INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(82199, 2019, 'bio', 'Joint analysis of individual-level and summary-level GWAS data by leveraging pleiotropy', 'Dai, Mingwei|Wan, Xiang|Peng, Hao|Wang, Yao|Liu, Yue|Liu, Jin|Xu, Zongben|Yang, Can', 'Department of Applied Mathematics, School of Mathematics and Statistics, Xian Jiaotong University, Xian, China.|ShenZhen Research Institute of Big Data, ShenZhen, China.|School of Business Administration, Southwestern University of Finance and Economics, Chengdu, China.|Department of Applied Mathematics, School of Mathematics and Statistics, Xian Jiaotong University, Xian, China.|Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, China.|Centre for Quantitative Medicine, Program in Health Services and Systems Research, Duke-NUS Medical School, Singapore.|Department of Applied Mathematics, School of Mathematics and Statistics, Xian Jiaotong University, Xian, China.|Department of Mathematics, Hong Kong University of Science and Technology, Hong Kong, China.', 'China|China|China|China|China|Singapore|China|China', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty870', '30307540', 'A large number of recent genome-wide association studies (GWASs) for complex phenotypes confirm the early conjecture for polygenicity, suggesting the presence of large number of variants with only tiny or moderate effects. However, due to the limited sample size of a single GWAS, many associated genetic variants are too weak to achieve the genome-wide significance. These undiscovered variants further limit the prediction capability of GWAS. Restricted access to the individual-level data and the increasing availability of the published GWAS results motivate the development of methods integrating both the individual-level and summary-level data. How to build the connection between the individual-level and summary-level data determines the efficiency of using the existing abundant summary-level resources with limited individual-level data, and this issue inspires more efforts in the existing area.'),
(82200, 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 Agriculture, Nutrition, and Food Systems, University of New Hampshire, Durham, NH, USA.|Department of Agriculture, Nutrition, and Food Systems, University of New Hampshire, Durham, NH, USA.', 'United States|United States', 3, '-', 'Journal Article', '10.1093/bioinformatics/bty873', '30321264', 'GBS-SNP-CROP is a bioinformatics pipeline originally developed to support the cost-effective genome-wide characterization of plant genetic resources through paired-end genotyping-by-sequencing (GBS), particularly in the absence of a reference genome. Since its 2016 release, the pipelines functionality has greatly expanded, its computational efficiency has improved, and its applicability to a broad set of genomic studies for both plants and animals has been demonstrated. This note details the suite of improvements to date, as realized in GBS-SNP-CROP v.4.0, with specific attention paid to a new integrated metric that facilitates reliable variant identification despite the complications of homologs. Using the new de novo GBS read simulator GBS-Pacecar, also introduced in this note, results show an improvement in overall pipeline accuracy from 66% (v.1.0) to 84% (v.4.0), with a time saving of xe2x88xbc70%. Both GBS-SNP-CROP versions significantly outperform TASSEL-UNEAK, and v.4.0 resolves the issue of non-overlapping variant calls observed between UNEAK and v.1.0.'),
(82201, 2019, 'bio', 'MetaSMC: a coalescent-based shotgun sequence simulator for evolving microbial populations', 'Liao, Ki-Hok|Hon, Wing-Kai|Tang, Chuan-Yi|Hsieh, Wen-Ping', 'Department of Computer Science, National Tsing-Hua University, Hsinchu, Taiwan.|Department of Computer Science, National Tsing-Hua University, Hsinchu, Taiwan.|Department of Computer Science, National Tsing-Hua University, Hsinchu, Taiwan.|Institute of Statistics, National Tsing-Hua University, Hsinchu, Taiwan.', '-|-|-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty840', '30321266', 'High-throughput sequencing technology has revolutionized the study of metagenomics and cancer evolution. In a relatively simple environment, a metagenomics sequencing data is dominated by a few species. By analyzing the alignment of reads from microbial species, single nucleotide polymorphisms can be discovered and the evolutionary history of the populations can be reconstructed. The ever-increasing read length will allow more detailed analysis about the evolutionary history of microbial or tumor cell population. A simulator of shotgun sequences from such populations will be helpful in the development or evaluation of analysis algorithms.'),
(82202, 2019, 'bio', 'SCALOP: sequence-based antibody canonical loop structure annotation', 'Wong, Wing Ki|Georges, Guy|Ros, Francesca|Kelm, Sebastian|Lewis, Alan P|Taddese, Bruck|Leem, Jinwoo|Deane, Charlotte M', 'Department of Statistics, University of Oxford, Oxford, UK.|Roche Pharma Research and Early Development, Large Molecule Research Roche Innovation Center Munich, Penzberg, Germany.|Roche Pharma Research and Early Development, Large Molecule Research Roche Innovation Center Munich, Penzberg, Germany.|UCB Pharma, Slough, UK.|Computational and Modelling Sciences, GlaxoSmithKline Research and Development, Stevenage, UK.|Antibody Discovery and Protein Engineering, MedImmune, Granta Park, Cambridge, UK.|Department of Statistics, University of Oxford, Oxford, UK.|Department of Statistics, University of Oxford, Oxford, UK.', 'United Kingdom|Germany|Germany|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty877', '30321295', 'Canonical forms of the antibody complementarity-determining regions (CDRs) were first described in 1987 and have been redefined on multiple occasions since. The canonical forms are often used to approximate the antibody binding site shape as they can be predicted from sequence. A rapid predictor would facilitate the annotation of CDR structures in the large amounts of repertoire data now becoming available from next generation sequencing experiments.'),
(82203, 2019, 'bio', 'Enhanced prediction of RNA solvent accessibility with long short-term memory neural networks and improved sequence profiles', 'Sun, Saisai|Wu, Qi|Peng, Zhenling|Yang, Jianyi', 'School of Mathematical Sciences, Nankai University, Tianjin, China.|School of Mathematical Sciences, Nankai University, Tianjin, China.|Center for Applied Mathematics, Tianjin University, Tianjin, China.|School of Mathematical Sciences, Nankai University, Tianjin, China.', 'China|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty876', '30321300', 'The de novo prediction of RNA tertiary structure remains a grand challenge. Predicted RNA solvent accessibility provides an opportunity to address this challenge. To the best of our knowledge, there is only one method (RNAsnap) available for RNA solvent accessibility prediction. However, its performance is unsatisfactory for protein-free RNAs.'),
(82204, 2019, 'bio', 'ParGenes: a tool for massively parallel model selection and phylogenetic tree inference on thousands of genes', 'Morel, Benoit|Kozlov, Alexey M|Stamatakis, Alexandros', 'Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, Heidelberg, Germany.|Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, Heidelberg, Germany.|Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, Heidelberg, Germany.', 'Germany|Germany|Germany', 4, '-', 'Journal Article', '10.1093/bioinformatics/bty839', '30321303', 'Coalescent- and reconciliation-based methods are now widely used to infer species phylogenies from genomic data. They typically use per-gene phylogenies as input, which requires conducting multiple individual tree inferences on a large set of multiple sequence alignments (MSAs). At present, no easy-to-use parallel tool for this task exists. Ad hoc scripts for this purpose do not only induce additional implementation overhead, but can also lead to poor resource utilization and long times-to-solution. We present ParGenes, a tool for simultaneously determining the best-fit model and inferring maximum likelihood (ML) phylogenies on thousands of independent MSAs using supercomputers.'),
(82205, 2019, 'bio', 'PopLDdecay: a fast and effective tool for linkage disequilibrium decay analysis based on variant call format files', 'Zhang, Chi|Dong, Shan-Shan|Xu, Jun-Yang|He, Wei-Ming|Yang, Tie-Lin', 'Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Molecular Genetics, School of Life Science and Technology, Xian Jiaotong University, Xian, China.|Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Molecular Genetics, School of Life Science and Technology, Xian Jiaotong University, Xian, China.|BGI Genomics, BGI-Shenzhen, Shenzhen, China.|Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Molecular Genetics, School of Life Science and Technology, Xian Jiaotong University, Xian, China.|Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Molecular Genetics, School of Life Science and Technology, Xian Jiaotong University, Xian, China.', 'China|China|China|China|China', 65, '-', 'Journal Article', '10.1093/bioinformatics/bty875', '30321304', 'Linkage disequilibrium (LD) decay is of great interest in population genetic studies. However, no tool is available now to do LD decay analysis from variant call format (VCF) files directly. In addition, generation of pair-wise LD measurements for whole genome SNPs usually resulting in large storage wasting files.'),
(82206, 2019, 'bio', 'ABC random forests for Bayesian parameter inference', 'Raynal, Louis|Marin, Jean-Michel|Pudlo, Pierre|Ribatet, Mathieu|Robert, Christian P|Estoup, Arnaud', 'IMAG, Univ Montpellier, CNRS, Montpellier, France.|IMAG, Univ Montpellier, CNRS, Montpellier, France.|Institut de Mathématiques de Marseille, Aix-Marseille Université, Marseille, France.|IMAG, Univ Montpellier, CNRS, Montpellier, France.|Université Paris Dauphine, PSL Research University, Paris, France.|IBC, Univ Montpellier, CNRS, Montpellier, France.', 'France|France|France|France|France|France', 14, '-', 'Journal Article', '10.1093/bioinformatics/bty867', '30321307', 'Approximate Bayesian computation (ABC) has grown into a standard methodology that manages Bayesian inference for models associated with intractable likelihood functions. Most ABC implementations require the preliminary selection of a vector of informative statistics summarizing raw data. Furthermore, in almost all existing implementations, the tolerance level that separates acceptance from rejection of simulated parameter values needs to be calibrated.'),
(82207, 2019, 'bio', 'CSBFinder: discovery of colinear syntenic blocks across thousands of prokaryotic genomes', 'Svetlitsky, Dina|Dagan, Tal|Chalifa-Caspi, Vered|Ziv-Ukelson, Michal', 'Department of Computer Science, Ben-Gurion University of the Negev, Beer-Sheva, Israel.|Institute of General Microbiology, Christian-Albrechts University Kiel, Kiel, Germany.|Bioinformatics Core Facility, National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva, Israel.|Department of Computer Science, Ben-Gurion University of the Negev, Beer-Sheva, Israel.', 'Israel|Germany|Israel|Israel', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty861', '30321308', 'Identification of conserved syntenic blocks across microbial genomes is important for several problems in comparative genomics such as gene annotation, study of genome organization and evolution and prediction of gene interactions. Current tools for syntenic block discovery do not scale up to the large quantity of prokaryotic genomes available today.'),
(82208, 2019, 'bio', 'Quality assessment for the putative intrinsic disorder in proteins', 'Hu, Gang|Wu, Zhonghua|Oldfield, Christopher J|Wang, Chen|Kurgan, Lukasz', 'School of Mathematical Sciences and LPMC, Nankai University, Tianjin, Peoples Republic of China.|School of Mathematical Sciences and LPMC, Nankai University, Tianjin, Peoples Republic of China.|Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA.|Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA.|Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA.', 'China|China|United States|United States|United States', 9, '-', 'Journal Article', '10.1093/bioinformatics/bty881', '30329008', 'While putative intrinsic disorder is widely used, none of the predictors provides quality assessment (QA) scores. QA scores estimate the likelihood that predictions are correct at a residue level and have been applied in other bioinformatics areas. We recently reported that QA scores derived from putative disorder propensities perform relatively poorly for native disordered residues. Here we design and validate a general approach to construct QA predictors for disorder predictions.'),
(82209, 2019, 'bio', 'farPPI: a webserver for accurate prediction of protein-ligand binding structures for small-molecule PPI inhibitors by MM/PB(GB)SA methods', 'Wang, Zhe|Wang, Xuwen|Li, Youyong|Lei, Tailong|Wang, Ercheng|Li, Dan|Kang, Yu|Zhu, Feng|Hou, Tingjun', 'College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China.|College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China.|Institute of Functional Nano and Soft Materials (FUNSOM), Soochow University, Suzhou, Jiangsu, China.|College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China.|College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China.|College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China.|College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China.|College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China.|College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China.', 'China|China|China|China|China|China|China|China|China', 4, '-', 'Journal Article', '10.1093/bioinformatics/bty879', '30329012', 'Protein-protein interactions (PPIs) have been regarded as an attractive emerging class of therapeutic targets for the development of new treatments. Computational approaches, especially molecular docking, have been extensively employed to predict the binding structures of PPI-inhibitors or discover novel small molecule PPI inhibitors. However, due to the relatively undruggable features of PPI interfaces, accurate predictions of the binding structures for ligands towards PPI targets are quite challenging for most docking algorithms. Here, we constructed a non-redundant pose ranking benchmark dataset for small-molecule PPI inhibitors, which contains 900 binding poses for 184 protein-ligand complexes. Then, we evaluated the performance of MM/PB(GB)SA approaches to identify the correct binding poses for PPI inhibitors, including two Prime MM/GBSA procedures from the Schrxc3xb6dinger suite and seven different MM/PB(GB)SA procedures from the Amber package. Our results showed that MM/PBSA outperformed the Glide SP scoring function (success rate of 58.6%) and MM/GBSA in most cases, especially the PB3 procedure which could achieve an overall success rate of xe2x88xbc74%. Moreover, the GB6 procedure (success rate of 68.9%) performed much better than the other MM/GBSA procedures, highlighting the excellent potential of the GBNSR6 implicit solvation model for pose ranking. Finally, we developed the webserver of Fast Amber Rescoring for PPI Inhibitors (farPPI), which offers a freely available service to rescore the docking poses for PPI inhibitors by using the MM/PB(GB)SA methods.'),
(82210, 2019, 'bio', 'Thalia: semantic search engine for biomedical abstracts', 'Soto, Axel J|Przybyxc5x82a, Piotr|Ananiadou, Sophia', 'National Centre for Text Mining, School of Computer Science, University of Manchester, Manchester, UK.|National Centre for Text Mining, School of Computer Science, University of Manchester, Manchester, UK.|National Centre for Text Mining, School of Computer Science, University of Manchester, Manchester, UK.', 'United Kingdom|United Kingdom|United Kingdom', 7, '-', 'Journal Article', '10.1093/bioinformatics/bty871', '30329013', 'Although the publication rate of the biomedical literature has been growing steadily during the last decades, the accessibility of pertinent research publications for biologist and medical practitioners remains a challenge. This article describes Thalia, which is a semantic search engine that can recognize eight different types of concepts occurring in biomedical abstracts. Thalia is available via a web-based interface or a RESTful API. A key aspect of our search engine is that it is updated from PubMed on a daily basis. We describe here the main building blocks of our tool as well as an evaluation of the retrieval capabilities of Thalia in the context of a precision medicine dataset.'),
(82211, 2019, 'bio', 'Landscape of multi-tissue global gene expression reveals the regulatory signatures of feed efficiency in beef cattle', 'Sun, Hui-Zeng|Zhao, Ke|Zhou, Mi|Chen, Yanhong|Guan, Le Luo', 'Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, Canada.|Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, Canada.|Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, Canada.|Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, Canada.|Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, Canada.', 'Canada|Canada|Canada|Canada|Canada', 4, '-', 'Journal Article', '10.1093/bioinformatics/bty883', '30329014', 'Feed efficiency is an important trait for sustainable beef production that is regulated by the complex biological process, but the mode of action behinds it has not been clearly defined. Here, we aimed to elucidate the regulatory mechanisms of this trait through studying the landscape of the genome-wide gene expression of rumen, liver, muscle and backfat tissues, the key ones involved in the energy metabolism.'),
(82212, 2019, 'bio', 'A natural upper bound to the accuracy of predicting protein stability changes upon mutations', 'Montanucci, Ludovica|Martelli, Pier Luigi|Ben-Tal, Nir|Fariselli, Piero', 'Department of Comparative Biomedicine and Food Science, University of Padova, Legnaro, Italy.|Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy.|Department of Biochemistry and Molecular Biology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel.|Department of Comparative Biomedicine and Food Science, University of Padova, Legnaro, Italy.', 'Italy|Italy|Israel|Italy', 6, '-', 'Journal Article', '10.1093/bioinformatics/bty880', '30329016', 'Accurate prediction of protein stability changes upon single-site variations (xcex94xcex94G) is important for protein design, as well as for our understanding of the mechanisms of genetic diseases. The performance of high-throughput computational methods to this end is evaluated mostly based on the Pearson correlation coefficient between predicted and observed data, assuming that the upper bound would be 1 (perfect correlation). However, the performance of these predictors can be limited by the distribution and noise of the experimental data. Here we estimate, for the first time, a theoretical upper-bound to the xcex94xcex94G prediction performances imposed by the intrinsic structure of currently available xcex94xcex94G data.'),
(82213, 2019, 'bio', 'Integrative cancer patient stratification via subspace merging', 'Ding, Hao|Sharpnack, Michael|Wang, Chao|Huang, Kun|Machiraju, Raghu', 'Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA.|Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA.|Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA.|Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA.|Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA.', 'United States|United States|United States|United States|United States', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty866', '30329022', 'Technologies that generate high-throughput omics data are flourishing, creating enormous, publicly available repositories of multi-omics data. As many data repositories continue to grow, there is an urgent need for computational methods that can leverage these data to create comprehensive clusters of patients with a given disease.'),
(82214, 2019, 'bio', 'BMDExpress 2: enhanced transcriptomic dose-response analysis workflow', 'Phillips, Jason R|Svoboda, Daniel L|Tandon, Arpit|Patel, Shyam|Sedykh, Alex|Mav, Deepak|Kuo, Byron|Yauk, Carole L|Yang, Longlong|Thomas, Russell S|Gift, Jeff S|Davis, J Allen|Olszyk, Louis|Merrick, B Alex|Paules, Richard S|Parham, Fred|Saddler, Trey|Shah, Ruchir R|Auerbach, Scott S', 'Sciome LLC, Research Triangle Park, NC, USA.|Sciome LLC, Research Triangle Park, NC, USA.|Sciome LLC, Research Triangle Park, NC, USA.|Sciome LLC, Research Triangle Park, NC, USA.|Sciome LLC, Research Triangle Park, NC, USA.|Sciome LLC, Research Triangle Park, NC, USA.|Environmental Health Science and Research Bureau, Environmental and Radiation Health Sciences Directorate, HECSB, Health Canada, Ottawa, ON, Canada.|Environmental Health Science and Research Bureau, Environmental and Radiation Health Sciences Directorate, HECSB, Health Canada, Ottawa, ON, Canada.|D.S. Technologies, Inc, Research Triangle Park, NC, USA.|National Center for Computational Toxicology, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA.|National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA.|National Center for Environmental Assessment, U.S. Environmental Protection Agency, Cincinnati, OH, USA.|CSRA Inc, Research Triangle Park, NC, USA.|National Toxicology Program, NIEHS, Research Triangle Park, NC, USA.|National Toxicology Program, NIEHS, Research Triangle Park, NC, USA.|National Toxicology Program, NIEHS, Research Triangle Park, NC, USA.|National Toxicology Program, NIEHS, Research Triangle Park, NC, USA.|Sciome LLC, Research Triangle Park, NC, USA.|National Toxicology Program, NIEHS, Research Triangle Park, NC, USA.', 'United States|United States|United States|United States|United States|United States|Canada|Canada|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 32, '-', 'Journal Article', '10.1093/bioinformatics/bty878', '30329029', 'A new version (version 2) of the genomic dose-response analysis software, BMDExpress, has been created. The software addresses the increasing use of transcriptomic dose-response data in toxicology, drug design, risk assessment and translational research. In this new version, we have implemented additional statistical filtering options (e.g. Williams trend test), curve fitting models, Linux and Macintosh compatibility and support for additional transcriptomic platforms with up-to-date gene annotations. Furthermore, we have implemented extensive data visualizations, on-the-fly data filtering, and a batch-wise analysis workflow. We have also significantly re-engineered the code base to reflect contemporary software engineering practices and streamline future development. The first version of BMDExpress was developed in 2007 to meet an unmet demand for easy-to-use transcriptomic dose-response analysis software. Since its original release, however, transcriptomic platforms, technologies, pathway annotations and quantitative methods for data analysis have undergone a large change necessitating a significant re-development of BMDExpress. To that end, as of 2016, the National Toxicology Program assumed stewardship of BMDExpress. The result is a modernized and updated BMDExpress 2 that addresses the needs of the growing toxicogenomics user community.'),
(82215, 2019, 'bio', 'GeneLab: Omics database for spaceflight experiments', 'Ray, Shayoni|Gebre, Samrawit|Fogle, Homer|Berrios, Daniel C|Tran, Peter B|Galazka, Jonathan M|Costes, Sylvain V', 'Space Biosciences Division, USRA/NASA Ames Research Center, Moffett Field, CA, USA.|Space Biosciences Division, KBRwyle/NASA Ames Research Center, Moffett Field, CA, USA.|Space Biosciences Division, KBRwyle/NASA Ames Research Center, Moffett Field, CA, USA.|Space Biosciences Division, USRA/NASA Ames Research Center, Moffett Field, CA, USA.|Intelligent Systems Division, NASA Ames Research Center, Moffett Field, CA, USA.|Space Biosciences Division, NASA Ames Research Center, Moffett Field, CA, USA.|Space Biosciences Division, NASA Ames Research Center, Moffett Field, CA, USA.', 'United States|United States|United States|United States|United States|United States|United States', 5, '-', 'Journal Article', '10.1093/bioinformatics/bty884', '30329036', 'To curate and organize expensive spaceflight experiments conducted aboard space stations and maximize the scientific return of investment, while democratizing access to vast amounts of spaceflight related omics data generated from several model organisms.'),
(82216, 2019, 'bio', 'Disease gene identification based on generic and disease-specific genome networks', 'Nam, Yonghyun|Jhee, Jong Ho|Cho, Junhee|Lee, Ji-Hyun|Shin, Hyunjung', 'Department of Industrial Engineering, Ajou University, Yeongtong-gu, Suwon, South Korea.|Department of Industrial Engineering, Ajou University, Yeongtong-gu, Suwon, South Korea.|Department of Industrial Engineering, Ajou University, Yeongtong-gu, Suwon, South Korea.|DR. Noah Biotech, Yeongtong-gu, Suwon, South Korea.|Department of Industrial Engineering, Ajou University, Yeongtong-gu, Suwon, South Korea.', '-|-|-|-|-', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty882', '30335143', 'Immune diseases have a strong genetic component with Mendelian patterns of inheritance. While the tight association has been a major understanding in the underlying pathophysiology for the category of immune diseases, the common features of these diseases remain unclear. Based on the potential commonality among immune genes, we design Gene Ranker for key gene identification. Gene Ranker is a network-based gene scoring algorithm that initially constructs a backbone network based on protein interactions. Patient gene expression networks are added into the network. An add-on process screens the networks of weighted gene co-expression network analysis (WGCNA) on the samples of immune patients. Gene Ranker is disease-specific, however, any WGCNA network that passes the screening procedure can be added on. With the constructed network, it employs the semi-supervised learning for gene scoring.'),
(82217, 2019, 'bio', 'Long-term context-dependent genetic adaptation of the viral genetic cloud', 'Braun, Tzipi|Bordería, Antonio V|Barbezange, Cyril|Vignuzzi, Marco|Louzoun, Yoram', 'Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel.|Institut Pasteur, Viral Populations and Pathogenesis, CNRS UMR 3569, Paris, France.|Institut Pasteur, Viral Populations and Pathogenesis, CNRS UMR 3569, Paris, France.|Institut Pasteur, Viral Populations and Pathogenesis, CNRS UMR 3569, Paris, France.|Department of Mathematics and Gonda Brain Research Center, Bar-Ilan University, Ramat Gan, Israel.', 'Israel|France|France|France|Israel', 3, '-', 'Journal Article', '10.1093/bioinformatics/bty891', '30346482', 'RNA viruses generate a cloud of genetic variants within each host. This cloud contains high-frequency genotypes, and many rare variants. The dynamics of these variants is crucial to understand viral evolution and their effect on their host.'),
(82218, 2019, 'bio', 'methylGSA: a Bioconductor package and Shiny app for DNA methylation data length bias adjustment in gene set testing', 'Ren, Xu|Kuan, Pei Fen', 'Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA.|Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA.', 'United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty892', '30346483', 'An important downstream analysis following differential expression from RNA sequencing (RNA-Seq) or DNA methylation analysis is the gene set testing to relate significant genes or CpGs to known biological properties. However, the traditional gene set testing approaches result in biased P-values due to the difference in gene length. Existing methods accounting for length bias were primarily developed for RNA-Seq data. For DNA methylation data profiled using the Illumina arrays, separate methods adjusting for the number of CpGs instead of gene length are necessary.'),
(82219, 2019, 'bio', 'SeeVis-3D space-time cube rendering for visualization of microfluidics image data', 'Hattab, Georges|Nattkemper, Tim W', 'International Research Training Group Computational Methods for the Analysis of the Diversity and Dynamics of Genomes, Faculty of Technology, Bielefeld University, Bielefeld, Germany.|Biodata Mining Group, Faculty of Technology, Bielefeld University, Bielefeld, Germany.', 'Germany|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty889', '30346487', 'Live cell imaging plays a pivotal role in understanding cell growth. Yet, there is a lack of visualization alternatives for quick qualitative characterization of colonies.'),
(82220, 2019, 'bio', 'TissueEnrich: Tissue-specific gene enrichment analysis', 'Jain, Ashish|Tuteja, Geetu', 'Bioinformatics and Computational Biology, Iowa State University, Ames, IA, USA.|Bioinformatics and Computational Biology, Iowa State University, Ames, IA, USA.', 'United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty890', '30346488', 'RNA-Seq data analysis results in lists of genes that may have a similar function, based on differential gene expression analysis or co-expression network analysis. While tools have been developed to identify biological processes that are enriched in the genes sets, there remains a need for tools that identify enrichment of tissue-specific genes. Therefore, we developed TissueEnrich, a tool that calculates tissue-specific gene enrichment in an input gene set. We demonstrated that TissueEnrich can assign tissue identities to single cell clusters and differentiated embryonic stem cells.'),
(82221, 2019, 'bio', 'SENSE: Siamese neural network for sequence embedding and alignment-free comparison', 'Zheng, Wei|Yang, Le|Genco, Robert J|Wactawski-Wende, Jean|Buck, Michael|Sun, Yijun', 'Department of Computer Science and Engineering, University at Buffalo, The State University of New York, Buffalo, NY, USA.|Department of Computer Science and Engineering, University at Buffalo, The State University of New York, Buffalo, NY, USA.|Department of Oral Biology, University at Buffalo, The State University of New York, Buffalo, NY, USA.|Department of Epidemiology and Environmental Health, University at Buffalo, The State University of New York, Buffalo, NY, USA.|Department of Biochemistry, University at Buffalo, The State University of New York, Buffalo, NY, USA.|Department of Computer Science and Engineering, University at Buffalo, The State University of New York, Buffalo, NY, USA.', 'United States|United States|United States|United States|United States|United States', 3, '-', 'Journal Article', '10.1093/bioinformatics/bty887', '30346493', 'Sequence analysis is arguably a foundation of modern biology. Classic approaches to sequence analysis are based on sequence alignment, which is limited when dealing with large-scale sequence data. A dozen of alignment-free approaches have been developed to provide computationally efficient alternatives to alignment-based approaches. However, existing methods define sequence similarity based on various heuristics and can only provide rough approximations to alignment distances.'),
(82222, 2019, 'bio', 'Alignment-free clustering of UMI tagged DNA molecules', 'Orabi, Baraa|Erhan, Emre|McConeghy, Brian|Volik, Stanislav V|Le Bihan, Stephane|Bell, Robert|Collins, Colin C|Chauve, Cedric|Hach, Faraz', 'School of Computing Science, Faculty of Applied Sciences, Simon Fraser University, Burnaby BC, Canada.|School of Computing Science, Faculty of Applied Sciences, Simon Fraser University, Burnaby BC, Canada.|Vancouver Prostate Centre, Vancouver BC, Canada.|Vancouver Prostate Centre, Vancouver BC, Canada.|Vancouver Prostate Centre, Vancouver BC, Canada.|Vancouver Prostate Centre, Vancouver BC, Canada.|Vancouver Prostate Centre, Vancouver BC, Canada.|Department of Mathematics, Simon Fraser University, Burnaby BC, Canada.|Vancouver Prostate Centre, Vancouver BC, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty888', '30351359', 'Next-Generation Sequencing has led to the availability of massive genomic datasets whose processing raises many challenges, including the handling of sequencing errors. This is especially pertinent in cancer genomics, e.g. for detecting low allele frequency variations from circulating tumor DNA. Barcode tagging of DNA molecules with unique molecular identifiers (UMI) attempts to mitigate sequencing errors, UMI tagged molecules are polymerase chain reaction (PCR) amplified, and the PCR copies of UMI tagged molecules are sequenced independently. However, the PCR and sequencing steps can generate errors in the sequenced reads that can be located in the barcode and/or the DNA sequence. Analyzing UMI tagged sequencing data requires an initial clustering step, with the aim of grouping reads sequenced from PCR duplicates of the same UMI tagged molecule into a single cluster, and the size of the current datasets requires this clustering process to be resource-efficient.'),
(82223, 2019, 'bio', 'VCPA: genomic variant calling pipeline and data management tool for Alzheimers Disease Sequencing Project', 'Leung, Yuk Yee|Valladares, Otto|Chou, Yi-Fan|Lin, Han-Jen|Kuzma, Amanda B|Cantwell, Laura|Qu, Liming|Gangadharan, Prabhakaran|Salerno, William J|Schellenberg, Gerard D|Wang, Li-San', 'Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Penn Neurodegeneration Genomics Center, Philadelphia, PA, USA.|Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Penn Neurodegeneration Genomics Center, Philadelphia, PA, USA.|Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Penn Neurodegeneration Genomics Center, Philadelphia, PA, USA.|Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Penn Neurodegeneration Genomics Center, Philadelphia, PA, USA.|Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Penn Neurodegeneration Genomics Center, Philadelphia, PA, USA.|Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Penn Neurodegeneration Genomics Center, Philadelphia, PA, USA.|Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Penn Neurodegeneration Genomics Center, Philadelphia, PA, USA.|Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Penn Neurodegeneration Genomics Center, Philadelphia, PA, USA.|Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.|Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Penn Neurodegeneration Genomics Center, Philadelphia, PA, USA.|Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Penn Neurodegeneration Genomics Center, Philadelphia, PA, 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/bty894', '30351394', 'We report VCPA, our SNP/Indel Variant Calling Pipeline and data management tool used for the analysis of whole genome and exome sequencing (WGS/WES) for the Alzheimers Disease Sequencing Project. VCPA consists of two independent but linkable components: pipeline and tracking database. The pipeline, implemented using the Workflow Description Language and fully optimized for the Amazon elastic compute cloud environment, includes steps from aligning raw sequence reads to variant calling using GATK. The tracking database allows users to view job running status in real time and visualize >100 quality metrics per genome. VCPA is functionally equivalent to the CCDG/TOPMed pipeline. Users can use the pipeline and the dockerized database to process large WGS/WES datasets on Amazon cloud with minimal configuration.'),
(82224, 2019, 'bio', 'Simple tricks of convolutional neural network architectures improve DNA-protein binding prediction', 'Cao, Zhen|Zhang, Shihua', 'NCMIS, CEMS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China.|NCMIS, CEMS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China.', 'China|China', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty893', '30351403', 'With the accumulation of DNA sequencing data, convolution neural network (CNN) based methods such as DeepBind and DeepSEA have achieved great success for predicting the function of primary DNA sequences. Previous studies confirm the importance of utilizing the reverse complement and flanking DNA sequences, which has a natural connection with data augmentation. However, it is not fully understood how these DNA sequences work during model training and testing.'),
(82225, 2019, 'bio', 'pJRES Binning Algorithm (JBA): a new method to facilitate the recovery of metabolic information from pJRES 1H NMR spectra', 'Rodriguez-Martinez, Andrea|Ayala, Rafael|Posma, Joram M|Harvey, Nikita|Jimxc3xa9nez, Beatriz|Sonomura, Kazuhiro|Sato, Taka-Aki|Matsuda, Fumihiko|Zalloua, Pierre|Gauguier, Dominique|Nicholson, Jeremy K|Dumas, Marc-Emmanuel', 'Division of Integrative Systems Medicine and Digestive Diseases, Department of Surgery and Cancer, Imperial College London, London, UK.|Section of Structural Biology, Department of Medicine, Shimadzu Corporation, Kyoto, Japan.|Division of Integrative Systems Medicine and Digestive Diseases, Department of Surgery and Cancer, Imperial College London, London, UK.|Division of Integrative Systems Medicine and Digestive Diseases, Department of Surgery and Cancer, Imperial College London, London, UK.|Division of Integrative Systems Medicine and Digestive Diseases, Department of Surgery and Cancer, Imperial College London, London, UK.|Life Science Research Center, Technology Research Laboratory, Shimadzu Corporation, Kyoto, Japan.|Life Science Research Center, Technology Research Laboratory, Shimadzu Corporation, Kyoto, Japan.|Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan.|School of Medicine, Lebanese American University, Beirut, Lebanon.|Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan.|Division of Integrative Systems Medicine and Digestive Diseases, Department of Surgery and Cancer, Imperial College London, London, UK.|Division of Integrative Systems Medicine and Digestive Diseases, Department of Surgery and Cancer, Imperial College London, London, UK.', 'United Kingdom|Japan|United Kingdom|United Kingdom|United Kingdom|Japan|Japan|Japan|Lebanon|Japan|United Kingdom|United Kingdom', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty837', '30351417', 'Data processing is a key bottleneck for 1H NMR-based metabolic profiling of complex biological mixtures, such as biofluids. These spectra typically contain several thousands of signals, corresponding to possibly few hundreds of metabolites. A number of binning-based methods have been proposed to reduce the dimensionality of 1xe2x80x89D 1H NMR datasets, including statistical recoupling of variables (SRV). Here, we introduce a new binning method, named JBA (pJRES Binning Algorithm), which aims to extend the applicability of SRV to pJRES spectra.'),
(82226, 2019, 'bio', 'Clustermatch: discovering hidden relations in highly diverse kinds of qualitative and quantitative data without standardization', 'Pividori, Milton|Cernadas, Andres|de Haro, Luis A|Carrari, Fernando|Stegmayer, Georgina|Milone, Diego H', 'Center for Translational Data Science, The University of Chicago, Chicago IL, USA.|Instituto de Fisiología, Biología Molecular y Neurociencias (IFIBYNE-UBA-CONICET) Ciudad Universitaria, C1428EHA Buenos Aires, Argentina.|Instituto de Fisiología, Biología Molecular y Neurociencias (IFIBYNE-UBA-CONICET) Ciudad Universitaria, C1428EHA Buenos Aires, Argentina.|Instituto de Fisiología, Biología Molecular y Neurociencias (IFIBYNE-UBA-CONICET) Ciudad Universitaria, C1428EHA Buenos Aires, Argentina.|Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH/UNL-CONICET, Santa Fe, Argentina.|Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH/UNL-CONICET, Santa Fe, Argentina.', 'United States|Argentina|Argentina|Argentina|Argentina|Argentina', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty899', '30357313', 'Heterogeneous and voluminous data sources are common in modern datasets, particularly in systems biology studies. For instance, in multi-holistic approaches in the fruit biology field, data sources can include a mix of measurements such as morpho-agronomic traits, different kinds of molecules (nucleic acids and metabolites) and consumer preferences. These sources not only have different types of data (quantitative and qualitative), but also large amounts of variables with possibly non-linear relationships among them. An integrative analysis is usually hard to conduct, since it requires several manual standardization steps, with a direct and critical impact on the results obtained. These are important issues in clustering applications, which highlight the need of new methods for uncovering complex relationships in such diverse repositories.'),
(82227, 2019, 'bio', 'BatchI: Batch effect Identification in high-throughput screening data using a dynamic programming algorithm', 'Papiez, Anna|Marczyk, Michal|Polanska, Joanna|Polanski, Andrzej', 'Institute of Automatic Control, Silesian University of Technology, Gliwice, Poland.|Institute of Automatic Control, Silesian University of Technology, Gliwice, Poland.|Institute of Automatic Control, Silesian University of Technology, Gliwice, Poland.|Institute of Informatics, Silesian University of Technology, Gliwice, Poland.', 'Poland|Poland|Poland|Poland', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty900', '30357412', 'In contemporary biological experiments, bias, which interferes with the measurements, requires attentive processing. Important sources of bias in high-throughput biological experiments are batch effects and diverse methods towards removal of batch effects have been established. These include various normalization techniques, yet many require knowledge on the number of batches and assignment of samples to batches. Only few can deal with the problem of identification of batch effect of unknown structure. For this reason, an original batch identification algorithm through dynamical programming is introduced for omics data that may be sorted on a timescale.'),
(82228, 2019, 'bio', 'Flexible design of multiple metagenomics classification pipelines with UGENE', 'Rose, Rebecca|Golosova, Olga|Sukhomlinov, Dmitrii|Tiunov, Aleksey|Prosperi, Mattia', 'BioInfoExperts, LLC, Thibodaux, LA, USA.|UNIPRO, LLC, Novosibirsk, Russia.|UNIPRO, LLC, Novosibirsk, Russia.|UNIPRO, LLC, Novosibirsk, Russia.|University of Florida, Gainesville, FL, USA.', 'United States|United States|United States|United States|United States', 5, '-', 'Journal Article', '10.1093/bioinformatics/bty901', '30358807', 'UGENE is a free, open-source, cross-platform bioinformatics software. UGENE deploys pre-defined pipelines and a flexible instrument to design new workflows and visually build multi-step analytics pipelines. The new UGENE v.1.31 release offers graphical, user-friendly wrapping of a number of popular command-line metagenomics classification programs (Kraken, CLARK, DIAMOND), combinable serially and in parallel through the workflow designer, with multiple, customizable reference databases. Ensemble classification voting is available through the WEVOTE algorithm, with augmented output in the form of detailed table reports. Pre-built workflows (which include all steps from data cleaning to summaries) are included with the installation and a tutorial is available on the UGENE website. Further expansion with multiple visualization tools for reports is planned.'),
(82229, 2019, 'bio', 'A Bayesian model for single cell transcript expression analysis on MERFISH data', 'Köster, Johannes|Brown, Myles|Liu, X Shirley', 'University of Florida, Gainesville, FL, USA.|University of Florida, Gainesville, FL, USA.|University of Florida, Gainesville, FL, USA.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty857', '30358813', '-'),
(82230, 2019, 'bio', 'Measuring phylogenetic signal between categorical traits and phylogenies', 'Borges, Rui|Machado, Joxc3xa3o Paulo|Gomes, Cidxc3xa1lia|Rocha, Ana Paula|Antunes, Agostinho', 'CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, Terminal de Cruzeiros do Porto de Leixxc3xb5es, Matosinhos, Portugal.|CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, Terminal de Cruzeiros do Porto de Leixxc3xb5es, Matosinhos, Portugal.|CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, Terminal de Cruzeiros do Porto de Leixxc3xb5es, Matosinhos, Portugal.|Department of Biology, Faculty of Sciences of the University of Porto, FCUP, Porto, Portugal.|CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, Terminal de Cruzeiros do Porto de Leixxc3xb5es, Matosinhos, Portugal.', 'Portugal|Portugal|Portugal|Portugal|Portugal', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty800', '30358816', 'Determining whether a trait and phylogeny share some degree of phylogenetic signal is a flagship goal in evolutionary biology. Signatures of phylogenetic signal can assist the resolution of a broad range of evolutionary questions regarding the tempo and mode of phenotypic evolution. However, despite the considerable number of strategies to measure it, few and limited approaches exist for categorical traits. Here, we used the concept of Shannon entropy and propose the xcexb4 statistic for evaluating the degree of phylogenetic signal between a phylogeny and categorical traits.'),
