INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(81372, 2018, 'bio', 'Sequential feature selection and inference using multi-variate random forests', 'Mayer, Joshua|Rahman, Raziur|Ghosh, Souparno|Pal, Ranadip', 'Department of Mathematics and Statistics, Texas Tech University, Lubbock, TX 79409, USA.|Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA.|Department of Mathematics and Statistics, Texas Tech University, Lubbock, TX 79409, USA.|Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA.', 'United States|United States|United States|United States', 2, 'Algorithms|Antineoplastic Agents|Genomics|Humans|Neoplasms', 'Journal Article', '10.1093/bioinformatics/btx784', '29267851', 'Random forest (RF) has become a widely popular prediction generating mechanism. Its strength lies in its flexibility, interpretability and ability to handle large number of features, typically larger than the sample size. However, this methodology is of limited use if one wishes to identify statistically significant features. Several ranking schemes are available that provide information on the relative importance of the features, but there is a paucity of general inferential mechanism, particularly in a multi-variate set up. We use the conditional inference tree framework to generate a RF where features are deleted sequentially based on explicit hypothesis testing. The resulting sequential algorithm offers an inferentially justifiable, but model-free, variable selection procedure. Significant features are then used to generate predictive RF. An added advantage of our methodology is that both variable selection and prediction are based on conditional inference framework and hence are coherent.'),
(81373, 2018, 'bio', 'GIGI-Quick: a fast approach to impute missing genotypes in genome-wide association family data', 'Kunji, Khalid|Ullah, Ehsan|Nato, Alejandro Q|Wijsman, Ellen M|Saad, Mohamad', 'Data Analytics Department, Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.|Data Analytics Department, Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.|Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA 98195-7720, USA.|Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA 98195-7720, USA.|Data Analytics Department, Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.', 'Qatar|Qatar|United States|United States|Qatar', 1, 'Genome-Wide Association Study|Genotype|Pedigree|Software', 'Journal Article', '10.1093/bioinformatics/btx782', '29267877', 'Genome-wide association studies have become common over the last ten years, with a shift towards targeting rare variants, especially in pedigree-data. Despite lower costs, sequencing for rare variants still remains expensive. To have a relatively large sample with acceptable cost, imputation approaches may be used, such as GIGI for pedigree data. GIGI is an imputation method that handles large pedigrees and is particularly good for rare variant imputation. GIGI requires a subset of individuals in a pedigree to be fully sequenced, while other individuals are sequenced only at relevant markers. The imputation will infer the missing genotypes at untyped markers. Running GIGI on large pedigrees for large numbers of markers can be very time consuming. We present GIGI-Quick as a method to efficiently split GIGIs input, run GIGI in parallel and efficiently merge the output to reduce the runtime with the number of cores. This allows obtaining imputation results faster, and therefore all subsequent association analyses.'),
(81374, 2018, 'bio', 'Computational identification of micro-structural variations and their proteogenomic consequences in cancer', 'Lin, Yen-Yi|Gawronski, Alexander|Hach, Faraz|Li, Sujun|Numanagic, Ibrahim|Sarrafi, Iman|Mishra, Swati|McPherson, Andrew|Collins, Colin C|Radovich, Milan|Tang, Haixu|Sahinalp, S Cenk', 'School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.|School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.|School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.|Department of Computer Science, Indiana University, Bloomington, IN 47405, USA.|School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.|School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.|Department of Surgery, Indiana University, School of Medicine, Indianapolis, IN 46202, USA.|School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.|Vancouver Prostate Centre, Vancouver, BC V6H 3Z6, Canada.|Department of Surgery, Indiana University, School of Medicine, Indianapolis, IN 46202, USA.|Department of Computer Science, Indiana University, Bloomington, IN 47405, USA.|Vancouver Prostate Centre, Vancouver, BC V6H 3Z6, Canada.', 'Canada|Canada|Canada|India|Canada|Canada|India|Canada|Canada|India|India|Canada', 1, 'Breast Neoplasms|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Gene Fusion|Humans|Mass Spectrometry|Neoplasm Proteins|Proteogenomics|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx807', '29267878', 'Rapid advancement in high throughput genome and transcriptome sequencing (HTS) and mass spectrometry (MS) technologies has enabled the acquisition of the genomic, transcriptomic and proteomic data from the same tissue sample. We introduce a computational framework, ProTIE, to integratively analyze all three types of omics data for a complete molecular profile of a tissue sample. Our framework features MiStrVar, a novel algorithmic method to identify micro structural variants (microSVs) on genomic HTS data. Coupled with deFuse, a popular gene fusion detection method we developed earlier, MiStrVar can accurately profile structurally aberrant transcripts in tumors. Given the breakpoints obtained by MiStrVar and deFuse, our framework can then identify all relevant peptides that span the breakpoint junctions and match them with unique proteomic signatures. Observing structural aberrations in all three types of omics data validates their presence in the tumor samples.'),
(81375, 2018, 'bio', 'GRAM-CNN: a deep learning approach with local context for named entity recognition in biomedical text', 'Zhu, Qile|Li, Xiaolin|Conesa, Ana|Pereira, Cécile', 'National Science Foundation Center for Big Learning, University of Florida, Gainesville, FL 32611, USA.|National Science Foundation Center for Big Learning, University of Florida, Gainesville, FL 32611, USA.|Department of Microbiology and Cell Science, Institute for Food and Agricultural Sciences, University of Florida, Gainesville, FL 32611, USA.|Department of Microbiology and Cell Science, Institute for Food and Agricultural Sciences, University of Florida, Gainesville, FL 32611, USA.', 'United States|United States|United States|United States', 27, 'Computational Biology|Deep Learning|Software', 'Journal Article', '10.1093/bioinformatics/btx815', '29272325', 'Best performing named entity recognition (NER) methods for biomedical literature are based on hand-crafted features or task-specific rules, which are costly to produce and difficult to generalize to other corpora. End-to-end neural networks achieve state-of-the-art performance without hand-crafted features and task-specific knowledge in non-biomedical NER tasks. However, in the biomedical domain, using the same architecture does not yield competitive performance compared with conventional machine learning models.'),
(81376, 2018, 'bio', 'Personal Cancer Genome Reporter: variant interpretation report for precision oncology', 'Nakken, Sigve|Fournous, Ghislain|Vodák, Daniel|Aasheim, Lars Birger|Myklebost, Ola|Hovig, Eivind', 'Norwegian Cancer Genomics Consortium, Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, Norway.|Norwegian Cancer Genomics Consortium, Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, Norway.|Norwegian Cancer Genomics Consortium, Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, Norway.|Norwegian Cancer Genomics Consortium, Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, Norway.|Norwegian Cancer Genomics Consortium, Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, Norway.|Norwegian Cancer Genomics Consortium, Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, Norway.', 'Norway|Norway|Norway|Norway|Norway|Norway', 5, 'Genetic Variation|Genome, Human|Humans|Neoplasms|Precision Medicine|Software', 'Journal Article', '10.1093/bioinformatics/btx817', '29272339', 'Individual tumor genomes pose a major challenge for clinical interpretation due to their unique sets of acquired mutations. There is a general scarcity of tools that can (i) systematically interrogate cancer genomes in the context of diagnostic, prognostic, and therapeutic biomarkers, (ii) prioritize and highlight the most important findings and (iii) present the results in a format accessible to clinical experts. We have developed a stand-alone, open-source software package for somatic variant annotation that integrates a comprehensive set of knowledge resources related to tumor biology and therapeutic biomarkers, both at the gene and variant level. Our application generates a tiered report that will aid the interpretation of individual cancer genomes in a clinical setting.'),
(81377, 2018, 'bio', 'TCGA-assembler 2: software pipeline for retrieval and processing of TCGA/CPTAC data', 'Wei, Lin|Jin, Zhilin|Yang, Shengjie|Xu, Yanxun|Zhu, Yitan|Ji, Yuan', 'Program of Computational Genomics & Medicine, NorthShore University HealthSystem, Evanston, IL 60201, USA.|Department of Applied Mathematics & Statistics, Johns Hopkins University, Baltimore, MD 21218, USA.|Program of Computational Genomics & Medicine, NorthShore University HealthSystem, Evanston, IL 60201, USA.|Department of Applied Mathematics & Statistics, Johns Hopkins University, Baltimore, MD 21218, USA.|Program of Computational Genomics & Medicine, NorthShore University HealthSystem, Evanston, IL 60201, USA.|Program of Computational Genomics & Medicine, NorthShore University HealthSystem, Evanston, IL 60201, USA.', 'United States|United States|United States|United States|United States|United States', 56, 'Genome|Genomics|Information Storage and Retrieval|Neoplasms|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btx812', '29272348', 'The Cancer Genome Atlas (TCGA) program has produced huge amounts of cancer genomics data providing unprecedented opportunities for research. In 2014, we developed TCGA-Assembler, a software pipeline for retrieval and processing of public TCGA data. In 2016, TCGA data were transferred from the TCGA data portal to the Genomic Data Commons (GDCs), which is supported by a different set of data storage and retrieval mechanisms. In addition, new proteomics data of TCGA samples have been generated by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) program, which were not available for downloading through TCGA-Assembler. It is desirable to acquire and integrate data from both GDC and CPTAC.'),
(81378, 2018, 'bio', 'Missing value imputation for LC-MS metabolomics data by incorporating metabolic network and adduct ion relations', 'Jin, Zhuxuan|Kang, Jian|Yu, Tianwei', 'Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA.|Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.|Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA.', 'United States|United States|United States', 7, 'Algorithms|Chromatography, Liquid|Coronary Disease|Humans|Mass Spectrometry|Metabolic Networks and Pathways|Metabolomics|Software', 'Journal Article', '10.1093/bioinformatics/btx816', '29272352', 'Metabolomics data generated from liquid chromatography-mass spectrometry platforms often contain missing values. Existing imputation methods do not consider underlying feature relations and the metabolic network information. As a result, the imputation results may not be optimal.'),
(81379, 2018, 'bio', 'DPMIND: degradome-based plant miRNA-target interaction and network database', 'Fei, Yuhan|Wang, Rui|Li, Haoyuan|Liu, Shu|Zhang, Hongsheng|Huang, Ji', 'State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Agriculture.|State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Agriculture.|College of Life Sciences, Nanjing Agricultural University, Nanjing 210095, China.|State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Agriculture.|State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Agriculture.|State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Agriculture.', '-|-|China|-|-|-', 11, 'Gene Expression Regulation, Plant|MicroRNAs|Plants|RNA, Plant|Software', 'Journal Article', '10.1093/bioinformatics/btx824', '29280990', 'MicroRNAs (miRNAs) play essential roles in plant growth, development and stress responses through post-transcriptionally regulating the expression levels of their target mRNAs. Although some tools and databases were developed for predicting the relationships between miRNAs and their targets (miR-Tar), most of them were dependent on computational methods without experimental validations. With the development of degradome sequencing techniques, researchers can identify potential interactions based on degradome sequencing data. The validations with specific degradome data are useful to identify the miR-Tar interactions (MTIs) occurring in/under some specific tissues or treatments. Degradome-based plant miRNA-target interaction and network database (DPMIND) collected almost all available plant degradome data and built a retrieval and analysis platform of miRNA-target interactions and miRNA regulatory networks (MRNs).'),
(81380, 2018, 'bio', 'R2DGC: threshold-free peak alignment and identification for 2D gas chromatography-mass spectrometry in R', 'Ramaker, Ryne C|Gordon, Emily R|Cooper, Sara J', 'HudsonAlpha Institute for Biotechnology, Huntsville, AL 35806, USA.|HudsonAlpha Institute for Biotechnology, Huntsville, AL 35806, USA.|HudsonAlpha Institute for Biotechnology, Huntsville, AL 35806, USA.', 'United States|United States|United States', 3, 'Gas Chromatography-Mass Spectrometry|Limit of Detection|Software', 'Journal Article', '10.1093/bioinformatics/btx825', '29280991', 'Comprehensive 2D gas chromatography-mass spectrometry is a powerful method for analyzing complex mixtures of volatile compounds, but produces a large amount of raw data that requires downstream processing to align signals of interest (peaks) across multiple samples and match peak characteristics to reference standard libraries prior to downstream statistical analysis. Very few existing tools address this aspect of analysis and those that do have shortfalls in usability or performance. We have developed an R package that implements retention time and mass spectra similarity threshold-free alignments, seamlessly integrates retention time standards for universally reproducible alignments, performs common ion filtering and provides compatibility with multiple peak quantification methods. We demonstrate that our packages performance compares favorably to existing tools on a controlled mix of metabolite standards separated under variable chromatography conditions and data generated from cell lines.'),
(81381, 2018, 'bio', 'PRAPI: post-transcriptional regulation analysis pipeline for Iso-Seq', 'Gao, Yubang|Wang, Huiyuan|Zhang, Hangxiao|Wang, Yongsheng|Chen, Jinfeng|Gu, Lianfeng', 'Basic Forestry and Proteomics Research Center, Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology, College of Life Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China.|Basic Forestry and Proteomics Research Center, Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology, College of Life Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China.|Basic Forestry and Proteomics Research Center, Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology, College of Life Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China.|Basic Forestry and Proteomics Research Center, Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology, College of Life Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China.|Department of Plant Pathology and Microbiology, Institute of Integrative Genome Biology, University of California, Riverside, CA 92521, USA.|Basic Forestry and Proteomics Research Center, Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology, College of Life Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China.', 'China|China|China|China|United States|China', 8, 'Alternative Splicing|Molecular Sequence Annotation|Polyadenylation|Protein Isoforms|Protein Processing, Post-Translational|RNA|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btx830', '29280994', 'The single-molecule real-time (SMRT) isoform sequencing (Iso-Seq) based on Pacific Bioscience (PacBio) platform has received increasing attention for its ability to explore full-length isoforms. Thus, comprehensive tools for Iso-Seq bioinformatics analysis are extremely useful. Here, we present a one-stop solution for Iso-Seq analysis, called PRAPI to analyze alternative transcription initiation (ATI), alternative splicing (AS), alternative cleavage and polyadenylation (APA), natural antisense transcripts (NAT), and circular RNAs (circRNAs) comprehensively. PRAPI is capable of combining Iso-Seq full-length isoforms with short read data, such as RNA-Seq or polyadenylation site sequencing (PAS-seq) for differential expression analysis of NAT, AS, APA and circRNAs. Furthermore, PRAPI can annotate new genes and correct mis-annotated genes when gene annotation is available. Finally, PRAPI generates high-quality vector graphics to visualize and highlight the Iso-Seq results.'),
(81382, 2018, 'bio', 'CRNET: an efficient sampling approach to infer functional regulatory networks by integrating large-scale ChIP-seq and time-course RNA-seq data', 'Chen, Xi|Gu, Jinghua|Wang, Xiao|Jung, Jin-Gyoung|Wang, Tian-Li|Hilakivi-Clarke, Leena|Clarke, Robert|Xuan, Jianhua', 'Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.|Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.|Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.|Department of Pathology, Johns Hopkins Medical Institutions, Baltimore, MD 21231, USA.|Department of Pathology, Johns Hopkins Medical Institutions, Baltimore, MD 21231, USA.|Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC 20057, USA.|Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC 20057, USA.|Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 4, 'Bayes Theorem|Breast Neoplasms|Humans|Promoter Regions, Genetic|RNA|Sequence Analysis, RNA|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btx827', '29280996', 'NGS techniques have been widely applied in genetic and epigenetic studies. Multiple ChIP-seq and RNA-seq profiles can now be jointly used to infer functional regulatory networks (FRNs). However, existing methods suffer from either oversimplified assumption on transcription factor (TF) regulation or slow convergence of sampling for FRN inference from large-scale ChIP-seq and time-course RNA-seq data.'),
(81383, 2018, 'bio', 'DeepSig: deep learning improves signal peptide detection in proteins', 'Savojardo, Castrense|Martelli, Pier Luigi|Fariselli, Piero|Casadio, Rita', 'Biocomputing Group, Department of Pharmacy and Biotechnology - Interdepartmental Centre L. Galvani for Integrated Studies of Bioinformatics, Biophysics and Biocomplexity, University of Bologna, 40126 Bologna, Italy.|Biocomputing Group, Department of Pharmacy and Biotechnology - Interdepartmental Centre L. Galvani for Integrated Studies of Bioinformatics, Biophysics and Biocomplexity, University of Bologna, 40126 Bologna, Italy.|Department of Comparative Biomedicine and Food Science (BCA), University of Padova, Padova, Italy.|Biocomputing Group, Department of Pharmacy and Biotechnology - Interdepartmental Centre L. Galvani for Integrated Studies of Bioinformatics, Biophysics and Biocomplexity, University of Bologna, 40126 Bologna, Italy.', 'Italy|Italy|Italy|Italy', 19, 'Bacteria|Deep Learning|Eukaryota|Internet|Protein Sorting Signals|Protein Transport|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btx818', '29280997', 'The identification of signal peptides in protein sequences is an important step toward protein localization and function characterization.'),
(81384, 2018, 'bio', 'Multiple hot-deck imputation for network inference from RNA sequencing data', 'Imbert, Alyssa|Valsesia, Armand|Le Gall, Caroline|Armenise, Claudia|Lefebvre, Gregory|Gourraud, Pierre-Antoine|Viguerie, Nathalie|Villa-Vialaneix, Nathalie', 'MIAT, Université de Toulouse, INRA, F-31326 Castanet-Tolosan, France.|Nestlé Institute of Health Sciences, CH-1015 Lausanne, Switzerland.|Methodomics, F-31200 Toulouse, France.|Quartzbio, CH-1202 Geneva, Switzerland.|Nestlé Institute of Health Sciences, CH-1015 Lausanne, Switzerland.|Methodomics, F-31200 Toulouse, France.|UMR1048, Obesity Research Laboratory, Institute of Metabolic and Cardiovascular Diseases (I2MC), Inserm, F-31024 Toulouse, France.|MIAT, Université de Toulouse, INRA, F-31326 Castanet-Tolosan, France.', 'France|Switzerland|France|Switzerland|Switzerland|France|France|France', 2, 'Base Sequence|Humans|RNA|Reproducibility of Results|Sequence Analysis, RNA|Software|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btx819', '29280999', 'Network inference provides a global view of the relations existing between gene expression in a given transcriptomic experiment (often only for a restricted list of chosen genes). However, it is still a challenging problem: even if the cost of sequencing techniques has decreased over the last years, the number of samples in a given experiment is still (very) small compared to the number of genes.'),
(81385, 2018, 'bio', 'A high-resolution map of the human small non-coding transcriptome', 'Fehlmann, Tobias|Backes, Christina|Alles, Julia|Fischer, Ulrike|Hart, Martin|Kern, Fabian|Langseth, Hilde|Rounge, Trine|Umu, Sinan Ugur|Kahraman, Mustafa|Laufer, Thomas|Haas, Jan|Staehler, Cord|Ludwig, Nicole|Hübenthal, Matthias|Meder, Benjamin|Franke, Andre|Lenhof, Hans-Peter|Meese, Eckart|Keller, Andreas', 'Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany.|Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany.|Department of Human Genetics, Saarland University, 66421 Homburg, Germany.|Department of Human Genetics, Saarland University, 66421 Homburg, Germany.|Department of Human Genetics, Saarland University, 66421 Homburg, Germany.|Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany.|Cancer Registry of Norway, Institute of Population-based Cancer Research, N-0304 Oslo, Norway.|Cancer Registry of Norway, Institute of Population-based Cancer Research, N-0304 Oslo, Norway.|Cancer Registry of Norway, Institute of Population-based Cancer Research, N-0304 Oslo, Norway.|Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany.|Hummingbird Diagnostics GmbH, 69120 Heidelberg, Germany.|Department of Internal Medicine III, University Hospital Heidelberg, 69120 Heidelberg, Germany.|Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany.|Department of Human Genetics, Saarland University, 66421 Homburg, Germany.|Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, 24105 Kiel, Germany.|Department of Internal Medicine III, University Hospital Heidelberg, 69120 Heidelberg, Germany.|Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, 24105 Kiel, Germany.|Center for Bioinformatics, Saarland University, 66123 Saarbrücken, Germany.|Department of Human Genetics, Saarland University, 66421 Homburg, Germany.|Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Norway|Norway|Norway|Germany|Germany|Germany|Germany', 0, 'Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Lung Neoplasms|MicroRNAs|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Sequence Analysis, RNA|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btx814', '29281000', 'Although the amount of small non-coding RNA-sequencing data is continuously increasing, it is still unclear to which extent small RNAs are represented in the human genome.'),
(81386, 2018, 'bio', 'Genome U-Plot: a whole genome visualization', 'Gaitatzes, Athanasios|Johnson, Sarah H|Smadbeck, James B|Vasmatzis, George', 'Biomarker Discovery Program, Center for Individualized Medicine, Mayo Clinic.|Biomarker Discovery Program, Center for Individualized Medicine, Mayo Clinic.|Biomarker Discovery Program, Center for Individualized Medicine, Mayo Clinic.|Biomarker Discovery Program, Center for Individualized Medicine, Mayo Clinic.', '-|-|-|-', 7, 'Data Visualization|Genome, Human|Genomic Structural Variation|Genomics|Humans|Software|Whole Genome Sequencing', 'Journal Article', '10.1093/bioinformatics/btx829', '29281001', 'The ability to produce and analyze whole genome sequencing (WGS) data from samples with structural variations (SV) generated the need to visualize such abnormalities in simplified plots. Conventional two-dimensional representations of WGS data frequently use either circular or linear layouts. There are several diverse advantages regarding both these representations, but their major disadvantage is that they do not use the two-dimensional space very efficiently. We propose a layout, termed the Genome U-Plot, which spreads the chromosomes on a two-dimensional surface and essentially quadruples the spatial resolution. We present the Genome U-Plot for producing clear and intuitive graphs that allows researchers to generate novel insights and hypotheses by visualizing SVs such as deletions, amplifications, and chromoanagenesis events. The main features of the Genome U-Plot are its layered layout, its high spatial resolution and its improved aesthetic qualities. We compare conventional visualization schemas with the Genome U-Plot using visualization metrics such as number of line crossings and crossing angle resolution measures. Based on our metrics, we improve the readability of the resulting graph by at least 2-fold, making apparent important features and making it easy to identify important genomic changes.'),
(81387, 2018, 'bio', 'An automated benchmarking platform for MHC class II binding prediction methods', 'Andreatta, Massimo|Trolle, Thomas|Yan, Zhen|Greenbaum, Jason A|Peters, Bjoern|Nielsen, Morten', 'Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, CP1650, San Martín, Buenos Aires, Argentina.|Evaxion Biotech, DK-2200 Copenhagen N, Denmark.|Bioinformatics Core Facility.|Bioinformatics Core Facility.|Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA.|Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, CP1650, San Martín, Buenos Aires, Argentina.', 'Argentina|Denmark|United States|United States|United States|Argentina', 22, 'Algorithms|Animals|Benchmarking|Computational Biology|Databases, Factual|Epitopes, T-Lymphocyte|Histocompatibility Antigens Class II|Humans|Protein Binding|Software', 'Journal Article', '10.1093/bioinformatics/btx820', '29281002', 'Computational methods for the prediction of peptide-MHC binding have become an integral and essential component for candidate selection in experimental T cell epitope discovery studies. The sheer amount of published prediction methods-and often discordant reports on their performance-poses a considerable quandary to the experimentalist who needs to choose the best tool for their research.'),
