031', '30689724', 'Bioinformatics research frequently involves handling gene-centric data such as exons, transcripts, proteins and their positions relative to a reference coordinate system. The ensembldb Bioconductor package retrieves and stores Ensembl-based genetic annotations and positional information, and furthermore offers identifier conversion and coordinates mappings for gene-associated data. In support of reproducible research, data are tied to Ensembl releases and are kept separately from the software. Premade data packages are available for a variety of genomes and Ensembl releases. Three examples demonstrate typical use cases of this software.'),
(82526, 2019, 'bio', 'Haplotype assembly of autotetraploid potato using integer linear programing', 'Siragusa, Enrico|Haiminen, Niina|Finkers, Richard|Visser, Richard|Parida, Laxmi', 'IBM T J Watson Research Center, Yorktown Heights, NY, USA.|IBM T J Watson Research Center, Yorktown Heights, NY, USA.|Wageningen UR Plant Breeding, Wageningen, The Netherlands.|Wageningen UR Plant Breeding, Wageningen, The Netherlands.|IBM T J Watson Research Center, Yorktown Heights, NY, USA.', 'United States|United States|Netherlands|Netherlands|United States', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz060', '30689725', 'Haplotype assembly of polyploids is an open issue in plant genomics. Recent experimental studies on highly heterozygous autotetraploid potato have shown that available methods do not deliver satisfying results in practice. We propose an optimal method to assemble haplotypes of highly heterozygous polyploids from Illumina short-sequencing reads. Our method is based on a generalization of the existing minimum fragment removal model to the polyploid case and on new integer linear programs to reconstruct optimal haplotypes. We validate our methods experimentally by means of a combined evaluation on simulated and experimental data based on 83 previously sequenced autotetraploid potato cultivars. Results on simulated data show that our methods produce highly accurate haplotype assemblies, while results on experimental data confirm a sensible improvement over the state of the art.'),
(82527, 2019, 'bio', 'Tracing the ancestry of operons in bacteria', 'Nguyen, Huy N|Jain, Ashish|Eulenstein, Oliver|Friedberg, Iddo', 'Department of Veterinary Microbiology and Preventive Medicine, lowa State University, Ames, IA, USA.|Department of Genetics, Development, and Cell Biology, Iowa State University, Ames, IA, USA.|Department of Computer Science, Iowa State University, Ames, IA, USA.|Department of Veterinary Microbiology and Preventive Medicine, lowa State University, Ames, IA, USA.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz053', '30689726', 'Complexity is a fundamental attribute of life. Complex systems are made of parts that together perform functions that a single component, or subsets of components, cannot. Examples of complex molecular systems include protein structures such as the F1Fo-ATPase, the ribosome, or the flagellar motor: each one of these structures requires most or all of its components to function properly. Given the ubiquity of complex systems in the biosphere, understanding the evolution of complexity is central to biology. At the molecular level, operons are classic examples of a complex system. An operons genes are co-transcribed under the control of a single promoter to a polycistronic mRNA molecule, and the operons gene products often form molecular complexes or metabolic pathways. With the large number of complete bacterial genomes available, we now have the opportunity to explore the evolution of these complex entities, by identifying possible intermediate states of operons.'),
(82528, 2019, 'bio', 'DiffNetFDR: differential network analysis with false discovery rate control', 'Zhang, Xiao-Fei|Ou-Yang, Le|Yang, Shuo|Hu, Xiaohua|Yan, Hong', 'Department of Statistics, School of Mathematics and Statistics & Hubei Key Laboratory of Mathematical Sciences, Central China Normal University, Wuhan, China.|Department of Electronic Engineering, Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security, Shenzhen University, Shenzhen, China.|Department of Respiratory Medicine, Wuhan Number 1 Hospital, Wuhan, China.|Department of Information Science, College of Computing and Informatics, Drexel University, Philadelphia, USA.|Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China.', 'China|China|China|United States|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz051', '30689728', 'To identify biological network rewiring under different conditions, we develop a user-friendly R package, named DiffNetFDR, to implement two methods developed for testing the difference in different Gaussian graphical models. Compared to existing tools, our methods have the following features: (i) they are based on Gaussian graphical models which can capture the changes of conditional dependencies, (ii) they determine the tuning parameters in a data-driven manner, (iii) they take a multiple testing procedure to control the overall false discovery rate, and (iv) our approach defines the differential network based on partial correlation coefficients so that the spurious differential edges caused by the variants of conditional variances can be excluded. We also develop a Shiny application to provide easier analysis and visualization. Simulation studies are conducted to evaluate the performance of our methods. We also apply our methods to two real gene expression datasets. The effectiveness of our methods is validated by the biological significance of the identified differential networks.'),
(82529, 2019, 'bio', 'Comparison of statistical algorithms for daily syndromic surveillance aberration detection', 'Noufaily, Angela|Morbey, Roger A|Colxc3xb3n-Gonzxc3xa1lez, Felipe J|Elliot, Alex J|Smith, Gillian E|Lake, Iain R|McCarthy, Noel', 'Statistics and Epidemiology, Warwick Medical School, University of Warwick, Coventry, UK.|Real-time Syndromic Surveillance Team, National Infection Service, Public Health England, Birmingham, UK.|School of Environmental Sciences, University of East Anglia, Norwich, UK.|Real-time Syndromic Surveillance Team, National Infection Service, Public Health England, Birmingham, UK.|Real-time Syndromic Surveillance Team, National Infection Service, Public Health England, Birmingham, UK.|School of Environmental Sciences, University of East Anglia, Norwich, UK.|Population Evidence and Technologies, Warwick Medical School, University of Warwick, Coventry, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty997', '30689731', 'Public health authorities can provide more effective and timely interventions to protect populations during health events if they have effective multi-purpose surveillance systems. These systems rely on aberration detection algorithms to identify potential threats within large datasets. Ensuring the algorithms are sensitive, specific and timely is crucial for protecting public health. Here, we evaluate the performance of three detection algorithms extensively used for syndromic surveillance: the rising activity, multilevel mixed effects, indicator emphasis (RAMMIE) method and the improved quasi-Poisson regression-based method known as Farrington Flexible both currently used at Public Health England, and the Early Aberration Reporting System (EARS) method used at the US Centre for Disease Control and Prevention. We model the wide range of data structures encountered within the daily syndromic surveillance systems used by PHE. We undertake extensive simulations to identify which algorithms work best across different types of syndromes and different outbreak sizes. We evaluate RAMMIE for the first time since its introduction. Performance metrics were computed and compared in the presence of a range of simulated outbreak types that were added to baseline data.'),
(82530, 2019, 'bio', 'DeepAMR for predicting co-occurrent resistance of Mycobacterium tuberculosis', 'Yang, Yang|Walker, Timothy M|Walker, A Sarah|Wilson, Daniel J|Peto, Timothy E A|Crook, Derrick W|Shamout, Farah|CRyPTIC Consortium |Zhu, Tingting|Clifton, David A', 'Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK.|Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital Headley Way, Oxford, UK.|Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital Headley Way, Oxford, UK.|Big Data Institute, Nuffield Department of Population Health, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Old Road Campus, Oxford, UK.|Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital Headley Way, Oxford, UK.|Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital Headley Way, Oxford, UK.|Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK.|Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK.|Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK.|Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz067', '30689732', 'Resistance co-occurrence within first-line anti-tuberculosis (TB) drugs is a common phenomenon. Existing methods based on genetic data analysis of Mycobacterium tuberculosis (MTB) have been able to predict resistance of MTB to individual drugs, but have not considered the resistance co-occurrence and cannot capture latent structure of genomic data that corresponds to lineages.'),
(82531, 2019, 'bio', 'Predicting the optimal growth temperatures of prokaryotes using only genome derived features', 'Sauer, David B|Wang, Da-Neng', 'Department of Cell Biology, and The Helen L. and Martin S. Kimmel Center for Biology and Medicine, Skirball Institute of Biomolecular Medicine, New York University School of Medicine, New York, New York, USA.|Department of Cell Biology, and The Helen L. and Martin S. Kimmel Center for Biology and Medicine, Skirball Institute of Biomolecular Medicine, New York University School of Medicine, New York, New York, USA.', 'United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz059', '30689741', 'Optimal growth temperature is a fundamental characteristic of all living organisms. Knowledge of this temperature is central to the study of a prokaryote, the thermal stability and temperature dependent activity of its genes, and the bioprospecting of its genome for thermally adapted proteins. While high throughput sequencing methods have dramatically increased the availability of genomic information, the growth temperatures of the source organisms are often unknown. This limits the study and technological application of these species and their genomes. Here, we present a novel method for the prediction of growth temperatures of prokaryotes using only genomic sequences.'),
(82532, 2019, 'bio', 'Comorbidity4j: a tool for interactive analysis of disease comorbidities over large patient datasets', 'Ronzano, Francesco|Gutiérrez-Sacristán, Alba|Furlong, Laura I', 'Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences (DCEXS), Universitat Pompeu Fabra (UPF), Barcelona, Spain.|Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.|Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences (DCEXS), Universitat Pompeu Fabra (UPF), Barcelona, Spain.', 'Spain|United States|Spain', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz061', '30689768', 'Pushed by the growing availability of Electronic Health Records for data mining, the identification of relevant patterns of co-occurring diseases over a population of individuals-referred to as comorbidity analysis-has become a common practice due to its great impact on life expectancy, quality of life and healthcare costs. In this scenario, the availability of scalable, easy-to-use software frameworks tailored to support the study of comorbidities over large datasets of patients is essential. We introduce Comorbidity4j, an open-source Java tool to perform systematic analyses of comorbidities by generating interactive Web visualizations to explore and refine results. Comorbidity4j processes user-provided clinical data by identifying significant disease co-occurrences and computing a comprehensive set of comorbidity indices. Patients can be stratified by sex, age and user-defined criteria. Comorbidity4j supports the analysis of the temporal directionality and the sex ratio of diseases. The incremental upload and validation of clinical input data and the customization of comorbidity analyses are performed by an interactive Web interface. With a Web browser, the results of such analyses can be filtered with respect to comorbidity indexes and disease names and explored by means of heat maps and network charts of disease associations. Comorbidity4j is optimized to efficiently process large datasets of clinical data. Besides a software tool for local execution, we provide Comorbidity4j as a Web service to enable users to perform online comorbidity analyses.'),
(82533, 2019, 'bio', 'GRIMM: GRaph IMputation and matching for HLA genotypes', 'Maiers, Martin|Halagan, Michael|Gragert, Loren|Bashyal, Pradeep|Brelsford, Jason|Schneider, Joel|Lutsker, Polina|Louzoun, Yoram', 'Department of biomedical informatics, Center for Blood and Marrow Transplant Research, Minneapolis, MN, USA.|Department of biomedical informatics, Center for Blood and Marrow Transplant Research, Minneapolis, MN, USA.|Department of biomedical informatics, Center for Blood and Marrow Transplant Research, Minneapolis, MN, USA.|Department of biomedical informatics, Center for Blood and Marrow Transplant Research, Minneapolis, MN, USA.|Department of biomedical informatics, Center for Blood and Marrow Transplant Research, Minneapolis, MN, USA.|Department of biomedical informatics, Center for Blood and Marrow Transplant Research, Minneapolis, MN, USA.|Department of Mathematics, Bar Ilan University, Ramat Gan, Israel.|Department of Mathematics, Bar Ilan University, Ramat Gan, Israel.', 'United States|United States|United States|United States|United States|United States|Israel|Israel', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz050', '30689784', 'For over 10xe2x80x89years allele-level HLA matching for bone marrow registries has been performed in a probabilistic context. HLA typing technologies provide ambiguous results in that they could not distinguish among all known HLA alleles equences, therefore registries have implemented matching algorithms that provide lists of donor and cord blood units ordered in terms of the likelihood of allele-level matching at specific HLA loci. With the growth of registry sizes, current match algorithm implementations are unable to provide match results in real time.'),
(82534, 2019, 'bio', 'NEMO: cancer subtyping by integration of partial multi-omic data', 'Rappoport, Nimrod|Shamir, Ron', 'Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.|Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.', 'Israel|Israel', 8, '-', 'Journal Article', '10.1093/bioinformatics/btz058', '30698637', 'Cancer subtypes were usually defined based on molecular characterization of single omic data. Increasingly, measurements of multiple omic profiles for the same cohort are available. Defining cancer subtypes using multi-omic data may improve our understanding of cancer, and suggest more precise treatment for patients.'),
(82535, 2019, 'bio', 'Reviewer-coerced citation: case report, update on journal policy and suggestions for future prevention', 'Wren, Jonathan D|Valencia, Alfonso|Kelso, Janet', 'Arthritis and Clinical Immunology Research Program, Division of Genomics and Data Sciences, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA.|ICREA, Barcelona, 08010, Spain & Barcelona Supercomputing Center (BSC), Barcelona, Spain.|Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany.', 'United States|Spain|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz071', '30698640', '-'),
(82536, 2019, 'bio', 'Predicting runtimes of bioinformatics tools based on historical data: five years of Galaxy usage', 'Tyryshkina, Anastasia|Coraor, Nate|Nekrutenko, Anton', 'Huck Institute of Life Sciences, Neuroscience Program, The Pennsylvania State University, University Park, USA.|Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, USA.|Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, USA.', 'United States|United States|United States', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz054', '30698642', 'One of the many technical challenges that arises when scheduling bioinformatics analyses at scale is determining the appropriate amount of memory and processing resources. Both over- and under-allocation leads to an inefficient use of computational infrastructure. Over allocation locks resources that could otherwise be used for other analyses. Under-allocation causes job failure and requires analyses to be repeated with a larger memory or runtime allowance. We address this challenge by using a historical dataset of bioinformatics analyses run on the Galaxy platform to demonstrate the feasibility of an online service for resource requirement estimation.'),
(82537, 2019, 'bio', 'Rapid alignment-free phylogenetic identification of metagenomic sequences', 'Linard, Benjamin|Swenson, Krister|Pardi, Fabio', 'LIRMM, University of Montpellier, CNRS, Montpellier, France.|LIRMM, University of Montpellier, CNRS, Montpellier, France.|LIRMM, University of Montpellier, CNRS, Montpellier, France.', 'France|France|France', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz068', '30698645', 'Taxonomic classification is at the core of environmental DNA analysis. When a phylogenetic tree can be built as a prior hypothesis to such classification, phylogenetic placement (PP) provides the most informative type of classification because each query sequence is assigned to its putative origin in the tree. This is useful whenever precision is sought (e.g. in diagnostics). However, likelihood-based PP algorithms struggle to scale with the ever-increasing throughput of DNA sequencing.'),
(82538, 2019, 'bio', 'Aligning optical maps to de Bruijn graphs', 'Mukherjee, Kingshuk|Alipanahi, Bahar|Kahveci, Tamer|Salmela, Leena|Boucher, Christina', 'Department of Computer and Information Science and Engineering, College of Engineering, University of Florida, Gainesville, USA.|Department of Computer and Information Science and Engineering, College of Engineering, University of Florida, Gainesville, USA.|Department of Computer and Information Science and Engineering, College of Engineering, University of Florida, Gainesville, USA.|Department of Computer Science, Helsinki Institute for Information Technology HIIT, University of Helsinki, Helsinki, Finland.|Department of Computer and Information Science and Engineering, College of Engineering, University of Florida, Gainesville, USA.', 'United States|United States|United States|Finland|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz069', '30698651', 'Optical maps are high-resolution restriction maps (Rmaps) that give a unique numeric representation to a genome. Used in concert with sequence reads, they provide a useful tool for genome assembly and for discovering structural variations and rearrangements. Although they have been a regular feature of modern genome assembly projects, optical maps have been mainly used in post-processing step and not in the genome assembly process itself. Several methods have been proposed for pairwise alignment of single molecule optical maps-called Rmaps, or for aligning optical maps to assembled reads. However, the problem of aligning an Rmap to a graph representing the sequence data of the same genome has not been studied before. Such an alignment provides a mapping between two sets of data: optical maps and sequence data which will facilitate the usage of optical maps in the sequence assembly step itself.'),
(82539, 2019, 'bio', 'Calculation of accurate interatomic contact surface areas for the quantitative analysis of non-bonded molecular interactions', 'Ribeiro, Judemir|Ríos-Vera, Carlos|Melo, Francisco|Schüller, Andreas', 'Department of Molecular Genetics and Microbiology, School of Biological Sciences, Pontificia Universidad Católica de Chile, Santiago, Chile.|Department of Molecular Genetics and Microbiology, School of Biological Sciences, Pontificia Universidad Católica de Chile, Santiago, Chile.|Department of Molecular Genetics and Microbiology, School of Biological Sciences, Pontificia Universidad Católica de Chile, Santiago, Chile.|Department of Molecular Genetics and Microbiology, School of Biological Sciences, Pontificia Universidad Católica de Chile, Santiago, Chile.', 'Chile|Chile|Chile|Chile', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz062', '30698657', 'Intra- and intermolecular contact surfaces are routinely calculated for a large array of applications in bioinformatics but are typically approximated from differential solvent accessible surface area calculations and not calculated directly. These approximations do not properly take the effects of neighboring atoms into account and tend to deviate considerably from the true contact surface. We implemented an extension of the original Shrake-Rupley algorithm to accurately estimate interatomic contact surface areas of molecular structures and complexes. Our extended algorithm is able to calculate the contact area of an atom to all nearby atoms by directly calculating overlapping surface patches, taking into account the possible shielding effects of neighboring atoms. Here, we present a versatile software tool and web server for the calculation of contact surface areas, as well as buried surface areas and solvent accessible surface areas (SASA) for different types of biomolecules, such as proteins, nucleic acids and small organic molecules. Detailed results are provided in tab-separated values format for analysis and Protein Databank files for visualization. Direct contact surface area calculation resulted in improved accuracy in a benchmark with a non-redundant set of 245 protein-DNA complexes. SASA-based approximations underestimated protein-DNA contact surfaces on average by 40%. This software tool may be useful for surface-based intra- and intermolecular interaction analyses and scoring function development.'),
(82540, 2019, 'bio', 'A unified approach for sparse dynamical system inference from temporal measurements', 'Pantazis, Yannis|Tsamardinos, Ioannis', 'Institute of Applied and Computational Mathematics, Foundation for Research and Technology - Hellas (FORTH), Heraklion, Greece.|Institute of Applied and Computational Mathematics, Foundation for Research and Technology - Hellas (FORTH), Heraklion, Greece.', 'Greece|Greece', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz065', '30715136', 'Temporal variations in biological systems and more generally in natural sciences are typically modeled as a set of ordinary, partial or stochastic differential or difference equations. Algorithms for learning the structure and the parameters of a dynamical system are distinguished based on whether time is discrete or continuous, observations are time-series or time-course and whether the system is deterministic or stochastic, however, there is no approach able to handle the various types of dynamical systems simultaneously.'),
(82541, 2019, 'bio', 'Assembly Graph Browser: interactive visualization of assembly graphs', 'Mikheenko, Alla|Kolmogorov, Mikhail', 'Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, Saint Petersburg State University, Saint Petersburg, Russia.|Department of Computer Science and Engineering, University of California, San Diego, CA, USA.', 'United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz072', '30715194', 'Currently, most genome assembly projects focus on contigs and scaffolds rather than assembly graphs that provide a more comprehensive representation of an assembly. Since interactive visualization of large assembly graphs remains an open problem, we developed an Assembly Graph Browser (AGB) tool that visualizes large assembly graphs, extending the functionality of previously developed visualization approaches. Assembly Graph Browser includes a number of novel functions including repeat analysis, construction of the contracted assembly graphs (i.e. the graphs obtained by collapsing a selected set of edges) and a new approach to visualizing large assembly graphs.'),
(82542, 2019, 'bio', 'GLAD: GLycan Array Dashboard, a visual analytics tool for glycan microarrays', 'Mehta, Akul Y|Cummings, Richard D', 'National Center for Functional Glycomics, Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.|National Center for Functional Glycomics, Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.', 'Israel|Israel', 13, '-', 'Journal Article', '10.1093/bioinformatics/btz075', '30715201', 'Traditional glycan microarray data is typically presented as excel files with limited visualization and interactivity. Thus, comparisons and analysis of glycan array data have been difficult, and there is need for a tool to facilitate data mining of glycan array data.'),
(82543, 2019, 'bio', 'Top-Down Garbage Collector: a tool for selecting high-quality top-down proteomics mass spectra', 'Lima, Diogo B|Silva, André R F|Dupré, Mathieu|Santos, Marlon D M|Clasen, Milan A|Kurt, Louise U|Aquino, Priscila F|Barbosa, Valmir C|Carvalho, Paulo C|Chamot-Rooke, Julia', 'Mass Spectrometry for Biology Unit, CNRS USR 2000, Institut Pasteur, Paris, France.|Laboratory for Structural and Computational Proteomics, Carlos Chagas Institute, Fiocruz, Paraná, Brazil.|Mass Spectrometry for Biology Unit, CNRS USR 2000, Institut Pasteur, Paris, France.|Laboratory for Structural and Computational Proteomics, Carlos Chagas Institute, Fiocruz, Paraná, Brazil.|Laboratory for Structural and Computational Proteomics, Carlos Chagas Institute, Fiocruz, Paraná, Brazil.|Laboratory for Structural and Computational Proteomics, Carlos Chagas Institute, Fiocruz, Paraná, Brazil.|Leônidas and Maria Deane Institute, Fiocruz, Amazonas, Brazil.|Systems Engineering and Computer Science Program, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.|Laboratory for Structural and Computational Proteomics, Carlos Chagas Institute, Fiocruz, Paraná, Brazil.|Mass Spectrometry for Biology Unit, CNRS USR 2000, Institut Pasteur, Paris, France.', 'France|Brazil|France|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|France', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz085', '30715205', 'We present the first tool for unbiased quality control of top-down proteomics datasets. Our tool can select high-quality top-down proteomics spectra, serve as a gateway for building top-down spectral libraries and, ultimately, improve identification rates.'),
(82544, 2019, 'bio', 'ChimeraUGEM: unsupervised gene expression modeling in any given organism', 'Diament, Alon|Weiner, Iddo|Shahar, Noam|Landman, Shira|Feldman, Yael|Atar, Shimshi|Avitan, Meital|Schweitzer, Shira|Yacoby, Iftach|Tuller, Tamir', 'Department of Biomedical Engineering, The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv, Israel.|Department of Biomedical Engineering, The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv, Israel.|School of Plant Sciences and Food Security, The George S. Wise Faculty of Life Sciences, Tel Aviv, Israel.|School of Plant Sciences and Food Security, The George S. Wise Faculty of Life Sciences, Tel Aviv, Israel.|School of Plant Sciences and Food Security, The George S. Wise Faculty of Life Sciences, Tel Aviv, Israel.|Department of Biomedical Engineering, The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv, Israel.|Department of Biomedical Engineering, The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv, Israel.|School of Plant Sciences and Food Security, The George S. Wise Faculty of Life Sciences, Tel Aviv, Israel.|School of Plant Sciences and Food Security, The George S. Wise Faculty of Life Sciences, Tel Aviv, Israel.|Department of Biomedical Engineering, The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv, Israel.', 'Israel|Israel|Israel|Israel|Israel|Israel|Israel|Israel|Israel|Israel', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz080', '30715207', 'Regulation of the amount of protein that is synthesized from genes has proved to be a serious challenge in terms of analysis and prediction, and in terms of engineering and optimization, due to the large diversity in expression machinery across species.'),
(82545, 2019, 'bio', 'Batch-normalization of cerebellar and medulloblastoma gene expression datasets utilizing empirically defined negative control genes', 'Weishaupt, Holger|Johansson, Patrik|Sundström, Anders|Lubovac-Pilav, Zelmina|Olsson, Björn|Nelander, Sven|Swartling, Fredrik J', 'Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden.|Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden.|Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden.|Division for Biology and Bioinformatics, School of Bioscience, The Systems Biology Research Centre, University of Skövde, Skövde, Sweden.|Division for Biology and Bioinformatics, School of Bioscience, The Systems Biology Research Centre, University of Skövde, Skövde, Sweden.|Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden.|Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden.', 'Sweden|Sweden|Sweden|Sweden|Sweden|Sweden|Sweden', 4, '-', 'Journal Article', '10.1093/bioinformatics/btz066', '30715209', 'Medulloblastoma (MB) is a brain cancer predominantly arising in children. Roughly 70% of patients are cured today, but survivors often suffer from severe sequelae. MB has been extensively studied by molecular profiling, but often in small and scattered cohorts. To improve cure rates and reduce treatment side effects, accurate integration of such data to increase analytical power will be important, if not essential.'),
(82546, 2019, 'bio', 'PROSSTT: probabilistic simulation of single-cell RNA-seq data for complex differentiation processes', 'Papadopoulos, Nikolaos|Gonzalo, Parra R|Söding, Johannes', 'Quantitative and Computational Biology, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany.|Quantitative and Computational Biology, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany.|Quantitative and Computational Biology, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany.', 'Germany|Germany|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz078', '30715210', 'Cellular lineage trees can be derived from single-cell RNA sequencing snapshots of differentiating cells. Currently, only datasets with simple topologies are available. To test and further develop tools for lineage tree reconstruction, we need test datasets with known complex topologies. PROSSTT can simulate scRNA-seq datasets for differentiation processes with lineage trees of any desired complexity, noise level, noise model and size. PROSSTT also provides scripts to quantify the quality of predicted lineage trees.'),
(82547, 2019, 'bio', 'ebGSEA: an improved Gene Set Enrichment Analysis method for Epigenome-Wide-Association Studies', 'Dong, Danyue|Tian, Yuan|Zheng, Shijie C|Teschendorff, Andrew E', 'CAS Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institute for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.|CAS Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institute for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.|CAS Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institute for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.|CAS Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institute for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.', 'China|China|China|China', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz073', '30715212', 'The biological interpretation of differentially methylated sites derived from Epigenome-Wide-Association Studies (EWAS) remains a significant challenge. Gene Set Enrichment Analysis (GSEA) is a general tool to aid biological interpretation, yet its correct and unbiased implementation in the EWAS context is difficult due to the differential probe representation of Illumina Infinium DNA methylation beadchips.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(82548, 2019, 'bio', 'SaGePhy: an improved phylogenetic simulation framework for gene and subgene evolution', 'Kundu, Soumya|Bansal, Mukul S', 'Department of Computer Science & Engineering, Storrs, CT, USA.|Department of Computer Science & Engineering, Storrs, CT, USA.', 'United States|United States', 4, '-', 'Journal Article', '10.1093/bioinformatics/btz081', '30715213', 'SaGePhy is a software package for improved phylogenetic simulation of gene and subgene evolution. SaGePhy can be used to generate species trees, gene trees and subgene or (protein) domain trees using a probabilistic birth-death process that allows for gene and subgene duplication, horizontal gene and subgene transfer and gene and subgene loss. SaGePhy implements a range of important features not found in other phylogenetic simulation frameworks/software. These include (i) simulation of subgene or domain level evolution inside one or more gene trees, (ii) simultaneous simulation of both additive and replacing horizontal gene/subgene transfers and (iii) probabilistic sampling of species tree and gene tree nodes, respectively, for gene- and domain-family birth. SaGePhy is open-source, platform independent and written in Java and Python.'),
(82549, 2019, 'bio', 'PMC text mining subset in BioC: about three million full-text articles and growing', 'Comeau, Donald C|Wei, Chih-Hsuan|Islamaj Doxc4x9fan, Rezarta|Lu, Zhiyong', 'National Center for Biotechnology Information (NCBI), U.S. Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD, USA.|National Center for Biotechnology Information (NCBI), U.S. Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD, USA.|National Center for Biotechnology Information (NCBI), U.S. Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD, USA.|National Center for Biotechnology Information (NCBI), U.S. Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD, USA.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz070', '30715220', 'Interest in text mining full-text biomedical research articles is growing. To facilitate automated processing of nearly 3 million full-text articles (in PubMed Centralxc2xae Open Access and Author Manuscript subsets) and to improve interoperability, we convert these articles to BioC, a community-driven simple data structure in either XML or JavaScript Object Notation format for conveniently sharing text and annotations.'),
(82550, 2019, 'bio', 'Multispectral tracing in densely labeled mouse brain with nTracer', 'Roossien, Douglas H|Sadis, Benjamin V|Yan, Yan|Webb, John M|Min, Lia Y|Dizaji, Aslan S|Bogart, Luke J|Mazuski, Cristina|Huth, Robert S|Stecher, Johanna S|Akula, Sriakhila|Shen, Fred|Li, Ye|Xiao, Tingxin|Vandenbrink, Madeleine|Lichtman, Jeff W|Hensch, Takao K|Herzog, Erik D|Cai, Dawen', 'Department of Cell and Developmental Biology, University of Michigan Medical School, Ann Arbor, MI, USA.|Department of Cell and Developmental Biology, University of Michigan Medical School, Ann Arbor, MI, USA.|Department of Cell and Developmental Biology, University of Michigan Medical School, Ann Arbor, MI, USA.|Department of Biology, Washington University in St. Louis, St. Louis, MO, USA.|Department of Cell and Developmental Biology, University of Michigan Medical School, Ann Arbor, MI, USA.|Department of Cell and Developmental Biology, University of Michigan Medical School, Ann Arbor, MI, USA.|Department of Neurology, Boston Childrens Hospital, Harvard Medical School, Boston, MA, USA.|Department of Biology, Washington University in St. Louis, St. Louis, MO, USA.|Department of Cell and Developmental Biology, University of Michigan Medical School, Ann Arbor, MI, USA.|Department of Cell and Developmental Biology, University of Michigan Medical School, Ann Arbor, MI, USA.|Department of Cell and Developmental Biology, University of Michigan Medical School, Ann Arbor, MI, USA.|Department of Cell and Developmental Biology, University of Michigan Medical School, Ann Arbor, MI, USA.|Department of Cell and Developmental Biology, University of Michigan Medical School, Ann Arbor, MI, USA.|Department of Biology, University of Toronto, Toronto, ON, Canada.|Department of Cell and Developmental Biology, University of Michigan Medical School, Ann Arbor, MI, USA.|Department of Molecular and Cellular Biology Cambridge, MA, USA.|Department of Neurology, Boston Childrens Hospital, Harvard Medical School, Boston, MA, USA.|Department of Biology, Washington University in St. Louis, St. Louis, MO, USA.|Department of Cell and Developmental Biology, University of Michigan Medical School, Ann Arbor, MI, 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|Canada|United States|United States|United States|United States|United Sta', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz084', '30715234', 'This note describes nTracer, an ImageJ plug-in for user-guided, semi-automated tracing of multispectral fluorescent tissue samples. This approach allows for rapid and accurate reconstruction of whole cell morphology of large neuronal populations in densely labeled brains.'),
(82551, 2019, 'bio', 'G4Hunter web application: a web server for G-quadruplex prediction', 'Brxc3xa1zda, Vxc3xa1clav|Kolomaznxc3xadk, Jan|Lxc3xbdsek, Jixc5x99xc3xad|Bartas, Martin|Fojta, Miroslav|xc5xa0xc5xa5astnxc3xbd, Jixc5x99xc3xad|Mergny, Jean-Louis', 'Department of biophysical chemistry and molecular oncology, Institute of Biophysics, Academy of Sciences of the Czech Republic, Brno, Czech Republic.|Department of Informatics, Mendel University in Brno, Brno, Czech Republic.|Department of Informatics, Mendel University in Brno, Brno, Czech Republic.|Department of biophysical chemistry and molecular oncology, Institute of Biophysics, Academy of Sciences of the Czech Republic, Brno, Czech Republic.|Department of biophysical chemistry and molecular oncology, Institute of Biophysics, Academy of Sciences of the Czech Republic, Brno, Czech Republic.|Department of Informatics, Mendel University in Brno, Brno, Czech Republic.|Department of biophysical chemistry and molecular oncology, Institute of Biophysics, Academy of Sciences of the Czech Republic, Brno, Czech Republic.', '-|-|-|-|-|-|-', 15, '-', 'Journal Article', '10.1093/bioinformatics/btz087', '30721922', 'Expanding research highlights the importance of guanine quadruplex structures. Therefore, easy-accessible tools for quadruplex analyses in DNA and RNA molecules are important for the scientific community.'),