(82231, 2019, 'bio', 'A framework for identifying dysregulated chromatin regulators as master regulators in human cancer', 'Ru, Beibei|Sun, Jianlong|Kang, Qingzheng|Tong, Yin|Zhang, Jiangwen', 'CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, Terminal de Cruzeiros do Porto de Leixxc3xb5es, Matosinhos, Portugal.|CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, Terminal de Cruzeiros do Porto de Leixxc3xb5es, Matosinhos, Portugal.|CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, Terminal de Cruzeiros do Porto de Leixxc3xb5es, Matosinhos, Portugal.|CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, Terminal de Cruzeiros do Porto de Leixxc3xb5es, Matosinhos, Portugal.|CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, Terminal de Cruzeiros do Porto de Leixxc3xb5es, Matosinhos, Portugal.', 'Portugal|Portugal|Portugal|Portugal|Portugal', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty836', '30358822', 'Chromatin regulators (CRs) are frequently dysregulated to reprogram the epigenetic landscape of the cancer genome. However, the underpinnings of the dysregulation of CRs and their downstream effectors remain to be elucidated.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(82232, 2019, 'bio', 'ELMER v2: an R/Bioconductor package to reconstruct gene regulatory networks from DNA methylation and transcriptome profiles', 'Silva, Tiago C|Coetzee, Simon G|Gull, Nicole|Yao, Lijing|Hazelett, Dennis J|Noushmehr, Houtan|Lin, De-Chen|Berman, Benjamin P', 'Department of Biomedical Sciences, Center for Bioinformatics and Functional Genomics, Cedars-Sinai Medical Center, Los Angeles, CA, USA.|Department of Biomedical Sciences, Center for Bioinformatics and Functional Genomics, Cedars-Sinai Medical Center, Los Angeles, CA, USA.|Department of Biomedical Sciences, Center for Bioinformatics and Functional Genomics, Cedars-Sinai Medical Center, Los Angeles, CA, USA.|Bioinformatics Research & Early Development, Roche Sequencing Solutions, Belmont, CA, USA.|Department of Biomedical Sciences, Center for Bioinformatics and Functional Genomics, Cedars-Sinai Medical Center, Los Angeles, CA, USA.|Department of Genetics, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil.|Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.|Department of Biomedical Sciences, Center for Bioinformatics and Functional Genomics, Cedars-Sinai Medical Center, Los Angeles, CA, USA.', 'United States|United States|United States|United States|United States|Brazil|United States|United States', 14, '-', 'Journal Article', '10.1093/bioinformatics/bty902', '30364927', 'DNA methylation has been used to identify functional changes at transcriptional enhancers and other cis-regulatory modules (CRMs) in tumors and other disease tissues. Our R/Bioconductor package ELMER (Enhancer Linking by Methylation/Expression Relationships) provides a systematic approach that reconstructs altered gene regulatory networks (GRNs) by combining enhancer methylation and gene expression data derived from the same sample set.'),
(82233, 2019, 'bio', 'simGWAS: a fast method for simulation of large scale case-control GWAS summary statistics', 'Fortune, Mary D|Wallace, Chris', 'MRC Biostatistics Unit, Cambridge Institute of Public Health, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK.|MRC Biostatistics Unit, Cambridge Institute of Public Health, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK.', 'United Kingdom|United Kingdom', 3, '-', 'Journal Article', '10.1093/bioinformatics/bty898', '30371734', 'Methods for analysis of GWAS summary statistics have encouraged data sharing and democratized the analysis of different diseases. Ideal validation for such methods is application to simulated data, where some truth is known. As GWAS increase in size, so does the computational complexity of such evaluations, standard practice repeatedly simulates and analyses genotype data for all individuals in an example study.'),
(82234, 2019, 'bio', 'VarSome: the human genomic variant search engine', 'Kopanos, Christos|Tsiolkas, Vasilis|Kouris, Alexandros|Chapple, Charles E|Albarca Aguilera, Monica|Meyer, Richard|Massouras, Andreas', 'Saphetor S.A., EPFL Innovation Park - C, Lausanne, Switzerland.|Saphetor S.A., EPFL Innovation Park - C, Lausanne, Switzerland.|Saphetor S.A., EPFL Innovation Park - C, Lausanne, Switzerland.|Saphetor S.A., EPFL Innovation Park - C, Lausanne, Switzerland.|Saphetor S.A., EPFL Innovation Park - C, Lausanne, Switzerland.|Saphetor S.A., EPFL Innovation Park - C, Lausanne, Switzerland.|Saphetor S.A., EPFL Innovation Park - C, Lausanne, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 116, '-', 'Journal Article', '10.1093/bioinformatics/bty897', '30376034', 'VarSome.com is a search engine, aggregator and impact analysis tool for human genetic variation and a community-driven project aiming at sharing global expertise on human variants.'),
(82235, 2019, 'bio', 'Statistical inference of the rate of RNA polymerase II elongation by total RNA sequencing', 'Kawamura, Yumi|Koyama, Shinsuke|Yoshida, Ryo', 'Department of Statistical Science, The Graduate University for Advanced Studies (SOKENDAI), Tachikawa, Japan.|Department of Statistical Science, The Graduate University for Advanced Studies (SOKENDAI), Tachikawa, Japan.|Department of Statistical Science, The Graduate University for Advanced Studies (SOKENDAI), Tachikawa, Japan.', 'Japan|Japan|Japan', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty886', '30376061', 'Sequencing total RNA without poly-A selection enables us to obtain a transcriptomic profile of nascent RNAs undergoing transcription with co-transcriptional splicing. In general, the RNA-seq reads exhibit a sawtooth pattern in a gene, which is characterized by a monotonically decreasing gradient across introns in the 5-3 direction, and by substantially higher levels of RNA-seq reads present in exonic regions. Such patterns result from the process of underlying transcription elongation by RNA polymerase II, which traverses the DNA strand in a 5-3 direction as it performs a complex series of mRNA synthesis and processing. Therefore, data of sequenced total RNAs could be utilized to infer the rate of transcription elongation by solving the inverse problem.'),
(82236, 2019, 'bio', 'TPMCalculator: one-step software to quantify mRNA abundance of genomic features', 'Vera Alvarez, Roberto|Pongor, Lorinc Sandor|Mariño-Ramírez, Leonardo|Landsman, David', 'Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.|Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.|Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.|Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.', 'United States|United States|United States|United States', 10, '-', 'Journal Article', '10.1093/bioinformatics/bty896', '30379987', 'The quantification of RNA sequencing (RNA-seq) abundance using a normalization method that calculates transcripts per million (TPM) is a key step to compare multiple samples from different experiments. TPMCalculator is a one-step software to process RNA-seq alignments in BAM format and reports TPM values, raw read counts and feature lengths for genes, transcripts, exons and introns. The program describes the genomic features through a model generated from the gene transfer format file used during alignments reporting of the TPM values and the raw read counts for each feature. In this paper, we show the correlation for 1256 samples from the TCGA-BRCA project between TPM and FPKM reported by TPMCalculator and RSeQC. We also show the correlation for raw read counts reported by TPMCalculator, HTSeq and featureCounts.'),
(82237, 2019, 'bio', 'Batch effects correction for microbiome data with Dirichlet-multinomial regression', 'Dai, Zhenwei|Wong, Sunny H|Yu, Jun|Wei, Yingying', 'Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.|Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.|Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.|Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty874', '30388197', '-'),
(82238, 2019, 'bio', 'Bastion3: a two-layer ensemble predictor of type III secreted effectors', 'Wang, Jiawei|Li, Jiahui|Yang, Bingjiao|Xie, Ruopeng|Marquez-Lago, Tatiana T|Leier, André|Hayashida, Morihiro|Akutsu, Tatsuya|Zhang, Yanju|Chou, Kuo-Chen|Selkrig, Joel|Zhou, Tieli|Song, Jiangning|Lithgow, Trevor', 'Infection and Immunity Program, Biomedicine Discovery Institute and Department of Microbiology, Monash University, Melbourne, VIC, Australia.|Infection and Immunity Program, Biomedicine Discovery Institute and Department of Microbiology, Monash University, Melbourne, VIC, Australia.|School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China.|School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China.|Department of Genetics, School of Medicine, University of Alabama at Birmingham, AL, USA.|Department of Genetics, School of Medicine, University of Alabama at Birmingham, AL, USA.|National Institute of Technology, Matsue College, Matsue, Shimane, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto, Japan.|School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China.|Gordon Life Science Institute, Boston, MA, USA.|European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany.|Department of Clinical Laboratory, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.|Infection and Immunity Program, Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC, Australia.|Infection and Immunity Program, Biomedicine Discovery Institute and Department of Microbiology, Monash University, Melbourne, VIC, Australia.', 'Australia|Australia|China|China|United States|United States|Japan|Japan|China|United States|Germany|China|Australia|Australia', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty914', '30388198', 'Type III secreted effectors (T3SEs) can be injected into host cell cytoplasm via type III secretion systems (T3SSs) to modulate interactions between Gram-negative bacterial pathogens and their hosts. Due to their relevance in pathogen-host interactions, significant computational efforts have been put toward identification of T3SEs and these in turn have stimulated new T3SE discoveries. However, as T3SEs with new characteristics are discovered, these existing computational tools reveal important limitations: (i) most of the trained machine learning models are based on the N-terminus (or incorporating also the C-terminus) instead of the proteins complete sequences, and (ii) the underlying models (trained with classic algorithms) employed only few features, most of which were extracted based on sequence-information alone. To achieve better T3SE prediction, we must identify more powerful, informative features and investigate how to effectively integrate these into a comprehensive model.'),
(82239, 2019, 'bio', 'SJARACNe: a scalable software tool for gene network reverse engineering from big data', 'Khatamian, Alireza|Paull, Evan O|Califano, Andrea|Yu, Jiyang', 'Department of Computational Biology, St. Jude Childrens Research Hospital, Memphis, TN, USA.|Department of Systems Biology, Columbia University, New York, NY, USA.|Department of Systems Biology, Columbia University, New York, NY, USA.|Department of Computational Biology, St. Jude Childrens Research Hospital, Memphis, TN, USA.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty907', '30388204', 'Over the last two decades, we have observed an exponential increase in the number of generated array or sequencing-based transcriptomic profiles. Reverse engineering of biological networks from high-throughput gene expression profiles has been one of the grand challenges in systems biology. The Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe) represents one of the most effective and widely-used tools to address this challenge. However, existing ARACNe implementations do not efficiently process big input data with thousands of samples. Here we present an improved implementation of the algorithm, SJARACNe, to solve this big data problem, based on sophisticated software engineering. The new scalable SJARACNe package achieves a dramatic improvement in computational performance in both time and memory usage and implements new features while preserving the network inference accuracy of the original algorithm. Given that large-sampled transcriptomic data is increasingly available and ARACNe is extremely demanding for network reconstruction, the scalable SJARACNe will allow even researchers with modest computational resources to efficiently construct complex regulatory and signaling networks from thousands of gene expression profiles.'),
(82240, 2019, 'bio', 'Set of approaches based on 3D structure and position specific-scoring matrix for predicting DNA-binding proteins', 'Nanni, Loris|Brahnam, Sheryl', 'Department of Information Engineering, University of Padova, Padova, Italy.|Department of Computer Information Systems, Missouri State University, Springfield, MO, USA.', 'Italy|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty912', '30395157', 'Because DNA-binding proteins (DNA-BPs) play a vital role in all aspects of genetic activity, the development of reliable and efficient systems for automatic DNA-BP classification is becoming a crucial proteomic technology. Key to this technology is the discovery of powerful protein representations and feature extraction methods. The goal of this article is to develop experimentally a system for automatic DNA-BP classification by comparing and combining different descriptors taken from different types of protein representations.'),
(82241, 2019, 'bio', 'DrugThatGene: integrative analysis to streamline the identification of druggable genes, pathways and protein complexes from CRISPR screens', 'Canver, Matthew C|Bauer, Daniel E|Maeda, Takahiro|Pinello, Luca', 'Molecular Pathology Unit, Center for Computational and Integrative Biology, Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA, USA.|Division of Hematology/Oncology, Boston Childrens Hospital, Boston, USA.|Center for Cellular and Molecular Medicine, Kyushu University Hospital, Fukuoka, Japan.|Molecular Pathology Unit, Center for Computational and Integrative Biology, Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA, USA.', 'United States|United States|Japan|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty913', '30395160', 'The clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated (Cas) nuclease system has allowed for high-throughput, large scale pooled screens for functional genomic studies. To aid in the translation of functional genomics to therapeutics, we developed DrugThatGene (DTG) as a web-based application that streamlines analysis of potential therapeutic targets identified from functional genetic screens.'),
(82242, 2019, 'bio', 'Partially non-homogeneous dynamic Bayesian networks based on Bayesian regression models with partitioned design matrices', 'Shafiee Kamalabad, Mahdi|Heberle, Alexander Martin|Thedieck, Kathrin|Grzegorczyk, Marco', 'Department of Mathematics, Bernoulli Institute, Faculty of Science and Engineering, University of Groningen, AG Groningen, The Netherlands.|Laboratory of Pediatrics, Section Systems Medicine of Metabolism and Signaling, University of Groningen, University Medical Center Groningen, AV Groningen, The Netherlands.|Laboratory of Pediatrics, Section Systems Medicine of Metabolism and Signaling, University of Groningen, University Medical Center Groningen, AV Groningen, The Netherlands.|Department of Mathematics, Bernoulli Institute, Faculty of Science and Engineering, University of Groningen, AG Groningen, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty917', '30395165', 'Non-homogeneous dynamic Bayesian networks (NH-DBNs) are a popular modelling tool for learning cellular networks from time series data. In systems biology, time series are often measured under different experimental conditions, and not rarely only some network interaction parameters depend on the condition while the other parameters stay constant across conditions. For this situation, we propose a new partially NH-DBN, based on Bayesian hierarchical regression models with partitioned design matrices. With regard to our main application to semi-quantitative (immunoblot) timecourse data from mammalian target of rapamycin complex 1 (mTORC1) signalling, we also propose a Gaussian process-based method to solve the problem of non-equidistant time series measurements.'),
(82243, 2019, 'bio', 'ukbREST: efficient and streamlined data access for reproducible research in large biobanks', 'Pividori, Milton|Im, Hae Kyung', 'Department of Medicine, Section of Genetic Medicine, The University of Chicago, Chicago, IL, USA.|Department of Medicine, Section of Genetic Medicine, The University of Chicago, Chicago, IL, USA.', 'United States|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty925', '30395166', 'Large biobanks, such as UK Biobank with half a million participants, are changing the scale and availability of genotypic and phenotypic data for researchers to ask fundamental questions about the biology of health and disease. The breadth of the UK Biobank data is enabling discoveries at an unprecedented pace. However, this size and complexity pose new challenges to investigators who need to keep the accruing data up to date, comply with potential consent changes, and efficiently and reproducibly extract subsets of the data to answer specific scientific questions. Here we propose a tool called ukbREST designed for the UK Biobank study (easily extensible to other biobanks), which allows authorized users to efficiently retrieve phenotypic and genetic data. It exposes a REST API that makes data highly accessible inside a private and secure network, allowing the data specification in a human readable text format easily shareable with other researchers. These characteristics make ukbREST an important tool to make biobanks valuable data more readily accessible to the research community and facilitate reproducibility of the analysis, a key aspect of science.'),
(82244, 2019, 'bio', 'gwasurvivr: an R package for genome-wide survival analysis', 'Rizvi, Abbas A|Karaesmen, Ezgi|Morgan, Martin|Preus, Leah|Wang, Junke|Sovic, Michael|Hahn, Theresa|Sucheston-Campbell, Lara E', 'Division of Pharmaceutics and Pharmaceutical Chemistry, College of Pharmacy, The Ohio State University, Columbus, OH, USA.|Division of Pharmaceutics and Pharmaceutical Chemistry, College of Pharmacy, The Ohio State University, Columbus, OH, USA.|Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.|Division of Pharmacy Practice and Science, College of Pharmacy, The Ohio State University, Columbus, OH, USA.|Division of Pharmacy Practice and Science, College of Pharmacy, The Ohio State University, Columbus, OH, USA.|Division of Pharmacy Practice and Science, College of Pharmacy, The Ohio State University, Columbus, OH, USA.|Department of Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.|Division of Pharmacy Practice and Science, College of Pharmacy, The Ohio State University, Columbus, OH, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty920', '30395168', 'To address the limited software options for performing survival analyses with millions of SNPs, we developed gwasurvivr, an R/Bioconductor package with a simple interface for conducting genome-wide survival analyses using VCF (outputted from Michigan or Sanger imputation servers), IMPUTE2 or PLINK files. To decrease the number of iterations needed for convergence when optimizing the parameter estimates in the Cox model, we modified the R package survival, covariates in the model are first fit without the SNP, and those parameter estimates are used as initial points. We benchmarked gwasurvivr with other software capable of conducting genome-wide survival analysis (genipe, SurvivalGWAS_SV and GWASTools). gwasurvivr is significantly faster and shows better scalability as sample size, number of SNPs and number of covariates increases.'),
(82245, 2019, 'bio', 'Supervised non-negative matrix factorization methods for MALDI imaging applications', 'Leuschner, Johannes|Schmidt, Maximilian|Fernsel, Pascal|Lachmund, Delf|Boskamp, Tobias|Maass, Peter', 'Department of Mathematics, Center for Industrial Mathematics, University of Bremen, Bremen, Germany.|Department of Mathematics, Center for Industrial Mathematics, University of Bremen, Bremen, Germany.|Department of Mathematics, Center for Industrial Mathematics, University of Bremen, Bremen, Germany.|Department of Mathematics, Center for Industrial Mathematics, University of Bremen, Bremen, Germany.|Department of Mathematics, Center for Industrial Mathematics, University of Bremen, Bremen, Germany.|Department of Mathematics, Center for Industrial Mathematics, University of Bremen, Bremen, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 3, '-', 'Journal Article', '10.1093/bioinformatics/bty909', '30395171', 'Non-negative matrix factorization (NMF) is a common tool for obtaining low-rank approximations of non-negative data matrices and has been widely used in machine learning, e.g. for supporting feature extraction in high-dimensional classification tasks. In its classical form, NMF is an unsupervised method, i.e. the class labels of the training data are not used when computing the NMF. However, incorporating the classification labels into the NMF algorithms allows to specifically guide them toward the extraction of data patterns relevant for discriminating the respective classes. This approach is particularly suited for the analysis of mass spectrometry imaging (MSI) data in clinical applications, such as tumor typing and classification, which are among the most challenging tasks in pathology. Thus, we investigate algorithms for extracting tumor-specific spectral patterns from MSI data by NMF methods.'),
(82246, 2019, 'bio', 'FAVITES: simultaneous simulation of transmission networks, phylogenetic trees and sequences', 'Moshiri, Niema|Ragonnet-Cronin, Manon|Wertheim, Joel O|Mirarab, Siavash', 'Bioinformatics and Systems Biology Graduate Program, UC San Diego, La Jolla, USA.|Department of Medicine, UC San Diego, La Jolla, USA.|Department of Medicine, UC San Diego, La Jolla, USA.|Department of Electrical and Computer Engineering, UC San Diego, La Jolla, USA.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty921', '30395173', 'The ability to simulate epidemics as a function of model parameters allows insights that are unobtainable from real datasets. Further, reconstructing transmission networks for fast-evolving viruses like Human Immunodeficiency Virus (HIV) may have the potential to greatly enhance epidemic intervention, but transmission network reconstruction methods have been inadequately studied, largely because it is difficult to obtain truth sets on which to test them and properly measure their performance.'),
(82247, 2019, 'bio', 'Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences', 'Zhu, Anqi|Ibrahim, Joseph G|Love, Michael I', 'Department of Biostatistics, University of North Carolina-Chapel Hill, NC, USA.|Department of Biostatistics, University of North Carolina-Chapel Hill, NC, USA.|Department of Biostatistics, University of North Carolina-Chapel Hill, NC, USA.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty895', '30395178', 'In RNA-seq differential expression analysis, investigators aim to detect those genes with changes in expression level across conditions, despite technical and biological variability in the observations. A common task is to accurately estimate the effect size, often in terms of a logarithmic fold change (LFC).'),
(82248, 2019, 'bio', 'BiXGBoost: a scalable, flexible boosting-based method for reconstructing gene regulatory networks', 'Zheng, Ruiqing|Li, Min|Chen, Xiang|Wu, Fang-Xiang|Pan, Yi|Wang, Jianxin', 'School of Information Science and Engineering, Central South University, Changsha, China.|School of Information Science and Engineering, Central South University, Changsha, China.|School of Information Science and Engineering, Central South University, Changsha, China.|School of Information Science and Engineering, Central South University, Changsha, China.|School of Information Science and Engineering, Central South University, Changsha, China.|School of Information Science and Engineering, Central South University, Changsha, China.', 'China|China|China|China|China|China', 10, '-', 'Journal Article', '10.1093/bioinformatics/bty908', '30395189', 'Reconstructing gene regulatory networks (GRNs) based on gene expression profiles is still an enormous challenge in systems biology. Random forest-based methods have been proved a kind of efficient methods to evaluate the importance of gene regulations. Nevertheless, the accuracy of traditional methods can be further improved. With time-series gene expression data, exploiting inherent time information and high order time lag are promising strategies to improve the power and accuracy of GRNs inference.'),
(82249, 2019, 'bio', 'Antibody interface prediction with 3D Zernike descriptors and SVM', 'Daberdaku, Sebastian|Ferrari, Carlo', 'Department of Comparative Biomedicine and Food Science, University of Padova, Legnaro, Italy.|Department of Information Engineering, University of Padova, Padova, Italy.', 'Italy|Italy', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty918', '30395191', 'Antibodies are a class of proteins capable of specifically recognizing and binding to a virtually infinite number of antigens. This binding malleability makes them the most valuable category of biopharmaceuticals for both diagnostic and therapeutic applications. The correct identification of the antigen-binding residues in the antibody is crucial for all antibody design and engineering techniques and could also help to understand the complex antigen binding mechanisms. However, the antibody-binding interface prediction field appears to be still rather underdeveloped.'),
(82250, 2019, 'bio', 'Discovering network phenotype between genetic risk factors and disease status via diagnosis-aligned multi-modality regression method in Alzheimers disease', 'Wang, Meiling|Hao, Xiaoke|Huang, Jiashuang|Shao, Wei|Zhang, Daoqiang', 'Department of Computer Science and Technology, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China.|Department of Internet of Things Engineering, School of Artificial Intelligence, Hebei University of Technology, Tianjin, China.|Department of Computer Science and Technology, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China.|Department of Computer Science and Technology, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China.|Department of Computer Science and Technology, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China.', 'China|China|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty911', '30395195', 'Neuroimaging genetics is an emerging field to identify the associations between genetic variants [e.g. single-nucleotide polymorphisms (SNPs)] and quantitative traits (QTs) such as brain imaging phenotypes. However, most of the current studies focus only on the associations between brain structure imaging and genetic variants, while neglecting the connectivity information between brain regions. In addition, the brain itself is a complex network, and the higher-order interaction may contain useful information for the mechanistic understanding of diseases [i.e. Alzheimers disease (AD)].'),
(82251, 2019, 'bio', 'High-complexity regions in mammalian genomes are enriched for developmental genes', 'Pirogov, Anton|Pfaffelhuber, Peter|Börsch-Haubold, Angelika|Haubold, Bernhard', 'Lehrstuhl für Informatik, RWTH Aachen University, Max-Planck-Institute for Evolutionary Biology, Plön, Germany.|Department of Mathematical Stochastics, Freiburg University, Germany.|24306 Plön, Germany.|Department of Evolutionary Genetics, Max-Planck-Institute for Evolutionary Biology, Plön, Germany.', 'Germany|Germany|Germany|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty922', '30395202', 'Unique sequence regions are associated with genetic function in vertebrate genomes. However, measuring uniqueness, or absence of long repeats, along a genome is conceptually and computationally difficult. Here we use a variant of the Lempel-Ziv complexity, the match complexity, Cm, and augment it by deriving its null distribution for random sequences. We then apply Cm to the human and mouse genomes to investigate the relationship between sequence complexity and function.'),
(82252, 2019, 'bio', 'Improved inference of intermolecular contacts through protein-protein interaction prediction using coevolutionary analysis', 'Correa Marrero, Miguel|Immink, Richard G H|de Ridder, Dick|van Dijk, Aalt D J', 'Bioinformatics Group, Department of Plant Sciences.|Laboratory of Molecular Biology, Department of Plant Sciences.|Bioinformatics Group, Department of Plant Sciences.|Bioinformatics Group, Department of Plant Sciences.', '-|-|-|-', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty924', '30398547', 'Predicting residue-residue contacts between interacting proteins is an important problem in bioinformatics. The growing wealth of sequence data can be used to infer these contacts through correlated mutation analysis on multiple sequence alignments of interacting homologs of the proteins of interest. This requires correct identification of pairs of interacting proteins for many species, in order to avoid introducing noise (i.e. non-interacting sequences) in the analysis that will decrease predictive performance.'),
(82253, 2019, 'bio', 'Computational modeling of cellular structures using conditional deep generative networks', 'Yuan, Hao|Cai, Lei|Wang, Zhengyang|Hu, Xia|Zhang, Shaoting|Ji, Shuiwang', 'School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, USA.|School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, USA.|Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA.|Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA.|Department of Computer Science, University of North Carolina at Charlotte, Charlotte, NC, USA.|Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA.', 'United States|United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty923', '30398548', 'Cellular function is closely related to the localizations of its sub-structures. It is, however, challenging to experimentally label all sub-cellular structures simultaneously in the same cell. This raises the need of building a computational model to learn the relationships among these sub-cellular structures and use reference structures to infer the localizations of other structures.'),
(82254, 2019, 'bio', 'wgd-simple command line tools for the analysis of ancient whole-genome duplications', 'Zwaenepoel, Arthur|Van de Peer, Yves', 'Department of Plant Biotechnology and Bioinformatics, Ghent University.|Department of Plant Biotechnology and Bioinformatics, Ghent University.', '-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty915', '30398564', 'Ancient whole-genome duplications (WGDs) have been uncovered in almost all major lineages of life on Earth and the search for traces or remnants of such events has become standard practice in most genome analyses. This is especially true for plants, where ancient WGDs are abundant. Common approaches to find evidence for ancient WGDs include the construction of KS distributions and the analysis of intragenomic colinearity. Despite the increased interest in WGDs and the acknowledgment of their evolutionary importance, user-friendly and comprehensive tools for their analysis are lacking. Here, we present an easy to use command-line tool for KS distribution construction named wgd. The wgd suite provides commonly used KS and colinearity analysis workflows together with tools for modeling and visualization, rendering these analyses accessible to genomics researchers in a convenient manner.'),
(82255, 2019, 'bio', 'Index suffix-prefix overlaps by (w, k)-minimizer to generate long contigs for reads compression', 'Liu, Yuansheng|Yu, Zuguo|Dinger, Marcel E|Li, Jinyan', 'Advanced Analytics Institute, Faculty of Engineering and IT, University of Technology Sydney, Ultimo, Australia.|Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Hunan, China.|Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Sydney, NSW, Australia.|Advanced Analytics Institute, Faculty of Engineering and IT, University of Technology Sydney, Ultimo, Australia.', 'Australia|China|Australia|Australia', 7, '-', 'Journal Article', '10.1093/bioinformatics/bty936', '30407482', 'Advanced high-throughput sequencing technologies have produced massive amount of reads data, and algorithms have been specially designed to contract the size of these datasets for efficient storage and transmission. Reordering reads with regard to their positions in de novo assembled contigs or in explicit reference sequences has been proven to be one of the most effective reads compression approach. As there is usually no good prior knowledge about the reference sequence, current focus is on the novel construction of de novo assembled contigs.'),
(82256, 2019, 'bio', 'BacPaCS-Bacterial Pathogenicity Classification via Sparse-SVM', 'Barash, Eran|Sal-Man, Neta|Sabato, Sivan|Ziv-Ukelson, Michal', 'Department of Computer Science, Faculty of Natural Sciences.|The Shraga Segal Department of Microbiology Immunology and Genetics, Faculty of Health Sciences, Ben-Gurion University of the Negev, BeerSheva, Israel.|Department of Computer Science, Faculty of Natural Sciences.|Department of Computer Science, Faculty of Natural Sciences.', '-|Israel|-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty928', '30407484', 'Bacterial infections are a major cause of illness worldwide. However, most bacterial strains pose no threat to human health and may even be beneficial. Thus, developing powerful diagnostic bioinformatic tools that differentiate pathogenic from commensal bacteria are critical for effective treatment of bacterial infections.'),
(82257, 2019, 'bio', 'Whisper: read sorting allows robust mapping of DNA sequencing data', 'Deorowicz, Sebastian|Debudaj-Grabysz, Agnieszka|Gudyxc5x9b, Adam|Grabowski, Szymon', 'Institute of Informatics, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Akademicka 16, Gliwice, PL, Poland.|Institute of Informatics, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Akademicka 16, Gliwice, PL, Poland.|Institute of Informatics, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Akademicka 16, Gliwice, PL, Poland.|Institute of Applied Computer Science, Faculty of Electrical, Electronic, Computer and Control Engineering, Lodz University of Technology, Stefanowskiego 18/22, xc5x81xc3xb3dxc5xba, PL, Poland.', 'Poland|Poland|Poland|Poland', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty927', '30407485', 'Mapping reads to a reference genome is often the first step in a sequencing data analysis pipeline. The reduction of sequencing costs implies a need for algorithms able to process increasing amounts of generated data in reasonable time.'),
(82258, 2019, 'bio', 'Measurement error and variant-calling in deep Illumina sequencing of HIV', 'Howison, Mark|Coetzer, Mia|Kantor, Rami', 'Watson Institute for International and Public Affairs.|Division of Infectious Diseases, The Alpert Medical School, Brown University, Providence, RI, USA.|Division of Infectious Diseases, The Alpert Medical School, Brown University, Providence, RI, USA.', 'United States|United States|United States', 11, '-', 'Journal Article', '10.1093/bioinformatics/bty919', '30407489', 'Next-generation deep sequencing of viral genomes, particularly on the Illumina platform, is increasingly applied in HIV research. Yet, there is no standard protocol or method used by the research community to account for measurement errors that arise during sample preparation and sequencing. Correctly calling high and low-frequency variants while controlling for erroneous variants is an important precursor to downstream interpretation, such as studying the emergence of HIV drug-resistance mutations, which in turn has clinical applications and can improve patient care.'),
(82259, 2019, 'bio', 'OPA2Vec: combining formal and informal content of biomedical ontologies to improve similarity-based prediction', 'Smaili, Fatima Zohra|Gao, Xin|Hoehndorf, Robert', 'Computer, Electrical & Mathematical Sciences and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.|Computer, Electrical & Mathematical Sciences and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.|Computer, Electrical & Mathematical Sciences and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.', 'Saudi Arabia|Saudi Arabia|Saudi Arabia', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty933', '30407490', 'Ontologies are widely used in biology for data annotation, integration and analysis. In addition to formally structured axioms, ontologies contain meta-data in the form of annotation axioms which provide valuable pieces of information that characterize ontology classes. Annotation axioms commonly used in ontologies include class labels, descriptions or synonyms. Despite being a rich source of semantic information, the ontology meta-data are generally unexploited by ontology-based analysis methods such as semantic similarity measures.'),
(82260, 2019, 'bio', 'dtangle: accurate and robust cell type deconvolution', 'Hunt, Gregory J|Freytag, Saskia|Bahlo, Melanie|Gagnon-Bartsch, Johann A', 'Department of Statistics, University of Michigan, Ann Arbor, MI, USA.|Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.|Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.|Department of Statistics, University of Michigan, Ann Arbor, MI, USA.', 'United States|Australia|Australia|United States', 5, '-', 'Journal Article', '10.1093/bioinformatics/bty926', '30407492', 'Cell type composition of tissues is important in many biological processes. To help understand cell type composition using gene expression data, methods of estimating (deconvolving) cell type proportions have been developed. Such estimates are often used to adjust for confounding effects of cell type in differential expression analysis (DEA).'),
(82261, 2019, 'bio', 'High efficiency referential genome compression algorithm', 'Shi, Wei|Chen, Jianhua|Luo, Mao|Chen, Min', 'School of Information, Yunnan University, Kunming, China.|School of Information, Yunnan University, Kunming, China.|School of Information, Yunnan University, Kunming, China.|Information Security College, Yunnan Police College, Kunming, China.', 'China|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty934', '30407493', 'With the development and the gradually popularized application of next-generation sequencing technologies (NGS), genome sequencing has been becoming faster and cheaper, creating a massive amount of genome sequence data which still grows at an explosive rate. The time and cost of transmission, storage, processing and analysis of these genetic data have become bottlenecks that hinder the development of genetics and biomedicine. Although there are many common data compression algorithms, they are not effective for genome sequences due to their inability to consider and exploit the inherent characteristics of genome sequence data. Therefore, the development of a fast and efficient compression algorithm specific to genome data is an important and pressing issue.'),
(82262, 2019, 'bio', 'Clusterdv: a simple density-based clustering method that is robust, general and automatic', 'Marques, João C|Orger, Michael B', 'Champalimaud Research, Champalimaud Centre for the Unknown, Avenida Brasília, Doca de Pedrouços, Lisboa, Portugal.|Champalimaud Research, Champalimaud Centre for the Unknown, Avenida Brasília, Doca de Pedrouços, Lisboa, Portugal.', 'Portugal|Portugal', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty932', '30407500', 'How to partition a dataset into a set of distinct clusters is a ubiquitous and challenging problem. The fact that data vary widely in features such as cluster shape, cluster number, density distribution, background noise, outliers and degree of overlap, makes it difficult to find a single algorithm that can be broadly applied. One recent method, clusterdp, based on search of density peaks, can be applied successfully to cluster many kinds of data, but it is not fully automatic, and fails on some simple data distributions.'),
(82263, 2019, 'bio', 'DeepT3: deep convolutional neural networks accurately identify Gram-negative bacterial type III secreted effectors using the N-terminal sequence', 'Xue, Li|Tang, Bin|Chen, Wei|Luo, Jiesi', 'School of Public Health, Southwest Medical University, Luzhou, Sichuan, PR, China.|Basic Medical College of Southwest Medical University, Luzhou, Sichuan, PR, China.|Integrative Genomics Core, City of Hope National Medical Center, Duarte, CA, USA.|Key Laboratory for Aging and Regenerative Medicine, Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou, Sichuan, China.', 'China|China|United States|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty931', '30407530', 'Various bacterial pathogens can deliver their secreted substrates also called effectors through Type III secretion systems (T3SSs) into host cells and cause diseases. Since T3SS secreted effectors (T3SEs) play important roles in pathogen-host interactions, identifying them is crucial to our understanding of the pathogenic mechanisms of T3SSs. However, the effectors display high level of sequence diversity, therefore making the identification a difficult process. There is a need to develop a novel and effective method to screen and select putative novel effectors from bacterial genomes that can be validated by a smaller number of key experiments.'),
(82264, 2019, 'bio', 'Pan-genomic analysis provides novel insights into the association of Ecoli with human host and its minimal genome', 'Yang, Zhi-Kai|Luo, Hao|Zhang, Yanming|Wang, Baijing|Gao, Feng', 'Department of Physics, School of Science.|Department of Physics, School of Science.|SinoGenoMax Co., Ltd./Chinese National Human Genome Center, Beijing, China.|SinoGenoMax Co., Ltd./Chinese National Human Genome Center, Beijing, China.|Department of Physics, School of Science.', '-|-|China|China|-', 4, '-', 'Journal Article', '10.1093/bioinformatics/bty938', '30418478', 'Bacteria can usually acquire certain advantageous genes that enable the bacteria to adapt to rapidly changing niches, thereby leading to a wide range of intraspecific genome content and genetic redundancy. The minimal genome of Escherichia coli, which is the most important bacterial species, and the association between E.coli and its human host are worthy of further exploration.'),
(82265, 2019, 'bio', 'BayMAP: a Bayesian hierarchical model for the analysis of PAR-CLIP data', 'Huessler, Eva-Maria|Schäfer, Martin|Schwender, Holger|Landgraf, Pablo', 'Mathematical Institute, Heinrich Heine University, Düsseldorf, Germany.|Mathematical Institute, Heinrich Heine University, Düsseldorf, Germany.|Mathematical Institute, Heinrich Heine University, Düsseldorf, Germany.|Department of Pediatric Oncology and Hematology, Childrens Hospital, University of Cologne, Cologne, Germany.', 'Germany|Germany|Germany|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty904', '30418480', 'Photoactivatable-Ribonucleoside-Enhanced Crosslinking and Immunoprecipitation (PAR-CLIP) is a biochemical method for detecting interaction sites of proteins with mRNA. This method introduces T-to-C substitutions at sequenced cDNA that help to detect binding sites on mRNA. However, T-to-C substitutions can also occur due to other reasons such as mismatches or SNPs. Only few statistical procedures exist for detecting binding sites in PAR-CLIP data. Most of these methods do not account for other types of substitutions than those induced by PAR-CLIP, and therefore, also report positions with high T-to-C substitution rates, e.g. SNPs, as binding sites. Moreover, none of these procedures allow to include additional information, e.g. the type of mRNA region, relevant for the biology of microRNA-binding sites.'),
(82266, 2019, 'bio', 'MAGpy: a reproducible pipeline for the downstream analysis of metagenome-assembled genomes (MAGs)', 'Stewart, Robert D|Auffret, Marc D|Snelling, Timothy J|Roehe, Rainer|Watson, Mick', 'The Roslin Institute and R(D)SVS, University of Edinburgh, Easter Bush, UK.|Scotlands Rural College, Easter Bush, UK.|The Rowett Institute of Nutrition and Health, University of Aberdeen, Kings College, Aberdeen, UK.|Scotlands Rural College, Easter Bush, UK.|The Roslin Institute and R(D)SVS, University of Edinburgh, Easter Bush, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 8, '-', 'Journal Article', '10.1093/bioinformatics/bty905', '30418481', 'Metagenomics is a powerful tool for assaying the DNA from every genome present in an environment. Recent advances in bioinformatics have enabled the rapid assembly of near-complete metagenome-assembled genomes (MAGs), and there is a need for reproducible pipelines that can annotate and characterize thousands of genomes simultaneously, to enable identification and functional characterization.'),
(82267, 2019, 'bio', 'Identifying antimicrobial peptides using word embedding with deep recurrent neural networks', 'Hamid, Md-Nafiz|Friedberg, Iddo', 'Interdepartmental program in Bioinformatics and Computational Biology.|Interdepartmental program in Bioinformatics and Computational Biology.', '-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty937', '30418485', 'Antibiotic resistance constitutes a major public health crisis, and finding new sources of antimicrobial drugs is crucial to solving it. Bacteriocins, which are bacterially produced antimicrobial peptide products, are candidates for broadening the available choices of antimicrobials. However, the discovery of new bacteriocins by genomic mining is hampered by their sequences low complexity and high variance, which frustrates sequence similarity-based searches.'),