(81388, 2018, 'bio', 'Computational identification of binding energy hot spots in protein-RNA complexes using an ensemble approach', 'Pan, Yuliang|Wang, Zixiang|Zhan, Weihua|Deng, Lei', 'School of Software, Central South University, Changsha 410075, China.|School of Software, Central South University, Changsha 410075, China.|School of Electronics and Computer Science, Zhejiang Wanli University, Ningbo 315100, China.|School of Software, Central South University, Changsha 410075, China.', 'China|China|China|China', 0, 'Binding Sites|Computational Biology|Databases, Protein|Machine Learning|Models, Molecular|Protein Binding|Proteins|RNA', 'Journal Article', '10.1093/bioinformatics/btx822', '29281004', 'Identifying RNA-binding residues, especially energetically favored hot spots, can provide valuable clues for understanding the mechanisms and functional importance of protein-RNA interactions. Yet, limited availability of experimentally recognized energy hot spots in protein-RNA crystal structures leads to the difficulties in developing empirical identification approaches. Computational prediction of RNA-binding hot spot residues is still in its infant stage.'),
(81389, 2018, 'bio', 'PINE-SPARKY2 for automated NMR-based protein structure research', 'Lee, Woonghee|Markley, John L', 'National Magnetic Resonance Facility at Madison, Biochemistry Department, University of Wisconsin-Madison, Madison, WI 53706, USA.|National Magnetic Resonance Facility at Madison, Biochemistry Department, University of Wisconsin-Madison, Madison, WI 53706, USA.', 'United States|United States', 9, 'Automation, Laboratory|Cryoelectron Microscopy|Models, Molecular|Nuclear Magnetic Resonance, Biomolecular|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btx785', '29281006', 'Nuclear magnetic resonance (NMR) spectroscopy, along with X-ray crystallography and cryoelectron microscopy, is one of the three major tools that enable the determination of atomic-level structural models of biological macromolecules. Of these, NMR has the unique ability to follow important processes in solution, including conformational changes, internal dynamics and protein-ligand interactions. As a means for facilitating the handling and analysis of spectra involved in these types of NMR studies, we have developed PINE-SPARKY.2, a software package that integrates and automates discrete tasks that previously required interaction with separate software packages. The graphical user interface of PINE-SPARKY.2 simplifies chemical shift assignment and verification, automated detection of secondary structural elements, predictions of flexibility and hydrophobic cores, and calculation of three-dimensional structural models.'),
(81390, 2018, 'bio', 'mTM-align: an algorithm for fast and accurate multiple protein structure alignment', 'Dong, Runze|Peng, Zhenling|Zhang, Yang|Yang, Jianyi', 'School of Mathematical Sciences, Nankai University, Tianjin 300071, China.|Center for Applied Mathematics, Tianjin University, Tianjin 300072, China.|Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109-2218, USA.|School of Mathematical Sciences, Nankai University, Tianjin 300071, China.', 'China|China|United States|China', 9, 'Algorithms|Databases, Protein|Protein Conformation|Proteins|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btx828', '29281009', 'As protein structure is more conserved than sequence during evolution, multiple structure alignment can be more informative than multiple sequence alignment, especially for distantly related proteins. With the rapid increase of the number of protein structures in the Protein Data Bank, it becomes urgent to develop efficient algorithms for multiple structure alignment.'),
(81391, 2018, 'bio', 'A benchmark for comparing precision medicine methods in thyroid cancer diagnosis using tissue microarrays', 'Wang, Ching-Wei|Lee, Yu-Ching|Calista, Evelyne|Zhou, Fan|Zhu, Hongtu|Suzuki, Ryohei|Komura, Daisuke|Ishikawa, Shumpei|Cheng, Shih-Ping', 'Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan.|NTUST Center of Computer Vision and Medical Imaging, Taipei, Taiwan.|Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan.|Department of Biostatistics, University of North Carolina at Chapel Hill, NC, USA.|Department of Biostatistics, University of North Carolina at Chapel Hill, NC, USA.|Department of Genomic Pathology, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan.|Department of Genomic Pathology, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan.|Department of Genomic Pathology, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan.|Department of Surgery, Mackay Memorial Hospital, Taipei, Taiwan.', 'United States|United States|United States|United States|United States|Japan|Japan|Japan|United States', 0, 'Algorithms|Benchmarking|Humans|Precision Medicine|Software|Thyroid Neoplasms', 'Comparative Study', '10.1093/bioinformatics/btx838', '29293907', 'The aim of precision medicine is to harness new knowledge and technology to optimize the timing and targeting of interventions for maximal therapeutic benefit. This study explores the possibility of building AI models without precise pixel-level annotation in prediction of the tumor size, extrathyroidal extension, lymph node metastasis, cancer stage and BRAF mutation in thyroid cancer diagnosis, providing the patients background information, histopathological and immunohistochemical tissue images.'),
(81392, 2018, 'bio', 'Towards self-learning based hypotheses generation in biomedical text domain', 'Gopalakrishnan, Vishrawas|Jha, Kishlay|Xun, Guangxu|Ngo, Hung Q|Zhang, Aidong', 'Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, NY, USA.|Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, NY, USA.|Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, NY, USA.|Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, NY, USA.|Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, NY, USA.', 'United States|United States|United States|United States|United States', 7, 'Algorithms|Animals|Data Mining|Humans|Software', 'Journal Article', '10.1093/bioinformatics/btx837', '29293920', 'The overwhelming amount of research articles in the domain of bio-medicine might cause important connections to remain unnoticed. Literature Based Discovery is a sub-field within biomedical text mining that peruses these articles to formulate high confident hypotheses on possible connections between medical concepts. Although many alternate methodologies have been proposed over the last decade, they still suffer from scalability issues. The primary reason, apart from the dense inter-connections between biological concepts, is the absence of information on the factors that lead to the edge-formation. In this work, we formulate this problem as a collaborative filtering task and leverage a relatively new concept of word-vectors to learn and mimic the implicit edge-formation process. Along with single-class classifier, we prune the search-space of redundant and irrelevant hypotheses to increase the efficiency of the system and at the same time maintaining and in some cases even boosting the overall accuracy.'),
(81393, 2018, 'bio', 'DyNetViewer: a Cytoscape app for dynamic network construction, analysis and visualization', 'Li, Min|Yang, Jie|Wu, Fang-Xiang|Pan, Yi|Wang, Jianxin', 'School of Information Science and Engineering, Central South University, Changsha 410083, China.|School of Information Science and Engineering, Central South University, Changsha 410083, China.|Division of Biomedical Engineering, University of Saskatchewan, Saskatoon SKS7N5A9, Canada.|Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA.|School of Information Science and Engineering, Central South University, Changsha 410083, China.', 'China|China|Canada|Georgia|China', 0, 'Algorithms|Cluster Analysis|Computational Biology|Protein Interaction Maps|Software', 'Journal Article', '10.1093/bioinformatics/btx821', '29293938', 'The molecular interactions in a cell are varying with time and surrounded environmental cues. The construction and analysis of dynamic molecular networks can elucidate dynamic cellular mechanisms of different biological functions and provide a chance to understand complex diseases at the systems level. Here, we develop DyNetViewer, a Cytoscape application that provides a range of functionalities for the construction, analysis and visualization of dynamic protein-protein interaction networks. The current version of DyNetViewer consists of four different dynamic network construction methods, twelve topological variation analysis methods and four clustering algorithms. Moreover, visualization of different topological variation of nodes and clusters over time enables users to quickly identify the most variations across many network states.'),
(81394, 2018, 'bio', 'polyPK: an R package for pharmacokinetic analysis of multi-component drugs using a metabolomics approach', 'Li, Mengci|Wang, Shouli|Xie, Guoxiang|Ma, Xiaohui|Chen, Tianlu|Jia, Wei', 'Center for Translational Medicine, Shanghai Jiao Tong University Affiliated Sixth Peoples Hospital, Shanghai 200233, China.|Center for Translational Medicine, Shanghai Jiao Tong University Affiliated Sixth Peoples Hospital, Shanghai 200233, China.|University of Hawaii Cancer Center, Honolulu 96813, USA.|State Key Laboratory of Core Technology in Innovative Chinese Medicine, Pharmacology and Toxicology Research Centre, Tasly Academy, Tasly Holding Group Co., Ltd, Tianjin 300410, China.|Center for Translational Medicine, Shanghai Jiao Tong University Affiliated Sixth Peoples Hospital, Shanghai 200233, China.|Center for Translational Medicine, Shanghai Jiao Tong University Affiliated Sixth Peoples Hospital, Shanghai 200233, China.', 'China|China|United States|China|China|China', 4, 'Metabolic Networks and Pathways|Metabolomics|Pharmacokinetics|Software', 'Journal Article', '10.1093/bioinformatics/btx834', '29293946', 'Pharmacokinetics (PK) is a long-standing bottleneck for botanical drug and traditional medicine research. By using an integrated phytochemical and metabolomics approach coupled with multivariate statistical analysis, we propose a new strategy, Poly-PK, to simultaneously monitor the performance of drug constituents and endogenous metabolites, taking into account both the diversity of the drugs chemical composition and its complex effects on the mammalian metabolic pathways. Poly-PK is independent of specific measurement platforms and has been successfully applied in the PK studies of Puerh tea, a traditional Chinese medicine Huangqi decoction and many other multi-component drugs. Here, we introduce an R package, polyPK, the first and only automation of the data analysis pipeline of Poly-PK strategy. polyPK provides 10 functions for data pre-processing, differential compound identification and grouping, traditional PK parameters calculation, multivariate statistical analysis, correlations, cluster analyses and resulting visualization. It may serve a wide range of users, including pharmacologists, biologists and doctors, in understanding the metabolic fate of multi-component drugs.'),
(81395, 2018, 'bio', 'Ontological function annotation of long non-coding RNAs through hierarchical multi-label classification', 'Zhang, Jingpu|Zhang, Zuping|Wang, Zixiang|Liu, Yuting|Deng, Lei', 'School of Information Science and Engineering, Central South University, Changsha 410083, China.|School of Information Science and Engineering, Central South University, Changsha 410083, China.|School of Software, Central South University, Changsha 410075, China.|School of Software, Central South University, Changsha 410075, China.|School of Software, Central South University, Changsha 410075, China.', 'China|China|China|China|China', 29, 'Humans|Molecular Sequence Annotation|Neoplasms|Neural Networks (Computer)|RNA, Long Noncoding|Software', 'Journal Article', '10.1093/bioinformatics/btx833', '29293953', 'Long non-coding RNAs (lncRNAs) are an enormous collection of functional non-coding RNAs. Over the past decades, a large number of novel lncRNA genes have been identified. However, most of the lncRNAs remain function uncharacterized at present. Computational approaches provide a new insight to understand the potential functional implications of lncRNAs.'),
(81396, 2018, 'bio', 'MIIC online: a web server to reconstruct causal or non-causal networks from non-perturbative data', 'Sella, Nadir|Verny, Louis|Uguzzoni, Guido|Affeldt, Séverine|Isambert, Hervé', 'Institut Curie, PSL Research University, CNRS UMR168.|Institut Curie, PSL Research University, CNRS UMR168.|Institut Curie, PSL Research University, CNRS UMR168.|Institut Curie, PSL Research University, CNRS UMR168.|Institut Curie, PSL Research University, CNRS UMR168.', '-|-|-|-|-', 0, 'Algorithms|Computers|Neural Networks (Computer)|Software', 'Journal Article', '10.1093/bioinformatics/btx844', '29300827', 'We present a web server running the MIIC algorithm, a network learning method combining constraint-based and information-theoretic frameworks to reconstruct causal, non-causal or mixed networks from non-perturbative data, without the need for an a priori choice on the class of reconstructed network. Starting from a fully connected network, the algorithm first removes dispensable edges by iteratively subtracting the most significant information contributions from indirect paths between each pair of variables. The remaining edges are then filtered based on their confidence assessment or oriented based on the signature of causality in observational data. MIIC online server can be used for a broad range of biological data, including possible unobserved (latent) variables, from single-cell gene expression data to protein sequence evolution and outperforms or matches state-of-the-art methods for either causal or non-causal network reconstruction.'),
(81397, 2018, 'bio', 'PGA: post-GWAS analysis for disease gene identification', 'Lin, Jhih-Rong|Jaroslawicz, Daniel|Cai, Ying|Zhang, Quanwei|Wang, Zhen|Zhang, Zhengdong D', 'Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA.|Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA.|Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA.|Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA.|Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA.|Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA.', 'United States|United States|United States|United States|United States|United States', 2, 'Gene Regulatory Networks|Genetic Predisposition to Disease|Genome-Wide Association Study|Genomics|Humans|Prostaglandins A|Quantitative Trait Loci|Software', 'Journal Article', '10.1093/bioinformatics/btx845', '29300829', 'Although the genome-wide association study (GWAS) is a powerful method to identify disease-associated variants, it does not directly address the biological mechanisms underlying such genetic association signals. Here, we present PGA, a Perl- and Java-based program for post-GWAS analysis that predicts likely disease genes given a list of GWAS-reported variants. Designed with a command line interface, PGA incorporates genomic and eQTL data in identifying disease gene candidates and uses gene network and ontology data to score them based upon the strength of their relationship to the disease in question.'),
(81398, 2018, 'bio', 'SV2: accurate structural variation genotyping and de novo mutation detection from whole genomes', 'Antaki, Danny|Brandler, William M|Sebat, Jonathan', 'Beyster Center for Genomics of Psychiatric Diseases.|Beyster Center for Genomics of Psychiatric Diseases.|Beyster Center for Genomics of Psychiatric Diseases.', '-|-|-', 10, 'Algorithms|Genome, Human|Genotype|High-Throughput Nucleotide Sequencing|Humans|Mutation|Sequence Analysis, DNA|Software|Whole Genome Sequencing', 'Journal Article', '10.1093/bioinformatics/btx813', '29300834', 'Structural variation (SV) detection from short-read whole genome sequencing is error prone, presenting significant challenges for population or family-based studies of disease.'),
(81399, 2018, 'bio', 'CHROMIXS: automatic and interactive analysis of chromatography-coupled small-angle X-ray scattering data', 'Panjkovich, Alejandro|Svergun, Dmitri I', 'European Molecular Biology Laboratory, Hamburg Outstation, EMBL c/o DESY, 22607 Hamburg, Germany.|European Molecular Biology Laboratory, Hamburg Outstation, EMBL c/o DESY, 22607 Hamburg, Germany.', 'Germany|Germany', 0, 'Animals|Chromatography|Protein Conformation|Scattering, Small Angle|Serum Albumin, Bovine|Software|X-Ray Diffraction', 'Journal Article', '10.1093/bioinformatics/btx846', '29300836', 'Size-exclusion chromatography (SEC) coupled to small-angle X-ray scattering (SAXS), also known as inline SEC-SAXS, is being increasingly used for the structural analysis of biological macromolecules, complexes and mixtures in solution. A single SEC-SAXS run generates thousands of individual SAXS profiles from the eluting solute and their analysis requires a correct identification of buffer and sample regions, a rather laborous task. We present CHROMIXS (as in CHROMatography Inline X-ray Scattering), a program for rapid reduction and analysis, both automatically and interactively, of SEC-SAXS data.'),
(81400, 2018, 'bio', 'ChronQC: a quality control monitoring system for clinical next generation sequencing', 'Tawari, Nilesh R|Seow, Justine Jia Wen|Perumal, Dharuman|Ow, Jack L|Ang, Shimin|Devasia, Arun George|Ng, Pauline C', 'Computational and Systems Biology, Genome Institute of Singapore, Genome, #02-01, Singapore 138672, Singapore.|Computational and Systems Biology, Genome Institute of Singapore, Genome, #02-01, Singapore 138672, Singapore.|Computational and Systems Biology, Genome Institute of Singapore, Genome, #02-01, Singapore 138672, Singapore.|Computational and Systems Biology, Genome Institute of Singapore, Genome, #02-01, Singapore 138672, Singapore.|Computational and Systems Biology, Genome Institute of Singapore, Genome, #02-01, Singapore 138672, Singapore.|Computational and Systems Biology, Genome Institute of Singapore, Genome, #02-01, Singapore 138672, Singapore.|Computational and Systems Biology, Genome Institute of Singapore, Genome, #02-01, Singapore 138672, Singapore.', 'Singapore|Singapore|Singapore|Singapore|Singapore|Singapore|Singapore', 0, 'Data Accuracy|High-Throughput Nucleotide Sequencing|Quality Control|Software', 'Journal Article', '10.1093/bioinformatics/btx843', '29300845', 'ChronQC is a quality control (QC) tracking system for clinical implementation of next-generation sequencing (NGS). ChronQC generates time series plots for various QC metrics to allow comparison of current runs to historical runs. ChronQC has multiple features for tracking QC data including Westgard rules for clinical validity, laboratory-defined thresholds and historical observations within a specified time period. Users can record their notes and corrective actions directly onto the plots for long-term recordkeeping. ChronQC facilitates regular monitoring of clinical NGS to enable adherence to high quality clinical standards.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(81401, 2018, 'bio', 'ChopStitch: exon annotation and splice graph construction using transcriptome assembly and whole genome sequencing data', 'Khan, Hamza|Mohamadi, Hamid|Vandervalk, Benjamin P|Warren, Rene L|Chu, Justin|Birol, Inanc', 'Canadas Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, BC V5Z 4S6, Canada.|Canadas Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, BC V5Z 4S6, Canada.|Canadas Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, BC V5Z 4S6, Canada.|Canadas Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, BC V5Z 4S6, Canada.|Canadas Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, BC V5Z 4S6, Canada.|Canadas Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, BC V5Z 4S6, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada', 3, 'Algorithms|Alternative Splicing|Exons|Genome|High-Throughput Nucleotide Sequencing|RNA|Sequence Analysis, RNA|Software|Transcriptome|Whole Genome Sequencing', 'Journal Article', '10.1093/bioinformatics/btx839', '29300846', 'Sequencing studies on non-model organisms often interrogate both genomes and transcriptomes with massive amounts of short sequences. Such studies require de novo analysis tools and techniques, when the species and closely related species lack high quality reference resources. For certain applications such as de novo annotation, information on putative exons and alternative splicing may be desirable.'),
(81402, 2018, 'bio', 'Aligning dynamic networks with DynaWAVE', 'Vijayan, Vipin|Milenkovic, Tijana', 'Department of Computer Science and Engineering, ECK Institute for Global Health, and Interdisciplinary Center for Network Science and Applications (iCeNSA), University of Notre Dame, Notre Dame, IN 46556, USA.|Department of Computer Science and Engineering, ECK Institute for Global Health, and Interdisciplinary Center for Network Science and Applications (iCeNSA), University of Notre Dame, Notre Dame, IN 46556, USA.', 'United States|United States', 0, 'Algorithms|Data Accuracy|Software', 'Journal Article', '10.1093/bioinformatics/btx841', '29300873', 'Network alignment (NA) aims to find similar (conserved) regions between networks, such as cellular networks of different species. Until recently, existing methods were limited to aligning static networks. However, real-world systems, including cellular functioning, are dynamic. Hence, in our previous work, we introduced the first ever dynamic NA method, DynaMAGNA++, which improved upon the traditional static NA. However, DynaMAGNA++xe2x80x89does not necessarily scale well to larger networks in terms of alignment quality or runtime.'),
(81403, 2018, 'bio', 'QUENTIN: reconstruction of disease transmissions from viral quasispecies genomic data', 'Skums, Pavel|Zelikovsky, Alex|Singh, Rahul|Gussler, Walker|Dimitrova, Zoya|Knyazev, Sergey|Mandric, Igor|Ramachandran, Sumathi|Campo, David|Jha, Deeptanshu|Bunimovich, Leonid|Costenbader, Elizabeth|Sexton, Connie|OConnor, Siobhan|Xia, Guo-Liang|Khudyakov, Yury', 'Department of Computer Science, Georgia State University.|Department of Computer Science, Georgia State University.|Department of Computer Science, San Francisco State University, San Francisco, CA 94132, USA.|Centers for Disease Control and Prevention, Division of Viral Hepatitis, Atlanta, GA 30303, USA.|Centers for Disease Control and Prevention, Division of Viral Hepatitis, Atlanta, GA 30303, USA.|Department of Computer Science, Georgia State University.|Department of Computer Science, Georgia State University.|Centers for Disease Control and Prevention, Division of Viral Hepatitis, Atlanta, GA 30303, USA.|Centers for Disease Control and Prevention, Division of Viral Hepatitis, Atlanta, GA 30303, USA.|Department of Computer Science, San Francisco State University, San Francisco, CA 94132, USA.|School of Mathematics, Georgia Institute of Technology, Atlanta, GA 30313, USA.|FHI 360, Durham, NC 27701, USA.|Centers for Disease Control and Prevention, Division of Viral Hepatitis, Atlanta, GA 30303, USA.|Centers for Disease Control and Prevention, Division of Viral Hepatitis, Atlanta, GA 30303, USA.|Centers for Disease Control and Prevention, Division of Viral Hepatitis, Atlanta, GA 30303, USA.|Centers for Disease Control and Prevention, Division of Viral Hepatitis, Atlanta, GA 30303, USA.', 'Georgia|Georgia|United States|United States|United States|Georgia|Georgia|United States|United States|United States|Georgia|United States|United States|United States|United States|United States', 11, 'Bayes Theorem|Disease Outbreaks|Genome, Viral|Genomics|Hepacivirus|High-Throughput Nucleotide Sequencing|Humans|Quasispecies|Sequence Analysis, DNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx402', '29304222', 'Genomic analysis has become one of the major tools for disease outbreak investigations. However, existing computational frameworks for inference of transmission history from viral genomic data often do not consider intra-host diversity of pathogens and heavily rely on additional epidemiological data, such as sampling times and exposure intervals. This impedes genomic analysis of outbreaks of highly mutable viruses associated with chronic infections, such as human immunodeficiency virus and hepatitis C virus, whose transmissions are often carried out through minor intra-host variants, while the additional epidemiological information often is either unavailable or has a limited use.'),
(81404, 2018, 'bio', 'Mustguseal: a server for multiple structure-guided sequence alignment of protein families', 'Suplatov, Dmitry A|Kopylov, Kirill E|Popova, Nina N|Voevodin, Vladimir V|Švedas, Vytas K', 'Belozersky Institute of Physicochemical Biology.|Faculty of Bioengineering and Bioinformatics.|Faculty of Computational Mathematics and Cybernetics.|Faculty of Computational Mathematics and Cybernetics.|Belozersky Institute of Physicochemical Biology.', '-|-|-|-|-', 11, 'Computers|Ligands|Models, Molecular|Protein Structure, Tertiary|Proteins|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btx831', '29309510', 'Comparative analysis of homologous proteins in a functionally diverse superfamily is a valuable tool at studying structure-function relationship, but represents a methodological challenge.'),
(81405, 2018, 'bio', 'DiffGraph: an R package for identifying gene network rewiring using differential graphical models', 'Zhang, Xiao-Fei|Ou-Yang, Le|Yang, Shuo|Hu, Xiaohua|Yan, Hong', 'Department of Statistics, School of Mathematics and Statistics, Central China Normal University, Wuhan 430079, China.|Department of Electronic Engineering, College of Information Engineering, Shenzhen University, Shenzhen 518060, China.|Department of Respiratory Medicine, Wuhan Number 1 Hospital, Wuhan 430022, China.|Department of Computer Science, School of Computer, Central China Normal University, Wuhan 430079, China.|Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China.', 'China|China|China|China|China', 5, 'Computational Biology|Data Visualization|Gene Regulatory Networks|Models, Genetic|Software', 'Journal Article', '10.1093/bioinformatics/btx836', '29309511', 'We develop DiffGraph, an R package that integrates four influential differential graphical models for identifying gene network rewiring under two different conditions from gene expression data. The input and output of different models are packaged in the same format, making it convenient for users to compare different models using a wide range of datasets and carry out follow-up analysis. Furthermore, the inferred differential networks can be visualized both non-interactively and interactively. The package is useful for identifying gene network rewiring from input datasets, comparing the predictions of different methods and visualizing the results.'),