(82552, 2019, 'bio', 'IGLOSS: iterative gapless local similarity search', 'Rabar, Braslav|Zagorxc5xa1xc4x8dak, Maja|Ristov, Strahil|Rosenzweig, Martin|Goldstein, Pavle', 'Mathematics Department, Faculty of Natural Sciences and Mathematics, Zagreb, Croatia.|Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, Slovenia.|Division of Electronics, Ruxc4x91er Boxc5xa1kovixc4x87 Institute, Zagreb, Croatia.|Mathematics Department, Faculty of Natural Sciences and Mathematics, Zagreb, Croatia.|Mathematics Department, Faculty of Natural Sciences and Mathematics, Zagreb, Croatia.', 'Croatia|Slovenia|Croatia|Croatia|Croatia', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz086', '30721953', 'Searching for local sequence patterns is one of the basic tasks in bioinformatics. Sequence patterns might have structural, functional or some other relevance, and numerous methods have been developed to detect and analyze them. These methods often depend on the wealth of information already collected. The explosion in the number of newly available sequences calls for novel methods to explore local sequence similarity. We have developed a new method for iterative motif scanning that will look for ungapped sequence patterns similar to a submitted query. Using careful parameter estimation and an adaptation of a fast string-matching algorithm, the method performs significantly better in this context than the existing software.'),
(82553, 2019, 'bio', 'Structured crowdsourcing enables convolutional segmentation of histology images', 'Amgad, Mohamed|Elfandy, Habiba|Hussein, Hagar|Atteya, Lamees A|Elsebaie, Mai A T|Abo Elnasr, Lamia S|Sakr, Rokia A|Salem, Hazem S E|Ismail, Ahmed F|Saad, Anas M|Ahmed, Joumana|Elsebaie, Maha A T|Rahman, Mustafijur|Ruhban, Inas A|Elgazar, Nada M|Alagha, Yahya|Osman, Mohamed H|Alhusseiny, Ahmed M|Khalaf, Mariam M|Younes, Abo-Alela F|Abdulkarim, Ali|Younes, Duaa M|Gadallah, Ahmed M|Elkashash, Ahmad M|Fala, Salma Y|Zaki, Basma M|Beezley, Jonathan|Chittajallu, Deepak R|Manthey, David|Gutman, David A|Cooper, Lee A D', 'Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA.|Department of Pathology, National Cancer Institute, Cairo, Egypt.|Department of Medicine, Cairo University, Cairo, Egypt.|Egyptian Ministry of Health, Cairo, Egypt.|Department of Medicine, Ain Shams University, Cairo, Egypt.|Department of Medicine, Menoufia University, Menoufia, Egypt.|Department of Medicine, Menoufia University, Menoufia, Egypt.|Department of Medicine, Ain Shams University, Cairo, Egypt.|Department of Pathology, Medical Research Institute, Alexandria University, Alexandria, Egypt.|Department of Medicine, Ain Shams University, Cairo, Egypt.|Department of Medicine, Cairo University, Cairo, Egypt.|Department of Medicine, Ain Shams University, Cairo, Egypt.|Department of Medicine, Chittagong University, Chittagong, Bangladesh.|Department of Medicine, Damascus University, Damascus, Syria.|Department of Medicine, Mansoura University, Mansoura, Egypt.|Department of Medicine, Cairo University, Cairo, Egypt.|Department of Medicine, Zagazig University, Zagazig, Egypt.|Department of Medicine, Mansoura University, Mansoura, Egypt.|Department of Medicine, Batterjee Medical College, Jeddah, Saudi Arabia.|Department of Medicine, Ain Shams University, Cairo, Egypt.|Department of Medicine, Cairo University, Cairo, Egypt.|Department of Medicine, Ain Shams University, Cairo, Egypt.|Department of Medicine, Ain Shams University, Cairo, Egypt.|Department of Medicine, Cairo University, Cairo, Egypt.|Department of Medicine, Suez Canal University, Ismailia, Egypt.|Department of Medicine, Suez Canal University, Ismailia, Egypt.|Kitware Inc., Clifton Park, NY, USA.|Kitware Inc., Clifton Park, NY, USA.|Kitware Inc., Clifton Park, NY, USA.|Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA.|Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA.', 'United States|Egypt|Egypt|Egypt|Egypt|Egypt|Egypt|Egypt|Egypt|Egypt|Egypt|Egypt|Bangladesh|United States|Egypt|Egypt|Egypt|E', 9, '-', 'Journal Article', '10.1093/bioinformatics/btz083', '30726865', 'While deep-learning algorithms have demonstrated outstanding performance in semantic image segmentation tasks, large annotation datasets are needed to create accurate models. Annotation of histology images is challenging due to the effort and experience required to carefully delineate tissue structures, and difficulties related to sharing and markup of whole-slide images.'),
(82554, 2019, 'bio', 'RiboD: a comprehensive database for prokaryotic riboswitches', 'Mukherjee, Sumit|Das Mandal, Sukhen|Gupta, Nikita|Drory-Retwitzer, Matan|Barash, Danny|Sengupta, Supratim', 'Department of Physical Sciences, Indian Institute of Science Education and Research Kolkata, Mohanpur-741246, India.|Department of Biological Sciences, Indian Institute of Science Education and Research Kolkata, Mohanpur-741246, India.|Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (BHU) Varanasi, Varanasi, India.|Department of Computer Science, Ben-Gurion University, Beer-Sheva, Israel.|Department of Computer Science, Ben-Gurion University, Beer-Sheva, Israel.|Department of Physical Sciences, Indian Institute of Science Education and Research Kolkata, Mohanpur-741246, India.', 'India|India|India|Israel|Israel|India', 7, '-', 'Journal Article', '10.1093/bioinformatics/btz093', '30726866', 'Riboswitches are cis-regulatory non-coding genomic segments that control the expression of downstream genes by undergoing conformational change upon ligand binding. We present a comprehensive database of prokaryotic riboswitches that allows the user to search for riboswitches using multiple criteria, extract information about riboswitch location and gene/operon it regulates. RiboD provides a very useful resource that can be utilized for the better understanding of riboswitch-based gene regulation in bacteria and archaea.'),
(82555, 2019, 'bio', 'Classifying tumors by supervised network propagation', 'Zhang, Wei|Ma, Jianzhu|Ideker, Trey', 'Department of Physical Sciences, Indian Institute of Science Education and Research Kolkata, Mohanpur-741246, India.|Department of Physical Sciences, Indian Institute of Science Education and Research Kolkata, Mohanpur-741246, India.|Department of Physical Sciences, Indian Institute of Science Education and Research Kolkata, Mohanpur-741246, India.', 'India|India|India', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty1072', '30726869', '-'),
(82556, 2019, 'bio', 'How well do RNA-Seq differential gene expression tools perform in a complex eukaryote? A case study in Arabidopsis thaliana', 'Froussios, Kimon|Schurch, Nick J|Mackinnon, Katarzyna|Gierlixc5x84ski, Marek|Duc, Cxc3xa9line|Simpson, Gordon G|Barton, Geoffrey J', 'Division of Computational Biology, University of Dundee, Dundee, UK.|Division of Computational Biology, University of Dundee, Dundee, UK.|Centre for Gene Regulation and Expression, School of Life Sciences, University of Dundee, Dundee, UK.|Division of Computational Biology, University of Dundee, Dundee, UK.|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.|Division of Computational Biology, University of Dundee, Dundee, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz089', '30726870', 'RNA-seq experiments are usually carried out in three or fewer replicates. In order to work well with so few samples, differential gene expression (DGE) tools typically assume the form of the underlying gene expression distribution. In this paper, the statistical properties of gene expression from RNA-seq are investigated in the complex eukaryote, Arabidopsis thaliana, extending and generalizing the results of previous work in the simple eukaryote Saccharomyces cerevisiae.'),
(82557, 2019, 'bio', 'Incorporating alignment uncertainty into Felsensteins phylogenetic bootstrap to improve its reliability', 'Chang, Jia-Ming|Floden, Evan W|Herrero, Javier|Gascuel, Olivier|Di Tommaso, Paolo|Notredame, Cedric', 'European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom.|Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain.|European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom.|Unité Bioinformatique Evolutive, Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI)-USR 3756 CNRS and Institut Pasteur, Paris, France.|Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain.|Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain.', 'United Kingdom|Spain|United Kingdom|France|Spain|Spain', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz082', '30726875', 'Most evolutionary analyses are based on pre-estimated multiple sequence alignment. Wong et al. established the existence of an uncertainty induced by multiple sequence alignment when reconstructing phylogenies. They were able to show that in many cases different aligners produce different phylogenies, with no simple objective criterion sufficient to distinguish among these alternatives.'),
(82558, 2019, 'bio', 'Miso: an R package for multiple isotope labeling assisted metabolomics data analysis', 'Dong, Yonghui|Feldberg, Liron|Aharoni, Asaph', 'Department of Plant Sciences, Weizmann Institute of Science, Rehovot, Israel.|Department of Analytical chemistry, Israel Institute for Biological Research, Ness Ziona, Israel.|Department of Plant Sciences, Weizmann Institute of Science, Rehovot, Israel.', 'Israel|Israel|Israel', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz092', '30726876', 'The use of stable isotope labeling is highly advantageous for structure elucidation in metabolomics studies. However, computational tools dealing with multiple-precursor-based labeling studies are still missing. Hence, we developed Miso, an R package providing automated and efficient data analysis workflow to detect the complete repertoire of labeled molecules from multiple-precursor-based labeling experiments.'),
(82559, 2019, 'bio', 'KOnezumi: a web application for automating gene disruption strategies to generate knockout mice', 'Kuno, Akihiro|Mizuno, Seiya|Takahashi, Satoru', 'Department of Anatomy and Embryology, Faculty of Medicine, Tsukuba, Ibaraki, Japan.|Laboratory Animal Resource Center, University of Tsukuba, Tsukuba, Ibaraki, Japan.|Department of Anatomy and Embryology, Faculty of Medicine, Tsukuba, Ibaraki, Japan.', 'Japan|Japan|Japan', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz090', '30726877', 'Although gene editing using the CRISPR/Cas9 system enables the rapid generation of knockout mice, constructing an optimal gene disruption strategy is still labourious. Here, we propose KOnezumi, a simple and user-friendly web application, for use in automating the design of knockout strategies for multiple genes. Users only need to input gene symbols, and then KOnezumi returns target exons, gRNA candidates to delete the target exons, genotyping PCR primers, nucleotide sequences of the target exons and coding sequences of expected deletion products. KOnezumi enables users to easily and rapidly apply a rational strategy to accelerate the generation of KO mice using the CRISPR/Cas9 system.'),
(82560, 2019, 'bio', 'Finding de novo methylated DNA motifs', 'Ngo, Vu|Wang, Mengchi|Wang, Wei', 'Graduate Program of Bioinformatics and Systems Biology, University of California at San Diego, La Jolla, CA, USA.|Graduate Program of Bioinformatics and Systems Biology, University of California at San Diego, La Jolla, CA, USA.|Graduate Program of Bioinformatics and Systems Biology, University of California at San Diego, La Jolla, CA, USA.', 'United States|United States|United States', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz079', '30726880', 'Increasing evidence has shown that nucleotide modifications such as methylation and hydroxymethylation on cytosine would greatly impact the binding of transcription factors (TFs). However, there is a lack of motif finding algorithms with the function to search for motifs with modified bases. In this study, we expand on our previous motif finding pipeline Epigram to provide systematic de novo motif discovery and performance evaluation on methylated DNA motifs.'),
(82561, 2019, 'bio', 'Variational infinite heterogeneous mixture model for semi-supervised clustering of heart enhancers', 'Mehdi, Tahmid F|Singh, Gurdeep|Mitchell, Jennifer A|Moses, Alan M', 'Department of Computer Science, University of Toronto, Toronto, ON, Canada.|Department of Cell & Systems Biology, University of Toronto, Toronto, ON, Canada.|Department of Cell & Systems Biology, University of Toronto, Toronto, ON, Canada.|Department of Computer Science, University of Toronto, Toronto, ON, Canada.', 'Canada|Canada|Canada|Canada', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz064', '30753279', 'Mammalian genomes can contain thousands of enhancers but only a subset are actively driving gene expression in a given cellular context. Integrated genomic datasets can be harnessed to predict active enhancers. One challenge in integration of large genomic datasets is the increasing heterogeneity: continuous, binary and discrete features may all be relevant. Coupled with the typically small numbers of training examples, semi-supervised approaches for heterogeneous data are needed, however, current enhancer prediction methods are not designed to handle heterogeneous data in the semi-supervised paradigm.'),
(82562, 2019, 'bio', 'Biological sequence modeling with convolutional kernel networks', 'Chen, Dexiong|Jacob, Laurent|Mairal, Julien', 'Université Grenoble Alpes, INRIA, CNRS, Grenoble INP, LJK, Grenoble, Isère France.|University of Lyon, Université Lyon 1, CNRS, Laboratoire de Biométrie et Biologie Évolutive UMR 5558, Lyon, Rhône France.|Université Grenoble Alpes, INRIA, CNRS, Grenoble INP, LJK, Grenoble, Isère France.', 'France|France|France', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz094', '30753280', 'The growing number of annotated biological sequences available makes it possible to learn genotype-phenotype relationships from data with increasingly high accuracy. When large quantities of labeled samples are available for training a model, convolutional neural networks can be used to predict the phenotype of unannotated sequences with good accuracy. Unfortunately, their performance with medium- or small-scale datasets is mitigated, which requires inventing new data-efficient approaches.'),
(82563, 2019, 'bio', 'Two-Tier Mapper, an unbiased topology-based clustering method for enhanced global gene expression analysis', 'Jeitziner, Rachel|Carrière, Mathieu|Rougemont, Jacques|Oudot, Steve|Hess, Kathryn|Brisken, Cathrin', 'School of Life Sciences, Swiss Institute for Experimental Cancer Research, Ecole Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland.|INRIA Saclay, Palaiseau FR-91120, France.|DP Physique théorique, Université de Genève, Genève CH-1205, Switzerland.|INRIA Saclay, Palaiseau FR-91120, France.|Brain and Mind Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland.|School of Life Sciences, Swiss Institute for Experimental Cancer Research, Ecole Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland.', 'Switzerland|France|Switzerland|France|Switzerland|Switzerland', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz052', '30753284', 'Unbiased clustering methods are needed to analyze growing numbers of complex datasets. Currently available clustering methods often depend on parameters that are set by the user, they lack stability, and are not applicable to small datasets. To overcome these shortcomings we used topological data analysis, an emerging field of mathematics that discerns additional feature and discovers hidden insights on datasets and has a wide application range.'),
(82564, 2019, 'bio', 'Modeling cell proliferation in human acute myeloid leukemia xenografts', 'Nobile, Marco S|Vlachou, Thalia|Spolaor, Simone|Bossi, Daniela|Cazzaniga, Paolo|Lanfrancone, Luisa|Mauri, Giancarlo|Pelicci, Pier Giuseppe|Besozzi, Daniela', 'Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy.|Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy.|Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy.|Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy.|SYSBIO.IT Centre of Systems Biology, Milan, Italy.|Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy.|Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy.|Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy.|Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz063', '30753298', 'Acute myeloid leukemia (AML) is one of the most common hematological malignancies, characterized by high relapse and mortality rates. The inherent intra-tumor heterogeneity in AML is thought to play an important role in disease recurrence and resistance to chemotherapy. Although experimental protocols for cell proliferation studies are well established and widespread, they are not easily applicable to in vivo contexts, and the analysis of related time-series data is often complex to achieve. To overcome these limitations, model-driven approaches can be exploited to investigate different aspects of cell population dynamics.'),
(82565, 2019, 'bio', 'Expanded functionality, increased accuracy, and enhanced speed in the de novo genotyping-by-sequencing pipeline GBS-SNP-CROP', 'Melo, Arthur T O|Hale, Iago', 'Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy.|Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy.', 'Italy|Italy', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty1073', '30753299', '-'),
(82566, 2019, 'bio', 'PairedFB: a full hierarchical Bayesian model for paired RNA-seq data with heterogeneous treatment effects', 'Bian, Yuanyuan|He, Chong|Hou, Jie|Cheng, Jianlin|Qiu, Jing', 'Department of Statistics, University of Missouri, Columbia, MO, USA.|Department of Statistics, 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 Applied Economics and Statistics, University of Delaware, Newark, DE, USA.', 'United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty731', '30753300', 'Several methods have been proposed for the paired RNA-seq analysis. However, many of them do not consider the heterogeneity in treatment effect among pairs that can naturally arise in real data. In addition, it has been reported in literature that the false discovery rate (FDR) control of some popular methods has been problematic. In this paper, we present a full hierarchical Bayesian model for the paired RNA-seq count data that accounts for variation of treatment effects among pairs and controls the FDR through the posterior expected FDR.'),
(82567, 2019, 'bio', 'mCSEA: detecting subtle differentially methylated regions', 'Martorell-Marugán, Jordi|González-Rumayor, Víctor|Carmona-Sáez, Pedro', 'Bioinformatics Unit. GENYO. Centre for Genomics and Oncological Research: Pfizer, University of Granada, Andalusian Regional Government, Granada, Spain.|Atrys Health, Barcelona, Spain.|Bioinformatics Unit. GENYO. Centre for Genomics and Oncological Research: Pfizer, University of Granada, Andalusian Regional Government, Granada, Spain.', 'Spain|Spain|Spain', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz096', '30753302', 'The identification of differentially methylated regions (DMRs) among phenotypes is one of the main goals of epigenetic analysis. Although there are several methods developed to detect DMRs, most of them are focused on detecting relatively large differences in methylation levels and fail to detect moderate, but consistent, methylation changes that might be associated to complex disorders.'),
(82568, 2019, 'bio', 'Gag-protease coevolution shapes the outcome of lopinavir-inclusive treatment regimens in chronically infected HIV-1 subtype C patients', 'Marie, V|Gordon, M', 'KwaZulu-Natal Research Innovation and Sequencing Platform, University of KwaZulu-Natal, Durban, South Africa.|KwaZulu-Natal Research Innovation and Sequencing Platform, University of KwaZulu-Natal, Durban, South Africa.', 'South Africa|South Africa', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz076', '30753326', 'Commonly, protease inhibitor failure is characterized by the development of multiple protease resistance mutations (PRMs). While the impact of PRMs on therapy failure are understood, the introduction of Gag mutations with protease remains largely unclear.'),
(82569, 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', 'KwaZulu-Natal Research Innovation and Sequencing Platform, University of KwaZulu-Natal, Durban, South Africa.|KwaZulu-Natal Research Innovation and Sequencing Platform, University of KwaZulu-Natal, Durban, South Africa.|KwaZulu-Natal Research Innovation and Sequencing Platform, University of KwaZulu-Natal, Durban, South Africa.|KwaZulu-Natal Research Innovation and Sequencing Platform, University of KwaZulu-Natal, Durban, South Africa.|KwaZulu-Natal Research Innovation and Sequencing Platform, University of KwaZulu-Natal, Durban, South Africa.|KwaZulu-Natal Research Innovation and Sequencing Platform, University of KwaZulu-Natal, Durban, South Africa.|KwaZulu-Natal Research Innovation and Sequencing Platform, University of KwaZulu-Natal, Durban, South Africa.|KwaZulu-Natal Research Innovation and Sequencing Platform, University of KwaZulu-Natal, Durban, South Africa.', 'South Africa|South Africa|South Africa|South Africa|South Africa|South Africa|South Africa|South Afr', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz049', '30753340', '-'),
(82570, 2019, 'bio', 'refineD: improved protein structure refinement using machine learning based restrained relaxation', 'Bhattacharya, Debswapna', 'Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, USA.', 'United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz101', '30759180', 'Protein structure refinement aims to bring moderately accurate template-based protein models closer to the native state through conformational sampling. However, guiding the sampling towards the native state by effectively using restraints remains a major issue in structure refinement.'),
(82571, 2019, 'bio', 'Temporal network alignment via GoT-WAVE', 'Aparxc3xadcio, David|Ribeiro, Pedro|Milenkovixc4x87, Tijana|Silva, Fernando', 'CRACS & INESC-TEC, Departamento de Cixc3xaancia de Computadores, Faculdade de Cixc3xaancias, Universidade do Porto, Porto, Portugal.|CRACS & INESC-TEC, Departamento de Cixc3xaancia de Computadores, Faculdade de Cixc3xaancias, Universidade do Porto, Porto, Portugal.|Department of Computer Science and Engineering, Interdisciplinary Center for Network Science and Applications, and ECK Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA.|CRACS & INESC-TEC, Departamento de Cixc3xaancia de Computadores, Faculdade de Cixc3xaancias, Universidade do Porto, Porto, Portugal.', 'Portugal|Portugal|United States|Portugal', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz119', '30759185', 'Network alignment (NA) finds conserved regions between two networks. NA methods optimize node conservation (NC) and edge conservation. Dynamic graphlet degree vectors are a state-of-the-art dynamic NC measure, used within the fastest and most accurate NA method for temporal networks: DynaWAVE. Here, we use graphlet-orbit transitions (GoTs), a different graphlet-based measure of temporal node similarity, as a new dynamic NC measure within DynaWAVE, resulting in GoT-WAVE.'),
(82572, 2019, 'bio', 'rMETL: sensitive mobile element insertion detection with long read realignment', 'Jiang, Tao|Liu, Bo|Li, Junyi|Wang, Yadong', 'Center for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China.|Center for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China.|Center for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China.|Center for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China.', 'China|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz106', '30759188', 'Mobile element insertion (MEI) is a major category of structure variations (SVs). The rapid development of long read sequencing technologies provides the opportunity to detect MEIs sensitively. However, the signals of MEI implied by noisy long reads are highly complex due to the repetitiveness of mobile elements as well as the high sequencing error rates. Herein, we propose the Realignment-based Mobile Element insertion detection Tool for Long read (rMETL). Benchmarking results of simulated and real datasets demonstrate that rMETL enables to handle the complex signals to discover MEIs sensitively. It is suited to produce high-quality MEI callsets in many genomics studies.'),
(82573, 2019, 'bio', 'Identification of caveolin-1 domain signatures via machine learning and graphlet analysis of single-molecule super-resolution data', 'Khater, Ismail M|Meng, Fanrui|Nabi, Ivan Robert|Hamarneh, Ghassan', 'Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.|Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, BC, Canada.|Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, BC, Canada.|Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.', 'Canada|Canada|Canada|Canada', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz113', '30759191', 'Network analysis and unsupervised machine learning processing of single-molecule localization microscopy of caveolin-1 (Cav1) antibody labeling of prostate cancer cells identified biosignatures and structures for caveolae and three distinct non-caveolar scaffolds (S1A, S1B and S2). To obtain further insight into low-level molecular interactions within these different structural domains, we now introduce graphlet decomposition over a range of proximity thresholds and show that frequency of different subgraph (k = 4 nodes) patterns for machine learning approaches (classification, identification, automatic labeling, etc.) effectively distinguishes caveolae and scaffold blobs.'),
(82574, 2019, 'bio', 'Comparison of six breast cancer classifiers using qPCR', 'Berchtold, Evi|Vetter, Martina|Gündert, Melanie|Csaba, Gergely|Fathke, Christine|Ulbrich, Susanne E|Thomssen, Christoph|Zimmer, Ralf|Kantelhardt, Eva J', 'Department of Informatics, Institute of Bioinformatics, Ludwig-Maximilians-Universität München, München, Germany.|Department of Gynecology, Institute of Clinical Epidemiology, Martin-Luther-Universität, Halle an der Saale, Germany.|Physiology Weihenstephan, Technical University of Munich, Freising, Germany.|Department of Informatics, Institute of Bioinformatics, Ludwig-Maximilians-Universität München, München, Germany.|Department of Gynecology, Institute of Clinical Epidemiology, Martin-Luther-Universität, Halle an der Saale, Germany.|Physiology Weihenstephan, Technical University of Munich, Freising, Germany.|Department of Gynecology, Institute of Clinical Epidemiology, Martin-Luther-Universität, Halle an der Saale, Germany.|Department of Informatics, Institute of Bioinformatics, Ludwig-Maximilians-Universität München, München, Germany.|Department of Gynecology, Institute of Clinical Epidemiology, Martin-Luther-Universität, Halle an der Saale, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz103', '30759193', 'Several gene expression-based risk scores and subtype classifiers for breast cancer were developed to distinguish high- and low-risk patients. Evaluating the performance of these classifiers helps to decide which classifiers should be used in clinical practice for personal therapeutic recommendations. So far, studies that compared multiple classifiers in large independent patient cohorts mostly used microarray measurements. qPCR-based classifiers were not included in the comparison or had to be adapted to the different experimental platforms.'),
(82575, 2019, 'bio', 'Finding enzyme cofactors in Protein Data Bank', 'Mukhopadhyay, Abhik|Borkakoti, Neera|Pravda, Lukxc3xa1xc5xa1|Tyzack, Jonathan D|Thornton, Janet M|Velankar, Sameer', 'European Molecular Biology Laboratory (EMBL), European Bioinformatics Institute (EMBL-EBI), Cambridge, UK.|European Molecular Biology Laboratory (EMBL), European Bioinformatics Institute (EMBL-EBI), Cambridge, UK.|European Molecular Biology Laboratory (EMBL), European Bioinformatics Institute (EMBL-EBI), Cambridge, UK.|European Molecular Biology Laboratory (EMBL), European Bioinformatics Institute (EMBL-EBI), Cambridge, UK.|European Molecular Biology Laboratory (EMBL), European Bioinformatics Institute (EMBL-EBI), Cambridge, UK.|European Molecular Biology Laboratory (EMBL), European Bioinformatics Institute (EMBL-EBI), Cambridge, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz115', '30759194', 'Cofactors are essential for many enzyme reactions. The Protein Data Bank (PDB) contains >67xe2x80x89000 entries containing enzyme structures, many with bound cofactor or cofactor-like molecules. This work aims to identify and categorize these small molecules in the PDB and make it easier to find them.'),
(82576, 2019, 'bio', 'Gsmodutils: a python based framework for test-driven genome scale metabolic model development', 'Gilbert, James|Pearcy, Nicole|Norman, Rupert|Millat, Thomas|Winzer, Klaus|King, John|Hodgman, Charlie|Minton, Nigel|Twycross, Jamie', 'Synthetic Biology Research Centre, University of Nottingham, Nottingham, UK.|Synthetic Biology Research Centre, University of Nottingham, Nottingham, UK.|Synthetic Biology Research Centre, University of Nottingham, Nottingham, UK.|Synthetic Biology Research Centre, University of Nottingham, Nottingham, UK.|Synthetic Biology Research Centre, University of Nottingham, Nottingham, UK.|School of Mathematical Sciences, University of Nottingham, Nottingham, UK.|Synthetic Biology Research Centre, University of Nottingham, Nottingham, UK.|Synthetic Biology Research Centre, University of Nottingham, Nottingham, UK.|School of Computer Science, University of Nottingham, Nottingham, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz088', '30759197', 'Genome scale metabolic models (GSMMs) are increasingly important for systems biology and metabolic engineering research as they are capable of simulating complex steady-state behaviour. Constraints based models of this form can include thousands of reactions and metabolites, with many crucial pathways that only become activated in specific simulation settings. However, despite their widespread use, power and the availability of tools to aid with the construction and analysis of large scale models, little methodology is suggested for their continued management. For example, when genome annotations are updated or new understanding regarding behaviour is discovered, models often need to be altered to reflect this. This is quickly becoming an issue for industrial systems and synthetic biotechnology applications, which require good quality reusable models integral to the design, build, test and learn cycle.'),
(82577, 2019, 'bio', 'Biomolecular Reaction and Interaction Dynamics Global Environment (BRIDGE)', 'Senapathi, Tharindu|Bray, Simon|Barnett, Christopher B|Grüning, Björn|Naidoo, Kevin J', 'Scientific Computing Research Unit and Department of Chemistry, University of Cape Town, Rondebosch, South Africa.|Department of Computer Science, University of Freiburg, Freiburg, Germany.|Scientific Computing Research Unit and Department of Chemistry, University of Cape Town, Rondebosch, South Africa.|Department of Computer Science, University of Freiburg, Freiburg, Germany.|Scientific Computing Research Unit and Department of Chemistry, University of Cape Town, Rondebosch, South Africa.', 'South Africa|Germany|South Africa|Germany|South Africa', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz107', '30759217', 'The pathway from genomics through proteomics and onto a molecular description of biochemical processes makes the discovery of drugs and biomaterials possible. A research framework common to genomics and proteomics is needed to conduct biomolecular simulations that will connect biological data to the dynamic molecular mechanisms of enzymes and proteins. Novice biomolecular modelers are faced with the daunting task of complex setups and a myriad of possible choices preventing their use of molecular simulations and their ability to conduct reliable and reproducible computations that can be shared with collaborators and verified for procedural accuracy.'),
(82578, 2019, 'bio', 'ST-Steiner: a spatio-temporal gene discovery algorithm', 'Norman, Utku|Cicek, A Ercument', 'Computer Engineering Department, Bilkent University, Ankara, Turkey.|Computer Engineering Department, Bilkent University, Ankara, Turkey.', 'Turkey|Turkey', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz110', '30759247', 'Whole exome sequencing (WES) studies for autism spectrum disorder (ASD) could identify only around six dozen risk genes to date because the genetic architecture of the disorder is highly complex. To speed the gene discovery process up, a few network-based ASD gene discovery algorithms were proposed. Although these methods use static gene interaction networks, functional clustering of genes is bound to evolve during neurodevelopment and disruptions are likely to have a cascading effect on the future associations. Thus, approaches that disregard the dynamic nature of neurodevelopment are limited.'),
(82579, 2019, 'bio', '3DBIONOTES v30: crossing molecular and structural biology data with genomic variations', 'Segura, Joan|Sanchez-Garcia, Ruben|Sorzano, C O S|Carazo, J M', 'Spanish National Institute for Bioinformatics (INB ELIXIR-ES) and Biocomputing Unit, National Centre of Biotechnology (CSIC)/Instruct Image Processing Centre, Madrid, Spain.|Spanish National Institute for Bioinformatics (INB ELIXIR-ES) and Biocomputing Unit, National Centre of Biotechnology (CSIC)/Instruct Image Processing Centre, Madrid, Spain.|Spanish National Institute for Bioinformatics (INB ELIXIR-ES) and Biocomputing Unit, National Centre of Biotechnology (CSIC)/Instruct Image Processing Centre, Madrid, Spain.|Spanish National Institute for Bioinformatics (INB ELIXIR-ES) and Biocomputing Unit, National Centre of Biotechnology (CSIC)/Instruct Image Processing Centre, Madrid, Spain.', 'Spain|Spain|Spain|Spain', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz118', '30768147', 'Many diseases are associated to single nucleotide polymorphisms that affect critical regions of proteins as binding sites or post translational modifications. Therefore, analysing genomic variants with structural and molecular biology data is a powerful framework in order to elucidate the potential causes of such diseases.'),
(82580, 2019, 'bio', 'Driver network as a biomarker: systematic integration and network modeling of multi-omics data to derive driver signaling pathways for drug combination prediction', 'Huang, Lei|Brunell, David|Stephan, Clifford|Mancuso, James|Yu, Xiaohui|He, Bin|Thompson, Timothy C|Zinner, Ralph|Kim, Jeri|Davies, Peter|Wong, Stephen T C', 'Department of Systems Medicine and Bioengineering, Houston Methodist Research Institute, Weill Cornell Medicine of Cornell University, Houston, TX, USA.|Center for Clinical and Translational Cancer Research, Texas A&M Health Sciences Center, Institute of Biosciences and Technology, Houston, TX, USA.|Center for Clinical and Translational Cancer Research, Texas A&M Health Sciences Center, Institute of Biosciences and Technology, Houston, TX, USA.|Department of Systems Medicine and Bioengineering, Houston Methodist Research Institute, Weill Cornell Medicine of Cornell University, Houston, TX, USA.|Department of Systems Medicine and Bioengineering, Houston Methodist Research Institute, Weill Cornell Medicine of Cornell University, Houston, TX, USA.|Department of Urology, Immunobiology & Transplant Science Center, Houston Methodist Cancer Center, Houston Methodist Hospital, Houston, TX, USA.|Division of Cancer Medicine, Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.|Department of Medical Oncology, Thomas Jefferson University, Philadelphia, PA, USA.|Division of Cancer Medicine, Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.|Center for Clinical and Translational Cancer Research, Texas A&M Health Sciences Center, Institute of Biosciences and Technology, Houston, TX, USA.|Department of Systems Medicine and Bioengineering, Houston Methodist Research Institute, Weill Cornell Medicine of Cornell University, Houston, TX, USA.', '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/btz109', '30768150', 'Drug combinations that simultaneously suppress multiple cancer driver signaling pathways increase therapeutic options and may reduce drug resistance. We have developed a computational systems biology tool, DrugComboExplorer, to identify driver signaling pathways and predict synergistic drug combinations by integrating the knowledge embedded in vast amounts of available pharmacogenomics and omics data.'),
(82581, 2019, 'bio', 'Explore, edit and leverage genomic annotations using Python GTF toolkit', 'Lopez, F|Charbonnier, G|Kermezli, Y|Belhocine, M|Ferré, Q|Zweig, N|Aribi, M|Gonzalez, A|Spicuglia, S|Puthier, D', 'Aix Marseille Univ, INSERM, TAGC, UMR U1090, Marseille, France.|Aix Marseille Univ, INSERM, TAGC, UMR U1090, Marseille, France.|Aix Marseille Univ, INSERM, TAGC, UMR U1090, Marseille, France.|Molecular Biology and Genetics Laboratory, Dubai, United Arab Emirates.|Aix Marseille Univ, INSERM, TAGC, UMR U1090, Marseille, France.|Aix Marseille Univ.|The Laboratory of Applied Molecular Biology and Immunology, Tlemcen University, Algeria.|Aix Marseille Univ, INSERM, TAGC, UMR U1090, Marseille, France.|Aix Marseille Univ, INSERM, TAGC, UMR U1090, Marseille, France.|Aix Marseille Univ, INSERM, TAGC, UMR U1090, Marseille, France.', 'France|France|France|United Arab Emirates|France|-|Algeria|France|France|France', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz116', '30768152', 'While Python has become very popular in bioinformatics, a limited number of libraries exist for fast manipulation of gene coordinates in Ensembl GTF format.'),