(82268, 2018, 'bio', 'Understanding the evolution of functional redundancy in metabolic networks', 'Sambamoorthy, Gayathri|Raman, Karthik', 'Department of Biotechnology, Bhupat Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India.|Department of Biotechnology, Bhupat Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India.', 'India|India', 8, 'Algorithms|Metabolic Networks and Pathways|Phenotype', 'Journal Article', '10.1093/bioinformatics/bty604', '30423058', 'Metabolic networks have evolved to reduce the disruption of key metabolic pathways by the establishment of redundant genes/reactions. Synthetic lethals in metabolic networks provide a window to study these functional redundancies. While synthetic lethals have been previously studied in different organisms, there has been no study on how the synthetic lethals are shaped during adaptation/evolution.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(82269, 2018, 'bio', 'An ontology-based method for assessing batch effect adjustment approaches in heterogeneous datasets', 'Schmidt, Florian|List, Markus|Cukuroglu, Engin|Köhler, Sebastian|Göke, Jonathan|Schulz, Marcel H', 'Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany.|Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany.|Genome Institute of Singapore, Computational Genomics and Transcriptomics, Singapore.|Berlin Institute of Health (BIH), Berlin, Germany.|Genome Institute of Singapore, Computational Genomics and Transcriptomics, Singapore.|Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany.', 'Germany|Germany|Singapore|Germany|Singapore|Germany', 0, 'Data Accuracy|Datasets as Topic|Gene Ontology|Genome, Human|Genomics|Humans|RNA|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/bty553', '30423059', 'International consortia such as the Genotype-Tissue Expression (GTEx) project, The Cancer Genome Atlas (TCGA) or the International Human Epigenetics Consortium (IHEC) have produced a wealth of genomic datasets with the goal of advancing our understanding of cell differentiation and disease mechanisms. However, utilizing all of these data effectively through integrative analysis is hampered by batch effects, large cell type heterogeneity and low replicate numbers. To study if batch effects across datasets can be observed and adjusted for, we analyze RNA-seq data of 215 samples from ENCODE, Roadmap, BLUEPRINT and DEEP as well as 1336 samples from GTEx and TCGA. While batch effects are a considerable issue, it is non-trivial to determine if batch adjustment leads to an improvement in data quality, especially in cases of low replicate numbers.'),
(82270, 2018, 'bio', 'S-Cluster++: a fast program for solving the cluster containment problem for phylogenetic networks', 'Yan, Hongwei|Gunawan, Andreas D M|Zhang, Louxin', 'Department of Mathematics, National University of Singapore, Singapore.|Department of Mathematics, National University of Singapore, Singapore.|Department of Mathematics, National University of Singapore, Singapore.', 'Singapore|Singapore|Singapore', 4, 'Algorithms|Evolution, Molecular|Genome|Genomics|Models, Genetic|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/bty594', '30423060', 'Comparative genomic studies indicate that extant genomes are more properly considered to be a fusion product of random mutations over generations (vertical evolution) and genomic material transfers between individuals of different lineages (reticulate transfer). This has motivated biologists to use phylogenetic networks and other general models to study genome evolution. Two fundamental algorithmic problems arising from verification of phylogenetic networks and from computing Robinson-Foulds distance in the space of phylogenetic networks are the tree and cluster containment problems. The former asks how to decide whether or not a phylogenetic tree is displayed in a phylogenetic network. The latter is to decide whether a subset of taxa appears as a cluster in some tree displayed in a phylogenetic network. The cluster containment problem (CCP) is also closely related to testing the infinite site model on a recombination network. Both the tree containment and CCP are NP-complete. Although the CCP was introduced a decade ago, there has been little progress in developing fast algorithms for it on arbitrary phylogenetic networks.'),
(82271, 2018, 'bio', 'Higher-order molecular organization as a source of biological function', 'Gaudelet, Thomas|Malod-Dognin, Noël|Pržulj, Nataša', 'Department of Computer Science, University College London, London, UK.|Department of Computer Science, University College London, London, UK.|Department of Computer Science, University College London, London, UK.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Humans|Protein Interaction Mapping|Proteins|Saccharomyces cerevisiae', 'Journal Article', '10.1093/bioinformatics/bty570', '30423061', 'Molecular interactions have widely been modelled as networks. The local wiring patterns around molecules in molecular networks are linked with their biological functions. However, networks model only pairwise interactions between molecules and cannot explicitly and directly capture the higher-order molecular organization, such as protein complexes and pathways. Hence, we ask if hypergraphs (hypernetworks), that directly capture entire complexes and pathways along with protein-protein interactions (PPIs), carry additional functional information beyond what can be uncovered from networks of pairwise molecular interactions. The mathematical formalism of a hypergraph has long been known, but not often used in studying molecular networks due to the lack of sophisticated algorithms for mining the underlying biological information hidden in the wiring patterns of molecular systems modelled as hypernetworks.'),
(82272, 2018, 'bio', 'Discovering epistatic feature interactions from neural network models of regulatory DNA sequences', 'Greenside, Peyton|Shimko, Tyler|Fordyce, Polly|Kundaje, Anshul', 'Biomedical Informatics Training Program, Stanford University, Stanford, CA.|Genetics, Stanford University, Stanford, CA.|Genetics, Stanford University, Stanford, CA.|Genetics, Stanford University, Stanford, CA.', '-|-|-|-', 12, 'Binding Sites|Chromatin|DNA|Genomics|High-Throughput Nucleotide Sequencing|Neural Networks (Computer)|Protein Binding|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/bty575', '30423062', 'Transcription factors bind regulatory DNA sequences in a combinatorial manner to modulate gene expression. Deep neural networks (DNNs) can learn the cis-regulatory grammars encoded in regulatory DNA sequences associated with transcription factor binding and chromatin accessibility. Several feature attribution methods have been developed for estimating the predictive importance of individual features (nucleotides or motifs) in any input DNA sequence to its associated output prediction from a DNN model. However, these methods do not reveal higher-order feature interactions encoded by the models.'),
(82273, 2018, 'bio', 'Scalable and exhaustive screening of metabolic functions carried out by microbial consortia', 'Frioux, Clémence|Fremy, Enora|Trottier, Camille|Siegel, Anne', 'Univ Rennes, Inria, CNRS, IRISA, Rennes, France.|Univ Rennes, Inria, CNRS, IRISA, Rennes, France.|Université de Nantes, LS2N, CNRS, Nantes, France.|Univ Rennes, Inria, CNRS, IRISA, Rennes, France.', 'France|France|France|France', 4, 'Humans|Microbial Consortia|Microbiota|Software', 'Journal Article', '10.1093/bioinformatics/bty588', '30423063', 'The selection of species exhibiting metabolic behaviors of interest is a challenging step when switching from the investigation of a large microbiota to the study of functions effectiveness. Approaches based on a compartmentalized framework are not scalable. The output of scalable approaches based on a non-compartmentalized modeling may be so large that it has neither been explored nor handled so far.'),
(82274, 2018, 'bio', 'Towards an accurate and efficient heuristic for species/gene tree co-estimation', 'Wang, Yaxuan|Nakhleh, Luay', 'Department of Computer Science, Rice University, Houston, TX, USA.|Department of Computer Science, Rice University, Houston, TX, USA.', 'United States|United States', 0, 'Bayes Theorem|Computer Simulation|Genomics|Machine Learning|Models, Genetic', 'Journal Article', '10.1093/bioinformatics/bty599', '30423064', 'Species and gene trees represent how species and individual loci within their genomes evolve from their most recent common ancestors. These trees are central to addressing several questions in biology relating to, among other issues, species conservation, trait evolution and gene function. Consequently, their accurate inference from genomic data is a major endeavor. One approach to their inference is to co-estimate species and gene trees from genome-wide data. Indeed, Bayesian methods based on this approach already exist. However, these methods are very slow, limiting their applicability to datasets with small numbers of taxa. The more commonly used approach is to first infer gene trees individually, and then use gene tree estimates to infer the species tree. Methods in this category rely significantly on the accuracy of the gene trees which is often not high when the dataset includes closely related species.'),
(82275, 2018, 'bio', 'Recognition of CRISPR/Cas9 off-target sites through ensemble learning of uneven mismatch distributions', 'Peng, Hui|Zheng, Yi|Zhao, Zhixun|Liu, Tao|Li, Jinyan', 'Advanced Analytics Institute, Faculty of Engineering and Information Technology, University of Technology Sydney, Broadway, Australia.|Advanced Analytics Institute, Faculty of Engineering and Information Technology, University of Technology Sydney, Broadway, Australia.|Advanced Analytics Institute, Faculty of Engineering and Information Technology, University of Technology Sydney, Broadway, Australia.|Centre for Childhood Cancer Research, University of New South Wales, Kensington, Australia.|Advanced Analytics Institute, Faculty of Engineering and Information Technology, University of Technology Sydney, Broadway, Australia.', 'Australia|Australia|Australia|Australia|Australia', 8, 'Base Composition|CRISPR-Cas Systems|Genome|High-Throughput Nucleotide Sequencing|Machine Learning|RNA, Guide|Software', 'Journal Article', '10.1093/bioinformatics/bty558', '30423065', 'CRISPR/Cas9 is driving a broad range of innovative applications from basic biology to biotechnology and medicine. One of its current issues is the effect of off-target editing that should be critically resolved and should be completely avoided in the ideal use of this system.'),
(82276, 2018, 'bio', 'Conditional generative adversarial network for gene expression inference', 'Wang, Xiaoqian|Ghasedi Dizaji, Kamran|Huang, Heng', 'Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA.|Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA.|Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA.', 'United States|United States|United States', 0, 'Gene Expression|Gene Expression Profiling|Gene Regulatory Networks|Genome|Linear Models', 'Journal Article', '10.1093/bioinformatics/bty563', '30423066', 'The rapid progress of gene expression profiling has facilitated the prosperity of recent biological studies in various fields, where gene expression data characterizes various cell conditions and regulatory mechanisms under different experimental circumstances. Despite the widespread application of gene expression profiling and advances in high-throughput technologies, profiling in genome-wide level is still expensive and difficult. Previous studies found that high correlation exists in the expression pattern of different genes, such that a small subset of genes can be informative to approximately describe the entire transcriptome. In the Library of Integrated Network-based Cell-Signature program, a set of xe2x88xbc1000 landmark genes have been identified that contain xe2x88xbc80% information of the whole genome and can be used to predict the expression of remaining genes. For a cost-effective profiling strategy, traditional methods measure the profiles of landmark genes and then infer the expression of other target genes via linear models. However, linear models do not have the capacity to capture the non-linear associations in gene regulatory networks.'),
(82277, 2018, 'bio', 'Prioritising candidate genes causing QTL using hierarchical orthologous groups', 'Warwick Vesztrocy, Alex|Dessimoz, Christophe|Redestig, Henning', 'Department of Genetics, Evolution and Environment, University College London, London, UK.|Department of Genetics, Evolution and Environment, University College London, London, UK.|Bayer CropScience NV, Ghent, Belgium.', 'United Kingdom|United Kingdom|Belgium', 2, 'Arabidopsis|Genomics|Molecular Sequence Annotation|Oryza|Quantitative Trait Loci|Software', 'Journal Article', '10.1093/bioinformatics/bty615', '30423067', 'A key goal in plant biotechnology applications is the identification of genes associated to particular phenotypic traits (for example: yield, fruit size, root length). Quantitative Trait Loci (QTL) studies identify genomic regions associated with a trait of interest. However, to infer potential causal genes in these regions, each of which can contain hundreds of genes, these data are usually intersected with prior functional knowledge of the genes. This process is however laborious, particularly if the experiment is performed in a non-model species, and the statistical significance of the inferred candidates is typically unknown.'),
(82278, 2018, 'bio', 'Ontology-based validation and identification of regulatory phenotypes', 'Kulmanov, Maxat|Schofield, Paul N|Gkoutos, Georgios V|Hoehndorf, Robert', 'Computer, Electrical and Mathematical Sciences and Engineering Division, Computational Bioscience Research Centre, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.|Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK.|College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, Birmingham, UK.|Computer, Electrical and Mathematical Sciences and Engineering Division, Computational Bioscience Research Centre, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.', 'Saudi Arabia|United Kingdom|United Kingdom|Saudi Arabia', 0, 'Animals|Computational Biology|Gene Ontology|Humans|Mice|Molecular Sequence Annotation|Phenotype|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/bty605', '30423068', 'Function annotations of gene products, and phenotype annotations of genotypes, provide valuable information about molecular mechanisms that can be utilized by computational methods to identify functional and phenotypic relatedness, improve our understanding of disease and pathobiology, and lead to discovery of drug targets. Identifying functions and phenotypes commonly requires experiments which are time-consuming and expensive to carry out, creating the annotations additionally requires a curator to make an assertion based on reported evidence. Support to validate the mutual consistency of functional and phenotype annotations as well as a computational method to predict phenotypes from function annotations, would greatly improve the utility of function annotations.'),
(82279, 2018, 'bio', 'PAIPline: pathogen identification in metagenomic and clinical next generation sequencing samples', 'Andrusch, Andreas|Dabrowski, Piotr W|Klenner, Jeanette|Tausch, Simon H|Kohl, Claudia|Osman, Abdalla A|Renard, Bernhard Y|Nitsche, Andreas', 'Highly Pathogenic Viruses (ZBS1), Robert Koch Institute, Berlin, Germany.|Bioinformatics Unit (MF1), Robert Koch Institute, Berlin, Germany.|Highly Pathogenic Viruses (ZBS1), Robert Koch Institute, Berlin, Germany.|Highly Pathogenic Viruses (ZBS1), Robert Koch Institute, Berlin, Germany.|Highly Pathogenic Viruses (ZBS1), Robert Koch Institute, Berlin, Germany.|National Public Health Laboratory, Karthoum, Sudan.|Bioinformatics Unit (MF1), Robert Koch Institute, Berlin, Germany.|Highly Pathogenic Viruses (ZBS1), Robert Koch Institute, Berlin, Germany.', 'Germany|Germany|Germany|Germany|Germany|Sudan|Germany|Germany', 0, 'Bacteria|Computational Biology|High-Throughput Nucleotide Sequencing|Humans|Metagenomics|Software', 'Journal Article', '10.1093/bioinformatics/bty595', '30423069', 'Next generation sequencing (NGS) has provided researchers with a powerful tool to characterize metagenomic and clinical samples in research and diagnostic settings. NGS allows an open view into samples useful for pathogen detection in an unbiased fashion and without prior hypothesis about possible causative agents. However, NGS datasets for pathogen detection come with different obstacles, such as a very unfavorable ratio of pathogen to host reads. Alongside often appearing false positives and irrelevant organisms, such as contaminants, tools are often challenged by samples with low pathogen loads and might not report organisms present below a certain threshold. Furthermore, some metagenomic profiling tools are only focused on one particular set of pathogens, for example bacteria.'),
(82280, 2018, 'bio', 'SPhyR: tumor phylogeny estimation from single-cell sequencing data under loss and error', 'El-Kebir, Mohammed', 'Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA.', 'United States', 14, 'Algorithms|Humans|Mutation|Neoplasms|Phylogeny|Sequence Analysis|Single-Cell Analysis', 'Journal Article', '10.1093/bioinformatics/bty589', '30423070', 'Cancer is characterized by intra-tumor heterogeneity, the presence of distinct cell populations with distinct complements of somatic mutations, which include single-nucleotide variants (SNVs) and copy-number aberrations (CNAs). Single-cell sequencing technology enables one to study these cell populations at single-cell resolution. Phylogeny estimation algorithms that employ appropriate evolutionary models are key to understanding the evolutionary mechanisms behind intra-tumor heterogeneity.'),
(82281, 2018, 'bio', 'Computational enhancement of single-cell sequences for inferring tumor evolution', 'Miura, Sayaka|Huuki, Louise A|Buturla, Tiffany|Vu, Tracy|Gomez, Karen|Kumar, Sudhir', 'Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, USA.|Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, USA.|Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, USA.|Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, USA.|Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, USA.|Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, USA.', 'United States|United States|United States|United States|United States|United States', 7, 'Bayes Theorem|High-Throughput Nucleotide Sequencing|Humans|Neoplasms|Phylogeny|Single-Cell Analysis', 'Journal Article', '10.1093/bioinformatics/bty571', '30423071', 'Tumor sequencing has entered an exciting phase with the advent of single-cell techniques that are revolutionizing the assessment of single nucleotide variation (SNV) at the highest cellular resolution. However, state-of-the-art single-cell sequencing technologies produce data with many missing bases (MBs) and incorrect base designations that lead to false-positive (FP) and false-negative (FN) detection of somatic mutations. While computational methods are available to make biological inferences in the presence of these errors, the accuracy of the imputed MBs and corrected FPs and FNs remains unknown.'),
(82282, 2018, 'bio', 'Off-target predictions in CRISPR-Cas9 gene editing using deep learning', 'Lin, Jiecong|Wong, Ka-Chun', 'Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR.|Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR.', 'Hong Kong|Hong Kong', 0, 'Algorithms|Area Under Curve|CRISPR-Cas Systems|Deep Learning|Gene Editing|Logistic Models', 'Journal Article', '10.1093/bioinformatics/bty554', '30423072', 'The prediction of off-target mutations in CRISPR-Cas9 is a hot topic due to its relevance to gene editing research. Existing prediction methods have been developed, however, most of them just calculated scores based on mismatches to the guide sequence in CRISPR-Cas9. Therefore, the existing prediction methods are unable to scale and improve their performance with the rapid expansion of experimental data in CRISPR-Cas9. Moreover, the existing methods still cannot satisfy enough precision in off-target predictions for gene editing at the clinical level.'),
(82283, 2018, 'bio', 'iCFN: an efficient exact algorithm for multistate protein design', 'Karimi, Mostafa|Shen, Yang', 'Department of Electrical and Computer Engineering and TEES-AgriLife Center for Bioinformatics and Genomic Systems Engineering, Texas A&M University, College Station, USA.|Department of Electrical and Computer Engineering and TEES-AgriLife Center for Bioinformatics and Genomic Systems Engineering, Texas A&M University, College Station, USA.', 'United States|United States', 6, 'Algorithms|Humans|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/bty564', '30423073', 'Multistate protein design addresses real-world challenges, such as multi-specificity design and backbone flexibility, by considering both positive and negative protein states with an ensemble of substates for each. It also presents an enormous challenge to exact algorithms that guarantee the optimal solutions and enable a direct test of mechanistic hypotheses behind models. However, efficient exact algorithms are lacking for multistate protein design.'),
(82284, 2018, 'bio', 'A Boolean network inference from time-series gene expression data using a genetic algorithm', 'Barman, Shohag|Kwon, Yung-Keun', 'Department of IT Convergence, University of Ulsan, 93 Nam-gu, Ulsan, Republic of Korea.|Department of IT Convergence, University of Ulsan, 93 Nam-gu, Ulsan, Republic of Korea.', '-|-', 6, 'Algorithms|Gene Expression|Gene Regulatory Networks|Reproducibility of Results', 'Journal Article', '10.1093/bioinformatics/bty584', '30423074', 'Inferring a gene regulatory network from time-series gene expression data is a fundamental problem in systems biology, and many methods have been proposed. However, most of them were not efficient in inferring regulatory relations involved by a large number of genes because they limited the number of regulatory genes or computed an approximated reliability of multivariate relations. Therefore, an improved method is needed to efficiently search more generalized and scalable regulatory relations.'),
(82285, 2018, 'bio', 'Comparative Network Reconstruction using mixed integer programming', 'Bosdriesz, Evert|Prahallad, Anirudh|Klinger, Bertram|Sieber, Anja|Bosma, Astrid|Bernards, René|Blüthgen, Nils|Wessels, Lodewyk F A', 'Division of Molecular Carcinogenesis, The Oncode Institute, The Netherlands Cancer Institute, Amsterdam, The Netherlands.|Division of Molecular Carcinogenesis, The Oncode Institute, The Netherlands Cancer Institute, Amsterdam, The Netherlands.|Institute of Pathology, Charité Universitätsmedizin, Berlin, Germany.|Institute of Pathology, Charité Universitätsmedizin, Berlin, Germany.|Division of Molecular Carcinogenesis, The Oncode Institute, The Netherlands Cancer Institute, Amsterdam, The Netherlands.|Division of Molecular Carcinogenesis, The Oncode Institute, The Netherlands Cancer Institute, Amsterdam, The Netherlands.|Institute of Pathology, Charité Universitätsmedizin, Berlin, Germany.|Division of Molecular Carcinogenesis, The Oncode Institute, The Netherlands Cancer Institute, Amsterdam, The Netherlands.', 'Netherlands|Netherlands|Germany|Germany|Netherlands|Netherlands|Germany|Netherlands', 0, 'Neural Networks (Computer)|Signal Transduction|Software', 'Comparative Study', '10.1093/bioinformatics/bty616', '30423075', 'Signal-transduction networks are often aberrated in cancer cells, and new anti-cancer drugs that specifically target oncogenes involved in signaling show great clinical promise. However, the effectiveness of such targeted treatments is often hampered by innate or acquired resistance due to feedbacks, crosstalks or network adaptations in response to drug treatment. A quantitative understanding of these signaling networks and how they differ between cells with different oncogenic mutations or between sensitive and resistant cells can help in addressing this problem.'),
(82286, 2018, 'bio', 'DeepDiff: DEEP-learning for predicting DIFFerential gene expression from histone modifications', 'Sekhon, Arshdeep|Singh, Ritambhara|Qi, Yanjun', 'Department of Computer Science, University of Virginia, Charlottesville, VA, USA.|Department of Computer Science, University of Virginia, Charlottesville, VA, USA.|Department of Computer Science, University of Virginia, Charlottesville, VA, USA.', 'United States|United States|United States', 0, 'Gene Expression|Histone Code|Histones|Humans|Machine Learning|Protein Processing, Post-Translational|Software', 'Journal Article', '10.1093/bioinformatics/bty612', '30423076', 'Computational methods that predict differential gene expression from histone modification signals are highly desirable for understanding how histone modifications control the functional heterogeneity of cells through influencing differential gene regulation. Recent studies either failed to capture combinatorial effects on differential prediction or primarily only focused on cell type-specific analysis. In this paper we develop a novel attention-based deep learning architecture, DeepDiff, that provides a unified and end-to-end solution to model and to interpret how dependencies among histone modifications control the differential patterns of gene regulation. DeepDiff uses a hierarchy of multiple Long Short-Term Memory (LSTM) modules to encode the spatial structure of input signals and to model how various histone modifications cooperate automatically. We introduce and train two levels of attention jointly with the target prediction, enabling DeepDiff to attend differentially to relevant modifications and to locate important genome positions for each modification. Additionally, DeepDiff introduces a novel deep-learning based multi-task formulation to use the cell-type-specific gene expression predictions as auxiliary tasks, encouraging richer feature embeddings in our primary task of differential expression prediction.'),
(82287, 2018, 'bio', 'Semantic Disease Gene Embeddings (SmuDGE): phenotype-based disease gene prioritization without phenotypes', 'Alshahrani, Mona|Hoehndorf, Robert', 'Computer, Electrical and Mathematical Sciences and Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.|Computer, Electrical and Mathematical Sciences and Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.', 'Saudi Arabia|Saudi Arabia', 9, 'Animals|Disease|Humans|Mice|Phenotype|Semantics|Software', 'Journal Article', '10.1093/bioinformatics/bty559', '30423077', 'In the past years, several methods have been developed to incorporate information about phenotypes into computational disease gene prioritization methods. These methods commonly compute the similarity between a diseases (or patients) phenotypes and a database of gene-to-phenotype associations to find the phenotypically most similar match. A key limitation of these methods is their reliance on knowledge about phenotypes associated with particular genes which is highly incomplete in humans as well as in many model organisms such as the mouse.'),
(82288, 2018, 'bio', 'A deep neural network approach for learning intrinsic protein-RNA binding preferences', 'Ben-Bassat, Ilan|Chor, Benny|Orenstein, Yaron', 'Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel.|Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel.|Department of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel.', 'Israel|Israel|Israel', 12, 'Deep Learning|Neural Networks (Computer)|RNA|RNA-Binding Proteins|Software', 'Journal Article', '10.1093/bioinformatics/bty600', '30423078', 'The complexes formed by binding of proteins to RNAs play key roles in many biological processes, such as splicing, gene expression regulation, translation and viral replication. Understanding protein-RNA binding may thus provide important insights to the functionality and dynamics of many cellular processes. This has sparked substantial interest in exploring protein-RNA binding experimentally, and predicting it computationally. The key computational challenge is to efficiently and accurately infer protein-RNA binding models that will enable prediction of novel protein-RNA interactions to additional transcripts of interest.'),
(82289, 2018, 'bio', 'Liquid-chromatography retention order prediction for metabolite identification', 'Bach, Eric|Szedmak, Sandor|Brouard, Céline|Böcker, Sebastian|Rousu, Juho', 'Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Espoo, Finland.|Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Espoo, Finland.|Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Espoo, Finland.|Department for Computer Science, Chair for Bioinformatics, Friedrich-Schiller-University, Jena, Germany.|Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Espoo, Finland.', 'Finland|Finland|Finland|Germany|Finland', 0, 'Chromatography, Liquid|Tandem Mass Spectrometry', 'Journal Article', '10.1093/bioinformatics/bty590', '30423079', 'Liquid Chromatography (LC) followed by tandem Mass Spectrometry (MS/MS) is one of the predominant methods for metabolite identification. In recent years, machine learning has started to transform the analysis of tandem mass spectra and the identification of small molecules. In contrast, LC data is rarely used to improve metabolite identification, despite numerous published methods for retention time prediction using machine learning.'),
(82290, 2018, 'bio', 'DREAM-Yara: an exact read mapper for very large databases with short update time', 'Dadi, Temesgen Hailemariam|Siragusa, Enrico|Piro, Vitor C|Andrusch, Andreas|Seiler, Enrico|Renard, Bernhard Y|Reinert, Knut', 'Algorithmic Bioinformatics, Institute for Bioinformatics, FU Berlin, Berlin, Germany.|Computational Genomics, IBM Thomas J Watson Research Center, Yorktown Heights, NY, USA.|Bioinformatics Unit (MF1), Robert Koch Institute, Berlin, Germany.|Centre for Biological Threats and Special Pathogens (ZBS1), Robert Koch Institute, Berlin, Germany.|Algorithmic Bioinformatics, Institute for Bioinformatics, FU Berlin, Berlin, Germany.|Bioinformatics Unit (MF1), Robert Koch Institute, Berlin, Germany.|Algorithmic Bioinformatics, Institute for Bioinformatics, FU Berlin, Berlin, Germany.', 'Germany|United States|Germany|Germany|Germany|Germany|Germany', 0, 'Databases, Factual|Humans|Software|Time Factors', 'Journal Article', '10.1093/bioinformatics/bty567', '30423080', 'Mapping-based approaches have become limited in their application to very large sets of references since computing an FM-index for very large databases (e.g. >10 GB) has become a bottleneck. This affects many analyses that need such index as an essential step for approximate matching of the NGS reads to reference databases. For instance, in typical metagenomics analysis, the size of the reference sequences has become prohibitive to compute a single full-text index on standard machines. Even on large memory machines, computing such index takes about 1 day of computing time. As a result, updates of indices are rarely performed. Hence, it is desirable to create an alternative way of indexing while preserving fast search times.'),
(82291, 2018, 'bio', 'IRSOM, a reliable identifier of ncRNAs based on supervised self-organizing maps with rejection', 'Platon, Ludovic|Zehraoui, Farida|Bendahmane, Abdelhafid|Tahi, Fariza', 'IBISC, Universitxc3xa9 Evry, Universitxc3xa9 Paris-Saclay, Evry, France.|IBISC, Universitxc3xa9 Evry, Universitxc3xa9 Paris-Saclay, Evry, France.|Institute of Plant Sciences Paris-Saclay, INRA, CNRS, Universitxc3xa9 Paris-Sud, Universitxc3xa9 dEvry, Universitxc3xa9 Paris-Diderot, Orsay, France.|IBISC, Universitxc3xa9 Evry, Universitxc3xa9 Paris-Saclay, Evry, France.', 'France|France|France|France', 0, 'Algorithms|RNA, Untranslated|Software', 'Journal Article', '10.1093/bioinformatics/bty572', '30423081', 'Non-coding RNAs (ncRNAs) play important roles in many biological processes and are involved in many diseases. Their identification is an important task, and many tools exist in the literature for this purpose. However, almost all of them are focused on the discrimination of coding and ncRNAs without giving more biological insight. In this paper, we propose a new reliable method called IRSOM, based on a supervised Self-Organizing Map (SOM) with a rejection option, that overcomes these limitations. The rejection option in IRSOM improves the accuracy of the method and also allows identifing the ambiguous transcripts. Furthermore, with the visualization of the SOM, we analyze the rejected predictions and highlight the ambiguity of the transcripts.'),
(82292, 2018, 'bio', 'Accurate and adaptive imputation of summary statistics in mixed-ethnicity cohorts', 'Togninalli, Matteo|Roqueiro, Damian|COPDGene Investigators|Borgwardt, Karsten M', 'Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.|Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.|Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.|Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland', 1, 'Cohort Studies|Ethnic Groups|Gene Frequency|Genome-Wide Association Study|Genotype|Humans|Software', 'Journal Article', '10.1093/bioinformatics/bty596', '30423082', 'Methods based on summary statistics obtained from genome-wide association studies have gained considerable interest in genetics due to the computational cost and privacy advantages they present. Imputing missing summary statistics has therefore become a key procedure in many bioinformatics pipelines, but available solutions may rely on additional knowledge about the populations used in the original study and, as a result, may not always ensure feasibility or high accuracy of the imputation procedure.'),
(82293, 2018, 'bio', 'Learning structural motif representations for efficient protein structure search', 'Liu, Yang|Ye, Qing|Wang, Liwei|Peng, Jian', 'Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA.|Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA.|Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA.|Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA.', 'United States|United States|United States|United States', 0, 'Amino Acid Motifs|Binding Sites|Databases, Protein|Neural Networks (Computer)|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/bty585', '30423083', 'Given a protein of unknown function, fast identification of similar protein structures from the Protein Data Bank (PDB) is a critical step for inferring its biological function. Such structural neighbors can provide evolutionary insights into protein conformation, interfaces and binding sites that are not detectable from sequence similarity. However, the computational cost of performing pairwise structural alignment against all structures in PDB is prohibitively expensive. Alignment-free approaches have been introduced to enable fast but coarse comparisons by representing each protein as a vector of structure features or fingerprints and only computing similarity between vectors. As a notable example, FragBag represents each protein by a bag of fragments, which is a vector of frequencies of contiguous short backbone fragments from a predetermined library. Despite being efficient, the accuracy of FragBag is unsatisfactory because its backbone fragment library may not be optimally constructed and long-range interacting patterns are omitted.'),
(82294, 2018, 'bio', 'iTOP: inferring the topology of omics data', 'Aben, Nanne|Westerhuis, Johan A|Song, Yipeng|Kiers, Henk A L|Michaut, Magali|Smilde, Age K|Wessels, Lodewyk F A', 'Division of Molecular Carcinogenesis, Oncode Institute, Netherlands Cancer Institute, Amsterdam, The Netherlands.|Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands.|Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands.|Heymans Institute, University of Groningen, Groningen, The Netherlands.|Division of Molecular Carcinogenesis, Oncode Institute, Netherlands Cancer Institute, Amsterdam, The Netherlands.|Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands.|Division of Molecular Carcinogenesis, Oncode Institute, Netherlands Cancer Institute, Amsterdam, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 3, 'Algorithms|Humans|Neoplasms|Proteomics', 'Journal Article', '10.1093/bioinformatics/bty636', '30423084', 'In biology, we are often faced with multiple datasets recorded on the same set of objects, such as multi-omics and phenotypic data of the same tumors. These datasets are typically not independent from each other. For example, methylation may influence gene expression, which may, in turn, influence drug response. Such relationships can strongly affect analyses performed on the data, as we have previously shown for the identification of biomarkers of drug response. Therefore, it is important to be able to chart the relationships between datasets.'),
(82295, 2018, 'bio', 'An accurate and rapid continuous wavelet dynamic time warping algorithm for end-to-end mapping in ultra-long nanopore sequencing', 'Han, Renmin|Li, Yu|Gao, Xin|Wang, Sheng', 'King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal, Saudi Arabia.|King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal, Saudi Arabia.|King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal, Saudi Arabia.|King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal, Saudi Arabia.', 'Saudi Arabia|Saudi Arabia|Saudi Arabia|Saudi Arabia', 0, 'Algorithms|Humans|Nanopores|Sequence Analysis|Time Factors', 'Journal Article', '10.1093/bioinformatics/bty555', '30423085', 'Long-reads, point-of-care and polymerase chain reaction-free are the promises brought by nanopore sequencing. Among various steps in nanopore data analysis, the end-to-end mapping between the raw electrical current signal sequence and the reference expected signal sequence serves as the key building block to signal labeling, and the following signal visualization, variant identification and methylation detection. One of the classic algorithms to solve the signal mapping problem is the dynamic time warping (DTW). However, the ultra-long nanopore sequencing and an order of magnitude difference in the sampling speed complexify the scenario and make the classical DTW infeasible to solve the problem.'),
(82296, 2018, 'bio', 'fastp: an ultra-fast all-in-one FASTQ preprocessor', 'Chen, Shifu|Zhou, Yanqing|Chen, Yaru|Gu, Jia', 'Department of Bioinformatics, HaploX Biotechnology, Shenzhen, China.|Department of Bioinformatics, HaploX Biotechnology, Shenzhen, China.|Department of Bioinformatics, HaploX Biotechnology, Shenzhen, China.|Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.', 'China|China|China|China', 477, 'Humans|Programming Languages|Quality Control', 'Journal Article', '10.1093/bioinformatics/bty560', '30423086', 'Quality control and preprocessing of FASTQ files are essential to providing clean data for downstream analysis. Traditionally, a different tool is used for each operation, such as quality control, adapter trimming and quality filtering. These tools are often insufficiently fast as most are developed using high-level programming languages (e.g. Python and Java) and provide limited multi-threading support. Reading and loading data multiple times also renders preprocessing slow and I/O inefficient.'),
(82297, 2018, 'bio', 'piMGM: incorporating multi-source priors in mixed graphical models for learning disease networks', 'Manatakis, Dimitris V|Raghu, Vineet K|Benos, Panayiotis V', 'Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.|Department of Computer Science, University of Pittsburgh, Pittsburgh, PA, USA.|Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.', 'United States|United States|United States', 0, 'Databases, Factual|Disease|Humans|Machine Learning|Reproducibility of Results|Sample Size|Software', 'Journal Article', '10.1093/bioinformatics/bty591', '30423087', 'Learning probabilistic graphs over mixed data is an important way to combine gene expression and clinical disease data. Leveraging the existing, yet imperfect, information in pathway databases for mixed graphical model (MGM) learning is an understudied problem with tremendous potential applications in systems medicine, the problems of which often involve high-dimensional data.'),
(82298, 2018, 'bio', 'Hierarchical HotNet: identifying hierarchies of altered subnetworks', 'Reyna, Matthew A|Leiserson, Mark D M|Raphael, Benjamin J', 'Department of Computer Science, Princeton University, Princeton, NJ, USA.|Department of Computer Science, University of Maryland, College Park, MD, USA.|Department of Computer Science, Princeton University, Princeton, NJ, USA.', 'United States|United States|United States', 0, 'Algorithms|Gene Regulatory Networks|Humans|Neoplasms|Oncogenes|Protein Interaction Maps', 'Journal Article', '10.1093/bioinformatics/bty613', '30423088', 'The analysis of high-dimensional omics data is often informed by the use of biological interaction networks. For example, protein-protein interaction networks have been used to analyze gene expression data, to prioritize germline variants, and to identify somatic driver mutations in cancer. In these and other applications, the underlying computational problem is to identify altered subnetworks containing genes that are both highly altered in an omics dataset and are topologically close (e.g. connected) on an interaction network.'),
(82299, 2018, 'bio', 'Bayesian inference on stochastic gene transcription from flow cytometry data', 'Tiberi, Simone|Walsh, Mark|Cavallaro, Massimo|Hebenstreit, Daniel|Finkenstädt, Bärbel', 'Institute of Molecular Life Sciences, University of Zürich, Zürich, Switzerland.|School of Life Sciences, University of Warwick, Coventry, UK.|Department of Statistics, University of Warwick, Coventry, UK.|School of Life Sciences, University of Warwick, Coventry, UK.|Department of Statistics, University of Warwick, Coventry, UK.', 'Switzerland|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Bayes Theorem|Flow Cytometry|In Situ Hybridization, Fluorescence|Software|Stochastic Processes|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/bty568', '30423089', 'Transcription in single cells is an inherently stochastic process as mRNA levels vary greatly between cells, even for genetically identical cells under the same experimental and environmental conditions. We present a stochastic two-state switch model for the population of mRNA molecules in single cells where genes stochastically alternate between a more active ON state and a less active OFF state. We prove that the stationary solution of such a model can be written as a mixture of a Poisson and a Poisson-beta probability distribution. This finding facilitates inference for single cell expression data, observed at a single time point, from flow cytometry experiments such as FACS or fluorescence in situ hybridization (FISH) as it allows one to sample directly from the equilibrium distribution of the mRNA population. We hence propose a Bayesian inferential methodology using a pseudo-marginal approach and a recent approximation to integrate over unobserved states associated with measurement error.'),
(82300, 2018, 'bio', 'CNEFinder: finding conserved non-coding elements in genomes', 'Ayad, Lorraine A K|Pissis, Solon P|Polychronopoulos, Dimitris', 'Department of Informatics, Kings College London, London, UK.|Department of Informatics, Kings College London, London, UK.|Genomics England, Charterhouse Square, London, UK.', 'United Kingdom|United Kingdom|United Kingdom', 2, 'Conserved Sequence|Genome|Humans|RNA, Untranslated|Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/bty601', '30423090', 'Conserved non-coding elements (CNEs) represent an enigmatic class of genomic elements which, despite being extremely conserved across evolution, do not encode for proteins. Their functions are still largely unknown. Thus, there exists a need to systematically investigate their roles in genomes. Towards this direction, identifying sets of CNEs in a wide range of organisms is an important first step. Currently, there are no tools published in the literature for systematically identifying CNEs in genomes.'),
(82301, 2018, 'bio', 'Predicting protein-protein interactions through sequence-based deep learning', 'Hashemifar, Somaye|Neyshabur, Behnam|Khan, Aly A|Xu, Jinbo', 'Toyota Technological Institute at Chicago, Chicago, IL, USA.|Toyota Technological Institute at Chicago, Chicago, IL, USA.|Toyota Technological Institute at Chicago, Chicago, IL, USA.|Toyota Technological Institute at Chicago, Chicago, IL, USA.', 'United States|United States|United States|United States', 0, 'Animals|Area Under Curve|Deep Learning|Humans|Mice|Protein Binding|Protein Interaction Mapping|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/bty573', '30423091', 'High-throughput experimental techniques have produced a large amount of protein-protein interaction (PPI) data, but their coverage is still low and the PPI data is also very noisy. Computational prediction of PPIs can be used to discover new PPIs and identify errors in the experimental PPI data.'),