(81406, 2018, 'bio', 'Protein classification using modified n-grams and skip-grams', 'Islam, S M Ashiqul|Heil, Benjamin J|Kearney, Christopher Michel|Baker, Erich J', 'Institute of Biomedical Studies.|Department of Computer Science.|Institute of Biomedical Studies.|Institute of Biomedical Studies.', '-|-|-|-', 0, 'Models, Molecular|Molecular Sequence Annotation|Natural Language Processing|Protein Conformation|Proteins|Sequence Analysis, Protein|Supervised Machine Learning', 'Journal Article', '10.1093/bioinformatics/btx823', '29309523', 'Classification by supervised machine learning greatly facilitates the annotation of protein characteristics from their primary sequence. However, the feature generation step in this process requires detailed knowledge of attributes used to classify the proteins. Lack of this knowledge risks the selection of irrelevant features, resulting in a faulty model. In this study, we introduce a supervised protein classification method with a novel means of automating the work-intensive feature generation step via a Natural Language Processing (NLP)-dependent model, using a modified combination of n-grams and skip-grams (m-NGSG).'),
(81407, 2018, 'bio', 'myTAI: evolutionary transcriptomics with R', 'Drost, Hajk-Georg|Gabel, Alexander|Liu, Jialin|Quint, Marcel|Grosse, Ivo', 'Sainsbury Laboratory Cambridge, University of Cambridge, Cambridge CB2 1LR, UK.|Institute of Computer Science, Martin Luther University Halle-Wittenberg, 06120 Halle (Saale), Germany.|Université de Lausanne, Département dEcologie et dEvolution, Quartier Sorge, 1015 Lausanne, Switzerland.|Institute of Agricultural and Nutritional Sciences, Martin Luther University Halle-Wittenberg, 06120 Halle (Saale), Germany.|Institute of Computer Science, Martin Luther University Halle-Wittenberg, 06120 Halle (Saale), Germany.', 'United Kingdom|Germany|Switzerland|Germany|Germany', 0, 'Biological Evolution|Genomics|Software|Transcriptome', 'Journal Article', '10.1093/bioinformatics/btx835', '29309527', 'Next Generation Sequencing (NGS) technologies generate a large amount of high quality transcriptome datasets enabling the investigation of molecular processes on a genomic and metagenomic scale. These transcriptomics studies aim to quantify and compare the molecular phenotypes of the biological processes at hand. Despite the vast increase of available transcriptome datasets, little is known about the evolutionary conservation of those characterized transcriptomes.'),
(81408, 2018, 'bio', 'Algorithmic identification of discrepancies between published ratios and their reported confidence intervals and P-values', 'Georgescu, Constantin|Wren, Jonathan D', 'Arthritis and Clinical Immunology Research Program, Division of Genomics and Data Sciences, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma 73104-5005.|Arthritis and Clinical Immunology Research Program, Division of Genomics and Data Sciences, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma 73104-5005.', '-|-', 0, 'Algorithms|Confidence Intervals|Humans|MEDLINE|Publications', 'Journal Article', '10.1093/bioinformatics/btx811', '29309530', 'Studies, mostly from the operations/management literature, have shown that the rate of human error increases with task complexity. What is not known is how many errors make it into the published literature, given that they must slip by peer-review. By identifying paired, dependent values within text for reported calculations of varying complexity, we can identify discrepancies, quantify error rates and identify mitigating factors.'),
(81409, 2018, 'bio', 'CGManalyzer: an R package for analyzing continuous glucose monitoring studies', 'Zhang, Xiaohua Douglas|Zhang, Zhaozhi|Wang, Dandan', 'Faculty of Health Sciences, University of Macau, Taipa, Macau.|Department of Statistical Sciences, Duke University, Durham, NC 27708, USA.|Faculty of Health Sciences, University of Macau, Taipa, Macau.', 'United States|United States|United States', 0, 'Blood Glucose|Blood Glucose Self-Monitoring|Entropy|Software', 'Journal Article', '10.1093/bioinformatics/btx826', '29315360', 'The R package CGManalyzer contains functions for analyzing data from a continuous glucose monitoring (CGM) study. It covers a wide and comprehensive range of data analysis methods including reading a series of datasets, obtaining summary statistics of glucose levels, plotting data, transforming the time stamp format, fixing missing values, evaluating the mean of daily difference and continuous overlapping net glycemic action, calculating multiscale sample entropy, conducting pairwise comparison, displaying results using various plots including a new type of plot called an antenna plot, etc. This package has been developed from our work in directly analyzing data from various CGM devices such as the FreeStyle Libre, Glutalor, Dexcom and Medtronic CGM. Thus, this package should greatly facilitate the analysis of various CGM studies.'),
(81410, 2018, 'bio', 'Tumor purity quantification by clonal DNA methylation signatures', 'Benelli, Matteo|Romagnoli, Dario|Demichelis, Francesca', 'Centre for Integrative Biology, University of Trento, Trento, Italy.|Centre for Integrative Biology, University of Trento, Trento, Italy.|Centre for Integrative Biology, University of Trento, Trento, Italy.', 'Italy|Italy|Italy', 0, 'CpG Islands|DNA Methylation|Epigenomics|High-Throughput Nucleotide Sequencing|Humans|Neoplasms|Nucleotide Motifs|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bty011', '29325057', 'Controlling for tumor purity in molecular analyses is essential to allow for reliable genomic aberration calls, for inter-sample comparison and to monitor heterogeneity of cancer cell populations. In genome wide screening studies, the assessment of tumor purity is typically performed by means of computational methods that exploit somatic copy number aberrations.'),
(81411, 2018, 'bio', 'Toppar: an interactive browser for viewing association study results', 'Juliusdottir, Thorhildur|Banasik, Karina|Robertson, Neil R|Mott, Richard|McCarthy, Mark I', 'Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK.|Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK.|Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK.|UCL Genetics Institute, University College London, London WC1E6BT, UK.|Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Data Visualization|Databases, Factual|Genome-Wide Association Study|Internet|Software', 'Journal Article', '10.1093/bioinformatics/btx840', '29325066', 'Data integration and visualization help geneticists make sense of large amounts of data. To help facilitate interpretation of genetic association data we developed Toppar, a customizable visualization tool that stores results from association studies and enables browsing over multiple results, by combining features from existing tools and linking to appropriate external databases.'),
(81412, 2018, 'bio', 'Sensitive and specific post-call filtering of genetic variants in xenograft and primary tumors', 'Mannakee, Brian K|Balaji, Uthra|Witkiewicz, Agnieszka K|Gutenkunst, Ryan N|Knudsen, Erik S', 'Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health.|McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.|University of Arizona Cancer Center, University of Arizona, Tucson, AZ 85721, USA.|Department of Molecular and Cellular Biology, University of Arizona, Tucson, AZ 85721, USA.|University of Arizona Cancer Center, University of Arizona, Tucson, AZ 85721, USA.', 'United States|United States|United States|United States|United States', 1, 'Algorithms|Animals|Genetic Variation|Heterografts|High-Throughput Nucleotide Sequencing|Humans|Mice|Neoplasms|Software', 'Journal Article', '10.1093/bioinformatics/bty010', '29325072', 'Tumor genome sequencing offers great promise for guiding research and therapy, but spurious variant calls can arise from multiple sources. Mouse contamination can generate many spurious calls when sequencing patient-derived xenografts. Paralogous genome sequences can also generate spurious calls when sequencing any tumor. We developed a BLAST-based algorithm, Mouse And Paralog EXterminator (MAPEX), to identify and filter out spurious calls from both these sources.'),
(81413, 2018, 'bio', 'pyHVis3D: visualising molecular simulation deduced H-bond networks in 3D: application to T-cell receptor interactions', 'Knapp, Bernhard|Alcala, Marta|Zhang, Hao|West, Clare E|van der Merwe, P Anton|Deane, Charlotte M', 'Department of Statistics, University of Oxford, 24-29 St Giles, Oxford OX1 3LB, UK.|Department of Basic Sciences, International University of Catalonia, Josep Trueta, s/n 08195 Sant Cugat del Vallès, Spain.|Sir William Dunn School of Pathology, University of Oxford, S Parks Rd, Oxford OX1 3RE, UK.|Department of Statistics, University of Oxford, 24-29 St Giles, Oxford OX1 3LB, UK.|Sir William Dunn School of Pathology, University of Oxford, S Parks Rd, Oxford OX1 3RE, UK.|Department of Statistics, University of Oxford, 24-29 St Giles, Oxford OX1 3LB, UK.', 'United Kingdom|Spain|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 2, 'Algorithms|Computational Biology|Humans|Hydrogen Bonding|Molecular Dynamics Simulation|Mutation|Protein Conformation|Receptors, Antigen, T-Cell|Software', 'Journal Article', '10.1093/bioinformatics/btx842', '29329361', 'Hydrogen bonds (H-bonds) play an essential role for many molecular interactions but are also often transient, making visualising them in a flexible system challenging.'),
(81414, 2018, 'bio', 'Discovering personalized driver mutation profiles of single samples in cancer by network control strategy', 'Guo, Wei-Feng|Zhang, Shao-Wu|Liu, Li-Li|Liu, Fei|Shi, Qian-Qian|Zhang, Lei|Tang, Ying|Zeng, Tao|Chen, Luonan', 'Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xian 710072, China.|Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xian 710072, China.|Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xian 710072, China.|Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xian 710072, China.|Key Laboratory of Systems Biology, CAS Center for Excellence in Molecular Cell Science, Innovation Center for Cell Signaling Network, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Science, University of Chinese Academy of Sciences, Shanghai 200031, China.|Key Laboratory of Systems Biology, CAS Center for Excellence in Molecular Cell Science, Innovation Center for Cell Signaling Network, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Science, University of Chinese Academy of Sciences, Shanghai 200031, China.|Key Laboratory of Systems Biology, CAS Center for Excellence in Molecular Cell Science, Innovation Center for Cell Signaling Network, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Science, University of Chinese Academy of Sciences, Shanghai 200031, China.|Key Laboratory of Systems Biology, CAS Center for Excellence in Molecular Cell Science, Innovation Center for Cell Signaling Network, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Science, University of Chinese Academy of Sciences, Shanghai 200031, China.|Key Laboratory of Systems Biology, CAS Center for Excellence in Molecular Cell Science, Innovation Center for Cell Signaling Network, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Science, University of Chinese Academy of Sciences, Shanghai 200031, China.', 'China|China|China|China|China|China|China|China|China', 0, 'Algorithms|Genomics|Humans|Mutation|Neoplasms|Precision Medicine|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bty006', '29329368', 'It is a challenging task to discover personalized driver genes that provide crucial information on disease risk and drug sensitivity for individual patients. However, few methods have been proposed to identify the personalized-sample driver genes from the cancer omics data due to the lack of samples for each individual. To circumvent this problem, here we present a novel single-sample controller strategy (SCS) to identify personalized driver mutation profiles from network controllability perspective.'),
(81415, 2018, 'bio', 'A network approach to exploring the functional basis of gene-gene epistatic interactions in disease susceptibility', 'Yip, Danny Kit-Sang|Chan, Landon L|Pang, Iris K|Jiang, Wei|Tang, Nelson L S|Yu, Weichuan|Yip, Kevin Y', 'Department of Computer Science and Engineering.|Department of Computer Science and Engineering.|School of Life Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.|Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong.|Department of Chemical Pathology.|Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong.|Department of Computer Science and Engineering.', '-|-|Hong Kong|Hong Kong|-|Hong Kong|-', 0, 'Disease Susceptibility|Epistasis, Genetic|Humans|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/bty005', '29329369', 'Individual genetic variants explain only a small fraction of heritability in some diseases. Some variants have weak marginal effects on disease risk, but their joint effects are significantly stronger when occurring together. Most studies on such epistatic interactions have focused on methods for identifying the interactions and interpreting individual cases, but few have explored their general functional basis. This was due to the lack of a comprehensive list of epistatic interactions and uncertainties in associating variants to genes.'),
(81416, 2018, 'bio', 'Enrichment analysis with EpiAnnotator', 'Pageaud, Yoann|Plass, Christoph|Assenov, Yassen', 'Epigenomics and Cancer Risk Factors, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany.|Epigenomics and Cancer Risk Factors, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany.|Epigenomics and Cancer Risk Factors, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany.', 'Germany|Germany|Germany', 2, 'Animals|Epigenomics|Genome|Humans|Mice|Software', 'Journal Article', '10.1093/bioinformatics/bty007', '29329372', 'Deciphering relevant biological insights from epigenomic data can be a challenging task. One commonly used approach is to perform enrichment analysis. However, finding, downloading and using the publicly available functional annotations require time, programming skills and IT infrastructure. Here we describe the online tool EpiAnnotator for performing enrichment analyses on epigenomic data in a fast and user-friendly way.'),
(81417, 2018, 'bio', 'SimulaTE: simulating complex landscapes of transposable elements of populations', 'Kofler, Robert', 'Epigenomics and Cancer Risk Factors, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany.', 'Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/btx832', '29329374', '-'),
(81418, 2018, 'bio', 'LeNup: learning nucleosome positioning from DNA sequences with improved convolutional neural networks', 'Zhang, Juhua|Peng, Wenbo|Wang, Lei', 'Department of Biomedical Engineering.|Department of Biomedical Engineering.|Department of Biomedical Engineering.', '-|-|-', 11, 'Algorithms|Animals|Caenorhabditis elegans|Chromatin Assembly and Disassembly|DNA|Drosophila melanogaster|Genome|Humans|Neural Networks (Computer)|Nucleosomes|Saccharomyces cerevisiae|Software', 'Journal Article', '10.1093/bioinformatics/bty003', '29329398', 'Nucleosome positioning plays significant roles in proper genome packing and its accessibility to execute transcription regulation. Despite a multitude of nucleosome positioning resources available on line including experimental datasets of genome-wide nucleosome occupancy profiles and computational tools to the analysis on these data, the complex language of eukaryotic Nucleosome positioning remains incompletely understood.'),
(81419, 2018, 'bio', 'A benchmark study of scoring methods for non-coding mutations', 'Drubay, Damien|Gautheret, Daniel|Michiels, Stefan', 'INSERM U1018, CESP, Fac. de Mxc3xa9decine-Univ. Paris-Sud-UVSQ, INSERM, Universitxc3xa9 Paris-Saclay, 94807 Villejuif cedex, France.|Institute for Integrative Biology of the Cell, Universitxc3xa9 Paris-Sud, CNRS, CEA, 91198 Gif-sur-Yvette, France.|INSERM U1018, CESP, Fac. de Mxc3xa9decine-Univ. Paris-Sud-UVSQ, INSERM, Universitxc3xa9 Paris-Saclay, 94807 Villejuif cedex, France.', 'France|France|France', 6, 'Benchmarking|Genome, Human|Humans|Mutation|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bty008', '29340599', 'Detailed knowledge of coding sequences has led to different candidate models for pathogenic variant prioritization. Several deleteriousness scores have been proposed for the non-coding part of the genome, but no large-scale comparison has been realized to date to assess their performance.'),
(81420, 2018, 'bio', 'PyChimera: use UCSF Chimera modules in any Python 27 project', 'Rodrxc3xadguez-Guerra Pedregal, Jaime|Marxc3xa9chal, Jean-Didier', 'InsiliChem, Departament de Quxc3xadmica, Universitat Autxc3xb2noma de Barcelona, 08193 Barcelona, Spain.|InsiliChem, Departament de Quxc3xadmica, Universitat Autxc3xb2noma de Barcelona, 08193 Barcelona, Spain.', 'Spain|Spain', 0, 'Computational Biology|Molecular Dynamics Simulation|Software', 'Journal Article', '10.1093/bioinformatics/bty021', '29340616', 'UCSF Chimera is a powerful visualization tool remarkably present in the computational chemistry and structural biology communities. Built on a C++xe2x80x89core wrapped under a Python 2.7 environment, one could expect to easily import UCSF Chimeras arsenal of resources in custom scripts or software projects. Nonetheless, this is not readily possible if the script is not executed within UCSF Chimera due to the isolation of the platform. UCSF ChimeraX, successor to the original Chimera, partially solves the problem but yet major upgrades need to be undergone so that this updated version can offer all UCSF Chimera features.'),
(81421, 2018, 'bio', 'A rapid epistatic mixed-model association analysis by linear retransformations of genomic estimated values', 'Ning, Chao|Wang, Dan|Kang, Huimin|Mrode, Raphael|Zhou, Lei|Xu, Shizhong|Liu, Jian-Feng', 'National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China.|National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China.|National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China.|Animal Biosciences, International Livestock Institute, Nairobi 00100, Kenya.|National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China.|Department of Botany and Plant Science, University of California, Riverside, CA 20705, USA.|National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China.', 'China|China|China|Kenya|China|United States|China', 0, 'Algorithms|Animals|Epistasis, Genetic|Genome-Wide Association Study|Genomics|Humans|Mice|Models, Genetic|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/bty017', '29342229', 'Epistasis provides a feasible way for probing potential genetic mechanism of complex traits. However, time-consuming computation challenges successful detection of interaction in practice, especially when linear mixed model (LMM) is used to control type I error in the presence of population structure and cryptic relatedness.'),
(81422, 2018, 'bio', 'Informational and linguistic analysis of large genomic sequence collections via efficient Hadoop cluster algorithms', 'Ferraro Petrillo, Umberto|Roscigno, Gianluca|Cattaneo, Giuseppe|Giancarlo, Raffaele', 'Dipartimento di Scienze Statistiche, Università di Roma - La Sapienza, Rome 00185, Italy.|Dipartimento di Informatica, Università di Salerno, Fisciano, SA 84084, Italy.|Dipartimento di Informatica, Università di Salerno, Fisciano, SA 84084, Italy.|Dipartimento di Matematica ed Informatica, Università di Palermo, Palermo 90133, Italy.', 'Italy|Italy|Italy|Italy', 11, 'Algorithms|Animals|Bacteria|Cluster Analysis|Epigenomics|Eukaryota|Genomics|Humans|Linguistics|Metagenome|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bty018', '29342232', 'Information theoretic and compositional/linguistic analysis of genomes have a central role in bioinformatics, even more so since the associated methodologies are becoming very valuable also for epigenomic and meta-genomic studies. The kernel of those methods is based on the collection of k-mer statistics, i.e. how many times each k-mer in {A,C,G,T}k occurs in a DNA sequence. Although this problem is computationally very simple and efficiently solvable on a conventional computer, the sheer amount of data available now in applications demands to resort to parallel and distributed computing. Indeed, those type of algorithms have been developed to collect k-mer statistics in the realm of genome assembly. However, they are so specialized to this domain that they do not extend easily to the computation of informational and linguistic indices, concurrently on sets of genomes.'),
(81423, 2018, 'bio', 'QuantumClone: clonal assessment of functional mutations in cancer based on a genotype-aware method for clonal reconstruction', 'Deveau, Paul|Colmet Daage, Leo|Oldridge, Derek|Bernard, Virginie|Bellini, Angela|Chicard, Mathieu|Clement, Nathalie|Lapouble, Eve|Combaret, Valerie|Boland, Anne|Meyer, Vincent|Deleuze, Jean-Francois|Janoueix-Lerosey, Isabelle|Barillot, Emmanuel|Delattre, Olivier|Maris, John M|Schleiermacher, Gudrun|Boeva, Valentina', 'Institut Curie, PSL Research University, Mines Paris Tech, INSERM U900, Paris 75005, France.|Département de Recherche Translationnelle, Institut Curie, PSL Research University, INSERM U830, Laboratoire RTOP (Recherche Translationelle en Oncologie Pédiatrique), SIREDO Oncology Center (Care, Innovation and research for children and AYA with cancer), Paris 75005, France.|Division of Oncology, Childrens Hospital of Philadelphia, Philadelphia, PA, USA.|Institut Curie, PSL Research University, NGS platform ICGex, Paris 75005, France.|Département de Recherche Translationnelle, Institut Curie, PSL Research University, INSERM U830, Laboratoire RTOP (Recherche Translationelle en Oncologie Pédiatrique), SIREDO Oncology Center (Care, Innovation and research for children and AYA with cancer), Paris 75005, France.|Département de Recherche Translationnelle, Institut Curie, PSL Research University, INSERM U830, Laboratoire RTOP (Recherche Translationelle en Oncologie Pédiatrique), SIREDO Oncology Center (Care, Innovation and research for children and AYA with cancer), Paris 75005, France.|Département de Recherche Translationnelle, Institut Curie, PSL Research University, INSERM U830, Laboratoire RTOP (Recherche Translationelle en Oncologie Pédiatrique), SIREDO Oncology Center (Care, Innovation and research for children and AYA with cancer), Paris 75005, France.|Unité de Génétique Somatique, Institut Curie, PSL Research University, Paris 75005, France.|Centre Léon-Bérard Laboratoire de Recherche Translationnelle, Lyon, France.|Centre National de Recherche en Génomique Humaine (CNRGH), Institut de biologie François Jacob, CEA, Evry 91057, France.|Centre National de Recherche en Génomique Humaine (CNRGH), Institut de biologie François Jacob, CEA, Evry 91057, France.|Centre National de Recherche en Génomique Humaine (CNRGH), Institut de biologie François Jacob, CEA, Evry 91057, France.|Institut Curie, PSL Research University, INSERM U830, SIREDO Oncology Center (Care, Innovation and research for children and AYA with cancer), Equipe labellisée Ligue Nationale contre le cancer, Paris 75005, France.|Institut Curie, PSL Research University, Mines Paris Tech, INSERM U900, Paris 75005, France.|Institut Curie, PSL Research University, INSERM U830, SIREDO Oncology Center (Care, Innovation and research for children and AYA with cancer), Equipe labellisée Ligue Nationale contre le cancer, Paris 75005, France.|Division of Oncology, Childrens Hospital of Philadelphia, Philadelphia, PA, USA.|Département de Recherche Translationnelle, Institut Curie, PSL Research University, INSERM U830, Laboratoire RTOP (Recherche Translationelle en Oncologie Pédiatrique), SIREDO Oncology Center (Care, Innovation and research for children and AYA with cancer), Paris 75005, France.|Institut Curie, PSL Research University, Mines Paris Tech, INSERM U900, Paris 75005, France.', 'France|France|United States|France|France|France|France|France|France|France|France|France|France|France|France|', 0, 'Clonal Evolution|Cluster Analysis|DNA Copy Number Variations|DNA Mutational Analysis|Gene Frequency|High-Throughput Nucleotide Sequencing|Humans|Molecular Typing|Mutation|Neoplasms|Software|Whole Genome Sequencing', 'Journal Article', '10.1093/bioinformatics/bty016', '29342233', 'In cancer, clonal evolution is assessed based on information coming from single nucleotide variants and copy number alterations. Nonetheless, existing methods often fail to accurately combine information from both sources to truthfully reconstruct clonal populations in a given tumor sample or in a set of tumor samples coming from the same patient. Moreover, previously published methods detect clones from a single set of variants. As a result, compromises have to be done between stringent variant filtering [reducing dispersion in variant allele frequency estimates (VAFs)] and using all biologically relevant variants.'),
(81424, 2018, 'bio', 'MAGNAMWAR: an R package for genome-wide association studies of bacterial orthologs', 'Sexton, Corinne E|Smith, Hayden Z|Newell, Peter D|Douglas, Angela E|Chaston, John M', 'Department of Biology, Brigham Young University, Provo, UT 84602, USA.|Department of Biology, Brigham Young University, Provo, UT 84602, USA.|Department of Biological Sciences, SUNY Oswego, Oswego, NY 13126, USA.|Department of Entomology and Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, USA.|Department of Plant & Wildlife Sciences, Brigham Young University, Provo, UT 84602, USA.', 'United States|United States|United States|United States|United States', 4, 'Animals|Bacteria|Bacterial Proteins|Genome, Bacterial|Genome-Wide Association Study|Metagenome|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/bty001', '29342236', 'Here we report on an R package for genome-wide association studies of orthologous genes in bacteria. Before using the software, orthologs from bacterial genomes or metagenomes are defined using local or online implementations of OrthoMCL. These presence-absence patterns are statistically associated with variation in user-collected phenotypes using the Mono-Associated GNotobiotic Animals Metagenome-Wide Association R package (MAGNAMWAR). Genotype-phenotype associations can be performed with several different statistical tests based on the type and distribution of the data.'),
(81425, 2018, 'bio', 'iPat: intelligent prediction and association tool for genomic research', 'Chen, Chunpeng James|Zhang, Zhiwu', 'Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USA.|Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USA.', 'United States|United States', 0, 'Genome-Wide Association Study|Genomics|Genotype|Humans|Phenotype|Software', 'Journal Article', '10.1093/bioinformatics/bty015', '29342241', 'The ultimate goal of genomic research is to effectively predict phenotypes from genotypes so that medical management can improve human health and molecular breeding can increase agricultural production. Genomic prediction or selection (GS) plays a complementary role to genome-wide association studies (GWAS), which is the primary method to identify genes underlying phenotypes. Unfortunately, most computing tools cannot perform data analyses for both GWAS and GS. Furthermore, the majority of these tools are executed through a command-line interface (CLI), which requires programming skills. Non-programmers struggle to use them efficiently because of the steep learning curves and zero tolerance for data formats and mistakes when inputting keywords and parameters. To address these problems, this study developed a software package, named the Intelligent Prediction and Association Tool (iPat), with a user-friendly graphical user interface. With iPat, GWAS or GS can be performed using a pointing device to simply drag and/or click on graphical elements to specify input data files, choose input parameters and select analytical models. Models available to users include those implemented in third party CLI packages such as GAPIT, PLINK, FarmCPU, BLINK, rrBLUP and BGLR. Users can choose any data format and conduct analyses with any of these packages. File conversions are automatically conducted for specified input data and selected packages. A GWAS-assisted genomic prediction method was implemented to perform genomic prediction using any GWAS method such as FarmCPU. iPat was written in Java for adaptation to multiple operating systems including Windows, Mac and Linux.'),