(82582, 2019, 'bio', 'DeepAffinity: interpretable deep learning of compound-protein affinity through unified recurrent and convolutional neural networks', 'Karimi, Mostafa|Wu, Di|Wang, Zhangyang|Shen, Yang', 'Department of Electrical and Computer Engineering, College Station, TX, USA.|Department of Electrical and Computer Engineering, College Station, TX, USA.|Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA.|Department of Electrical and Computer Engineering, College Station, TX, USA.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz111', '30768156', 'Drug discovery demands rapid quantification of compound-protein interaction (CPI). However, there is a lack of methods that can predict compound-protein affinity from sequences alone with high applicability, accuracy and interpretability.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(82583, 2019, 'bio', 'BioKEEN: a library for learning and evaluating biological knowledge graph embeddings', 'Ali, Mehdi|Hoyt, Charles Tapley|Domingo-Fernández, Daniel|Lehmann, Jens|Jabeen, Hajira', 'Department of Computer Science, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany.|Department of Life Science Informatics, Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany.|Department of Life Science Informatics, Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany.|Department of Computer Science, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany.|Department of Computer Science, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany.', 'Germany|Germany|Germany|Germany|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz117', '30768158', 'Knowledge graph embeddings (KGEs) have received significant attention in other domains due to their ability to predict links and create dense representations for graphs nodes and edges. However, the software ecosystem for their application to bioinformatics remains limited and inaccessible for users without expertise in programing and machine learning. Therefore, we developed BioKEEN (Biological KnowlEdge EmbeddiNgs) and PyKEEN (Python KnowlEdge EmbeddiNgs) to facilitate their easy use through an interactive command line interface. Finally, we present a case study in which we used a novel biological pathway mapping resource to predict links that represent pathway crosstalks and hierarchies.'),
(82584, 2019, 'bio', 'Dhaka: Variational Autoencoder for Unmasking Tumor Heterogeneity from Single Cell Genomic Data', 'Rashid, Sabrina|Shah, Sohrab|Bar-Joseph, Ziv|Pandya, Ravi', 'Computational Biology Dept., Carnegie Mellon University, Pittsburgh, USA.|Dept. of Computer Science and Dept. of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, Canada.|Machine Learning Dept. and Computational Biology Dept., Carnegie Mellon University, Pittsburgh, USA.|Microsoft Research, Redmond, USA.', 'United States|Canada|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz095', '30768159', 'Intra-tumor heterogeneity is one of the key confounding factors in deciphering tumor evolution. Malignant cells exhibit variations in their gene expression, copy numbers, and mutation even when originating from a single progenitor cell. Single cell sequencing of tumor cells has recently emerged as a viable option for unmasking the underlying tumor heterogeneity. However, extracting features from single cell genomic data in order to infer their evolutionary trajectory remains computationally challenging due to the extremely noisy and sparse nature of the data.'),
(82585, 2019, 'bio', 'SUBSTRA: Supervised Bayesian Patient Stratification', 'Khakabimamaghani, Sahand|Kelkar, Yogeshwar D|Grande, Bruno M|Morin, Ryan D|Ester, Martin|Ziemek, Daniel', 'School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.|Computational Systems Immunology, Pfizer Worldwide R&D, Cambridge, MA, USA.|Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada.|School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.|School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.|Computational Systems Immunology, Pfizer Worldwide R&D, Berlin, Germany.', 'Canada|United States|Canada|Canada|Canada|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz112', '30768166', 'Patient stratification methods are key to the vision of precision medicine. Here, we consider transcriptional data to segment the patient population into subsets relevant to a given phenotype. Whereas most existing patient stratification methods focus either on predictive performance or interpretable features, we developed a method striking a balance between these two important goals.'),
(82586, 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', 'Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan.|Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan.', 'Japan|Japan', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty663', '30776078', 'Detection of DNA at low abundance with respect to the entire sample is an important problem in areas such as epidemiology and field research, as these samples are highly contaminated with non-target DNA. To solve this problem, many methods have been developed to date, but all require additional time-consuming and costly procedures. Meanwhile, the MinION sequencer developed by Oxford Nanopore Technology (ONT) is considered a powerful tool for tackling this problem, as it allows selective sequencing of target DNA. The main technology employed involves rejection of an undesirable read from a specific pore by inverting the voltage of that pore, which is referred to as Read Until. Despite its usefulness, several issues remain to be solved in real situations. First, limited computational resources are available in field research and epidemiological applications. In addition, a high-speed online classification algorithm is required to make a prompt decision. Lastly, the lack of a theoretical approach for modeling of selective sequencing makes it difficult to analyze and justify a given algorithm.'),
(82587, 2019, 'bio', 'Inter-Modular Linkers play a crucial role in governing the biosynthesis of non-ribosomal peptides', 'Farag, Sherif|Bleich, Rachel M|Shank, Elizabeth A|Isayev, Olexandr|Bowers, Albert A|Tropsha, Alexander', 'Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.|Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.|Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.|Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.|Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.|Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.', 'United States|United States|United States|United States|United States|United States', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz127', '30785185', 'Non-ribosomal peptide synthetases (NRPSs) are modular enzymatic machines that catalyze the ribosome-independent production of structurally complex small peptides, many of which have important clinical applications as antibiotics, antifungals and anti-cancer agents. Several groups have tried to expand natural product diversity by intermixing different NRPS modules to create synthetic peptides. This approach has not been as successful as anticipated, suggesting that these modules are not fully interchangeable.'),
(82588, 2019, 'bio', 'Toward perfect reads: self-correction of short reads via mapping on de Bruijn graphs', 'Limasset, Antoine|Flot, Jean-François|Peterlongo, Pierre', 'Université libre de Bruxelles (ULB), Evolutionary Biology & Ecology, Bruxelles, Belgium.|Université libre de Bruxelles (ULB), Evolutionary Biology & Ecology, Bruxelles, Belgium.|Univ Rennes, Inria, CNRS, IRISA, Rennes, France.', 'Belgium|Belgium|France', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz102', '30785192', 'Motivations Short-read accuracy is important for downstream analyses such as genome assembly and hybrid long-read correction. Despite much work on short-read correction, present-day correctors either do not scale well on large data sets or consider reads as mere suites of k-mers, without taking into account their full-length read information. Results We propose a new method to correct short reads using de Bruijn graphs, and implement it as a tool called Bcool. As a first step, Bcool constructs a compacted de Bruijn graph from the reads. This graph is filteblack on the basis of k-mer abundance then of unitig abundance, thereby removing most sequencing errors. The cleaned graph is then used as a reference on which the reads are mapped to correct them. We show that this approach yields more accurate reads than k-mer-spectrum correctors while being scalable to human-size genomic datasets and beyond. Availability and Implementation The implementation is open source, available at http://github.com/Malfoy/BCOOL under the Affero GPL license and as a Bioconda package. Contact Antoine Limasset antoine.limasset@gmail.com & Jean-François Flot jflot@ulb.ac.be & Pierre Peterlongo pierre.peterlongo@inria.fr.'),
(82589, 2019, 'bio', 'GlycanAnalyzer: software for automated interpretation of N-glycan profiles after exoglycosidase digestions', 'Walsh, Ian|Nguyen-Khuong, Terry|Wongtrakul-Kish, Katherine|Jie Tay, Shi|Chew, Daniel|José, Tasha|Taron, Christopher H|Rudd, Pauline M', 'Univ Rennes, Inria, CNRS, IRISA, Rennes, France.|Univ Rennes, Inria, CNRS, IRISA, Rennes, France.|Univ Rennes, Inria, CNRS, IRISA, Rennes, France.|Univ Rennes, Inria, CNRS, IRISA, Rennes, France.|Univ Rennes, Inria, CNRS, IRISA, Rennes, France.|Univ Rennes, Inria, CNRS, IRISA, Rennes, France.|Univ Rennes, Inria, CNRS, IRISA, Rennes, France.|Univ Rennes, Inria, CNRS, IRISA, Rennes, France.', 'France|France|France|France|France|France|France|France', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz077', '30789217', '-'),
(82590, 2019, 'bio', 'LIONS: analysis suite for detecting and quantifying transposable element initiated transcription from RNA-seq', 'Babaian, Artem|Thompson, I Richard|Lever, Jake|Gagnier, Liane|Karimi, Mohammad M|Mager, Dixie L', 'Terry Fox Laboratory, BC Cancer, Vancouver, BC, Canada.|Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Doha, Qatar.|University of British Columbia, Vancouver, BC, Canada.|Terry Fox Laboratory, BC Cancer, Vancouver, BC, Canada.|MRC London Institute of Medical Sciences, Imperial College, London, UK.|Terry Fox Laboratory, BC Cancer, Vancouver, BC, Canada.', 'Canada|Qatar|Canada|Canada|United Kingdom|Canada', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz130', '30793157', 'Transposable elements (TEs) influence the evolution of novel transcriptional networks yet the specific and meaningful interpretation of how TE-derived transcriptional initiation contributes to the transcriptome has been marred by computational and methodological deficiencies. We developed LIONS for the analysis of RNA-seq data to specifically detect and quantify TE-initiated transcripts.'),
(82591, 2019, 'bio', 'Anduril 2: upgraded large-scale data integration framework', 'Cervera, Alejandra|Rantanen, Ville|Ovaska, Kristian|Laakso, Marko|Nuñez-Fontarnau, Javier|Alkodsi, Amjad|Casado, Julia|Facciotto, Chiara|Häkkinen, Antti|Louhimo, Riku|Karinen, Sirkku|Zhang, Kaiyang|Lavikka, Kari|Lyly, Lauri|Pal Singh, Maninder|Hautaniemi, Sampsa', 'Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki.|Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki.|Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki.|Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki.|Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland.|Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki.|Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki.|Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki.|Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki.|Finnish Institute of Occupational Health, Helsinki, Finland.|Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki.|Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki.|Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki.|Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki.|Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki.|Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki.', '-|-|-|-|Finland|-|-|-|-|Finland|-|-|-|-|-|-', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz133', '30793160', 'Anduril is an analysis and integration framework that facilitates the design, use, parallelization and reproducibility of bioinformatics workflows. Anduril has been upgraded to use Scala for pipeline construction, which simplifies software maintenance, and facilitates design of complex pipelines. Additionally, Andurils bioinformatics repository has been expanded with multiple components, and tutorial pipelines, for next-generation sequencing data analysis.'),
(82592, 2019, 'bio', 'The TMCrys server for supporting crystallization of transmembrane proteins', 'Varga, Julia K|Tusnády, Gábor E', 'Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki.|Momentum Membrane Protein Bioinformatics Research Group, Institute of Enzymology, Research Center of Natural Sciences, Hungarian Academy of Sciences, H-1117 Budapest, Magyar tudósok körútja 2.', '-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz108', '30793168', 'Due to their special properties, the structures of transmembrane proteins are extremely hard to determine. Several methods exist to predict the propensity of successful completion of the structure determination process. However, available predictors incorporate data of any kind of proteins, hence they can hardly differentiate between crystallizable and non-crystallizable membrane proteins.'),
(82593, 2019, 'bio', 'CausalTAB: the PSI-MITAB 28 updated format for signalling data representation and dissemination', 'Perfetto, L|Acencio, M L|Bradley, G|Cesareni, G|Del Toro, N|Fazekas, D|Hermjakob, H|Korcsmaros, T|Kuiper, M|Lægreid, A|Lo Surdo, P|Lovering, R C|Orchard, S|Porras, P|Thomas, P D|Touré, V|Zobolas, J|Licata, L', 'Department of Biology, University of Rome Tor Vergata, Rome, Italy.|Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.|Computational Biology and Statistics, Target Sciences, GSK, UK.|Department of Biology, University of Rome Tor Vergata, Rome, Italy.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, UK.|Department of Genetics, Eötvös Loránd University, Budapest, Hungary.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, UK.|Earlham Institute, Norwich, UK.|Department of Biology, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.|Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.|Department of Biology, University of Rome Tor Vergata, Rome, Italy.|Department of Preclinical and Fundamental Science, Institute of Cardiovascular Science, University College London, 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.|Division of Bioinformatics, Department of Preventive Medicine, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA.|Department of Biology, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.|Department of Biology, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.|Department of Biology, University of Rome Tor Vergata, Rome, Italy.', 'Italy|Norway|United Kingdom|Italy|United Kingdom|Hungary|United Kingdom|United Kingdom|Norway|Norway|Italy|United Kingdom|United Kingdom|United Kingdom|United Kingdom|Norway|Norway|Italy', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz132', '30793173', 'Combining multiple layers of information underlying biological complexity into a structured framework represent a challenge in systems biology. A key task is the formalization of such information in models describing how biological entities interact to mediate the response to external and internal signals. Several databases with signalling information, focus on capturing, organizing and displaying signalling interactions by representing them as binary, causal relationships between biological entities. The curation efforts that build these individual databases demand a concerted effort to ensure interoperability among resources.'),
(82594, 2019, 'bio', 'Parameter balancing: consistent parameter sets for kinetic metabolic models', 'Lubitz, Timo|Liebermeister, Wolfram', 'Theoretische Biophysik, Institut für Biologie, Humboldt-Universität zu Berlin, Berlin, Germany.|INRA, UR1404, MaIAGE, Université Paris-Saclay, Jouy-en-Josas, France.', 'Germany|France', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz129', '30793200', 'Measured kinetic constants are key input data for metabolic models, but they are often uncertain, inconsistent and incomplete. Parameter balancing translates such data into complete and consistent parameter sets while accounting for predefined ranges and physical constraints. Based on Bayesian regression, it determines a most plausible parameter set as well as uncertainty ranges for all model parameters. Our tools for parameter balancing support standard model and data formats and enable an easy customization of prior distributions and constraints for biochemical constants. Modellers can balance kinetic constants, thermodynamic data and metabolomic data to obtain thermodynamically consistent metabolic states that comply with user-defined flux directions.'),
(82595, 2019, 'bio', 'Missing value estimation methods for DNA methylation data', 'Di Lena, Pietro|Sala, Claudia|Prodi, Andrea|Nardini, Christine', 'Department of Computer Science and Engineering, University of Bologna, Mura Anteo Zamboni 7, Bologna, Italy.|Department of Physics and Astronomy, University of Bologna, Viale Berti Pichat 6/2, Bologna, Italy.|Smart Cities Living Lab, Istituto per la Sintesi Organica e la Fotoreattività, Consiglio Nazionale delle Ricerche (CNR), Via P. Gobetti 101, Bologna, Italy.|Department of Laboratory Medicine, Karolinska Institutet, Alfred Nobels Allé 8, Stockholm, Sweden.', 'Italy|Italy|Italy|Sweden', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz134', '30796811', 'DNA methylation is a stable epigenetic mark with major implications in both physiological (development, aging) and pathological conditions (cancers and numerous diseases). Recent research involving methylation focuses on the development of molecular age estimation methods based on DNA methylation levels (mAge). An increasing number of studies indicate that divergences between mAge and chronological age may be associated to age-related diseases. Current advances in high-throughput technologies have allowed the characterization of DNA methylation levels throughout the human genome. However, experimental methylation profiles often contain multiple missing values that can affect the analysis of the data and also mAge estimation. Although several imputation methods exist, a major deficiency lies in the inability to cope with large datasets, such as DNA methylation chips. Specific methods for imputing missing methylation data are therefore needed.'),
(82596, 2019, 'bio', 'Easybiotics: a GUI for 3D physical modelling of multi-species bacterial populations', 'Naylor, Jonathan|Fellermann, Harold|Krasnogor, Natalio', 'Interdisciplinary Computing and Complex BioSystems (ICOS) research group, School of Computing Science, Newcastle University, Newcastle upon Tyne NE4 5TG, UK.|Interdisciplinary Computing and Complex BioSystems (ICOS) research group, School of Computing Science, Newcastle University, Newcastle upon Tyne NE4 5TG, UK.|Interdisciplinary Computing and Complex BioSystems (ICOS) research group, School of Computing Science, Newcastle University, Newcastle upon Tyne NE4 5TG, UK.', 'United Kingdom|United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz131', '30796819', '3D physical modelling is a powerful computational technique that allows for the simulation of complex systems such as consortia of mixed bacterial species. The complexities in physical modelling reside in the knowledge intensive model building process and the computational expense in calculating their numerical solutions. These models can offer insights into microbiology, both in understanding natural systems and as design tools for developing novel synthetic bacterial systems. Developing a robust synthetic system typically requires multiple iterations around the specifyxe2x86x92designxe2x86x92buildxe2x86x92test cycle to meet specifications. This process is laborious and expensive for both the computational and laboratory aspects, hence any improvement in any of the workflow steps would be welcomed. We have previously introduced Simbiotics, a powerful and flexible platform for designing and analyzing 3D simulations of mixed species bacterial populations. Simbiotics requires programming experience to use which creates barriers to entry for use of the tool.'),
(82597, 2019, 'bio', 'SPIM workflow manager for HPC', 'Koxc5xbeusznik, Jan|Bainar, Petr|Klxc3xadmovxc3xa1, Jana|Krumnikl, Michal|Moravec, Pavel|Svatoxc5x88, Vxc3xa1clav|Tomanxc4x8dxc3xa1k, Pavel', 'Department of Computer Science, FEECS VSB-Technical University of Ostrava, Ostrava, Czech Republic.|IT4Innovations, VSB-Technical University of Ostrava, Ostrava, Czech Republic.|IT4Innovations, VSB-Technical University of Ostrava, Ostrava, Czech Republic.|Department of Computer Science, FEECS VSB-Technical University of Ostrava, Ostrava, Czech Republic.|Department of Computer Science, FEECS VSB-Technical University of Ostrava, Ostrava, Czech Republic.|IT4Innovations, VSB-Technical University of Ostrava, Ostrava, Czech Republic.|IT4Innovations, VSB-Technical University of Ostrava, Ostrava, Czech Republic.', '-|-|-|-|-|-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz140', '30799494', 'Here we introduce a Fiji plugin utilizing the HPC-as-a-Service concept, significantly mitigating the challenges life scientists face when delegating complex data-intensive processing workflows to HPC clusters. We demonstrate on a common Selective Plane Illumination Microscopy image processing task that execution of a Fiji workflow on a remote supercomputer leads to improved turnaround time despite the data transfer overhead. The plugin allows the end users to conveniently transfer image data to remote HPC resources, manage pipeline jobs and visualize processed results directly from the Fiji graphical user interface.'),
(82598, 2019, 'bio', 'Nucleotide Archival Format (NAF) enables efficient lossless reference-free compression of DNA sequences', 'Kryukov, Kirill|Ueda, Mahoko Takahashi|Nakagawa, So|Imanishi, Tadashi', 'Department of Molecular Life Science, Tokai University School of Medicine, Isehara, Japan.|Micro/Nano Technology Center, Tokai University, Hiratsuka, Japan.|Department of Molecular Life Science, Tokai University School of Medicine, Isehara, Japan.|Department of Molecular Life Science, Tokai University School of Medicine, Isehara, Japan.', 'Japan|Japan|Japan|Japan', 4, '-', 'Journal Article', '10.1093/bioinformatics/btz144', '30799504', 'DNA sequence databases use compression such as gzip to reduce the required storage space and network transmission time. We describe Nucleotide Archival Format (NAF)-a new file format for lossless reference-free compression of FASTA and FASTQ-formatted nucleotide sequences. Nucleotide Archival Format compression ratio is comparable to the best DNA compressors, while providing dramatically faster decompression. We compared our format with DNA compressors: DELIMINATE and MFCompress, and with general purpose compressors: gzip, bzip2, xz, brotli and zstd.'),
(82599, 2019, 'bio', 'BioMethyl: an R package for biological interpretation of DNA methylation data', 'Wang, Yue|Franks, Jennifer M|Whitfield, Michael L|Cheng, Chao', 'Department of Molecular and Systems 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.|Department of Molecular and Systems 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', 4, '-', 'Journal Article', '10.1093/bioinformatics/btz137', '30799505', 'The accumulation of publicly available DNA methylation datasets has resulted in the need for tools to interpret the specific cellular phenotypes in bulk tissue data. Current approaches use either single differentially methylated CpG sites or differentially methylated regions that map to genes. However, these approaches may introduce biases in downstream analyses of biological interpretation, because of the variability in gene length. There is a lack of approaches to interpret DNA methylation effectively. Therefore, we have developed computational models to provide biological interpretation of relevant gene sets using DNA methylation data in the context of The Cancer Genome Atlas.'),
(82600, 2019, 'bio', 'DIFFpop: a stochastic computational approach to simulate differentiation hierarchies with single cell barcoding', 'Ferlic, Jeremy|Shi, Jiantao|McDonald, Thomas O|Michor, Franziska', 'Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA.|Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA.|Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA.|Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz074', '30816920', 'DIFFpop is an R package designed to simulate cellular differentiation hierarchies using either exponentially-expanding or fixed population sizes. The software includes functionalities to simulate clonal evolution due to the emergence of driver mutations under the infinite-allele assumption as well as options for simulation and analysis of single cell barcoding and labeling data. The software uses the Gillespie Stochastic Simulation Algorithm and a modification of expanding or fixed-size stochastic process models expanded to a large number of cell types and scenarios.'),
(82601, 2019, 'bio', 'PANDA: A comprehensive and flexible tool for quantitative proteomics data analysis', 'Chang, Cheng|Li, Mansheng|Guo, Chaoping|Ding, Yuqing|Xu, Kaikun|Han, Mingfei|He, Fuchu|Zhu, Yunping', 'State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (Beijing), Beijing, Peoples Republic of China.|State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (Beijing), Beijing, Peoples Republic of China.|Beijing Key Laboratory of Human Computer Interactions, Institute of Software Chinese, Academy of Sciences, Beijing, P.R. China.|Beijing Key Laboratory of Human Computer Interactions, Institute of Software Chinese, Academy of Sciences, Beijing, P.R. China.|State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (Beijing), Beijing, Peoples Republic of China.|State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (Beijing), Beijing, Peoples Republic of China.|State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (Beijing), Beijing, Peoples Republic of China.|State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (Beijing), Beijing, Peoples Republic of China.', 'China|China|China|China|China|China|China|China', 5, '-', 'Journal Article', '10.1093/bioinformatics/bty727', '30816924', 'As the experiment techniques and strategies in quantitative proteomics are improving rapidly, the corresponding algorithms and tools for protein quantification with high accuracy and precision are continuously required to be proposed. Here, we present a comprehensive and flexible tool named PANDA for proteomics data quantification. PANDA, which supports both label-free and labeled quantifications, is compatible with existing peptide identification tools and pipelines with considerable flexibility. Compared with MaxQuant on several complex datasets, PANDA was proved to be more accurate and precise with less computation time. Additionally, PANDA is an easy-to-use desktop application tool with user-friendly interfaces.'),
(82602, 2019, 'bio', 'C3D: a tool to predict 3D genomic interactions between cis-regulatory elements', 'Mehdi, Tahmid|Bailey, Swneke D|Guilhamon, Paul|Lupien, Mathieu', 'Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.|Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.|Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.|Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.', 'Canada|Canada|Canada|Canada', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty717', '30816925', 'The 3D genome architecture influences the regulation of genes by facilitating chromatin interactions between distal cis-regulatory elements and gene promoters. We implement Cross Cell-type Correlation based on DNA accessibility (C3D), a customizable computational tool that predicts chromatin interactions using an unsupervised algorithm that utilizes correlations in chromatin measurements, such as DNaseI hypersensitivity signals.'),
(82603, 2019, 'bio', 'QuantiNemo 2: a Swiss knife to simulate complex demographic and genetic scenarios, forward and backward in time', 'Neuenschwander, Samuel|Michaud, Frédéric|Goudet, Jérôme', 'Vital-IT, Swiss Institute of Bioinformatics, Lausanne, Switzerland.|Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland.|Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland.', 'Switzerland|Switzerland|Switzerland', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty737', '30816926', 'QuantiNemo 2 is a stochastic simulation program for quantitative population genetics. It was developed to investigate the effects of selection, mutation, recombination and drift on quantitative traits and neutral markers in structured populations connected by migration and located in heterogeneous habitats. A specific feature is that it allows to switch between an individual-based full-featured mode and a population-based faster mode. Several demographic, genetic and selective parameters can be fine-tuned in QuantiNemo 2: population, selection, trait(s) architecture, genetic map for QTL and/or markers, environment, demography and mating system are the main features.'),
(82604, 2019, 'bio', 'Batch effects correction for microbiome data with Dirichlet-multinomial regression', 'Dai, Zhenwei|Wong, Sunny H|Yu, Jun|Wei, Yingying', 'Institute of Digestive Disease and Department of Medicine and Therapeutics, State Key Laboratory of Digestive Disease, Li Ka Shing Institute of Health Sciences, Hong Kong.|Institute of Digestive Disease and Department of Medicine and Therapeutics, State Key Laboratory of Digestive Disease, Li Ka Shing Institute of Health Sciences, Hong Kong.|Institute of Digestive Disease and Department of Medicine and Therapeutics, State Key Laboratory of Digestive Disease, Li Ka Shing Institute of Health Sciences, Hong Kong.|Department of Statistics, The Chinese University of Hong Kong, Hong Kong.', 'Hong Kong|Hong Kong|Hong Kong|Hong Kong', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty729', '30816927', 'Metagenomic sequencing techniques enable quantitative analyses of the microbiome. However, combining the microbial data from these experiments is challenging due to the variations between experiments. The existing methods for correcting batch effects do not consider the interactions between variables-microbial taxa in microbial studies-and the overdispersion of the microbiome data. Therefore, they are not applicable to microbiome data.'),
(82605, 2019, 'bio', 'SpaRC: scalable sequence clustering using Apache Spark', 'Shi, Lizhen|Meng, Xiandong|Tseng, Elizabeth|Mascagni, Michael|Wang, Zhong', 'Department of Computer Science, School of Computer Science, Florida State University, Tallahassee, FL, USA.|US Department of Energy, Joint Genome Institute, Walnut Creek, CA, USA.|Pacific Biosciences Inc, Menlo Park, CA, USA.|Department of Computer Science, School of Computer Science, Florida State University, Tallahassee, FL, USA.|US Department of Energy, Joint Genome Institute, Walnut Creek, CA, USA.', 'United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty733', '30816928', 'Whole genome shotgun based next-generation transcriptomics and metagenomics studies often generate 100-1000 GB sequence data derived from tens of thousands of different genes or microbial species. Assembly of these data sets requires tradeoffs between scalability and accuracy. Current assembly methods optimized for scalability often sacrifice accuracy and vice versa. An ideal solution would both scale and produce optimal accuracy for individual genes or genomes.'),
(82606, 2019, 'bio', 'Benchmarking optimization methods for parameter estimation in large kinetic models', 'Villaverde, Alejandro F|Fröhlich, Fabian|Weindl, Daniel|Hasenauer, Jan|Banga, Julio R', 'Bioprocess Engineering Group, IIM-CSIC, Vigo, Spain.|Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.|Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.|Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.|Bioprocess Engineering Group, IIM-CSIC, Vigo, Spain.', 'Spain|Germany|Germany|Germany|Spain', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty736', '30816929', 'Kinetic models contain unknown parameters that are estimated by optimizing the fit to experimental data. This task can be computationally challenging due to the presence of local optima and ill-conditioning. While a variety of optimization methods have been suggested to surmount these issues, it is difficult to choose the best one for a given problem a priori. A systematic comparison of parameter estimation methods for problems with tens to hundreds of optimization variables is currently missing, and smaller studies provided contradictory findings.'),
(82607, 2019, 'bio', 'SinNLRR: a robust subspace clustering method for cell type detection by non-negative and low-rank representation', 'Zheng, Ruiqing|Li, Min|Liang, Zhenlan|Wu, Fang-Xiang|Pan, Yi|Wang, Jianxin', 'School of Computer Science and Engineering, Central South University, Changsha, China.|School of Computer Science and Engineering, Central South University, Changsha, China.|School of Computer Science and Engineering, Central South University, Changsha, China.|School of Computer Science and Engineering, Central South University, Changsha, China.|School of Computer Science and Engineering, Central South University, Changsha, China.|School of Computer Science and Engineering, Central South University, Changsha, China.', 'China|China|China|China|China|China', 9, '-', 'Journal Article', '10.1093/bioinformatics/btz139', '30821315', 'The development of single-cell RNA-sequencing (scRNA-seq) provides a new perspective to study biological problems at the single-cell level. One of the key issues in scRNA-seq analysis is to resolve the heterogeneity and diversity of cells, which is to cluster the cells into several groups. However, many existing clustering methods are designed to analyze bulk RNA-seq data, it is urgent to develop the new scRNA-seq clustering methods. Moreover, the high noise in scRNA-seq data also brings a lot of challenges to computational methods.'),
(82608, 2019, 'bio', 'metagenomeFeatures: an R package for working with 16S rRNA reference databases and marker-gene survey feature data', 'Olson, Nathan D|Shah, Nidhi|Kancherla, Jayaram|Wagner, Justin|Paulson, Joseph N|Corrada Bravo, Hector', 'Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA.|Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA.|Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA.|Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA.|Department of Biostatistics, Product Development, Genentech Inc., South San Francisco, CA, USA.|Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA.', 'United States|United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz136', '30821316', 'We developed the metagenomeFeatures R Bioconductor package along with annotation packages for three 16S rRNA databases (Greengenes, RDP and SILVA) to facilitate working with 16S rRNA databases and marker-gene survey feature data. The metagenomeFeatures package defines two classes, MgDb for working with 16S rRNA sequence databases, and mgFeatures for marker-gene survey feature data. The associated annotation packages provide a consistent interface to the different databases facilitating database comparison and exploration. The mgFeatures-class represents a crucial step in the development of a common data structure for working with 16S marker-gene survey data in R.'),
(82609, 2019, 'bio', 'Functional geometry of protein interactomes', 'Malod-Dognin, Noël|Pržulj, Nataša', 'Department of Life Sciences, Barcelona Supercomputing Center, Barcelona, Spain.|Department of Life Sciences, Barcelona Supercomputing Center, Barcelona, Spain.', 'Spain|Spain', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz146', '30821317', 'Protein-protein interactions (PPIs) are usually modeled as networks. These networks have extensively been studied using graphlets, small induced subgraphs capturing the local wiring patterns around nodes in networks. They revealed that proteins involved in similar functions tend to be similarly wired. However, such simple models can only represent pairwise relationships and cannot fully capture the higher-order organization of protein interactomes, including protein complexes.'),
(82610, 2019, 'bio', 'SW-Tandem: a highly efficient tool for large-scale peptide identification with parallel spectrum dot product on Sunway TaihuLight', 'Li, Chuang|Li, Kenli|Chen, Tao|Zhu, Yunping|He, Qiang', 'College of Computer Science and Electronic Engineering, Hunan University, National Supercomputing Center, Changsha, China.|College of Computer Science and Electronic Engineering, Hunan University, National Supercomputing Center, Changsha, China.|State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Life Omics, Beijing, China.|State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Life Omics, Beijing, China.|School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, Australia.', 'China|China|China|China|Australia', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz147', '30821318', 'Tandem mass spectrometry based database searching is a widely acknowledged and adopted method that identifies peptide sequence in shotgun proteomics. However, database searching is extremely computationally expensive, which can take days even weeks to process a large spectra dataset. To address this critical issue, this paper presents SW-Tandem, a new tool for large-scale peptide sequencing. SW-Tandem parallelizes the spectrum dot product scoring algorithm and leverages the advantages of Sunway TaihuLight, the No. 1 supercomputer in the world in 2017. Sunway TaihuLight is powered by the brand new many-core SW26010 processors and provides a peak computation performance greater than 100PFlops. To fully utilize the Sunway TaihuLights capacity, SW-Tandem employs three mechanisms to accelerate large-scale peptide identification, memory-access optimizations, double buffering and vectorization. The results of experiments conducted on multiple datasets demonstrate the performance of SW-Tandem against three state-of-the-art tools for peptide identification, including X!! Tandem, MR-Tandem and MSFragger. In addition, it shows high scalability in the experiments on extremely large datasets sized up to 12 GB.'),