(82302, 2018, 'bio', 'Fast characterization of segmental duplications in genome assemblies', 'Numanagic, Ibrahim|Gxc3xb6kkaya, Alim S|Zhang, Lillian|Berger, Bonnie|Alkan, Can|Hach, Faraz', 'Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA.|Department of Computer Engineering, Bilkent University, Ankara, Turkey.|Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA.|Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA.|Department of Computer Engineering, Bilkent University, Ankara, Turkey.|Vancouver Prostate Centre, Vancouver, Canada.', 'United States|Turkey|United States|United States|Turkey|Canada', 5, 'Algorithms|Genome|Genomics|Humans|Segmental Duplications, Genomic', 'Journal Article', '10.1093/bioinformatics/bty586', '30423092', 'Segmental duplications (SDs) or low-copy repeats, are segments of DNAxe2x80x89>xe2x80x891 Kbp with high sequence identity that are copied to other regions of the genome. SDs are among the most important sources of evolution, a common cause of genomic structural variation and several are associated with diseases of genomic origin including schizophrenia and autism. Despite their functional importance, SDs present one of the major hurdles for de novo genome assembly due to the ambiguity they cause in building and traversing both state-of-the-art overlap-layout-consensus and de Bruijn graphs. This causes SD regions to be misassembled, collapsed into a unique representation, or completely missing from assembled reference genomes for various organisms. In turn, this missing or incorrect information limits our ability to fully understand the evolution and the architecture of the genomes. Despite the essential need to accurately characterize SDs in assemblies, there has been only one tool that was developed for this purpose, called Whole-Genome Assembly Comparison (WGAC), its primary goal is SD detection. WGAC is comprised of several steps that employ different tools and custom scripts, which makes this strategy difficult and time consuming to use. Thus there is still a need for algorithms to characterize within-assembly SDs quickly, accurately, and in a user friendly manner.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(82303, 2018, 'bio', 'Insights on the alteration of functionality of a tyrosine kinase 2 variant: a molecular dynamics study', 'Lesgidou, Nastazia|Eliopoulos, Elias|Goulielmos, George N|Vlassi, Metaxia', 'Laboratory of Protein Structure & Molecular Modeling, Institute of Biosciences & Applications, National Center for Scientific Research DEMOKRITOS, Athens, Greece.|Laboratory of Genetics, Department of Biotechnology, Agricultural University of Athens, Athens, Greece.|Section of Molecular Pathology and Human Genetics, Department of Internal Medicine, School of Medicine, University of Crete, Heraklion, Greece.|Laboratory of Protein Structure & Molecular Modeling, Institute of Biosciences & Applications, National Center for Scientific Research DEMOKRITOS, Athens, Greece.', 'Greece|Greece|Greece|Greece', 0, 'Molecular Dynamics Simulation|Point Mutation|Polymorphism, Single Nucleotide|Proteins|TYK2 Kinase', 'Journal Article', '10.1093/bioinformatics/bty556', '30423093', 'The tyrosine kinase 2 protein (Tyk2), encoded by the TYK2 gene, has a crucial role in signal transduction and the pathogenesis of many diseases. A single nucleotide polymorphism of the TYK2 gene, SNP rs34536443, is of major importance, since it has been shown to confer protection against various, mainly, autoimmune diseases. This polymorphism results in a Pro to Ala change at amino acid position 1104 of the encoded Tyk2 protein that affects its enzymatic activity. However, the details of the underlined mechanism are unknown. To address this issue, in this study, we used molecular dynamics simulations on the kinase domains of both wild type and variant Tyk2 protein.'),
(82304, 2018, 'bio', 'A fast adaptive algorithm for computing whole-genome homology maps', 'Jain, Chirag|Koren, Sergey|Dilthey, Alexander|Phillippy, Adam M|Aluru, Srinivas', 'School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA.|Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.|Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.|Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.|School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA.', 'Georgia|United States|United States|United States|Georgia', 13, 'Algorithms|Base Sequence|Chromosome Mapping|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Segmental Duplications, Genomic|Sequence Alignment|Software|Time Factors', 'Journal Article', '10.1093/bioinformatics/bty597', '30423094', 'Whole-genome alignment is an important problem in genomics for comparing different species, mapping draft assemblies to reference genomes and identifying repeats. However, for large plant and animal genomes, this task remains compute and memory intensive. In addition, current practical methods lack any guarantee on the characteristics of output alignments, thus making them hard to tune for different application requirements.'),
(82305, 2018, 'bio', 'ECCB 2018 Organization', '-', '-', '-', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty739', '30423095', '-'),
(82306, 2018, 'bio', 'FLYCOP: metabolic modeling-based analysis and engineering microbial communities', 'García-Jiménez, Beatriz|García, José Luis|Nogales, Juan', 'Department of Systems Biology, Centro Nacional de Biotecnología, Consejo Superior de Investigaciones Científicas (CNB-CSIC), 28049 Madrid, Spain.|Microbial and Plant Biotechnology Department, Centro de Investigaciones Biológicas (CIB-CSIC), 28040 Madrid, Spain.|Department of Systems Biology, Centro Nacional de Biotecnología, Consejo Superior de Investigaciones Científicas (CNB-CSIC), 28049 Madrid, Spain.', 'Spain|Spain|Spain', 0, 'Escherichia coli|Metabolic Engineering|Microbial Consortia|Microbiota|Software', 'Journal Article', '10.1093/bioinformatics/bty561', '30423096', 'Synthetic microbial communities begin to be considered as promising multicellular biocatalysts having a large potential to replace engineered single strains in biotechnology applications, in pharmaceutical, chemical and living architecture sectors. In contrast to single strain engineering, the effective and high-throughput analysis and engineering of microbial consortia face the lack of knowledge, tools and well-defined workflows. This manuscript contributes to fill this important gap with a framework, called FLYCOP (FLexible sYnthetic Consortium OPtimization), which contributes to microbial consortia modeling and engineering, while improving the knowledge about how these communities work. FLYCOP selects the best consortium configuration to optimize a given goal, among multiple and diverse configurations, in a flexible way, taking temporal changes in metabolite concentrations into account.'),
(82307, 2018, 'bio', 'DeepDTA: deep drug-target binding affinity prediction', 'Öztürk, Hakime|Özgür, Arzucan|Ozkirimli, Elif', 'Department of Computer Engineering, Bogazici University, Istanbul, Turkey.|Department of Computer Engineering, Bogazici University, Istanbul, Turkey.|Department of Chemical Engineering, Bogazici University, Istanbul, Turkey.', 'Turkey|Turkey|Turkey', 53, 'Algorithms|Amino Acid Sequence|Ligands|Machine Learning|Neural Networks (Computer)|Protein Interaction Mapping|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/bty593', '30423097', 'The identification of novel drug-target (DT) interactions is a substantial part of the drug discovery process. Most of the computational methods that have been proposed to predict DT interactions have focused on binary classification, where the goal is to determine whether a DT pair interacts or not. However, protein-ligand interactions assume a continuum of binding strength values, also called binding affinity and predicting this value still remains a challenge. The increase in the affinity data available in DT knowledge-bases allows the use of advanced learning techniques such as deep learning architectures in the prediction of binding affinities. In this study, we propose a deep-learning based model that uses only sequence information of both targets and drugs to predict DT interaction binding affinities. The few studies that focus on DT binding affinity prediction use either 3D structures of protein-ligand complexes or 2D features of compounds. One novel approach used in this work is the modeling of protein sequences and compound 1D representations with convolutional neural networks (CNNs).'),
(82308, 2018, 'bio', 'Approximate, simultaneous comparison of microbial genome architectures via syntenic anchoring of quiver representations', 'Salazar, Alex N|Abeel, Thomas', 'Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands.|Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands.', 'Netherlands|Netherlands', 0, 'Genome, Microbial|Software|Synteny', 'Comparative Study', '10.1093/bioinformatics/bty614', '30423098', 'A long-standing limitation in comparative genomic studies is the dependency on a reference genome, which hinders the spectrum of genetic diversity that can be identified across a population of organisms. This is especially true in the microbial world where genome architectures can significantly vary. There is therefore a need for computational methods that can simultaneously analyze the architectures of multiple genomes without introducing bias from a reference.'),
(82309, 2018, 'bio', 'CisPi: a transcriptomic score for disclosing cis-acting disease-associated lincRNAs', 'Wang, Zhezhen|Cunningham, John M|Yang, Xinan H', 'Department of Pediatrics, University of Chicago, Chicago, IL, USA.|Department of Pediatrics, University of Chicago, Chicago, IL, USA.|Department of Pediatrics, University of Chicago, Chicago, IL, USA.', 'United States|United States|United States', 0, 'Humans|RNA, Long Noncoding|Transcriptome', 'Journal Article', '10.1093/bioinformatics/bty574', '30423099', 'Long intergenic noncoding RNAs (lincRNAs) have risen to prominence in cancer biology as new biomarkers of disease. Those lincRNAs transcribed from active cis-regulatory elements (enhancers) have provided mechanistic insight into cis-acting regulation, however, in the absence of an enhancer hallmark, computational prediction of cis-acting transcription of lincRNAs remains challenging. Here, we introduce a novel transcriptomic method: a cis-regulatory lincRNA-gene associating metric, termed CisPi. CisPi quantifies the mutual information between lincRNAs and local gene expression regarding their response to perturbation, such as disease risk-dependence. To predict risk-dependent lincRNAs in neuroblastoma, an aggressive pediatric cancer, we advance this scoring scheme to measure lincRNAs that represent the minority of reads in RNA-Seq libraries by a novel side-by-side analytical pipeline.'),
(82310, 2018, 'bio', 'Single cell network analysis with a mixture of Nested Effects Models', 'Pirkl, Martin|Beerenwinkel, Niko', 'Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.|Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.', 'Switzerland|Switzerland', 2, 'Humans|Models, Biological|Probability|Single-Cell Analysis', 'Journal Article', '10.1093/bioinformatics/bty602', '30423100', 'New technologies allow for the elaborate measurement of different traits of single cells under genetic perturbations. These interventional data promise to elucidate intra-cellular networks in unprecedented detail and further help to improve treatment of diseases like cancer. However, cell populations can be very heterogeneous.'),
(82311, 2018, 'bio', 'Quantitative trait loci identification for brain endophenotypes via new additive model with random networks', 'Wang, Xiaoqian|Chen, Hong|Yan, Jingwen|Nho, Kwangsik|Risacher, Shannon L|Saykin, Andrew J|Shen, Li|Huang, Heng|ADNI', 'Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA.|Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA.|Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA.|Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA.|Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA.|Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA.|Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.|Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA.|Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA.', 'United States|United States|India|India|India|India|United States|United States|United States', 1, 'Alzheimer Disease|Brain|Endophenotypes|Genotype|Humans|Phenotype|Polymorphism, Single Nucleotide|Quantitative Trait Loci|Software', 'Journal Article', '10.1093/bioinformatics/bty557', '30423101', 'The identification of quantitative trait loci (QTL) is critical to the study of causal relationships between genetic variations and disease abnormalities. We focus on identifying the QTLs associated to the brain endophenotypes in imaging genomics study for Alzheimers Disease (AD). Existing research works mainly depict the association between single nucleotide polymorphisms (SNPs) and the brain endophenotypes via the linear methods, which may introduce high bias due to the simplicity of the models. Since the influence of QTLs on brain endophenotypes is quite complex, it is desired to design the appropriate non-linear models to investigate the associations of genotypes and endophenotypes.'),
(82312, 2018, 'bio', 'ECCB 2018: The 17th European Conference on Computational Biology', 'ECCB 2018 Steering Committee|Hatzigeorgiou, Artemis G|Bagos, Pantelis|Benos, Panayiotis V|Nikolaou, Christoforos|Moreau, Yves|Kavakiotis, Ioannis', 'Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA.|DIANA-Lab, Department of Electrical & Computer Engineering, University of Thessaly, Volos, Greece.|Department of Computer Science and Biomedical Informatics, University of Thessaly, Papasiopoulou 24, Lamia, Greece.|Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.|Computational Genomics Group, Department of Biology, University of Crete, Herakleion, Greece.|Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, University of Leuven, Belgium.|DIANA-Lab, Department of Electrical & Computer Engineering, University of Thessaly, Volos, Greece.', 'United States|Greece|Greece|United States|Greece|Belgium|Greece', 0, '-', 'Introductory Journal Article', '10.1093/bioinformatics/bty740', '30423102', '-'),
(82313, 2018, 'bio', 'Analysis of single amino acid variations in singlet hot spots of protein-protein interfaces', 'Ozdemir, E Sila|Gursoy, Attila|Keskin, Ozlem', 'Department of Chemical and Biological Engineering, Koc University, Istanbul, Turkey.|Department of Computer Engineering, Koc University, Istanbul, Turkey.|Department of Chemical and Biological Engineering, Koc University, Istanbul, Turkey.', 'Turkey|Turkey|Turkey', 0, 'Amino Acids|Humans|Protein Binding|Protein Conformation|Protein Interaction Mapping|Proteins|Software|Thermodynamics', 'Journal Article', '10.1093/bioinformatics/bty569', '30423104', 'Single amino acid variations (SAVs) in protein-protein interaction (PPI) sites play critical roles in diseases. PPI sites (interfaces) have a small subset of residues called hot spots that contribute significantly to the binding energy, and they may form clusters called hot regions. Singlet hot spots are the single amino acid hot spots outside of the hot regions. The distribution of SAVs on the interface residues may be related to their disease association.'),
(82314, 2018, 'bio', 'Protein pocket detection via convex hull surface evolution and associated Reeb graph', 'Zhao, Rundong|Cang, Zixuan|Tong, Yiying|Wei, Guo-Wei', 'Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA.|Department of Mathematics, Michigan State University, East Lansing, MI, USA.|Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA.|Department of Mathematics, Michigan State University, East Lansing, MI, USA.', 'United States|United States|United States|United States', 2, 'Algorithms|Binding Sites|Ligands|Protein Binding|Protein Conformation|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/bty598', '30423105', 'Protein pocket information is invaluable for drug target identification, agonist design, virtual screening and receptor-ligand binding analysis. A recent study indicates that about half holoproteins can simultaneously bind multiple interacting ligands in a large pocket containing structured sub-pockets. Although this hierarchical pocket and sub-pocket structure has a significant impact to multi-ligand synergistic interactions in the protein binding site, there is no method available for this analysis. This work introduces a computational tool based on differential geometry, algebraic topology and physics-based simulation to address this pressing issue.'),
(82315, 2018, 'bio', 'Topology independent structural matching discovers novel templates for protein interfaces', 'Mirabello, Claudio|Wallner, Björn', 'Division of Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Linköping SE, Sweden.|Division of Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Linköping SE, Sweden.', 'Sweden|Sweden', 0, 'Algorithms|Protein Conformation|Protein Interaction Mapping|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/bty587', '30423106', 'Protein-protein interactions (PPI) are essential for the function of the cellular machinery. The rapid growth of protein-protein complexes with known 3D structures offers a unique opportunity to study PPI to gain crucial insights into protein function and the causes of many diseases. In particular, it would be extremely useful to compare interaction surfaces of monomers, as this would enable the pinpointing of potential interaction surfaces based solely on the monomer structure, without the need to predict the complete complex structure. While there are many structural alignment algorithms for individual proteins, very few have been developed for protein interfaces, and none that can align only the interface residues to other interfaces or surfaces of interacting monomer subunits in a topology independent (non-sequential) manner.'),
(82316, 2018, 'bio', 'MDPbiome: microbiome engineering through prescriptive perturbations', 'García-Jiménez, Beatriz|de la Rosa, Tomás|Wilkinson, Mark D', 'Center for Plant Biotechnology and Genomics UPM - INIA, Universidad Politecnica de Madrid, Madrid, Spain.|Department of Computer Science, Universidad Carlos III de Madrid, Madrid, Spain.|Center for Plant Biotechnology and Genomics UPM - INIA, Universidad Politecnica de Madrid, Madrid, Spain.', 'Spain|Spain|Spain', 5, 'Algorithms|Markov Chains|Metagenomics|Microbiota|Molecular Conformation|Software', 'Journal Article', '10.1093/bioinformatics/bty562', '30423107', 'Recent microbiome dynamics studies highlight the current inability to predict the effects of external perturbations on complex microbial populations. To do so would be particularly advantageous in fields such as medicine, bioremediation or industrial scenarios.'),
(82317, 2018, 'bio', 'Branch-recombinant Gaussian processes for analysis of perturbations in biological time series', 'Penfold, Christopher A|Sybirna, Anastasiya|Reid, John E|Huang, Yun|Wernisch, Lorenz|Ghahramani, Zoubin|Grant, Murray|Surani, M Azim', 'Wellcome Trust/Cancer Research UK Gurdon Institute, Henry Wellcome Building of Cancer and Developmental Biology, Cambridge, UK.|Wellcome Trust/Cancer Research UK Gurdon Institute, Henry Wellcome Building of Cancer and Developmental Biology, Cambridge, UK.|MRC Biostatistics Unit, University of Cambridge, Cambridge Institute of Public Health, Cambridge Biomedical Campus, Cambridge, UK.|Wellcome Trust/Cancer Research UK Gurdon Institute, Henry Wellcome Building of Cancer and Developmental Biology, Cambridge, UK.|MRC Biostatistics Unit, University of Cambridge, Cambridge Institute of Public Health, Cambridge Biomedical Campus, Cambridge, UK.|Department of Engineering, University of Cambridge, Cambridge, UK.|School of Life Sciences, Gibbet Hill Campus, The University of Warwick, Coventry, UK.|Wellcome Trust/Cancer Research UK Gurdon Institute, Henry Wellcome Building of Cancer and Developmental Biology, Cambridge, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Arabidopsis|Arabidopsis Proteins|Computational Biology|Pseudomonas syringae|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/bty603', '30423108', 'A common class of behaviour encountered in the biological sciences involves branching and recombination. During branching, a statistical process bifurcates resulting in two or more potentially correlated processes that may undergo further branching, the contrary is true during recombination, where two or more statistical processes converge. A key objective is to identify the time of this bifurcation (branch or recombination time) from time series measurements, e.g. by comparing a control time series with perturbed time series. Gaussian processes (GPs) represent an ideal framework for such analysis, allowing for nonlinear regression that includes a rigorous treatment of uncertainty. Currently, however, GP models only exist for two-branch systems. Here, we highlight how arbitrarily complex branching processes can be built using the correct composition of covariance functions within a GP framework, thus outlining a general framework for the treatment of branching and recombination in the form of branch-recombinant Gaussian processes (B-RGPs).'),
(82318, 2019, 'bio', 'SeQuiLa: an elastic, fast and scalable SQL-oriented solution for processing and querying genomic intervals', 'Wiewixc3xb3rka, Marek|Lexc5x9bniewska, Anna|Szmurxc5x82o, Agnieszka|Stxc4x99piexc5x84, Kacper|Borowiak, Mateusz|Okoniewski, Michaxc5x82|Gambin, Tomasz', 'Institute of Computer Science, Warsaw University of Technology, Warsaw, Poland.|Department of Computer Science, Poznan University of Technology, Poznaxc5x84, Poland.|Institute of Computer Science, Warsaw University of Technology, Warsaw, Poland.|Department of Computer Science, Poznan University of Technology, Poznaxc5x84, Poland.|Department of Computer Science, Poznan University of Technology, Poznaxc5x84, Poland.|Scientific IT Services, ETH Zurich, Zxc3xbcrich, Switzerland.|Institute of Computer Science, Warsaw University of Technology, Warsaw, Poland.', 'Poland|Poland|Poland|Poland|Poland|Switzerland|Poland', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty940', '30428005', 'Efficient processing of large-scale genomic datasets has recently become possible due to the application of big data technologies in bioinformatics pipelines. We present SeQuiLa-a distributed, ANSI SQL-compliant solution for speedy querying and processing of genomic intervals that is available as an Apache Spark package. Proposed range join strategy is significantly (xe2x88xbc22xc3x97) faster than the default Apache Spark implementation and outperforms other state-of-the-art tools for genomic intervals processing.'),
(82319, 2019, 'bio', 'Metrics for regulated biochemical pathway systems', 'Davis, Jacob D|Voit, Eberhard O', 'Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.|Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.', 'Georgia|Georgia', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty942', '30428007', 'The assessment of graphs through crisp numerical metrics has long been a hallmark of biological network analysis. However, typical graph metrics ignore regulatory signals that are crucially important for optimal pathway operation, for instance, in biochemical or metabolic studies. Here we introduce adjusted metrics that are applicable to both static networks and dynamic systems.'),
(82320, 2019, 'bio', 'Identify origin of replication in Saccharomyces cerevisiae using two-step feature selection technique', 'Dao, Fu-Ying|Lv, Hao|Wang, Fang|Feng, Chao-Qin|Ding, Hui|Chen, Wei|Lin, Hao', 'Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.|Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.|Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.|Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.|Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.|Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.|Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.', 'China|China|China|China|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty943', '30428009', 'DNA replication is a key step to maintain the continuity of genetic information between parental generation and offspring. The initiation site of DNA replication, also called origin of replication (ORI), plays an extremely important role in the basic biochemical process. Thus, rapidly and effectively identifying the location of ORI in genome will provide key clues for genome analysis. Although biochemical experiments could provide detailed information for ORI, it requires high experimental cost and long experimental period. As good complements to experimental techniques, computational methods could overcome these disadvantages.'),
(82321, 2019, 'bio', 'A Drug-Side Effect Context-Sensitive Network approach for drug target prediction', 'Zhou, Mengshi|Chen, Yang|Xu, Rong', 'Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.|Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.|Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.', 'China|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty906', '30428013', 'Computational drug target prediction has become an important process in drug discovery. Network-based approaches are commonly used in computational drug-target interaction (DTI) prediction. Existing network-based approaches are limited in capturing the contextual information on how diseases, drugs and genes are connected. Here, we proposed a context-sensitive network (CSN) model for DTI prediction by modeling contextual drug phenotypic relationships. We constructed a Drug-Side Effect Context-Sensitive Network (DSE-CSN) of 139 760 drug-side effect pairs, representing 1480 drugs and 5868 side effects. We also built a protein-protein interaction network (PPIN) of 15 267 gene nodes and 178 972 weighted edges. A heterogeneous network was built by connecting the DSE-CSN and the PPIN through 3684 known DTIs. For each drug on the DSE-CSN, its genetic targets were predicted and prioritized using a network-based ranking algorithm. Our approach was evaluated in both de novo and leave-one-out cross-validation analysis using known DTIs as the gold standard. We compared our DSE-CSN-based model to the traditional similarity-based network (SBN)-based prediction model. The results suggested that the DSE-CSN-based model was able to rank known DTIs highly. In a de novo cross-validation, the area under the receiver operating characteristic (ROC) curve was 0.95. In a leave-one-out cross-validation, the average rank was top 3.2% for known DTIs. When it was compared to the SBN-based model using the Precision-Recall curve, our CSN-based model achieved a higher mean average precision (MAP) (0.23 versus 0.19, P-value<1e-4) in a de novo cross-validation analysis. We further improved the CSN-based DTI prediction by differentially weighting the drug-side effect pairs on the network and showed a significant improvement of the MAP (0.29 versus 0.23, P-value<1e-4). We also showed that the CSN-based model consistently achieved better performances than the traditional SBN-based model across different drug classes. Moreover, we demonstrated that our novel DTI predictions can be supported by published literature. In summary, the CSN-based model, by modeling the context-specific inter-relationships among drugs and side effects, has a high potential in drug target prediction.'),
(82322, 2019, 'bio', 'FactorialHMM: fast and exact inference in factorial hidden Markov models', 'Schweiger, Regev|Erlich, Yaniv|Carmi, Shai', 'Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.|MyHeritage, Or Yehuda, Israel.|Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel.', 'Israel|Israel|Israel', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty944', '30445428', 'Hidden Markov models (HMMs) are powerful tools for modeling processes along the genome. In a standard genomic HMM, observations are drawn, at each genomic position, from a distribution whose parameters depend on a hidden state, and the hidden states evolve along the genome as a Markov chain. Often, the hidden state is the Cartesian product of multiple processes, each evolving independently along the genome. Inference in these so-called Factorial HMMs has a naïve running time that scales as the square of the number of possible states, which by itself increases exponentially with the number of sub-chains, such a running time scaling is impractical for many applications. While faster algorithms exist, there is no available implementation suitable for developing bioinformatics applications.'),
(82323, 2019, 'bio', 'Semi-supervised learning of Hidden Markov Models for biological sequence analysis', 'Tamposis, Ioannis A|Tsirigos, Konstantinos D|Theodoropoulou, Margarita C|Kontou, Panagiota I|Bagos, Pantelis G', 'Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece.|Department of Bio and Health Informatics, Technical University of Denmark, Kgs Lyngby, Denmark.|Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece.|Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece.|Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece.', 'Greece|Denmark|Greece|Greece|Greece', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty910', '30445435', 'Hidden Markov Models (HMMs) are probabilistic models widely used in applications in computational sequence analysis. HMMs are basically unsupervised models. However, in the most important applications, they are trained in a supervised manner. Training examples accompanied by labels corresponding to different classes are given as input and the set of parameters that maximize the joint probability of sequences and labels is estimated. A main problem with this approach is that, in the majority of the cases, labels are hard to find and thus the amount of training data is limited. On the other hand, there are plenty of unclassified (unlabeled) sequences deposited in the public databases that could potentially contribute to the training procedure. This approach is called semi-supervised learning and could be very helpful in many applications.'),
(82324, 2019, 'bio', 'G4HunterApps', 'Lacroix, Laurent', 'IBENS, ENS-CNRS UMR8197, Inserm U1024, Ecole Normale Supérieure, Paris Cedex 05, France.', 'France', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty951', '30445487', 'In order to help G4Hunter users and make it more accessible, I have developed a set of small applications within the Shiny/R framework.'),
(82325, 2019, 'bio', 'Pair Matcher (PaM): fast model-based optimization of treatment/case-control matches', 'Elhaik, Eran|Ryan, Desmond M', 'Department of Animal and Plant Sciences, University of Sheffield, Sheffield UK, UK.|Department of Animal and Plant Sciences, University of Sheffield, Sheffield UK, UK.', 'United Kingdom|United Kingdom', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty946', '30445488', 'In clinical trials, individuals are matched using demographic criteria, paired and then randomly assigned to treatment and control groups to determine a drugs efficacy. A chief cause for the irreproducibility of results across pilot to Phase-III trials is population stratification bias caused by the uneven distribution of ancestries in the treatment and control groups.'),
(82326, 2019, 'bio', 'GRNBoost2 and Arboreto: efficient and scalable inference of gene regulatory networks', 'Moerman, Thomas|Aibar Santos, Sara|Bravo González-Blas, Carmen|Simm, Jaak|Moreau, Yves|Aerts, Jan|Aerts, Stein', 'KU Leuven ESAT/STADIUS, VDA-lab.|Laboratory of Computational Biology, VIB Center for Brain & Disease Research.|Laboratory of Computational Biology, VIB Center for Brain & Disease Research.|IMEC Smart Applications and Innovation Services.|IMEC Smart Applications and Innovation Services.|KU Leuven ESAT/STADIUS, VDA-lab.|Laboratory of Computational Biology, VIB Center for Brain & Disease Research.', '-|-|-|-|-|-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty916', '30445495', 'Inferring a Gene Regulatory Network (GRN) from gene expression data is a computationally expensive task, exacerbated by increasing data sizes due to advances in high-throughput gene profiling technology, such as single-cell RNA-seq. To equip researchers with a toolset to infer GRNs from large expression datasets, we propose GRNBoost2 and the Arboreto framework. GRNBoost2 is an efficient algorithm for regulatory network inference using gradient boosting, based on the GENIE3 architecture. Arboreto is a computational framework that scales up GRN inference algorithms complying with this architecture. Arboreto includes both GRNBoost2 and an improved implementation of GENIE3, as a user-friendly open source Python package.'),
(82327, 2019, 'bio', 'Condition-specific series of metabolic sub-networks and its application for gene set enrichment analysis', 'Tran, Van Du T|Moretti, Sébastien|Coste, Alix T|Amorim-Vaz, Sara|Sanglard, Dominique|Pagni, Marco', 'Vital-IT Group, SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland.|Vital-IT Group, SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland.|Institute of Microbiology, University Hospital and University of Lausanne, CH-1015 Lausanne, Switzerland.|Institute of Microbiology, University Hospital and University of Lausanne, CH-1015 Lausanne, Switzerland.|Institute of Microbiology, University Hospital and University of Lausanne, CH-1015 Lausanne, Switzerland.|Vital-IT Group, SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty929', '30445518', 'Genome-scale metabolic networks and transcriptomic data represent complementary sources of knowledge about an organisms metabolism, yet their integration to achieve biological insight remains challenging.'),
(82328, 2019, 'bio', 'BGSA: a bit-parallel global sequence alignment toolkit for multi-core and many-core architectures', 'Zhang, Jikai|Lan, Haidong|Chan, Yuandong|Shang, Yuan|Schmidt, Bertil|Liu, Weiguo', 'School of Software, Shandong University, Jinan, China.|School of Software, Shandong University, Jinan, China.|School of Software, Shandong University, Jinan, China.|Supercomputer Center, Zhengzhou University, Zhengzhou City, China.|Institut für Informatik, Johannes Gutenberg University, Mainz, Germany.|School of Software, Shandong University, Jinan, China.', 'China|China|China|China|Germany|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty930', '30445566', 'Modern bioinformatics tools for analyzing large-scale NGS datasets often need to include fast implementations of core sequence alignment algorithms in order to achieve reasonable execution times. We address this need by presenting the BGSA toolkit for optimized implementations of popular bit-parallel global pairwise alignment algorithms on modern microprocessors.'),
(82329, 2019, 'bio', 'PAVOOC: designing CRISPR sgRNAs using 3D protein structures and functional domain annotations', 'Schaefer, Moritz|Clevert, Djork-Arné|Weiss, Bertram|Steffen, Andreas', 'Computer Science, TU Berlin, D-10623 Berlin, Germany.|Bioinformatics, Bayer AG, D-13342 Berlin, Germany.|Bioinformatics, Bayer AG, D-13342 Berlin, Germany.|Bioinformatics, Bayer AG, D-13342 Berlin, Germany.', 'Germany|Germany|Germany|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty935', '30445568', 'Single-guide RNAs (sgRNAs) targeting the same gene can significantly vary in terms of efficacy and specificity. PAVOOC (Prediction And Visualization of On- and Off-targets for CRISPR) is a web-based CRISPR sgRNA design tool that employs state of the art machine learning models to prioritize most effective candidate sgRNAs. In contrast to other tools, it maps sgRNAs to functional domains and protein structures and visualizes cut sites on corresponding protein crystal structures. Furthermore, PAVOOC supports homology-directed repair template generation for genome editing experiments and the visualization of the mutated amino acids in 3D.'),
(82330, 2019, 'bio', 'pyNVR: investigating factors affecting feature selection from scRNA-seq data for lineage reconstruction', 'Chen, Bob|Herring, Charles A|Lau, Ken S', 'Epithelial Biology Center, Vanderbilt University Medical Center, Nashville, TN, USA.|Epithelial Biology Center, Vanderbilt University Medical Center, Nashville, TN, USA.|Epithelial Biology Center, Vanderbilt University Medical Center, Nashville, TN, USA.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty950', '30445607', 'The emergence of single-cell RNA-sequencing has enabled analyses that leverage transitioning cell states to reconstruct pseudotemporal trajectories. Multidimensional data sparsity, zero inflation and technical variation necessitate the selection of high-quality features that feed downstream analyses. Despite the development of numerous algorithms for the unsupervised selection of biologically relevant features, their differential performance remains largely unaddressed.'),
(82331, 2019, 'bio', 'Genetic Neural Networks: an artificial neural network architecture for capturing gene expression relationships', 'Eetemadi, Ameen|Tagkopoulos, Ilias', 'Department of Computer Science, University of California, Davis, CA, USA.|Department of Computer Science, University of California, Davis, CA, USA.', 'United States|United States', 4, '-', 'Journal Article', '10.1093/bioinformatics/bty945', '30452523', 'Gene expression prediction is one of the grand challenges in computational biology. The availability of transcriptomics data combined with recent advances in artificial neural networks provide an unprecedented opportunity to create predictive models of gene expression with far reaching applications.'),
(82332, 2019, 'bio', 'Clustering of samples with a tree-shaped dependence structure, with an application to microscopic time lapse imaging', 'Failmezger, Henrik|Dursun, Ezgi|Dümcke, Sebastian|Endele, Max|Poron, Don|Schroeder, Timm|Krug, Anne|Tresch, Achim', 'Department of Molecular Pathology, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK.|Department of Medicine, Institute for Immunology, Biomedical Center, Ludwig-Maximilians-University Munich, Martinsried, Germany.|Department of medicine, Institute of Medical Statistics and Computational Biology, University Hospital Cologne, Cologne, Germany.|Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.|Department of medicine, Institute of Medical Statistics and Computational Biology, University Hospital Cologne, Cologne, Germany.|Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.|Department of Medicine, Institute for Immunology, Biomedical Center, Ludwig-Maximilians-University Munich, Martinsried, Germany.|Department of medicine, Institute of Medical Statistics and Computational Biology, University Hospital Cologne, Cologne, Germany.', 'United Kingdom|Germany|Germany|Switzerland|Germany|Switzerland|Germany|Germany', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty939', '30452534', 'Recent imaging technologies allow for high-throughput tracking of cells as they migrate, divide, express fluorescent markers and change their morphology. The interpretation of these data requires unbiased, efficient statistical methods that model the dynamics of cell phenotypes.'),
(82333, 2019, 'bio', 'CAARS: comparative assembly and annotation of RNA-Seq data', 'Rey, Carine|Veber, Philippe|Boussau, Bastien|Sémon, Marie', 'UnivLyon, Université Claude Bernard Lyon 1, ENS de Lyon, CNRS UMR, INSERM U1210, LBMC, F-69007, Lyon, France.|UnivLyon, Université Claude Bernard Lyon 1, CNRS, UMR, LBBE, F-69100, Villeurbanne, France.|UnivLyon, Université Claude Bernard Lyon 1, CNRS, UMR, LBBE, F-69100, Villeurbanne, France.|UnivLyon, Université Claude Bernard Lyon 1, ENS de Lyon, CNRS UMR, INSERM U1210, LBMC, F-69007, Lyon, France.', 'France|France|France|France', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty903', '30452539', 'RNA sequencing (RNA-Seq) is a widely used approach to obtain transcript sequences in non-model organisms, notably for performing comparative analyses. However, current bioinformatic pipelines do not take full advantage of pre-existing reference data in related species for improving RNA-Seq assembly, annotation and gene family reconstruction.'),
(82334, 2019, 'bio', 'DiPhiSeq: robust comparison of expression levels on RNA-Seq data with large sample sizes', 'Li, Jun|Lamere, Alicia T', 'Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, USA.|Mathematics Department, Bryant University, Smithfield, RI, USA.', 'United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty952', '30452547', 'In the analysis of RNA-Seq data, detecting differentially expressed (DE) genes has been a hot research area in recent years and many methods have been proposed. DE genes show different average expression levels in different sample groups, and thus can be important biological markers. While generally very successful, these methods need to be further tailored and improved for cancerous data, which often features quite diverse expression in the samples from the cancer group, and this diversity is much larger than that in the control group.'),
(82335, 2019, 'bio', 'BulkVis: a graphical viewer for Oxford nanopore bulk FAST5 files', 'Payne, Alexander|Holmes, Nadine|Rakyan, Vardhman|Loose, Matthew', 'School of Life Sciences, University of Nottingham, Nottingham, UK.|DeepSeq, School of Life Sciences, University of Nottingham, Nottingham, UK.|The Blizard Institute, Barts and The London School of Medicine and Dentistry and Centre for Genomic Health, LSI, Queen Mary University of London, London, UK.|School of Life Sciences, University of Nottingham, Nottingham, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 39, '-', 'Journal Article', '10.1093/bioinformatics/bty841', '30462145', 'The Oxford Nanopore Technologies (ONT) MinION is used for sequencing a wide variety of sample types with diverse methods of sample extraction. Nanopore sequencers output FAST5 files containing signal data subsequently base called to FASTQ format. Optionally, ONT devices can collect data from all sequencing channels simultaneously in a bulk FAST5 file enabling inspection of signal in any channel at any point. We sought to visualize this signal to inspect challenging or difficult to sequence samples.'),
(82336, 2019, 'bio', 'Directional allelic imbalance profiling and visualization from multi-sample data with RECUR', 'Jakubek, Yasminka A|San Lucas, F Anthony|Scheet, Paul', 'Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.|Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.|Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.', 'United States|United States|United States', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty885', '30462146', 'Genetic analysis of cancer regularly includes two or more samples from the same patient. Somatic copy number alterations leading to allelic imbalance (AI) play a critical role in cancer initiation and progression. Directional analysis and visualization of the alleles in imbalance in multi-sample settings allow for inference of recurrent mutations, providing insights into mutation rates, clonality and the genomic architecture and etiology of cancer.'),
(82337, 2019, 'bio', 'Application of machine learning techniques to tuberculosis drug resistance analysis', 'Kouchaki, Samaneh|Yang, Yang|Walker, Timothy M|Sarah Walker, A|Wilson, Daniel J|Peto, Timothy E A|Crook, Derrick W|CRyPTIC Consortium|Clifton, David A', 'Department of Engineering Science, Institute of Biomedical Engineering.|Department of Engineering Science, Institute of Biomedical Engineering.|Nuffield Department of Medicine, University of Oxford.|Nuffield Department of Medicine, University of Oxford.|Nuffield Department of Population Health, Big Data Institute, University of Oxford, Li Ka Shing Centre for Health Information and Discovery, Oxford, UK.|Nuffield Department of Medicine, University of Oxford.|Nuffield Department of Medicine, University of Oxford.|Nuffield Department of Medicine, University of Oxford.|Department of Engineering Science, Institute of Biomedical Engineering.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty949', '30462147', 'Timely identification of Mycobacterium tuberculosis (MTB) resistance to existing drugs is vital to decrease mortality and prevent the amplification of existing antibiotic resistance. Machine learning methods have been widely applied for timely predicting resistance of MTB given a specific drug and identifying resistance markers. However, they have been not validated on a large cohort of MTB samples from multi-centers across the world in terms of resistance prediction and resistance marker identification. Several machine learning classifiers and linear dimension reduction techniques were developed and compared for a cohort of 13 402 isolates collected from 16 countries across 6 continents and tested 11 drugs.'),
(82338, 2019, 'bio', 'The Microbiome Modeling Toolbox: from microbial interactions to personalized microbial communities', 'Baldini, Federico|Heinken, Almut|Heirendt, Laurent|Magnusdottir, Stefania|Fleming, Ronan M T|Thiele, Ines', 'Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette L-4362, Luxembourg.|Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette L-4362, Luxembourg.|Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette L-4362, Luxembourg.|Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette L-4362, Luxembourg.|Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette L-4362, Luxembourg.|Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette L-4362, Luxembourg.', 'Luxembourg|Luxembourg|Luxembourg|Luxembourg|Luxembourg|Luxembourg', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty941', '30462168', 'The application of constraint-based modeling to functionally analyze metagenomic data has been limited so far, partially due to the absence of suitable toolboxes.'),