(81426, 2018, 'bio', 'PEATH: single-individual haplotyping by a probabilistic evolutionary algorithm with toggling', 'Na, Joong Chae|Lee, Jong-Chan|Rhee, Je-Keun|Shin, Soo-Yong', 'Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea.|Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea.|Cancer Research Institute, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea.|Department of Digital Health, Samsung Advanced Institute for Health Science and Technology, Sungkyunkwan University, Seoul 06351, Korea.', '-|-|-|-', 3, 'Algorithms|Genome, Human|Genomics|Haplotypes|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bty012', '29342247', 'Single-individual haplotyping (SIH) is critical in genomic association studies and genetic diseases analysis. However, most genomic analysis studies do not perform haplotype-phasing analysis due to its complexity. Several computational methods have been developed to solve the SIH problem, but these approaches have not generated sufficiently reliable haplotypes.'),
(81427, 2018, 'bio', 'Splice Expression Variation Analysis (SEVA) for inter-tumor heterogeneity of gene isoform usage in cancer', 'Afsari, Bahman|Guo, Theresa|Considine, Michael|Florea, Liliana|Kagohara, Luciane T|Stein-OBrien, Genevieve L|Kelley, Dylan|Flam, Emily|Zambo, Kristina D|Ha, Patrick K|Geman, Donald|Ochs, Michael F|Califano, Joseph A|Gaykalova, Daria A|Favorov, Alexander V|Fertig, Elana J', 'Division of Biostatistics and Bioinformatics, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center.|Department of Otolaryngology-Head and Neck Surgery.|Division of Biostatistics and Bioinformatics, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center.|McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.|Division of Biostatistics and Bioinformatics, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center.|Division of Biostatistics and Bioinformatics, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center.|Department of Otolaryngology-Head and Neck Surgery.|Department of Otolaryngology-Head and Neck Surgery.|Department of Otolaryngology-Head and Neck Surgery.|Department of Otolaryngology-Head and Neck Surgery, University of California, San Francisco, CA 94158, USA.|Department of Applied Mathematics & Statistics, Johns Hopkins University, Baltimore, MD 21218, USA.|Department of Mathematics & Statistics, The College of New Jersey, Ewing, NJ 08628, USA.|Division of Otolaryngology, Department of Surgery, University of California, San Diego, CA 92093, USA.|Department of Otolaryngology-Head and Neck Surgery.|Division of Biostatistics and Bioinformatics, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center.|Division of Biostatistics and Bioinformatics, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|Jersey|United States|United States|United States|United States', 0, 'Alternative Splicing|Computational Biology|Gene Expression Regulation, Neoplastic|Head and Neck Neoplasms|Humans|Models, Genetic|Neoplasms|Protein Isoforms|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/bty004', '29342249', 'Current bioinformatics methods to detect changes in gene isoform usage in distinct phenotypes compare the relative expected isoform usage in phenotypes. These statistics model differences in isoform usage in normal tissues, which have stable regulation of gene splicing. Pathological conditions, such as cancer, can have broken regulation of splicing that increases the heterogeneity of the expression of splice variants. Inferring events with such differential heterogeneity in gene isoform usage requires new statistical approaches.'),
(81428, 2018, 'bio', 'BAUM: improving genome assembly by adaptive unique mapping and local overlap-layout-consensus approach', 'Wang, Anqi|Wang, Zhanyu|Li, Zheng|Li, Lei M', 'National Center of Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China.|National Center of Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China.|National Center of Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China.|National Center of Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China.', 'China|China|China|China', 6, 'Genomics|High-Throughput Nucleotide Sequencing|Repetitive Sequences, Nucleic Acid|Sequence Analysis, DNA|Software|Uncertainty', 'Journal Article', '10.1093/bioinformatics/bty020', '29346504', 'It is highly desirable to assemble genomes of high continuity and consistency at low cost. The current bottleneck of draft genome continuity using the second generation sequencing (SGS) reads is primarily caused by uncertainty among repetitive sequences. Even though the single-molecule real-time sequencing technology is very promising to overcome the uncertainty issue, its relatively high cost and error rate add burden on budget or computation. Many long-read assemblers take the overlap-layout-consensus (OLC) paradigm, which is less sensitive to sequencing errors, heterozygosity and variability of coverage. However, current assemblers of SGS data do not sufficiently take advantage of the OLC approach.'),
(81429, 2018, 'bio', 'A distance-based approach for testing the mediation effect of the human microbiome', 'Zhang, Jie|Wei, Zhi|Chen, Jun', 'Adobe Systems Incorporated, San Jose, CA 95110, USA.|Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102, USA.|Division of Biomedical Statistics and Informatics and Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA.', 'United States|Jersey|United States', 0, 'Bacteria|Computational Biology|Computer Simulation|Gastrointestinal Microbiome|Microbiota|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/bty014', '29346509', 'Recent studies have revealed a complex interplay between environment, the human microbiome and health and disease. Mediation analysis of the human microbiome in these complex relationships could potentially provide insights into the role of the microbiome in the etiology of disease and, more importantly, lead to novel clinical interventions by modulating the microbiome. However, due to the high dimensionality, sparsity, non-normality and phylogenetic structure of microbiome data, none of the existing methods are suitable for testing such clinically important mediation effect.'),
(81430, 2018, 'bio', 'VIPER: a web application for rapid expert review of variant calls', 'Wöste, Marius|Dugas, Martin', 'Institute of Medical Informatics, University of Münster, Münster 48149, Germany.|Institute of Medical Informatics, University of Münster, Münster 48149, Germany.', 'Germany|Germany', 0, 'Genomics|Genotyping Techniques|High-Throughput Nucleotide Sequencing|Humans|Internet|Mutation|Myelodysplastic Syndromes|Polymorphism, Genetic|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bty022', '29346510', 'With the rapid development in next-generation sequencing, cost and time requirements for genomic sequencing are decreasing, enabling applications in many areas such as cancer research. Many tools have been developed to analyze genomic variation ranging from single nucleotide variants to whole chromosomal aberrations. As sequencing throughput increases, the number of variants called by such tools also grows. Often employed manual inspection of such calls is thus becoming a time-consuming procedure. We developed the Variant InsPector and Expert Rating tool (VIPER) to speed up this process by integrating the Integrative Genomics Viewer into a web application. Analysts can then quickly iterate through variants, apply filters and make decisions based on the generated images and variant metadata. VIPER was successfully employed in analyses with manual inspection of more than 10 000 calls.'),
(81431, 2018, 'bio', 'Optimal water networks in protein cavities with GAsol and 3D-RISM', 'Fusani, Lucia|Wall, Ian|Palmer, David|Cortes, Alvaro', 'Platform Technology and Science, GSK Medicines Research Centre, Stevenage, Hertfordshire SG1 2NY, UK.|Platform Technology and Science, GSK Medicines Research Centre, Stevenage, Hertfordshire SG1 2NY, UK.|Department of Pure and Applied Chemistry, University of Strathclyde, Glasgow G1 1XL, UK.|Platform Technology and Science, GSK Medicines Research Centre, Stevenage, Hertfordshire SG1 2NY, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 5, 'Binding Sites|Computational Biology|Models, Molecular|Protein Conformation|Proteins|Software|Water', 'Journal Article', '10.1093/bioinformatics/bty024', '29346514', 'Water molecules in protein binding sites play essential roles in biological processes. The popular 3D-RISM prediction method can calculate the solvent density distribution within minutes, but is difficult to convert it into explicit water molecules.'),
(81432, 2018, 'bio', 'Computational deconvolution of transcriptomics data from mixed cell populations', 'Avila Cobos, Francisco|Vandesompele, Jo|Mestdagh, Pieter|De Preter, Katleen', 'Center for Medical Genetics Ghent (CMGG), Ghent University, 9000 Ghent, Belgium.|Center for Medical Genetics Ghent (CMGG), Ghent University, 9000 Ghent, Belgium.|Center for Medical Genetics Ghent (CMGG), Ghent University, 9000 Ghent, Belgium.|Center for Medical Genetics Ghent (CMGG), Ghent University, 9000 Ghent, Belgium.', 'Belgium|Belgium|Belgium|Belgium', 30, 'Algorithms|Animals|Computational Biology|Gene Expression Profiling|Humans|Sequence Analysis, RNA|Single-Cell Analysis', 'Journal Article', '10.1093/bioinformatics/bty019', '29351586', 'Gene expression analyses of bulk tissues often ignore cell type composition as an important confounding factor, resulting in a loss of signal from lowly abundant cell types. In this review, we highlight the importance and value of computational deconvolution methods to infer the abundance of different cell types and/or cell type-specific expression profiles in heterogeneous samples without performing physical cell sorting. We also explain the various deconvolution scenarios, the mathematical approaches used to solve them and the effect of data processing and different confounding factors on the accuracy of the deconvolution results.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(81433, 2018, 'bio', 'GTC: how to maintain huge genotype collections in a compressed form', 'Danek, Agnieszka|Deorowicz, Sebastian', 'Institute of Informatics, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Gliwice, Poland.|Institute of Informatics, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Gliwice, Poland.', 'Poland|Poland', 0, 'Algorithms|Data Compression|Databases, Genetic|Genomics|Genotype|Genotyping Techniques|Haplotypes|Humans|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bty023', '29351600', 'Nowadays, genome sequencing is frequently used in many research centers. In projects, such as the Haplotype Reference Consortium or the Exome Aggregation Consortium, huge databases of genotypes in large populations are determined. Together with the increasing size of these collections, the need for fast and memory frugal ways of representation and searching in them becomes crucial.'),
(81434, 2018, 'bio', 'OPAL: prediction of MoRF regions in intrinsically disordered protein sequences', 'Sharma, Ronesh|Raicar, Gaurav|Tsunoda, Tatsuhiko|Patil, Ashwini|Sharma, Alok', 'School of Engineering and Physics, The University of the South Pacific, Suva, Fiji.|School of Engineering and Physics, The University of the South Pacific, Suva, Fiji.|Laboratory of Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan.|Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo 108-8639, Japan.|School of Engineering and Physics, The University of the South Pacific, Suva, Fiji.', 'Fiji|Fiji|Japan|Japan|Fiji', 22, 'Computational Biology|Humans|Intrinsically Disordered Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/bty032', '29360926', 'Intrinsically disordered proteins lack stable 3-dimensional structure and play a crucial role in performing various biological functions. Key to their biological function are the molecular recognition features (MoRFs) located within long disordered regions. Computationally identifying these MoRFs from disordered protein sequences is a challenging task. In this study, we present a new MoRF predictor, OPAL, to identify MoRFs in disordered protein sequences. OPAL utilizes two independent sources of information computed using different component predictors. The scores are processed and combined using common averaging method. The first score is computed using a component MoRF predictor which utilizes composition and sequence similarity of MoRF and non-MoRF regions to detect MoRFs. The second score is calculated using half-sphere exposure (HSE), solvent accessible surface area (ASA) and backbone angle information of the disordered protein sequence, using information from the amino acid properties of flanks surrounding the MoRFs to distinguish MoRF and non-MoRF residues.'),
(81435, 2018, 'bio', 'PhenoRank: reducing study bias in gene prioritization through simulation', 'Cornish, Alex J|David, Alessia|Sternberg, Michael J E', 'Department of Life Sciences, Center of Bioinformatics and Systems Biology, Imperial College London, London, UK.|Department of Life Sciences, Center of Bioinformatics and Systems Biology, Imperial College London, London, UK.|Department of Life Sciences, Center of Bioinformatics and Systems Biology, Imperial College London, London, UK.', 'United Kingdom|United Kingdom|United Kingdom', 8, 'Animals|Bias|Genetic Predisposition to Disease|Genome-Wide Association Study|Humans|Mice|Phenotype|Polymorphism, Genetic|Protein Interaction Maps|ROC Curve|Software', 'Journal Article', '10.1093/bioinformatics/bty028', '29360927', 'Genome-wide association studies have identified thousands of loci associated with human disease, but identifying the causal genes at these loci is often difficult. Several methods prioritize genes most likely to be disease causing through the integration of biological data, including protein-protein interaction and phenotypic data. Data availability is not the same for all genes however, potentially influencing the performance of these methods.'),
(81436, 2018, 'bio', 'APAtrap: identification and quantification of alternative polyadenylation sites from RNA-seq data', 'Ye, Congting|Long, Yuqi|Ji, Guoli|Li, Qingshun Quinn|Wu, Xiaohui', 'Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Xiamen, Fujian 361102, China.|Department of Automation, Xiamen University, Xiamen, Fujian 361005, China.|Department of Automation, Xiamen University, Xiamen, Fujian 361005, China.|Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Xiamen, Fujian 361102, China.|Department of Automation, Xiamen University, Xiamen, Fujian 361005, China.', 'China|China|China|China|China', 18, 'Arabidopsis|Humans|Poly A|Polyadenylation|Sequence Analysis, RNA|Software|Transcriptome', 'Journal Article', '10.1093/bioinformatics/bty029', '29360928', 'Alternative polyadenylation (APA) has been increasingly recognized as a crucial mechanism that contributes to transcriptome diversity and gene expression regulation. As RNA-seq has become a routine protocol for transcriptome analysis, it is of great interest to leverage such unprecedented collection of RNA-seq data by new computational methods to extract and quantify APA dynamics in these transcriptomes. However, research progress in this area has been relatively limited. Conventional methods rely on either transcript assembly to determine transcript 3 ends or annotated poly(A) sites. Moreover, they can neither identify more than two poly(A) sites in a gene nor detect dynamic APA site usage considering more than two poly(A) sites.'),
(81437, 2018, 'bio', 'ST Spot Detector: a web-based application for automatic spot and tissue detection for spatial Transcriptomics image datasets', 'Wong, Kim|Navarro, José Fernández|Bergenstråhle, Ludvig|Ståhl, Patrik L|Lundeberg, Joakim', 'Science for Life Laboratory, Division of Gene Technology, School of Biotechnology, Royal Institute of Technology (KTH), SE-106 91 Solna, Sweden.|Science for Life Laboratory, Division of Gene Technology, School of Biotechnology, Royal Institute of Technology (KTH), SE-106 91 Solna, Sweden.|Science for Life Laboratory, Division of Gene Technology, School of Biotechnology, Royal Institute of Technology (KTH), SE-106 91 Solna, Sweden.|Science for Life Laboratory, Division of Gene Technology, School of Biotechnology, Royal Institute of Technology (KTH), SE-106 91 Solna, Sweden.|Science for Life Laboratory, Division of Gene Technology, School of Biotechnology, Royal Institute of Technology (KTH), SE-106 91 Solna, Sweden.', 'Sweden|Sweden|Sweden|Sweden|Sweden', 4, 'Animals|Gene Expression Profiling|Humans|Image Interpretation, Computer-Assisted|Internet|Plants|Sequence Analysis, RNA|Software|Spatial Analysis', 'Journal Article', '10.1093/bioinformatics/bty030', '29360929', 'Spatial Transcriptomics (ST) is a method which combines high resolution tissue imaging with high troughput transcriptome sequencing data. This data must be aligned with the images for correct visualization, a process that involves several manual steps.'),
(81438, 2018, 'bio', 'ADEPTUS: a discovery tool for disease prediction, enrichment and network analysis based on profiles from many diseases', 'Amar, David|Vizel, Amir|Levy, Carmit|Shamir, Ron', 'Department of Cardiovascular Medicine, Stanford University, Stanford, CA 94305, USA.|The Blavatnik School of Computer Science.|Department of Human Genetics and Biochemistry, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.|The Blavatnik School of Computer Science.', 'United States|United States|Israel|United States', 2, 'Biological Ontologies|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Gene Regulatory Networks|Genetic Predisposition to Disease|Genomics|Humans|Melanoma|Mutation|Phenotype|Skin Neoplasms|Software', 'Journal Article', '10.1093/bioinformatics/bty027', '29360930', 'Large-scale publicly available genomic data on many disease phenotypes could improve our understanding of the molecular basis of disease. Tools that undertake this challenge by jointly analyzing multiple phenotypes are needed.'),
(81439, 2018, 'bio', 'DExSI: a new tool for the rapid quantitation of 13C-labelled metabolites detected by GC-MS', 'Dagley, Michael J|McConville, Malcolm J', 'Department of Biochemistry and Molecular Biology, Bio21 Institute of Molecular Science and Biotechnology, University of Melbourne, Parkville, VIC 3010, Australia.|Department of Biochemistry and Molecular Biology, Bio21 Institute of Molecular Science and Biotechnology, University of Melbourne, Parkville, VIC 3010, Australia.', 'Australia|Australia', 13, 'Animals|Bacteria|Carbon Isotopes|Data Visualization|Eukaryota|Gas Chromatography-Mass Spectrometry|Isotope Labeling|Metabolomics|Software', 'Journal Article', '10.1093/bioinformatics/bty025', '29360933', 'Stable isotope directed metabolomics is increasingly being used to measure metabolic fluxes in microbial, plant and animal cells. Incorporation of 13C/15N isotopes into a wide range of metabolites is typically determined using gas chromatography-mass spectrometry (GC/MS) or other hyphenated mass spectrometry approaches. The DExSI (Data Extraction for Stable Isotope-labelled metabolites) pipeline is an interactive graphical software package which can be used to rapidly quantitate isotopologues for a wide variety of metabolites detected by GC/MS. DExSI performs automated metabolite annotation, mass and positional isotopomer abundance determination and natural isotope abundance correction. It provides a range of output options and is suitable for high throughput analyses.'),
(81440, 2018, 'bio', 'Haystack: systematic analysis of the variation of epigenetic states and cell-type specific regulatory elements', 'Pinello, Luca|Farouni, Rick|Yuan, Guo-Cheng', 'Department of Molecular Pathology, Massachusetts General Hospital, Boston, MA, USA.|Department of Molecular Pathology, Massachusetts General Hospital, Boston, MA, USA.|Dana-Farber Cancer Institute, Boston, MA, USA.', 'United States|United States|United States', 4, 'Epigenomics|Genome, Human|Humans|Organ Specificity|Regulatory Sequences, Nucleic Acid|Software', 'Journal Article', '10.1093/bioinformatics/bty031', '29360936', 'With the increasing amount of genomic and epigenomic data in the public domain, a pressing challenge is to integrate these data to investigate the role of epigenetic mechanisms in regulating gene expression and maintenance of cell-identity. To this end, we have implemented a computational pipeline to systematically study epigenetic variability and uncover regulatory DNA sequences.'),
(81441, 2018, 'bio', 'Primer3_masker: integrating masking of template sequence with primer design software', 'Kõressaar, Triinu|Lepamets, Maarja|Kaplinski, Lauris|Raime, Kairi|Andreson, Reidar|Remm, Maido', 'Department of Bioinformatics, Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia.|Department of Bioinformatics, Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia.|Department of Bioinformatics, Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia.|Department of Bioinformatics, Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia.|Department of Bioinformatics, Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia.|Department of Bioinformatics, Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia.', 'Estonia|Estonia|Estonia|Estonia|Estonia|Estonia', 34, 'Animals|DNA Primers|Humans|Plants|Polymerase Chain Reaction|Repetitive Sequences, Nucleic Acid|Software', 'Journal Article', '10.1093/bioinformatics/bty036', '29360956', 'Designing PCR primers for amplifying regions of eukaryotic genomes is a complicated task because the genomes contain a large number of repeat sequences and other regions unsuitable for amplification by PCR. We have developed a novel k-mer based masking method that uses a statistical model to detect and mask failure-prone regions on the DNA template prior to primer design. We implemented the software as a standalone software primer3_masker and integrated it into the primer design program Primer3.'),
(81442, 2018, 'bio', 'Feature specific quantile normalization enables cross-platform classification of molecular subtypes using gene expression data', 'Franks, Jennifer M|Cai, Guoshuai|Whitfield, Michael L', 'Department of Molecular and Systems Biology.|Department of Environmental Health Sciences, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA.|Department of Molecular and Systems Biology.', 'United States|United States|United States', 14, 'Breast Neoplasms|Colonic Neoplasms|Colorectal Neoplasms|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Neoplasms|Oligonucleotide Array Sequence Analysis|Sequence Analysis, RNA|Software|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/bty026', '29360996', 'Molecular subtypes of cancers and autoimmune disease, defined by transcriptomic profiling, have provided insight into disease pathogenesis, molecular heterogeneity and therapeutic responses. However, technical biases inherent to different gene expression profiling platforms present a unique problem when analyzing data generated from different studies. Currently, there is a lack of effective methods designed to eliminate platform-based bias. We present a method to normalize and classify RNA-seq data using machine learning classifiers trained on DNA microarray data and molecular subtypes in two datasets: breast invasive carcinoma (BRCA) and colorectal cancer (CRC).'),
(81443, 2018, 'bio', 'Sequanix: a dynamic graphical interface for Snakemake workflows', 'Desvillechabrol, Dimitri|Legendre, Rachel|Rioualen, Claire|Bouchier, Christiane|van Helden, Jacques|Kennedy, Sean|Cokelaer, Thomas', 'Institut Pasteur-Biomics Pole-CITECH, F-75015, Paris, France.|Institut Pasteur-Biomics Pole-CITECH, F-75015, Paris, France.|Aix Marseille Univ, INSERM, TAGC, UMR_S 1090, Marseille 13288, France.|Institut Pasteur-Biomics Pole-CITECH, F-75015, Paris, France.|Aix Marseille Univ, INSERM, TAGC, UMR_S 1090, Marseille 13288, France.|Institut Pasteur-Biomics Pole-CITECH, F-75015, Paris, France.|Institut Pasteur-Biomics Pole-CITECH, F-75015, Paris, France.', 'France|France|France|France|France|France|France', 8, 'Computational Biology|Data Visualization|Software', 'Journal Article', '10.1093/bioinformatics/bty034', '29361152', 'We designed a PyQt graphical user interface-Sequanix-aimed at democratizing the use of Snakemake pipelines in the NGS space and beyond. By default, Sequanix includes Sequana NGS pipelines (Snakemake format) (http://sequana.readthedocs.io), and is also capable of loading any external Snakemake pipeline. New users can easily, visually, edit configuration files of expert-validated pipelines and can interactively execute these production-ready workflows. Sequanix will be useful to both Snakemake developers in exposing their pipelines and to a wide audience of users.'),
(81444, 2018, 'bio', 'DincRNA: a comprehensive web-based bioinformatics toolkit for exploring disease associations and ncRNA function', 'Cheng, Liang|Hu, Yang|Sun, Jie|Zhou, Meng|Jiang, Qinghua', 'Department of College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Sheng 150081, China.|Department of Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang Sheng 150001, China.|Department of College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Sheng 150081, China.|Department of College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Sheng 150081, China.|Department of Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang Sheng 150001, China.', 'China|China|China|China|China', 102, 'Algorithms|Computational Biology|Genetic Predisposition to Disease|Humans|Internet|MicroRNAs|RNA, Long Noncoding|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/bty002', '29365045', 'DincRNA aims to provide a comprehensive web-based bioinformatics toolkit to elucidate the entangled relationships among diseases and non-coding RNAs (ncRNAs) from the perspective of disease similarity. The quantitative way to illustrate relationships of pair-wise diseases always depends on their molecular mechanisms, and structures of the directed acyclic graph of Disease Ontology (DO). Corresponding methods for calculating similarity of pair-wise diseases involve Resniks, Lins, Wangs, PSB and SemFunSim methods. Recently, disease similarity was validated suitable for calculating functional similarities of ncRNAs and prioritizing ncRNA-disease pairs, and it has been widely applied for predicting the ncRNA function due to the limited biological knowledge from wet lab experiments of these RNAs. For this purpose, a large number of algorithms and priori knowledge need to be integrated. e.g. pair-wise best, pairs-average (PBPA) and pair-wise all, pairs-maximum (PAPM) methods for calculating functional similarities of ncRNAs, and random walk with restart (RWR) method for prioritizing ncRNA-disease pairs. To facilitate the exploration of disease associations and ncRNA function, DincRNA implemented all of the above eight algorithms based on DO and disease-related genes. Currently, it provides the function to query disease similarity scores, miRNA and lncRNA functional similarity scores, and the prioritization scores of lncRNA-disease and miRNA-disease pairs.'),