(82611, 2019, 'bio', 'FORESEE: a tool for the systematic comparison of translational drug response modeling pipelines', 'Turnhoff, Lisa-Katrin|Hadizadeh Esfahani, Ali|Montazeri, Maryam|Kusch, Nina|Schuppert, Andreas', 'Joint Research Center for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, Aachen, Germany.|Joint Research Center for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, Aachen, Germany.|Joint Research Center for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, Aachen, Germany.|Joint Research Center for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, Aachen, Germany.|Joint Research Center for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, Aachen, Germany.', 'Germany|Germany|Germany|Germany|Germany', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz145', '30821320', 'Translational models that utilize omics data generated in in vitro studies to predict the drug efficacy of anti-cancer compounds in patients are highly distinct, which complicates the benchmarking process for new computational approaches. In reaction to this, we introduce the uniFied translatiOnal dRug rESponsE prEdiction platform FORESEE, an open-source R-package. FORESEE not only provides a uniform data format for public cell line and patient datasets, but also establishes a standardized environment for drug response prediction pipelines, incorporating various state-of-the-art pre-processing methods, model training algorithms and validation techniques. The modular implementation of individual elements of the pipeline facilitates a straightforward development of combinatorial models, which can be used to re-evaluate and improve already existing pipelines as well as to develop new ones.'),
(82612, 2019, 'bio', 'Destin: toolkit for single-cell analysis of chromatin accessibility', 'Urrutia, Eugene|Chen, Li|Zhou, Haibo|Jiang, Yuchao', 'Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.|Department of Health Outcomes Research and Policy, Harrison School of Pharmacy, Auburn University, Auburn, AL, USA.|Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.|Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz141', '30821321', 'Single-cell assay of transposase-accessible chromatin followed by sequencing (scATAC-seq) is an emerging new technology for the study of gene regulation with single-cell resolution. The data from scATAC-seq are unique-sparse, binary and highly variable even within the same cell type. As such, neither methods developed for bulk ATAC-seq nor single-cell RNA-seq data are appropriate. Here, we present Destin, a bioinformatic and statistical framework for comprehensive scATAC-seq data analysis. Destin performs cell-type clustering via weighted principle component analysis, weighting accessible chromatin regions by existing genomic annotations and publicly available regulomic datasets. The weights and additional tuning parameters are determined via model-based likelihood. We evaluated the performance of Destin using downsampled bulk ATAC-seq data of purified samples and scATAC-seq data from seven diverse experiments. Compared to existing methods, Destin was shown to outperform across all datasets and platforms. For demonstration, we further applied Destin to 2088 adult mouse forebrain cells and identified cell-type-specific association of previously reported schizophrenia GWAS loci.'),
(82613, 2019, 'bio', 'RECAP reveals the true statistical significance of ChIP-seq peak calls', 'Chitpin, Justin G|Awdeh, Aseel|Perkins, Theodore J', 'Translational and Molecular Medicine Program, University of Ottawa, Ottawa, ON K1H8M5, Canada.|Regenerative Medicine Program, Ottawa Hospital Research Institute, Ottawa, ON K1H8L6, Canada.|Regenerative Medicine Program, Ottawa Hospital Research Institute, Ottawa, ON K1H8L6, Canada.', 'Canada|Canada|Canada', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz150', '30824903', 'Chromatin Immunopreciptation (ChIP)-seq is used extensively to identify sites of transcription factor binding or regions of epigenetic modifications to the genome. A key step in ChIP-seq analysis is peak calling, where genomic regions enriched for ChIP versus control reads are identified. Many programs have been designed to solve this task, but nearly all fall into the statistical trap of using the data twice-once to determine candidate enriched regions, and again to assess enrichment by classical statistical hypothesis testing. This double use of the data invalidates the statistical significance assigned to enriched regions, thus the true significance or reliability of peak calls remains unknown.'),
(82614, 2019, 'bio', 'Surrogate minimal depth as an importance measure for variables in random forests', 'Seifert, Stephan|Gundlach, Sven|Szymczak, Silke', 'Institute of Medical Informatics and Statistics, Kiel University, University Hospital Schleswig-Holstein, Kiel, ermany.|Institute of Medical Informatics and Statistics, Kiel University, University Hospital Schleswig-Holstein, Kiel, ermany.|Institute of Medical Informatics and Statistics, Kiel University, University Hospital Schleswig-Holstein, Kiel, ermany.', '-|-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz149', '30824905', 'It has been shown that the machine learning approach random forest can be successfully applied to omics data, such as gene expression data, for classification or regression and to select variables that are important for prediction. However, the complex relationships between predictor variables, in particular between causal predictor variables, make the interpretation of currently applied variable selection techniques difficult.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(82615, 2019, 'bio', 'DECO: decompose heterogeneous population cohorts for patient stratification and discovery of sample biomarkers using omic data profiling', 'Campos-Laborie, F J|Risueño, A|Ortiz-Estévez, M|Rosón-Burgo, B|Droste, C|Fontanillo, C|Loos, R|Sánchez-Santos, J M|Trotter, M W|De Las Rivas, J', 'Bioinformatics and Functional Genomics Group, Cancer Research Center (CiC-IMBCC, CSIC/USAL/IBSAL), Consejo Superior de Investigaciones Científicas (CSIC), University of Salamanca (USAL), Campus Miguel de Unamuno s/n, Salamanca, Spain.|Celgene Institute for Translational Research Europe (CITRE), Parque Científico y Tecnológico Cartuja 93, Sevilla, Spain.|Celgene Institute for Translational Research Europe (CITRE), Parque Científico y Tecnológico Cartuja 93, Sevilla, Spain.|Bioinformatics and Functional Genomics Group, Cancer Research Center (CiC-IMBCC, CSIC/USAL/IBSAL), Consejo Superior de Investigaciones Científicas (CSIC), University of Salamanca (USAL), Campus Miguel de Unamuno s/n, Salamanca, Spain.|Bioinformatics and Functional Genomics Group, Cancer Research Center (CiC-IMBCC, CSIC/USAL/IBSAL), Consejo Superior de Investigaciones Científicas (CSIC), University of Salamanca (USAL), Campus Miguel de Unamuno s/n, Salamanca, Spain.|Celgene Institute for Translational Research Europe (CITRE), Parque Científico y Tecnológico Cartuja 93, Sevilla, Spain.|Celgene Institute for Translational Research Europe (CITRE), Parque Científico y Tecnológico Cartuja 93, Sevilla, Spain.|Bioinformatics and Functional Genomics Group, Cancer Research Center (CiC-IMBCC, CSIC/USAL/IBSAL), Consejo Superior de Investigaciones Científicas (CSIC), University of Salamanca (USAL), Campus Miguel de Unamuno s/n, Salamanca, Spain.|Celgene Institute for Translational Research Europe (CITRE), Parque Científico y Tecnológico Cartuja 93, Sevilla, Spain.|Bioinformatics and Functional Genomics Group, Cancer Research Center (CiC-IMBCC, CSIC/USAL/IBSAL), Consejo Superior de Investigaciones Científicas (CSIC), University of Salamanca (USAL), Campus Miguel de Unamuno s/n, Salamanca, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz148', '30824909', 'Patient and sample diversity is one of the main challenges when dealing with clinical cohorts in biomedical genomics studies. During last decade, several methods have been developed to identify biomarkers assigned to specific individuals or subtypes of samples. However, current methods still fail to discover markers in complex scenarios where heterogeneity or hidden phenotypical factors are present. Here, we propose a method to analyze and understand heterogeneous data avoiding classical normalization approaches of reducing or removing variation.'),
(82616, 2019, 'bio', 'deTS: tissue-specific enrichment analysis to decode tissue specificity', 'Pei, Guangsheng|Dai, Yulin|Zhao, Zhongming|Jia, Peilin', 'School of Biomedical Informatics, Center for Precision Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.|School of Biomedical Informatics, Center for Precision Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.|School of Biomedical Informatics, Center for Precision Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.|School of Biomedical Informatics, Center for Precision Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.', 'United States|United States|United States|United States', 4, '-', 'Journal Article', '10.1093/bioinformatics/btz138', '30824912', 'Diseases and traits are under dynamic tissue-specific regulation. However, heterogeneous tissues are often collected in biomedical studies, which reduce the power in the identification of disease-associated variants and gene expression profiles.'),
(82617, 2019, 'bio', 'VHost-Classifier: virus-host classification using natural language processing', 'Kitson, Ezra|Suttle, Curtis A', 'Department of Microbiology and Immunology, University of British Columbia, Vancouver, BC, Canada.|Department of Microbiology and Immunology, University of British Columbia, Vancouver, BC, Canada.', 'Canada|Canada', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz151', '30824917', 'When analyzing viral metagenomic sequences, it is often desired to filter the results of a BLAST analysis by the host species of the virus. VHost-Classifier automates this procedure using a natural language processing algorithm written in Python 3, which takes a list of taxonomic identifiers (taxids) returned from a BLAST query using viral sequences as input. The taxid output is binned by the evolutionary lineage of their host, based on string matching the words in their English names. If VHost-Classifier cannot identify a host, it attempts to bin the sequences by the environment from which the sample originated. VHost-Classifier predicts the evolutionary lineage of the host from the virus name and does not rely on referencing taxids against a database, therefore, it is not constrained by the size of a database and can host classify newly characterized viruses.'),
(82618, 2019, 'bio', 'Enhancing the prediction of disease-gene associations with multimodal deep learning', 'Luo, Ping|Li, Yuanyuan|Tian, Li-Ping|Wu, Fang-Xiang', 'Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, Canada.|Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, Canada.|School of Information, Beijing Wuzi University, Beijing, China.|Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, Canada.', 'Canada|Canada|China|Canada', 10, '-', 'Journal Article', '10.1093/bioinformatics/btz155', '30825303', 'Computationally predicting disease genes helps scientists optimize the in-depth experimental validation and accelerates the identification of real disease-associated genes. Modern high-throughput technologies have generated a vast amount of omics data, and integrating them is expected to improve the accuracy of computational prediction. As an integrative model, multimodal deep belief net (DBN) can capture cross-modality features from heterogeneous datasets to model a complex system. Studies have shown its power in image classification and tumor subtype prediction. However, multimodal DBN has not been used in predicting disease-gene associations.'),
(82619, 2019, 'bio', 'VERA: agent-based modeling transmission of antibiotic resistance between human pathogens and gut microbiota', 'Glushchenko, Oksana E|Prianichnikov, Nikita A|Olekhnovich, Evgenii I|Manolov, Alexander I|Tyakht, Alexander V|Starikova, Elizaveta V|Odintsova, Vera E|Kostryukova, Elena S|Ilina, Elena I', 'Federal Research and Clinical Center of Physical-Chemical Medicine, Federal Medical Biological Agency, Moscow, Russia.|Federal Research and Clinical Center of Physical-Chemical Medicine, Federal Medical Biological Agency, Moscow, Russia.|Federal Research and Clinical Center of Physical-Chemical Medicine, Federal Medical Biological Agency, Moscow, Russia.|Federal Research and Clinical Center of Physical-Chemical Medicine, Federal Medical Biological Agency, Moscow, Russia.|Federal Research and Clinical Center of Physical-Chemical Medicine, Federal Medical Biological Agency, Moscow, Russia.|Federal Research and Clinical Center of Physical-Chemical Medicine, Federal Medical Biological Agency, Moscow, Russia.|Federal Research and Clinical Center of Physical-Chemical Medicine, Federal Medical Biological Agency, Moscow, Russia.|Federal Research and Clinical Center of Physical-Chemical Medicine, Federal Medical Biological Agency, Moscow, Russia.|Federal Research and Clinical Center of Physical-Chemical Medicine, Federal Medical Biological Agency, Moscow, Russia.', '-|-|-|-|-|-|-|-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz154', '30825306', 'The resistance of bacterial pathogens to antibiotics is one of the most important issues of modern health care. The human microbiota can accumulate resistance determinants and transfer them to pathogenic microbiota by means of horizontal gene transfer. Thus, it is important to develop methods of prediction and monitoring of antibiotics resistance in human populations.'),
(82620, 2019, 'bio', 'Aggrescan3D standalone package for structure-based prediction of protein aggregation properties', 'Kuriata, Aleksander|Iglesias, Valentin|Kurcinski, Mateusz|Ventura, Salvador|Kmiecik, Sebastian', 'Biological and Chemical Research Centre, Department of Chemistry, University of Warsaw, Warsaw, Poland.|Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Barcelona, Spain.|Biological and Chemical Research Centre, Department of Chemistry, University of Warsaw, Warsaw, Poland.|Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Barcelona, Spain.|Biological and Chemical Research Centre, Department of Chemistry, University of Warsaw, Warsaw, Poland.', 'Poland|Spain|Poland|Spain|Poland', 4, '-', 'Journal Article', '10.1093/bioinformatics/btz143', '30825368', 'Aggrescan3D (A3D) standalone is a multiplatform Python package for structure-based prediction of protein aggregation properties and rational design of protein solubility. A3D allows the re-design of protein solubility by combining structural aggregation propensity and stability predictions, as demonstrated by a recent experimental study. It also enables predicting the impact of protein conformational fluctuations on the aggregation properties. The standalone A3D version is an upgrade of the original web server implementation-it introduces a number of customizable options, automated analysis of multiple mutations and offers a flexible computational framework for merging it with other computational tools.'),
(82621, 2019, 'bio', 'AutoDock Bias: improving binding mode prediction and virtual screening using known protein-ligand interactions', 'Arcon, Juan Pablo|Modenutti, Carlos P|Avendaño, Demian|Lopez, Elias D|Defelipe, Lucas A|Ambrosio, Francesca Alessandra|Turjanski, Adrian G|Forli, Stefano|Marti, Marcelo A', 'Departamento de Química Biológica e IQUIBICEN-CONICET, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Buenos Aires C1428EHA, Argentina.|Departamento de Química Biológica e IQUIBICEN-CONICET, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Buenos Aires C1428EHA, Argentina.|Departamento de Química Biológica e IQUIBICEN-CONICET, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Buenos Aires C1428EHA, Argentina.|Departamento de Química Biológica e IQUIBICEN-CONICET, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Buenos Aires C1428EHA, Argentina.|Departamento de Química Biológica e IQUIBICEN-CONICET, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Buenos Aires C1428EHA, Argentina.|Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA.|Departamento de Química Biológica e IQUIBICEN-CONICET, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Buenos Aires C1428EHA, Argentina.|Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA.|Departamento de Química Biológica e IQUIBICEN-CONICET, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Buenos Aires C1428EHA, Argentina.', 'Argentina|Argentina|Argentina|Argentina|Argentina|United States|Argentina|United States|Argentina', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz152', '30825370', 'The performance of docking calculations can be improved by tuning parameters for the system of interest, e.g. biasing the results towards the formation of relevant protein-ligand interactions, such as known ligand pharmacophore or interaction sites derived from cosolvent molecular dynamics. AutoDock Bias is a straightforward and easy to use script-based method that allows the introduction of different types of user-defined biases for fine-tuning AutoDock4 docking calculations.'),
(82622, 2019, 'bio', 'MetaMarker: a pipeline for de novo discovery of novel metagenomic biomarkers', 'Koohi-Moghadam, Mohamad|Borad, Mitesh J|Tran, Nhan L|Swanson, Kristin R|Boardman, Lisa A|Sun, Hongzhe|Wang, Junwen', 'Department of Health Sciences Research and Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ, USA.|Department of Hematology, Mayo Clinic, Scottsdale, AZ, USA.|Department of Cancer Biology, Mayo Clinic, Scottsdale, AZ, USA.|Department of Neurologic Surgery, Mayo Clinic, Scottsdale, AZ, USA.|Division of Gastroenterology and Hepatology, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA.|Department of Chemistry, The University of Hong Kong, Hong Kong SAR, China.|Department of Health Sciences Research and Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ, USA.', 'United States|United States|United States|United States|United States|China|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz123', '30825371', 'We present MetaMarker, a pipeline for discovering metagenomic biomarkers from whole-metagenome sequencing samples. Different from existing methods, MetaMarker is based on a de novo approach that does not require mapping raw reads to a reference database. We applied MetaMarker on whole-metagenome sequencing of colorectal cancer (CRC) stool samples from France to discover CRC specific metagenomic biomarkers. We showed robustness of the discovered biomarkers by validating in independent samples from Hong Kong, Austria, Germany and Denmark. We further demonstrated these biomarkers could be used to build a machine learning classifier for CRC prediction.'),
(82623, 2019, 'bio', 'NFPws: a web server for delineating broadly neutralizing antibody specificities from serum HIV-1 neutralization data', 'Raju, Nagarajan|Setliff, Ian|Georgiev, Ivelin S', 'Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, TN, USA.|Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, TN, USA.|Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, TN, USA.', 'United States|United States|United States', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz097', '30838378', 'A better understanding of antibody responses to HIV-1 infection in humans can provide novel insights for the development of an effective HIV-1 vaccine. Neutralization fingerprinting (NFP) is an efficient and accurate algorithm for delineating the epitope specificities found in polyclonal antibody responses to HIV-1 infection. Here, we report the development of NFPws, a web server implementation of the NFP algorithm. The server takes as input serum neutralization data for a set of diverse viral strains, and uses a mathematical model to identify similarities between the serum neutralization pattern and the patterns for known broadly neutralizing monoclonal antibodies (bNAbs), in order to predict the prevalence of bNAb epitope specificities in the given serum. In addition, NFPws also computes and displays a number of estimates related to prediction confidence, as well as the likelihood of presence of novel, previously uncharacterized, antibody specificities in a given serum. NFPws also implements a JSmol viewer for molecular structure visualization of the prediction results. Overall, the NFPws server will be an important tool for the identification and analysis of epitope specificities of bNAb responses against HIV-1.'),
(82624, 2019, 'bio', 'eBDIMS server: protein transition pathways with ensemble analysis in 2D-motion spaces', 'Orellana, Laura|Gustavsson, Johan|Bergh, Cathrine|Yoluk, Ozge|Lindahl, Erik', 'Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Stockholm, Sweden.|Department of Computational Science and Technology, KTH Royal Institute of Technology, Stockholm, Sweden.|Department of Applied Physics, Swedish e-Science Research Center, KTH Royal Institute of Technology, Solna, Sweden.|Department of Applied Physics, Swedish e-Science Research Center, KTH Royal Institute of Technology, Solna, Sweden.|Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Stockholm, Sweden.', 'Sweden|Sweden|Sweden|Sweden|Sweden', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz104', '30838394', 'Understanding how proteins transition between different conformers, and how conformers relate to each other in terms of structure and function, is not trivial. Here, we present an online tool for transition pathway generation between two protein conformations using Elastic Network Driven Brownian Dynamics Importance Sampling, a coarse-grained simulation algorithm, which spontaneously predicts transition intermediates trapped experimentally. In addition to path-generation, the server provides an interactive 2D-motion landscape graphical representation of the transitions or any additional conformers to explore their structural relationships.'),
(82625, 2019, 'bio', 'A new computational drug repurposing method using established disease-drug pair knowledge', 'Saberian, Nafiseh|Peyvandipour, Azam|Donato, Michele|Ansari, Sahar|Draghici, Sorin', 'Department of Computer Science, Wayne State University, Detroit, MI, USA.|Department of Computer Science, Wayne State University, Detroit, MI, USA.|Department of Computer Science, Wayne State University, Detroit, MI, USA.|Department of Computer Science, Wayne State University, Detroit, MI, USA.|Department of Computer Science, Wayne State University, Detroit, MI, USA.', 'United States|United States|United States|United States|United States', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz156', '30840053', 'Drug repurposing is a potential alternative to the classical drug discovery pipeline. Repurposing involves finding novel indications for already approved drugs. In this work, we present a novel machine learning-based method for drug repurposing. This method explores the anti-similarity between drugs and a disease to uncover new uses for the drugs. More specifically, our proposed method takes into account three sources of information: (i) large-scale gene expression profiles corresponding to human cell lines treated with small molecules, (ii) gene expression profile of a human disease and (iii) the known relationship between Food and Drug Administration (FDA)-approved drugs and diseases. Using these data, our proposed method learns a similarity metric through a supervised machine learning-based algorithm such that a disease and its associated FDA-approved drugs have smaller distance than the other disease-drug pairs.'),
(82626, 2019, 'bio', 'aphid: an R package for analysis with profile hidden Markov models', 'Wilkinson, Shaun P', 'School of Biological Sciences, Victoria University of Wellington, Wellington, New Zealand.', 'New Zealand', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz159', '30840059', 'Hidden Markov models (HMMs) and profile HMMs form an integral part of biological sequence analysis, supporting an ever-growing list of applications. The aphid R package can be used to derive, train, plot, import and export HMMs and profile HMMs in the R environment. Computationally-intensive dynamic programing recursions, such as the Viterbi, forward and backward algorithms are implemented in C++ and parallelized for increased speed and efficiency.'),
(82627, 2019, 'bio', 'PhenoPro: a novel toolkit for assisting in the diagnosis of Mendelian disease', 'Li, Zixiu|Zhang, Feng|Wang, Yukai|Qiu, Yue|Wu, Yang|Lu, Yulan|Yang, Lin|Qu, William J|Wang, Huijun|Zhou, Wenhao|Tian, Weidong', 'State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, Shanghai, China.|State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, Shanghai, China.|Department of Biostatistics and Computational Biology, School of Life Sciences, Fudan University, Shanghai, China.|Department of Biostatistics and Computational Biology, School of Life Sciences, Fudan University, Shanghai, China.|Department of Biostatistics and Computational Biology, School of Life Sciences, Fudan University, Shanghai, China.|The Molecular Genetic Diagnosis Center, Shanghai Key Lab of Birth Defect, Translational Medicine Research Center of Children Development and Diseases, Pediatrics Research Institute, Shanghai, China.|The Molecular Genetic Diagnosis Center, Shanghai Key Lab of Birth Defect, Translational Medicine Research Center of Children Development and Diseases, Pediatrics Research Institute, Shanghai, China.|Centennial High School, Ellicott City, MD, USA.|The Molecular Genetic Diagnosis Center, Shanghai Key Lab of Birth Defect, Translational Medicine Research Center of Children Development and Diseases, Pediatrics Research Institute, Shanghai, China.|The Molecular Genetic Diagnosis Center, Shanghai Key Lab of Birth Defect, Translational Medicine Research Center of Children Development and Diseases, Pediatrics Research Institute, Shanghai, China.|State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, Shanghai, China.', 'China|China|China|China|China|China|China|United States|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz100', '30843052', 'Whole-exome sequencing (WES) is now being used in clinical practice for the diagnosis of the causal genes of Mendelian diseases. In order to make the diagnosis, however, the clinical phenotypes [e.g. Human Phenotype Ontology (HPO) terms] of a patient are needed for prioritizing the variants called from the WES data of the patient. Computational tools are therefore needed to standardize and accelerate this process.'),
(82628, 2019, 'bio', 'Bacterial Feature Finder (BaFF)-a system for extracting features overrepresented in sets of prokaryotic organisms', 'Lxc3xb3pez-Ibxc3xa1xc3xb1ez, Javier|Martxc3xadn, Laura T|Chagoyen, Mxc3xb3nica|Pazos, Florencio', 'Computational Systems Biology Group, Systems Biology Program, Spanish National Centre for Biotechnology (CNB-CSIC), Madrid, Spain.|Computational Systems Biology Group, Systems Biology Program, Spanish National Centre for Biotechnology (CNB-CSIC), Madrid, Spain.|Computational Systems Biology Group, Systems Biology Program, Spanish National Centre for Biotechnology (CNB-CSIC), Madrid, Spain.|Computational Systems Biology Group, Systems Biology Program, Spanish National Centre for Biotechnology (CNB-CSIC), Madrid, Spain.', 'Spain|Spain|Spain|Spain', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz099', '30844057', 'The results of some experimental and computational techniques are given in terms of large sets of organisms, especially prokaryotic. While their distinctive features can provide useful data regarding specific phenomenon, there are no automated tools for extracting them.'),
(82629, 2019, 'bio', 'Automated exploration of gene ontology term and pathway networks with ClueGO-REST', 'Mlecnik, Bernhard|Galon, Jérôme|Bindea, Gabriela', 'INSERM, Laboratory of Integrative Cancer Immunology, Equipe Labellisée Ligue Contre le Cancer, Sorbonne Université, Université Sorbonne Paris Cité, Université Paris Descartes, Université Paris Diderot, Centre de Recherche des Cordeliers, Paris F-75006, France.|INSERM, Laboratory of Integrative Cancer Immunology, Equipe Labellisée Ligue Contre le Cancer, Sorbonne Université, Université Sorbonne Paris Cité, Université Paris Descartes, Université Paris Diderot, Centre de Recherche des Cordeliers, Paris F-75006, France.|INSERM, Laboratory of Integrative Cancer Immunology, Equipe Labellisée Ligue Contre le Cancer, Sorbonne Université, Université Sorbonne Paris Cité, Université Paris Descartes, Université Paris Diderot, Centre de Recherche des Cordeliers, Paris F-75006, France.', 'France|France|France', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz163', '30847467', 'Large scale technologies produce massive amounts of experimental data that need to be investigated. To improve their biological interpretation we have developed ClueGO, a Cytoscape App that selects representative Gene Onology terms and pathways for one or multiple lists of genes/proteins and visualizes them into functionally organized networks. Because of its reliability, userfriendliness and support of many species ClueGO gained a large community of users. To further allow scientists programmatic access to ClueGO with R, Python, JavaScript etc., we implemented the cyREST API into ClueGO. In this article we describe this novel, complementary way of accessing ClueGO via REST, and provide R and Phyton examples to demonstrate how ClueGO workflows can be integrated into bioinformatic analysis pipelines.'),
(82630, 2019, 'bio', 'G2P: a Genome-Wide-Association-Study simulation tool for genotype simulation, phenotype simulation and power evaluation', 'Tang, You|Liu, Xiaolei', 'Electrical and Information Engineering College, Jilin Agricultural Science and Technology University, Jilin, China.|Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China.', 'China|China', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz126', '30848784', 'Plenty of Genome-Wide-Association-Study (GWAS) methods have been developed for mapping genetic markers that associated with human diseases and agricultural economic traits. Computer simulation is a nice tool to test the performances of various GWAS methods under certain scenarios. Existing tools are either inefficient in terms of computation and memory efficiency or inconvenient to use to simulate big, realistic genotype data and phenotype data to evaluate available GWAS methods.'),
(82631, 2019, 'bio', 'A Gaussian process model and Bayesian variable selection for mapping function-valued quantitative traits with incomplete phenotypic data', 'Vanhatalo, Jarno|Li, Zitong|Sillanpää, Mikko J', 'Department of Mathematics and Statistics and Organismal and Evolutionary Biology Research Programme, University of Helsinki, Helsinki, Finland.|CSIRO Agriculture & Food, GPO Box 1600, Canberra, ACT 2601, Australia.|Department of Mathematical Sciences, Biocenter Oulu and Infotech Oulu University of Oulu, Oulu FI-90014, Finland.', 'Finland|Australia|Finland', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz164', '30850830', 'Recent advances in high dimensional phenotyping bring time as an extra dimension into the phenotypes. This promotes the quantitative trait locus (QTL) studies of function-valued traits such as those related to growth and development. Existing approaches for analyzing functional traits utilize either parametric methods or semi-parametric approaches based on splines and wavelets. However, very limited choices of software tools are currently available for practical implementation of functional QTL mapping and variable selection.'),
(82632, 2019, 'bio', 'PyFeat: a Python-based effective feature generation tool for DNA, RNA and protein sequences', 'Muhammod, Rafsanjani|Ahmed, Sajid|Md Farid, Dewan|Shatabda, Swakkhar|Sharma, Alok|Dehzangi, Abdollah', 'Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh.|Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh.|Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh.|Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh.|School of Engineering and Physics, University of the South Pacific, Private Mail Bag, Laucala Campus, Suva, Fiji.|Department of Computer Science, Morgan State University, Baltimore, MD, USA.', 'Bangladesh|Bangladesh|Bangladesh|Bangladesh|Fiji|United States', 14, '-', 'Journal Article', '10.1093/bioinformatics/btz165', '30850831', 'Extracting useful feature set which contains significant discriminatory information is a critical step in effectively presenting sequence data to predict structural, functional, interaction and expression of proteins, DNAs and RNAs. Also, being able to filter features with significant information and avoid sparsity in the extracted features require the employment of efficient feature selection techniques. Here we present PyFeat as a practical and easy to use toolkit implemented in Python for extracting various features from proteins, DNAs and RNAs. To build PyFeat we mainly focused on extracting features that capture information about the interaction of neighboring residues to be able to provide more local information. We then employ AdaBoost technique to select features with maximum discriminatory information. In this way, we can significantly reduce the number of extracted features and enable PyFeat to represent the combination of effective features from large neighboring residues. As a result, PyFeat is able to extract features from 13 different techniques and represent context free combination of effective features. The source code for PyFeat standalone toolkit and employed benchmarks with a comprehensive user manual explaining its system and workflow in a step by step manner are publicly available.'),
(82633, 2019, 'bio', 'DrVAE: improving drug response prediction via modeling of drug perturbation effects', 'Rampášek, Ladislav|Hidru, Daniel|Smirnov, Petr|Haibe-Kains, Benjamin|Goldenberg, Anna', 'Department of Computer Science, University of Toronto, Toronto, ON, Canada.|Department of Computer Science, University of Toronto, Toronto, ON, Canada.|Vector Institute, Toronto, ON, Canada.|Department of Computer Science, University of Toronto, Toronto, ON, Canada.|Department of Computer Science, University of Toronto, Toronto, ON, Canada.', 'Canada|Canada|Canada|Canada|Canada', 7, '-', 'Journal Article', '10.1093/bioinformatics/btz158', '30850846', 'Individualized drug response prediction is a fundamental part of personalized medicine for cancer. Great effort has been made to discover biomarkers or to develop machine learning methods for accurate drug response prediction in cancers. Incorporating prior knowledge of biological systems into these methods is a promising avenue to improve prediction performance. High-throughput cell line assays of drug-induced transcriptomic perturbation effects are a prior knowledge that has not been fully incorporated into a drug response prediction model yet.'),
(82634, 2019, 'bio', 'Integration of methylation QTL and enhancer-target gene maps with schizophrenia GWAS summary results identifies novel genes', 'Wu, Chong|Pan, Wei', 'Department of Statistics, Florida State University, Tallahassee, FL, USA.|Division of Biostatistics, University of Minnesota, Minneapolis, MN, USA.', 'United States|United States', 4, '-', 'Journal Article', '10.1093/bioinformatics/btz161', '30850848', 'Most trait-associated genetic variants identified in genome-wide association studies (GWASs) are located in non-coding regions of the genome and thought to act through their regulatory roles.'),
(82635, 2019, 'bio', 'MeSHProbeNet: a self-attentive probe net for MeSH indexing', 'Xun, Guangxu|Jha, Kishlay|Yuan, Ye|Wang, Yaqing|Zhang, Aidong', 'Department of Computer Science, University of Virginia, Charlottesville, VA, USA.|Department of Computer Science, University of Virginia, Charlottesville, VA, USA.|Department of Information and Communication Engineering, Beijing University of Technology, Beijing, China.|Department of Computer Science and Engineering, SUNY at Buffalo, Buffalo, NY, USA.|Department of Computer Science, University of Virginia, Charlottesville, VA, USA.', 'United States|United States|China|United States|United States', 4, '-', 'Journal Article', '10.1093/bioinformatics/btz142', '30851089', 'MEDLINE is the primary bibliographic database maintained by National Library of Medicine (NLM). MEDLINE citations are indexed with Medical Subject Headings (MeSH), which is a controlled vocabulary curated by the NLM experts. This greatly facilitates the applications of biomedical research and knowledge discovery. Currently, MeSH indexing is manually performed by human experts. To reduce the time and monetary cost associated with manual annotation, many automatic MeSH indexing systems have been proposed to assist manual annotation, including DeepMeSH and NLMs official model Medical Text Indexer (MTI). However, the existing models usually rely on the intermediate results of other models and suffer from efficiency issues. We propose an end-to-end framework, MeSHProbeNet (formerly named as xgx), which utilizes deep learning and self-attentive MeSH probes to index MeSH terms. Each MeSH probe enables the model to extract one specific aspect of biomedical knowledge from an input article, thus comprehensive biomedical information can be extracted with different MeSH probes and interpretability can be achieved at word level. MeSH terms are finally recommended with a unified classifier, making MeSHProbeNet both time efficient and space efficient.'),