(82339, 2019, 'bio', 'TAIJI: approaching experimental replicates-level accuracy for drug synergy prediction', 'Li, Hongyang|Hu, Shuai|Neamati, Nouri|Guan, Yuanfang', 'Department of Computational Medicine and Bioinformatics.|Department of Computational Medicine and Bioinformatics.|Department of Medicinal Chemistry, College of Pharmacy, Rogel Cancer Center and.|Department of Computational Medicine and Bioinformatics.', '-|-|-|-', 4, '-', 'Journal Article', '10.1093/bioinformatics/bty955', '30462169', 'Combination therapy is widely used in cancer treatment to overcome drug resistance. High-throughput drug screening is the standard approach to study the drug combination effects, yet it becomes impractical when the number of drugs under consideration is large. Therefore, accurate and fast computational tools for predicting drug synergistic effects are needed to guide experimental design for developing candidate drug pairs.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(82340, 2019, 'bio', 'DeepCrystal: a deep learning framework for sequence-based protein crystallization prediction', 'Elbasir, Abdurrahman|Moovarkumudalvan, Balasubramanian|Kunji, Khalid|Kolatkar, Prasanna R|Mall, Raghvendra|Bensmail, Halima', 'College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.|Qatar Biomedical Research Institute and Hamad Bin Khalifa University, Doha, Qatar.|Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.|Qatar Biomedical Research Institute and Hamad Bin Khalifa University, Doha, Qatar.|Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.|College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.', 'Qatar|Qatar|Qatar|Qatar|Qatar|Qatar', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty953', '30462171', 'Protein structure determination has primarily been performed using X-ray crystallography. To overcome the expensive cost, high attrition rate and series of trial-and-error settings, many in-silico methods have been developed to predict crystallization propensities of proteins based on their sequences. However, the majority of these methods build their predictors by extracting features from protein sequences, which is computationally expensive and can explode the feature space. We propose DeepCrystal, a deep learning framework for sequence-based protein crystallization prediction. It uses deep learning to identify proteins which can produce diffraction-quality crystals without the need to manually engineer additional biochemical and structural features from sequence. Our model is based on convolutional neural networks, which can exploit frequently occurring k-mers and sets of k-mers from the protein sequences to distinguish proteins that will result in diffraction-quality crystals from those that will not.'),
(82341, 2019, 'bio', 'circtools-a one-stop software solution for circular RNA research', 'Jakobi, Tobias|Uvarovskii, Alexey|Dieterich, Christoph', 'Department of Cardiology, Angiology, and Pneumology, University Hospital Heidelberg.|Department of Cardiology, Angiology, and Pneumology, University Hospital Heidelberg.|Department of Cardiology, Angiology, and Pneumology, University Hospital Heidelberg.', '-|-|-', 10, '-', 'Journal Article', '10.1093/bioinformatics/bty948', '30462173', 'Circular RNAs (circRNAs) originate through back-splicing events from linear primary transcripts, are resistant to exonucleases, are not polyadenylated and have been shown to be highly specific for cell type and developmental stage. CircRNA detection starts from high-throughput sequencing data and is a multi-stage bioinformatics process yielding sets of potential circRNA candidates that require further analyses. While a number of tools for the prediction process already exist, publicly available analysis tools for further characterization are rare. Our work provides researchers with a harmonized workflow that covers different stages of in silico circRNA analyses, from prediction to first functional insights.'),
(82342, 2019, 'bio', 'Bias in the intervention in prediction measure in random forests: illustrations and recommendations', 'Nembrini, Stefano', 'Department of Cardiology, Angiology, and Pneumology, University Hospital Heidelberg.', '-', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty959', '30462187', 'Supplementary data are available at Bioinformatics online.'),
(82343, 2019, 'bio', 'ProteomicsBrowser: MS/proteomics data visualization and investigation', 'Peng, Gang|Wilson, Rashaun|Tang, Yishuo|Lam, TuKiet T|Nairn, Angus C|Williams, Kenneth|Zhao, Hongyu', 'Department of Biostatistics, School of Medicine, Yale University, New Haven, CT, USA.|Department of Psychiatry, School of Medicine, Yale University, New Haven, CT, USA.|Department of Genetics, School of Medicine, Yale University, New Haven, CT, USA.|Department of Molecular Biophysics and Biochemistry, School of Medicine, Yale University, New Haven, CT, USA.|Department of Psychiatry, School of Medicine, Yale University, New Haven, CT, USA.|Department of Molecular Biophysics and Biochemistry, School of Medicine, Yale University, New Haven, CT, USA.|Department of Biostatistics, School of Medicine, Yale University, New Haven, CT, USA.', 'United States|United States|United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty958', '30462190', 'Large-scale, quantitative proteomics data are being generated at ever increasing rates by high-throughput, mass spectrometry technologies. However, due to the complexity of these large datasets as well as the increasing numbers of post-translational modifications (PTMs) that are being identified, developing effective methods for proteomic visualization has been challenging. ProteomicsBrowser was designed to meet this need for comprehensive data visualization. Using peptide information files exported from mass spectrometry search engines or quantitative tools as input, the peptide sequences are aligned to an internal protein database such as UniProtKB. Each identified peptide ion including those with PTMs is then visualized along the parent protein in the Browser. A unique property of ProteomicsBrowser is the ability to combine overlapping peptides in different ways to focus analysis of sequence coverage, charge state or PTMs. ProteomicsBrowser includes other useful functions, such as a data filtering tool and basic statistical analyses to qualify quantitative data.'),
(82344, 2019, 'bio', 'RT States: systematic annotation of the human genome using cell type-specific replication timing programs', 'Poulet, Axel|Li, Ben|Dubos, Tristan|Rivera-Mulia, Juan Carlos|Gilbert, David M|Qin, Zhaohui S', 'Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA.|Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA.|Laboratoire de Génétique, CHRU-Nancy, Nancy, France.|Department of Biological Science, Center for Genomics and Personalized Medicine, Florida State University, Tallahassee, FL, USA.|Department of Biological Science, Center for Genomics and Personalized Medicine, Florida State University, Tallahassee, FL, USA.|Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA.', 'United States|United States|France|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty957', '30475980', 'The replication timing (RT) program has been linked to many key biological processes including cell fate commitment, 3D chromatin organization and transcription regulation. Significant technology progress now allows to characterize the RT program in the entire human genome in a high-throughput and high-resolution fashion. These experiments suggest that RT changes dynamically during development in coordination with gene activity. Since RT is such a fundamental biological process, we believe that an effective quantitative profile of the local RT program from a diverse set of cell types in various developmental stages and lineages can provide crucial biological insights for a genomic locus.'),
(82345, 2019, 'bio', 'SGTK: a toolkit for visualization and assessment of scaffold graphs', 'Kunyavskaya, Olga|Prjibelski, Andrey D', 'Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg 199034, Russia.|Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg 199034, Russia.', '-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty956', '30475983', 'Scaffolding is an important step in every genome assembly pipeline, which allows to order contigs into longer sequences using various types of linkage information, such as mate-pair libraries and long reads. In this work, we operate with a notion of a scaffold graph-a graph, vertices of which correspond to the assembled contigs and edges represent connections between them. We present a software package called Scaffold Graph ToolKit that allows to construct and visualize scaffold graphs using different kinds of sequencing data. We show that the scaffold graph appears to be useful for analyzing and assessing genome assemblies, and demonstrate several use cases that can be helpful for both assembly software developers and their users.'),
(82346, 2019, 'bio', 'A plugin for the Ensembl Variant Effect Predictor that uses MaxEntScan to predict variant spliceogenicity', 'Shamsani, Jannah|Kazakoff, Stephen H|Armean, Irina M|McLaren, Will|Parsons, Michael T|Thompson, Bryony A|OMara, Tracy A|Hunt, Sarah E|Waddell, Nicola|Spurdle, Amanda B', 'Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane QLD, Australia.|Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane QLD, Australia.|European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.|Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane QLD, Australia.|Centre for Epidemiology and Biostatistics, School of Population and Global Health, University of Melbourne, Melbourne VIC, Australia.|Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane QLD, Australia.|European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.|Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane QLD, Australia.|Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane QLD, Australia.', 'Australia|Australia|United Kingdom|United Kingdom|Australia|Australia|Australia|United Kingdom|Australia|Australia', 3, '-', 'Journal Article', '10.1093/bioinformatics/bty960', '30475984', 'Assessing the pathogenicity of genetic variants can be a complex and challenging task. Spliceogenic variants, which alter mRNA splicing, may yield mature transcripts that encode non-functional protein products, an important predictor of Mendelian disease risk. However, most variant annotation tools do not adequately assess spliceogenicity outside the native splice site and thus the disease-causing potential of variants in other intronic and exonic regions is often overlooked. Here, we present a plugin for the Ensembl Variant Effect Predictor that packages MaxEntScan and extends its functionality to provide splice site predictions using a maximum entropy model. The plugin incorporates a sliding window algorithm to predict splice site loss or gain for any variant that overlaps a transcript feature. We also demonstrate the utility of the plugin by comparing our predictions to two mRNA splicing datasets containing several cancer-susceptibility genes.'),
(82347, 2019, 'bio', 'GUV-AP: multifunctional FIJI-based tool for quantitative image analysis of Giant Unilamellar Vesicles', 'Sych, Taras|Schubert, Thomas|Vauchelles, Romain|Madl, Josef|Omidvar, Ramin|Thuenauer, Roland|Richert, Ludovic|Mély, Yves|Römer, Winfried', 'Faculty of Biology, Albert Ludwigs University Freiburg, Schänzlestraße 1, Freiburg im Breisgau, Germany.|Faculty of Biology, Albert Ludwigs University Freiburg, Schänzlestraße 1, Freiburg im Breisgau, Germany.|Laboratory of Bioimaging and Pathologies, UMR 7021 CNRS, Faculty of Pharmacy, University of Strasbourg, 74 route du Rhin, Illkirch, France.|Faculty of Biology, Albert Ludwigs University Freiburg, Schänzlestraße 1, Freiburg im Breisgau, Germany.|Faculty of Biology, Albert Ludwigs University Freiburg, Schänzlestraße 1, Freiburg im Breisgau, Germany.|Faculty of Biology, Albert Ludwigs University Freiburg, Schänzlestraße 1, Freiburg im Breisgau, Germany.|Laboratory of Bioimaging and Pathologies, UMR 7021 CNRS, Faculty of Pharmacy, University of Strasbourg, 74 route du Rhin, Illkirch, France.|Laboratory of Bioimaging and Pathologies, UMR 7021 CNRS, Faculty of Pharmacy, University of Strasbourg, 74 route du Rhin, Illkirch, France.|Faculty of Biology, Albert Ludwigs University Freiburg, Schänzlestraße 1, Freiburg im Breisgau, Germany.', 'Germany|Germany|France|Germany|Germany|Germany|France|France|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty962', '30475993', 'Giant Unilamellar Vesicles (GUVs) are widely used synthetic membrane systems that mimic native membranes and cellular processes. Various fluorescence imaging techniques can be employed for their characterization. In order to guarantee a fast and unbiased analysis of imaging data, the development of automated recognition and processing steps is required.'),
(82348, 2019, 'bio', 'GenCoF: a graphical user interface to rapidly remove human genome contaminants from metagenomic datasets', 'Czajkowski, Matthew D|Vance, Daniel P|Frese, Steven A|Casaburi, Giorgio', 'Evolve BioSystems, Inc., Davis, CA, USA.|Evolve BioSystems, Inc., Davis, CA, USA.|Evolve BioSystems, Inc., Davis, CA, USA.|Evolve BioSystems, Inc., Davis, CA, USA.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty963', '30475995', 'The removal of human genomic reads from shotgun metagenomic sequencing is a critical step in protecting subject privacy. Freely available tools addressing this issue require advanced programing knowledge or are limited by analytical time and data load due to their server-based nature. Here, we compared the most cited tools for host-DNA removal using synthetic and real metagenomic datasets. Then, we integrated the most efficient pipeline in a graphical user interface to make these tools available without command line use. This interface, GenCoF, rapidly removes human genome contaminants from metagenomic datasets. Additionally, the tool offers quality-filtering, data reduction and interactive modification of any parameter in order to customize the analysis. GenCoF offers both quality and host-associated filtering in a non-commercial, freely available tool in a local, interactive and easy-to-use interface.'),
(82349, 2019, 'bio', 'Integrate multiple traits to detect novel trait-gene association using GWAS summary data with an adaptive test approach', 'Guo, Bin|Wu, Baolin', 'Evolve BioSystems, Inc., Davis, CA, USA.|Evolve BioSystems, Inc., Davis, CA, USA.', 'United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty961', '30476000', 'Genetics hold great promise to precision medicine by tailoring treatment to the individual patient based on their genetic profiles. Toward this goal, many large-scale genome-wide association studies (GWAS) have been performed in the last decade to identify genetic variants associated with various traits and diseases. They have successfully identified tens of thousands of disease-related variants. However they have explained only a small proportion of the overall trait heritability for most traits and are of very limited clinical use. This is partly owing to the small effect sizes of most genetic variants, and the common practice of testing association between one trait and one genetic variant at a time in most GWAS, even when multiple related traits are often measured for each individual. Increasing evidence suggests that many genetic variants can influence multiple traits simultaneously, and we can gain more power by testing association of multiple traits simultaneously. It is appealing to develop novel multi-trait association test methods that need only GWAS summary data, since it is generally very hard to access the individual-level GWAS phenotype and genotype data.'),
(82350, 2019, 'bio', 'SkeletalVis: an exploration and meta-analysis data portal of cross-species skeletal transcriptomics data', 'Soul, Jamie|Hardingham, Tim E|Boot-Handford, Ray P|Schwartz, Jean-Marc', 'Division of Evolution & Genomic Sciences, University of Manchester, Manchester, MUK.|Wellcome Centre for Cell-Matrix Research, Division of Cell-Matrix Biology and Regenerative Medicine, Faculty of Biology Medicine and Health, University of Manchester, Manchester, MUK.|Wellcome Centre for Cell-Matrix Research, Division of Cell-Matrix Biology and Regenerative Medicine, Faculty of Biology Medicine and Health, University of Manchester, Manchester, MUK.|Division of Evolution & Genomic Sciences, University of Manchester, Manchester, MUK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 3, '-', 'Journal Article', '10.1093/bioinformatics/bty947', '30481257', 'Skeletal diseases are prevalent in society, but improved molecular understanding is required to formulate new therapeutic strategies. Large and increasing quantities of available skeletal transcriptomics experiments give the potential for mechanistic insight of both fundamental skeletal biology and skeletal disease. However, no current repository provides access to processed, readily interpretable analysis of this data. To address this, we have developed SkeletalVis, an exploration portal for skeletal gene expression experiments.'),
(82351, 2019, 'bio', 'An equivariant Bayesian convolutional network predicts recombination hotspots and accurately resolves binding motifs', 'Brown, Richard C|Lunter, Gerton', 'Division of Evolution & Genomic Sciences, University of Manchester, Manchester, MUK.|Division of Evolution & Genomic Sciences, University of Manchester, Manchester, MUK.', 'United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty964', '30481258', 'Convolutional neural networks (CNNs) have been tremendously successful in many contexts, particularly where training data are abundant and signal-to-noise ratios are large. However, when predicting noisily observed phenotypes from DNA sequence, each training instance is only weakly informative, and the amount of training data is often fundamentally limited, emphasizing the need for methods that make optimal use of training data and any structure inherent in the process.'),
(82352, 2019, 'bio', 'EpistasisRank and EpistasisKatz: interaction network centrality methods that integrate prior knowledge networks', 'Parvandeh, Saeid|McKinney, Brett A', 'Tandy School of Computer Science.|Tandy School of Computer Science.', '-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty965', '30481259', 'An important challenge in gene expression analysis is to improve hub gene selection to enrich for biological relevance or improve classification accuracy for a given phenotype. In order to incorporate phenotypic context into co-expression, we recently developed an epistasis-expression network centrality method that blends the importance of gene-gene interactions (epistasis) and main effects of genes. Further blending of prior knowledge from functional interactions has the potential to enrich for relevant genes and stabilize classification.'),
(82353, 2019, 'bio', 'PhastWeb: a web interface for evolutionary conservation scoring of multiple sequence alignments using phastCons and phyloP', 'Ramani, Ritika|Krumholz, Katie|Huang, Yi-Fei|Siepel, Adam', 'Tandy School of Computer Science.|Tandy School of Computer Science.|Tandy School of Computer Science.|Tandy School of Computer Science.', '-|-|-|-', 8, '-', 'Journal Article', '10.1093/bioinformatics/bty966', '30481262', 'The Phylogenetic Analysis with Space/Time models (PHAST) package is a widely used software package for comparative genomics that has been freely available for download since 2002. Here, we introduce a web interface (phastWeb) that makes it possible to use two of the most popular programs in PHAST, phastCons and phyloP, without downloading and installing the PHAST software. This interface allows users to upload a sequence alignment and either upload a corresponding phylogeny or have one estimated from the alignment. After processing, users can visualize alignments and conservation scores as genome browser tracks and download estimated tree models and raw scores for further analysis. Altogether, this resource makes key features of the PHAST package conveniently available to a broad audience.'),
(82354, 2019, 'bio', 'Computational modeling reveals MAP3K8 as mediator of resistance to vemurafenib in thyroid cancer stem cells', 'Gianì, Fiorenza|Russo, Giulia|Pennisi, Marzio|Sciacca, Laura|Frasca, Francesco|Pappalardo, Francesco', 'Endocrinology Unit, Department of Clinical and Molecular BioMedicine, Garibaldi-Nesima Medical Center.|Department of Biomedical and Biotechnological Sciences.|Department of Mathematics and Computer Science.|Endocrinology Unit, Department of Clinical and Molecular BioMedicine, Garibaldi-Nesima Medical Center.|Endocrinology Unit, Department of Clinical and Molecular BioMedicine, Garibaldi-Nesima Medical Center.|Department of Drug Sciences, University of Catania, Catania, Italy.', '-|-|-|-|-|Italy', 8, '-', 'Journal Article', '10.1093/bioinformatics/bty969', '30481266', 'Val600Glu (V600E) mutation is the most common BRAF mutation detected in thyroid cancer. Hence, recent research efforts have been performed trying to explore several inhibitors of the V600E mutation-containing BRAF kinase as potential therapeutic options in thyroid cancer refractory to standard interventions. Among them, vemurafenib is a selective BRAF inhibitor approved by Food and Drug Administration for clinical practice. Unfortunately, vemurafenib often displays limited efficacy in poorly differentiated and anaplastic thyroid carcinomas probably because of intrinsic and/or acquired resistance mechanisms. In this view, cancer stem cells (CSCs) may represent a possible mechanism of resistance to vemurafenib, due to their self-renewal and chemo resistance properties.'),
(82355, 2019, 'bio', 'BlendMol: advanced macromolecular visualization in Blender', 'Durrant, Jacob D', 'Department of Drug Sciences, University of Catania, Catania, Italy.', 'Italy', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty968', '30481283', 'Programs such as VMD and PyMOL are excellent tools for analyzing macromolecular structures, but they do not implement many of the advanced rendering techniques common in the film and video-game industries. In contrast, the open-source program Blender is a general-purpose tool for industry-standard rendering/visualization, but its user interface is poorly suited for rigorous scientific analysis. We present BlendMol, a Blender plugin that imports VMD or PyMOL scenes into Blender. BlendMol-generated images are well suited for use in manuscripts, outreach programs, websites and classes.'),
(82356, 2019, 'bio', 'A structural homology approach for computational protein design with flexible backbone', 'Simoncini, David|Zhang, Kam Y J|Schiex, Thomas|Barbe, Sophie', 'Laboratoire dIngénierie des Systèmes Biologiques et des Procédés, LISBP, Université de Toulouse, CNRS, INRA, INSA, F Toulouse cedex 04, France.|Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, Yokohama, Kanagawa, Japan.|Institut de recherche en informatique de Toulouse, UMR 5505-CNRS, Université de Toulouse, Cedex 9, France.|Laboratoire dIngénierie des Systèmes Biologiques et des Procédés, LISBP, Université de Toulouse, CNRS, INRA, INSA, F Toulouse cedex 04, France.', 'France|Japan|France|France', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty975', '30496341', 'Structure-based Computational Protein design (CPD) plays a critical role in advancing the field of protein engineering. Using an all-atom energy function, CPD tries to identify amino acid sequences that fold into a target structure and ultimately perform a desired function. Energy functions remain however imperfect and injecting relevant information from known structures in the design process should lead to improved designs.'),
(82357, 2019, 'bio', 'Genome-wide search of nucleosome patterns using visual analytics', 'Santamaría, Rodrigo|Therón, Roberto|Durán, Laura|García, Alicia|González, Sara|Sánchez, Mar|Antequera, Francisco', 'Departmento de Informática y Automática, Universidad de Salamanca, Salamanca, Spain.|Departmento de Informática y Automática, Universidad de Salamanca, Salamanca, Spain.|Instituto de Biología Funcional y Genómica, Unidad de Dinámica del Genoma y Epigenética, CSIC.USAL, Salamanca, Spain.|Instituto de Biología Funcional y Genómica, Unidad de Dinámica del Genoma y Epigenética, CSIC.USAL, Salamanca, Spain.|Instituto de Biología Funcional y Genómica, Unidad de Dinámica del Genoma y Epigenética, CSIC.USAL, Salamanca, Spain.|Instituto de Biología Funcional y Genómica, Unidad de Dinámica del Genoma y Epigenética, CSIC.USAL, Salamanca, Spain.|Instituto de Biología Funcional y Genómica, Unidad de Dinámica del Genoma y Epigenética, CSIC.USAL, Salamanca, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty971', '30496344', 'The Burrows-Wheeler transform (BWT) is widely used for the fast alignment of high-throughput sequence data. This method also has potential applications in other areas of bioinformatics, and it can be specially useful for the fast searching of patterns on coverage data from different sources.'),
(82358, 2019, 'bio', 'GUESS: projecting machine learning scores to well-calibrated probability estimates for clinical decision-making', 'Schwarz, Johanna|Heider, Dominik', 'Instituto de Biología Funcional y Genómica, Unidad de Dinámica del Genoma y Epigenética, CSIC.USAL, Salamanca, Spain.|Instituto de Biología Funcional y Genómica, Unidad de Dinámica del Genoma y Epigenética, CSIC.USAL, Salamanca, Spain.', 'Spain|Spain', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty984', '30496351', 'Clinical decision support systems have been applied in numerous fields, ranging from cancer survival toward drug resistance prediction. Nevertheless, clinical decision support systems typically have a caveat: many of them are perceived as black-boxes by non-experts and, unfortunately, the obtained scores cannot usually be interpreted as class probability estimates. In probability-focused medical applications, it is not sufficient to perform well with regards to discrimination and, consequently, various calibration methods have been developed to enable probabilistic interpretation. The aims of this study were (i) to develop a tool for fast and comparative analysis of different calibration methods, (ii) to demonstrate their limitations for the use on clinical data and (iii) to introduce our novel method GUESS.'),
(82359, 2019, 'bio', 'On the biases in predictions of protein stability changes upon variations: the INPS test case', 'Montanucci, Ludovica|Savojardo, Castrense|Martelli, Pier Luigi|Casadio, Rita|Fariselli, Piero', 'Department of Comparative Biomedicine and Food Science, University of Padova, Legnaro, Padova, Italy.|Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy.|Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy.|Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy.|Department of Medical Sciences, University of Torino, Italy.', 'Italy|Italy|Italy|Italy|Italy', 4, '-', 'Journal Article', '10.1093/bioinformatics/bty979', '30496382', '-'),
(82360, 2019, 'bio', 'DiTaxa: nucleotide-pair encoding of 16S rRNA for host phenotype and biomarker detection', 'Asgari, Ehsaneddin|Münch, Philipp C|Lesker, Till R|McHardy, Alice C|Mofrad, Mohammad R K', 'Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, Berkeley, CA, USA.|Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Brunswick, Germany.|Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Brunswick, Germany.|Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Brunswick, Germany.|Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, Berkeley, CA, USA.', 'United States|Germany|Germany|Germany|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty954', '30500871', 'Identifying distinctive taxa for micro-biome-related diseases is considered key to the establishment of diagnosis and therapy options in precision medicine and imposes high demands on the accuracy of micro-biome analysis techniques. We propose an alignment- and reference- free subsequence based 16S rRNA data analysis, as a new paradigm for micro-biome phenotype and biomarker detection. Our method, called DiTaxa, substitutes standard operational taxonomic unit (OTU)-clustering by segmenting 16S rRNA reads into the most frequent variable-length subsequences. We compared the performance of DiTaxa to the state-of-the-art methods in phenotype and biomarker detection, using human-associated 16S rRNA samples for periodontal disease, rheumatoid arthritis and inflammatory bowel diseases, as well as a synthetic benchmark dataset. DiTaxa performed competitively to the k-mer based state-of-the-art approach in phenotype prediction while outperforming the OTU-based state-of-the-art approach in finding biomarkers in both resolution and coverage evaluated over known links from literature and synthetic benchmark datasets.'),
(82361, 2019, 'bio', 'Computational functional genomics-based reduction of disease-related gene sets to their key components', 'Lippmann, Catharina|Ultsch, Alfred|Lxc3xb6tsch, Jxc3xb6rn', 'Fraunhofer Institute of Molecular Biology and Applied Ecology - Project Group Translational Medicine and Pharmacology (IME-TMP), Frankfurt am Main, Germany.|DataBionics Research Group, University of Marburg, Marburg, Germany.|Fraunhofer Institute of Molecular Biology and Applied Ecology - Project Group Translational Medicine and Pharmacology (IME-TMP), Frankfurt am Main, Germany.', 'Germany|Germany|Germany', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty986', '30500872', 'The genetic architecture of diseases becomes increasingly known. This raises difficulties in picking suitable targets for further research among an increasing number of candidates. Although expression based methods of gene set reduction are applied to laboratory-derived genetic data, the analysis of topical sets of genes gathered from knowledge bases requires a modified approach as no quantitative information about gene expression is available.'),
(82362, 2019, 'bio', 'ConDo: protein domain boundary prediction using coevolutionary information', 'Hong, Seung Hwan|Joo, Keehyoung|Lee, Jooyoung', 'School of Computational Sciences.|Center for Advanced Computation, Korea Institute for Advanced Study, Korea.|School of Computational Sciences.', '-|-|-', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty973', '30500873', 'Domain boundary prediction is one of the most important problems in the study of protein structure and function. Many sequence-based domain boundary prediction methods are either template-based or machine learning (ML) based. ML-based methods often perform poorly due to their use of only local (i.e. short-range) features. These conventional features such as sequence profiles, secondary structures and solvent accessibilities are typically restricted to be within 20 residues of the domain boundary candidate.'),
(82363, 2019, 'bio', 'PPaxe: easy extraction of protein occurrence and interactions from the scientific literature', 'Castillo-Lara, S|Abril, J F', 'School of Computational Sciences.|School of Computational Sciences.', '-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty988', '30500875', 'Protein-protein interactions (PPIs) are very important to build models for understanding many biological processes. Although several databases hold many of these interactions, exploring them, selecting those relevant for a given subject and contextualizing them can be a difficult task for researchers. Extracting PPIs directly from the scientific literature can be very helpful for providing such context, as the sentences describing these interactions may give insights to researchers in helpful ways.'),
(82364, 2019, 'bio', 'TADOSS: computational estimation of tandem domain swap stability', 'Lafita, Aleix|Tian, Pengfei|Best, Robert B|Bateman, Alex', 'European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.|Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA.|Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA.|European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty974', '30500878', 'Proteins with highly similar tandem domains have shown an increased propensity for misfolding and aggregation. Several molecular explanations have been put forward, such as swapping of adjacent domains, but there is a lack of computational tools to systematically analyze them. We present the TAndem DOmain Swap Stability predictor (TADOSS), a method to computationally estimate the stability of tandem domain-swapped conformations from the structures of single domains, based on previous coarse-grained simulation studies. The tool is able to discriminate domains susceptible to domain swapping and to identify structural regions with high propensity to form hinge loops. TADOSS is a scalable method and suitable for large scale analyses.'),
(82365, 2019, 'bio', 'CRISPR-Local: a local single-guide RNA (sgRNA) design tool for non-reference plant genomes', 'Sun, Jiamin|Liu, Hao|Liu, Jianxiao|Cheng, Shikun|Peng, Yong|Zhang, Qinghua|Yan, Jianbing|Liu, Hai-Jun|Chen, Ling-Ling', 'National Key Laboratory of Crop Genetic Improvement.|National Key Laboratory of Crop Genetic Improvement.|National Key Laboratory of Crop Genetic Improvement.|Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, Peoples Republic of China.|National Key Laboratory of Crop Genetic Improvement.|National Key Laboratory of Crop Genetic Improvement.|National Key Laboratory of Crop Genetic Improvement.|National Key Laboratory of Crop Genetic Improvement.|National Key Laboratory of Crop Genetic Improvement.', '-|-|-|China|-|-|-|-|-', 5, '-', 'Journal Article', '10.1093/bioinformatics/bty970', '30500879', 'CRISPR-Local is a high-throughput local tool for designing single-guide RNAs (sgRNAs) in plants and other organisms that factors in genetic variation and is optimized to generate genome-wide sgRNAs. CRISPR-Local outperforms other sgRNA design tools in the following respects: (i) designing sgRNAs suitable for non-reference varieties, (ii) screening for sgRNAs that are capable of simultaneously targeting multiple genes, (iii) saving computational resources by avoiding repeated calculations from multiple submissions and (iv) running offline, with both command-line and graphical user interface modes and the ability to export multiple formats for further batch analysis or visualization. We have applied CRISPR-Local to 71 public plant genomes, using both CRISPR/Cas9 and CRISPR/cpf1 systems.'),
(82366, 2019, 'bio', 'DeeReCT-PolyA: a robust and generic deep learning method for PAS identification', 'Xia, Zhihao|Li, Yu|Zhang, Bin|Li, Zhongxiao|Hu, Yuhui|Chen, Wei|Gao, Xin', 'Department of Computer Science and Engineering (CSE), Washington University in St Louis, St Louis, MO, USA.|Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Thuwal, Saudi Arabia.|Department of Biology, Southern University of Science and Technology (SUSTC), Shenzhen, China.|Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Thuwal, Saudi Arabia.|Department of Biology, Southern University of Science and Technology (SUSTC), Shenzhen, China.|Department of Biology, Southern University of Science and Technology (SUSTC), Shenzhen, China.|Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Thuwal, Saudi Arabia.', 'United States|Saudi Arabia|China|Saudi Arabia|China|China|Saudi Arabia', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty991', '30500881', 'Polyadenylation is a critical step for gene expression regulation during the maturation of mRNA. An accurate and robust method for poly(A) signals (PASs) identification is not only desired for the purpose of better transcripts end annotation, but can also help us gain a deeper insight of the underlying regulatory mechanism. Although many methods have been proposed for PAS recognition, most of them are PAS motif- and human-specific, which leads to high risks of overfitting, low generalization power, and inability to reveal the connections between the underlying mechanisms of different mammals.'),
(82367, 2019, 'bio', 'Detection of rare disease variants in extended pedigrees using RVS', 'Sherman, Thomas|Fu, Jack|Scharpf, Robert B|Bureau, Alexandre|Ruczinski, Ingo', 'Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health.|Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health.|Department of Oncology, Johns Hopkins School of Medicine, Baltimore, MD, USA.|Département de Médecine Sociale et Préventive, Université Laval, Québec, QC, Canada.|Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health.', 'United States|United States|United States|Canada|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty976', '30500888', 'Family-based sequencing studies enable researchers to identify highly penetrant genetic variants too rare to be tested in conventional case-control studies, by studying co-segregation of variant and disease phenotypes. When multiple affected subjects in a family are sequenced, the probability that a variant or a set of variants is shared identical-by-descent by some or all affected relatives provides evidence against the null hypothesis of complete absence of linkage and association. The Rare Variant Sharing software package RVS implements a suite of tools to assess association and linkage between rare genetic variants and a dichotomous disease indicator in family pedigrees.'),
(82368, 2019, 'bio', 'Validation of electron microscopy initial models via small angle X-ray scattering curves', 'Jiménez, Amaya|Jonic, Slavica|Majtner, Tomas|Otón, Joaquín|Vilas, Jose Luis|Maluenda, David|Mota, Javier|Ramírez-Aportela, Erney|Martínez, Marta|Rancel, Yaiza|Segura, Joan|Sánchez-García, Ruben|Melero, Roberto|Del Cano, Laura|Conesa, Pablo|Skjaerven, Lars|Marabini, Roberto|Carazo, Jose M|Sorzano, Carlos Oscar S', 'Biocomputing Unit, Centro Nac. Biotecnología (CSIC), Cantoblanco, Madrid, Spain.|UMR CNRS 7590, Muséum National d ´Histoire Naturelle, IRD, Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, IMPMC, Sorbonne Université, Paris, France.|Biocomputing Unit, Centro Nac. Biotecnología (CSIC), Cantoblanco, Madrid, Spain.|Biocomputing Unit, Centro Nac. Biotecnología (CSIC), Cantoblanco, Madrid, Spain.|Biocomputing Unit, Centro Nac. Biotecnología (CSIC), Cantoblanco, Madrid, Spain.|Biocomputing Unit, Centro Nac. Biotecnología (CSIC), Cantoblanco, Madrid, Spain.|Biocomputing Unit, Centro Nac. Biotecnología (CSIC), Cantoblanco, Madrid, Spain.|Biocomputing Unit, Centro Nac. Biotecnología (CSIC), Cantoblanco, Madrid, Spain.|Biocomputing Unit, Centro Nac. Biotecnología (CSIC), Cantoblanco, Madrid, Spain.|Biocomputing Unit, Centro Nac. Biotecnología (CSIC), Cantoblanco, Madrid, Spain.|Biocomputing Unit, Centro Nac. Biotecnología (CSIC), Cantoblanco, Madrid, Spain.|Biocomputing Unit, Centro Nac. Biotecnología (CSIC), Cantoblanco, Madrid, Spain.|Biocomputing Unit, Centro Nac. Biotecnología (CSIC), Cantoblanco, Madrid, Spain.|Biocomputing Unit, Centro Nac. Biotecnología (CSIC), Cantoblanco, Madrid, Spain.|Biocomputing Unit, Centro Nac. Biotecnología (CSIC), Cantoblanco, Madrid, Spain.|Department of Biomedicine, University of Bergen, Bergen, Norway.|Biocomputing Unit, Centro Nac. Biotecnología (CSIC), Cantoblanco, Madrid, Spain.|Biocomputing Unit, Centro Nac. Biotecnología (CSIC), Cantoblanco, Madrid, Spain.|Biocomputing Unit, Centro Nac. Biotecnología (CSIC), Cantoblanco, Madrid, Spain.', 'Spain|France|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Norway|Sp', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty985', '30500892', 'Cryo electron microscopy (EM) is currently one of the main tools to reveal the structural information of biological macromolecules. The re-construction of three-dimensional (3D) maps is typically carried out following an iterative process that requires an initial estimation of the 3D map to be refined in subsequent steps. Therefore, its determination is key in the quality of the final results, and there are cases in which it is still an open issue in single particle analysis (SPA). Small angle X-ray scattering (SAXS) is a well-known technique applied to structural biology. It is useful from small nanostructures up to macromolecular ensembles for its ability to obtain low resolution information of the biological sample measuring its X-ray scattering curve. These curves, together with further analysis, are able to yield information on the sizes, shapes and structures of the analyzed particles.'),
(82369, 2019, 'bio', 'GWASpro: a high-performance genome-wide association analysis server', 'Kim, Bongsong|Dai, Xinbin|Zhang, Wenchao|Zhuang, Zhaohong|Sanchez, Darlene L|Lübberstedt, Thomas|Kang, Yun|Udvardi, Michael K|Beavis, William D|Xu, Shizhong|Zhao, Patrick X', 'Noble Research Institute, Ardmore, OK, USA.|Noble Research Institute, Ardmore, OK, USA.|Noble Research Institute, Ardmore, OK, USA.|Noble Research Institute, Ardmore, OK, USA.|AgriLife Research Center, Texas A&M University, Beaumont, TX, USA.|Department of Agronomy, Iowa State University, Ames, IA, USA.|Noble Research Institute, Ardmore, OK, USA.|Noble Research Institute, Ardmore, OK, USA.|Department of Agronomy, Iowa State University, Ames, IA, USA.|Department of Botany and Plant Sciences, University of California, Riverside, CA, USA.|Noble Research Institute, Ardmore, OK, 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/bty989', '30508039', 'We present GWASpro, a high-performance web server for the analyses of large-scale genome-wide association studies (GWAS). GWASpro was developed to provide data analyses for large-scale molecular genetic data, coupled with complex replicated experimental designs such as found in plant science investigations and to overcome the steep learning curves of existing GWAS software tools. GWASpro supports building complex design matrices, by which complex experimental designs that may include replications, treatments, locations and times, can be accounted for in the linear mixed model. GWASpro is optimized to handle GWAS data that may consist of up to 10 million markers and 10 000 samples from replicable lines or hybrids. GWASpro provides an interface that significantly reduces the learning curve for new GWAS investigators.'),
(82370, 2019, 'bio', 'GEMPROT: visualization of the impact on the protein of the genetic variants found on each haplotype', 'Cuppens, Tania|Ludwig, Thomas E|Trouvé, Pascal|Genin, Emmanuelle', 'Noble Research Institute, Ardmore, OK, USA.|Noble Research Institute, Ardmore, OK, USA.|Noble Research Institute, Ardmore, OK, USA.|Noble Research Institute, Ardmore, OK, USA.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty993', '30508040', 'When analyzing sequence data, genetic variants are considered one by one, taking no account of whether or not they are found in the same individual. However, variant combinations might be key players in some diseases as variants that are neutral on their own can become deleterious when associated together. GEMPROT is a new analysis tool that allows, from a phased vcf file, to visualize the consequences of the genetic variants on the protein. At the level of an individual, the program shows the variants on each of the two protein sequences and the Pfam functional protein domains. When data on several individuals are available, GEMPROT lists the haplotypes found in the sample and can compare the haplotype distributions between different sub-groups of individuals. By offering a global visualization of the gene with the genetic variants present, GEMPROT makes it possible to better understand the impact of combinations of genetic variants on the protein sequence.'),
(82371, 2019, 'bio', 'iHam and pyHam: visualizing and processing hierarchical orthologous groups', 'Train, Clément-Marie|Pignatelli, Miguel|Altenhoff, Adrian|Dessimoz, Christophe', 'SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.|Open Targets.|SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.|SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.', 'Switzerland|-|Switzerland|Switzerland', 6, '-', 'Journal Article', '10.1093/bioinformatics/bty994', '30508066', 'The evolutionary history of gene families can be complex due to duplications and losses. This complexity is compounded by the large number of genomes simultaneously considered in contemporary comparative genomic analyses. As provided by several orthology databases, hierarchical orthologous groups (HOGs) are sets of genes that are inferred to have descended from a common ancestral gene within a species clade. This implies that the set of HOGs defined for a particular clade correspond to the ancestral genes found in its last common ancestor. Furthermore, by keeping track of HOG composition along the species tree, it is possible to infer the emergence, duplications and losses of genes within a gene family of interest. However, the lack of tools to manipulate and analyse HOGs has made it difficult to extract, display and interpret this type of information. To address this, we introduce interactive HOG analysis method, an interactive JavaScript widget to visualize and explore gene family history encoded in HOGs and python HOG analysis method, a python library for programmatic processing of genes families. These complementary open source tools greatly ease adoption of HOGs as a scalable and interpretable concept to relate genes across multiple species.'),