(81445, 2018, 'bio', 'Computational drug repositioning using low-rank matrix approximation and randomized algorithms', 'Luo, Huimin|Li, Min|Wang, Shaokai|Liu, Quan|Li, Yaohang|Wang, Jianxin', 'School of Information Science and Engineering, Central South University, ChangSha 410083, China.|School of Information Science and Engineering, Central South University, ChangSha 410083, China.|School of Information Science and Engineering, Central South University, ChangSha 410083, China.|School of Information Science and Engineering, Central South University, ChangSha 410083, China.|Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA.|School of Information Science and Engineering, Central South University, ChangSha 410083, China.', 'China|China|China|China|United States|China', 46, 'Algorithms|Computational Biology|Drug Discovery|Drug Repositioning|Humans', 'Journal Article', '10.1093/bioinformatics/bty013', '29365057', 'Computational drug repositioning is an important and efficient approach towards identifying novel treatments for diseases in drug discovery. The emergence of large-scale, heterogeneous biological and biomedical datasets has provided an unprecedented opportunity for developing computational drug repositioning methods. The drug repositioning problem can be modeled as a recommendation system that recommends novel treatments based on known drug-disease associations. The formulation under this recommendation system is matrix completion, assuming that the hidden factors contributing to drug-disease associations are highly correlated and thus the corresponding data matrix is low-rank. Under this assumption, the matrix completion algorithm fills out the unknown entries in the drug-disease matrix by constructing a low-rank matrix approximation, where new drug-disease associations having not been validated can be screened.'),
(81446, 2018, 'bio', 'An easy and efficient approach for testing identifiability', 'Kreutz, Clemens', 'Center for Systems Biology (ZBSA), Habsburger Str. 49, University of Freiburg, 79104 Freiburg, Germany.', 'Germany', 5, 'Computational Biology|Least-Squares Analysis|Models, Biological|Probability|Software', 'Journal Article', '10.1093/bioinformatics/bty035', '29365095', 'The feasibility of uniquely estimating parameters of dynamical systems from observations is a widely discussed aspect of mathematical modelling. Several approaches have been published for analyzing this so-called identifiability of model parameters. However, they are typically computationally demanding, difficult to perform and/or not applicable in many application settings.'),
(81447, 2018, 'bio', 'pepKalc: scalable and comprehensive calculation of electrostatic interactions in random coil polypeptides', 'Tamiola, Kamil|Scheek, Ruud M|van der Meulen, Pieter|Mulder, Frans A A', 'Peptone - The Protein Intelligence Company, Amsterdam, The Netherlands.|Department of Molecular Dynamics, GBB, University of Groningen, Groningen, The Netherlands.|Department of Molecular Dynamics, GBB, University of Groningen, Groningen, The Netherlands.|Department of Molecular Dynamics, GBB, University of Groningen, Groningen, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands', 6, 'Computational Biology|Humans|Monte Carlo Method|Peptides|Software|Static Electricity|alpha-Synuclein', 'Journal Article', '10.1093/bioinformatics/bty033', '29365182', 'Polypeptide sequence length is the single dominant factor hampering the effectiveness of currently available software tools for de novo calculation of amino acid-specific protonation constants in disordered polypeptides.'),
(81448, 2018, 'bio', 'HPViewer: sensitive and specific genotyping of human papillomavirus in metagenomic DNA', 'Hao, Yuhan|Yang, Liying|Galvao Neto, Antonio|Amin, Milan R|Kelly, Dervla|Brown, Stuart M|Branski, Ryan C|Pei, Zhiheng', 'Department of Pathology.|Department of Pathology.|Department of Pathology.|Department of Otolaryngology-Head and Neck Surgery.|Department of Pathology.|Applied Bioinformatics Laboratories.|Department of Otolaryngology-Head and Neck Surgery.|Department of Pathology.', '-|-|-|-|-|-|-|-', 0, 'Genotyping Techniques|Humans|Metagenomics|Papillomaviridae|Papillomavirus Infections|Respiratory Tract Infections|Sensitivity and Specificity|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bty037', '29377990', 'Shotgun DNA sequencing provides sensitive detection of all 182 HPV types in tissue and body fluid. However, existing computational methods either produce false positives misidentifying HPV types due to shared sequences among HPV, human and prokaryotes, or produce false negative since they identify HPV by assembled contigs requiring large abundant of HPV reads.'),
(81449, 2018, 'bio', 'Accurity: accurate tumor purity and ploidy inference from tumor-normal WGS data by jointly modelling somatic copy number alterations and heterozygous germline single-nucleotide-variants', 'Luo, Zhihui|Fan, Xinping|Su, Yao|Huang, Yu S', 'State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.|State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.|State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.|State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.', 'China|China|China|China', 7, 'Algorithms|Computational Biology|Computer Simulation|DNA Copy Number Variations|Germ-Line Mutation|High-Throughput Nucleotide Sequencing|Humans|Neoplasms|Ploidies|Software|Whole Genome Sequencing', 'Journal Article', '10.1093/bioinformatics/bty043', '29385401', 'Tumor purity and ploidy have a substantial impact on next-gen sequence analyses of tumor samples and may alter the biological and clinical interpretation of results. Despite the existence of several computational methods that are dedicated to estimate tumor purity and/or ploidy from The Cancer Genome Atlas (TCGA) tumor-normal whole-genome-sequencing (WGS) data, an accurate, fast and fully-automated method that works in a wide range of sequencing coverage, level of tumor purity and level of intra-tumor heterogeneity, is still missing.'),
(81450, 2018, 'bio', 'G2S: a web-service for annotating genomic variants on 3D protein structures', 'Wang, Juexin|Sheridan, Robert|Sumer, S Onur|Schultz, Nikolaus|Xu, Dong|Gao, Jianjiong', 'Department of Electrical Engineering & Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA.|Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.|Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.|Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.|Department of Electrical Engineering & Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA.|Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.', 'United States|United States|United States|United States|United States|United States', 2, 'Amino Acid Sequence|Computational Biology|Humans|Internet|Mutation|Polymorphism, Genetic|Protein Conformation|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/bty047', '29385402', 'Accurately mapping and annotating genomic locations on 3D protein structures is a key step in structure-based analysis of genomic variants detected by recent large-scale sequencing efforts. There are several mapping resources currently available, but none of them provides a web API (Application Programming Interface) that supports programmatic access.'),
(81451, 2018, 'bio', 'TSAPA: identification of tissue-specific alternative polyadenylation sites in plants', 'Ji, Guoli|Chen, Moliang|Ye, Wenbin|Zhu, Sheng|Ye, Congting|Su, Yaru|Peng, Haonan|Wu, Xiaohui', 'Department of Automation.|Department of Automation.|Department of Automation.|Department of Automation.|Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Xiamen, China.|College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China.|Department of Automation.|Department of Automation.', '-|-|-|-|China|China|-|-', 4, 'Computational Biology|Machine Learning|Plants|Poly A|Polyadenylation|Sequence Analysis, DNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/bty044', '29385403', 'Alternative polyadenylation (APA) is now emerging as a widespread mechanism modulated tissue-specifically, which highlights the need to define tissue-specific poly(A) sites for profiling APA dynamics across tissues. We have developed an R package called TSAPA based on the machine learning model for identifying tissue-specific poly(A) sites in plants. A feature space including more than 200 features was assembled to specifically characterize poly(A) sites in plants. The classification model in TSAPA can be customized by selecting desirable features or classifiers. TSAPA is also capable of predicting tissue-specific poly(A) sites in unannotated intergenic regions. TSAPA will be a valuable addition to the community for studying dynamics of APA in plants.'),
(81452, 2018, 'bio', 'TOXsIgN: a cross-species repository for toxicogenomic signatures', 'Darde, Thomas A|Gaudriault, Pierre|Beranger, Rémi|Lancien, Clément|Caillarec-Joly, Annaëlle|Sallou, Olivier|Bonvallot, Nathalie|Chevrier, Cécile|Mazaud-Guittot, Séverine|Jégou, Bernard|Collin, Olivier|Becker, Emmanuelle|Rolland, Antoine D|Chalmel, Frédéric', 'Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S1085, F-35000 Rennes, France.|Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S1085, F-35000 Rennes, France.|Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S1085, F-35000 Rennes, France.|Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S1085, F-35000 Rennes, France.|Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S1085, F-35000 Rennes, France.|Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA/INRIA) - GenOuest Platform, Université de Rennes 1, F-35042 Rennes, France.|Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S1085, F-35000 Rennes, France.|Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S1085, F-35000 Rennes, France.|Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S1085, F-35000 Rennes, France.|Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S1085, F-35000 Rennes, France.|Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA/INRIA) - GenOuest Platform, Université de Rennes 1, F-35042 Rennes, France.|Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S1085, F-35000 Rennes, France.|Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S1085, F-35000 Rennes, France.|Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S1085, F-35000 Rennes, France.', 'France|France|France|France|France|France|France|France|France|France|France|France|France|France', 10, 'Animals|Databases, Factual|Humans|Software|Toxicogenetics', 'Journal Article', '10.1093/bioinformatics/bty040', '29385404', 'At the same time that toxicologists express increasing concern about reproducibility in this field, the development of dedicated databases has already smoothed the path toward improving the storage and exchange of raw toxicogenomic data. Nevertheless, none provides access to analyzed and interpreted data as originally reported in scientific publications. Given the increasing demand for access to this information, we developed TOXsIgN, a repository for TOXicogenomic sIgNatures.'),
(81453, 2018, 'bio', 'EPS-LASSO: test for high-dimensional regression under extreme phenotype sampling of continuous traits', 'Xu, Chao|Fang, Jian|Shen, Hui|Wang, Yu-Ping|Deng, Hong-Wen', 'Center of Bioinformatics and Genomics, Tulane University, New Orleans, LA, USA.|Center of Bioinformatics and Genomics, Tulane University, New Orleans, LA, USA.|Center of Bioinformatics and Genomics, Tulane University, New Orleans, LA, USA.|Center of Bioinformatics and Genomics, Tulane University, New Orleans, LA, USA.|Center of Bioinformatics and Genomics, Tulane University, New Orleans, LA, USA.', 'United States|United States|United States|United States|United States', 5, 'Gene Expression Profiling|Genomics|Humans|Models, Genetic|Phenotype|Quantitative Trait Loci|Software', 'Journal Article', '10.1093/bioinformatics/bty042', '29385408', 'Extreme phenotype sampling (EPS) is a broadly-used design to identify candidate genetic factors contributing to the variation of quantitative traits. By enriching the signals in extreme phenotypic samples, EPS can boost the association power compared to random sampling. Most existing statistical methods for EPS examine the genetic factors individually, despite many quantitative traits have multiple genetic factors underlying their variation. It is desirable to model the joint effects of genetic factors, which may increase the power and identify novel quantitative trait loci under EPS. The joint analysis of genetic data in high-dimensional situations requires specialized techniques, e.g. the least absolute shrinkage and selection operator (LASSO). Although there are extensive research and application related to LASSO, the statistical inference and testing for the sparse model under EPS remain unknown.'),
(81454, 2018, 'bio', 'DIBS: a repository of disordered binding sites mediating interactions with ordered proteins', 'Schad, Eva|Fichó, Erzsébet|Pancsa, Rita|Simon, István|Dosztányi, Zsuzsanna|Mészáros, Bálint', 'Research Centre for Natural Sciences, Institute of Enzymology, Hungarian Academy of Sciences, Budapest H-1117, Hungary.|Research Centre for Natural Sciences, Institute of Enzymology, Hungarian Academy of Sciences, Budapest H-1117, Hungary.|MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus, Cambridge CB2 0QH, UK.|Research Centre for Natural Sciences, Institute of Enzymology, Hungarian Academy of Sciences, Budapest H-1117, Hungary.|MTA-ELTE Momentum Bioinformatics Research Group, Department of Biochemistry, Eötvös Loránd University, Budapest H-1117, Hungary.|Research Centre for Natural Sciences, Institute of Enzymology, Hungarian Academy of Sciences, Budapest H-1117, Hungary.', 'Hungary|Hungary|United Kingdom|Hungary|Hungary|Hungary', 19, 'Binding Sites|Databases, Protein|Intrinsically Disordered Proteins|Protein Interaction Domains and Motifs', 'Journal Article', '10.1093/bioinformatics/btx640', '29385418', 'Intrinsically Disordered Proteins (IDPs) mediate crucial protein-protein interactions, most notably in signaling and regulation. As their importance is increasingly recognized, the detailed analyses of specific IDP interactions opened up new opportunities for therapeutic targeting. Yet, large scale information about IDP-mediated interactions in structural and functional details are lacking, hindering the understanding of the mechanisms underlying this distinct binding mode.'),
(81455, 2018, 'bio', 'JABAWS 22 distributed web services for Bioinformatics: protein disorder, conservation and RNA secondary structure', 'Troshin, Peter V|Procter, James B|Sherstnev, Alexander|Barton, Daniel L|Madeira, Fábio|Barton, Geoffrey J', 'Division of Computational Biology, School of Life Sciences, University of Dundee, Dundee DD1 4HN, UK.|Division of Computational Biology, School of Life Sciences, University of Dundee, Dundee DD1 4HN, UK.|Division of Computational Biology, School of Life Sciences, University of Dundee, Dundee DD1 4HN, UK.|Division of Computational Biology, School of Life Sciences, University of Dundee, Dundee DD1 4HN, UK.|Division of Computational Biology, School of Life Sciences, University of Dundee, Dundee DD1 4HN, UK.|Division of Computational Biology, School of Life Sciences, University of Dundee, Dundee DD1 4HN, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 5, 'Algorithms|Computational Biology|Internet|Models, Molecular|Nucleic Acid Conformation|Proteostasis Deficiencies|RNA|Sequence Alignment|Sequence Analysis, Protein|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/bty045', '29390042', 'JABAWS 2.2 is a computational framework that simplifies the deployment of web services for Bioinformatics. In addition to the five multiple sequence alignment (MSA) algorithms in JABAWS 1.0, JABAWS 2.2 includes three additional MSA programs (Clustal Omega, MSAprobs, GLprobs), four protein disorder prediction methods (DisEMBL, IUPred, Ronn, GlobPlot), 18 measures of protein conservation as implemented in AACon, and RNA secondary structure prediction by the RNAalifold program. JABAWS 2.2 can be deployed on a variety of in-house or hosted systems. JABAWS 2.2 web services may be accessed from the Jalview multiple sequence analysis workbench (Version 2.8 and later), as well as directly via the JABAWS command line interface (CLI) client. JABAWS 2.2 can be deployed on a local virtual server as a Virtual Appliance (VA) or simply as a Web Application Archive (WAR) for private use. Improvements in JABAWS 2.2 also include simplified installation and a range of utility tools for usage statistics collection, and web services querying and monitoring. The JABAWS CLI client has been updated to support all the new services and allow integration of JABAWS 2.2 services into conventional scripts. A public JABAWS 2 server has been in production since December 2011 and served over 800 000 analyses for users worldwide.'),
(81456, 2018, 'bio', 'Evolutionary relationship between the cysteine and histidine rich domains (CHORDs) and Btk-type zinc fingers', 'Kaur, Gurmeet|Subramanian, Srikrishna', 'CSIR-Institute of Microbial Technology (IMTECH), Sector 39-A, Chandigarh, India.|CSIR-Institute of Microbial Technology (IMTECH), Sector 39-A, Chandigarh, India.', 'India|India', 1, 'Agammaglobulinaemia Tyrosine Kinase|Amino Acid Sequence|Animals|Binding Sites|Cysteine|Eukaryota|Evolution, Molecular|Histidine|Humans|Metalloproteins|Protein Structural Elements|Protein-Tyrosine Kinases|Sequence Alignment|Signal Transduction|Zinc|Zinc Fingers', 'Journal Article', '10.1093/bioinformatics/bty041', '29390068', 'Cysteine and histidine rich domains (CHORDs), implicated in immunity and disease resistance signaling in plants, and in development and signal transduction in muscles and tumorigenesis in animals, are seen to have a cylindrical three-dimensional structure stabilized by the tetrahedral chelation of two zinc ions. CHORDs are regarded as novel zinc-binding domains and classified independently in Pfam and ECOD. Our sequence and structure analysis reveals that both the zinc-binding sites in CHORD possess a zinc ribbon fold and are likely related to each other by duplication and circular permutation. Interestingly, we also detect an evolutionary relationship between each of the CHORD zinc fingers (ZFs) and the Brutons tyrosine kinase (Btk)-type ZF of the zinc ribbon fold group. Btk_ZF is found in eukaryotic Tec kinase family proteins that are also implicated in signaling pathways in several lineages of hematopoietic cells involved in mammalian immunity. Our analysis suggests that the unique zinc-stabilized fold seen only in the CHORD and Btk_ZFs likely emerged specifically in eukaryotes to mediate diverse signaling pathways.'),
(81457, 2018, 'bio', 'Co-complex protein membership evaluation using Maximum Entropy on GO ontology and InterPro annotation', 'Armean, Irina M|Lilley, Kathryn S|Trotter, Matthew W B|Pilkington, Nicholas C V|Holden, Sean B', 'Department of Biochemistry, Cambridge Centre for Proteomics, University of Cambridge, Cambridge CB2 1GA, UK.|Department of Biochemistry, Cambridge Centre for Proteomics, University of Cambridge, Cambridge CB2 1GA, UK.|Celegene Institute for Translational Research Europe (CITRE), Sevilla 41092, Spain.|Department of Computer Science, Computer Laboratory, University of Cambridge, Cambridge CB3 0FD, UK.|Department of Computer Science, Computer Laboratory, University of Cambridge, Cambridge CB3 0FD, UK.', 'United Kingdom|United Kingdom|Spain|United Kingdom|United Kingdom', 3, 'Computational Biology|Entropy|Gene Ontology|Molecular Sequence Annotation|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/btx803', '29390084', 'Protein-protein interactions (PPI) play a crucial role in our understanding of protein function and biological processes. The standardization and recording of experimental findings is increasingly stored in ontologies, with the Gene Ontology (GO) being one of the most successful projects. Several PPI evaluation algorithms have been based on the application of probabilistic frameworks or machine learning algorithms to GO properties. Here, we introduce a new training set design and machine learning based approach that combines dependent heterogeneous protein annotations from the entire ontology to evaluate putative co-complex protein interactions determined by empirical studies.'),
(81458, 2018, 'bio', 'A low-complexity add-on score for protein remote homology search with COMER', 'Margelevicius, Mindaugas', 'Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania.', 'Lithuania', 2, 'Amino Acid Sequence|Computational Biology|Data Accuracy|Protein Conformation|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/bty048', '29390109', 'Protein sequence alignment forms the basis for comparative modeling, the most reliable approach to protein structure prediction, among many other applications. Alignment between sequence families, or profile-profile alignment, represents one of the most, if not the most, sensitive means for homology detection but still necessitates improvement. We aim at improving the quality of profile-profile alignments and the sensitivity induced by them by refining profile-profile substitution scores.'),
(81459, 2018, 'bio', 'pymzML v20: introducing a highly compressed and seekable gzip format', 'Kxc3xb6sters, M|Leufken, J|Schulze, S|Sugimoto, K|Klein, J|Zahedi, R P|Hippler, M|Leidel, S A|Fufezan, C', 'Institute of Plant Biology and Biotechnology, WWU Mxc3xbcnster, Mxc3xbcnster, Germany.|Institute of Plant Biology and Biotechnology, WWU Mxc3xbcnster, Mxc3xbcnster, Germany.|Institute of Plant Biology and Biotechnology, WWU Mxc3xbcnster, Mxc3xbcnster, Germany.|Institute of Plant Biology and Biotechnology, WWU Mxc3xbcnster, Mxc3xbcnster, Germany.|Bioinformatics Program, Boston University, One Silber Way, Boston, MA, USA.|Gerald Bronfman Department of Oncology, Jewish General Hospital, McGill University, 5100 de Maisonneuve Boulevard West, Suite 720, Montreal, Quebec, Canada.|Institute of Plant Biology and Biotechnology, WWU Mxc3xbcnster, Mxc3xbcnster, Germany.|Max Planck Institute for Molecular Biomedicine, Mxc3xbcnster, Germany.|Institute of Plant Biology and Biotechnology, WWU Mxc3xbcnster, Mxc3xbcnster, Germany.', 'Germany|Germany|Germany|Germany|United States|Canada|Germany|Germany|Germany', 13, 'Algorithms|Data Compression|Mass Spectrometry|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/bty046', '29394323', 'In the new release of pymzML (v2.0), we have optimized the speed of this established tool for mass spectrometry data analysis to adapt to increasing amounts of data in mass spectrometry. Thus, we integrated faster libraries for numerical calculations, improved data retrieving algorithms and have optimized the source code. Importantly, to adapt to rapidly growing file sizes, we developed a generalizable compression scheme for very fast random access and applied this concept to mzML files to retrieve spectral data.'),
(81460, 2018, 'bio', 'GEMMER: GEnome-wide tool for Multi-scale Modeling data Extraction and Representation for Saccharomyces cerevisiae', 'Mondeel, Thierry D G A|Crémazy, Frédéric|Barberis, Matteo', 'Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, XH Amsterdam, The Netherlands.|Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, XH Amsterdam, The Netherlands.|Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, XH Amsterdam, The Netherlands.', 'Netherlands|Netherlands|Netherlands', 0, 'Data Visualization|Databases, Factual|Gene Regulatory Networks|Information Storage and Retrieval|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Software', 'Journal Article', '10.1093/bioinformatics/bty052', '29401212', 'Multi-scale modeling of biological systems requires integration of various information about genes and proteins that are connected together in networks. Spatial, temporal and functional information is available, however, it is still a challenge to retrieve and explore this knowledge in an integrated, quick and user-friendly manner.'),
(81461, 2018, 'bio', 'MutHTP: mutations in human transmembrane proteins', 'Kulandaisamy, A|Binny Priya, S|Sakthivel, R|Tarnovskaya, Svetlana|Bizin, Ilya|Hönigschmid, Peter|Frishman, Dmitrij|Gromiha, M Michael', 'Department of Biotechnology, Bhupat and Jyoti Mehta School of BioSciences, Indian Institute of Technology Madras, Chennai, Tamilnadu, India.|Department of Biotechnology, Bhupat and Jyoti Mehta School of BioSciences, Indian Institute of Technology Madras, Chennai, Tamilnadu, India.|Department of Biotechnology, Bhupat and Jyoti Mehta School of BioSciences, Indian Institute of Technology Madras, Chennai, Tamilnadu, India.|Department of Bioinformatics, Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russian Federation.|Department of Bioinformatics, Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russian Federation.|Department of Bioinformatics, Technische Universität München, WissenschaftszentrumWeihenstephan, Freising, Germany.|Department of Bioinformatics, Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russian Federation.|Department of Biotechnology, Bhupat and Jyoti Mehta School of BioSciences, Indian Institute of Technology Madras, Chennai, Tamilnadu, India.', 'India|India|India|Russian Federation|Russian Federation|Germany|Russian Federation|India', 0, 'Databases, Factual|Humans|Membrane Proteins|Mutation|Software', 'Journal Article', '10.1093/bioinformatics/bty054', '29401218', 'Existing sources of experimental mutation data do not consider the structural environment of amino acid substitutions and distinguish between soluble and membrane proteins. They also suffer from a number of further limitations, including data redundancy, lack of disease classification, incompatible information content, and ambiguous annotations (e.g. the same mutation being annotated as disease and benign).'),
(81462, 2018, 'bio', 'Aligning dynamic networks with DynaWAVE', 'Vijayan, Vipin|Milenkovic, Tijana', 'Department of Biotechnology, Bhupat and Jyoti Mehta School of BioSciences, Indian Institute of Technology Madras, Chennai, Tamilnadu, India.|Department of Biotechnology, Bhupat and Jyoti Mehta School of BioSciences, Indian Institute of Technology Madras, Chennai, Tamilnadu, India.', 'India|India', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty038', '29401295', '-'),