(82636, 2019, 'bio', 'Interoperable and scalable data analysis with microservices: applications in metabolomics', 'Emami Khoonsari, Payam|Moreno, Pablo|Bergmann, Sven|Burman, Joachim|Capuccini, Marco|Carone, Matteo|Cascante, Marta|de Atauri, Pedro|Foguet, Carles|Gonzalez-Beltran, Alejandra N|Hankemeier, Thomas|Haug, Kenneth|He, Sijin|Herman, Stephanie|Johnson, David|Kale, Namrata|Larsson, Anders|Neumann, Steffen|Peters, Kristian|Pireddu, Luca|Rocca-Serra, Philippe|Roger, Pierrick|Rueedi, Rico|Ruttkies, Christoph|Sadawi, Noureddin|Salek, Reza M|Sansone, Susanna-Assunta|Schober, Daniel|Selivanov, Vitaly|Thévenot, Etienne A|van Vliet, Michael|Zanetti, Gianluigi|Steinbeck, Christoph|Kultima, Kim|Spjuth, Ola', 'Department of Medical Sciences, Clinical Chemistry, Uppsala University, Uppsala, Sweden.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK.|Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.|Department of Neuroscience, Uppsala University, Uppsala, Sweden.|Department of Information Technology, Uppsala University, Uppsala, Sweden.|Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.|Department of Biochemistry and Molecular Biomedicine, and Institute of Biomedicine (IBUB), Faculty of Biology, Universitat de Barcelona (IBUB), Barcelona, Spain.|Department of Biochemistry and Molecular Biomedicine, and Institute of Biomedicine (IBUB), Faculty of Biology, Universitat de Barcelona (IBUB), Barcelona, Spain.|Department of Biochemistry and Molecular Biomedicine, and Institute of Biomedicine (IBUB), Faculty of Biology, Universitat de Barcelona (IBUB), Barcelona, Spain.|Oxford e-Research Centre, Department of Engineering Science, University of Oxford, Oxford, UK.|Division of Analytical Biosciences, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK.|Department of Medical Sciences, Clinical Chemistry, Uppsala University, Uppsala, Sweden.|Oxford e-Research Centre, Department of Engineering Science, University of Oxford, Oxford, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK.|Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.|Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry, Halle, Germany.|Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry, Halle, Germany.|CRS4: Center for Advanced Studies, Research and Development in Sardinia, Distributed Computing Group, Pula, Italy.|Oxford e-Research Centre, Department of Engineering Science, University of Oxford, Oxford, UK.|CEA, LIST, Laboratory for Data Analysis and Systems Intelligence, MetaboHUB, Gif-sur-Yvette, France.|Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.|Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry, Halle, Germany.|Faculty of Medicine, Department of Surgery & Cancer, Imperial College London, London, UK.|International Agency for Research on Cancer, 69372 Lyon CEDEX 08, France.|Oxford e-Research Centre, Department of Engineering Science, University of Oxford, Oxford, UK.|Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry, Halle, Germany.|Department of Biochemistry and Molecular Biomedicine, and Institute of Biomedicine (IBUB), Faculty of Biology, Universitat de Barcelona (IBUB), Barcelona, Spain.|CEA, LIST, Laboratory for Data Analysis and Systems Intelligence, MetaboHUB, Gif-sur-Yvette, France.|Division of Analytical Biosciences, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands.|CRS4: Center for Advanced Studies, Research and Development in Sardinia, Distributed Computing Group, Pula, Italy.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK.|Department of Medical Sciences, Clinical Chemistry, Uppsala University, Uppsala, Sweden.|Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.', 'Sweden|United Kingdom|Switzerland|Sweden|Sweden|Sweden|Spain|Spain|Spain|United Kingdom|Netherlands|United Kingdom|United Kingdom|Sweden|United Kingdom|United Kingdom|Sweden|Germ', 5, '-', 'Journal Article', '10.1093/bioinformatics/btz160', '30851093', 'Developing a robust and performant data analysis workflow that integrates all necessary components whilst still being able to scale over multiple compute nodes is a challenging task. We introduce a generic method based on the microservice architecture, where software tools are encapsulated as Docker containers that can be connected into scientific workflows and executed using the Kubernetes container orchestrator.'),
(82637, 2019, 'bio', 'Bit-parallel sequence-to-graph alignment', 'Rautiainen, Mikko|Mxc3xa4kinen, Veli|Marschall, Tobias', 'Center for Bioinformatics, Saarland University, Saarland Informatics Campus E2.1, 66123 Saarbrxc3xbccken, Germany.|Department of Computer Science, University of Helsinki, Helsinki, Finland.|Center for Bioinformatics, Saarland University, Saarland Informatics Campus E2.1, 66123 Saarbrxc3xbccken, Germany.', 'Germany|Finland|Germany', 13, '-', 'Journal Article', '10.1093/bioinformatics/btz162', '30851095', 'Graphs are commonly used to represent sets of sequences. Either edges or nodes can be labeled by sequences, so that each path in the graph spells a concatenated sequence. Examples include graphs to represent genome assemblies, such as string graphs and de Bruijn graphs, and graphs to represent a pan-genome and hence the genetic variation present in a population. Being able to align sequencing reads to such graphs is a key step for many analyses and its applications include genome assembly, read error correction and variant calling with respect to a variation graph.'),
(82638, 2019, 'bio', 'ppsPCP: A Plant Presence/absence Variants Scanner and Pan-genome Construction Pipeline', 'Tahir Ul Qamar, Muhammad|Zhu, Xitong|Xing, Feng|Chen, Ling-Ling', 'National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, P. R. China.|National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, P. R. China.|National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, P. R. China.|National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, P. R. China.', 'China|China|China|China', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz168', '30851098', 'Since the idea of pan-genomics emerged several tools and pipelines have been introduced for prokaryotic pan-genomics. However, not a single comprehensive pipeline has been reported which could overcome multiple challenges associated with eukaryotic pan-genomics. To aid the eukaryotic pan-genomic studies, here we present ppsPCP pipeline which is designed for eukaryotes especially for plants. It is capable of scanning presence/absence variants (PAVs) and constructing a fully annotated pan-genome. We believe with these unique features of PAV scanning and building a pan-genome together with its annotation, ppsPCP will be useful for plant pan-genomic studies and aid researchers to study genetic/phenotypic variations and genomic diversity.'),
(82639, 2019, 'bio', 'VIMCO: variational inference for multiple correlated outcomes in genome-wide association studies', 'Shi, Xingjie|Jiao, Yuling|Yang, Yi|Cheng, Ching-Yu|Yang, Can|Lin, Xinyi|Liu, Jin', 'Department of Statistics, Nanjing University of Finance and Economics, Nanjing, China.|School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, China.|School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China.|Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore.|Department of Mathematics, Hong Kong University of Science and Technology, Hong Kong.|Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore.|Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore.', 'China|China|China|Singapore|Hong Kong|Singapore|Singapore', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz167', '30851102', 'In genome-wide association studies (GWASs) where multiple correlated traits have been measured on participants, a joint analysis strategy, whereby the traits are analyzed jointly, can improve statistical power over a single-trait analysis strategy. There are two questions of interest to be addressed when conducting a joint GWAS analysis with multiple traits. The first question examines whether a genetic loci is significantly associated with any of the traits being tested. The second question focuses on identifying the specific trait(s) that is associated with the genetic loci. Since existing methods primarily focus on the first question, this article seeks to provide a complementary method that addresses the second question.'),
(82640, 2019, 'bio', 'Response envelope analysis for quantitative evaluation of drug combinations', 'Du, Di|Chang, Chia-Hua|Wang, Yumeng|Tong, Pan|Chan, Wai Kin|Chiu, Yulun|Peng, Bo|Tan, Lin|Weinstein, John N|Lorenzi, Philip L', 'Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 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/btz091', '30851108', 'The concept of synergy between two agents, over a century old, is important to the fields of biology, chemistry, pharmacology and medicine. A key step in drug combination analysis is the selection of an additivity model to identify combination effects including synergy, additivity and antagonism. Existing methods for identifying and interpreting those combination effects have limitations.'),
(82641, 2019, 'bio', 'Brain annotation toolbox: exploring the functional and genetic associations of neuroimaging results', 'Liu, Zhaowen|Rolls, Edmund T|Liu, Zhi|Zhang, Kai|Yang, Ming|Du, Jingnan|Gong, Weikang|Cheng, Wei|Dai, Fei|Wang, He|Ugurbil, Kamil|Zhang, Jie|Feng, Jianfeng', 'Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.|Department of Computer Science, University of Warwick, Coventry, UK.|The School of Information Science and Engineering, Shandong University, Jinan, China.|Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA.|Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China.|Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China.|Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China.|Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China.|Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China.|Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China.|Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA.|Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China.|Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China.', 'United Kingdom|United Kingdom|China|United Kingdom|China|China|China|China|China|China|United Kingdom|China|China', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz128', '30854545', 'Advances in neuroimaging and sequencing techniques provide an unprecedented opportunity to map the function of brain regions and identify the roots of psychiatric diseases. However, the results from most neuroimaging studies, i.e. activated clusters/regions or functional connectivities between brain regions, frequently cannot be conveniently and systematically interpreted, rendering the biological meaning unclear.'),
(82642, 2019, 'bio', 'Holistic optimization of an RNA-seq workflow for multi-threaded environments', 'Hung, Ling-Hong|Lloyd, Wes|Agumbe Sridhar, Radhika|Devi Athmalingam Ravishankar, Saranya|Xiong, Yuguang|Sobie, Eric|Yeung, Ka Yee', 'Institute of Technology, University of Washington, Tacoma, WA, USA.|Institute of Technology, University of Washington, Tacoma, WA, USA.|Institute of Technology, University of Washington, Tacoma, WA, USA.|Institute of Technology, University of Washington, Tacoma, WA, USA.|Ichahn School of Medicine at Mount Sinai, 1468 Madison Ave, New York, NY, US.|Ichahn School of Medicine at Mount Sinai, 1468 Madison Ave, New York, NY, US.|Institute of Technology, University of Washington, Tacoma, WA, USA.', 'United States|United States|United States|United States|United States|United States|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz169', '30859176', 'For many next-generation sequencing pipelines, the most computationally intensive step is the alignment of reads to a reference sequence. As a result, alignment software such as the Burrows-Wheeler Aligner (BWA) is optimized for speed and is often executed in parallel on the cloud. However, there are other less demanding steps that can also be optimized to significantly increase the speed especially when using many threads. We demonstrate this using a unique molecular identifier (UMI) RNA sequencing pipeline consisting of 3 steps: split, align, and merge. Optimization of all three steps yields a 40% increase in speed when executed using a single thread. However, when executed using 16 threads, we observe a 4-fold improvement over the original parallel implementation and more than an 8-fold improvement over the original single-threaded implementation. In contrast, optimizing only the alignment step results in just a 13% improvement over the original parallel workflow using 16 threads.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(82643, 2019, 'bio', 'Choice of species affects phylogenetic stability of deep nodes: an empirical example in Terrabacteria', 'Superson, Ashley A|Phelan, Doug|Dekovich, Allyson|Battistuzzi, Fabia U', 'Department of Biological Sciences, Oakland University, Rochester, MI, USA.|Department of Biological Sciences, Oakland University, Rochester, MI, USA.|Department of Biological Sciences, Oakland University, Rochester, MI, USA.|Department of Biological Sciences, Oakland University, Rochester, MI, USA.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz121', '30859177', 'The promise of higher phylogenetic stability through increased dataset sizes within tree of life (TOL) reconstructions has not been fulfilled. Among the many possible causes are changes in species composition (taxon sampling) that could influence phylogenetic accuracy of the methods by altering the relative weight of the evolutionary histories of each individual species. This effect would be stronger in clades that are represented by few lineages, which is common in many prokaryote phyla. Indeed, phyla with fewer taxa showed the most discordance among recent TOL studies. We implemented an approach to systematically test how the identity of taxa among a larger dataset and the number of taxa included affected the accuracy of phylogenetic reconstruction.'),
(82644, 2019, 'bio', 'GREP: genome for REPositioning drugs', 'Sakaue, Saori|Okada, Yukinori', 'Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan.|Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan.', 'Japan|Japan', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz166', '30859178', 'Making use of accumulated genetic knowledge for clinical practice is our next goal in human genetics. Here we introduce GREP (Genome for REPositioning drugs), a standalone python software to quantify an enrichment of the user-defined set of genes in the target of clinical indication categories and to capture potentially repositionable drugs targeting the gene set. We show that genes identified by the large-scale genome-wide association studies were robustly enriched in the approved drugs to treat the trait of interest. This enrichment analysis was also highly applicable to other sets of biological genes such as those identified by gene expression studies and genes somatically mutated in cancers. This software should accelerate investigators to reposition drugs to other indications with the guidance of known genomics.'),
(82645, 2019, 'bio', 'Using local alignment to enhance single-cell bisulfite sequencing data efficiency', 'Wu, Peng|Gao, Yan|Guo, Weilong|Zhu, Ping', 'State Key Laboratory of Experimental Hematology, Institute of Hematology and Blood Disease Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China.|Center for Breast Cancer Prevention and TherapyTianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China.|State Key Laboratory for Agrobiotechnology, College of Agronomy and Biotechnology, China Agricultural University, Beijing, China.|State Key Laboratory of Experimental Hematology, Institute of Hematology and Blood Disease Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China.', 'China|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz125', '30859188', 'Single-cell bisulfite sequencing (BS-seq) techniques have been developed for DNA methylation heterogeneity detection and studies with limited materials. However, the data deficiency such as low read mapping ratio is still a critical issue.'),
(82646, 2019, 'bio', 'Gene-based association tests using GWAS summary statistics', 'Svishcheva, Gulnara R|Belonogova, Nadezhda M|Zorkoltseva, Irina V|Kirichenko, Anatoly V|Axenovich, Tatiana I', 'Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia.|Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia.|Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia.|Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia.|Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia.', '-|-|-|-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz172', '30860568', 'A huge number of genome-wide association studies (GWAS) summary statistics freely available in databases provide a new material for gene-based association analysis aimed at identifying rare genetic variants. Only a few of the many popular gene-based methods developed for individual genotype and phenotype data are adapted for the practical use of the GWAS summary statistics as input.'),
(82647, 2019, 'bio', 'DEFOR: depth- and frequency-based somatic copy number alteration detector', 'Zhang, He|Zhan, Xiaowei|Brugarolas, James|Xie, Yang', 'Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA.|Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA.|Department of Internal Medicine and Kidney Cancer Program, Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.|Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA.', 'United States|United States|United States|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz170', '30860569', 'Detection of somatic copy number alterations (SCNAs) using high-throughput sequencing has become popular because of rapid developments in sequencing technology. Existing methods do not perform well in calling SCNAs for the unstable tumor genomes.'),
(82648, 2019, 'bio', 'Stable H3K4me3 is associated with transcription initiation during early embryo development', 'Huang, Xin|Gao, Xudong|Li, Wanying|Jiang, Shuai|Li, Ruijiang|Hong, Hao|Zhao, Chenghui|Zhou, Pingkun|Chen, Hebing|Bo, Xiaochen|Li, Hao', 'Beijing Institute of Radiation Medicine, Beijing, China.|Beijing Institute of Radiation Medicine, Beijing, China.|Beijing Institute of Radiation Medicine, Beijing, China.|Beijing Institute of Radiation Medicine, Beijing, China.|Beijing Institute of Radiation Medicine, Beijing, China.|Beijing Institute of Radiation Medicine, Beijing, China.|Beijing Institute of Radiation Medicine, Beijing, China.|Beijing Institute of Radiation Medicine, Beijing, China.|Beijing Institute of Radiation Medicine, Beijing, China.|Beijing Institute of Radiation Medicine, Beijing, China.|Beijing Institute of Radiation Medicine, Beijing, China.', 'China|China|China|China|China|China|China|China|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz173', '30860576', 'During development of the mammalian embryo, histone modification H3K4me3 plays an important role in regulating gene expression and exhibits extensive reprogramming on the parental genomes. In addition to these dramatic epigenetic changes, certain unchanging regulatory elements are also essential for embryonic development.'),
(82649, 2019, 'bio', 'Using association signal annotations to boost similarity network fusion', 'Ruan, Peifeng|Wang, Ya|Shen, Ronglai|Wang, Shuang', 'Department of Statistics, Columbian College of Arts and Sciences, The George Washington University, Washington, DC, USA.|Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA.|Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY, USA.|Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz124', '30863842', 'Recent technology developments have made it possible to generate various kinds of omics data, which provides opportunities to better solve problems such as disease subtyping or disease mapping using more comprehensive omics data jointly. Among many developed data-integration methods, the similarity network fusion (SNF) method has shown a great potential to identify new disease subtypes through separating similar subjects using multi-omics data. SNF effectively fuses similarity networks with pairwise patient similarity measures from different types of omics data into one fused network using both shared and complementary information across multiple types of omics data.'),
(82650, 2019, 'bio', 'pldist: ecological dissimilarities for paired and longitudinal microbiome association analysis', 'Plantinga, Anna M|Chen, Jun|Jenq, Robert R|Wu, Michael C', 'Department of Mathematics and Statistics, Williams College, Williamstown, MA, USA.|Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA.|Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.|Department of Biostatistics and Biomathematics Program, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.', 'United States|United States|United States|United States', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz120', '30863868', 'The human microbiome is notoriously variable across individuals, with a wide range of healthy microbiomes. Paired and longitudinal studies of the microbiome have become increasingly popular as a way to reduce unmeasured confounding and to increase statistical power by reducing large inter-subject variability. Statistical methods for analyzing such datasets are scarce.'),
(82651, 2019, 'bio', 'Drug repositioning through integration of prior knowledge and projections of drugs and diseases', 'Xuan, Ping|Cao, Yangkun|Zhang, Tiangang|Wang, Xiao|Pan, Shuxiang|Shen, Tonghui', 'School of Computer Science and Technology, Heilongjiang University, Harbin, China.|School of Computer Science and Technology, Heilongjiang University, Harbin, China.|School of Mathematical Science, Heilongjiang University, Harbin, China.|School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China.|School of Computer Science and Technology, Heilongjiang University, Harbin, China.|School of Computer Science and Technology, Heilongjiang University, Harbin, China.', 'China|China|China|China|China|China', 13, '-', 'Journal Article', '10.1093/bioinformatics/btz182', '30865257', 'Identifying and developing novel therapeutic effects for existing drugs contributes to reduction of drug development costs. Most of the previous methods focus on integration of the heterogeneous data of drugs and diseases frommultiple sources for predicting the candidate drug-disease associations. However, they fail to take the prior knowledge of drugs and diseases and their sparse characteristic into account. It is essential to develop a method that exploits the more useful information to predict the reliable candidate associations.'),
(82652, 2019, 'bio', 'CABS-dock standalone: a toolbox for flexible protein-peptide docking', 'Kurcinski, Mateusz|Ciemny, Maciej Pawel|Oleniecki, Tymoteusz|Kuriata, Aleksander|Badaczewska-Dawid, Aleksandra E|Kolinski, Andrzej|Kmiecik, Sebastian', 'Biological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, Zwirki i Wigury 101, 02-089 Warsaw, Poland.|Biological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, Zwirki i Wigury 101, 02-089 Warsaw, Poland.|Biological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, Zwirki i Wigury 101, 02-089 Warsaw, Poland.|Biological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, Zwirki i Wigury 101, 02-089 Warsaw, Poland.|Biological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, Zwirki i Wigury 101, 02-089 Warsaw, Poland.|Biological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, Zwirki i Wigury 101, 02-089 Warsaw, Poland.|Biological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, Zwirki i Wigury 101, 02-089 Warsaw, Poland.', 'Poland|Poland|Poland|Poland|Poland|Poland|Poland', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz185', '30865258', 'CABS-dock standalone is a multiplatform Python package for protein-peptide docking with backbone flexibility. The main feature of the CABS-dock method is its ability to simulate significant backbone flexibility of the entire protein-peptide system in a reasonable computational time. In the default mode, the package runs a simulation of fully flexible peptide searching for a binding site on the surface of a flexible protein receptor. The flexibility level of the molecules may be defined by the user. Furthermore, the CABS-dock standalone application provides users with full control over the docking simulation from the initial setup to the analysis of results. The standalone version is an upgrade of the original web server implementation - it introduces a number of customizable options, provides support for large-sized systems and offers a framework for deeper analysis of docking results.'),
(82653, 2019, 'bio', 'SCL: A lattice-based approach to infer three-dimensional chromosome structures from single-cell Hi-C data', 'Zhu, Hao|Wang, Zheng', 'School of Computing Sciences and Computer Engineering, University of Southern Mississippi, 118 College Drive #5106, Hattiesburg, MS, USA.|Department of Computer Science, University of Miami, 1364 Memorial Drive, Coral Gables, FL, USA.', 'United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz181', '30865261', 'In contrast to population-based Hi-C data, single-cell Hi-C data are zero-inflated and do not indicate the frequency of proximate DNA segments. There are a limited number of computational tools that can model the three-dimensional structures of chromosomes based on single-cell Hi-C data.'),
(82654, 2019, 'bio', 'Multiresolution correction of GC bias and application to identification of copy number alterations', 'Jang, Ho|Lee, Hyunju', 'School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, South Korea.|School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, South Korea.', '-|-', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz174', '30865265', 'Whole-genome sequencing (WGS) data are affected by various sequencing biases such as GC bias and mappability bias. These biases degrade performance on detection of genetic variations such as copy number alterations. The existing methods use a relation between the GC proportion and depth of coverage (DOC) of markers by means of regression models. Nonetheless, severity of the GC bias varies from sample to sample. We developed a new method for correction of GC bias on the basis of multiresolution analysis. We used a translation-invariant wavelet transform to decompose biased raw signals into high- and low-frequency coefficients. Then, we modeled the relation between GC proportion and DOC of the genomic regions and constructed new control DOC signals that reflect the GC bias. The control DOC signals are used for normalizing genomic sequences by correcting the GC bias.'),
(82655, 2019, 'bio', 'GToTree: a user-friendly workflow for phylogenomics', 'Lee, Michael D', 'Exobiology Branch, NASA Ames Research Center, Moffett Field, CA, USA.', 'United States', 20, '-', 'Journal Article', '10.1093/bioinformatics/btz188', '30865266', 'Genome-level evolutionary inference (i.e., phylogenomics) is becoming an increasingly essential step in many biologists work. Accordingly, there are several tools available for the major steps in a phylogenomics workflow. But for the biologist whose main focus is not bioinformatics, much of the computational work required - such as accessing genomic data on large scales, integrating genomes from different file formats, performing required filtering, stitching different tools together, etc. - can be prohibitive. Here I introduce GToTree, a command-line tool that can take any combination of fasta files, GenBank files, and/or NCBI assembly accessions as input and outputs an alignment file, estimates of genome completeness and redundancy, and a phylogenomic tree based on a specified single-copy gene (SCG) set. While GToTree can work with any custom hidden Markov Models (HMMs), also included are 13 newly generated SCG-set HMMs for different lineages and levels of resolution, built based on searches of xe2x88xbc12,000 bacterial and archaeal high-quality genomes. GToTree aims to give more researchers the capability to make phylogenomic trees.'),
(82656, 2019, 'bio', 'Probabilistic Count Matrix Factorization for Single Cell Expression Data Analysis', 'Durif, G|Modolo, L|Mold, J E|Lambert-Lacroix, S|Picard, F', 'Univ Lyon, Université Lyon 1, CNRS, LBBE UMR 5558, F-69622 Villeurbanne, France.|Univ Lyon, Université Lyon 1, CNRS, LBBE UMR 5558, F-69622 Villeurbanne, France.|Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden.|Université Grenoble Alpes, CNRS, TIMC-IMAG UMR 5525, F-38041 Grenoble, France.|Univ Lyon, Université Lyon 1, CNRS, LBBE UMR 5558, F-69622 Villeurbanne, France.', 'France|France|Sweden|France|France', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz177', '30865271', 'The development of high throughput single-cell sequencing technologies now allows the investigation of the population diversity of cellular transcriptomes. The expression dynamics (gene-to-gene variability) can be quantified more accurately, thanks to the measurement of lowly-expressed genes. In addition, the cell-to-cell variability is high, with a low proportion of cells expressing the same genes at the same time/level. Those emerging patterns appear to be very challenging from the statistical point of view, especially to represent a summarized view of single-cell expression data. PCA is a most powerful tool for high dimensional data representation, by searching for latent directions catching the most variability in the data. Unfortunately, classical PCA is based on Euclidean distance and projections that poorly work in presence of over-dispersed count data with dropout events like single-cell expression data.'),
(82657, 2019, 'bio', 'PleioNet: a web-based visualization tool for exploring pleiotropy across complex traits', 'Gao, X Raymond|Huang, Hua', 'Departments of Ophthalmology and Visual Science and Biomedical Informatics, Division of Human Genetics, The Ohio State University, Columbus, OH, USA.|Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL,USA.', 'United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz179', '30865284', 'Pleiotropy plays an important role in furthering our understanding of the shared genetic architecture of different human diseases and traits. However, exploring and visualizing pleiotropic information with currently publicly available tools is limiting and challenging. To aid researchers in constructing and digesting pleiotropic networks, we present PleioNet, a web-based visualization tool for exploring this information across human diseases and traits. This program provides an intuitive and interactive web interface that seamlessly integrates large database queries with visualizations that enable users to quickly explore complex high-dimensional pleiotropic information. PleioNet works on all modern computer and mobile web browsers, making pleiotropic information readily available to a broad range of researchers and clinicians with diverse technical backgrounds. We expect that PleioNet will be an important tool for studying the underlying pleiotropic connections among human diseases and traits.'),
(82658, 2019, 'bio', 'SodaPop: A Forward Simulation Suite for the Evolutionary Dynamics of Asexual Populations on Protein Fitness Landscapes', 'Gauthier, Louis|Di Franco, Rémicia|Serohijos, Adrian W R', 'Département de Biochimie, Université de Montréal, Montréal, Québec, Canada.|Département de Biochimie, Université de Montréal, Montréal, Québec, Canada.|Département de Biochimie, Université de Montréal, Montréal, Québec, Canada.', 'Canada|Canada|Canada', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz175', '30873519', 'Protein evolution is determined by forces at multiple levels of biological organization. Random mutations have an immediate effect on the biophysical properties, structure and function of proteins. These same mutations also affect the fitness of the organism. However, the evolutionary fate of mutations, whether they succeed to fixation or are purged, also depends on population size and dynamics. There is an emerging interest, both theoretically and experimentally, to integrate these two factors in protein evolution. Although there are several tools available for simulating protein evolution, most of them focus on either the biophysical or the population-level determinants, but not both. Hence, there is a need for a publicly available computational tool to explore both the effects of protein biophysics and population dynamics on protein evolution.'),
(82659, 2019, 'bio', 'onlineFDR: an R package to control the false discovery rate for growing data repositories', 'Robertson, David S|Wildenhain, Jan|Javanmard, Adel|Karp, Natasha A', 'MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.|Quantitative Biology, Discovery Sciences, IMED Biotech Unit, AstraZeneca, Cambridge, UK.|Department of Data Sciences and Operations, University of Southern California, CA, USA.|Quantitative Biology, Discovery Sciences, IMED Biotech Unit, AstraZeneca, Cambridge, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz191', '30873526', 'In many areas of biological research, hypotheses are tested in a sequential manner, without having access to future p-values or even the number of hypotheses to be tested. A key setting where this online hypothesis testing occurs is in the context of publicly available data repositories, where the family of hypotheses to be tested is continually growing as new data is accumulated over time. Recently, Javanmard and Montanari (Ann. Stat. 46:526-554, 2018) proposed the first procedures that control the FDR for online hypothesis testing. We present an R package, onlineFDR, which implements these procedures and provides wrapper functions to apply them to a historic dataset or a growing data repository.'),
(82660, 2019, 'bio', 'Classical scoring functions for docking are unable to exploit large volumes of structural and interaction data', 'Li, Hongjian|Peng, Jiangjun|Sidorov, Pavel|Leung, Yee|Leung, Kwong-Sak|Wong, Man-Hon|Lu, Gang|Ballester, Pedro J', 'SDIVF R&D Centre, Hong Kong Science Park, Sha Tin, New Territories, Hong Kong.|School of Mathematics and Statistics, Xian Jiaotong University, Xian, China.|Cancer Research Center of Marseille, CRCM, INSERM, Institut Paoli-Calmettes, Aix-Marseille Univ, CNRS, F-13009 Marseille, France.|Institute of Future Cities, The Chinese University of Hong Kong, Sha Tin, New Territories, Hong Kong.|Institute of Future Cities, The Chinese University of Hong Kong, Sha Tin, New Territories, Hong Kong.|Department of Computer Science and Engineering, The Chinese University of Hong Kong, Sha Tin, New Territories, Hong Kong.|CUHK-SDU Joint Laboratory on Reproductive Genetics, School of Biomedical Sciences, The Chinese University of Hong Kong, Sha Tin, New Territories, Hong Kong.|Cancer Research Center of Marseille, CRCM, INSERM, Institut Paoli-Calmettes, Aix-Marseille Univ, CNRS, F-13009 Marseille, France.', 'Hong Kong|China|France|Hong Kong|Hong Kong|Hong Kong|Hong Kong|France', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz183', '30873528', 'Studies have shown that the accuracy of Random Forest (RF)-based Scoring Functions (SFs), such as RF-Score-v3, increases with more training samples, whereas that of classical SFs, such as X-Score, does not. Nevertheless, the impact of the similarity between training and test samples on this matter has not been studied in a systematic manner. It is therefore unclear how these SFs would perform when only trained on protein-ligand complexes that are highly dissimilar or highly similar to the test set. It is also unclear whether SFs based on machine learning algorithms other than RF can also improve accuracy with increasing training set size and to what extent they learn from dissimilar or similar training complexes.'),
(82661, 2019, 'bio', 'Lemon: a framework for rapidly mining structural information from the Protein Data Bank', 'Fine, Jonathan|Chopra, Gaurav', 'Department of Chemistry, Purdue University, 720 Clinic Drive, West Lafayette, Indiana, USA.|Department of Chemistry, Purdue University, 720 Clinic Drive, West Lafayette, Indiana, USA.', 'India|India', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz178', '30873531', 'The protein data bank (PDB) currently holds over 140,000 biomolecular structures and continues to release new structures on a weekly basis. The PDB is an essential resource to the structural bioinformatics community to develop software that mine, use, categorize, and analyze such data. New computational biology methods are evaluated using custom benchmarking sets derived as subsets of 3D experimentally determined structures and structural features from the PDB. Currently, such benchmarking features are manually curated with custom scripts in a non-standardized manner that results in slow distribution and updates with new experimental structures. Finally, there is a scarcity of standardized tools to rapidly query 3D descriptors of the entire PDB.'),