(82372, 2019, 'bio', 'Drug Targetor: a web interface to investigate the human druggome for over 500 phenotypes', 'Gaspar, Héléna A|Hübel, Christopher|Breen, Gerome', 'Institute of Psychiatry, Psychology and Neuroscience, Social, Genetic and Developmental Psychiatry (SGDP) Centre, Kings College London, London, UK.|Institute of Psychiatry, Psychology and Neuroscience, Social, Genetic and Developmental Psychiatry (SGDP) Centre, Kings College London, London, UK.|Institute of Psychiatry, Psychology and Neuroscience, Social, Genetic and Developmental Psychiatry (SGDP) Centre, Kings College London, London, UK.', 'United Kingdom|United Kingdom|United Kingdom', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty982', '30517594', 'Results from hundreds of genome-wide association studies (GWAS) are now freely available and offer a catalogue of the association between phenotypes across medicine with variants in the genome. With the aim of using this data to better understand therapeutic mechanisms, we have developed Drug Targetor, a web interface that allows the generation and exploration of drug-target networks of hundreds of phenotypes using GWAS data. Drug Targetor networks consist of drug and target nodes ordered by genetic association and connected by drug-target or drug-gene relationship. We show that Drug Targetor can help prioritize drugs, targets and drug-target interactions for a specific phenotype based on genetic evidence.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(82373, 2019, 'bio', 'ARGDIT: a validation and integration toolkit for Antimicrobial Resistance Gene Databases', 'Chiu, Jimmy Ka Ho|Ong, Rick Twee-Hee', 'Institute of Psychiatry, Psychology and Neuroscience, Social, Genetic and Developmental Psychiatry (SGDP) Centre, Kings College London, London, UK.|Institute of Psychiatry, Psychology and Neuroscience, Social, Genetic and Developmental Psychiatry (SGDP) Centre, Kings College London, London, UK.', 'United Kingdom|United Kingdom', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty987', '30520940', 'Antimicrobial resistance is currently one of the main challenges in public health due to the excessive use of antimicrobials in medical treatments and agriculture. The advancements in high-throughput next-generation sequencing and development of bioinformatics tools allow simultaneous detection and identification of antimicrobial resistance genes (ARGs) from clinical, food and environment samples, to monitor the prevalence and track the dissemination of these ARGs. Such analyses are however reliant on a comprehensive database of ARGs with accurate sequence content and annotation. Most of the current ARG databases are therefore manually curated, but this is a time-consuming process and the resulting curation errors could be hard to detect. Several secondary ARG databases consolidate contents from different source ARG databases, and hence modifications in the primary databases might not be propagated and updated promptly in the secondary ARG databases.'),
(82374, 2019, 'bio', 'Alfred: interactive multi-sample BAM alignment statistics, feature counting and feature annotation for long- and short-read sequencing', 'Rausch, Tobias|Hsi-Yang Fritz, Markus|Korbel, Jan O|Benes, Vladimir', 'Genomics Core Facility, European Molecular Biology Laboratory (EMBL), Meyerhofstrasse 1, Heidelberg, Germany.|Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Meyerhofstrasse 1, Heidelberg, Germany.|Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Meyerhofstrasse 1, Heidelberg, Germany.|Genomics Core Facility, European Molecular Biology Laboratory (EMBL), Meyerhofstrasse 1, Heidelberg, Germany.', 'Germany|Germany|Germany|Germany', 8, '-', 'Journal Article', '10.1093/bioinformatics/bty1007', '30520945', 'Harmonizing quality control (QC) of large-scale second and third-generation sequencing datasets is key for enabling downstream computational and biological analyses. We present Alfred, an efficient and versatile command-line application that computes multi-sample QC metrics in a read-group aware manner, across a wide variety of sequencing assays and technologies. In addition to standard QC metrics such as GC bias, base composition, insert size and sequencing coverage distributions it supports haplotype-aware and allele-specific feature counting and feature annotation. The versatility of Alfred allows for easy pipeline integration in high-throughput settings, including DNA sequencing facilities and large-scale research initiatives, enabling continuous monitoring of sequence data quality and characteristics across samples. Alfred supports haplo-tagging of BAM/CRAM files to conduct haplotype-resolved analyses in conjunction with a variety of next-generation sequencing based assays. Alfreds companion web application enables interactive exploration of results and comparison to public datasets.'),
(82375, 2019, 'bio', 'In silico error correction improves cfDNA mutation calling', 'Kim, Chang Sik|Mohan, Sumitra|Ayub, Mahmood|Rothwell, Dominic G|Dive, Caroline|Brady, Ged|Miller, Crispin', 'Clinical and Experimental Pharmacology Group, Cancer Research UK Manchester Institute.|Clinical and Experimental Pharmacology Group, Cancer Research UK Manchester Institute.|Clinical and Experimental Pharmacology Group, Cancer Research UK Manchester Institute.|Clinical and Experimental Pharmacology Group, Cancer Research UK Manchester Institute.|Clinical and Experimental Pharmacology Group, Cancer Research UK Manchester Institute.|Clinical and Experimental Pharmacology Group, Cancer Research UK Manchester Institute.|Medical Research Council Manchester Single Cell Research Centre.', '-|-|-|-|-|-|-', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty1004', '30520956', 'Circulating-free DNA (cfDNA) profiling by sequencing is an important minimally invasive protocol for monitoring the mutation profile of solid tumours in cancer patients. Since the concentration of available cfDNA is limited, sample library generation relies on multiple rounds of PCR amplification, during which the accumulation of errors results in reduced sensitivity and lower accuracy.'),
(82376, 2019, 'bio', 'Protein-protein interaction sites prediction by ensemble random forests with synthetic minority oversampling technique', 'Wang, Xiaoying|Yu, Bin|Ma, Anjun|Chen, Cheng|Liu, Bingqiang|Ma, Qin', 'College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, China.|College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, China.|Bioinformatics and Mathematical Biosciences Lab, Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, USA.|College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, China.|School of Mathematics, Shandong University, Jinan, China.|Bioinformatics and Mathematical Biosciences Lab, Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, USA.', 'China|China|United States|China|China|United States', 20, '-', 'Journal Article', '10.1093/bioinformatics/bty995', '30520961', 'The prediction of protein-protein interaction (PPI) sites is a key to mutation design, catalytic reaction and the reconstruction of PPI networks. It is a challenging task considering the significant abundant sequences and the imbalance issue in samples.'),
(82377, 2019, 'bio', 'Global Biobank Engine: enabling genotype-phenotype browsing for biobank summary statistics', 'McInnes, Gregory|Tanigawa, Yosuke|DeBoever, Chris|Lavertu, Adam|Olivieri, Julia Eve|Aguirre, Matthew|Rivas, Manuel A', 'Biomedical Informatics Training Program, Stanford University, CA, USA.|Biomedical Informatics Training Program, Stanford University, CA, USA.|Department of Biomedical Data Science, Stanford University, CA, USA.|Biomedical Informatics Training Program, Stanford University, CA, USA.|Institute for Computational and Mathematical Engineering, Stanford University, CA, USA.|Department of Biomedical Data Science, Stanford University, CA, USA.|Department of Biomedical Data Science, Stanford University, CA, USA.', 'United States|United States|United States|United States|United States|United States|United States', 12, '-', 'Journal Article', '10.1093/bioinformatics/bty999', '30520965', 'Large biobanks linking phenotype to genotype have led to an explosion of genetic association studies across a wide range of phenotypes. Sharing the knowledge generated by these resources with the scientific community remains a challenge due to patient privacy and the vast amount of data. Here, we present Global Biobank Engine (GBE), a web-based tool that enables exploration of the relationship between genotype and phenotype in biobank cohorts, such as the UK Biobank. GBE supports browsing for results from genome-wide association studies, phenome-wide association studies, gene-based tests and genetic correlation between phenotypes. We envision GBE as a platform that facilitates the dissemination of summary statistics from biobanks to the scientific and clinical communities.'),
(82378, 2019, 'bio', 'Histones predate the split between bacteria and archaea', 'Alva, Vikram|Lupas, Andrei N', 'Department of Biomedical Data Science, Stanford University, CA, USA.|Department of Biomedical Data Science, Stanford University, CA, USA.', 'United States|United States', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty1000', '30520969', 'Histones form octameric complexes called nucleosomes, which organize the genomic DNA of eukaryotes into chromatin. Each nucleosome comprises two copies each of the histones H2A, H2B, H3 and H4, which share a common ancestry. Although histones were initially thought to be a eukaryotic innovation, the subsequent identification of archaeal homologs led to the notion that histones emerged before the divergence of archaea and eukaryotes.'),
(82379, 2019, 'bio', 'Capsule network for protein post-translational modification site prediction', 'Wang, Duolin|Liang, Yanchun|Xu, Dong', 'Department of Electrical Engineering and Computer Science, and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, USA.|Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China.|Department of Electrical Engineering and Computer Science, and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, USA.', 'United States|China|United States', 12, '-', 'Journal Article', '10.1093/bioinformatics/bty977', '30520972', 'Computational methods for protein post-translational modification (PTM) site prediction provide a useful approach for studying protein functions. The prediction accuracy of the existing methods has significant room for improvement. A recent deep-learning architecture, Capsule Network (CapsNet), which can characterize the internal hierarchical representation of input data, presents a great opportunity to solve this problem, especially using small training data.'),
(82380, 2019, 'bio', 'New models of atherosclerosis and multi-drug therapeutic interventions', 'Parton, Andrew|McGilligan, Victoria|Chemaly, Melody|OKane, Maurice|Watterson, Steven', 'Northern Ireland Centre for Stratified Medicine, School of Biomedical Sciences, Ulster University, Altnagelvin Hospital Campus, Derry, Co Londonderry, UK.|Northern Ireland Centre for Stratified Medicine, School of Biomedical Sciences, Ulster University, Altnagelvin Hospital Campus, Derry, Co Londonderry, UK.|Northern Ireland Centre for Stratified Medicine, School of Biomedical Sciences, Ulster University, Altnagelvin Hospital Campus, Derry, Co Londonderry, UK.|Western Health and Social Care Trust, Altnagelvin Hospital, Derry, Co Londonderry, UK.|Northern Ireland Centre for Stratified Medicine, School of Biomedical Sciences, Ulster University, Altnagelvin Hospital Campus, Derry, Co Londonderry, UK.', 'Ireland|Ireland|Ireland|United Kingdom|Ireland', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty980', '30520978', 'Atherosclerosis is amongst the leading causes of death globally. However, it is challenging to study in vivo or in vitro and no detailed, openly-available computational models exist. Clinical studies hint that pharmaceutical therapy may be possible. Here, we develop the first detailed, computational model of atherosclerosis and use it to develop multi-drug therapeutic hypotheses.'),
(82381, 2019, 'bio', 'miRDeep-P2: accurate and fast analysis of the microRNA transcriptome in plants', 'Kuang, Zheng|Wang, Ying|Li, Lei|Yang, Xiaozeng', 'Beijing Agro-biotechnology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.|Beijing Agro-biotechnology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.|Peking-Tsinghua Center for Life Sciences, School of Advanced Agricultural Sciences and School of Life Sciences, Peking University, Beijing, China.|Beijing Agro-biotechnology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.', 'China|China|China|China', 8, '-', 'Journal Article', '10.1093/bioinformatics/bty972', '30521000', 'Two major challenges arise when employing next-generation sequencing methods to comprehensively identify microRNAs (miRNAs) in plants: (i) how to minimize the false-positive inheritable to computational predictions and (ii) how to minimize the computational time required for analyzing the miRNA transcriptome in plants with complex and large genomes.'),
(82382, 2019, 'bio', 'BaiHui: cross-species brain-specific network built with hundreds of hand-curated datasets', 'Li, Hong-Dong|Bai, Tianjian|Sandford, Erin|Burmeister, Margit|Guan, Yuanfang', 'Center for Bioinformatics, School of Information Science and Engineering, Central South University, Changsha, Peoples Republic of China.|Department of Computational Medicine and Bioinformatics.|Molecular and Behavioral Neuroscience Institute, University of Michigan, Ann Arbor, MI, USA.|Department of Computational Medicine and Bioinformatics.|Department of Computational Medicine and Bioinformatics.', 'China|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty1001', '30521009', 'Functional gene networks, representing how likely two genes work in the same biological process, are important models for studying gene interactions in complex tissues. However, a limitation of the current network-building scheme is the lack of leveraging evidence from multiple model organisms as well as the lack of expert curation and quality control of the input genomic data.'),
(82383, 2019, 'bio', 'BIOLITMAP: a web-based geolocated, temporal and thematic visualization of the evolution of bioinformatics publications', 'Bazaga, Adrián|Valencia, Alfonso|JoséRementeria, María-', 'Barcelona Supercomputing Center (BSC).|Barcelona Supercomputing Center (BSC).|Barcelona Supercomputing Center (BSC).', '-|-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty967', '30521012', 'The fast growth of bioinformatics adds a significant difficulty to assess the contribution, geographical and thematic distribution of the research publications.'),
(82384, 2019, 'bio', 'Accurate and efficient estimation of small P-values with the cross-entropy method: applications in genomic data analysis', 'Shi, Yang|Wang, Mengqiao|Shi, Weiping|Lee, Ji-Hyun|Kang, Huining|Jiang, Hui', 'Division of Biostatistics and Data Science, Department of Population Health Sciences, Medical College of Georgia, Augusta University, Augusta, Georgia, USA.|Department of Epidemiology and Biostatistics, West China School of Public Health, Sichuan University, Chengdu, Sichuan, China.|College of Mathematics, Jilin University, Changchun, Jilin, China.|Division of Quantitative Sciences, University of Florida Health Cancer Center and Department of Biostatistics, University of Florida, Gainesville, Florida, USA.|Biostatistics Shared Resource, University of New Mexico Comprehensive Cancer Center and Department of Internal Medicine, University of New Mexico, Albuquerque, New Mexico, USA.|Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.', 'Georgia|China|China|United States|Mexico|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty1005', '30521030', 'Small P-values are often required to be accurately estimated in large-scale genomic studies for the adjustment of multiple hypothesis tests and the ranking of genomic features based on their statistical significance. For those complicated test statistics whose cumulative distribution functions are analytically intractable, existing methods usually do not work well with small P-values due to lack of accuracy or computational restrictions. We propose a general approach for accurately and efficiently estimating small P-values for a broad range of complicated test statistics based on the principle of the cross-entropy method and Markov chain Monte Carlo sampling techniques.'),
(82385, 2019, 'bio', 'atSNP Search: a web resource for statistically evaluating influence of human genetic variation on transcription factor binding', 'Shin, Sunyoung|Hudson, Rebecca|Harrison, Christopher|Craven, Mark|Kelexc5x9f, Sxc3xbcndxc3xbcz', 'Department of Mathematical Sciences, University of Texas at Dallas, Richardson, TX, USA.|Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA.|Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA.|Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA.|Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA.', 'United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty1010', '30534948', 'Understanding the regulatory roles of non-coding genetic variants has become a central goal for interpreting results of genome-wide association studies. The regulatory significance of the variants may be interrogated by assessing their influence on transcription factor binding. We have developed atSNP Search, a comprehensive web database for evaluating motif matches to the human genome with both reference and variant alleles and assessing the overall significance of the variant alterations on the motif matches. Convenient search features, comprehensive search outputs and a useful help menu are key components of atSNP Search. atSNP Search enables convenient interpretation of regulatory variants by statistical significance testing and composite logo plots, which are graphical representations of motif matches with the reference and variant alleles. Existing motif-based regulatory variant discovery tools only consider a limited pool of variants due to storage or other limitations. In contrast, atSNP Search users can test more than 37 billion variant-motif pairs with marginal significance in motif matches or match alteration. Computational evidence from atSNP Search, when combined with experimental validation, may help with the discovery of underlying disease mechanisms.'),
(82386, 2019, 'bio', 'Optimal Gene Filtering for Single-Cell data (OGFSC)-a gene filtering algorithm for single-cell RNA-seq data', 'Hao, Jie|Cao, Wei|Huang, Jian|Zou, Xin|Han, Ze-Guang', 'Shanghai Centre for Systems Biomedicine, Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Centre for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China.|Department of Oral Maxillofacial-Head and Neck Oncology, Ninth Peoples Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.|Shanghai Centre for Systems Biomedicine, Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Centre for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China.|Shanghai Centre for Systems Biomedicine, Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Centre for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China.|Shanghai Centre for Systems Biomedicine, Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Centre for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China.', 'China|China|China|China|China', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty1016', '30535000', 'Single-cell transcriptomic data are commonly accompanied by extremely high technical noise due to the low RNA concentrations from individual cells. Precise identification of differentially expressed genes and cell populations are heavily dependent on the effective reduction of technical noise, e.g. by gene filtering. However, there is still no well-established standard in the current approaches of gene filtering. Investigators usually filter out genes based on single fixed threshold, which commonly leads to both over- and under-stringent errors.'),
(82387, 2019, 'bio', 'A novel measure of non-coding genome conservation identifies genomic regulatory blocks within primates', 'Nash, Alexander J|Lenhard, Boris', 'Computational Regulatory Genomics Group, MRC London Institute of Medical Sciences.|Computational Regulatory Genomics Group, MRC London Institute of Medical Sciences.', '-|-', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty1014', '30535005', 'Clusters of extremely conserved non-coding elements (CNEs) mark genomic regions devoted to cis-regulation of key developmental genes in Metazoa. We have recently shown that their span coincides with that of topologically associating domains (TADs), making them useful for estimating conserved TAD boundaries in the absence of Hi-C data. The standard approach-detecting CNEs in genome alignments and then establishing the boundaries of their clusters-requires tuning of several parameters and breaks down when comparing closely related genomes.'),
(82388, 2019, 'bio', 'SPRING: a next-generation compressor for FASTQ data', 'Chandak, Shubham|Tatwawadi, Kedar|Ochoa, Idoia|Hernaez, Mikel|Weissman, Tsachy', 'Department of Electrical Engineering, Stanford University, Stanford, CA, USA.|Department of Electrical Engineering, Stanford University, Stanford, CA, USA.|Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA.|Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.|Department of Electrical Engineering, Stanford University, Stanford, CA, USA.', 'United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty1015', '30535063', 'High-Throughput Sequencing technologies produce huge amounts of data in the form of short genomic reads, associated quality values and read identifiers. Because of the significant structure present in these FASTQ datasets, general-purpose compressors are unable to completely exploit much of the inherent redundancy. Although there has been a lot of work on designing FASTQ compressors, most of them lack in support of one or more crucial properties, such as support for variable length reads, scalability to high coverage datasets, pairing-preserving compression and lossless compression.'),
(82389, 2019, 'bio', 'Improving prediction of protein secondary structure, backbone angles, solvent accessibility and contact numbers by using predicted contact maps and an ensemble of recurrent and residual convolutional neural networks', 'Hanson, Jack|Paliwal, Kuldip|Litfin, Thomas|Yang, Yuedong|Zhou, Yaoqi', 'Signal Processing Laboratory, Griffith University, Brisbane, QLD, Australia.|Signal Processing Laboratory, Griffith University, Brisbane, QLD, Australia.|School of Information and Communication Technology, Griffith University, Gold Coast, QLD, Australia.|School of Data and Computer Science, Sun-Yat Sen University, Guangzhou, Guangdong, China.|School of Information and Communication Technology, Griffith University, Gold Coast, QLD, Australia.', 'Australia|Australia|Australia|China|Australia', 19, '-', 'Journal Article', '10.1093/bioinformatics/bty1006', '30535134', 'Sequence-based prediction of one dimensional structural properties of proteins has been a long-standing subproblem of protein structure prediction. Recently, prediction accuracy has been significantly improved due to the rapid expansion of protein sequence and structure libraries and advances in deep learning techniques, such as residual convolutional networks (ResNets) and Long-Short-Term Memory Cells in Bidirectional Recurrent Neural Networks (LSTM-BRNNs). Here we leverage an ensemble of LSTM-BRNN and ResNet models, together with predicted residue-residue contact maps, to continue the push towards the attainable limit of prediction for 3- and 8-state secondary structure, backbone angles (xcexb8, xcfx84, xcfx95 and xcfx88), half-sphere exposure, contact numbers and solvent accessible surface area (ASA).'),
(82390, 2019, 'bio', 'WIlsON: Web-based Interactive Omics VisualizatioN', 'Schultheis, Hendrik|Kuenne, Carsten|Preussner, Jens|Wiegandt, Rene|Fust, Annika|Bentsen, Mette|Looso, Mario', 'Max Planck Institute for Heart and Lung Research, Bioinformatics Core Unit (BCU), 61231 Bad Nauheim, Germany.|Max Planck Institute for Heart and Lung Research, Bioinformatics Core Unit (BCU), 61231 Bad Nauheim, Germany.|Max Planck Institute for Heart and Lung Research, Bioinformatics Core Unit (BCU), 61231 Bad Nauheim, Germany.|Max Planck Institute for Heart and Lung Research, Bioinformatics Core Unit (BCU), 61231 Bad Nauheim, Germany.|Max Planck Institute for Heart and Lung Research, Bioinformatics Core Unit (BCU), 61231 Bad Nauheim, Germany.|Max Planck Institute for Heart and Lung Research, Bioinformatics Core Unit (BCU), 61231 Bad Nauheim, Germany.|Max Planck Institute for Heart and Lung Research, Bioinformatics Core Unit (BCU), 61231 Bad Nauheim, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 3, '-', 'Journal Article', '10.1093/bioinformatics/bty711', '30535135', 'High throughput (HT) screens in the omics field are typically analyzed by automated pipelines that generate static visualizations and comprehensive spreadsheet data for scientists. However, exploratory and hypothesis driven data analysis are key aspects of the understanding of biological systems, both generating extensive need for customized and dynamic visualization.'),
(82391, 2019, 'bio', 'AStrap: identification of alternative splicing from transcript sequences without a reference genome', 'Ji, Guoli|Ye, Wenbin|Su, Yaru|Chen, Moliang|Huang, Guangzao|Wu, Xiaohui', 'Department of Automation, Xiamen University, Xiamen, China.|Department of Automation, Xiamen University, Xiamen, China.|College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China.|Department of Automation, Xiamen University, Xiamen, China.|Department of Automation, Xiamen University, Xiamen, China.|Department of Automation, Xiamen University, Xiamen, China.', 'China|China|China|China|China|China', 3, '-', 'Journal Article', '10.1093/bioinformatics/bty1008', '30535139', 'Alternative splicing (AS) is a well-established mechanism for increasing transcriptome and proteome diversity, however, detecting AS events and distinguishing among AS types in organisms without available reference genomes remains challenging. We developed a de novo approach called AStrap for AS analysis without using a reference genome. AStrap identifies AS events by extensive pair-wise alignments of transcript sequences and predicts AS types by a machine-learning model integrating more than 500 assembled features. We evaluated AStrap using collected AS events from reference genomes of rice and human as well as single-molecule real-time sequencing data from Amborella trichopoda. Results show that AStrap can identify much more AS events with comparable or higher accuracy than the competing method. AStrap also possesses a unique feature of predicting AS types, which achieves an overall accuracy of xe2x88xbc0.87 for different species. Extensive evaluation of AStrap using different parameters, sample sizes and machine-learning models on different species also demonstrates the robustness and flexibility of AStrap. AStrap could be a valuable addition to the community for the study of AS in non-model organisms with limited genetic resources.'),
(82392, 2019, 'bio', 'Deciphering the complexity of human non-coding promoter-proximal transcriptome', 'Mapelli, Sarah N|Napoli, Sara|Pisignano, Giuseppina|Garcia-Escudero, Ramon|Carbone, Giuseppina M|Catapano, Carlo V', 'Tumor Biology and Experimental Therapeutics Program, Institute of Oncology Research (IOR), Università della Svizzera Italiana (USI), Bellinzona, Switzerland.|Tumor Biology and Experimental Therapeutics Program, Institute of Oncology Research (IOR), Università della Svizzera Italiana (USI), Bellinzona, Switzerland.|Tumor Biology and Experimental Therapeutics Program, Institute of Oncology Research (IOR), Università della Svizzera Italiana (USI), Bellinzona, Switzerland.|Tumor Biology and Experimental Therapeutics Program, Institute of Oncology Research (IOR), Università della Svizzera Italiana (USI), Bellinzona, Switzerland.|Tumor Biology and Experimental Therapeutics Program, Institute of Oncology Research (IOR), Università della Svizzera Italiana (USI), Bellinzona, Switzerland.|Tumor Biology and Experimental Therapeutics Program, Institute of Oncology Research (IOR), Università della Svizzera Italiana (USI), Bellinzona, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty981', '30535182', 'Long non-coding RNAs (lncRNAs) have gained increasing relevance in epigenetic regulation and nuclear functional organization. High-throughput sequencing approaches have revealed frequent non-coding transcription in promoter-proximal regions. However, a comprehensive catalogue of promoter-associated RNAs (paRNAs) and an analysis of the possible interactions with neighboring genes and genomic regulatory elements are missing.'),
(82393, 2019, 'bio', 'GlycanFormatConverter: a conversion tool for translating the complexities of glycans', 'Tsuchiya, Shinichiro|Yamada, Issaku|Aoki-Kinoshita, Kiyoko F', 'Graduate School of Engineering, Soka University, Hachioji, Tokyo, Japan.|The Noguchi Institute, Itabashi, Tokyo, Japan.|Graduate School of Engineering, Soka University, Hachioji, Tokyo, Japan.', 'Japan|Japan|Japan', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty990', '30535258', 'Glycans are biomolecules that take an important role in the biological processes of living organisms. They form diverse, complicated structures such as branched and cyclic forms. Web3 Unique Representation of Carbohydrate Structures (WURCS) was proposed as a new linear notation for uniquely representing glycans during the GlyTouCan project. WURCS defines rules for complex glycan structures that other text formats did not support, and so it is possible to represent a wide variety glycans. However, WURCS uses a complicated nomenclature, so it is not human-readable. Therefore, we aimed to support the interpretation of WURCS by converting WURCS to the most basic and widely used format IUPAC.'),
(82394, 2019, 'bio', 'MapOptics: a light-weight, cross-platform visualization tool for optical mapping alignment', 'Burgin, Josephine|Molitor, Corentin|Mohareb, Fady', 'The Bioinformatics Group, Cranfield Soil and Agrifood Institute, School of Water, Energy and Environment, Cranfield University, Bedford MK43 0AL, UK.|The Bioinformatics Group, Cranfield Soil and Agrifood Institute, School of Water, Energy and Environment, Cranfield University, Bedford MK43 0AL, UK.|The Bioinformatics Group, Cranfield Soil and Agrifood Institute, School of Water, Energy and Environment, Cranfield University, Bedford MK43 0AL, UK.', 'United Kingdom|United Kingdom|United Kingdom', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty1013', '30535283', 'Bionano optical mapping is a technology that can assist in the final stages of genome assembly by lengthening and ordering scaffolds in a draft assembly by aligning the assembly to a genomic map. However, currently, tools for visualization are limited to use on a Windows operating system or are developed initially for visualizing large-scale structural variation. MapOptics is a lightweight cross-platform tool that enables the user to visualize and interact with the alignment of Bionano optical mapping data and can be used for in depth exploration of hybrid scaffolding alignments. It provides a fast, simple alternative to the large optical mapping analysis programs currently available for this area of research.'),
(82395, 2019, 'bio', 'Insights on protein thermal stability: a graph representation of molecular interactions', 'Miotto, Mattia|Olimpieri, Pier Paolo|Di Rienzo, Lorenzo|Ambrosetti, Francesco|Corsi, Pietro|Lepore, Rosalba|Tartaglia, Gian Gaetano|Milanetti, Edoardo', 'Department of Physics, Sapienza University, Piazzale Aldo Moro 5, Rome, Italy.|Department of Physics, Sapienza University, Piazzale Aldo Moro 5, Rome, Italy.|Department of Physics, Sapienza University, Piazzale Aldo Moro 5, Rome, Italy.|Department of Physics, Sapienza University, Piazzale Aldo Moro 5, Rome, Italy.|Department of Science, Università degli Studi Roma Tre, via della Vasca Navale 84, Rome, Italy.|Biozentrum, University of Basel, Klingelbergstrasse 50-70, CH-4056 Basel, Switzerland.|Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader St. 88, Barcelona, Spain.|Department of Physics, Sapienza University, Piazzale Aldo Moro 5, Rome, Italy.', 'Italy|Italy|Italy|Italy|Italy|Switzerland|Spain|Italy', 7, '-', 'Journal Article', '10.1093/bioinformatics/bty1011', '30535291', 'Understanding the molecular mechanisms of thermal stability is a challenge in protein biology. Indeed, knowing the temperature at which proteins are stable has important theoretical implications, which are intimately linked with properties of the native fold, and a wide range of potential applications from drug design to the optimization of enzyme activity.'),
(82396, 2018, 'bio', 'pyseer: a comprehensive tool for microbial pangenome-wide association studies', 'Lees, John A|Galardini, Marco|Bentley, Stephen D|Weiser, Jeffrey N|Corander, Jukka', 'Department of Microbiology, New York University School of Medicine, New York, NY, USA.|European Bioinformatics Institute (EMBL-EBI), Hinxton, UK.|Department of Parasites and Microbes, Wellcome Sanger Institute, Hinxton, UK.|Department of Microbiology, New York University School of Medicine, New York, NY, USA.|Department of Parasites and Microbes, Wellcome Sanger Institute, Hinxton, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 20, '-', 'Journal Article', '10.1093/bioinformatics/bty539', '30535304', 'Genome-wide association studies (GWAS) in microbes have different challenges to GWAS in eukaryotes. These have been addressed by a number of different methods. pyseer brings these techniques together in one package tailored to microbial GWAS, allows greater flexibility of the input data used, and adds new methods to interpret the association results.'),
(82397, 2018, 'bio', 'PopViz: a webserver for visualizing minor allele frequencies and damage prediction scores of human genetic variations', 'Zhang, Peng|Bigio, Benedetta|Rapaport, Franck|Zhang, Shen-Ying|Casanova, Jean-Laurent|Abel, Laurent|Boisson, Bertrand|Itan, Yuval', 'St. Giles Laboratory of Human Genetics of Infectious Diseases, The Rockefeller University, New York, NY, USA.|St. Giles Laboratory of Human Genetics of Infectious Diseases, The Rockefeller University, New York, NY, USA.|St. Giles Laboratory of Human Genetics of Infectious Diseases, The Rockefeller University, New York, NY, USA.|St. Giles Laboratory of Human Genetics of Infectious Diseases, The Rockefeller University, New York, NY, USA.|St. Giles Laboratory of Human Genetics of Infectious Diseases, The Rockefeller University, New York, NY, USA.|St. Giles Laboratory of Human Genetics of Infectious Diseases, The Rockefeller University, New York, NY, USA.|St. Giles Laboratory of Human Genetics of Infectious Diseases, The Rockefeller University, New York, NY, USA.|The Charles Bronfman Institute for Personalized Medicine.', 'United States|United States|United States|United States|United States|United States|United States|United States', 11, '-', 'Journal Article', '10.1093/bioinformatics/bty536', '30535305', 'Next-generation sequencing (NGS) generates large amounts of genomic data and reveals about 20 000 genetic coding variants per individual studied. Several mutation damage prediction scores are available to prioritize variants, but there is currently no application to help investigators to determine the relevance of the candidate genes and variants quickly and visually from population genetics data and deleteriousness scores. Here, we present PopViz, a user-friendly, rapid, interactive, mobile-compatible webserver providing a gene-centric visualization of the variants of any human gene, with (i) population-specific minor allele frequencies from the gnomAD population genetic database, (ii) mutation damage prediction scores from CADD, EIGEN and LINSIGHT and (iii) amino-acid positions and protein domains. This application will be particularly useful in investigations of NGS data for new disease-causing genes and variants, by reinforcing or rejecting the plausibility of the candidate genes, and by selecting and prioritizing, the candidate variants for experimental testing.'),
(82398, 2018, 'bio', 'Database of evidence for precision oncology portal', 'Sun, Sam Q|Mashl, R Jay|Sengupta, Sohini|Scott, Adam D|Wang, Weihua|Batra, Prag|Wang, Liang-Bo|Wyczalkowski, Matthew A|Ding, Li', 'Division of Oncology, School of Medicine, St. Louis, MO, USA.|Division of Oncology, School of Medicine, St. Louis, MO, USA.|Division of Oncology, School of Medicine, St. Louis, MO, USA.|Division of Oncology, School of Medicine, St. Louis, MO, USA.|Division of Oncology, School of Medicine, St. Louis, MO, USA.|McDonnell Genome Institute, School of Medicine, St. Louis, MO, USA.|Division of Oncology, School of Medicine, St. Louis, MO, USA.|Division of Oncology, School of Medicine, St. Louis, MO, USA.|Division of Oncology, School of Medicine, St. Louis, MO, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty531', '30535306', 'A database of curated genomic variants with clinically supported drug therapies and other oncological annotations is described. The accompanying web portal provides a search engine with two modes: one that allows users to query gene, cancer type, variant type or position for druggable mutations, and another to search for and to visualize, on three-dimensional protein structures, putative druggable sites that cluster with known druggable mutations.'),
(82399, 2019, 'bio', 'Evaluation of methods for generative modeling of cell and nuclear shape', 'Ruan, Xiongtao|Murphy, Robert F', 'Computational Biology Department, School of Computer Science.|Computational Biology Department, School of Computer Science.', '-|-', 4, '-', 'Journal Article', '10.1093/bioinformatics/bty983', '30535313', 'Cell shape provides both geometry for, and a reflection of, cell function. Numerous methods for describing and modeling cell shape have been described, but previous evaluation of these methods in terms of the accuracy of generative models has been limited.'),
(82400, 2019, 'bio', 'Increasing the accuracy of protein loop structure prediction with evolutionary constraints', 'Marks, Claire|Deane, Charlotte M', 'Department of Statistics, University of Oxford, Oxford, UK.|Department of Statistics, University of Oxford, Oxford, UK.', 'United Kingdom|United Kingdom', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty996', '30535347', 'Accurate prediction of loop structures remains challenging. This is especially true for long loops where the large conformational space and limited coverage of experimentally determined structures often leads to low accuracy. Co-evolutionary contact predictors, which provide information about the proximity of pairs of residues, have been used to improve whole-protein models generated through de novo techniques. Here we investigate whether these evolutionary constraints can enhance the prediction of long loop structures.'),
(82401, 2019, 'bio', 'DensityPath: an algorithm to visualize and reconstruct cell state-transition path on density landscape for single-cell RNA sequencing data', 'Chen, Ziwei|An, Shaokun|Bai, Xiangqi|Gong, Fuzhou|Ma, Liang|Wan, Lin', 'NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing.|NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing.|NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing.|NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing.|University of Chinese Academy of Sciences, Beijing.|NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing.', '-|-|-|-|-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty1009', '30535348', 'Visualizing and reconstructing cell developmental trajectories intrinsically embedded in high-dimensional expression profiles of single-cell RNA sequencing (scRNA-seq) snapshot data are computationally intriguing, but challenging.'),
(82402, 2019, 'bio', 'DeepIsoFun: a deep domain adaptation approach to predict isoform functions', 'Shaw, Dipan|Chen, Hao|Jiang, Tao', 'Department of Computer Science and Engineering, University of California, Riverside, CA, USA.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA.|Department of Computer Science and Engineering, University of California, Riverside, CA, USA.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty1017', '30535380', 'Isoforms are mRNAs produced from the same gene locus by alternative splicing and may have different functions. Although gene functions have been studied extensively, little is known about the specific functions of isoforms. Recently, some computational approaches based on multiple instance learning have been proposed to predict isoform functions from annotated gene functions and expression data, but their performance is far from being desirable primarily due to the lack of labeled training data. To improve the performance on this problem, we propose a novel deep learning method, DeepIsoFun, that combines multiple instance learning with domain adaptation. The latter technique helps to transfer the knowledge of gene functions to the prediction of isoform functions and provides additional labeled training data. Our model is trained on a deep neural network architecture so that it can adapt to different expression distributions associated with different gene ontology terms.'),
(82403, 2019, 'bio', 'Gene expression models based on transcription factor binding events confer insight into functional cis-regulatory variants', 'Shi, Wenqiang|Fornes, Oriol|Wasserman, Wyeth W', 'Department of Medical Genetics, Centre for Molecular Medicine and Therapeutics, BC Childrens Hospital Research Institute, University of British Columbia, Vancouver, BC, Canada.|Department of Medical Genetics, Centre for Molecular Medicine and Therapeutics, BC Childrens Hospital Research Institute, University of British Columbia, Vancouver, BC, Canada.|Department of Medical Genetics, Centre for Molecular Medicine and Therapeutics, BC Childrens Hospital Research Institute, University of British Columbia, Vancouver, BC, Canada.', 'Canada|Canada|Canada', 3, '-', 'Journal Article', '10.1093/bioinformatics/bty992', '30541050', 'Deciphering the functional roles of cis-regulatory variants is a critical challenge in genome analysis and interpretation. It has been hypothesized that altered transcription factor (TF) binding events are a central mechanism by which cis-regulatory variants impact gene expression levels. However, we lack a computational framework to understand and quantify such mechanistic contributions.'),
(82404, 2019, 'bio', 'Commonly misunderstood parameters of NCBI BLAST and important considerations for users', 'González-Pech, Raúl A|Stephens, Timothy G|Chan, Cheong Xin', 'Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia.|Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia.|Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia.', 'Australia|Australia|Australia', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty1018', '30541060', '-'),
(82405, 2019, 'bio', 'CASMAP: detection of statistically significant combinations of SNPs in association mapping', 'Llinares-López, Felipe|Papaxanthos, Laetitia|Roqueiro, Damian|Bodenham, Dean|Borgwardt, Karsten', 'Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland.|Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland.|Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland.|Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland.|Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty1020', '30541062', 'Combinatorial association mapping aims to assess the statistical association of higher-order interactions of genetic markers with a phenotype of interest. This article presents combinatorial association mapping (CASMAP), a software package that leverages recent advances in significant pattern mining to overcome the statistical and computational challenges that have hindered combinatorial association mapping. CASMAP can be used to perform region-based association studies and to detect higher-order epistatic interactions of genetic variants. Most importantly, unlike other existing significant pattern mining-based tools, CASMAP allows for the correction of categorical covariates such as age or gender, making it suitable for genome-wide association studies.'),
(82406, 2019, 'bio', 'ModL: exploring and restoring regularity when testing for positive selection', 'Mingrone, Joseph|Susko, Edward|Bielawski, Joseph P', 'Department of Mathematics and Statistics, Dalhousie University, Halifax, NS, Canada.|Department of Mathematics and Statistics, Dalhousie University, Halifax, NS, Canada.|Department of Mathematics and Statistics, Dalhousie University, Halifax, NS, Canada.', 'Canada|Canada|Canada', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty1019', '30541063', 'Likelihood ratio tests are commonly used to test for positive selection acting on proteins. They are usually applied with thresholds for declaring a protein under positive selection determined from a chi-square or mixture of chi-square distributions. Although it is known that such distributions are not strictly justified due to the statistical irregularity of the problem, the hope has been that the resulting tests are conservative and do not lose much power in comparison with the same test using the unknown, correct threshold. We show that commonly used thresholds need not yield conservative tests, but instead give larger than expected Type I error rates. Statistical regularity can be restored by using a modified likelihood ratio test.'),