(81463, 2018, 'bio', 'ARGs-OAP v20 with an expanded SARG database and Hidden Markov Models for enhancement characterization and quantification of antibiotic resistance genes in environmental metagenomes', 'Yin, Xiaole|Jiang, Xiao-Tao|Chai, Benli|Li, Liguan|Yang, Ying|Cole, James R|Tiedje, James M|Zhang, Tong', 'Environmental Biotechnology Lab, Department of Civil Engineering, The University of Hong Kong, Hong Kong, China.|Environmental Biotechnology Lab, Department of Civil Engineering, The University of Hong Kong, Hong Kong, China.|Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, MI, USA.|Environmental Biotechnology Lab, Department of Civil Engineering, The University of Hong Kong, Hong Kong, China.|South China Sea Resource Exploitation and Protection Collaborative Innovation Center, School of Marine Sciences, Sun Yat-sen University, Guangzhou, China.|Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, MI, USA.|Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, MI, USA.|Environmental Biotechnology Lab, Department of Civil Engineering, The University of Hong Kong, Hong Kong, China.', 'China|China|United States|China|China|United States|United States|China', 55, 'Archaea|Bacteria|Bacterial Physiological Phenomena|Databases, Factual|Drug Resistance, Microbial|Genome, Archaeal|Genome, Bacterial|Metagenome|Metagenomics|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bty053', '29408954', 'Much global attention has been paid to antibiotic resistance in monitoring its emergence, accumulation and dissemination. For rapid characterization and quantification of antibiotic resistance genes (ARGs) in metagenomic datasets, an online analysis pipeline, ARGs-OAP has been developed consisting of a database termed Structured Antibiotic Resistance Genes (the SARG) with a hierarchical structure (ARGs type-subtype-reference sequence).');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(81464, 2018, 'bio', 'AbDesigner3D: a structure-guided tool for peptide-based antibody production', 'Saethang, Thammakorn|Hodge, Kenneth|Kimkong, Ingorn|Payne, D Michael|Knepper, Mark A|Pisitkun, Trairak', 'Center of Excellence in Systems Biology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.|Center of Excellence in Systems Biology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.|Department of Microbiology, Faculty of Science, Kasetsart University, Bangkok, Thailand.|Center of Excellence in Systems Biology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.|Epithelial Systems Biology Laboratory, NHLBI, National Institutes of Health, Bethesda, MD, USA.|Center of Excellence in Systems Biology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.', 'Thailand|Thailand|Thailand|Thailand|United States|Thailand', 0, 'Antibodies|Computational Biology|Databases, Protein|Models, Molecular|Peptides|Protein Conformation|Software|Synthetic Biology', 'Journal Article', '10.1093/bioinformatics/bty055', '29408997', 'We present AbDesigner3D, a new tool for identification of optimal immunizing peptides for antibody production using a peptide-based strategy. AbDesigner3D integrates 3D structural data from the Protein Data Bank (PDB) with UniProt data, which includes basic sequence data, post-translational modification sites, SNP occurrences and more. Other features, such as uniqueness and conservation scores, are calculated based on sequences from UniProt. The 3D visualization capabilities allow an intuitive interface, while an abundance of quantitative output simplifies the process of comparing immunogen peptides. Important quantitative features added in this tool include calculation and display of accessible surface area (ASA) and protein-protein interacting residues (PPIR). The specialized data visualization features of AbDesigner3D will greatly assist users to optimize their choice of immunizing peptides.'),
(81465, 2018, 'bio', 'Novel overlapping subgraph clustering for the detection of antigen epitopes', 'Zhao, Liang|Wu, Shaogui|Jiang, Jiawen|Li, Wencui|Luo, Jie|Li, Jinyan', 'Department of Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Hubei, China.|Department of Computer Science, School of Computing and Electronic Information, Guangxi University, Nanning, China.|Department of Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Hubei, China.|Department of Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Hubei, China.|Department of Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Hubei, China.|Department of Data Science, Advanced Analytics Institute, Faculty of Engineering and IT, University of Technology Sydney, Broadway, NSW 2007, Australia.', 'China|China|China|China|China|Australia', 0, 'Algorithms|Animals|Cluster Analysis|Computational Biology|Data Visualization|Epitopes|Humans|Software', 'Journal Article', '10.1093/bioinformatics/bty051', '29409062', 'Antigens that contain overlapping epitopes have been occasionally reported. As current algorithms mainly take a one-antigen-one-epitope approach to the prediction of epitopes, they are not capable of detecting these multiple and overlapping epitopes accurately, or even those multiple and separated epitopes existing in some other antigens.'),
(81466, 2018, 'bio', 'Integrative pipeline for profiling DNA copy number and inferring tumor phylogeny', 'Urrutia, Eugene|Chen, Hao|Zhou, Zilu|Zhang, Nancy R|Jiang, Yuchao', 'Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA.|Department of Statistics, University of California, Davis, Davis, CA, USA.|Genomics and Computational Biology Graduate Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.|Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA.|Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA.', 'United States|United States|United States|United States|United States', 9, 'Algorithms|DNA Copy Number Variations|Genomics|High-Throughput Nucleotide Sequencing|Humans|Models, Genetic|Models, Statistical|Neoplasms|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bty057', '29415173', 'Copy number variation is an important and abundant source of variation in the human genome, which has been associated with a number of diseases, especially cancer. Massively parallel next-generation sequencing allows copy number profiling with fine resolution. Such efforts, however, have met with mixed successes, with setbacks arising partly from the lack of reliable analytical methods to meet the diverse and unique challenges arising from the myriad experimental designs and study goals in genetic studies. In cancer genomics, detection of somatic copy number changes and profiling of allele-specific copy number (ASCN) are complicated by experimental biases and artifacts as well as normal cell contamination and cancer subclone admixture. Furthermore, careful statistical modeling is warranted to reconstruct tumor phylogeny by both somatic ASCN changes and single nucleotide variants. Here we describe a flexible computational pipeline, MARATHON, which integrates multiple related statistical software for copy number profiling and downstream analyses in disease genetic studies.'),
(81467, 2018, 'bio', 'scEpath: energy landscape-based inference of transition probabilities and cellular trajectories from single-cell transcriptomic data', 'Jin, Suoqin|MacLean, Adam L|Peng, Tao|Nie, Qing', 'Department of Mathematics and Center for Complex Biological Systems.|Department of Mathematics and Center for Complex Biological Systems.|Department of Mathematics and Center for Complex Biological Systems.|Department of Mathematics and Center for Complex Biological Systems.', '-|-|-|-', 17, 'Algorithms|Animals|Cell Differentiation|Embryo, Mammalian|Embryonic Development|Gene Expression Profiling|Gene Expression Regulation, Developmental|Humans|Mice|Myoblasts|Probability|Sequence Analysis, RNA|Single-Cell Analysis|Software', 'Journal Article', '10.1093/bioinformatics/bty058', '29415263', 'Single-cell RNA-sequencing (scRNA-seq) offers unprecedented resolution for studying cellular decision-making processes. Robust inference of cell state transition paths and probabilities is an important yet challenging step in the analysis of these data.'),
(81468, 2018, 'bio', 'GCPred: a web tool for guanylyl cyclase functional centre prediction from amino acid sequence', 'Xu, Nuo|Fu, Dongfang|Li, Shiang|Wang, Yuxuan|Wong, Aloysius', 'Department of Computer Science.|Department of Computer Science.|Department of Computer Science.|Department of Psychology.|Department of Biology, Wenzhou-Kean University, Ouhai, Wenzhou, Zhejiang Province, China.', '-|-|-|-|China', 11, 'Amino Acid Sequence|Catalytic Domain|Cloud Computing|Guanylate Cyclase|Humans|Interleukin-1 Receptor-Associated Kinases|Plant Proteins|Plants|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/bty067', '29420675', 'GCPred is a webserver for the prediction of guanylyl cyclase (GC) functional centres from amino acid sequence. GCs are enzymes that generate the signalling molecule cyclic guanosine 3, 5-monophosphate from guanosine-5-triphosphate. A novel class of GC centres (GCCs) has been identified in complex plant proteins. Using currently available experimental data, GCPred is created to automate and facilitate the identification of similar GCCs. The server features GCC values that consider in its calculation, the physicochemical properties of amino acids constituting the GCC and the conserved amino acids within the centre. From user input amino acid sequence, the server returns a table of GCC values and graphs depicting deviations from mean values. The utility of this server is demonstrated using plant proteins and the human interleukin-1 receptor-associated kinase family of proteins as example.'),
(81469, 2018, 'bio', 'Principal metabolic flux mode analysis', 'Bhadra, Sahely|Blomberg, Peter|Castillo, Sandra|Rousu, Juho', 'Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Espoo, Finland.|VTT Technical Research Centre of Finland Ltd, Espoo, Finland.|VTT Technical Research Centre of Finland Ltd, Espoo, Finland.|Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Espoo, Finland.', 'Finland|Finland|Finland|Finland', 1, 'Metabolic Flux Analysis|Metabolic Networks and Pathways|Models, Biological|Principal Component Analysis|Software', 'Journal Article', '10.1093/bioinformatics/bty049', '29420676', 'In the analysis of metabolism, two distinct and complementary approaches are frequently used: Principal component analysis (PCA) and stoichiometric flux analysis. PCA is able to capture the main modes of variability in a set of experiments and does not make many prior assumptions about the data, but does not inherently take into account the flux mode structure of metabolism. Stoichiometric flux analysis methods, such as Flux Balance Analysis (FBA) and Elementary Mode Analysis, on the other hand, are able to capture the metabolic flux modes, however, they are primarily designed for the analysis of single samples at a time, and not best suited for exploratory analysis on a large sets of samples.'),
(81470, 2018, 'bio', 'GCevobase: an evolution-based database for GC content in eukaryotic genomes', 'Wang, Dapeng', 'Department of Plant Sciences, University of Oxford, Oxford OX1 3RB, UK.', 'United Kingdom', 0, 'Animals|Base Composition|Codon|Databases, Genetic|Eukaryota|Evolution, Molecular|Genomics|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/bty068', '29420682', 'How to comprehend the underlying mechanism behind the origin and evolution of genome composition such as GC content has been regarded as a long-standing crucial question, highlighting its biological significance and functional relevance. To varying extents, several systematically identified patterns of GC content variations are shown to be linked to a set of genomic features in the events of replication, transcription, translation and recombination, with strong contrasts between diverse phylogenetic or taxonomical groups. In this situation, we develop a repository-GCevobase-which houses compositional and size related data presented in various forms from 1118 genomes including 5 major clades of eukaryotic species such as vertebrates, invertebrates, plants, fungi and protists. It analyzes the cautiously selected sequences with clearly-defined bases and structures them under the taxonomical classification system (kingdom, phylum, class, order and family) at the genome and gene scales. It uses the diversified and intelligible graphs to show the statistical measurements of GC content in the sequence, at the three codon positions and at 4-fold degenerate sites and CDS length and their genome-wide correlations and display the evolutionary pathways of GC content by taking into account between-species orthologs and within-species paralogs for each annotated gene. In addition, a lot of internal and external links have been created, making it an effective communication between the data from individual genomes and the raw data are downloadable.'),
(81471, 2018, 'bio', 'omicsPrint: detection of data linkage errors in multiple omics studies', 'van Iterson, Maarten|Cats, Davy|Hop, Paul|BIOS Consortium|Heijmans, Bastiaan T', 'Molecular Epidemiology, Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, ZC Leiden, The Netherlands.|Molecular Epidemiology, Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, ZC Leiden, The Netherlands.|Molecular Epidemiology, Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, ZC Leiden, The Netherlands.|Molecular Epidemiology, Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, ZC Leiden, The Netherlands.|Molecular Epidemiology, Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, ZC Leiden, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 7, 'Algorithms|DNA Methylation|Female|Gene Expression Profiling|Genomics|Humans|Information Storage and Retrieval|Male|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Sequence Analysis, RNA|Software|Transcriptome', 'Journal Article', '10.1093/bioinformatics/bty062', '29420690', 'OmicsPrint is a versatile method for the detection of data linkage errors in multiple omics studies encompassing genetic, transcriptome and/or methylome data. OmicsPrint evaluates data linkage within and between omics data types using genotype calls from SNP arrays, DNA- or RNA-sequencing data and includes an algorithm to infer genotypes from Illumina DNA methylation array data. The method uses classification to verify assumed relationships and detect any data linkage errors, e.g. arising from sample mix-ups and mislabeling. Graphical and text output is provided to inspect and resolve putative data linkage errors. If sufficient genotype calls are available, first degree family relations also are revealed which can be used to check parent-offspring relations or zygosity in twin studies.'),
(81472, 2018, 'bio', 'FWER and FDR control when testing multiple mediators', 'Sampson, Joshua N|Boca, Simina M|Moore, Steven C|Heller, Ruth', 'Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.|Department of Oncology and Department of Biostatistics, Bioinformatics & Biomathematics, Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC, USA.|Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.|Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv, Israel.', 'United States|United States|United States|Israel', 0, 'Body Mass Index|Breast Neoplasms|Computational Biology|Environmental Exposure|Female|Humans|Metabolic Networks and Pathways|Risk|Software|Statistics as Topic', 'Journal Article', '10.1093/bioinformatics/bty064', '29420693', 'The biological pathways linking exposures and disease risk are often poorly understood. To gain insight into these pathways, studies may try to identify biomarkers that mediate the exposure/disease relationship. Such studies often simultaneously test hundreds or thousands of biomarkers.'),
(81473, 2018, 'bio', 'L1000FWD: fireworks visualization of drug-induced transcriptomic signatures', 'Wang, Zichen|Lachmann, Alexander|Keenan, Alexandra B|Maayan, Avi', 'Department of Pharmacological Sciences, BD2K-LINCS Data Coordination and Integration Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA.|Department of Pharmacological Sciences, BD2K-LINCS Data Coordination and Integration Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA.|Department of Pharmacological Sciences, BD2K-LINCS Data Coordination and Integration Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA.|Department of Pharmacological Sciences, BD2K-LINCS Data Coordination and Integration Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA.', 'United States|United States|United States|United States', 0, 'Cell Line|Cluster Analysis|Data Visualization|Gene Expression Regulation|Humans|Pharmacogenetics|Software|Transcriptome|Unsupervised Machine Learning', 'Journal Article', '10.1093/bioinformatics/bty060', '29420694', 'As part of the NIH Library of Integrated Network-based Cellular Signatures program, hundreds of thousands of transcriptomic signatures were generated with the L1000 technology, profiling the response of human cell lines to over 20 000 small molecule compounds. This effort is a promising approach toward revealing the mechanisms-of-action (MOA) for marketed drugs and other less studied potential therapeutic compounds.'),
(81474, 2018, 'bio', 'O-GlcNAcPRED-II: an integrated classification algorithm for identifying O-GlcNAcylation sites based on fuzzy undersampling and a K-means PCA oversampling technique', 'Jia, Cangzhi|Zuo, Yun|Zou, Quan', 'Department of Mathematics, Dalian Maritime University, Dalian, China.|Department of Mathematics, Dalian Maritime University, Dalian, China.|School of Computer Science and Technology, Tianjin University, Tianjin, China.', 'China|China|China', 60, 'Acetylglucosamine|Bayes Theorem|Computational Biology|Models, Biological|Protein Processing, Post-Translational|Proteomics|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/bty039', '29420699', 'Protein O-GlcNAcylation (O-GlcNAc) is an important post-translational modification of serine (S)/threonine (T) residues that involves multiple molecular and cellular processes. Recent studies have suggested that abnormal O-G1cNAcylation causes many diseases, such as cancer and various neurodegenerative diseases. With the available protein O-G1cNAcylation sites experimentally verified, it is highly desired to develop automated methods to rapidly and effectively identify O-GlcNAcylation sites. Although some computational methods have been proposed, their performance has been unsatisfactory, particularly in terms of prediction sensitivity.'),
(81475, 2018, 'bio', 'Kpax3: Bayesian bi-clustering of large sequence datasets', 'Pessia, Alberto|Corander, Jukka', 'Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.|Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.', 'Finland|Finland', 0, 'Algorithms|Bayes Theorem|Cluster Analysis|Sequence Alignment|Software|Viral Proteins|Viruses', 'Journal Article', '10.1093/bioinformatics/bty056', '29425273', 'Estimation of the hidden population structure is an important step in many genetic studies. Often the aim is also to identify which sequence locations are the most discriminative between groups of samples for a given data partition. Automated discovery of interesting patterns that are present in the data can help to generate new biological hypotheses.'),
(81476, 2018, 'bio', 'INfORM: Inference of NetwOrk Response Modules', 'Marwah, Veer Singh|Kinaret, Pia Anneli Sofia|Serra, Angela|Scala, Giovanni|Lauerma, Antti|Fortino, Vittorio|Greco, Dario', 'Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland.|Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland.|NeuRoNeLab, DISA-MIS, University of Salerno, Fisciano, Italy.|Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland.|Department of Dermatology and Allergology, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland.|Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland.|Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland.', 'Finland|Finland|Italy|Finland|Finland|Finland|Finland', 0, 'Algorithms|Computational Biology|Gene Expression|Gene Regulatory Networks|Software', 'Journal Article', '10.1093/bioinformatics/bty063', '29425308', 'Detecting and interpreting responsive modules from gene expression data by using network-based approaches is a common but laborious task. It often requires the application of several computational methods implemented in different software packages, forcing biologists to compile complex analytical pipelines. Here we introduce INfORM (Inference of NetwOrk Response Modules), an R shiny application that enables non-expert users to detect, evaluate and select gene modules with high statistical and biological significance. INfORM is a comprehensive tool for the identification of biologically meaningful response modules from consensus gene networks inferred by using multiple algorithms. It is accessible through an intuitive graphical user interface allowing for a level of abstraction from the computational steps.'),
(81477, 2018, 'bio', 'Selenzyme: enzyme selection tool for pathway design', 'Carbonell, Pablo|Wong, Jerry|Swainston, Neil|Takano, Eriko|Turner, Nicholas J|Scrutton, Nigel S|Kell, Douglas B|Breitling, Rainer|Faulon, Jean-Loup', 'BBSRC/EPSRC Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology.|BBSRC/EPSRC Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology.|BBSRC/EPSRC Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology.|BBSRC/EPSRC Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology.|BBSRC/EPSRC Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology.|BBSRC/EPSRC Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology.|BBSRC/EPSRC Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology.|BBSRC/EPSRC Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology.|BBSRC/EPSRC Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology.', '-|-|-|-|-|-|-|-|-', 9, 'Databases, Factual|Enzymes|Internet|Metabolic Networks and Pathways|Phylogeny|Software|Synthetic Biology', 'Journal Article', '10.1093/bioinformatics/bty065', '29425325', 'Synthetic biology applies the principles of engineering to biology in order to create biological functionalities not seen before in nature. One of the most exciting applications of synthetic biology is the design of new organisms with the ability to produce valuable chemicals including pharmaceuticals and biomaterials in a greener, sustainable fashion. Selecting the right enzymes to catalyze each reaction step in order to produce a desired target compound is, however, not trivial. Here, we present Selenzyme, a free online enzyme selection tool for metabolic pathway design. The user is guided through several decision steps in order to shortlist the best candidates for a given pathway step. The tool graphically presents key information about enzymes based on existing databases and tools such as: similarity of sequences and of catalyzed reactions, phylogenetic distance between source organism and intended host species, multiple alignment highlighting conserved regions, predicted catalytic site, and active regions and relevant properties such as predicted solubility and transmembrane regions. Selenzyme provides bespoke sequence selection for automated workflows in biofoundries.'),
(81478, 2018, 'bio', 'Improving SNP prioritization and pleiotropic architecture estimation by incorporating prior knowledge using graph-GPA', 'Kim, Hang J|Yu, Zhenning|Lawson, Andrew|Zhao, Hongyu|Chung, Dongjun', 'Department of Mathematical Sciences, University of Cincinnati, Cincinnati, OH, USA.|Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC 29425, USA.|Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC 29425, USA.|Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.|Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC 29425, USA.', 'United States|United States|United States|United States|United States', 2, 'Bayes Theorem|Computational Biology|Data Mining|Data Visualization|Genome-Wide Association Study|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/bty061', '29432514', 'Integration of genetic studies for multiple phenotypes is a powerful approach to improving the identification of genetic variants associated with complex traits. Although it has been shown that leveraging shared genetic basis among phenotypes, namely pleiotropy, can increase statistical power to identify risk variants, it remains challenging to effectively integrate genome-wide association study (GWAS) datasets for a large number of phenotypes. We previously developed graph-GPA, a Bayesian hierarchical model that integrates multiple GWAS datasets to boost statistical power for the identification of risk variants and to estimate pleiotropic architecture within a unified framework. Here we propose a novel improvement of graph-GPA which incorporates external knowledge about phenotype-phenotype relationship to guide the estimation of genetic correlation and the association mapping. The application of graph-GPA to GWAS datasets for 12 complex diseases with a prior disease graph obtained from a text mining of biomedical literature illustrates its power to improve the identification of risk genetic variants and to facilitate understanding of genetic relationship among complex diseases.'),
(81479, 2018, 'bio', 'Spectral clustering based on learning similarity matrix', 'Park, Seyoung|Zhao, Hongyu', 'Department of Biostatistics, School of Public Health, Yale University, New Haven, CT, USA.|Department of Biostatistics, School of Public Health, Yale University, New Haven, CT, USA.', 'United States|United States', 14, 'Algorithms|Cluster Analysis|Gene Expression Profiling|Sequence Analysis, RNA|Single-Cell Analysis|Software', 'Journal Article', '10.1093/bioinformatics/bty050', '29432517', 'Single-cell RNA-sequencing (scRNA-seq) technology can generate genome-wide expression data at the single-cell levels. One important objective in scRNA-seq analysis is to cluster cells where each cluster consists of cells belonging to the same cell type based on gene expression patterns.'),
(81480, 2018, 'bio', 'WDL-RF: predicting bioactivities of ligand molecules acting with G protein-coupled receptors by combining weighted deep learning and random forest', 'Wu, Jiansheng|Zhang, Qiuming|Wu, Weijian|Pang, Tao|Hu, Haifeng|Chan, Wallace K B|Ke, Xiaoyan|Zhang, Yang', 'School of Geographic and Biological Information, Nanjing University of Posts and Telecommunications, Nanjing, China.|School of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China.|College of Computer and Information, Hohai University, Nanjing, China.|Jiangsu Key Laboratory of Drug Screening, China Pharmaceutical University, Nanjing, China.|School of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China.|Department of Biological Chemistry, University of Michigan, Ann Arbor, USA.|Child Mental Health Research Center, Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China.|Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, USA.', 'China|China|China|China|China|United States|China|United States', 11, 'Animals|Computational Biology|Deep Learning|Drug Discovery|Drug Evaluation, Preclinical|Humans|Ligands|Receptors, G-Protein-Coupled', 'Journal Article', '10.1093/bioinformatics/bty070', '29432522', 'Precise assessment of ligand bioactivities (including IC50, EC50, Ki, Kd, etc.) is essential for virtual screening and lead compound identification. However, not all ligands have experimentally determined activities. In particular, many G protein-coupled receptors (GPCRs), which are the largest integral membrane protein family and represent targets of nearly 40% drugs on the market, lack published experimental data about ligand interactions. Computational methods with the ability to accurately predict the bioactivity of ligands can help efficiently address this problem.'),