(82662, 2019, 'bio', 'phylostratr: a framework for phylostratigraphy', 'Arendsee, Zebulun|Li, Jing|Singh, Urminder|Seetharam, Arun|Dorman, Karin|Wurtele, Eve Syrkin', 'Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA, USA.|Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA, USA.|Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA, USA.|Genetics, Development, and Cell Biology, Iowa State University, Ames, IA, USA.|Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA, USA.|Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA, USA.', 'United States|United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz171', '30873536', 'The goal of phylostratigraphy is to infer the evolutionary origin of each gene in an organism. This is done by searching for homologs within increasingly broad clades. The deepest clade that contains a homolog of the protein(s) encoded by a gene is that genes phylostratum.'),
(82663, 2019, 'bio', 'GladiaTOX: GLobal Assessment of Dose-IndicAtor in TOXicology', 'Belcastro, Vincenzo|Cano, Stephane|Marescotti, Diego|Acali, Stefano|Gonzalez-Suarez, Ignacio|Martin, Florian|Bonjour, Filipe|Ivanov, Nikolai V|Peitsch, Manuel C|Hoeng, Julia', 'PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchâtel, Switzerland.|PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchâtel, Switzerland.|PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchâtel, Switzerland.|PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchâtel, Switzerland.|PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchâtel, Switzerland.|PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchâtel, Switzerland.|PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchâtel, Switzerland.|PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchâtel, Switzerland.|PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchâtel, Switzerland.|PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchâtel, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Swit', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz187', '30873538', 'GladiaTOX R package is an open-source, flexible solution to high-content screening data processing and reporting in biomedical research. GladiaTOX takes advantage of the tcpl core functionalities and provides a number of extensions: it provides a web-service solution to fetch raw data, it computes severity scores and exports ToxPi formatted files, furthermore it contains a suite of functionalities to generate pdf reports for quality control and data processing.'),
(82664, 2019, 'bio', 'ECOGEMS: efficient compression and retrieve of SNP data of 2058 rice accessions with integer sparse matrices', 'Yao, Wen|Huang, Fangfang|Zhang, Xuehai|Tang, Jihua', 'National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou, China.|National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou, China.|National Key Laboratory of Wheat and Maize Crop Science, Collaborative Innovation Center of Henan Grain Crops, College of Agronomy, Henan Agricultural University, Zhengzhou, China.|National Key Laboratory of Wheat and Maize Crop Science, Collaborative Innovation Center of Henan Grain Crops, College of Agronomy, Henan Agricultural University, Zhengzhou, China.', 'China|China|China|China', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz186', '30873546', 'We proposed to store large-scale genotype data as integer sparse matrices, which consumed much fewer computing resources for storage and analysis than traditional approaches. In addition, the raw genotype data could be readily recovered from integer sparse matrices. Utilizing this approach, we stored the genotype data of 1612 Asian cultivated rice accessions and 446 Asian wild rice accessions across 8,584,244 SNP sites in the ECOGEMS database with 310xe2x80x89MB of disk usage. Graphical interface for visualization, analysis and download of SNP data were implemented in ECOGEMS, which made it a valuable resource for rice functional genomic studies.'),
(82665, 2019, 'bio', 'Protein model quality assessment using 3D oriented convolutional neural networks', 'Pagès, Guillaume|Charmettant, Benoit|Grudinin, Sergei', 'Université Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, Grenoble, France.|Université Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, Grenoble, France.|Université Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, Grenoble, France.', 'France|France|France', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz122', '30874723', 'Protein model quality assessment (QA) is a crucial and yet open problem in structural bioinformatics. The current best methods for single-model QA typically combine results from different approaches, each based on different input features constructed by experts in the field. Then, the prediction model is trained using a machine-learning algorithm. Recently, with the development of convolutional neural networks (CNN), the training paradigm has changed. In computer vision, the expert-developed features have been significantly overpassed by automatically trained convolutional filters. This motivated us to apply a three-dimensional (3D) CNN to the problem of protein model QA.'),
(82666, 2019, 'bio', 'mirtronDB: a mirtron knowledge base', 'Da Fonseca, Bruno Henrique Ribeiro|Domingues, Douglas Silva|Paschoal, Alexandre Rossi', 'Bioinformatics Graduation Program (PPGBIOINFO), Department of Computer Science, Federal University of Technology - Paraná, Cornélio Procópio, Paraná, Brazil.|Bioinformatics Graduation Program (PPGBIOINFO), Department of Computer Science, Federal University of Technology - Paraná, Cornélio Procópio, Paraná, Brazil.|Bioinformatics Graduation Program (PPGBIOINFO), Department of Computer Science, Federal University of Technology - Paraná, Cornélio Procópio, Paraná, Brazil.', 'Brazil|Brazil|Brazil', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz153', '30874795', 'Mirtrons arise from short introns with atypical cleavage by using the splicing mechanism. In the current literature, there is no repository centralizing and organizing the data available to the public. To fill this gap, we developed mirtronDB, the first knowledge database dedicated to mirtron, and it is available at http://mirtrondb.cp.utfpr.edu.br/. MirtronDB currently contains a total of 1407 mirtron precursors and 2426 mirtron mature sequences in 18 species.'),
(82667, 2019, 'bio', 'Significance tests for analyzing gene expression data with small sample sizes', 'Ullah, Insha|Paul, Sudhir|Hong, Zhenjie|Wang, You-Gan', 'School of Mathematical Sciences, Queensland University of Technology, QLD, Australia.|Department of Mathematics and Statistics, University of Windsor, Windsor, Canada.|College of Mathematics and Physics, Wenzhou University, China.|School of Mathematical Sciences, Queensland University of Technology, QLD, Australia.', 'Australia|Canada|China|Australia', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz189', '30874796', 'Under two biologically different conditions, we are often interested in identifying differentially expressed genes. It is usually the case that the assumption of equal variances on the two groups is violated for many genes where a large number of them are required to be filtered or ranked. In these cases, exact tests are unavailable and the Welchs approximate test is most reliable one. The Welchs test involves two layers of approximations: approximating the distribution of the statistic by a t-distribution, which in turn depends on an approximate degrees of freedom. This study attempts to improve upon Welchs approximate test by avoiding one layer of approximation.'),
(82668, 2019, 'bio', 'FoldX 50: Working with RNA, small molecules and a new graphical interface', 'Delgado, Javier|Radusky, Leandro G|Cianferoni, Damiano|Serrano, Luis', 'Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Dr. Aiguader 88,Barcelona, Spain.|Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Dr. Aiguader 88,Barcelona, Spain.|Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Dr. Aiguader 88,Barcelona, Spain.|Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Dr. Aiguader 88,Barcelona, Spain.', 'Spain|Spain|Spain|Spain', 7, '-', 'Journal Article', '10.1093/bioinformatics/btz184', '30874800', 'A new version of FoldX, whose main new features allows running classic FoldX commands on structures containing RNA molecules and includes a module that allows parametrization of ligands or small molecules (ParamX) that were not previously recognised in old versions, has been released. An extended FoldX graphical user interface has also being developed (available as a python plugin for the YASARA molecular viewer) allowing user-friendly parametrization of new custom user molecules encoded using JSON format.'),
(82669, 2019, 'bio', 'CyTOFmerge: Integrating mass cytometry data across multiple panels', 'Abdelaal, Tamim|Hxc3xb6llt, Thomas|van Unen, Vincent|Lelieveldt, Boudewijn P F|Koning, Frits|Reinders, Marcel J T|Mahfouz, Ahmed', 'Delft Bioinformatics Lab, Delft University of Technology, 2628xe2x80x89CD Delft, The Netherlands.|Leiden Computational Biology Center, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands.|Department of Immunohematology and Blood Transfusion, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands.|Delft Bioinformatics Lab, Delft University of Technology, 2628xe2x80x89CD Delft, The Netherlands.|Department of Immunohematology and Blood Transfusion, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands.|Delft Bioinformatics Lab, Delft University of Technology, 2628xe2x80x89CD Delft, The Netherlands.|Delft Bioinformatics Lab, Delft University of Technology, 2628xe2x80x89CD Delft, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz180', '30874801', 'High-dimensional mass cytometry (CyTOF) allows the simultaneous measurement of multiple cellular markers at single cell level, providing a comprehensive view of cell compositions. However, the power of CyTOF to explore the full heterogeneity of a biological sample at the single cell level is currently limited by the number of markers measured simultaneously on a single panel.'),
(82670, 2019, 'bio', 'ST viewer: a tool for analysis and visualization of spatial transcriptomics datasets', 'Fernández Navarro, José|Lundeberg, Joakim|Ståhl, Patrik L', 'Department of Gene Technology, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Science for Life Laboratory, Solna, Sweden.|Department of Gene Technology, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Science for Life Laboratory, Solna, Sweden.|Department of Gene Technology, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Science for Life Laboratory, Solna, Sweden.', 'Sweden|Sweden|Sweden', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty714', '30875427', 'Spatial Transcriptomics (ST) is a technique that combines high-resolution imaging with spatially resolved transcriptome-wide sequencing. This novel type of data opens up many possibilities for analysis and visualization, most of which are either not available with standard tools or too complex for normal users.'),
(82671, 2019, 'bio', 'Adjutant: an R-based tool to support topic discovery for systematic and literature reviews', 'Crisan, Anamaria|Munzner, Tamara|Gardy, Jennifer L', 'Department of Computer Science, University of British Columbia, Vancouver, BC, Canada.|Department of Computer Science, University of British Columbia, Vancouver, BC, Canada.|School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada.', 'Canada|Canada|Canada', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty722', '30875428', 'Adjutant is an open-source, interactive and R-based application to support mining PubMed for literature reviews. Given a PubMed-compatible search query, Adjutant downloads the relevant articles and allows the user to perform an unsupervised clustering analysis to identify data-driven topic clusters. Following clustering, users can also sample documents using different strategies to obtain a more manageable dataset for further analysis. Adjutant makes explicit trade-offs between speed and accuracy, which are modifiable by the user, such that a complete analysis of several thousand documents can take a few minutes. All analytic datasets generated by Adjutant are saved, allowing users to easily conduct other downstream analyses that Adjutant does not explicitly support.'),
(82672, 2019, 'bio', 'A Bayesian model for single cell transcript expression analysis on MERFISH data', 'Köster, Johannes|Brown, Myles|Liu, X Shirley', 'Algorithms for Reproducible Bioinformatics, Genome Informatics, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen, Essen, Germany.|Division of Molecular and Cellular Oncology, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.|Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA.', 'Germany|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty718', '30875429', 'Multiplexed error-robust fluorescence in-situ hybridization (MERFISH) is a recent technology to obtain spatially resolved gene or transcript expression profiles in single cells for hundreds to thousands of genes in parallel. So far, no statistical framework to analyze MERFISH data is available.'),
(82673, 2019, 'bio', 'Bigmelon: tools for analysing large DNA methylation datasets', 'Gorrie-Stone, Tyler J|Smart, Melissa C|Saffari, Ayden|Malki, Karim|Hannon, Eilis|Burrage, Joe|Mill, Jonathan|Kumari, Meena|Schalkwyk, Leonard C', 'School of Biological Sciences, University of Essex, Colchester, UK.|Institute for Social and Economic Research, University of Essex, Colchester, UK.|Department of Psychological Sciences, Birkbeck, University of London, London, UK.|Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, UK.|University of Exeter Medical School, University of Exeter, Exeter, UK.|University of Exeter Medical School, University of Exeter, Exeter, UK.|University of Exeter Medical School, University of Exeter, Exeter, UK.|Institute for Social and Economic Research, University of Essex, Colchester, UK.|School of Biological Sciences, University of Essex, Colchester, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 5, '-', 'Journal Article', '10.1093/bioinformatics/bty713', '30875430', 'The datasets generated by DNA methylation analyses are getting bigger. With the release of the HumanMethylationEPIC micro-array and datasets containing thousands of samples, analyses of these large datasets using R are becoming impractical due to large memory requirements. As a result there is an increasing need for computationally efficient methodologies to perform meaningful analysis on high dimensional data.'),
(82674, 2019, 'bio', 'Health Assistant: Answering Your Questions Anytime from Biomedical Literature', 'Jin, Zan-Xia|Zhang, Bo-Wen|Fang, Fan|Zhang, Le-Le|Yin, Xu-Cheng', 'School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China.|Alibaba Group, Hangzhou, China.|School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China.|School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China.|School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China.', 'China|China|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz195', '30887023', 'With the abundant medical resources, especially literature available online, it is possible for people to understand their own health status and relevant problems autonomously. However, how to obtain the most appropriate answer from the increasingly large-scale database, remains a great challenge. Here, we present a biomedical question answering framework and implement a system, Health Assistant, to enable the search process.'),
(82675, 2019, 'bio', 'ScanNeo: identifying indel derived neoantigens using RNA-Seq data', 'Wang, Ting-You|Wang, Li|Alam, Sk Kayum|Hoeppner, Luke H|Yang, Rendong', 'The Hormel Institute, University of Minnesota, 801 16th AVE NE, Austin, MN, USA.|The Hormel Institute, University of Minnesota, 801 16th AVE NE, Austin, MN, USA.|The Hormel Institute, University of Minnesota, 801 16th AVE NE, Austin, MN, USA.|The Hormel Institute, University of Minnesota, 801 16th AVE NE, Austin, MN, USA.|The Hormel Institute, University of Minnesota, 801 16th AVE NE, Austin, MN, USA.', 'United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz193', '30887025', 'Insertion and deletion (indels) have been recognized as an important source generating tumor-specific mutant peptides (neoantigens). The focus of indel derived neoantigen identification has been on leveraging DNA sequencing such as whole exome sequencing, with the effort of using RNA-seq less well explored. Here we present ScanNeo, a fast-streamlined computational pipeline for analyzing RNA-seq to predict neoepitopes derived from small to large-sized indels. We applied ScanNeo in a prostate cancer cell line and validated our predictions with matched mass spectrometry data. Finally, we demonstrated that indel neoantigens predicted from RNA-seq were associated with checkpoint inhibitor response in a cohort of melanoma patients.'),
(82676, 2019, 'bio', 'Differential network enrichment analysis reveals novel lipid pathways in chronic kidney disease', 'Ma, Jing|Karnovsky, Alla|Afshinnia, Farsad|Wigginton, Janis|Rader, Daniel J|Natarajan, Loki|Sharma, Kumar|Porter, Anna C|Rahman, Mahboob|He, Jiang|Hamm, Lee|Shafi, Tariq|Gipson, Debbie|Gadegbeku, Crystal|Feldman, Harold|Michailidis, George|Pennathur, Subramaniam', 'Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.|Department of Computational Medicine & Bioinformatics, Ann Arbor, MI, USA.|Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.|Michigan Regional Comprehensive Metabolomics Resource Core, Ann Arbor, MI, USA.|Department of Medicine, Translational-Clinical Research, University of Pennsylvania, Philadelphia, PA, USA.|Department of Family Medicine and Public Health, University of California at San Diego, San Diego, CA, USA.|Department of Internal Medicine, University of Texas Health at San Antonio, San Antonio, TX, USA.|Department of Internal Medicine, University of Illinois at Chicago, Chicago, IL, USA.|Department of Internal Medicine, Case-Western Reserve University, Cleveland, OH, USA.|Department of Epidemiology, Tulane University School of Medicine, Tulane University, New Orleans, LA, USA.|School of Medicine, Division of Nephrology and Hypertension, Tulane University, New Orleans, LA, USA.|Department of Internal Medicine, Johns Hopkins University, Baltimore, MD, USA.|Department of Pediatrics, University of Michigan, Ann Arbor, MI, USA.|Department of Internal Medicine, Temple University, Philadelphia, PA, USA.|Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA, USA.|Michigan Regional Comprehensive Metabolomics Resource Core, Ann Arbor, MI, USA.|Michigan Regional Comprehensive Metabolomics Resource Core, Ann Arbor, MI, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 9, '-', 'Journal Article', '10.1093/bioinformatics/btz114', '30887029', 'Functional enrichment testing methods can reduce data comprising hundreds of altered biomolecules to smaller sets of altered biological concepts that help generate testable hypotheses. This study leveraged differential network enrichment analysis methodology to identify and validate lipid subnetworks that potentially differentiate chronic kidney disease (CKD) by severity or progression.'),
(82677, 2019, 'bio', 'Compressed Filesystem for Managing Large Genome Collections', 'Navarro, Gonzalo|Sepxc3xbalveda, Vxc3xadctor|Marxc3xadn, Mauricio|Gonzxc3xa1lez, Senxc3xa9n', 'CeBiB - Center for Biotechnology and Bioengineering.|Department of Computer Science, University of Chile.|CeBiB - Center for Biotechnology and Bioengineering.|CeBiB - Center for Biotechnology and Bioengineering.', '-|Chile|-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz192', '30887042', 'Genome repositories are growing faster than our storage capacities, challenging our ability to store, transmit, process, and analyze them. While genomes are not very compressible individually, those repositories usually contain myriads of genomes or genome reads of the same species, thereby creating opportunities for orders-of-magnitude compression by exploiting inter-genome similarities. A useful compression system, however, cannot be only usable for archival, but it must allow direct access to the sequences, ideally in transparent form so that applications do not need to be rewritten.'),
(82678, 2019, 'bio', 'ERVcaller: Identifying polymorphic endogenous retrovirus and other transposable element insertions using whole-genome sequencing data', 'Chen, Xun|Li, Dawei', 'Department of Microbiology and Molecular Genetics,University of Vermont, Burlington, Vermont, USA.|Department of Microbiology and Molecular Genetics,University of Vermont, Burlington, Vermont, USA.', 'United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz205', '30895294', 'Approximately 8% of the human genome is derived from endogenous retroviruses (ERVs). In recent years, an increasing number of human diseases have been found to be associated with ERVs. However, it remains challenging to accurately detect the full spectrum of polymorphic (unfixed) ERVs using whole-genome sequencing (WGS) data.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(82679, 2019, 'bio', 'FLAS: fast and high throughput algorithm for PacBio long read self-correction', 'Bao, Ergude|Xie, Fei|Song, Changjin|Song, Dandan', 'School of Software Engineering, Beijing Jiaotong University, Beijing, China.|School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China.|School of Software Engineering, Beijing Jiaotong University, Beijing, China.|School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China.', 'China|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz206', '30895306', 'The third generation PacBio long reads have greatly facilitated sequencing projects with very large read lengths, but they contain about 15% sequencing errors and need error correction. For the projects with long reads only, it is challenging to make correction with fast speed, and also challenging to correct a sufficient amount of read bases, i.e. to achieve high throughput self-correction. MECAT is currently among the fastest self-correction algorithms, but its throughput is relatively small (Xiao et al., 2017).'),
(82680, 2019, 'bio', 'maTE: Discovering Expressed MicroRNA - Target Interactions', 'Yousef, Malik|Abddallah, Loai|Allmer, Jens', 'Department of Community Information Systems, Zefat Academic College, Zefat, Israel.|Department of Information Systems, The Max Stern Yezreel Valley Academic College, Israel.|Applied Bioinformatics, Bioscience, Wageningen University and Research, Wageningen, the Netherlands.', 'Israel|Israel|Netherlands', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz204', '30895309', 'Disease is often manifested via changes in transcript and protein abundance. MicroRNAs (miRNAs) are instrumental in regulating protein abundance and may measurably influence transcript levels. MicroRNAs often target more than one mRNA (for humans, the average is three), and mRNAs are often targeted by more than one miRNA (for the genes considered in this study, the average is also three). Therefore, it is difficult to determine the miRNAs that may cause the observed differential gene expression.We present a novel approach, maTE, which is based on machine learning, that integrates information about miRNA target genes with gene expression data. maTE depends on the availability of a sufficient amount of patient and control samples. The samples are used to train classifiers to accurately classify the samples on a per miRNA basis. Multiple high scoring miRNAs are used to build a final classifier to improve separation.'),
(82681, 2019, 'bio', 'SArKS: de novo discovery of gene expression regulatory motif sites and domains by suffix array kernel smoothing', 'Wylie, Dennis|Hofmann, Hans A|Zemelman, Boris V', 'Center for Computational Biology and Bioinformatics, University of Texas at Austin.|Center for Computational Biology and Bioinformatics, University of Texas at Austin.|Institute for Cellular and Molecular Biology, University of Texas at Austin, USA.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz198', '30903136', 'We set out to develop an algorithm that can mine differential gene expression data to identify candidate cell type-specific DNA regulatory sequences. Differential expression is usually quantified as a continuous score-fold-change, test-statistic, p-value-comparing biological classes. Unlike existing approaches, our de novo strategy, termed SArKS, applies nonparametric kernel smoothing to uncover promoter motif sites that correlate with elevated differential expression scores. SArKS detects motif k-mers by smoothing sequence scores over sequence similarity. A second round of smoothing over spatial proximity reveals multi-motif domains (MMDs). Discovered motif sites can then be merged or extended based on adjacency within MMDs. False positive rates are estimated and controlled by permutation testing.'),
(82682, 2019, 'bio', 'CytoBackBone: an Algorithm for Merging of Phenotypic Information from Different Cytometric Profiles', 'Pereira, Adrien Leite|Lambotte, Olivier|Grand, Roger Le|Cosma, Antonio|Tchitchek, Nicolas', 'CEA - Université Paris Sud 11 - INSERM U1184, Immunology of Viral Infections and Autoimmune Diseases, IDMIT Infrastructure, Fontenay-aux-Roses, France.|CEA - Université Paris Sud 11 - INSERM U1184, Immunology of Viral Infections and Autoimmune Diseases, IDMIT Infrastructure, Fontenay-aux-Roses, France.|CEA - Université Paris Sud 11 - INSERM U1184, Immunology of Viral Infections and Autoimmune Diseases, IDMIT Infrastructure, Fontenay-aux-Roses, France.|CEA - Université Paris Sud 11 - INSERM U1184, Immunology of Viral Infections and Autoimmune Diseases, IDMIT Infrastructure, Fontenay-aux-Roses, France.|CEA - Université Paris Sud 11 - INSERM U1184, Immunology of Viral Infections and Autoimmune Diseases, IDMIT Infrastructure, Fontenay-aux-Roses, France.', 'France|France|France|France|France', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz212', '30903138', 'Flow and mass cytometry are experimental techniques used to measure the level of proteins expressed by cells at the single-cell resolution. Several algorithms were developed in flow cytometry to increase the number of simultaneously measurable markers. These approaches aim to combine phenotypic information of different cytometric profiles obtained from different cytometry panels.'),
(82683, 2019, 'bio', 'ORE Identifies Extreme Expression Effects Enriched for Rare Variants', 'Richter, F|Hoffman, G E|Manheimer, K B|Patel, N|Sharp, A J|McKean, D|Morton, S U|DePalma, S|Gorham, J|Kitaygorodksy, A|Porter, G A|Giardini, A|Shen, Y|Chung, W K|Seidman, J G|Seidman, C E|Schadt, E E|Gelb, B D', 'Graduate School of Biomedical Sciences, New York, NY, USA.|Icahn Institute for Genomics and Multiscale Biology, New York, NY, USA.|Sema4, a Mount Sinai venture, Stamford, CT, USA.|Mindich Child Health and Development Institute, and Departments of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.|Mindich Child Health and Development Institute, and Departments of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.|Department of Genetics, Harvard Medical School, Boston, MA, USA.|Department of Genetics, Harvard Medical School, Boston, MA, USA.|Department of Genetics, Harvard Medical School, Boston, MA, USA.|Department of Genetics, Harvard Medical School, Boston, MA, USA.|Departments of Systems Biology, Columbia University, New York, NY, USA.|Department of Pediatrics, University of Rochester Medical Center, Rochester, NY, USA.|Cardiorespiratory Unit, Great Ormond Street Hospital and University College London, London UK.|Departments of Systems Biology, Columbia University, New York, NY, USA.|Pediatrics, and Medicine, Columbia University, New York, NY, USA.|Department of Genetics, Harvard Medical School, Boston, MA, USA.|Department of Genetics, Harvard Medical School, Boston, MA, USA.|Icahn Institute for Genomics and Multiscale Biology, New York, NY, USA.|Mindich Child Health and Development Institute, and Departments of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz202', '30903145', 'Noncoding rare variants (RVs) may contribute to Mendelian disorders but have been challenging to study due to small sample sizes, genetic heterogeneity, and uncertainty about relevant noncoding features. Previous studies identified RVs associated with expression outliers, but varying outlier definitions were employed and no comprehensive open-source software was developed.'),
(82684, 2019, 'bio', 'Enhanced Waddington Landscape Model with Cell-Cell Communication Can Explain Molecular Mechanisms of Self-Organization', 'Fooladi, Hosein|Moradi, Parsa|Sharifi-Zarchi, Ali|Khalaj, Babak Hosein', 'Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran.|Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran.|Department of Computer Engineering, Sharif University of Technology, Tehran, Iran.|Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran.', '-|-|-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz201', '30903147', 'The molecular mechanisms of self-organization that orchestrate embryonic cells to create astonishing patterns have been among major questions of developmental biology. It is recently shown that embryonic stem cells (ESCs), when cultured in particular micropatterns, can self-organize and mimic the early steps of pre-implantation embryogenesis. A systems-biology model to address this observation from a dynamical systems perspective is essential and can enhance understanding of the phenomenon.'),
(82685, 2019, 'bio', 'Differential proteostatic regulation of insoluble and abundant proteins', 'Ramakrishnan, Reshmi|Houben, Bert|Rousseau, Frederic|Schymkowitz, Joost', 'Switch Laboratory, VIB Center for Brain and Disease Research, Herestraat 49, Leuven, Belgium.|Switch Laboratory, VIB Center for Brain and Disease Research, Herestraat 49, Leuven, Belgium.|Switch Laboratory, VIB Center for Brain and Disease Research, Herestraat 49, Leuven, Belgium.|Switch Laboratory, VIB Center for Brain and Disease Research, Herestraat 49, Leuven, Belgium.', 'Belgium|Belgium|Belgium|Belgium', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz214', '30903148', 'Despite intense effort, it has been difficult to explain chaperone dependencies of proteins from sequence or structural properties.'),
(82686, 2019, 'bio', 'ngsLD: evaluating linkage disequilibrium using genotype likelihoods', 'Fox, Emma A|Wright, Alison E|Fumagalli, Matteo|Vieira, Filipe G', 'Department of Life Sciences, Silwood Park Campus, Imperial College London, Ascot, UK.|Department of Animal and Plant Sciences, University of Sheffield, Sheffield, UK.|Department of Life Sciences, Silwood Park Campus, Imperial College London, Ascot, UK.|Center for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, Copenhagen, Denmark.', 'United Kingdom|United Kingdom|United Kingdom|Denmark', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz200', '30903149', 'Linkage disequilibrium (LD) measures the correlation between genetic loci and is highly informative for association mapping and population genetics. As many studies rely on called genotypes for estimating LD, their results can be affected by data uncertainty, especially when employing a low read depth sequencing strategy. Furthermore, there is a manifest lack of tools for the analysis of large-scale, low-depth and short-read sequencing data from non-model organisms with limited sample sizes.'),
(82687, 2019, 'bio', 'TISIDB: an integrated repository portal for tumor-immune system interactions', 'Ru, Beibei|Wong, Ching Ngar|Tong, Yin|Yi Zhong, Jia|Wa Zhong, Sophia Shek|Wu, Wai Chung|Chu, Ka Chi|Wong, Choi Yiu|Lau, Chit Ying|Chen, Ian|Chan, Nam Wai|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.|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.|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.|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|China|China|China|China|China|China|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz210', '30903160', 'The interaction between tumor and immune system plays a crucial role in both cancer development and treatment response. To facilitate comprehensive investigation of tumor-immune interactions, we have designed a user-friendly web portal TISIDB, which integrated multiple types of data resources in oncoimmunology. First, we manually curated 4176 records from 2530 publications, which reported 988 genes related to anti-tumor immunity. Second, genes associated with the resistance or sensitivity of tumor cells to T cell-mediated killing and immunotherapy were identified by analyzing high-throughput screening and genomic profiling data. Third, associations between any gene and immune features, such as lymphocytes, immunomodulators and chemokines, were pre-calculated for 30 TCGA cancer types. In TISIDB, biologists can cross-check a gene of interest about its role in tumor-immune interactions through literature mining and high-throughput data analysis, and generate testable hypotheses and high quality figures for publication.'),
(82688, 2019, 'bio', 'PedigreeNet: A Web-based Pedigree Viewer for Biological Databases', 'Braun, Bremen L|Schott, David A|Portwood, John L|Andorf, Carson M|Sen, Taner Z', 'U.S. Department of Agriculture, Agricultural Research Service, Corn Insects and Crop Genetics Research Unit, Ames, IA, USA.|U.S. Department of Agriculture, Agricultural Research Service, Corn Insects and Crop Genetics Research Unit, Ames, IA, USA.|U.S. Department of Agriculture, Agricultural Research Service, Corn Insects and Crop Genetics Research Unit, Ames, IA, USA.|U.S. Department of Agriculture, Agricultural Research Service, Corn Insects and Crop Genetics Research Unit, Ames, IA, USA.|U.S. Department of Agriculture, Agricultural Research Service, Crop Improvement and Genetics Research Unit, Albany, CA USA.', 'United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz208', '30903182', 'Plant breeding aims to improve current germplasm that can tolerate a wide range of biotic and abiotic stresses. To accomplish this goal, breeders rely on developing a deeper understanding of genetic makeup and relationships between plant varieties to make informed plant selections. Although rapid advances in genotyping technology generated a large amount of data for breeders, tools that facilitate pedigree analysis and visualization are scant, leaving breeders to use classical, but inherently limited, hierarchical pedigree diagrams for a handful of plant varieties. To answer this need, we developed a simple web-based tool that can be easily implemented at biological databases, called PedigreeNet, to create and visualize customizable pedigree relationships in a network context, displaying pre- and user-uploaded data.'),
(82689, 2019, 'bio', 'IsoCor: isotope correction for high-resolution MS labeling experiments', 'Millard, Pierre|Delépine, Baudoin|Guionnet, Matthieu|Heuillet, Maud|Bellvert, Floriant|Létisse, Fabien', 'LISBP, Université de Toulouse, CNRS, INRA, INSA, Toulouse, France.|LISBP, Université de Toulouse, CNRS, INRA, INSA, Toulouse, France.|LISBP, Université de Toulouse, CNRS, INRA, INSA, Toulouse, France.|LISBP, Université de Toulouse, CNRS, INRA, INSA, Toulouse, France.|LISBP, Université de Toulouse, CNRS, INRA, INSA, Toulouse, France.|LISBP, Université de Toulouse, CNRS, INRA, INSA, Toulouse, France.', 'France|France|France|France|France|France', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz209', '30903185', 'Mass spectrometry (MS) is widely used for isotopic studies of metabolism and other (bio)chemical processes. Quantitative applications in systems and synthetic biology require to correct the raw MS data for the contribution of naturally occurring isotopes. Several tools are available to correct low-resolution MS data, and recent developments made substantial improvements by introducing resolution-dependent correction methods, hence opening the way to the correction of high-resolution MS (HRMS) data. Nevertheless, current HRMS correction methods partly fail to determine which isotopic species are resolved from the tracer isotopologues and should thus be corrected. We present an updated version of our isotope correction software (IsoCor) with a novel correction algorithm which ensures to accurately exploit any chemical species with any isotopic tracer, at any MS resolution. IsoCor v2 also includes a novel graphical user interface for intuitive use by end-users and a command-line interface to streamline integration into existing pipelines.'),