(82407, 2019, 'bio', 'Implementation of a Stirling number estimator enables direct calculation of population genetics tests for large sequence datasets', 'Chen, Swaine L', 'Division of Infectious Diseases, Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.', 'Singapore', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty1012', '30541067', 'Stirling numbers enter into the calculation of several population genetics statistics, including Fus Fs. However, as alignments become large (xe2x89xa550 sequences), the Stirling numbers required rapidly exceed the standard floating point range. Another recursive method for calculating Fus Fs suffers from floating point underflow issues.'),
(82408, 2019, 'bio', 'CNValidator: validating somatic copy-number inference', 'Smith, Lucian P|Yamato, Jon A|Kuhner, Mary K', 'Department of Genome Sciences, University of Washington, Seattle, WA, USA.|Department of Genome Sciences, University of Washington, Seattle, WA, USA.|Department of Genome Sciences, University of Washington, Seattle, WA, USA.', 'United States|United States|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty1022', '30541069', 'CNValidator assesses the quality of somatic copy-number calls based on coherency of haplotypes across multiple samples from the same individual. It is applicable to any copy-number calling algorithm, which makes calls independently for each sample. This test is useful in assessing the accuracy of copy-number calls, as well as choosing among alternative copy-number algorithms or tuning parameter values.'),
(82409, 2019, 'bio', 'Comment on: Empirical comparison of web-based antimicrobial peptide prediction tools', 'Vishnepolsky, Boris|Pirtskhalava, Malak', 'Ivane Beritashvili Center of Experimental Biomedicine, Tbilisi, Georgia.|Ivane Beritashvili Center of Experimental Biomedicine, Tbilisi, Georgia.', 'Georgia|Georgia', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty1023', '30561507', 'Supplementary data are available at Bioinformatics online.'),
(82410, 2019, 'bio', 'On what to permute in test-based approaches for variable importance measures in Random Forests', 'Nembrini, Stefano', 'Department of Pathology, Immunology and Laboratory Medicine, College of Medicine, Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA.', 'United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty1025', '30561510', 'In bioinformatics applications, it is currently customary to permute the outcome variable in order to produce inference on covariates to test novel methods or statistics whose distributions are poorly known. The seminal publication of Altmann et al. in Bioinformatics uses the same permutation scheme to obtain P-values that can be treated as corrected measure of feature importance to rectify the bias of the Gini variable importance in Random Forests. Since then, such method has been used in applied work to also draw statistical conclusions on variable importance measures from resulting P-values.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(82411, 2019, 'bio', 'Response to comments on Empirical comparison of web-based antimicrobial peptide prediction tools', 'Gabere, Musa Nur|Noble, William Stafford', 'Department of Biostatistics and Bioinformatics, King Abdullah International Medical Research Center/King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia.|Department of Genome Sciences, University of Washington, Seattle, WA, USA.', 'Saudi Arabia|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty1024', '30561528', '-'),
(82412, 2019, 'bio', 'GrandPrix: scaling up the Bayesian GPLVM for single-cell data', 'Ahmed, Sumon|Rattray, Magnus|Boukouvalas, Alexis', 'Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.|Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.|Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.', 'United Kingdom|United Kingdom|United Kingdom', 5, '-', 'Journal Article', '10.1093/bioinformatics/bty533', '30561544', 'The Gaussian Process Latent Variable Model (GPLVM) is a popular approach for dimensionality reduction of single-cell data and has been used for pseudotime estimation with capture time information. However, current implementations are computationally intensive and will not scale up to modern droplet-based single-cell datasets which routinely profile many tens of thousands of cells.'),
(82413, 2019, 'bio', 'pyTFA and matTFA: a Python package and a Matlab toolbox for Thermodynamics-based Flux Analysis', 'Salvy, Pierre|Fengos, Georgios|Ataman, Meric|Pathier, Thomas|Soh, Keng C|Hatzimanikatis, Vassily', 'Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.|Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.|Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.|CentraleSupélec, Université Paris Saclay, Gif-Sur-Yvette, France.|Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.|Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.', 'Switzerland|Switzerland|Switzerland|France|Switzerland|Switzerland', 13, '-', 'Journal Article', '10.1093/bioinformatics/bty499', '30561545', 'pyTFA and matTFA are the first published implementations of the original TFA paper. Specifically, they include explicit formulation of Gibbs energies and metabolite concentrations, which enables straightforward integration of metabolite concentration measurements.'),
(82414, 2019, 'bio', 'Seave: a comprehensive web platform for storing and interrogating human genomic variation', 'Gayevskiy, Velimir|Roscioli, Tony|Dinger, Marcel E|Cowley, Mark J', 'Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia.|Centre for Clinical Genetics, Sydney Childrens Hospital, Randwick, NSW, Australia.|Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia.|Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia.', 'Australia|Australia|Australia|Australia', 11, '-', 'Journal Article', '10.1093/bioinformatics/bty540', '30561546', 'Genome sequencing has had a remarkable impact on our ability to study the effects of human genetic variation, however, variant interpretation remains the major bottleneck. Understanding the potential impact of variants, including structural variants, requires extensive annotation from disparate sources of knowledge, and in silico prediction algorithms.'),
(82415, 2019, 'bio', 'Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy', 'Ghaemi, Mohammad Sajjad|DiGiulio, Daniel B|Contrepois, Kévin|Callahan, Benjamin|Ngo, Thuy T M|Lee-McMullen, Brittany|Lehallier, Benoit|Robaczewska, Anna|Mcilwain, David|Rosenberg-Hasson, Yael|Wong, Ronald J|Quaintance, Cecele|Culos, Anthony|Stanley, Natalie|Tanada, Athena|Tsai, Amy|Gaudilliere, Dyani|Ganio, Edward|Han, Xiaoyuan|Ando, Kazuo|McNeil, Leslie|Tingle, Martha|Wise, Paul|Maric, Ivana|Sirota, Marina|Wyss-Coray, Tony|Winn, Virginia D|Druzin, Maurice L|Gibbs, Ronald|Darmstadt, Gary L|Lewis, David B|Partovi Nia, Vahid|Agard, Bruno|Tibshirani, Robert|Nolan, Garry|Snyder, Michael P|Relman, David A|Quake, Stephen R|Shaw, Gary M|Stevenson, David K|Angst, Martin S|Gaudilliere, Brice|Aghaeepour, Nima', 'Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.|Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.|Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.|Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.|Department of Bioengineering, Stanford University, Stanford, CA, USA.|Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.|Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA.|Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.|Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA.|Institute for Immunity, Transplantation and Infection, Human Immune Monitoring Center Stanford, CA, USA.|Division of Neonatology, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA.|Division of Neonatology, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA.|Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.|Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.|Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.|Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.|Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.|Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.|Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.|Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.|Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.|Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.|Division of Neonatology, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA.|Division of Neonatology, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA.|Institute for Computational Health Sciences, University of California San Francisco, San Francisco, CA, USA.|Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA.|Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA, USA.|Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA, USA.|Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA, USA.|Division of Neonatology, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA.|Division of Neonatology, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA.|Département de Mathématiques et de Génie Industriel, École Polytechnique de Montréal, QC, Canada.|Département de Mathématiques et de Génie Industriel, École Polytechnique de Montréal, QC, Canada.|Departments of Biomedical Data Sciences and Statistics, Stanford University, Stanford, CA, USA.|Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA.|Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.|Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.|Department of Bioengineering, Stanford University, Stanford, CA, USA.|Division of Neonatology, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA.|Division of Neonatology, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA.|Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.|Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.|Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|Uni', 26, '-', 'Journal Article', '10.1093/bioinformatics/bty537', '30561547', 'Multiple biological clocks govern a healthy pregnancy. These biological mechanisms produce immunologic, metabolomic, proteomic, genomic and microbiomic adaptations during the course of pregnancy. Modeling the chronology of these adaptations during full-term pregnancy provides the frameworks for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia.'),
(82416, 2019, 'bio', 'NeoDTI: neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions', 'Wan, Fangping|Hong, Lixiang|Xiao, An|Jiang, Tao|Zeng, Jianyang', 'Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China.|Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China.|Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China.|Department of Computer Science and Technology, Tsinghua University, Beijing, China.|Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China.', 'China|China|China|China|China', 19, '-', 'Journal Article', '10.1093/bioinformatics/bty543', '30561548', 'Accurately predicting drug-target interactions (DTIs) in silico can guide the drug discovery process and thus facilitate drug development. Computational approaches for DTI prediction that adopt the systems biology perspective generally exploit the rationale that the properties of drugs and targets can be characterized by their functional roles in biological networks.'),
(82417, 2019, 'bio', 'ShinyCNV: a Shiny/R application to view and annotate DNA copy number variations', 'Gu, Zhaohui|Mullighan, Charles G', 'Department of Pathology, St. Jude Childrens Research Hospital, Memphis, TN, USA.|Department of Pathology, St. Jude Childrens Research Hospital, Memphis, TN, USA.', 'United States|United States', 3, '-', 'Journal Article', '10.1093/bioinformatics/bty546', '30561549', 'Single nucleotide polymorphism (SNP) array is the most widely used platform to assess somatic copy number variations (CNVs) in cancer studies. Many SNP data-based CNV callers are available, however, the false positive rates from automated calling are commonly high, and reported breakpoints can be inaccurate. Manual review for each reported CNV by visualizing the SNP data is important, but is challenging for users lacking computational experience. To address this, we present a Shiny/R application ShinyCNV, an interactive graphical user interface to view and annotate CNVs.'),
(82418, 2019, 'bio', 'lordFAST: sensitive and Fast Alignment Search Tool for LOng noisy Read sequencing Data', 'Haghshenas, Ehsan|Sahinalp, S Cenk|Hach, Faraz', 'School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.|School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.|Vancouver Prostate Centre, Vancouver, BC, Canada.', 'Canada|Canada|Canada', 6, '-', 'Journal Article', '10.1093/bioinformatics/bty544', '30561550', 'Recent advances in genomics and precision medicine have been made possible through the application of high throughput sequencing (HTS) to large collections of human genomes. Although HTS technologies have proven their use in cataloging human genome variation, computational analysis of the data they generate is still far from being perfect. The main limitation of Illumina and other popular sequencing technologies is their short read length relative to the lengths of (common) genomic repeats. Newer (single molecule sequencing - SMS) technologies such as Pacific Biosciences and Oxford Nanopore are producing longer reads, making it theoretically possible to overcome the difficulties imposed by repeat regions. Unfortunately, because of their high sequencing error rate, reads generated by these technologies are very difficult to work with and cannot be used in many of the standard downstream analysis pipelines. Note that it is not only difficult to find the correct mapping locations of such reads in a reference genome, but also to establish their correct alignment so as to differentiate sequencing errors from real genomic variants. Furthermore, especially since newer SMS instruments provide higher throughput, mapping and alignment need to be performed much faster than before, maintaining high sensitivity.'),
(82419, 2019, 'bio', 'Exploring drivers of gene expression in the Cancer Genome Atlas', 'Rau, Andrea|Flister, Michael|Rui, Hallgeir|Auer, Paul L', 'GABI, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France.|Department of Physiology, Medical College of Wisconsin, Milwaukee, WI, USA.|Department of Pathology, Medical College of Wisconsin, Milwaukee, WI, USA.|Joseph J. Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI, USA.', 'France|United States|United States|United States', 8, '-', 'Journal Article', '10.1093/bioinformatics/bty551', '30561551', 'The Cancer Genome Atlas (TCGA) has greatly advanced cancer research by generating, curating and publicly releasing deeply measured molecular data from thousands of tumor samples. In particular, gene expression measures, both within and across cancer types, have been used to determine the genes and proteins that are active in tumor cells.'),
(82420, 2019, 'bio', 'Pathogen-Host Analysis Tool (PHAT): an integrative platform to analyze next-generation sequencing data', 'Gibb, Christopher M|Jackson, Robert|Mohammed, Sabah|Fiaidhi, Jinan|Zehbe, Ingeborg', 'Probe Development and Biomarker Exploration, Thunder Bay Regional Health Research Institute.|Probe Development and Biomarker Exploration, Thunder Bay Regional Health Research Institute.|Department of Computer Science, Lakehead University, Thunder Bay, Ontario, Canada.|Department of Computer Science, Lakehead University, Thunder Bay, Ontario, Canada.|Probe Development and Biomarker Exploration, Thunder Bay Regional Health Research Institute.', '-|-|Canada|Canada|-', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty1003', '30561651', 'The Pathogen-Host Analysis Tool (PHAT) is an application for processing and analyzing next-generation sequencing (NGS) data as it relates to relationships between pathogens and their hosts. Unlike custom scripts and tedious pipeline programming, PHAT provides an integrative platform encompassing raw and aligned sequence and reference file input, quality control (QC) reporting, alignment and variant calling, linear and circular alignment viewing, and graphical and tabular output. This novel tool aims to be user-friendly for life scientists studying diverse pathogen-host relationships.'),
(82421, 2019, 'bio', 'Inter-residue interactions in alpha-helical transmembrane proteins', 'Mayol, Eduardo|Campillo, Mercedes|Cordomxc3xad, Arnau|Olivella, Mireia', 'Laboratori de Medicina Computacional, Unitat de Bioestadxc3xadstica, Facultat de Medicina, Universitat Autxc3xb2noma de Barcelona, Bellaterra, Spain.|Laboratori de Medicina Computacional, Unitat de Bioestadxc3xadstica, Facultat de Medicina, Universitat Autxc3xb2noma de Barcelona, Bellaterra, Spain.|Laboratori de Medicina Computacional, Unitat de Bioestadxc3xadstica, Facultat de Medicina, Universitat Autxc3xb2noma de Barcelona, Bellaterra, Spain.|Bioinformatics Area, School of International Studies, ESCI-UPF, Barcelona, Spain.', 'Spain|Spain|Spain|Spain', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty978', '30566615', 'The number of available membrane protein structures has markedly increased in the last years and, in parallel, the reliability of the methods to detect transmembrane (TM) segments. In the present report, we characterized inter-residue interactions in xcexb1-helical membrane proteins using a dataset of 3462 TM helices from 430 proteins. This is by far the largest analysis published to date.'),
(82422, 2019, 'bio', 'TriPOINT: a software tool to prioritize important genes in pathways and their non-coding regulators', 'Thibodeau, Asa|Shin, Dong-Guk', 'Department of Computer Science & Engineering, University of Connecticut, Storrs, CT, USA.|Department of Computer Science & Engineering, University of Connecticut, Storrs, CT, USA.', 'United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty998', '30566622', 'Current approaches for pathway analyses focus on representing gene expression levels on graph representations of pathways and conducting pathway enrichment among differentially expressed genes. However, gene expression levels by themselves do not reflect the overall picture as non-coding factors play an important role to regulate gene expression. To incorporate these non-coding factors into pathway analyses and to systematically prioritize genes in a pathway we introduce a new software: Triangulation of Perturbation Origins and Identification of Non-Coding Targets. Triangulation of Perturbation Origins and Identification of Non-Coding Targets is a pathway analysis tool, implemented in Java that identifies the significance of a gene under a condition (e.g. a disease phenotype) by studying graph representations of pathways, analyzing upstream and downstream gene interactions and integrating non-coding regions that may be regulating gene expression levels.'),
(82423, 2019, 'bio', 'DNABarcodeCompatibility: an R-package for optimizing DNA-barcode combinations in multiplex sequencing experiments', 'Trébeau, Céline|Boutet de Monvel, Jacques|Wong Jun Tai, Fabienne|Petit, Christine|Etournay, Raphaël', 'Unité de Génétique et Physiologie de lAudition, Département Neuroscience, Institut Pasteur, Paris, France.|Unité de Génétique et Physiologie de lAudition, Département Neuroscience, Institut Pasteur, Paris, France.|Unité de Génétique et Physiologie de lAudition, Département Neuroscience, Institut Pasteur, Paris, France.|Unité de Génétique et Physiologie de lAudition, Département Neuroscience, Institut Pasteur, Paris, France.|Unité de Génétique et Physiologie de lAudition, Département Neuroscience, Institut Pasteur, Paris, France.', 'France|France|France|France|France', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty1030', '30576403', 'Using adequate DNA barcodes is essential to unambiguously identify each DNA library within a multiplexed set of libraries sequenced using next-generation sequencers. We introduce DNABarcodeCompatibility, an R-package that allows one to design single or dual-barcoding multiplex experiments by imposing desired constraints on the barcodes (including sequencer chemistry, barcode pairwise minimal distance and nucleotide content), while optimizing barcode frequency usage, thereby allowing one to both facilitate the demultiplexing step and spare expensive library-preparation kits. The package comes with a user-friendly interface and a web app developed in Java and Shiny (https://dnabarcodecompatibility.pasteur.fr), respectively, with the aim to help bridge the expertise of core facilities with the experimental needs of non-experienced users.'),
(82424, 2019, 'bio', 'Very low-depth whole-genome sequencing in complex trait association studies', 'Gilly, Arthur|Southam, Lorraine|Suveges, Daniel|Kuchenbaecker, Karoline|Moore, Rachel|Melloni, Giorgio E M|Hatzikotoulas, Konstantinos|Farmaki, Aliki-Eleni|Ritchie, Graham|Schwartzentruber, Jeremy|Danecek, Petr|Kilian, Britt|Pollard, Martin O|Ge, Xiangyu|Tsafantakis, Emmanouil|Dedoussis, George|Zeggini, Eleftheria', 'Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.|Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.|Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.|Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.|Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.|Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.|Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.|Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University of Athens, Athens, Greece.|Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.|Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.|Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.|Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.|Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.|Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.|Anogia Medical Centre, Anogia, Greece.|Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University of Athens, Athens, Greece.|Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|Greece|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|Greece|Greece|United Kingdom', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty1032', '30576415', 'Very low-depth sequencing has been proposed as a cost-effective approach to capture low-frequency and rare variation in complex trait association studies. However, a full characterization of the genotype quality and association power for very low-depth sequencing designs is still lacking.'),
(82425, 2019, 'bio', 'PINSPlus: a tool for tumor subtype discovery in integrated genomic data', 'Nguyen, Hung|Shrestha, Sangam|Draghici, Sorin|Nguyen, Tin', 'Department of Computer Science and Engineering, University of Nevada, Reno, NV, USA.|Department of Computer Science and Engineering, University of Nevada, Reno, NV, USA.|Department of Computer Science, Wayne State University, Detroit, MI, USA.|Department of Computer Science and Engineering, University of Nevada, Reno, NV, USA.', 'United States|United States|United States|United States', 7, '-', 'Journal Article', '10.1093/bioinformatics/bty1049', '30590381', 'Since cancer is a heterogeneous disease, tumor subtyping is crucial for improved treatment and prognosis. We have developed a subtype discovery tool, called PINSPlus, that is: (i) robust against noise and unstable quantitative assays, (ii) able to integrate multiple types of omics data in a single analysis and (iii) dramatically superior to established approaches in identifying known subtypes and novel subgroups with significant survival differences. Our validation on 12,158 samples from 44 datasets shows that PINSPlus vastly outperforms other approaches. The software is easy-to-use and can partition hundreds of patients in a few minutes on a personal computer.'),
(82426, 2019, 'bio', 'Ancestral sequence reconstruction: accounting for structural information by averaging over replacement matrices', 'Moshe, Asher|Pupko, Tal', 'Department of Cell Research and Immunology, School of Molecular Cell Biology and Biotechnology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel.|Department of Cell Research and Immunology, School of Molecular Cell Biology and Biotechnology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel.', 'Israel|Israel', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty1031', '30590382', 'Ancestral sequence reconstruction (ASR) is widely used to understand protein evolution, structure and function. Current ASR methodologies do not fully consider differences in evolutionary constraints among positions imposed by the three-dimensional (3D) structure of the protein. Here, we developed an ASR algorithm that allows different protein sites to evolve according to different mixtures of replacement matrices. We show that assigning replacement matrices to protein positions based on their solvent accessibility leads to ASR with higher log-likelihoods compared to naïve models that assume a single replacement matrix for all sites. Improved ASR log-likelihoods are also demonstrated when solvent accessibility is predicted from protein sequences rather than inferred from a known 3D structure. Finally, we show that using such structure-aware mixture models results in substantial differences in the inferred ancestral sequences.'),
(82427, 2019, 'bio', 'Smooth orientation-dependent scoring function for coarse-grained protein quality assessment', 'Karasikov, Mikhail|Pagès, Guillaume|Grudinin, Sergei', 'Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, Grenoble, France.|Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, Grenoble, France.|Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, Grenoble, France.', 'France|France|France', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty1037', '30590384', 'Protein quality assessment (QA) is a crucial element of protein structure prediction, a fundamental and yet open problem in structural bioinformatics. QA aims at ranking predicted protein models to select the best candidates. The assessment can be performed based either on a single model or on a consensus derived from an ensemble of models. The latter strategy can yield very high performance but substantially depends on the pool of available candidate models, which limits its applicability. Hence, single-model QA methods remain an important research target, also because they can assist the sampling of candidate models.'),
(82428, 2019, 'bio', 'Computing irreversible minimal cut sets in genome-scale metabolic networks via flux cone projection', 'Röhl, Annika|Riou, Tanguy|Bockmayr, Alexander', 'Department of Mathematics and Computer Science, FB Mathematik und Informatik, Freie Universität Berlin, Berlin, Germany.|Department FRANCE, Ecole Centrale de Nantes, Nantes, France.|Department of Mathematics and Computer Science, FB Mathematik und Informatik, Freie Universität Berlin, Berlin, Germany.', 'Germany|France|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty1027', '30590390', 'Minimal cut sets (MCSs) for metabolic networks are sets of reactions which, if they are removed from the network, prevent a target reaction from carrying flux. To compute MCSs different methods exist, which may fail to find sufficiently many MCSs for larger genome-scale networks.'),
(82429, 2019, 'bio', 'Curse: building expression atlases and co-expression networks from public RNA-Seq data', 'Vaneechoutte, Dries|Vandepoele, Klaas', 'VIB Center for Plant Systems Biology, VIB, Ghent B, Belgium.|VIB Center for Plant Systems Biology, VIB, Ghent B, Belgium.', 'Belgium|Belgium', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty1052', '30590391', 'Public RNA-Sequencing (RNA-Seq) datasets are a valuable resource for transcriptome analyses, but their accessibility is hindered by the imperfect quality and presentation of their metadata and by the complexity of processing raw sequencing data. The Curse suite was created to alleviate these problems. It consists of an online curation tool named Curse to efficiently build compendia of experiments hosted on the Sequence Read Archive, and a lightweight pipeline named Prose to download and process the RNA-Seq data into expression atlases and co-expression networks. Curse networks showed improved linking of functionally related genes compared to the state-of-the-art.'),
(82430, 2019, 'bio', 'Systematic characterization and prediction of post-translational modification cross-talk between proteins', 'Huang, Rongting|Huang, Yuanhua|Guo, Yubin|Ji, Shangwei|Lu, Ming|Li, Tingting', 'Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK.|Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China.|Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China.|Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China.|Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China.', 'China|United Kingdom|China|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty1033', '30590394', 'Protein post-translational modifications (PTMs) regulate a wide range of cellular protein functions. Many PTM sites from the same (intra) or different (inter) proteins often cooperate with each other to perform a function, which is defined as PTM cross-talk. PTM cross-talk within proteins attracted great attentions in the past a few years. However, the inter-protein PTM cross-talk is largely under studied due to its large protein pair space and lack of a gold standard dataset, even though the PTM interplay between proteins is a key element in cell signaling and regulatory networks.'),
(82431, 2019, 'bio', 'PconsC4: fast, accurate and hassle-free contact predictions', 'Michel, Mirco|Menéndez Hurtado, David|Elofsson, Arne', 'Science for Life Laboratory and Department of Biochemistry and Biophysics, Stockholm University, Stockholm 10691, Sweden.|Science for Life Laboratory and Department of Biochemistry and Biophysics, Stockholm University, Stockholm 10691, Sweden.|Science for Life Laboratory and Department of Biochemistry and Biophysics, Stockholm University, Stockholm 10691, Sweden.', 'Sweden|Sweden|Sweden', 9, '-', 'Journal Article', '10.1093/bioinformatics/bty1036', '30590407', 'Residue contact prediction was revolutionized recently by the introduction of direct coupling analysis (DCA). Further improvements, in particular for small families, have been obtained by the combination of DCA and deep learning methods. However, existing deep learning contact prediction methods often rely on a number of external programs and are therefore computationally expensive.'),
(82432, 2019, 'bio', 'mAHTPred: a sequence-based meta-predictor for improving the prediction of anti-hypertensive peptides using effective feature representation', 'Manavalan, Balachandran|Basith, Shaherin|Shin, Tae Hwan|Wei, Leyi|Lee, Gwang', 'Department of Physiology, Ajou University School of Medicine, Suwon, Republic of Korea.|Department of Physiology, Ajou University School of Medicine, Suwon, Republic of Korea.|Department of Physiology, Ajou University School of Medicine, Suwon, Republic of Korea.|School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China.|Department of Physiology, Ajou University School of Medicine, Suwon, Republic of Korea.', '-|-|-|China|-', 40, '-', 'Journal Article', '10.1093/bioinformatics/bty1047', '30590410', 'Cardiovascular disease is the primary cause of death globally accounting for approximately 17.7 million deaths per year. One of the stakes linked with cardiovascular diseases and other complications is hypertension. Naturally derived bioactive peptides with antihypertensive activities serve as promising alternatives to pharmaceutical drugs. So far, there is no comprehensive analysis, assessment of diverse features and implementation of various machine-learning (ML) algorithms applied for antihypertensive peptide (AHTP) model construction.'),
(82433, 2019, 'bio', 'poreTally: run and publish de novo nanopore assembler benchmarks', 'de Lannoy, Carlos|Risse, Judith|de Ridder, Dick', 'Bioinformatics Group, WUR, 6700 AP Wageningen, The Netherlands.|Department of Animal Ecology, Netherlands Institute of Ecology (NIOO-KNAW), 6708 PB Wageningen, The Netherlands.|Bioinformatics Group, WUR, 6700 AP Wageningen, The Netherlands.', 'Netherlands|Netherlands|Netherlands', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty1045', '30590415', 'Nanopore sequencing is a novel development in nucleic acid analysis. As such, nanopore-sequencing hardware and software are updated frequently and extensively, which quickly renders peer-reviewed publications on analysis pipeline benchmarking efforts outdated. To provide the user community with a faster, more flexible alternative to peer-reviewed benchmark papers for de novo assembly tool performance we constructed poreTally, a comprehensive benchmarking tool. poreTally automatically assembles a given read set using several often-used assembly pipelines, analyzes the resulting assemblies for correctness and continuity, and finally generates a quality report, which can immediately be published on Github/Gitlab.'),
(82434, 2019, 'bio', 'CSHAP: efficient haplotype frequency estimation based on sparse representation', 'Zhou, Yinsheng|Zhang, Han|Yang, Yaning', 'Department of Statistics and Finance, University of Science and Technology of China, Hefei, Anhui, China.|Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.|Department of Statistics and Finance, University of Science and Technology of China, Hefei, Anhui, China.', 'China|United States|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty1040', '30590428', 'Estimating haplotype frequencies from genotype data plays an important role in genetic analysis. In silico methods are usually computationally involved since phase information is not available. Due to tight linkage disequilibrium and low recombination rates, the number of haplotypes observed in human populations is far less than all the possibilities. This motivates us to solve the estimation problem by maximizing the sparsity of existing haplotypes. Here, we propose a new algorithm by applying the compressive sensing (CS) theory in the field of signal processing, compressive sensing haplotype inference (CSHAP), to solve the sparse representation of haplotype frequencies based on allele frequencies and between-allele co-variances.'),
(82435, 2019, 'bio', 'Reply to the paper: Misunderstood parameters of NCBI BLAST impacts the correctness of bioinformatics workflows', 'Madden, Thomas L|Busby, Ben|Ye, Jian', 'National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty1026', '30590429', '-'),
(82436, 2019, 'bio', 'KinVis: a visualization tool to detect cryptic relatedness in genetic datasets', 'Ullah, Ehsan|Aupetit, Michaël|Das, Arun|Patil, Abhishek|Al Muftah, Noora|Rawi, Reda|Saad, Mohamad|Bensmail, Halima', 'Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.|Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.|Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.|Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.|Department of Computational Biology and Quantitative Genetics, Harvard School of Public Health, Boston, MA, USA.|Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.|Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.|Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.', 'Qatar|Qatar|Qatar|Qatar|United States|Qatar|Qatar|Qatar', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty1028', '30590437', 'It is important to characterize individual relatedness in terms of familial relationships and underlying population structure in genome-wide association studies for correct downstream analysis. The characterization of individual relatedness becomes vital if the cohort is to be used as reference panel in other studies for association tests and for identifying ethnic diversities. In this paper, we propose a kinship visualization tool to detect cryptic relatedness between subjects. We utilize multi-dimensional scaling, bar charts, heat maps and node-link visualizations to enable analysis of relatedness information.'),
(82437, 2019, 'bio', 'PredGly: predicting lysine glycation sites for Homo sapiens based on XGboost feature optimization', 'Yu, Jialin|Shi, Shaoping|Zhang, Fang|Chen, Guodong|Cao, Man', 'Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences, Nanchang University, Nanchang, China.|Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences, Nanchang University, Nanchang, China.|Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences, Nanchang University, Nanchang, China.|Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences, Nanchang University, Nanchang, China.|Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences, Nanchang University, Nanchang, China.', 'China|China|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty1043', '30590442', 'Protein glycation is a familiar post-translational modification (PTM) which is a two-step non-enzymatic reaction. Glycation not only impairs the function but also changes the characteristics of the proteins so that it is related to many human diseases. It is still much more difficult to systematically detect glycation sites due to the glycated residues without crucial patterns. Computational approaches, which can filter supposed sites prior to experimental verification, can extremely increase the efficiency of experiment work. However, the previous lysine glycation prediction method uses a small number of training datasets. Hence, the model is not generalized or pervasive.'),
(82438, 2019, 'bio', 'Improved mutant function prediction via PACT: Protein Analysis and Classifier Toolkit', 'Klesmith, Justin R|Hackel, Benjamin J', 'Department of Chemical Engineering and Materials Science, University of Minnesota, Twin Cities, Minneapolis, MN, USA.|Department of Chemical Engineering and Materials Science, University of Minnesota, Twin Cities, Minneapolis, MN, USA.', 'United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty1042', '30590444', 'Deep mutational scanning experiments have enabled the measurement of the sequence-function relationship for thousands of mutations in a single experiment. The Protein Analysis and Classifier Toolkit (PACT) is a Python software package that marries the fitness metric of a given mutation within these experiments to sequence and structural features enabling downstream analyses. PACT enables the easy development of user sharable protocols for custom deep mutational scanning experiments as all code is modular and reusable between protocols. Protocols for mutational libraries with single or multiple mutations are included. To exemplify its utility, PACT assessed two deep mutational scanning datasets that measured the tradeoff of enzyme activity and enzyme stability.'),
(82439, 2019, 'bio', 'Systematic selection of chemical fingerprint features improves the Gibbs energy prediction of biochemical reactions', 'Alazmi, Meshari|Kuwahara, Hiroyuki|Soufan, Othman|Ding, Lizhong|Gao, Xin', 'King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Thuwal, Saudi Arabia.|King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Thuwal, Saudi Arabia.|Institute of Parasitology, McGill University, Montreal, Quebec, Canada.|Inception Institute of Artificial Intelligence (IIAI), Abu Dhabi, UAE.|King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Thuwal, Saudi Arabia.', 'Saudi Arabia|Saudi Arabia|Canada|-|Saudi Arabia', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty1035', '30590445', 'Accurate and wide-ranging prediction of thermodynamic parameters for biochemical reactions can facilitate deeper insights into the workings and the design of metabolic systems.'),
(82440, 2019, 'bio', 'Fully unsupervised symmetry-based mitosis detection in time-lapse cell microscopy', 'Gilad, Topaz|Reyes, Jose|Chen, Jia-Yun|Lahav, Galit|Riklin Raviv, Tammy', 'Department of Electrical and Computer Engineering and the Zlotwoski Center for Neuroscience, Ben-Gurion University of the Negev, Beersheba, Israel.|Department of Systems Biology, Harvard Medical School, Boston, MA, USA.|Department of Systems Biology, Harvard Medical School, Boston, MA, USA.|Department of Systems Biology, Harvard Medical School, Boston, MA, USA.|Department of Electrical and Computer Engineering and the Zlotwoski Center for Neuroscience, Ben-Gurion University of the Negev, Beersheba, Israel.', 'Israel|United States|United States|United States|Israel', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty1034', '30590471', 'Cell microscopy datasets have great diversity due to variability in cell types, imaging techniques and protocols. Existing methods are either tailored to specific datasets or are based on supervised learning, which requires comprehensive manual annotations. Using the latter approach, however, poses a significant difficulty due to the imbalance between the number of mitotic cells with respect to the entire cell population in a time-lapse microscopy sequence.'),
(82441, 2019, 'bio', 'Integration of accessibility data from structure probing into RNA-RNA interaction prediction', 'Miladi, Milad|Montaseri, Soheila|Backofen, Rolf|Raden, Martin', 'Department of Computer Science, Bioinformatics Group, University of Freiburg, Freiburg D-79110, Germany.|Department of Computer Science, Bioinformatics Group, University of Freiburg, Freiburg D-79110, Germany.|Department of Computer Science, Bioinformatics Group, University of Freiburg, Freiburg D-79110, Germany.|Department of Computer Science, Bioinformatics Group, University of Freiburg, Freiburg D-79110, Germany.', 'Germany|Germany|Germany|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty1029', '30590479', 'Experimental structure probing data has been shown to improve thermodynamics-based RNA secondary structure prediction. To this end, chemical reactivity information (as provided e.g. by SHAPE) is incorporated, which encodes whether or not individual nucleotides are involved in intra-molecular structure. Since inter-molecular RNA-RNA interactions are often confined to unpaired RNA regions, SHAPE data is even more promising to improve interaction prediction. Here, we show how such experimental data can be incorporated seamlessly into accessibility-based RNA-RNA interaction prediction approaches, as implemented in IntaRNA. This is possible via the computation and use of unpaired probabilities that incorporate the structure probing information. We show that experimental SHAPE data can significantly improve RNA-RNA interaction prediction. We evaluate our approach by investigating interactions of a spliceosomal U1 snRNA transcript with its target splice sites. When SHAPE data is incorporated, known target sites are predicted with increased precision and specificity.'),
(82442, 2019, 'bio', 'M3Drop: dropout-based feature selection for scRNASeq', 'Andrews, Tallulah S|Hemberg, Martin', 'Department of Cellular Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridgshire, UK.|Department of Cellular Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridgshire, UK.', 'United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty1044', '30590489', 'Most genomes contain thousands of genes, but for most functional responses, only a subset of those genes are relevant. To facilitate many single-cell RNASeq (scRNASeq) analyses the set of genes is often reduced through feature selection, i.e. by removing genes only subject to technical noise.'),
(82443, 2019, 'bio', 'Multiview: a software package for multiview pattern recognition methods', 'Kanaan-Izquierdo, Samir|Ziyatdinov, Andrey|Burgueño, Maria Araceli|Perera-Lluna, Alexandre', 'Centre de Recerca en Enginyeria Biomèdica, Universitat Politècnica de Catalunya, Barcelona, Spain.|Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.|Universitat Oberta de Catalunya, Barcelona, Spain.|Centre de Recerca en Enginyeria Biomèdica, Universitat Politècnica de Catalunya, Barcelona, Spain.', 'Spain|United States|Spain|Spain', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty1039', '30596886', 'Multiview datasets are the norm in bioinformatics, often under the label multi-omics. Multiview data are gathered from several experiments, measurements or feature sets available for the same subjects. Recent studies in pattern recognition have shown the advantage of using multiview methods of clustering and dimensionality reduction, however, none of these methods are readily available to the extent of our knowledge. Multiview extensions of four well-known pattern recognition methods are proposed here. Three multiview dimensionality reduction methods: multiview t-distributed stochastic neighbour embedding, multiview multidimensional scaling and multiview minimum curvilinearity embedding, as well as a multiview spectral clustering method. Often they produce better results than their single-view counterparts, tested here on four multiview datasets.'),