(81481, 2018, 'bio', 'CrossPlan: systematic planning of genetic crosses to validate mathematical models', 'Pratapa, Aditya|Adames, Neil|Kraikivski, Pavel|Franzese, Nicholas|Tyson, John J|Peccoud, Jean|Murali, T M', 'Department of Computer Science, Virginia Tech, Blacksburg, USA.|Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, USA.|Department of Biological Sciences, Virginia Tech, Blacksburg, USA.|Department of Biology, Reed College, Portland, USA.|Department of Biological Sciences, Virginia Tech, Blacksburg, USA.|Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, USA.|Department of Computer Science, Virginia Tech, Blacksburg, USA.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Cell Division|Computational Biology|Crosses, Genetic|Gene Regulatory Networks|Models, Biological|Models, Theoretical|Mutation|Programming, Linear|Saccharomycetales|Software', 'Journal Article', '10.1093/bioinformatics/bty072', '29432533', 'Mathematical models of cellular processes can systematically predict the phenotypes of novel combinations of multi-gene mutations. Searching for informative predictions and prioritizing them for experimental validation is challenging since the number of possible combinations grows exponentially in the number of mutations. Moreover, keeping track of the crosses needed to make new mutants and planning sequences of experiments is unmanageable when the experimenter is deluged by hundreds of potentially informative predictions to test.'),
(81482, 2018, 'bio', 'chromswitch: a flexible method to detect chromatin state switches', 'Jessa, Selin|Kleinman, Claudia L', 'Department of Human Genetics, McGill University, Montreal, QC, Canada and Lady Davis Research Institute, Jewish General Hospital, Montreal, QC, Canada.|Department of Human Genetics, McGill University, Montreal, QC, Canada and Lady Davis Research Institute, Jewish General Hospital, Montreal, QC, Canada.', 'Canada|Canada', 2, 'Chromatin|Epigenesis, Genetic|Epigenomics|Genome|Software', 'Journal Article', '10.1093/bioinformatics/bty075', '29438498', 'Chromatin state plays a major role in controlling gene expression, and comparative analysis of ChIP-seq data is key to understanding epigenetic regulation. We present chromswitch, an R/Bioconductor package to integrate epigenomic data in a defined window of interest to detect an overall switch in chromatin state. Chromswitch accurately classifies a benchmarking dataset, and when applied genome-wide, the tool successfully detects chromatin changes that result in brain-specific expression.'),
(81483, 2018, 'bio', 'KMgene: a unified R package for gene-based association analysis for complex traits', 'Yan, Qi|Fang, Zhou|Chen, Wei', 'Division of Pulmonary Medicine, Allergy and Immunology, Department of Pediatrics, Childrens Hospital of Pittsburgh of UPMC, University of Pittsburgh, Pittsburgh, PA, USA.|Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA.|Division of Pulmonary Medicine, Allergy and Immunology, Department of Pediatrics, Childrens Hospital of Pittsburgh of UPMC, University of Pittsburgh, Pittsburgh, PA, USA.', 'United States|United States|United States', 3, 'Genetic Testing|Humans|Linear Models|Lung|Models, Genetic|Multifactorial Inheritance|Software', 'Journal Article', '10.1093/bioinformatics/bty066', '29438558', 'In this report, we introduce an R package KMgene for performing gene-based association tests for familial, multivariate or longitudinal traits using kernel machine (KM) regression under a generalized linear mixed model framework. Extensive simulations were performed to evaluate the validity of the approaches implemented in KMgene.'),
(81484, 2018, 'bio', 'HIITE: HIV-1 incidence and infection time estimator', 'Park, Sung Yong|Love, Tanzy M T|Kapoor, Shivankur|Lee, Ha Youn', 'Department of Molecular Microbiology and Immunology, Keck School of Medicine, University of Southern California, CA, USA.|Department of Biostatistics and Computational Biology, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA.|Department of Molecular Microbiology and Immunology, Keck School of Medicine, University of Southern California, CA, USA.|Department of Molecular Microbiology and Immunology, Keck School of Medicine, University of Southern California, CA, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Computational Biology|Cross-Sectional Studies|Female|HIV Infections|HIV-1|Humans|Incidence|Male|Models, Biological|Polymorphism, Genetic|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/bty073', '29438560', 'Around 2.1 million new HIV-1 infections were reported in 2015, alerting that the HIV-1 epidemic remains a significant global health challenge. Precise incidence assessment strengthens epidemic monitoring efforts and guides strategy optimization for prevention programs. Estimating the onset time of HIV-1 infection can facilitate optimal clinical management and identify key populations largely responsible for epidemic spread and thereby infer HIV-1 transmission chains. Our goal is to develop a genomic assay estimating the incidence and infection time in a single cross-sectional survey setting.'),
(81485, 2018, 'bio', 'Hierarchical analysis of RNA-seq reads improves the accuracy of allele-specific expression', 'Raghupathy, Narayanan|Choi, Kwangbom|Vincent, Matthew J|Beane, Glen L|Sheppard, Keith S|Munger, Steven C|Korstanje, Ron|Pardo-Manual de Villena, Fernando|Churchill, Gary A', 'The Jackson Laboratory, Bar Harbor, USA.|The Jackson Laboratory, Bar Harbor, USA.|The Jackson Laboratory, Bar Harbor, USA.|The Jackson Laboratory, Bar Harbor, USA.|The Jackson Laboratory, Bar Harbor, USA.|The Jackson Laboratory, Bar Harbor, USA.|The Jackson Laboratory, Bar Harbor, USA.|Department of Genetics, The University of North Carolina, Chapel Hill, USA.|The Jackson Laboratory, Bar Harbor, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 18, 'Alleles|Alternative Splicing|Animals|Genomics|Male|Mice|Sequence Analysis, RNA|Software|Transcriptome', 'Journal Article', '10.1093/bioinformatics/bty078', '29444201', 'Allele-specific expression (ASE) refers to the differential abundance of the allelic copies of a transcript. RNA sequencing (RNA-seq) can provide quantitative estimates of ASE for genes with transcribed polymorphisms. When short-read sequences are aligned to a diploid transcriptome, read-mapping ambiguities confound our ability to directly count reads. Multi-mapping reads aligning equally well to multiple genomic locations, isoforms or alleles can comprise the majority (>85%) of reads. Discarding them can result in biases and substantial loss of information. Methods have been developed that use weighted allocation of read counts but these methods treat the different types of multi-reads equivalently. We propose a hierarchical approach to allocation of read counts that first resolves ambiguities among genes, then among isoforms, and lastly between alleles. We have implemented our model in EMASE software (Expectation-Maximization for Allele Specific Expression) to estimate total gene expression, isoform usage and ASE based on this hierarchical allocation.'),
(81486, 2018, 'bio', 'ssbio: a Python framework for structural systems biology', 'Mih, Nathan|Brunk, Elizabeth|Chen, Ke|Catoiu, Edward|Sastry, Anand|Kavvas, Erol|Monk, Jonathan M|Zhang, Zhen|Palsson, Bernhard O', 'Department of Bioengineering, Bioinformatics and Systems Biology Graduate Program.|Department of Bioengineering, University of California, San Diego, CA, USA.|Department of Bioengineering, University of California, San Diego, CA, USA.|Department of Bioengineering, University of California, San Diego, CA, USA.|Department of Bioengineering, University of California, San Diego, CA, USA.|Department of Bioengineering, University of California, San Diego, CA, USA.|Department of Bioengineering, University of California, San Diego, CA, USA.|Department of Bioengineering, University of California, San Diego, CA, USA.|Department of Bioengineering, University of California, San Diego, CA, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Computational Biology|Models, Biological|Protein Conformation|Software', 'Journal Article', '10.1093/bioinformatics/bty077', '29444205', 'Working with protein structures at the genome-scale has been challenging in a variety of ways. Here, we present ssbio, a Python package that provides a framework to easily work with structural information in the context of genome-scale network reconstructions, which can contain thousands of individual proteins. The ssbio package provides an automated pipeline to construct high quality genome-scale models with protein structures (GEM-PROs), wrappers to popular third-party programs to compute associated protein properties, and methods to visualize and annotate structures directly in Jupyter notebooks, thus lowering the barrier of linking 3D structural data with established systems workflows.'),
(81487, 2018, 'bio', 'HiCapTools: a software suite for probe design and proximity detection for targeted chromosome conformation capture applications', 'Anil, Anandashankar|Spalinskas, Rapolas|Åkerborg, Örjan|Sahlén, Pelin', 'KTH - Royal Institute of Technology, Science for Life Laboratory, School of Biotechnology, Solna 171?65, Sweden.|KTH - Royal Institute of Technology, Science for Life Laboratory, School of Biotechnology, Solna 171?65, Sweden.|KTH - Royal Institute of Technology, Science for Life Laboratory, School of Biotechnology, Solna 171?65, Sweden.|KTH - Royal Institute of Technology, Science for Life Laboratory, School of Biotechnology, Solna 171?65, Sweden.', 'Sweden|Sweden|Sweden|Sweden', 8, 'Chromosomes|Eukaryota|Genomics|Molecular Conformation|Software', 'Journal Article', '10.1093/bioinformatics/btx625', '29444232', 'Folding of eukaryotic genomes within nuclear space enables physical and functional contacts between regions that are otherwise kilobases away in sequence space. Targeted chromosome conformation capture methods (T2C, chi-C and HiCap) are capable of informing genomic contacts for a subset of regions targeted by probes. We here present HiCapTools, a software package that can design sequence capture probes for targeted chromosome capture applications and analyse sequencing output to detect proximities involving targeted fragments. Two probes are designed for each feature while avoiding repeat elements and non-unique regions. The data analysis suite processes alignment files to report genomic proximities for each feature at restriction fragment level and is isoform-aware for gene features. Statistical significance of contact frequencies is evaluated using an empirically derived background distribution. Targeted chromosome conformation capture applications are invaluable for locating target genes of disease-associated variants found by genome-wide association studies. Hence, we believe our software suite will prove to be useful for a wider user base within clinical and functional applications.'),
(81488, 2018, 'bio', 'BetaSerpentine: a bioinformatics tool for reconstruction of amyloid structures', 'Bondarev, Stanislav A|Bondareva, Olga V|Zhouravleva, Galina A|Kajava, Andrey V', 'Laboratory of Amyloid Biology and Department of Genetics and Biotechnology, St. Petersburg State University, Saint Petersburg 199034, Russia.|Laboratory of Molecular Systematics, Zoological Institute RAS, Saint Petersburg 199034, Russia.|Laboratory of Amyloid Biology and Department of Genetics and Biotechnology, St. Petersburg State University, Saint Petersburg 199034, Russia.|Structural Bioinformatics and Molecular Modeling, Centre de Recherche en Biologie Cellulaire de Montpellier, CNRS, Universitxc3xa9 Montpellier, Montpellier 34293, France.', '-|-|-|France', 0, 'Amyloid|Animals|Computational Biology|Humans|Protein Conformation|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btx629', '29444233', 'Numerous experimental studies have suggested that polypeptide chains of large amyloidogenic regions zig-zag in xcexb2-serpentine arrangements. These xcexb2-serpentines are stacked axially and form the superpleated xcexb2-structure. Despite this progress in the understanding of amyloid folds, the determination of their 3D structure at the atomic level is still a problem due to the polymorphism of these fibrils and incompleteness of experimental structural data. Today, the way to get insight into the atomic structure of amyloids is a combination of experimental studies with bioinformatics.'),
(81489, 2018, 'bio', 'ThreaDNA: predicting DNA mechanics contribution to sequence selectivity of proteins along whole genomes', 'Cevost, Jasmin|Vaillant, Cédric|Meyer, Sam|Rost, Burkhard', 'Microbiologie, Adaptation et Pathogénie, UMR5240, INSA Lyon, Université de Lyon, 69622 Villeurbanne.|Laboratoire de Physique, UMR5672, ENS Lyon, CNRS, Université de Lyon, 69007 Lyon, France.|Microbiologie, Adaptation et Pathogénie, UMR5240, INSA Lyon, Université de Lyon, 69622 Villeurbanne.|Microbiologie, Adaptation et Pathogénie, UMR5240, INSA Lyon, Université de Lyon, 69622 Villeurbanne.', '-|France|-|-', 4, 'Computational Biology|DNA|DNA-Binding Proteins|Escherichia coli|Escherichia coli Proteins|Factor For Inversion Stimulation Protein|Histones|Models, Molecular|Nucleic Acid Conformation|Nucleosomes|Protein Conformation|Saccharomyces cerevisiae|Software', 'Journal Article', '10.1093/bioinformatics/btx634', '29444234', 'Many DNA-binding proteins recognize their target sequences indirectly, by sensing DNAs response to mechanical distortion. ThreaDNA estimates this response based on high-resolution structures of the protein-DNA complex of interest. Implementing an efficient nanoscale modeling of DNA deformations involving essentially no adjustable parameters, it returns the profile of deformation energy along whole genomes, at base-pair resolution, within minutes on usual laptop/desktop computers. Our predictions can also be easily combined with estimations of direct selectivity through a generalized form of position-weight-matrices. The formalism of ThreaDNA is accessible to a wide audience.'),
(81490, 2018, 'bio', 'Squeakr: an exact and approximate k-mer counting system', 'Pandey, Prashant|Bender, Michael A|Johnson, Rob|Patro, Rob|Berger, Bonnie', 'Department of Computer Science, Stony Brook University, Stony Brook, NY 11790, USA.|Department of Computer Science, Stony Brook University, Stony Brook, NY 11790, USA.|Department of Computer Science, Stony Brook University, Stony Brook, NY 11790, USA.|Department of Computer Science, Stony Brook University, Stony Brook, NY 11790, USA.|Department of Computer Science, Stony Brook University, Stony Brook, NY 11790, USA.', 'United States|United States|United States|United States|United States', 15, 'Algorithms|Animals|Eukaryota|Gene Expression Profiling|Genome|Genomics|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btx636', '29444235', 'k-mer-based algorithms have become increasingly popular in the processing of high-throughput sequencing data. These algorithms span the gamut of the analysis pipeline from k-mer counting (e.g. for estimating assembly parameters), to error correction, genome and transcriptome assembly, and even transcript quantification. Yet, these tasks often use very different k-mer representations and data structures. In this article, we show how to build a k-mer-counting and multiset-representation system using the counting quotient filter, a feature-rich approximate membership query data structure. We introduce the k-mer-counting/querying system Squeakr (Simple Quotient filter-based Exact and Approximate Kmer Representation), which is based on the counting quotient filter. This off-the-shelf data structure turns out to be an efficient (approximate or exact) representation for sets or multisets of k-mers.'),
(81491, 2018, 'bio', 'findGSE: estimating genome size variation within human and Arabidopsis using k-mer frequencies', 'Sun, Hequan|Ding, Jia|Piednoël, Mathieu|Schneeberger, Korbinian', 'Department of Plant Developmental Biology, Max Planck Institute for Plant Breeding Research, 50829 Cologne, Germany.|Department of Plant Breeding and Genetics, Max Planck Institute for Plant Breeding Research, 50829 Cologne, Germany.|Department of Plant Developmental Biology, Max Planck Institute for Plant Breeding Research, 50829 Cologne, Germany.|Department of Plant Developmental Biology, Max Planck Institute for Plant Breeding Research, 50829 Cologne, Germany.', 'Germany|Germany|Germany|Germany', 0, 'Arabidopsis|Female|Genetic Variation|Genome Size|Genome, Human|Genome, Plant|Genomics|Humans|Long Interspersed Nucleotide Elements|Male|Population Groups|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx637', '29444236', 'Analyzing k-mer frequencies in whole-genome sequencing data is becoming a common method for estimating genome size (GS). However, it remains uninvestigated how accurate the method is, especially if it can capture intra-species GS variation.'),
(81492, 2018, 'bio', 'Compression of genomic sequencing reads via hash-based reordering: algorithm and analysis', 'Chandak, Shubham|Tatwawadi, Kedar|Weissman, Tsachy', 'Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.|Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.|Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.', 'United States|United States|United States', 12, 'Algorithms|Bacteria|Data Compression|Eukaryota|Genome|Genomics|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btx639', '29444237', 'New Generation Sequencing (NGS) technologies for genome sequencing produce large amounts of short genomic reads per experiment, which are highly redundant and compressible. However, general-purpose compressors are unable to exploit this redundancy due to the special structure present in the data.'),
(81493, 2018, 'bio', 'ChemDistiller: an engine for metabolite annotation in mass spectrometry', 'Laponogov, Ivan|Sadawi, Noureddin|Galea, Dieter|Mirnezami, Reza|Veselkov, Kirill A', 'Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK.|Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK.|Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK.|Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK.|Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Databases, Factual|Metabolomics|Software|Tandem Mass Spectrometry', 'Journal Article', '10.1093/bioinformatics/bty080', '29447341', 'High-resolution mass spectrometry permits simultaneous detection of thousands of different metabolites in biological samples, however, their automated annotation still presents a challenge due to the limited number of tailored computational solutions freely available to the scientific community.'),
(81494, 2018, 'bio', 'Enhancing protein fold determination by exploring the complementary information of chemical cross-linking and coevolutionary signals', 'Dos Santos, Ricardo N|Ferrari, Allan J R|de Jesus, Hugo C R|Gozzo, Fábio C|Morcos, Faruck|Martínez, Leandro', 'Institute of Chemistry, University of Campinas, Campinas, Brazil.|Institute of Chemistry, University of Campinas, Campinas, Brazil.|Institute of Chemistry, University of Campinas, Campinas, Brazil.|Institute of Chemistry, University of Campinas, Campinas, Brazil.|Department of Biological Sciences, University of Texas at Dallas, Richardson, USA.|Institute of Chemistry, University of Campinas, Campinas, Brazil.', 'Brazil|Brazil|Brazil|Brazil|United States|Brazil', 7, 'Amino Acid Sequence|Cross-Linking Reagents|Mass Spectrometry|Molecular Dynamics Simulation|Protein Folding|Protein Structure, Secondary|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/bty074', '29447388', 'Elucidation of protein native states from amino acid sequences is a primary computational challenge. Modern computational and experimental methodologies, such as molecular coevolution and chemical cross-linking mass-spectrometry allowed protein structural characterization to previously intangible systems. Despite several independent successful examples, data from these distinct methodologies have not been systematically studied in conjunction. One challenge of structural inference using coevolution is that it is limited to sequence fragments within a conserved and unique domain for which sufficient sequence datasets are available. Therefore, coupling coevolutionary data with complimentary distance constraints from orthogonal sources can provide additional precision to structure prediction methodologies.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(81495, 2018, 'bio', 'RaMWAS: fast methylome-wide association study pipeline for enrichment platforms', 'Shabalin, Andrey A|Hattab, Mohammad W|Clark, Shaunna L|Chan, Robin F|Kumar, Gaurav|Aberg, Karolina A|van den Oord, Edwin J C G', 'Center for Biomarker Research and Precision Medicine, Virginia Commonwealth University, Richmond, USA.|Center for Biomarker Research and Precision Medicine, Virginia Commonwealth University, Richmond, USA.|Center for Biomarker Research and Precision Medicine, Virginia Commonwealth University, Richmond, USA.|Center for Biomarker Research and Precision Medicine, Virginia Commonwealth University, Richmond, USA.|Center for Biomarker Research and Precision Medicine, Virginia Commonwealth University, Richmond, USA.|Center for Biomarker Research and Precision Medicine, Virginia Commonwealth University, Richmond, USA.|Center for Biomarker Research and Precision Medicine, Virginia Commonwealth University, Richmond, USA.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Animals|Computational Biology|DNA Methylation|Genetic Association Studies|Humans|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/bty069', '29447401', 'Enrichment-based technologies can provide measurements of DNA methylation at tens of millions of CpGs for thousands of samples. Existing tools for methylome-wide association studies cannot analyze datasets of this size and lack important features like principal component analysis, combined analysis with SNP data and outcome predictions that are based on all informative methylation sites.'),
(81496, 2018, 'bio', 'SEED 2: a user-friendly platform for amplicon high-throughput sequencing data analyses', 'Vetrovskxc3xbd, Tomxc3xa1xc5xa1|Baldrian, Petr|Morais, Daniel', 'Institute of Microbiology of the CAS, 14220 Prague 4, Czech Republic.|Institute of Microbiology of the CAS, 14220 Prague 4, Czech Republic.|Institute of Microbiology of the CAS, 14220 Prague 4, Czech Republic.', '-|-|-', 48, 'Data Analysis|Genetic Markers|High-Throughput Nucleotide Sequencing|Microbiota|Phylogeny|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/bty071', '29452334', 'Modern molecular methods have increased our ability to describe microbial communities. Along with the advances brought by new sequencing technologies, we now require intensive computational resources to make sense of the large numbers of sequences continuously produced. The software developed by the scientific community to address this demand, although very useful, require experience of the command-line environment, extensive training and have steep learning curves, limiting their use. We created SEED 2, a graphical user interface for handling high-throughput amplicon-sequencing data under Windows operating systems.'),
(81497, 2018, 'bio', 'StructureMapper: a high-throughput algorithm for analyzing protein sequence locations in structural data', 'Nurminen, Anssi|Hytxc3xb6nen, Vesa P', 'Faculty of Medicine and Life Sciences and BioMediTech, University of Tampere, Tampere, Finland.|Faculty of Medicine and Life Sciences and BioMediTech, University of Tampere, Tampere, Finland.', 'Finland|Finland', 0, 'Algorithms|High-Throughput Nucleotide Sequencing|Humans|Models, Molecular|Protein Structure, Secondary|Protein Structure, Tertiary|Proteome|Proteomics|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/bty086', '29452363', 'StructureMapper is a high-throughput algorithm for automated mapping of protein primary amino sequence locations to existing three-dimensional protein structures. The algorithm is intended for facilitating easy and efficient utilization of structural information in protein characterization and proteomics. StructureMapper provides an analysis of the identified structural locations that includes surface accessibility, flexibility, protein-protein interfacing, intrinsic disorder prediction, secondary structure assignment, biological assembly information and sequence identity percentages, among other metrics.'),
(81498, 2018, 'bio', 'pBRIT: gene prioritization by correlating functional and phenotypic annotations through integrative data fusion', 'Kumar, Ajay Anand|Van Laer, Lut|Alaerts, Maaike|Ardeshirdavani, Amin|Moreau, Yves|Laukens, Kris|Loeys, Bart|Vandeweyer, Geert', 'Center of Medical Genetics, University of Antwerp and Antwerp University Hospital, Antwerp, Belgium.|Center of Medical Genetics, University of Antwerp and Antwerp University Hospital, Antwerp, Belgium.|Center of Medical Genetics, University of Antwerp and Antwerp University Hospital, Antwerp, Belgium.|Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Belgium.|Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Belgium.|Biomedical Informatics Research Network Antwerp (biomina), University of Antwerp, Antwerp, Belgium.|Center of Medical Genetics, University of Antwerp and Antwerp University Hospital, Antwerp, Belgium.|Center of Medical Genetics, University of Antwerp and Antwerp University Hospital, Antwerp, Belgium.', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 5, 'Animals|Bayes Theorem|Biological Ontologies|Computational Biology|Genomics|Humans|Information Storage and Retrieval|Phenotype|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bty079', '29452392', 'Computational gene prioritization can aid in disease gene identification. Here, we propose pBRIT (prioritization using Bayesian Ridge regression and Information Theoretic model), a novel adaptive and scalable prioritization tool, integrating Pubmed abstracts, Gene Ontology, Sequence similarities, Mammalian and Human Phenotype Ontology, Pathway, Interactions, Disease Ontology, Gene Association database and Human Genome Epidemiology database, into the prediction model. We explore and address effects of sparsity and inter-feature dependencies within annotation sources, and the impact of bias towards specific annotations.'),