(82690, 2019, 'bio', 'BrAPI - an Application Programming Interface for Plant Breeding Applications', 'Abbeloos, Rafael|Backlund, Jan Erik|Basterrechea Salido, Martin|Bauchet, Guillaume|Benites-Alfaro, Omar|Birkett, Clay|Calaminos, Viana C|Carceller, Pierre|Cornut, Guillaume|Vasques Costa, Bruno|Edwards, Jeremy D|Finkers, Richard|Gao, Star Yanxin|Ghaffar, Mehmood|Glaser, Philip|Guignon, Valentin|Hok, Puthick|Kilian, Andrzej|König, Patrick|Lagare, Jack Elendil B|Lange, Matthias|Laporte, Marie-Angélique|Larmande, Pierre|LeBauer, David|Lyon, David|Marshall, David|Matthews, Dave|Milne, Iain|Mistry, Naymesh|Morales, Nicolas|Mueller, Lukas|Neveu, Pascal|Papoutsoglou, Evangelia|Pearce, Brian|Perez-Masias, Ivan|Pommier, Cyril|Ramirez-Gonzalez, Ricardo H|Rathore, Abhishek|Raque, Angel M|Raubach, Sebastian|Rife, Trevor|Robbins, Kelly|Rouard, Mathieu|Sarma, Chaitanya|Scholz, Uwe|Selby, Peter|Sempéré, Guilhem|Shaw, Paul|Simon, Reinhard|Soldevilla, Nahuel|Stephen, Gordon|Sun, Qi|Tovar, Clarysabel|Uszynski, Grzegorz|Verouden, Maikel', 'VIB, Ghent, Belgium.|Integrated Breeding Program (IBP), CYMMIT, Texcoco, Mexico.|IPK-Gatersleben, Germany.|Boyce Thompson Institute, Ithaca, NY, USA.|International Potato Center (CIP), Lima, Peru.|USDA ARS, Ithaca NY.|International Rice Research Center (IRRI), Los Banos, The Philippines.|CIRAD, Montpellier, France.|INRA URGI, Versailles, France.|Instituto de Biologia Experimental e Tecnológica (iBET), Oeiras, Portugal.|USDA ARS, Stuttgart, Arkansas, USA.|Wageningen University, Wageningen, The Netherlands.|Cornell University, Ithaca, NY, USA.|IPK-Gatersleben, Germany.|Cornell University, Ithaca, NY, USA.|Bioversity International, Montpellier, France.|Diversity Arrays Technology, Bruce, Australia.|Diversity Arrays Technology, Bruce, Australia.|IPK-Gatersleben, Germany.|International Rice Research Center (IRRI), Los Banos, The Philippines.|IPK-Gatersleben, Germany.|Bioversity International, Montpellier, France.|Institute of Research and Development (IRD), Montpellier, France.|University of Arizona, Phoenix, Arizona.|Boyce Thompson Institute, Ithaca, NY, USA.|The James Hutton Institute, Dundee, UK.|USDA ARS, Ithaca NY.|The James Hutton Institute, Dundee, UK.|LeafNode Technology, Auckland, New Zealand.|Boyce Thompson Institute, Ithaca, NY, USA.|Boyce Thompson Institute, Ithaca, NY, USA.|MISTEA, INRA, Montpellier, France.|Wageningen University, Wageningen, The Netherlands.|Diversity Arrays Technology, Bruce, Australia.|International Potato Center (CIP), Lima, Peru.|INRA URGI, Versailles, France.|John Innes Centre (JIC), Norwich, UK.|ICRISAT, Hyderabad, India.|International Rice Research Center (IRRI), Los Banos, The Philippines.|The James Hutton Institute, Dundee, UK.|Kansas State University, Manhattan, KS, USA.|Cornell University, Ithaca, NY, USA.|Bioversity International, Montpellier, France.|ICRISAT, Hyderabad, India.|IPK-Gatersleben, Germany.|Cornell University, Ithaca, NY, USA.|CIRAD, Montpellier, France.|The James Hutton Institute, Dundee, UK.|Patranca E.I.R.L, Lima, Peru.|Integrated Breeding Program (IBP), CYMMIT, Texcoco, Mexico.|The James Hutton Institute, Dundee, UK.|Cornell University, Ithaca, NY, USA.|Integrated Breeding Program (IBP), CYMMIT, Texcoco, Mexico.|Diversity Arrays Technology, Bruce, Australia.|Wageningen University, Wageningen, The Netherlands.', 'Belgium|Mexico|Germany|United Kingdom|Peru|United Kingdom|Philippines|France|France|Portugal|United Kingdom|Netherlands|United Kingdom|Germany|United Kingdom|France|', 12, '-', 'Journal Article', '10.1093/bioinformatics/btz190', '30903186', 'Modern genomic breeding methods rely heavily on very large amounts of phenotyping and genotyping data, presenting new challenges in effective data management and integration. Recently, the size and complexity of datasets have increased significantly, with the result that data is often stored on multiple systems. As analyses of interest increasingly require aggregation of datasets from diverse sources, data exchange between disparate systems becomes a challenge.'),
(82691, 2019, 'bio', 'Dissecting differential signals in high-throughput data from complex tissues', 'Li, Ziyi|Wu, Zhijin|Jin, Peng|Wu, Hao', 'Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA USA.|Department of Biostatistics, Brown University, Providence, RI USA.|Department of Human Genetics, Emory University, Atlanta, GA USA.|Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA USA.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz196', '30903684', 'Samples from clinical practices are often mixtures of different cell types. The high-throughput data obtained from these samples are thus mixed signals. The cell mixture brings complications to data analysis, and will lead to biased results if not properly accounted for.'),
(82692, 2019, 'bio', 'ASTRAL-MP: scaling ASTRAL to very large datasets using randomization and parallelization', 'Yin, John|Zhang, Chao|Mirarab, Siavash', 'Department of Mathematics, University of California at San Diego, 9500 Gilman Drive, La Jolla, CA, USA.|Bioinformatics and Systems Biology, University of California at San Diego, 9500 Gilman Drive, La Jolla, CA, USA.|Department of Electrical and Computer Engineering, University of California, San Diego, USA.', 'United States|United States|United States', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz211', '30903685', 'Evolutionary histories can change from one part of the genome to another. The potential for discordance between the gene trees has motivated the development of summary methods that reconstruct a species tree from an input collection of gene trees. ASTRAL is a widely used summary method and has been able to scale to relatively large datasets. However, the size of genomic datasets is quickly growing. Despite its relative efficiency, the current single-threaded implementation of ASTRAL is falling behind the data growth trends is not able to analyze the largest available datasets in a reasonable time.'),
(82693, 2019, 'bio', 'SPRINT-Gly: Predicting N- and O-linked glycosylation sites of human and mouse proteins by using sequence and predicted structural properties', 'Taherzadeh, Ghazaleh|Dehzangi, Abdollah|Golchin, Maryam|Zhou, Yaoqi|Campbell, Matthew P', 'School of Information and Communication Technology, Griffith University, Gold Coast, QLD, Australia.|Department of Computer Science, Morgan State University, Baltimore, Maryland, USA.|School of Information and Communication Technology, Griffith University, Gold Coast, QLD, Australia.|School of Information and Communication Technology, Griffith University, Gold Coast, QLD, Australia.|Institute for Glycomics, Griffith University, Parklands Drive, Southport, Gold Coast, Australia.', 'Australia|United States|Australia|Australia|Australia', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz215', '30903686', 'Protein glycosylation is one of the most abundant post-translational modifications that plays an important role in immune responses, intercellular signaling, inflammation, and host-pathogen interactions. However, due to the poor ionization efficiency and microheterogeneity of glycopeptides identifying glycosylation sites is a challenging task, and there is a demand for computational methods. Here, we constructed the largest dataset of human and mouse glycosylation sites to train deep learning neural networks and support vector machine classifiers to predict N-/O-linked glycosylation sites, respectively.'),
(82694, 2019, 'bio', 'CliqueMS: A computational tool for annotating in-source metabolite ions from LC-MS untargeted metabolomics data based on a coelution similarity network', 'Senan, Oriol|Aguilar-Mogas, Antoni|Navarro, Miriam|Capellades, Jordi|Noon, Luke|Burks, Deborah|Yanes, Oscar|Guimerà, Roger|Sales-Pardo, Marta', 'Departament dEnginyeria Química, Universitat Rovira i Virgili, Av. Països Catalans 26, Tarragona, Catalonia, Spain.|Departament dEnginyeria Química, Universitat Rovira i Virgili, Av. Països Catalans 26, Tarragona, Catalonia, Spain.|Metabolomics Platform, IISPV, Department of Electronic Engineering, Universitat Rovira i Virgili, Avinguda Paisos Catalans 26, Tarragona, Catalonia, Spain.|Metabolomics Platform, IISPV, Department of Electronic Engineering, Universitat Rovira i Virgili, Avinguda Paisos Catalans 26, Tarragona, Catalonia, Spain.|CIBER of Diabetes and Associated Metabolic Diseases (CIBERDEM), Monforte de Lemos 3-5, Madrid, Spain.|CIBER of Diabetes and Associated Metabolic Diseases (CIBERDEM), Monforte de Lemos 3-5, Madrid, Spain.|Metabolomics Platform, IISPV, Department of Electronic Engineering, Universitat Rovira i Virgili, Avinguda Paisos Catalans 26, Tarragona, Catalonia, Spain.|Departament dEnginyeria Química, Universitat Rovira i Virgili, Av. Països Catalans 26, Tarragona, Catalonia, Spain.|Departament dEnginyeria Química, Universitat Rovira i Virgili, Av. Països Catalans 26, Tarragona, Catalonia, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain', 13, '-', 'Journal Article', '10.1093/bioinformatics/btz207', '30903689', 'The analysis of biological samples in untargeted metabolomic studies using LC-MS yields tens of thousands of ion signals. Annotating these features is of the utmost importance for answering questions as fundamental as, for example, how many metabolites are there in a given sample.'),
(82695, 2019, 'bio', 'Simulation assisted machine learning', 'Deist, Timo M|Patti, Andrew|Wang, Zhaoqi|Krane, David|Sorenson, Taylor|Craft, David', 'Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School.|Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School.|Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School.|Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School.|Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School.|Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School.', '-|-|-|-|-|-', 6, '-', 'Journal Article', '10.1093/bioinformatics/btz199', '30903692', 'In a predictive modeling setting, if sufficient details of the system behavior are known, one can build and use a simulation for making predictions. When sufficient system details are not known, one typically turns to machine learning, which builds a black-box model of the system using a large dataset of input sample features and outputs. We consider a setting which is between these two extremes: some details of the system mechanics are known but not enough for creating simulations that can be used to make high quality predictions. In this context we propose using approximate simulations to build a kernel for use in kernelized machine learning methods, such as support vector machines. The results of multiple simulations (under various uncertainty scenarios) are used to compute similarity measures between every pair of samples: sample pairs are given a high similarity score if they behave similarly under a wide range of simulation parameters. These similarity values, rather than the original high dimensional feature data, are used to build the kernel.'),
(82696, 2019, 'bio', 'SummaryAUC: a tool for evaluating the performance of polygenic risk prediction models in validation datasets with only summary level statistics', 'Song, Lei|Liu, Aiyi|Consortium, Molecular Genetics Of Schizophrenia|Shi, Jianxin|Gejman, P|Sanders, A|Duan, J|Cloninger, C|Svrakic, D|Buccola, N|Levinson, D|Mowry, B|Freedman, R|Olincy, A|Amin, F|Black, D|Silverman, J|Byerley, W', 'Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Mayland, USA.|Biostatistics and Bioinformatics Branch, Division of Intramural Population Health Research, National Institute of Child Health and Human Development, Bethesda, Mayland, USA.|Details of the MGS Consortium are in Acknowledgments.|Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Mayland, USA.|Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Mayland, USA.|Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Mayland, USA.|Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Mayland, USA.|Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Mayland, USA.|Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Mayland, USA.|Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Mayland, USA.|Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Mayland, USA.|Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Mayland, USA.|Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Mayland, USA.|Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Mayland, USA.|Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Mayland, USA.|Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Mayland, USA.|Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Mayland, USA.|Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Mayland, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz176', '30911754', 'Polygenic risk score (PRS) methods based on genome-wide association studies (GWAS) have a potential for predicting the risk of developing complex diseases and are expected to become more accurate with larger training data sets and innovative statistical methods. The area under the ROC curve (AUC) is often used to evaluate the performance of PRSs, which requires individual genotypic and phenotypic data in an independent GWAS validation dataset. We are motivated to develop methods for approximating AUC of PRSs based on the summary level data of the validation dataset, which will greatly facilitate the development of PRS models for complex diseases.'),
(82697, 2019, 'bio', 'GenomeWarp: an alignment-based variant coordinate transformation', 'McLean, Cory Y|Hwang, Yeongwoo|Poplin, Ryan|DePristo, Mark A', 'Verily Life Sciences, 1600 Amphitheatre Pkwy, Mountain View, CA, USA.|Verily Life Sciences, 1600 Amphitheatre Pkwy, Mountain View, CA, USA.|Verily Life Sciences, 1600 Amphitheatre Pkwy, Mountain View, CA, USA.|Verily Life Sciences, 1600 Amphitheatre Pkwy, Mountain View, CA, USA.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz218', '30916319', 'Reference genomes are refined to reflect error corrections and other improvements. While this process improves novel data generation and analysis, incorporating data analyzed on an older reference genome assembly requires transforming the coordinates and representations of the data to the new assembly. Multiple tools exist to perform this transformation for coordinate-only data types, but none supports accurate transformation of genome-wide short variation. Here we present GenomeWarp, a tool for efficiently transforming variants between genome assemblies. GenomeWarp transforms regions and short variants in a conservative manner to minimize false positive and negative variants in the target genome, and converts over 99% of regions and short variants from a representative human genome.'),
(82698, 2019, 'bio', 'PgpRules: a Decision Tree Based Prediction Server for P-glycoprotein Substrates and Inhibitors', 'Wang, Pei-Hua|Tu, Yi-Shu|Tseng, Yufeng J', 'Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, No.1xe2x80x89Sec.4, Roosevelt Road, Taipei, Taiwan.|Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, No.1xe2x80x89Sec.4, Roosevelt Road, Taipei, Taiwan.|Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, No.1xe2x80x89Sec.4, Roosevelt Road, Taipei, Taiwan.', '-|-|-', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz213', '30918935', 'P-glycoprotein (P-gp) is a member of ABC transporter family that actively pumps xenobiotics out of cells to protect organisms from toxic compounds. P-gp substrates can be easily pumped out of the cells to reduce their absorption, conversely P-gp inhibitors can reduce such pumping activity. Hence, it is crucial to know if a drug is a P-gp substrate or inhibitor in view of pharmacokinetics. Here we present PgpRules, an online P-gp substrate and P-gp inhibitor prediction server with ruled-sets. The two models were built using classification and regression tree (CART) algorithm. For each compound uploaded, PgpRules not only predicts whether the compound is a P-gp substrate or a P-gp inhibitor, but also provides the rules containing chemical structural features for further structural optimization.'),
(82699, 2019, 'bio', 'Kinpute: Using identity by descent to improve genotype imputation', 'Abney, Mark|Sherbiny, Aisha El', 'Department of Human Genetics, University of Chicago, Chicago, IL, USA.|Department of Human Genetics, University of Chicago, Chicago, IL, USA.', 'United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz221', '30918937', 'Genotype imputation, though generally accurate, often results in many genotypes being poorly imputed, particularly in studies where the individuals are notwell represented by standard reference panels. When individuals in the study share regions of the genome identical by descent (IBD), it is possible to use this information in combination with a study specific reference panel (SSRP) to improve the imputation results. Kinpute uses IBD information-due to either recent, familial relatedness or distant, unknown ancestors-in conjunction with the output from linkage disequilibrium (LD) based imputation methods to compute more accurate genotype probabilities. Kinpute uses a novel method for IBD imputation, which works even in the absence of a pedigree, and results in substantially improved imputation quality.'),
(82700, 2019, 'bio', 'parMATT: parallel multiple alignment of protein 3D-structures with translations and twists for distributed-memory systems', 'Shegay, Maksim V|Suplatov, Dmitry A|Popova, Nina N|Švedas, Vytas K|Voevodin, Vladimir V', 'Faculty of Computational Mathematics and Cybernetics, Vorobjev hills, Moscow, Russia.|Belozersky Institute of Physicochemical Biology, Vorobjev hills, Moscow, Russia.|Faculty of Computational Mathematics and Cybernetics, Vorobjev hills, Moscow, Russia.|Belozersky Institute of Physicochemical Biology, Vorobjev hills, Moscow, Russia.|Faculty of Computational Mathematics and Cybernetics, Vorobjev hills, Moscow, Russia.', '-|-|-|-|-', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz224', '30918940', 'Accurate structural alignment of proteins is crucial at studying structure-function relationship in evolutionarily distant homologues. Various software tools were proposed to align multiple protein 3D-structures utilizing one CPU and thus are of limited productivity at large-scale analysis of protein families/superfamilies.'),
(82701, 2019, 'bio', 'Simultaneous clustering of multiview biomedical data using manifold optimization', 'Yu, Yun|Zhang, Lei-Hong|Zhang, Shuqin', 'School of Mathematical Sciences, Fudan University, Shanghai, China.|School of Mathematical Sciences, Soochow University, Suzhou, China.|School of Mathematical Sciences, Fudan University, Shanghai, China.', 'China|China|China', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz217', '30918942', 'Multiview clustering has attracted much attention in recent years. Several models and algorithms have been proposed for finding the clusters. However, these methods are developed either to find the consistent/common clusters across different views, or to identify the differential clusters among different views. In reality, both consistent and differential clusters may exist in multiview datasets. Thus, development of simultaneous clustering methods such that both the consistent and the differential clusters can be identified is of great importance.'),
(82702, 2019, 'bio', 'Neural networks with circular filters enable data efficient inference of sequence motifs', 'Blum, Christopher F|Kollmann, Markus', 'Institute for Mathematical Modeling of Biological Systems, Heinrich-Heine University of Düsseldorf, Düsseldorf, Germany.|Institute for Mathematical Modeling of Biological Systems, Heinrich-Heine University of Düsseldorf, Düsseldorf, Germany.', 'Germany|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz194', '30918943', 'Nucleic acids and proteins often have localized sequence motifs that enable highly specific interactions. Due to the biological relevance of sequence motifs, numerous inference methods have been developed. Recently, convolutional neural networks (CNNs) have achieved state of the art performance. These methods were able to learn transcription factor binding sites from ChIP-seq data, resulting in accurate predictions on test data. However, CNNs typically distribute learned motifs across multiple filters, making them difficult to interpret. Furthermore, networks trained on small data sets often do not generalize well to new sequences.'),
(82703, 2019, 'bio', 'FusionLearn: A biomarker selection algorithm on cross-platform data', 'Xin, Gao|Yuan, Zhong', 'Department of Mathematics and Statistics, York University, Toronto, M3J 1P3, Canada.|Department of Mathematics and Statistics, York University, Toronto, M3J 1P3, Canada.', 'Canada|Canada', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz223', '30918944', 'In high dimensional genetic data analysis, the objective is to select important biomarkers which are involved in some biological processes such as disease progression, immune response, etc. The experimental data are often collected from different platforms including microarray experiments and proteomic experiments. The conventional single platform approach lacks the capability to learn from multiple platforms, and the resulted lists of biomarkers vary across different platforms. There is a great need to develop an algorithm which can aggregate information across platforms and provide a consolidated list of biomarkers across different platforms.'),
(82704, 2019, 'bio', 'Graph analysis of fragmented long-read bacterial genome assemblies', 'Marijon, Pierre|Chikhi, Rayan|Varré, Jean-Stéphane', 'Inria, Univ. Lille, CNRS, Centrale Lille, UMR 9189 - CRIStAL, Lille, France.|Institut Pasteur, C3BI USR 3756 IP CNRS, Paris, France.|Univ. Lille, CNRS, Centrale Lille, Inria, UMR 9189 - CRIStAL, Lille, France.', 'France|France|France', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz219', '30918948', 'Long-read genome assembly tools are expected to reconstruct bacterial genomes nearly perfectly, however they still produce fragmented assemblies in some cases. It would be beneficial to understand whether these cases are intrinsically impossible to resolve, or if assemblers are at fault, implying that genomes could be refined or even finished with little to no additional experimental cost.'),
(82705, 2019, 'bio', 'Shouji: A Fast and Efficient Pre-Alignment Filter for Sequence Alignment', 'Alser, Mohammed|Hassan, Hasan|Kumar, Akash|Mutlu, Onur|Alkan, Can', 'Computer Science Department, ETH Zürich, Zürich, Switzerland.|Computer Science Department, ETH Zürich, Zürich, Switzerland.|Institute for Computer Engineering, CfAED, Technische Universität Dresden, Germany.|Computer Science Department, ETH Zürich, Zürich, Switzerland.|Computer Engineering Department, Bilkent University, Bilkent, Ankara, Turkey.', 'Switzerland|Switzerland|Germany|Switzerland|Turkey', 7, '-', 'Journal Article', '10.1093/bioinformatics/btz234', '30923804', 'The ability to generate massive amounts of sequencing data continues to overwhelm the processing capability of existing algorithms and compute infrastructures. In this work, we explore the use of hardware/software co-design and hardware acceleration to significantly reduce the execution time of short sequence alignment, a crucial step in analyzing sequenced genomes. We introduce Shouji, a highly-parallel and accurate pre-alignment filter that remarkably reduces the need for computationally-costly dynamic programming algorithms. The first key idea of our proposed pre-alignment filter is to provide high filtering accuracy by correctly detecting all common subsequences shared between two given sequences. The second key idea is to design a hardware accelerator that adopts modern FPGA (Field-Programmable Gate Array) architectures to further boost the performance of our algorithm.'),
(82706, 2019, 'bio', 'rMTA: Robust Metabolic Transformation Analysis', 'Valcárcel, Luis V|Torrano, Verónica|Tobalina, Luis|Carracedo, Arkaitz|Planes, Francisco J', 'Tecnun, University of Navarra, Manuel de Lardizábal 15, 20018 San Sebastián, Spain.|CIC bioGUNE, Bizkaia Technology Park, 801 Building, Derio, Spain.|RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine, MTZ Pauwelsstrasse 19 D-52074 Aachen.|CIC bioGUNE, Bizkaia Technology Park, 801 Building, Derio, Spain.|Tecnun, University of Navarra, Manuel de Lardizábal 15, 20018 San Sebastián, Spain.', 'Spain|Spain|-|Spain|Spain', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz231', '30923806', 'The development of computational tools exploiting -omics data and high-quality genome-scale metabolic networks for the identification of novel drug targets is a relevant topic in Systems Medicine. Metabolic Transformation Algorithm (MTA) is one of these tools, which aims to identify targets that transform a disease metabolic state back into a healthy state, with potential application in any disease where a clear metabolic alteration is observed.'),
(82707, 2019, 'bio', 'Determining Parameters for Non-Linear Models of Multi-Loop Free Energy Change', 'Ward, Max|Sun, Hongying|Datta, Amitava|Wise, Michael|Mathews, David H', 'Computer Science & Software Engineering, The University of Western Australia.|Department of Biochemistry & Biophysics, and Center for RNA Biology, University of Rochester.|Computer Science & Software Engineering, The University of Western Australia.|Computer Science & Software Engineering, The University of Western Australia.|Department of Biostatistics & Computational Biology, University of Rochester.', 'Australia|-|Australia|Australia|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz222', '30923811', 'Predicting the secondary structure of RNA is a fundamental task in bioinformatics. Algorithms that predict secondary structure given only the primary sequence, and a model to evaluate the quality of a structure, are an integral part of this. These algorithms have been updated as our model of RNA thermodynamics changed and expanded. An exception to this has been the treatment of multi-loops. While more advanced models of multi-loop free energy change have been suggested, a simple, linear model has been used since the 1980s. However, recently, new dynamic programming algorithms for secondary structure prediction that could incorporate these models were presented. Unfortunately, these models appear to have lower accuracy for secondary structure prediction.'),
(82708, 2019, 'bio', 'epic2 efficiently finds diffuse domains in ChIP-seq data', 'Stovner, Endre Bakken|Sætrom, Pål', 'Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway.|Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway.', 'Norway|Norway', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz232', '30923821', 'Data from chromatin immunoprecipitation (ChIP) followed by high throughput sequencing (ChIP-seq) generally contain either narrow peaks or broad and diffusely enriched domains. The SICER ChIP-seq caller has proven adept at finding diffuse domains in ChIP-seq data, but it is slow, requires much memory, needs manual installation steps and is hard to use. epic2 is a complete rewrite of SICER that is focused on speed, low memory overhead and ease-of-use.'),
(82709, 2019, 'bio', 'Genome-wide identification of the essential protein-coding genes and long noncoding RNAs for human pan-cancer', 'Zhang, Yuwei|Tao, Yang|Ji, Huihui|Li, Wei|Guo, Xingli|Ng, Derry Minyao|Haleem, Maria|Xi, Yang|Dong, Changzheng|Zhao, Jinshun|Zhang, Lina|Zhang, Xiaohong|Xie, Yangyang|Dai, Xiaoyu|Liao, Qi', 'Department of Preventative Medicine, Zhejiang Provincial Key Laboratory of Pathological and Physiological Technology, Medicine School of Ningbo University, Ningbo, Zhejiang, China.|Department of Preventative Medicine, Zhejiang Provincial Key Laboratory of Pathological and Physiological Technology, Medicine School of Ningbo University, Ningbo, Zhejiang, China.|Zhejiang Provincial Key Laboratory of Pathophysiology, School of Medicine, Ningbo University, Ningbo, Zhejiang, China.|Center for Genetic Medicine Research, Childrens National Medical Center, Department of Genomics and Precision Medicine, George Washington University, Washington, DC, USA.|School of Computer Science and Technology, Xidian University, Xian, Shaanxi Province, China.|Department of Preventative Medicine, Zhejiang Provincial Key Laboratory of Pathological and Physiological Technology, Medicine School of Ningbo University, Ningbo, Zhejiang, China.|Department of Preventative Medicine, Zhejiang Provincial Key Laboratory of Pathological and Physiological Technology, Medicine School of Ningbo University, Ningbo, Zhejiang, China.|Zhejiang Provincial Key Laboratory of Pathophysiology, School of Medicine, Ningbo University, Ningbo, Zhejiang, China.|Department of Preventative Medicine, Zhejiang Provincial Key Laboratory of Pathological and Physiological Technology, Medicine School of Ningbo University, Ningbo, Zhejiang, China.|Department of Preventative Medicine, Zhejiang Provincial Key Laboratory of Pathological and Physiological Technology, Medicine School of Ningbo University, Ningbo, Zhejiang, China.|Department of Preventative Medicine, Zhejiang Provincial Key Laboratory of Pathological and Physiological Technology, Medicine School of Ningbo University, Ningbo, Zhejiang, China.|Department of Preventative Medicine, Zhejiang Provincial Key Laboratory of Pathological and Physiological Technology, Medicine School of Ningbo University, Ningbo, Zhejiang, China.|Anorectal Surgery, Ningbo Second Hospital, Ningbo, China.|Anorectal Surgery, Ningbo Second Hospital, Ningbo, China.|Department of Preventative Medicine, Zhejiang Provincial Key Laboratory of Pathological and Physiological Technology, Medicine School of Ningbo University, Ningbo, Zhejiang, China.', 'China|China|China|United States|China|China|China|China|China|China|China|China|China|China|China', 4, '-', 'Journal Article', '10.1093/bioinformatics/btz230', '30923830', 'Genome-scale CRISPR/Cas9 system has been a democratized gene editing technique and widely used to investigate gene functions in some biological processes and diseases especially cancers. Aiming to characterize gene aberrations and assess their effects on cancer, we designed a pipeline to identify the essential genes for pan-cancer.'),
(82710, 2019, 'bio', 'Pinetree: a step-wise gene expression simulator with codon-specific translation rates', 'Jack, Benjamin R|Wilke, Claus O', 'Department of Integrative Biology, The University of Texas at Austin, TX, USA.|Department of Integrative Biology, The University of Texas at Austin, TX, USA.', 'United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz203', '30923831', 'Stochastic gene expression simulations often assume steady-state transcript levels, or they model transcription in more detail than translation. Moreover, they lack accessible programming interfaces, which limits their utility.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(82711, 2019, 'bio', 'RTNsurvival : An R/Bioconductor package for regulatory network survival analysis', 'Groeneveld, Clarice S|Chagas, Vinicius S|Jones, Steven J M|Robertson, A Gordon|Ponder, Bruce A J|Meyer, Kerstin B|Castro, Mauro A A', 'Bioinformatics and Systems Biology Lab, Federal University of Paraná, Curitiba, Brazil.|Bioinformatics and Systems Biology Lab, Federal University of Paraná, Curitiba, Brazil.|Canadas Michael Smith Genome Sciences Center, BC Cancer Agency, Vancouver, Canada.|Canadas Michael Smith Genome Sciences Center, BC Cancer Agency, Vancouver, Canada.|Department of Oncology and Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, United Kingdom.|Department of Oncology and Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, United Kingdom.|Bioinformatics and Systems Biology Lab, Federal University of Paraná, Curitiba, Brazil.', 'Brazil|Brazil|Canada|Canada|United Kingdom|United Kingdom|Brazil', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz229', '30923832', 'Transcriptional networks are models that allow the biological state of cells or tumours to be described. Such networks consist of connected regulatory units known as regulons, each comprised of a regulator and its targets. Inferring a transcriptional network can be a helpful initial step in characterizing the different phenotypes within a cohort. While the network itself provides no information on molecular differences between samples, the per-sample state of each regulon, i.e. the regulon activity, can be used for describing subtypes in a cohort. Integrating regulon activities with clinical data and outcomes would extend this characterization of differences between subtypes.'),
(82712, 2019, 'bio', 'A Bayesian model integration for mutation calling through data partitioning', 'Moriyama, Takuya|Imoto, Seiya|Hayashi, Shuto|Shiraishi, Yuichi|Miyano, Satoru|Yamaguchi, Rui', 'Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo 108-8639.|Health Intelligence Center, The Institute of Medical Science, The University of Tokyo, Tokyo 108-8639.|Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo 108-8639.|Center for Cancer Genomics and Advanced Therapeutics, National Cancer Center, Tokyo, 104-0045.|Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo 108-8639.|Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo 108-8639.', '-|-|-|-|-|-', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz233', '30924874', 'Detection of somatic mutations from tumor and matched normal sequencing data has become among the most important analysis methods in cancer research. Some existing mutation callers have focused on additional information, e.g., heterozygous single-nucleotide polymorphisms~(SNPs) nearby mutation candidates or overlapping paired-end read information. However, existing methods cannot take multiple information sources into account simultaneously. Existing Bayesian hierarchical model-based methods construct two generative models, the tumor model and error model, and limited information sources have been modeled.'),
(82713, 2019, 'bio', 'Benefits of dimension reduction in penalized regression methods for high-dimensional grouped data: a case study in low sample size', 'Ajana, Soufiane|Acar, Niyazi|Bretillon, Lionel|Hejblum, Boris P|Jacqmin-Gadda, Hélène|Delcourt, Cécile|BLISAR Study Group ', 'Inserm, Bordeaux Population Health Research Center, Team LEHA, UMR 1219, University of Bordeaux, F-33000 Bordeaux, France.|Centre des Sciences du Goût et de lAlimentation, AgroSup Dijon, CNRS, INRA, Université Bourgogne Franche-Comté, Dijon, France.|Centre des Sciences du Goût et de lAlimentation, AgroSup Dijon, CNRS, INRA, Université Bourgogne Franche-Comté, Dijon, France.|ISPED, Inserm, Bordeaux Population Health Research Center 1219, Inria SISTM, University of Bordeaux, F-33000 Bordeaux, France.|Inserm, Bordeaux Population Health Research Center, Team Biostatistics, UMR 1219, University of Bordeaux, F-33000 Bordeaux, France.|Inserm, Bordeaux Population Health Research Center, Team LEHA, UMR 1219, University of Bordeaux, F-33000 Bordeaux, France.|Inserm, Bordeaux Population Health Research Center, Team LEHA, UMR 1219, University of Bordeaux, F-33000 Bordeaux, France.', 'France|France|France|France|France|France|France', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz135', '30931473', 'In some prediction analyses, predictors have a natural grouping structure and selecting predictors accounting for this additional information could be more effective for predicting the outcome accurately. Moreover, in a high dimension low sample size framework, obtaining a good predictive model becomes very challenging. The objective of this work was to investigate the benefits of dimension reduction in penalized regression methods, in terms of prediction performance and variable selection consistency, in high dimension low sample size data. Using two real datasets, we compared the performances of lasso, elastic net, group lasso, sparse group lasso, sparse partial least squares (PLS), group PLS and sparse group PLS.'),
(82714, 2019, 'bio', 'Network inference performance complexity: a consequence of topological, experimental and algorithmic determinants', 'Muldoon, Joseph J|Yu, Jessica S|Fassia, Mohammad-Kasim|Bagheri, Neda', 'Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA.|Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA.|Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA.|Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA.', 'United States|United States|United States|United States', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz105', '30932143', 'Network inference algorithms aim to uncover key regulatory interactions governing cellular decision-making, disease progression and therapeutic interventions. Having an accurate blueprint of this regulation is essential for understanding and controlling cell behavior. However, the utility and impact of these approaches are limited because the ways in which various factors shape inference outcomes remain largely unknown.'),
(82715, 2019, 'bio', 'Discovery of tandem and interspersed segmental duplications using high throughput sequencing', 'Soylev, Arda|Le, Thong|Amini, Hajar|Alkan, Can|Hormozdiari, Fereydoun', 'Department of Computer Engineering, Bilkent University, Ankara, Turkey.|UC-Davis Genome Center, University of California, Davis, CA, USA.|Department of Neurology, School of Medicine, University of California, Davis, CA, USA.|Department of Computer Engineering, Bilkent University, Ankara, Turkey.|UC-Davis Genome Center, University of California, Davis, CA, USA.', 'Turkey|United States|United States|Turkey|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz237', '30937433', 'Several algorithms have been developed that use high throughput sequencing technology to characterize structural variations. Most of the existing approaches focus on detecting relatively simple types of SVs such as insertions, deletions, and short inversions. In fact, complex SVs are of crucial importance and several have been associated with genomic disorders. To better understand the contribution of complex SVs to human disease, we need new algorithms to accurately discover and genotype such variants. Additionally, due to similar sequencing signatures, inverted duplications or gene conversion events that include inverted segmental duplications are often characterized as simple inversions, likewise, duplications and gene conversions in direct orientation may be called as simple deletions. Therefore, there is still a need for accurate algorithms to fully characterize complex SVs and thus improve calling accuracy of more simple variants.'),
(82716, 2019, 'bio', 'Developing Structural Profile Matrices for Protein Secondary Structure and Solvent Accessibility Prediction', 'Aydin, Zafer|Azginoglu, Nuh|Bilgin, Halil Ibrahim|Celik, Mete', 'Department of Computer Engineering, Abdullah Gul University, Kayseri, Turkey.|Department of Computer Engineering, Nevsehir Haci Bektas Veli University, Nevsehir, Turkey.|Department of Computer Engineering, Abdullah Gul University, Kayseri, Turkey.|Department of Computer Engineering, Erciyes University, Kayseri, Turkey.', 'Turkey|Turkey|Turkey|Turkey', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz238', '30937435', 'Predicting secondary structure and solvent accessibility of proteins are among the essential steps that preclude more elaborate 3D structure prediction tasks. Incorporating class label information contained in templates with known structures has the potential to improve the accuracy of prediction methods. Building a structural profile matrix is one such technique that provides a distribution for class labels at each amino acid position of the target.'),