(82444, 2019, 'bio', 'Linking sequence patterns and functionality of alpha-helical antimicrobial peptides', 'Eliseev, Igor E|Terterov, Ivan N|Yudenko, Anna N|Shamova, Olga V', 'Nanobiotechnology Lab, St Petersburg Academic University, St Petersburg, Russian Federation.|Nanobiotechnology Lab, St Petersburg Academic University, St Petersburg, Russian Federation.|Nanobiotechnology Lab, St Petersburg Academic University, St Petersburg, Russian Federation.|Department of General Pathology and Pathophysiology, Institute of Experimental Medicine, St Petersburg, Russian Federation.', 'Russian Federation|Russian Federation|Russian Federation|Russian Federation', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty1048', '30596889', 'The rational design of antimicrobial peptides (AMPs) with increased therapeutic potential requires deep understanding of the determinants of their activities. Inspired by the computational linguistic approach, we hypothesized that sequence patterns may encode the functional features of AMPs.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(82445, 2019, 'bio', 'GfaViz: flexible and interactive visualization of GFA sequence graphs', 'Gonnella, Giorgio|Niehus, Niklas|Kurtz, Stefan', 'Universität Hamburg, MIN-Fakultät, ZBH - Center for Bioinformatics, Hamburg, Germany.|Universität Hamburg, MIN-Fakultät, ZBH - Center for Bioinformatics, Hamburg, Germany.|Universität Hamburg, MIN-Fakultät, ZBH - Center for Bioinformatics, Hamburg, Germany.', 'Germany|Germany|Germany', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty1046', '30596893', 'The graphical fragment assembly (GFA) formats are emerging standard formats for the representation of sequence graphs. Although GFA 1 was primarily targeting assembly graphs, the newer GFA 2 format introduces several features, which makes it suitable for representing other kinds of information, such as scaffolding graphs, variation graphs, alignment graphs and colored metagenomic graphs. Here, we present GfaViz, an interactive graphical tool for the visualization of sequence graphs in GFA format. The software supports all new features of GFA 2 and introduces conventions for their visualization. The user can choose between two different layouts and multiple styles for representing single elements or groups. All customizations can be stored in custom tags of the GFA format itself, without requiring external configuration files. Stylesheets are supported for storing standard configuration options for groups of files. The visualizations can be exported to raster and vector graphics formats. A command line interface allows for batch generation of images.'),
(82446, 2019, 'bio', 'ACE: absolute copy number estimation from low-coverage whole-genome sequencing data', 'Poell, Jos B|Mendeville, Matias|Sie, Daoud|Brink, Arjen|Brakenhoff, Ruud H|Ylstra, Bauke', 'Otolaryngology/Head and Neck Surgery, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.|Pathology, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, Amsterdam, the Netherlands.|Pathology, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, Amsterdam, the Netherlands.|Otolaryngology/Head and Neck Surgery, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.|Otolaryngology/Head and Neck Surgery, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.|Pathology, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, Amsterdam, the Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty1055', '30596895', 'Chromosomal copy number aberrations can be efficiently detected and quantified using low-coverage whole-genome sequencing, but analysis is hampered by the lack of knowledge on absolute DNA copy numbers and tumor purity. Here, we describe an analytical tool for Absolute Copy number Estimation, ACE, which scales relative copy number signals from chromosomal segments to optimally fit absolute copy numbers, without the need for additional genetic information, such as SNP data. In doing so, ACE derives an estimate of tumor purity as well. ACE facilitates analysis of large numbers of samples, while maintaining the flexibility to customize models and generate output of single samples.'),
(82447, 2019, 'bio', 'The COP9 signalosome influences the epigenetic landscape of Arabidopsis thaliana', 'Tuller, Tamir|Diament, Alon|Yahalom, Avital|Zemach, Assaf|Atar, Shimshi|Chamovitz, Daniel A', 'Department of Biomedical Engineering.|Department of Biomedical Engineering.|School of Plant Sciences and Food Security, Tel Aviv University, Tel Aviv, Israel.|School of Plant Sciences and Food Security, Tel Aviv University, Tel Aviv, Israel.|Department of Biomedical Engineering.|School of Plant Sciences and Food Security, Tel Aviv University, Tel Aviv, Israel.', '-|-|Israel|Israel|-|Israel', 3, '-', 'Journal Article', '10.1093/bioinformatics/bty1053', '30596896', 'The COP9 signalosome is a highly conserved multi-protein complex consisting of eight subunits, which influences key developmental pathways through its regulation of protein stability and transcription. In Arabidopsis thaliana, mutations in the COP9 signalosome exhibit a number of diverse pleiotropic phenotypes. Total or partial loss of COP9 signalosome function in Arabidopsis leads to misregulation of a number of genes involved in DNA methylation, suggesting that part of the pleiotropic phenotype is due to global effects on DNA methylation.'),
(82448, 2019, 'bio', 'Single-cell RNA-seq interpretations using evolutionary multiobjective ensemble pruning', 'Li, Xiangtao|Zhang, Shixiong|Wong, Ka-Chun', 'School of Computer Science and Information Technology, Northeast Normal University, Changchun, Jilin, China.|Department of Computer Science, City University of Hong Kong, Hong Kong SAR.|Department of Computer Science, City University of Hong Kong, Hong Kong SAR.', 'China|Hong Kong|Hong Kong', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty1056', '30596898', 'In recent years, single-cell RNA sequencing enables us to discover cell types or even subtypes. Its increasing availability provides opportunities to identify cell populations from single-cell RNA-seq data. Computational methods have been employed to reveal the gene expression variations among multiple cell populations. Unfortunately, the existing ones can suffer from realistic restrictions such as experimental noises, numerical instability, high dimensionality and computational scalability.'),
(82449, 2019, 'bio', 'MoMo: discovery of statistically significant post-translational modification motifs', 'Cheng, Alice|Grant, Charles E|Noble, William S|Bailey, Timothy L', 'Department of Genome Sciences, University of Washington, Seattle, WA, USA.|Department of Genome Sciences, University of Washington, Seattle, WA, USA.|Department of Genome Sciences, University of Washington, Seattle, WA, USA.|Department of Pharmacology, University of Nevada, Reno, NV, USA.', 'United States|United States|United States|United States', 14, '-', 'Journal Article', '10.1093/bioinformatics/bty1058', '30596994', 'Post-translational modifications (PTMs) of proteins are associated with many significant biological functions and can be identified in high throughput using tandem mass spectrometry. Many PTMs are associated with short sequence patterns called motifs that help localize the modifying enzyme. Accordingly, many algorithms have been designed to identify these motifs from mass spectrometry data. Accurate statistical confidence estimates for discovered motifs are critically important for proper interpretation and in the design of downstream experimental validation.'),
(82450, 2019, 'bio', 'AnnoFly: annotating Drosophila embryonic images based on an attention-enhanced RNN model', 'Yang, Yang|Zhou, Mingyu|Fang, Qingwei|Shen, Hong-Bin', 'Center for Brain-Like Computing and Machine Intelligence, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China.|Center for Brain-Like Computing and Machine Intelligence, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China.|School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.|Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China.', 'China|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty1064', '30601935', 'In the post-genomic era, image-based transcriptomics have received huge attention, because the visualization of gene expression distribution is able to reveal spatial and temporal expression pattern, which is significantly important for understanding biological mechanisms. The Berkeley Drosophila Genome Project has collected a large-scale spatial gene expression database for studying Drosophila embryogenesis. Given the expression images, how to annotate them for the study of Drosophila embryonic development is the next urgent task. In order to speed up the labor-intensive labeling work, automatic tools are highly desired. However, conventional image annotation tools are not applicable here, because the labeling is at the gene-level rather than the image-level, where each gene is represented by a bag of multiple related images, showing a multi-instance phenomenon, and the image quality varies by image orientations and experiment batches. Moreover, different local regions of an image correspond to different CV annotation terms, i.e. an image has multiple labels. Designing an accurate annotation tool in such a multi-instance multi-label scenario is a very challenging task.'),
(82451, 2019, 'bio', 'DeepPhos: prediction of protein phosphorylation sites with deep learning', 'Luo, Fenglin|Wang, Minghui|Liu, Yu|Zhao, Xing-Ming|Li, Ao', 'School of Information Science and Technology.|School of Information Science and Technology.|School of Information Science and Technology.|Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.|School of Information Science and Technology.', '-|-|-|China|-', 17, '-', 'Journal Article', '10.1093/bioinformatics/bty1051', '30601936', 'Phosphorylation is the most studied post-translational modification, which is crucial for multiple biological processes. Recently, many efforts have been taken to develop computational predictors for phosphorylation site prediction, but most of them are based on feature selection and discriminative classification. Thus, it is useful to develop a novel and highly accurate predictor that can unveil intricate patterns automatically for protein phosphorylation sites.'),
(82452, 2019, 'bio', 'SIAN: software for structural identifiability analysis of ODE models', 'Hong, Hoon|Ovchinnikov, Alexey|Pogudin, Gleb|Yap, Chee', 'Department of Mathematics, North Carolina State University, Raleigh, NC, USA.|Department of Mathematics, CUNY Queens College and Ph.D. Programs in Mathematics and Computer Science, CUNY Graduate Center, New York, NY, USA.|Department of Computer Science, Courant Institute of Mathematical Sciences, New York University, New York, NY, USA.|Department of Computer Science, Courant Institute of Mathematical Sciences, New York University, New York, NY, USA.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty1069', '30601937', 'Biological processes are often modeled by ordinary differential equations with unknown parameters. The unknown parameters are usually estimated from experimental data. In some cases, due to the structure of the model, this estimation problem does not have a unique solution even in the case of continuous noise-free data. It is therefore desirable to check the uniqueness a priori before carrying out actual experiments. We present a new software SIAN (Structural Identifiability ANalyser) that does this. Our software can tackle problems that could not be tackled by previously developed packages.'),
(82453, 2019, 'bio', 'MetFlow: an interactive and integrated workflow for metabolomics data cleaning and differential metabolite discovery', 'Shen, Xiaotao|Zhu, Zheng-Jiang', 'Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China.|Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China.', 'China|China', 5, '-', 'Journal Article', '10.1093/bioinformatics/bty1066', '30601938', 'Mass spectrometry-based metabolomics aims to profile the metabolic changes in biological systems and identify differential metabolites related to physiological phenotypes and aberrant activities. However, many confounding factors during data acquisition complicate metabolomics data, which is characterized by high dimensionality, uncertain degrees of missing and zero values, nonlinearity, unwanted variations and non-normality. Therefore, prior to differential metabolite discovery analysis, various types of data cleaning such as batch alignment, missing value imputation, data normalization and scaling are essentially required for data post-processing. Here, we developed an interactive web server, namely, MetFlow, to provide an integrated and comprehensive workflow for metabolomics data cleaning and differential metabolite discovery.'),
(82454, 2019, 'bio', 'UbiHub: a data hub for the explorers of ubiquitination pathways', 'Liu, Lihua|Damerell, David R|Koukouflis, Leonidas|Tong, Yufeng|Marsden, Brian D|Schapira, Matthieu', 'Structural Genomics Consortium, University of Toronto, Toronto, ON, Canada.|Structural Genomics Consortium, University of Oxford, Headington Oxford, Oxfordshire, UK.|Structural Genomics Consortium, University of Oxford, Headington Oxford, Oxfordshire, UK.|Structural Genomics Consortium, University of Toronto, Toronto, ON, Canada.|Structural Genomics Consortium, University of Oxford, Headington Oxford, Oxfordshire, UK.|Structural Genomics Consortium, University of Toronto, Toronto, ON, Canada.', 'Canada|United Kingdom|United Kingdom|Canada|United Kingdom|Canada', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty1067', '30601939', 'Protein ubiquitination plays a central role in important cellular machineries such as protein degradation or chromatin-mediated signaling. With the recent discovery of the first potent ubiquitin-specific protease inhibitors, and the maturation of proteolysis targeting chimeras as promising chemical tools to exploit the ubiquitin-proteasome system, protein target classes associated with ubiquitination pathways are becoming the focus of intense drug-discovery efforts.'),
(82455, 2019, 'bio', 'BreakID: genomics breakpoints identification to detect gene fusion events using discordant pairs and split reads', 'Jin, Linfang|Lai, Jinhuo|Zhang, Yang|Fu, Ying|Wang, Shuhang|Dai, Heng|Huang, Bingding', 'Department of Research and Development, Sinotech Genomics Inc., Shanghai, China.|Department of Medical Oncology, Fujian Medical University Union Hospital, Fuzhou, China.|Department of Research and Development, Sinotech Genomics Inc., Shanghai, China.|Department of Research and Development, Sinotech Genomics Inc., Shanghai, China.|Thoracic Medical Oncology Department, Peking University Cancer Hospital, Beijing, China.|Department of Research and Development, Sinotech Genomics Inc., Shanghai, China.|Department of Research and Development, Sinotech Genomics Inc., Shanghai, China.', 'China|China|China|China|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty1070', '30601940', 'Here we developed a tool called Breakpoint Identification (BreakID) to identity fusion events from targeted sequencing data. Taking discordant read pairs and split reads as supporting evidences, BreakID can identify gene fusion breakpoints at single nucleotide resolution. After validation with confirmed fusion events in cancer cell lines, we have proved that BreakID can achieve high sensitivity of 90.63% along with PPV of 100% at sequencing depth of 500× and perform better than other available fusion detection tools. We anticipate that BreakID will have an extensive popularity in the detection and analysis of fusions involved in clinical and research sequencing scenarios.'),
(82456, 2019, 'bio', 'DeepCoil-a fast and accurate prediction of coiled-coil domains in protein sequences', 'Ludwiczak, Jan|Winski, Aleksander|Szczepaniak, Krzysztof|Alva, Vikram|Dunin-Horkawicz, Stanislaw', 'Laboratory of Structural Bioinformatics, Centre of New Technologies, University of Warsaw, Warsaw, Poland.|Laboratory of Structural Bioinformatics, Centre of New Technologies, University of Warsaw, Warsaw, Poland.|Laboratory of Structural Bioinformatics, Centre of New Technologies, University of Warsaw, Warsaw, Poland.|Department of Protein Evolution, Max Planck Institute for Developmental Biology, Tübingen, Germany.|Laboratory of Structural Bioinformatics, Centre of New Technologies, University of Warsaw, Warsaw, Poland.', 'Poland|Poland|Poland|Germany|Poland', 11, '-', 'Journal Article', '10.1093/bioinformatics/bty1062', '30601942', 'Coiled coils are protein structural domains that mediate a plethora of biological interactions, and thus their reliable annotation is crucial for studies of protein structure and function.'),
(82457, 2019, 'bio', 'Promoter analysis and prediction in the human genome using sequence-based deep learning models', 'Umarov, Ramzan|Kuwahara, Hiroyuki|Li, Yu|Gao, Xin|Solovyev, Victor', 'Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.|Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.|Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.|Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.|Department of Cell Biology, Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia.', 'Saudi Arabia|Saudi Arabia|Saudi Arabia|Saudi Arabia|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty1068', '30601980', 'Computational identification of promoters is notoriously difficult as human genes often have unique promoter sequences that provide regulation of transcription and interaction with transcription initiation complex. While there are many attempts to develop computational promoter identification methods, we have no reliable tool to analyze long genomic sequences.'),
(82458, 2019, 'bio', 'Simulation of heterogeneous tumour genomes with HeteroGenesis and in silico whole exome sequencing', 'Tanner, Georgette|Westhead, David R|Droop, Alastair|Stead, Lucy F', 'Leeds Institute of Medical Research at St Jamess, St Jamess University Hospital, Leeds, UK.|School of Molecular and Cellular Biology, University of Leeds, Leeds, UK.|Leeds Institute of Data Analytics, University of Leeds, Leeds, UK.|Leeds Institute of Medical Research at St Jamess, St Jamess University Hospital, Leeds, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty1063', '30615054', 'Tumour evolution results in progressive cancer phenotypes such as metastatic spread and treatment resistance. To better treat cancers, we must characterize tumour evolution and the genetic events that confer progressive phenotypes. This is facilitated by high coverage genome or exome sequencing. However, the best approach by which, or indeed whether, these data can be used to accurately model and interpret underlying evolutionary dynamics is yet to be confirmed. Establishing this requires sequencing data from appropriately heterogeneous tumours in which the exact trajectory and combination of events occurring throughout its evolution are known. We therefore developed HeteroGenesis: a tool to generate realistically evolved tumour genomes, which can be sequenced using weighted-Wessim (w-Wessim), an in silico exome sequencing tool that we have adapted from previous methods. HeteroGenesis simulates more complex and realistic heterogeneous tumour genomes than existing methods, can model different evolutionary dynamics, and enables the creation of multi-region and longitudinal data.'),
(82459, 2019, 'bio', 'CRISPR-DT: designing gRNAs for the CRISPR-Cpf1 system with improved target efficiency and specificity', 'Zhu, Houxiang|Liang, Chun', 'Department of Biology, Miami University, Oxford, OH, USA.|Department of Biology, Miami University, Oxford, OH, USA.', 'United States|United States', 5, '-', 'Journal Article', '10.1093/bioinformatics/bty1061', '30615056', 'The Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-Cpf1 system has been successfully applied in genome editing. However, target efficiency of the CRISPR-Cpf1 system varies among different guide RNA (gRNA) sequences.'),
(82460, 2019, 'bio', 'Boost-HiC: computational enhancement of long-range contacts in chromosomal contact maps', 'Carron, L|Morlot, J B|Matthys, V|Lesne, A|Mozziconacci, J', 'Sorbonne Université, CNRS, Laboratoire de Physique Théorique de la Matière Condensée, LPTMC, Paris, F, France.|Sorbonne Université, CNRS, Laboratoire de Physique Théorique de la Matière Condensée, LPTMC, Paris, F, France.|Sorbonne Université, CNRS, Laboratoire de Physique Théorique de la Matière Condensée, LPTMC, Paris, F, France.|Sorbonne Université, CNRS, Laboratoire de Physique Théorique de la Matière Condensée, LPTMC, Paris, F, France.|Sorbonne Université, CNRS, Laboratoire de Physique Théorique de la Matière Condensée, LPTMC, Paris, F, France.', 'France|France|France|France|France', 3, '-', 'Journal Article', '10.1093/bioinformatics/bty1059', '30615061', 'Genome-wide chromosomal contact maps are widely used to uncover the 3D organization of genomes. They rely on collecting millions of contacting pairs of genomic loci. Contacts at short range are usually well measured in experiments, while there is a lot of missing information about long-range contacts.'),
(82461, 2019, 'bio', 'MMseqs2 desktop and local web server app for fast, interactive sequence searches', 'Mirdita, Milot|Steinegger, Martin|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', 8, '-', 'Journal Article', '10.1093/bioinformatics/bty1057', '30615063', 'The MMseqs2 desktop and web server app facilitates interactive sequence searches through custom protein sequence and profile databases on personal workstations. By eliminating MMseqs2s runtime overhead, we reduced response times to a few seconds at sensitivities close to BLAST.'),
(82462, 2019, 'bio', 'polyDFEv20: testing for invariance of the distribution of fitness effects within and across species', 'Tataru, Paula|Bataillon, Thomas', 'Bioinformatics Research Centre, Aarhus University, DK Aarhus, Denmark.|Bioinformatics Research Centre, Aarhus University, DK Aarhus, Denmark.', 'Denmark|Denmark', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty1060', '30615114', 'Distribution of fitness effects (DFE) of mutations can be inferred from site frequency spectrum (SFS) data. There is mounting interest to determine whether distinct genomic regions and/or species share a common DFE, or whether evidence exists for differences among them. polyDFEv2.0 fits multiple SFS datasets at once and provides likelihood ratio tests for DFE invariance across datasets. Simulations show that testing for DFE invariance across genomic regions within a species requires models accounting for distinct sources of heterogeneity (chance and genuine difference in DFE) underlying differences in SFS data in these regions. Not accounting for this will result in the spurious detection of DFE differences.'),
(82463, 2019, 'bio', 'The DNA walk and its demonstration of deterministic chaos-relevance to genomic alterations in lung cancer', 'Hewelt, Blake|Li, Haiqing|Jolly, Mohit Kumar|Kulkarni, Prakash|Mambetsariev, Isa|Salgia, Ravi', 'Department of Medical Oncology and Therapeutics Research.|Department of Computational & Quantitative Medicine, Beckman Research Institute, City of Hope, Duarte, CA, USA.|Center for Theoretical Biological Physics, Rice University, Houston, TX, USA.|Department of Medical Oncology and Therapeutics Research.|Department of Medical Oncology and Therapeutics Research.|Department of Medical Oncology and Therapeutics Research.', 'United States|United States|United States|United States|United States|United States', 2, '-', 'Journal Article', '10.1093/bioinformatics/bty1021', '30615123', 'Advancements in cancer genetics have facilitated the development of therapies with actionable mutations. Although mutated genes have been studied extensively, their chaotic behavior has not been appreciated. Thus, in contrast to naïve DNA, mutated DNA sequences can display characteristics of unpredictability and sensitivity to the initial conditions that may be dictated by the environment, expression patterns and presence of other genomic alterations. Employing a DNA walk as a form of 2D analysis of the nucleotide sequence, we demonstrate that chaotic behavior in the sequence of a mutated gene can be predicted.'),
(82464, 2019, 'bio', 'Breaking the paradigm: Dr Insight empowers signature-free, enhanced drug repurposing', 'Chan, Jinyan|Wang, Xuan|Turner, Jacob A|Baldwin, Nicole E|Gu, Jinghua', 'Baylor Scott & White Research Institute, Dallas, TX, USA.|Baylor Scott & White Research Institute, Dallas, TX, USA.|Department of Mathematics and Statistics, Stephen F. Austin State University, Nacogdoches, TX, USA.|Baylor Scott & White Research Institute, Dallas, TX, USA.|Baylor Scott & White Research Institute, Dallas, TX, USA.', 'United States|United States|United States|United States|United States', 6, '-', 'Journal Article', '10.1093/bioinformatics/btz006', '30624606', 'Transcriptome-based computational drug repurposing has attracted considerable interest by bringing about faster and more cost-effective drug discovery. Nevertheless, key limitations of the current drug connectivity-mapping paradigm have been long overlooked, including the lack of effective means to determine optimal query gene signatures.'),
(82465, 2019, 'bio', 'Benchmark problems for dynamic modeling of intracellular processes', 'Hass, Helge|Loos, Carolin|Raimúndez-Álvarez, Elba|Timmer, Jens|Hasenauer, Jan|Kreutz, Clemens', 'Center for Systems Biology (ZBSA), University of Freiburg, Freiburg 79104, Germany.|Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg 85764, Germany.|Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg 85764, Germany.|Center for Systems Biology (ZBSA), University of Freiburg, Freiburg 79104, Germany.|Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg 85764, Germany.|Center for Systems Biology (ZBSA), University of Freiburg, Freiburg 79104, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 14, '-', 'Journal Article', '10.1093/bioinformatics/btz020', '30624608', 'Dynamic models are used in systems biology to study and understand cellular processes like gene regulation or signal transduction. Frequently, ordinary differential equation (ODE) models are used to model the time and dose dependency of the abundances of molecular compounds as well as interactions and translocations. A multitude of computational approaches, e.g. for parameter estimation or uncertainty analysis have been developed within recent years. However, many of these approaches lack proper testing in application settings because a comprehensive set of benchmark problems is yet missing.'),
(82466, 2019, 'bio', 'Transcriptogramer: an R/Bioconductor package for transcriptional analysis based on protein-protein interaction', 'Morais, Diego A A|Almeida, Rita M C|Dalmolin, Rodrigo J S', 'Bioinformatics Multidisciplinary Environment, Federal University of Rio Grande do Norte, Natal, RN, Brazil.|Institute of Physics and National Institute of Science and Technology: Complex Systems, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil.|Bioinformatics Multidisciplinary Environment, Federal University of Rio Grande do Norte, Natal, RN, Brazil.', 'Brazil|Brazil|Brazil', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz007', '30624611', 'Several freely available tools perform analysis using algorithms developed to identify significant variation of gene expression individually. The transcriptogramer R package uses protein-protein interaction to perform differential expression of functionally associated genes. The software assesses expression profile of entire genetic systems and reveals which biological systems are significantly altered in case-control designed transcriptome experiments.'),
(82467, 2019, 'bio', 'i6mA-Pred: identifying DNA N6-methyladenine sites in the rice genome', 'Chen, Wei|Lv, Hao|Nie, Fulei|Lin, Hao', 'Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China.|Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.|Center for Genomics and Computational Biology, School of Life Sciences, North China University of Science and Technology, Tangshan, China.|Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.', 'China|China|China|China', 88, '-', 'Journal Article', '10.1093/bioinformatics/btz015', '30624619', 'DNA N6-methyladenine (6mA) is associated with a wide range of biological processes. Since the distribution of 6mA site in the genome is non-random, accurate identification of 6mA sites is crucial for understanding its biological functions. Although experimental methods have been proposed for this regard, they are still cost-ineffective for detecting 6mA site in genome-wide scope. Therefore, it is desirable to develop computational methods to facilitate the identification of 6mA site.'),
(82468, 2019, 'bio', 'KASPspoon: an in vitro and in silico PCR analysis tool for high-throughput SNP genotyping', 'Alsamman, Alsamman M|Ibrahim, Shafik D|Hamwieh, Aladdin', 'Department of Genome Mapping, Molecular Genetics and Genome Mapping Laboratory, Agricultural Genetic Engineering Research Institute, Giza, Egypt.|Department of Genome Mapping, Molecular Genetics and Genome Mapping Laboratory, Agricultural Genetic Engineering Research Institute, Giza, Egypt.|Department of Biotechnology, International Center for Agricultural Research in the Dry Areas (ICARDA), Cairo, Egypt.', 'Egypt|Egypt|Egypt', 6, '-', 'Journal Article', '10.1093/bioinformatics/btz004', '30624621', 'Fine mapping becomes a routine trial following quantitative trait loci (QTL) mapping studies to shrink the size of genomic segments underlying causal variants. The availability of whole genome sequences can facilitate the development of high marker density and predict gene content in genomic segments of interest. Correlations between genetic and physical positions of these loci require handling of different experimental genetic data types, and ultimately converting them into positioning markers using a routine and efficient tool.'),
(82469, 2019, 'bio', 'PopCluster: an algorithm to identify genetic variants with ethnicity-dependent effects', 'Gurinovich, Anastasia|Bae, Harold|Farrell, John J|Andersen, Stacy L|Monti, Stefano|Puca, Annibale|Atzmon, Gil|Barzilai, Nir|Perls, Thomas T|Sebastiani, Paola', 'Bioinformatics Program, Boston University, Boston, MA, USA.|College of Public Health and Human Sciences, Oregon State University, Corvallis, OR, USA.|Department of Medicine, Boston University School of Medicine, Boston, MA, USA.|Department of Medicine, Boston University School of Medicine, Boston, MA, USA.|Department of Medicine, Boston University School of Medicine, Boston, MA, USA.|Department of Medicine and Surgery, University of Salerno, Fisciano, Italy.|Department of Medicine and Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, USA.|Department of Medicine and Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, USA.|Department of Medicine, Boston University School of Medicine, Boston, MA, USA.|Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.', 'United States|United States|United States|United States|United States|Italy|United States|United States|United States|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz017', '30624692', 'Over the last decade, more diverse populations have been included in genome-wide association studies. If a genetic variant has a varying effect on a phenotype in different populations, genome-wide association studies applied to a dataset as a whole may not pinpoint such differences. It is especially important to be able to identify population-specific effects of genetic variants in studies that would eventually lead to development of diagnostic tests or drug discovery.'),
(82470, 2019, 'bio', 'polymapR-linkage analysis and genetic map construction from F1 populations of outcrossing polyploids', 'Bourke, Peter M|van Geest, Geert|Voorrips, Roeland E|Jansen, Johannes|Kranenburg, Twan|Shahin, Arwa|Visser, Richard G F|Arens, Paul|Smulders, Marinus J M|Maliepaard, Chris', 'Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.|Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.|Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.|Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.|Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.|Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.|Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.|Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.|Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.|Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty1002', '30629112', '-'),
(82471, 2019, 'bio', 'TOPAS: network-based structural alignment of RNA sequences', 'Chen, Chun-Chi|Jeong, Hyundoo|Qian, Xiaoning|Yoon, Byung-Jun', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA.|Department of Electronic Engineering, Chosun University, Gwangju, Republic of Korea.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz001', '30629122', 'For many RNA families, the secondary structure is known to be better conserved among the member RNAs compared to the primary sequence. For this reason, it is important to consider the underlying folding structures when aligning RNA sequences, especially for those with relatively low sequence identity. Given a set of RNAs with unknown structures, simultaneous RNA alignment and folding algorithms aim to accurately align the RNAs by jointly predicting their consensus secondary structure and the optimal sequence alignment. Despite the improved accuracy of the resulting alignment, the computational complexity of simultaneous alignment and folding for a pair of RNAs is O(N6), which is too costly to be used for large-scale analysis.'),
(82472, 2019, 'bio', 'Streamlined analysis of LINCS L1000 data with the slinky package for R', 'Kort, Eric J|Jovinge, Stefan', 'DeVos Cardiovascular Research Program, Spectrum Health and Van Andel Research Institute, Grand Rapids, MI, USA.|DeVos Cardiovascular Research Program, Spectrum Health and Van Andel Research Institute, Grand Rapids, MI, USA.', 'United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz002', '30629124', 'The L1000 dataset from the NIH LINCS program holds the promise to deconvolute a wide range of biological questions in transcriptional space. However, using this large and decentralized dataset presents its own challenges. The slinky package was created to streamline the process of identifying samples of interest and their corresponding control samples, and loading their associated expression data and metadata. The package can integrate with workflows leveraging the BioConductor collection of tools by encapsulating the L1000 data as a SummarizedExperiment object.'),
(82473, 2019, 'bio', 'Statistical force-field for structural modeling using chemical cross-linking/mass spectrometry distance constraints', 'Ferrari, Allan J R|Gozzo, Fabio C|Martínez, Leandro', 'Institute of Chemistry, University of Campinas, Campinas, SP, Brazil.|Institute of Chemistry, University of Campinas, Campinas, SP, Brazil.|Institute of Chemistry, University of Campinas, Campinas, SP, Brazil.', 'Brazil|Brazil|Brazil', 7, '-', 'Journal Article', '10.1093/bioinformatics/btz013', '30629125', 'Chemical cross-linking/mass spectrometry (XLMS) is an experimental method to obtain distance constraints between amino acid residues which can be applied to structural modeling of tertiary and quaternary biomolecular structures. These constraints provide, in principle, only upper limits to the distance between amino acid residues along the surface of the biomolecule. In practice, attempts to use of XLMS constraints for tertiary protein structure determination have not been widely successful. This indicates the need of specifically designed strategies for the representation of these constraints within modeling algorithms.'),
(82474, 2019, 'bio', 'TopoLink: evaluation of structural models using chemical crosslinking distance constraints', 'Ferrari, Allan J R|Clasen, Milan A|Kurt, Louise|Carvalho, Paulo C|Gozzo, Fabio C|Martínez, Leandro', 'Institute of Chemistry, University of Campinas, Campinas, SP, Brazil.|Carlos Chagas Institute, Fiocruz, Brazil.|Carlos Chagas Institute, Fiocruz, Brazil.|Carlos Chagas Institute, Fiocruz, Brazil.|Institute of Chemistry, University of Campinas, Campinas, SP, Brazil.|Institute of Chemistry, University of Campinas, Campinas, SP, Brazil.', 'Brazil|Brazil|Brazil|Brazil|Brazil|Brazil', 8, '-', 'Journal Article', '10.1093/bioinformatics/btz014', '30629147', 'A software was developed to evaluate structural models using chemical crosslinking experiments. The user provides the types of linkers used and their reactivity, and the observed crosslinks and dead-ends. The software computes the minimum length of a physically inspired linker that connects the reactive atoms of interest, and reports the consistency of each distance with the experimental observation. Statistics on model consistency with the links are provided. Tools to evaluate the correlation of crosslinks in ensembles of models were developed. TopoLink was used to evaluate the potential crosslinks of all structures of the CATH database. The number of crosslinks expected as a function of protein size and linker length can be used as guide for experimental design.'),
(82475, 2019, 'bio', 'MULTiPly: a novel multi-layer predictor for discovering general and specific types of promoters', 'Zhang, Meng|Li, Fuyi|Marquez-Lago, Tatiana T|Leier, André|Fan, Cunshuo|Kwoh, Chee Keong|Chou, Kuo-Chen|Song, Jiangning|Jia, Cangzhi', 'School of Science, Dalian Maritime University, Dalian, China.|Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology.|Department of Genetics, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA.|Department of Genetics, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA.|College of Information Engineering, Northwest A&F University, Yangling, China.|School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore.|Gordon Life Science Institute, Boston, MA, USA.|Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology.|School of Science, Dalian Maritime University, Dalian, China.', 'China|United States|United States|United States|China|Singapore|United States|United States|China', 34, '-', 'Journal Article', '10.1093/bioinformatics/btz016', '30649179', 'Promoters are short DNA consensus sequences that are localized proximal to the transcription start sites of genes, allowing transcription initiation of particular genes. However, the precise prediction of promoters remains a challenging task because individual promoters often differ from the consensus at one or more positions.'),
(82476, 2019, 'bio', 'Crisflash: open-source software to generate CRISPR guide RNAs against genomes annotated with individual variation', 'Jacquin, Adrien L S|Odom, Duncan T|Lukk, Margus', 'Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK and.|Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK and.|Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK and.', '-|-|-', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz019', '30649181', 'CRISPR/Cas9 system requires short guide RNAs (sgRNAs) to direct genome modification. Most currently available tools for sgRNA design operate only with standard reference genomes, and are best suited for small-scale projects. To address these limitations, we developed Crisflash, a software tool for fast sgRNA design and potential off-target discovery, built for performance and flexibility. Crisflash can rapidly design CRISPR guides against any sequenced genome or genome sequences, and can optimize guide accuracy by incorporating user-supplied variant data. Crisflash is over an order of magnitude faster than comparable tools, even using a single CPU core, and efficiently and robustly scores the potential off-targeting of all possible candidate CRISPR guide oligonucleotides.'),
(82477, 2019, 'bio', 'PyLink: a PyMOL plugin to identify links', 'Gierut, Aleksandra M|Dabrowski-Tumanski, Pawel|Niemyska, Wanda|Millett, Kenneth C|Sulkowska, Joanna I', 'Centre of New Technologies, University of Warsaw, Warsaw, Poland.|Centre of New Technologies, University of Warsaw, Warsaw, Poland.|Centre of New Technologies, University of Warsaw, Warsaw, Poland.|Department of Mathematics, University of California, Santa Barbara, CA, USA.|Centre of New Technologies, University of Warsaw, Warsaw, Poland.', 'Poland|Poland|Poland|United States|Poland', 4, '-', 'Journal Article', '10.1093/bioinformatics/bty1038', '30649182', 'Links are generalization of knots, that consist of several components. They appear in proteins, peptides and other biopolymers with disulfide bonds or ions interactions giving rise to the exceptional stability. Moreover because of this stability such biopolymers are the target of commercial and medical use (including anti-bacterial and insecticidal activity). Therefore, topological characterization of such biopolymers, not only provides explanation of their thermodynamical or mechanical properties, but paves the way to design templates in pharmaceutical applications. However, distinction between links and trivial topology is not an easy task. Here, we present PyLink-a PyMOL plugin suited to identify three types of links and perform comprehensive topological analysis of proteins rich in disulfide or ion bonds. PyLink can scan for the links automatically, or the user may specify their own components, including closed loops with several bridges and ion interactions. This creates the possibility of designing new biopolymers with desired properties.'),
(82478, 2019, 'bio', 'Deuteros: software for rapid analysis and visualization of data from differential hydrogen deuterium exchange-mass spectrometry', 'Lau, Andy M C|Ahdash, Zainab|Martens, Chloe|Politis, Argyris', 'Department of Chemistry, Kings College London, London, UK.|Department of Chemistry, Kings College London, London, UK.|Department of Chemistry, Kings College London, London, UK.|Department of Chemistry, Kings College London, London, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 12, '-', 'Journal Article', '10.1093/bioinformatics/btz022', '30649183', 'Hydrogen deuterium exchange-mass spectrometry (HDX-MS) has emerged as a powerful technique for interrogating the conformational dynamics of proteins and their complexes. Currently, analysis of HDX-MS data remains a laborious procedure, mainly due to the lack of streamlined software to process the large datasets. We present Deuteros which is a standalone software designed to be coupled with Waters DynamX HDX data analysis software, allowing the rapid analysis and visualization of data from differential HDX-MS.'),
(82479, 2019, 'bio', 'A simple convolutional neural network for prediction of enhancer-promoter interactions with DNA sequence data', 'Zhuang, Zhong|Shen, Xiaotong|Pan, Wei', 'Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, USA.|School of Statistics, University of Minnesota, Minneapolis, MN, USA.|Division of Biostatistics, University of Minnesota, Minneapolis, MN, USA.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty1050', '30649185', 'Enhancer-promoter interactions (EPIs) in the genome play an important role in transcriptional regulation. EPIs can be useful in boosting statistical power and enhancing mechanistic interpretation for disease- or trait-associated genetic variants in genome-wide association studies. Instead of expensive and time-consuming biological experiments, computational prediction of EPIs with DNA sequence and other genomic data is a fast and viable alternative. In particular, deep learning and other machine learning methods have been demonstrated with promising performance.'),
(82480, 2019, 'bio', 'COmplexome Profiling ALignment (COPAL) reveals remodeling of mitochondrial protein complexes in Barth syndrome', 'Van Strien, Joeri|Guerrero-Castillo, Sergio|Chatzispyrou, Iliana A|Houtkooper, Riekelt H|Brandt, Ulrich|Huynen, Martijn A', 'CMBI, Radboud Centre for Mitochondrial Medicine, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.|Department of Pediatrics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.|Laboratory Genetic Metabolic Diseases, Amsterdam UMC, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands.|Laboratory Genetic Metabolic Diseases, Amsterdam UMC, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands.|Department of Pediatrics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.|CMBI, Radboud Centre for Mitochondrial Medicine, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 6, '-', 'Journal Article', '10.1093/bioinformatics/btz025', '30649188', 'Complexome profiling combines native gel electrophoresis with mass spectrometry to obtain the inventory, composition and abundance of multiprotein assemblies in an organelle. Applying complexome profiling to determine the effect of a mutation on protein complexes requires separating technical and biological variations from the variations caused by that mutation.'),
(82481, 2019, 'bio', 'CluMSID: an R package for similarity-based clustering of tandem mass spectra to aid feature annotation in metabolomics', 'Depke, Tobias|Franke, Raimo|Brönstrup, Mark', 'Department of Chemical Biology, Helmholtz Centre for Infection Research, Braunschweig D-38124, Germany.|Department of Chemical Biology, Helmholtz Centre for Infection Research, Braunschweig D-38124, Germany.|Department of Chemical Biology, Helmholtz Centre for Infection Research, Braunschweig D-38124, Germany.', 'Germany|Germany|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz005', '30649189', 'Compound identification is one of the most eminent challenges in the untargeted analysis of complex mixtures of small molecules by mass spectrometry. Similarity of tandem mass spectra can provide valuable information on putative structural similarities between known and unknown analytes and hence aids feature identification in the bioanalytical sciences. We have developed CluMSID (Clustering of MS2 spectra for metabolite identification), an R package that enables researchers to make use of tandem mass spectra and neutral loss pattern similarities as a part of their metabolite annotation workflow. CluMSID offers functions for all analysis steps from import of raw data to data mining by unsupervised multivariate methods along with respective (interactive) visualizations. A detailed tutorial with example data is provided as supplementary information.');