(82717, 2019, 'bio', 'Open Agile Text Mining for Bioinformatics: The PubAnnotation Ecosystem', 'Kim, Jin-Dong|Wang, Yue|Fujiwara, Toyofumi|Okuda, Shujiro|Callahan, Tiffany J|Cohen, K Bretonnel', 'Database Center for Life Science, ROIS, Kashiwa, 178-4-4, Japan.|Database Center for Life Science, ROIS, Kashiwa, 178-4-4, Japan.|Database Center for Life Science, ROIS, Kashiwa, 178-4-4, Japan.|Graduate School of Medical and Dental Sciences, Niigata University, Niigata, 951-8510, Japan.|Computational Bioscience Program, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, USA.|Computational Bioscience Program, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, USA.', 'Japan|Japan|Japan|Japan|United States|United States', 5, '-', 'Journal Article', '10.1093/bioinformatics/btz227', '30937439', 'Most currently available text mining tools share two characteristics that make them less than optimal for use by biomedical researchers: they require extensive specialist skills in natural language processing and they were built on the assumption that they should optimize global performance metrics on representative datasets. This is a problem because most end-users are not natural language processing specialists and because biomedical researchers often care less about global metrics like F-measure or representative datasets than they do about more granular metrics such as precision and recall on their own specialized datasets. Thus, there are fundamental mismatches between the assumptions of much text mining work and the preferences of potential end-users.'),
(82718, 2019, 'bio', 'Structural variant analysis for linked-read sequencing data with gemtools', 'Greer, S U|Ji, H P', 'Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, United States.|Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, United States.', 'United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz239', '30938757', 'Linked-read sequencing generates synthetic long reads which are useful for the detection and analysis of structural variants (SVs). The software associated with 10X Genomics linked-read sequencing, Long Ranger, generates the essential output files (BAM, VCF, SV BEDPE) necessary for downstream analyses. However, to perform downstream analyses requires the user to customize their own tools to handle the unique features of linked-read sequencing data. Here, we describe gemtools, a collection of tools for the downstream and in-depth analysis of structural variants from linked-read data. Gemtools uses the barcoded aligned reads and the Megabase-scale phase blocks to determine haplotypes of structural variant breakpoints and delineate complex breakpoint configurations at the resolution of single DNA molecules. The gemtools package is a suite of tools that provides the user with the flexibility to perform basic functions on their linked-read sequencing output in order to address even more questions.'),
(82719, 2019, 'bio', 'Assessing reproducibility of matrix factorization methods in independent transcriptomes', 'Cantini, Laura|Kairov, Ulykbek|de Reyniès, Aurélien|Barillot, Emmanuel|Radvanyi, François|Zinovyev, Andrei', 'Institut Curie, PSL Research University, Paris, France.|Laboratory of bioinformatics and systems biology, Center for Life Sciences, National Laboratory Astana, Nazarbayev University, Astana, Kazakhstan.|Programme Cartes dIdentité des Tumeurs (CIT), Ligue Nationale Contre le Cancer, Paris, France.|Institut Curie, PSL Research University, Paris, France.|Institut Curie, PSL Research University, CNRS, UMR144, Equipe Labellisée Ligue Contre le Cancer, Paris, France.|Institut Curie, PSL Research University, Paris, France.', 'France|Kazakhstan|France|France|France|France', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz225', '30938767', 'Matrix factorization (MF) methods are widely used in order to reduce dimensionality of transcriptomic datasets to the action of few hidden factors (metagenes). MF algorithms have never been compared based on the between-datasets reproducibility of their outputs in similar independent datasets. Lack of this knowledge might have a crucial impact when generalizing the predictions made in a study to others.'),
(82720, 2019, 'bio', 'Protein Multiple Alignments: Sequence-based vs Structure-based Programs', 'Carpentier, Mathilde|Chomilier, Jacques', 'Institut Systématique Evolution Biodiversité (ISYEB), Sorbonne Université, MNHN, CNRS, EPHE, Paris, France.|Sorbonne Université, CNRS, MNHN, IRD, IMPMC, BiBiP, Paris, France.', 'France|France', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz236', '30942864', 'Multiple sequence alignment programs have proved to be very useful and have already been evaluated in the literature yet not alignment programs based on structure or both sequence and structure. In the present article we wish to evaluate the added value provided through considering structures.'),
(82721, 2019, 'bio', 'HLA*LA - HLA typing from linearly projected graph alignments', 'Dilthey, Alexander T|Mentzer, Alexander J|Carapito, Raphael|Cutland, Clare|Cereb, Nezih|Madhi, Shabir A|Rhie, Arang|Koren, Sergey|Bahram, Seiamak|McVean, Gil|Phillippy, Adam M', 'Institute of Medical Microbiology, University Hospital of Dusseldorf, Dusseldorf, North Rhine-Westphalia, Germany.|Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.|Laboratoire dImmunoRhumatologie Molxc3xa9culaire, Plateforme GENOMAX, INSERM UMR_S 1109, LabEx TRANSPLANTEX, Fxc3xa9dxc3xa9ration de Mxc3xa9decine Translationnelle de Strasbourg (FMTS), Facultxc3xa9 de Mxc3xa9decine, Universitxc3xa9 de Strasbourg, France. Service dImmunologie Biologique, Nouvel Hxc3xb4pital Civil, Strasbourg, France.|Medical Research Council: Respiratory and Meningeal Pathogens Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.|Histogenetics, Ossining, United States of America.|Medical Research Council: Respiratory and Meningeal Pathogens Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.|Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, Bethesda, Maryland, USA.|Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, Bethesda, Maryland, USA.|Laboratoire dImmunoRhumatologie Molxc3xa9culaire, Plateforme GENOMAX, INSERM UMR_S 1109, LabEx TRANSPLANTEX, Fxc3xa9dxc3xa9ration de Mxc3xa9decine Translationnelle de Strasbourg (FMTS), Facultxc3xa9 de Mxc3xa9decine, Universitxc3xa9 de Strasbourg, France. Service dImmunologie Biologique, Nouvel Hxc3xb4pital Civil, Strasbourg, France.|Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.|Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, Bethesda, Maryland, USA.', 'Germany|United Kingdom|France|South Africa|United States|South Africa|United Kingdom|United Kingdom|France|United Kingdom|United Kingdom', 6, '-', 'Journal Article', '10.1093/bioinformatics/btz235', '30942877', 'HLA*LA implements a new graph alignment model for HLA type inference, based on the projection of linear alignments onto a variation graph. It enables accurate HLA type inference from whole-genome (99% accuracy) and whole-exome (93% accuracy) Illumina data, from long-read Oxford Nanopore and Pacific Biosciences data (98% accuracy for whole-genome and targeted data), and from genome assemblies. Computational requirements for a typical sample vary between 0.7 and 14 CPU hours per sample.'),
(82722, 2019, 'bio', 'IntronDB: a database for eukaryotic intron features', 'Wang, Dapeng', 'Department of Plant Sciences, University of Oxford, S Parks Rd, Oxford OX1 3RB, UK.', 'United Kingdom', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz242', '30949679', 'The rate and extent of unbalanced eukaryotic intron changes exhibit dynamic patterns for different lineages of species or certain functional groups of genes with varied spatio-temporal expression modes affected by selective pressure. To date, only a few key conserved splicing signals or regulatory elements have been identified in introns and little is known about the remaining intronic regions. To trace the evolutionary trajectory of spliceosomal introns from available genomes under a unified framework, we present IntronDB, which catalogues approximately 50000000 introns from over 1000 genomes spanning the major eukaryotic clades in the tree of life. Based on the position of introns relative to coding regions, it categorizes introns into three groups such as 5UTR, CDS and 3UTR and subsequently divides CDS introns into three categories such as phase 0, phase 1 and phase 2. It provides the quality evaluation for each sequence entry and characterizes the intronic parameters including number, size, sequence composition and positioning information as well as the features for exons and genes, making possible the comparisons between introns and exons. It reports the dinucleotides around the intron boundary and displays the consensus sequence features for all introns, small introns and large introns for each genome. By incorporating the taxonomic assignment of genomes, it performs high-level or genome-wide statistical analysis for single feature and coupled features both in a single genome and across multiple genomes. It offers functionalities to browse the data from representative protein-coding transcripts and download the data from all transcripts from protein-coding genes.'),
(82723, 2019, 'bio', 'Figure and Caption Extraction from Biomedical Documents', 'Li, Pengyuan|Jiang, Xiangying|Shatkay, Hagit', 'Department of Computer and Information Sciences, University of Delaware, Newark, 19711, USA.|Department of Computer and Information Sciences, University of Delaware, Newark, 19711, USA.|Department of Computer and Information Sciences, University of Delaware, Newark, 19711, USA.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz228', '30949681', 'Figures and captions convey essential information in biomedical documents. As such, there is a growing interest in mining published biomedical figures and in utilizing their respective captions as a source of knowledge. Notably, an essential step underlying such mining is the extraction of figures and captions from publications. While several PDF parsing tools that extract information from such documents are publicly available, they attempt to identify images by analyzing the PDF encoding and structure and the complex graphical objects embedded within. As such, they often incorrectly identify figures and captions in scientific publications, whose structure is often non-trivial. The extraction of figures, captions and figure-caption pairs from biomedical publications is thus neither well-studied nor yet well-addressed.'),
(82724, 2019, 'bio', 'Nano-GLADIATOR: real-time detection of copy number alterations from nanopore sequencing data', 'Magi, Alberto|Bolognini, Davide|Bartalucci, Niccoló|Mingrino, Alessandra|Semeraro, Roberto|Giovannini, Luna|Bonifacio, Stefania|Parrini, Daniela|Pelo, Elisabetta|Mannelli, Francesco|Guglielmelli, Paola|Vannucchi, Alessandro Maria', 'Department of Information Engineering, University of Florence, Florence, Italy.|Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy.|Department of Experimental and Clinical Medicine, CRIMM, Center Research and Innovation of Myeloproliferative Neoplasms, Azienda Ospedaliera Universitaria Careggi, University of Florence, Florence, Italy.|Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy.|Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy.|Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy.|Department of Laboratory Diagnosis, Genetic Diagnosis Service, Careggi Teaching Hospital, Florence, Italy.|Department of Laboratory Diagnosis, Genetic Diagnosis Service, Careggi Teaching Hospital, Florence, Italy.|Department of Laboratory Diagnosis, Genetic Diagnosis Service, Careggi Teaching Hospital, Florence, Italy.|Department of Experimental and Clinical Medicine, CRIMM, Center Research and Innovation of Myeloproliferative Neoplasms, Azienda Ospedaliera Universitaria Careggi, University of Florence, Florence, Italy.|Department of Experimental and Clinical Medicine, CRIMM, Center Research and Innovation of Myeloproliferative Neoplasms, Azienda Ospedaliera Universitaria Careggi, University of Florence, Florence, Italy.|Department of Experimental and Clinical Medicine, CRIMM, Center Research and Innovation of Myeloproliferative Neoplasms, Azienda Ospedaliera Universitaria Careggi, University of Florence, Florence, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz241', '30949684', 'The past few years have seen the emergence of nanopore-based sequencing technologies which interrogate single molecule of DNA and generate reads sequentially.'),
(82725, 2019, 'bio', 'scRNAss: a single-cell RNA-seq assembler via imputing dropouts and combing junctions', 'Liu, Juntao|Liu, Xiangyu|Ren, Xianwen|Li, Guojun', 'School of Mathematics, Shandong University, Jinan, China.|School of Mathematics, Shandong University, Jinan, China.|Biodynamic Optical Imaging Center, Beijing Advanced Innovation Center for Genomics, and School of Life Sciences, Peking University, Beijing, China.|School of Mathematics, Shandong University, Jinan, China.', 'China|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz240', '30951147', 'Full-length transcript reconstruction is essential for single-cell RNA-seq data analysis, but dropout events, which can cause transcripts discarded completely or broken into pieces, pose great challenges for transcript assembly. Currently available RNA-seq assemblers are generally designed for bulk RNA sequencing. To fill the gap, we introduce scRNAss, a method that applies explicit strategies to impute lost information caused by dropout events and a combing strategy to infer transcripts using scRNA-seq.'),
(82726, 2019, 'bio', 'AbPredict 2: a server for accurate and unstrained structure prediction of antibody variable domains', 'Lapidoth, Gideon|Parker, Jake|Prilusky, Jaime|Fleishman, Sarel J', 'Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel.|Institute for Molecular Bioscience, The University of Queensland, St. Lucia, Australia.|Bioinformatics & Biological Computing Unit, Weizmann Institute of Science, Rehovot, Israel.|Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel.', 'Israel|Australia|Israel|Israel', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty822', '30951584', 'Methods for antibody structure prediction rely on sequence homology to experimentally determined structures. Resulting models may be accurate but are often stereochemically strained, limiting their usefulness in modeling and design workflows. We present the AbPredict 2 web-server, which instead of using sequence homology, conducts a Monte Carlo-based search for low-energy combinations of backbone conformations to yield accurate and unstrained antibody structures.'),
(82727, 2019, 'bio', 'TORMES: an automated pipeline for whole bacterial genome analysis', 'Quijada, Narciso M|Rodríguez-Lázaro, David|Hernández, Marta', 'Laboratory of Molecular Biology and Microbiology, Instituto Tecnológico Agrario de Castilla y León, Valladolid, Spain.|Department Division of Microbiology, Department of Biotechnology and Food Science, Universidad de Burgos, Burgos, Spain.|Laboratory of Molecular Biology and Microbiology, Instituto Tecnológico Agrario de Castilla y León, Valladolid, Spain.', 'Spain|Spain|Spain', 10, '-', 'Journal Article', '10.1093/bioinformatics/btz220', '30957837', 'The progress of High Throughput Sequencing (HTS) technologies and the reduction in the sequencing costs are such that Whole Genome Sequencing (WGS) could replace many traditional laboratory assays and procedures. Exploiting the volume of data produced by HTS platforms requires substantial computing skills and this is the main bottleneck in the implementation of WGS as a routine laboratory technique. The way in which the vast amount of results are presented to researchers and clinicians with no specialist knowledge of genome sequencing is also a significant issue.'),
(82728, 2019, 'bio', 'TeraPCA: a fast and scalable software package to study genetic variation in tera-scale genotypes', 'Bose, Aritra|Kalantzis, Vassilis|Kontopoulou, Eugenia-Maria|Elkady, Mai|Paschou, Peristera|Drineas, Petros', 'Computer Science Department, Purdue University, West Lafayette, IN, USA.|IBM Research, Thomas J. Watson Research Center, Yorktown Heights, NY, USA.|Computer Science Department, Purdue University, West Lafayette, IN, USA.|Computer Science Department, Purdue University, West Lafayette, IN, USA.|Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.|Computer Science Department, Purdue University, West Lafayette, IN, USA.', 'United States|United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz157', '30957838', 'Principal Component Analysis is a key tool in the study of population structure in human genetics. As modern datasets become increasingly larger in size, traditional approaches based on loading the entire dataset in the system memory (Random Access Memory) become impractical and out-of-core implementations are the only viable alternative.'),
(82729, 2019, 'bio', 'Unsupervised discovery of phenotype specific multi-omics networks', 'Shi, W Jenny|Zhuang, Yonghua|Russell, Pamela H|Hobbs, Brian D|Parker, Margaret M|Castaldi, Peter J|Rudra, Pratyaydipta|Vestal, Brian|Hersh, Craig P|Saba, Laura M|Kechris, Katerina', 'Computational Bioscience Program, University of Colorado, Aurora, CO, USA.|Department of Biostatistics and Informatics, University of Colorado, Aurora, CO, USA.|Department of Biostatistics and Informatics, University of Colorado, Aurora, CO, USA.|Channing Division of Network Medicine, Brigham and Womens Hospital, Boston, MA, USA.|Channing Division of Network Medicine, Brigham and Womens Hospital, Boston, MA, USA.|Channing Division of Network Medicine, Brigham and Womens Hospital, Boston, MA, USA.|Department of Biostatistics and Informatics, University of Colorado, Aurora, CO, USA.|Center for Genes, Environment & Health, National Jewish Health, Denver, CO, USA.|Channing Division of Network Medicine, Brigham and Womens Hospital, Boston, MA, USA.|Department of Pharmaceutical Sciences, University of Colorado, Aurora, CO, USA.|Department of Biostatistics and Informatics, University of Colorado, Aurora, CO, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz226', '30957844', 'Complex diseases often involve a wide spectrum of phenotypic traits. Better understanding of the biological mechanisms relevant to each trait promotes understanding of the etiology of the disease and the potential for targeted and effective treatment plans. There have been many efforts towards omics data integration and network reconstruction, but limited work has examined the incorporation of relevant (quantitative) phenotypic traits.'),
(82730, 2019, 'bio', 'A learning-based framework for miRNA-disease association identification using neural networks', 'Peng, Jiajie|Hui, Weiwei|Li, Qianqian|Chen, Bolin|Hao, Jianye|Jiang, Qinghua|Shang, Xuequn|Wei, Zhongyu', 'School of Computer Science, Northwestern Polytechnical University, Xian, China.|School of Computer Science, Northwestern Polytechnical University, Xian, China.|School of Computer Science, Northwestern Polytechnical University, Xian, China.|School of Computer Science, Northwestern Polytechnical University, Xian, China.|College of Intelligence and Computing, Tianjin University, Tianjin, China.|School of Life Science and Technology, Harbin Institute of Technology, Harbin, China.|School of Computer Science, Northwestern Polytechnical University, Xian, China.|School of Data Science, Fudan University, Shanghai, China.', 'China|China|China|China|China|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz254', '30977780', 'A microRNA (miRNA) is a type of non-coding RNA, which plays important roles in many biological processes. Lots of studies have shown that miRNAs are implicated in human diseases, indicating that miRNAs might be potential biomarkers for various types of diseases. Therefore, it is important to reveal the relationships between miRNAs and diseases/phenotypes.'),
(82731, 2019, 'bio', 'Accounting for ambiguity in ancestral sequence reconstruction', 'Oliva, A|Pulicani, S|Lefort, V|Bréhélin, L|Gascuel, O|Guindon, S', 'LIRMM, CNRS & Université de Montpellier, Montpellier, France.|LIRMM, CNRS & Université de Montpellier, Montpellier, France.|LIRMM, CNRS & Université de Montpellier, Montpellier, France.|LIRMM, CNRS & Université de Montpellier, Montpellier, France.|Unité Bioinformatique Evolutive, C3BI USR 3756, Institut Pasteur & CNRS, Paris, France.|LIRMM, CNRS & Université de Montpellier, Montpellier, France.', 'France|France|France|France|France|France', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz249', '30977781', 'The reconstruction of ancestral genetic sequences from the analysis of contemporaneous data is a powerful tool to improve our understanding of molecular evolution. Various statistical criteria defined in a phylogenetic framework can be used to infer nucleotide, amino-acid or codon states at internal nodes of the tree, for every position along the sequence. These criteria generally select the state that maximizes (or minimizes) a given criterion. Although it is perfectly sensible from a statistical perspective, that strategy fails to convey useful information about the level of uncertainty associated to the inference.'),
(82732, 2019, 'bio', 'Modelling G×E with historical weather information improves genomic prediction in new environments', 'Gillberg, Jussi|Marttinen, Pekka|Mamitsuka, Hiroshi|Kaski, Samuel', 'Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Aalto, Finland.|Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Aalto, Finland.|Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Aalto, Finland.|Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Aalto, Finland.', 'Finland|Finland|Finland|Finland', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz197', '30977782', 'Interaction between the genotype and the environment (G×E) has a strong impact on the yield of major crop plants. Although influential, taking G×E explicitly into account in plant breeding has remained difficult. Recently G×E has been predicted from environmental and genomic covariates, but existing works have not shown that generalization to new environments and years without access to in-season data is possible and practical applicability remains unclear. Using data from a Barley breeding program in Finland, we construct an in-silico experiment to study the viability of G×E prediction under practical constraints.'),
(82733, 2019, 'bio', 'SolidBin: Improving Metagenome Binning with Semi-supervised Normalized Cut', 'Wang, Ziye|Wang, Zhengyang|Lu, Yang Young|Sun, Fengzhu|Zhu, Shanfeng', 'Centre for Computational Systems Biology, School of Mathematical Sciences, Fudan University, Shanghai, China.|School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China.|Quantitative and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, California, United States of America.|Centre for Computational Systems Biology, School of Mathematical Sciences, Fudan University, Shanghai, China.|School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China.', 'China|China|United States|China|China', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz253', '30977806', 'Metagenomic contig binning is an important computational problem in metagenomic research, which aims to cluster contigs from the same genome into the same group. Unlike classical clustering problem, contig binning can utilize known relationships among some of the contigs or the taxonomic identity of some contigs. However, the current state-of-the-art contig binning methods do not make full use of the additional biological information except the coverage and sequence composition of the contigs.'),
(82734, 2019, 'bio', 'Lipid Mini-On: Mining and ontology tool for enrichment analysis of lipidomic data', 'Clair, Geremy|Reehl, Sarah|Stratton, Kelly G|Monroe, Matthew E|Tfaily, Malak M|Ansong, Charles|Kyle, Jennifer E', 'Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA.|Computing and Analytics Division, Pacific Northwest National Laboratory, Richland, WA, USA.|Computing and Analytics Division, Pacific Northwest National Laboratory, Richland, WA, USA.|Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA.|Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA.|Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA.|Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA.', 'United States|United States|United States|United States|United States|United States|United States', 8, '-', 'Journal Article', '10.1093/bioinformatics/btz250', '30977807', 'Here we introduce Lipid Mini-On, an open-source tool that performs lipid enrichment analyses and visualizations of lipidomics data. Lipid Mini-On uses a text-mining process to bin individual lipid names into multiple lipid ontology groups based on the classification (e.g., LipidMaps) and other characteristics, such as chain length. Lipid Mini-On provides users with the capability to conduct enrichment analysis of the lipid ontology terms using a Shiny app with options of five statistical approaches. Lipid classes can be added to customize the users database and remain updated as new lipid classes are discovered. Visualization of results is available for all classification options (e.g., lipid subclass and individual fatty acid chains). Results are also visualized through an editable network of relationships between the individual lipids and their associated lipid ontology terms. The utility of the tool is demonstrated using biological (e.g, human lung endothelial cells) and environmental (e.g., peat soil) samples.'),
(82735, 2019, 'bio', 'ASICS: an R package for a whole analysis workflow of 1D 1H NMR spectra', 'Lefort, Gaëlle|Liaubet, Laurence|Canlet, Cécile|Tardivel, Patrick|Père, Marie-Christine|Quesnel, Hélène|Paris, Alain|Iannuccelli, Nathalie|Vialaneix, Nathalie|Servien, Rémi', 'MIAT, Université de Toulouse, INRA, Castanet Tolosan, France.|GenPhySE, Université de Toulouse, INRA, ENVT, Castanet Tolosan, France.|Toxalim, Université de Toulouse, INRA, ENVT, INP-Purpan, UPS, Toulouse, France.|Institute of Mathematics, University of Wroclaw, Poland.|PEGASE, INRA, Agrocampus Ouest, Saint-Gilles, France.|PEGASE, INRA, Agrocampus Ouest, Saint-Gilles, France.|Unité Molécules de Communication et Adaptation des Microorganismes (MCAM), Muséum national dHistoire naturelle, CNRS, CP54, 57 rue Cuvier, Paris, France.|GenPhySE, Université de Toulouse, INRA, ENVT, Castanet Tolosan, France.|MIAT, Université de Toulouse, INRA, Castanet Tolosan, France.|INTHERES, Université de Toulouse, INRA, ENVT, Toulouse, France.', 'France|France|France|Poland|France|France|France|France|France|France', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz248', '30977816', 'In metabolomics, the detection of new biomarkers from NMR spectra is a promising approach. However, this analysis remains difficult due to the lack of a whole workflow that handles spectra pre-processing, automatic identification and quantification of metabolites and statistical analyses, in a reproducible way.'),
(82736, 2019, 'bio', 'Biomedical Image Augmentation Using Augmentor', 'Bloice, Marcus D|Roth, Peter|Holzinger, Andreas', 'Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Austria.|Institute of Computer Graphics and Vision, Graz University of Technology, Austria.|Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Austria.', 'Austria|Austria|Austria', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz259', '30989173', 'Image augmentation is a frequently used technique in computer vision and has been seeing increased interest since the popularity of deep learning. Its usefulness is becoming more and more recognised due to deep neural networks requiring larger amounts of data to train, and because in certain fields, such as biomedical imaging, large amounts of labelled data are difficult to come by or expensive to produce. In biomedical imaging, features specific to this domain need to be addressed.'),
(82737, 2019, 'bio', 'Identifying centromeric satellites with dna-brnn', 'Li, Heng', 'Department of data sciences, Dana-Farber Cancer Institute, 450 Brookline Ave, Boston, MA, USA.', 'United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz264', '30989183', 'Human alpha satellite and satellite 2/3 contribute to several percent of the human genome. However, identifying these sequences with traditional algorithms is computationally intensive. Here we develop dna-brnn, a recurrent neural network to learn the sequences of the two classes of centromeric repeats. It achieves high similarity to RepeatMasker and is times faster. Dna-brnn explores a novel application of deep learning and may accelerate the study of the evolution of the two repeat classes.'),
(82738, 2019, 'bio', 'IsoformSwitchAnalyzeR: Analysis of changes in genome-wide patterns of alternative splicing and its functional consequences', 'Vitting-Seerup, Kristoffer|Sandelin, Albin', 'The Bioinformatics Centre, Department of Biology and Biotech Research & Innovation Centre, University of Copenhagen, Ole Maaløes Vej 5, DK, Copenhagen N, Denmark.|The Bioinformatics Centre, Department of Biology and Biotech Research & Innovation Centre, University of Copenhagen, Ole Maaløes Vej 5, DK, Copenhagen N, Denmark.', 'Denmark|Denmark', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz247', '30989184', 'Alternative splicing is an important mechanism involved in both health and disease. Recent work highlights the importance of investigating genome-wide changes in patters of splicing and the subsequent functional consequences. Current computational methods only support such analysis on a gene-by-gene basis. Therefore, we extended IsoformSwitchAnalyzeR R library to enable analysis of genome-wide changes in specific types of alternative splicing and predicted functional consequences of the resulting isoform switches. As a case study, we analyzed RNA-seq data from The Cancer Genome Atlas and found systematic changes in alternative splicing and the consequences of the associated isoform switches.'),
(82739, 2019, 'bio', 'SurVIndel: improving CNV calling from high-throughput sequencing data through statistical testing', 'Rajaby, Ramesh|Sung, Wing-Kin', 'School of Computing, National University of Singapore, 13 Computing Drive, Singapore.|School of Computing, National University of Singapore, 13 Computing Drive, Singapore.', 'Singapore|Singapore', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz261', '30989231', 'Structural variations (SV) are large scale mutations in a genome, although less frequent than point mutations, due to their large size they are responsible for more heritable differences between individuals. Two prominent classes of SVs are deletions and tandem duplications. They play important roles in many devastating genetic diseases, such as Smith-Magenis syndrome, Potocki-Lupski syndrome and Williams-Beuren syndrome.Since paired-end whole genome sequencing data has become widespread and affordable, reliably calling deletions and tandem duplications has been a major target in bioinformatics, unfortunately, the problem is far from being solved, since existing solutions often offer poor results when applied to real data.'),
(82740, 2019, 'bio', 'A new statistic for efficient detection of repetitive sequences', 'Chen, Sijie|Chen, Yixin|Sun, Fengzhu|Waterman, Michael S|Zhang, Xuegong', 'Department of Automation, MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist, Tsinghua University, Beijing, China.|Department of Automation, MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist, Tsinghua University, Beijing, China.|Quantitative and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA.|Department of Automation, MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist, Tsinghua University, Beijing, China.|Department of Automation, MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist, Tsinghua University, Beijing, China.', 'China|China|United States|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz262', '30993316', 'Detecting sequences containing repetitive regions is a basic bioinformatics task with many applications. Several methods have been developed for various types of repeat detection tasks. An efficient generic method for detecting most types of repetitive sequences is still desirable. Inspired by the excellent properties and successful applications of the D2 family of statistics in comparative analyses of genomic sequences, we developed a new statistic D2R that can efficiently discriminate sequences with or without repetitive regions.'),
(82741, 2019, 'bio', 'PyPore: a python toolbox for nanopore sequencing data handling', 'Semeraro, Roberto|Magi, Alberto', 'Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy.|Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy.', 'Italy|Italy', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz269', '30993318', 'The recent technological improvement of Oxford Nanopore sequencing pushed the throughput of these devices to 10-20xe2x80x89Gb allowing the generation of millions of reads. For these reasons, the availability of fast software packages for evaluating experimental quality by generating highly informative and interactive summary plots is of fundamental importance.'),
(82742, 2019, 'bio', 'Discovering novel mutation signatures by latent Dirichlet allocation with variational Bayes inference', 'Matsutani, Taro|Ueno, Yuki|Fukunaga, Tsukasa|Hamada, Michiaki', 'Department of Electrical Engineering and Bioscience, Faculty of Science and Engineering, Waseda University.|Department of Electrical Engineering and Bioscience, Faculty of Science and Engineering, Waseda University.|Department of Electrical Engineering and Bioscience, Faculty of Science and Engineering, Waseda University.|Department of Electrical Engineering and Bioscience, Faculty of Science and Engineering, Waseda University.', '-|-|-|-', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz266', '30993319', 'A cancer genome includes many mutations derived from various mutagens and mutational processes, leading to specific mutation patterns. It is known that each mutational process leads to characteristic mutations, and when a mutational process has preferences for mutations, this situation is called a mutation signature. Identification of mutation signatures is an important task for elucidation of carcinogenic mechanisms. In previous studies, analyses with statistical approaches (e.g., non-negative matrix factorization and latent Dirichlet allocation) revealed a number of mutation signatures. Nonetheless, strictly speaking, these existing approaches employ an ad hoc method or incorrect approximation to estimate the number of mutation signatures, and the whole picture of mutation signatures is unclear.'),
(82743, 2019, 'bio', 'Spliceogen: An integrative, scalable tool for the discovery of splice-altering variants', 'Monger, Steven|Troup, Michael|Ip, Eddie|Dunwoodie, Sally L|Giannoulatou, Eleni', 'Victor Chang Cardiac Research Institute, Sydney, Australia.|Victor Chang Cardiac Research Institute, Sydney, Australia.|Victor Chang Cardiac Research Institute, Sydney, Australia.|Victor Chang Cardiac Research Institute, Sydney, Australia.|Victor Chang Cardiac Research Institute, Sydney, Australia.', 'Australia|Australia|Australia|Australia|Australia', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz263', '30993321', 'In silico prediction tools are essential for identifying variants which create or disrupt cis splicing motifs. However, there are limited options for genome-scale discovery of splice-altering variants.'),
(82744, 2019, 'bio', 'Automatic Curation of Large Comparative Animal MicroRNA Data Sets', 'Yazbeck, Ali M|Stadler, Peter F|Tout, Kifah|Fallmann, Jörg', 'Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, Härtelstraße 16-18, Leipzig, Germany.|Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, Härtelstraße 16-18, Leipzig, Germany.|Doctoral School of Science and Technology, Center for Biotechnology Research, Lebanese University, Hadath Campus, Beirut, Lebanon.|Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, Härtelstraße 16-18, Leipzig, Germany.', 'Germany|Germany|Lebanon|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz271', '30993337', 'MicroRNAs form an important class of RNA regulators that has been studied extensively. The miRBase and Rfam database provide rich, frequently updated information on both pre-miRNAs and their mature forms. These data sources, however, rely on individual data submission and thus are neither complete nor consistent in their coverage across different miRNA families. Quantitative studies of miRNA evolution therefore are difficult or impossible on this basis.'),
(82745, 2019, 'bio', 'PEPred-Suite: improved and robust prediction of therapeutic peptides using adaptive feature representation learning', 'Wei, Leyi|Zhou, Chen|Su, Ran|Zou, Quan', 'School of Computer Science and Technology, Tianjin University, Tianjin, China.|School of Computer Science and Technology, Tianjin University, Tianjin, China.|School of Computer Software, Tianjin University, Tianjin, China.|School of Computer Science and Technology, Tianjin University, Tianjin, China.', 'China|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz246', '30994882', 'Prediction of therapeutic peptides is critical for the discovery of novel and efficient peptide-based therapeutics. Computational methods, especially machine learning based methods, have been developed for addressing this need. However, most of existing methods are peptide-specific, currently, there is no generic predictor for multiple peptide types. Moreover, it is still challenging to extract informative feature representations from the perspective of primary sequences.');