(81499, 2018, 'bio', 'LS-align: an atom-level, flexible ligand structural alignment algorithm for high-throughput virtual screening', 'Hu, Jun|Liu, Zi|Yu, Dong-Jun|Zhang, Yang', 'School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China.|School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China.|School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China.|Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, USA.', 'China|China|China|United States', 9, 'Algorithms|Computational Biology|Drug Discovery|Drug Evaluation, Preclinical|High-Throughput Screening Assays|Ligands|Protein Binding|Protein Conformation|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/bty081', '29462237', 'Sequence-order independent structural comparison, also called structural alignment, of small ligand molecules is often needed for computer-aided virtual drug screening. Although many ligand structure alignment programs are proposed, most of them build the alignments based on rigid-body shape comparison which cannot provide atom-specific alignment information nor allow structural variation, both abilities are critical to efficient high-throughput virtual screening.'),
(81500, 2018, 'bio', 'SecretSanta: flexible pipelines for functional secretome prediction', 'Gogleva, Anna|Drost, Hajk-Georg|Schornack, Sebastian', 'Sainsbury Laboratory, University of Cambridge, Cambridge, UK.|Sainsbury Laboratory, University of Cambridge, Cambridge, UK.|Sainsbury Laboratory, University of Cambridge, Cambridge, UK.', 'United Kingdom|United Kingdom|United Kingdom', 4, 'Genomics|Programming Languages|Workflow', 'Journal Article', '10.1093/bioinformatics/bty088', '29462238', 'The secretome denotes the collection of secreted proteins exported outside of the cell. The functional roles of secreted proteins include the maintenance and remodelling of the extracellular matrix as well as signalling between host and non-host cells. These features make secretomes rich reservoirs of biomarkers for disease classification and host-pathogen interaction studies. Common biomarkers are extracellular proteins secreted via classical pathways that can be predicted from sequence by annotating the presence or absence of N-terminal signal peptides. Several heterogeneous command line tools and web-interfaces exist to identify individual motifs, signal sequences and domains that are either characteristic or strictly excluded from secreted proteins. However, a single flexible secretome-prediction workflow that combines all analytic steps is still missing.'),
(81501, 2018, 'bio', 'flowLearn: fast and precise identification and quality checking of cell populations in flow cytometry', 'Lux, Markus|Brinkman, Ryan Remy|Chauve, Cedric|Laing, Adam|Lorenc, Anna|Abeler-Dörner, Lucie|Hammer, Barbara', 'Computational Methods for the Analysis of the Diversity and Dynamics of Genomes, Bielefeld University, Bielefeld, Germany.|Terry Fox Laboratory, BC Cancer Research Centre, Vancouver, Canada.|Department of Mathematics, Simon Fraser University, Vancouver, Canada.|Department of Immunobiology, Kings College London, London, UK.|Department of Immunobiology, Kings College London, London, UK.|Department of Immunobiology, Kings College London, London, UK.|CITEC Centre of Excellence, Bielefeld, Germany.', 'Germany|Canada|Canada|United Kingdom|United Kingdom|United Kingdom|Germany', 11, 'Computational Biology|Flow Cytometry|Software', 'Journal Article', '10.1093/bioinformatics/bty082', '29462241', 'Identification of cell populations in flow cytometry is a critical part of the analysis and lays the groundwork for many applications and research discovery. The current paradigm of manual analysis is time consuming and subjective. A common goal of users is to replace manual analysis with automated methods that replicate their results. Supervised tools provide the best performance in such a use case, however they require fine parameterization to obtain the best results. Hence, there is a strong need for methods that are fast to setup, accurate and interpretable.'),
(81502, 2018, 'bio', 'Combining co-evolution and secondary structure prediction to improve fragment library generation', 'de Oliveira, Saulo H P|Deane, Charlotte M', 'Department of Statistics, University of Oxford, Oxford, UK.|Department of Statistics, University of Oxford, Oxford, UK.', 'United Kingdom|United Kingdom', 0, 'Algorithms|Computational Biology|Peptide Library|Protein Structure, Secondary|Software', 'Comparative Study', '10.1093/bioinformatics/bty084', '29462243', 'Recent advances in co-evolution techniques have made possible the accurate prediction of protein structures in the absence of a template. Here, we provide a general approach that further utilizes co-evolution constraints to generate better fragment libraries for fragment-based protein structure prediction.'),
(81503, 2018, 'bio', 'A new approach for interpreting Random Forest models and its application to the biology of ageing', 'Fabris, Fabio|Doherty, Aoife|Palmer, Daniel|de Magalhães, João Pedro|Freitas, Alex A', 'School of Computing, University of Kent, Canterbury, Kent, UK.|Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK.|Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK.|Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK.|School of Computing, University of Kent, Canterbury, Kent, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Aging|Animals|Brain|Computational Biology|Gene Expression Regulation|Gene Ontology|Humans|Machine Learning|Software', 'Journal Article', '10.1093/bioinformatics/bty087', '29462247', 'This work uses the Random Forest (RF) classification algorithm to predict if a gene is over-expressed, under-expressed or has no change in expression with age in the brain. RFs have high predictive power, and RF models can be interpreted using a feature (variable) importance measure. However, current feature importance measures evaluate a feature as a whole (all feature values). We show that, for a popular type of biological data (Gene Ontology-based), usually only one value of a feature is particularly important for classification and the interpretation of the RF model. Hence, we propose a new algorithm for identifying the most important and most informative feature values in an RF model.'),
(81504, 2018, 'bio', 'The lncLocator: a subcellular localization predictor for long non-coding RNAs based on a stacked ensemble classifier', 'Cao, Zhen|Pan, Xiaoyong|Yang, Yang|Huang, Yan|Shen, Hong-Bin', 'Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China.|Department of Medical Informatics, Erasmus MC, Rotterdam, The Netherlands.|Department of Computer Science, Shanghai Jiao Tong University, and Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai, China.|State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, China.|Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China.', 'China|Netherlands|China|China|China', 61, 'Animals|Computational Biology|Humans|Mice|RNA Transport|RNA, Long Noncoding|Software|Support Vector Machine', 'Journal Article', '10.1093/bioinformatics/bty085', '29462250', 'The long non-coding RNA (lncRNA) studies have been hot topics in the field of RNA biology. Recent studies have shown that their subcellular localizations carry important information for understanding their complex biological functions. Considering the costly and time-consuming experiments for identifying subcellular localization of lncRNAs, computational methods are urgently desired. However, to the best of our knowledge, there are no computational tools for predicting the lncRNA subcellular locations to date.'),
(81505, 2018, 'bio', 'Multiobjective multifactor dimensionality reduction to detect SNP-SNP interactions', 'Yang, Cheng-Hong|Chuang, Li-Yeh|Lin, Yu-Da', 'Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan.|Department of Chemical Engineering and Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung, Taiwan.|Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan.', '-|-|-', 0, 'Case-Control Studies|Coronary Artery Disease|Epistasis, Genetic|Genetic Predisposition to Disease|Humans|Models, Genetic|Multifactor Dimensionality Reduction|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/bty076', '29471406', 'Single-nucleotide polymorphism (SNP)-SNP interactions (SSIs) are popular markers for understanding disease susceptibility. Multifactor dimensionality reduction (MDR) can successfully detect considerable SSIs. Currently, MDR-based methods mainly adopt a single-objective function (a single measure based on contingency tables) to detect SSIs. However, generally, a single-measure function might not yield favorable results due to potential model preferences and disease complexities.'),
(81506, 2018, 'bio', 'Cost function network-based design of protein-protein interactions: predicting changes in binding affinity', 'Viricel, Clxc3xa9ment|de Givry, Simon|Schiex, Thomas|Barbe, Sophie', 'Laboratoire dIngxc3xa9nierie des Systxc3xa8mes Biologiques et des Procxc3xa9dxc3xa9s, Universitxc3xa9 de Toulouse, CNRS, INRA, INSA, Toulouse, France.|Unitxc3xa9 de Mathxc3xa9matiques et Informatique Appliquxc3xa9es de Toulouse, INRA, Castanet Tolosan cedex, France.|Unitxc3xa9 de Mathxc3xa9matiques et Informatique Appliquxc3xa9es de Toulouse, INRA, Castanet Tolosan cedex, France.|Laboratoire dIngxc3xa9nierie des Systxc3xa8mes Biologiques et des Procxc3xa9dxc3xa9s, Universitxc3xa9 de Toulouse, CNRS, INRA, INSA, Toulouse, France.', 'France|France|France|France', 3, 'Animals|Artificial Intelligence|Bacteria|Computational Biology|Entropy|Humans|Mutation|Protein Binding|Protein Conformation|Proteins|Software|Thermodynamics', 'Journal Article', '10.1093/bioinformatics/bty092', '29474517', 'Accurate and economic methods to predict change in protein binding free energy upon mutation are imperative to accelerate the design of proteins for a wide range of applications. Free energy is defined by enthalpic and entropic contributions. Following the recent progresses of Artificial Intelligence-based algorithms for guaranteed NP-hard energy optimization and partition function computation, it becomes possible to quickly compute minimum energy conformations and to reliably estimate the entropic contribution of side-chains in the change of free energy of large protein interfaces.'),
(81507, 2018, 'bio', 'ViCTree: an automated framework for taxonomic classification from protein sequences', 'Modha, Sejal|Thanki, Anil S|Cotmore, Susan F|Davison, Andrew J|Hughes, Joseph', 'MRC-University of Glasgow Centre for Virus Research, Glasgow, UK.|Earlham Institute, Norwich Research Park, Norwich, UK.|Yale University Medical School, New Haven, CT, USA.|MRC-University of Glasgow Centre for Virus Research, Glasgow, UK.|MRC-University of Glasgow Centre for Virus Research, Glasgow, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Amino Acid Sequence|Databases, Factual|Phylogeny|Sequence Analysis, Protein|Software|Viral Proteins|Viruses', 'Journal Article', '10.1093/bioinformatics/bty099', '29474519', 'The increasing rate of submission of genetic sequences into public databases is providing a growing resource for classifying the organisms that these sequences represent. To aid viral classification, we have developed ViCTree, which automatically integrates the relevant sets of sequences in NCBI GenBank and transforms them into an interactive maximum likelihood phylogenetic tree that can be updated automatically. ViCTree incorporates ViCTreeView, which is a JavaScript-based visualization tool that enables the tree to be explored interactively in the context of pairwise distance data.'),
(81508, 2018, 'bio', 'Progressive approach for SNP calling and haplotype assembly using single molecular sequencing data', 'Guo, Fei|Wang, Dan|Wang, Lusheng', 'School of Computer Science and Technology, Tianjin University, Tianjin Haihe Education Park, Tianjin, China.|Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong.|Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong.', 'China|Hong Kong|Hong Kong', 10, 'Algorithms|Haplotypes|High-Throughput Nucleotide Sequencing|Humans|Molecular Sequence Data|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bty059', '29474523', 'Haplotype information is essential to the complete description and interpretation of genomes, genetic diversity and genetic ancestry. The new technologies can provide Single Molecular Sequencing (SMS) data that cover about 90% of positions over chromosomes. However, the SMS data has a higher error rate comparing to 1% error rate for short reads. Thus, it becomes very difficult for SNP calling and haplotype assembly using SMS reads. Most existing technologies do not work properly for the SMS data.'),
(81509, 2018, 'bio', 'IWTomics: testing high-resolution sequence-based Omics data at multiple locations and scales', 'Cremona, Marzia A|Pini, Alessia|Cumbo, Fabio|Makova, Kateryna D|Chiaromonte, Francesca|Vantini, Simone', 'Department of Statistics, The Pennsylvania State University, University Park, USA.|MOX - Department of Mathematics, Politecnico di Milano, Milano, Italy.|Department of Engineering, Third University of Rome, Italy.|Center for Medical Genomics, The Huck Institutes of the Life Sciences.|Department of Statistics, The Pennsylvania State University, University Park, USA.|MOX - Department of Mathematics, Politecnico di Milano, Milano, Italy.', 'United States|Italy|Italy|United States|United States|Italy', 2, 'Databases, Factual|Genome|Genomics|Sequence Analysis|Software|Workflow', 'Journal Article', '10.1093/bioinformatics/bty090', '29474526', 'With increased generation of high-resolution sequence-based Omics data, detecting statistically significant effects at different genomic locations and scales has become key to addressing several scientific questions. IWTomics is an R/Bioconductor package (integrated in Galaxy) that, exploiting sophisticated Functional Data Analysis techniques (i.e. statistical techniques that deal with the analysis of curves), allows users to pre-process, visualize and test these data at multiple locations and scales. The package provides a friendly, flexible and complete workflow that can be employed in many genomic and epigenomic applications.'),
(81510, 2018, 'bio', 'EPIC-CoGe: managing and analyzing genomic data', 'Nelson, Andrew D L|Haug-Baltzell, Asher K|Davey, Sean|Gregory, Brian D|Lyons, Eric', 'BIO5 Institute, School of Plant Sciences, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, School of Plant Sciences, University of Arizona, Tucson, AZ, USA.|BIO5 Institute, School of Plant Sciences, University of Arizona, Tucson, AZ, USA.|Department of Biology, University of Pennsylvania, Philadelphia, PA, USA.|BIO5 Institute, School of Plant Sciences, University of Arizona, Tucson, AZ, USA.', 'United States|United States|United States|United States|United States', 5, 'Data Visualization|Genome|Genomics|Molecular Sequence Annotation|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bty106', '29474529', 'The EPIC-CoGe browser is a web-based genome visualization utility that integrates the GMOD JBrowse genome browser with the extensive CoGe genome database (currently containing over 30 000 genomes). In addition, the EPIC-CoGe browser boasts many additional features over basic JBrowse, including enhanced search capability and on-the-fly analyses for comparisons and analyses between all types of functional and diversity genomics data. There is no installation required and data (genome, annotation, functional genomic and diversity data) can be loaded by following a simple point and click wizard, or using a REST API, making the browser widely accessible and easy to use by researchers of all computational skill levels. In addition, EPIC-CoGe and data tracks are easily embedded in other websites and JBrowse instances.'),
(81511, 2018, 'bio', 'MUGAN: Multi-GPU accelerated AmpliconNoise server for rapid microbial diversity assessment', 'Lee, Byunghan|Min, Hyeyoung|Yoon, Sungroh', 'Electrical and Computer Engineering, Seoul National University, Seoul 08826, Korea.|College of Pharmacy, Chung-Ang University, Seoul 06974, Korea.|Electrical and Computer Engineering, Seoul National University, Seoul 08826, Korea.', '-|-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty096', '29474530', 'Metagenomic sequencing has become a crucial tool for obtaining a gene catalogue of operational taxonomic units (OTUs) in a microbial community. A typical metagenomic sequencing produces a large amount of data (often in the order of terabytes or more), and computational tools are indispensable for efficient processing. In particular, error correction in metagenomics is crucial for accurate and robust genetic cataloging of microbial communities. However, many existing error-correction tools take a prohibitively long time and often bottleneck the whole analysis pipeline.'),
(81512, 2018, 'bio', 'PennDiff: detecting differential alternative splicing and transcription by RNA sequencing', 'Hu, Yu|Lin, Jennie|Hu, Jian|Hu, Gang|Wang, Kui|Zhang, Hanrui|Reilly, Muredach P|Li, Mingyao', 'Department of Biostatistics, Epidemiology and Informatics.|Renal Electrolyte and Hypertension Division, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.|Department of Biostatistics, Epidemiology and Informatics.|Department of Information Theory and Data Science, School of Mathematical Sciences, Nankai University, Tianjin, China.|Department of Information Theory and Data Science, School of Mathematical Sciences, Nankai University, Tianjin, China.|Division of Cardiology, Department of Medicine, Columbia University Medical Center, New York City, NY, USA.|Division of Cardiology, Department of Medicine, Columbia University Medical Center, New York City, NY, USA.|Department of Biostatistics, Epidemiology and Informatics.', 'United States|United States|United States|China|China|United States|United States|United States', 3, 'Alternative Splicing|Gene Expression Profiling|Humans|RNA Isoforms|Sequence Analysis, RNA|Software|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/bty097', '29474557', 'Alternative splicing and alternative transcription are a major mechanism for generating transcriptome diversity. Differential alternative splicing and transcription (DAST), which describe different usage of transcript isoforms across different conditions, can complement differential expression in characterizing gene regulation. However, the analysis of DAST is challenging because only a small fraction of RNA-seq reads is informative for isoforms. Several methods have been developed to detect exon-based and gene-based DAST, but they suffer from power loss for genes with many isoforms.'),
(81513, 2018, 'bio', 'STAR Chimeric Post for rapid detection of circular RNA and fusion transcripts', 'Akers, Nicholas K|Schadt, Eric E|Losic, Bojan', 'Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.|Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.|Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.', 'United States|United States|United States', 6, 'Animals|Humans|Mice|RNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/bty091', '29474638', 'The biological relevance of chimeric RNA alignments is now well established. Chimera arising as chromosomal fusions are often drivers of cancer and recently discovered circular RNA (circRNA) are only now being characterized. While software already exists for fusion discovery and quantitation, high false positive rates and high run-times hamper scalable fusion discovery on large datasets. Furthermore, software available for circRNA detection and quantification is limited.'),
(81514, 2018, 'bio', 'Bacmeta: simulator for genomic evolution in bacterial metapopulations', 'Sipola, Aleksi|Marttinen, Pekka|Corander, Jukka', 'Department of Mathematics and Statistics, University of Helsinki, Finland.|Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Finland.|Department of Mathematics and Statistics, University of Helsinki, Finland.', 'Finland|Finland|Finland', 3, 'Bacteria|Evolution, Molecular|Genome, Bacterial|Genomics|Software', 'Journal Article', '10.1093/bioinformatics/bty093', '29474733', 'The advent of genomic data from densely sampled bacterial populations has created a need for flexible simulators by which models and hypotheses can be efficiently investigated in the light of empirical observations. Bacmeta provides fast stochastic simulation of neutral evolution within a large collection of interconnected bacterial populations with completely adjustable connectivity network. Stochastic events of mutations, recombinations, insertions/deletions, migrations and micro-epidemics can be simulated in discrete non-overlapping generations with a Wright-Fisher model that operates on explicit sequence data of any desired genome length. Each model component, including locus, bacterial strain, population and ultimately the whole metapopulation, is efficiently simulated using C++xe2x80x89objects and detailed metadata from each level can be acquired. The software can be executed in a cluster environment using simple textual input files, enabling, e.g. large-scale simulations and likelihood-free inference.'),
(81515, 2018, 'bio', 'PhyloMAd: efficient assessment of phylogenomic model adequacy', 'Duchêne, David A|Duchêne, Sebastian|Ho, Simon Y W', 'School of Life and Environmental Sciences, University of Sydney, Sydney, Australia.|Bio21 Molecular Science and Biotechnology Institute, University of Melbourne, Melbourne, VIC, Australia.|School of Life and Environmental Sciences, University of Sydney, Sydney, Australia.', 'Australia|Australia|Australia', 8, 'Biological Evolution|Genomics|Likelihood Functions|Models, Genetic|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/bty103', '29481585', 'Statistical phylogenetic inference plays an important role in evolutionary biology. The accuracy of phylogenetic methods relies on having suitable models of the evolutionary process. Various tools allow comparisons of candidate phylogenetic models, but assessing the absolute performance of models remains a considerable challenge. We introduce PhyloMAd, a user-friendly application for assessing the adequacy of commonly used models of nucleotide substitution and among-lineage rate variation. Our software implements a fast, likelihood-based method of model assessment that is tractable for analyses of large multi-locus datasets. PhyloMAd provides a means of informing model improvement, or selecting data to enhance the evolutionary signal in phylogenomic analyses.'),
(81516, 2018, 'bio', 'A global network of biomedical relationships derived from text', 'Percha, Bethany|Altman, Russ B', 'Biomedical Informatics Training Program, Stanford University, Stanford, CA, USA.|Department of Bioengineering, Stanford University, Stanford, CA, USA.', 'United States|United States', 12, 'Algorithms|Biological Variation, Population|Data Mining|Drug Interactions|Humans|MEDLINE|Semantics|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/bty114', '29490008', 'The biomedical communitys collective understanding of how chemicals, genes and phenotypes interact is distributed across the text of over 24 million research articles. These interactions offer insights into the mechanisms behind higher order biochemical phenomena, such as drug-drug interactions and variations in drug response across individuals. To assist their curation at scale, we must understand what relationship types are possible and map unstructured natural language descriptions onto these structured classes. We used NCBIs PubTator annotations to identify instances of chemical, gene and disease names in Medline abstracts and applied the Stanford dependency parser to find connecting dependency paths between pairs of entities in single sentences. We combined a published ensemble biclustering algorithm (EBC) with hierarchical clustering to group the dependency paths into semantically-related categories, which we annotated with labels, or themes (inhibition and activation, for example). We evaluated our theme assignments against six human-curated databases: DrugBank, Reactome, SIDER, the Therapeutic Target Database, OMIM and PharmGKB.'),
(81517, 2018, 'bio', 'SECLAF: a webserver and deep neural network design tool for hierarchical biological sequence classification', 'Szalkai, Balázs|Grolmusz, Vince', 'PIT Bioinformatics Group, Institute of Mathematics, Eötvös University, H-1117 Budapest, Hungary.|PIT Bioinformatics Group, Institute of Mathematics, Eötvös University, H-1117 Budapest, Hungary.', 'Hungary|Hungary', 7, 'Computational Biology|Gene Ontology|Internet|Neural Networks (Computer)|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/bty116', '29490010', 'Artificial intelligence tools are gaining more and more ground each year in bioinformatics. Learning algorithms can be taught for specific tasks by using the existing enormous biological databases, and the resulting models can be used for the high-quality classification of novel, un-categorized data in numerous areas, including biological sequence analysis. Here, we introduce SECLAF, a webserver that uses deep neural networks for hierarchical biological sequence classification. By applying SECLAF for residue-sequences, we have reported [Methods (2018), https://doi.org/10.1016/j.ymeth.2017.06.034] the most accurate multi-label protein classifier to date (UniProt-into 698 classes-AUC 99.99%, Gene Ontology-into 983 classes-AUC 99.45%). Our framework SECLAF can be applied for other sequence classification tasks, as we describe in the present contribution.'),
(81518, 2018, 'bio', 'Isoform-level gene expression patterns in single-cell RNA-sequencing data', 'Vu, Trung Nghia|Wills, Quin F|Kalari, Krishna R|Niu, Nifang|Wang, Liewei|Pawitan, Yudi|Rantalainen, Mattias', 'Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.|Novo Nordisk Research Centre Oxford, Oxford, UK.|Department of Health Sciences Research.|Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA.|Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA.|Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.|Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.', 'Sweden|United Kingdom|United Kingdom|United Kingdom|United Kingdom|Sweden|Sweden', 4, 'Breast Neoplasms|Cell Line, Tumor|Female|Gene Expression|Gene Expression Profiling|Humans|RNA Isoforms|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/bty100', '29490015', 'RNA sequencing of single cells enables characterization of transcriptional heterogeneity in seemingly homogeneous cell populations. Single-cell sequencing has been applied in a wide range of researches fields. However, few studies have focus on characterization of isoform-level expression patterns at the single-cell level. In this study, we propose and apply a novel method, ISOform-Patterns (ISOP), based on mixture modeling, to characterize the expression patterns of isoform pairs from the same gene in single-cell isoform-level expression data.'),
(81519, 2018, 'bio', 'Prediction of potential disease-associated microRNAs using structural perturbation method', 'Zeng, Xiangxiang|Liu, Li|Lxc3xbc, Linyuan|Zou, Quan', 'Department of Computer Science, Xiamen University, Xiamen, China.|Department of Computer Science, Xiamen University, Xiamen, China.|Alibaba Research Center for Complexity Sciences, Alibaba Business College, Hangzhou Normal University, Hangzhou, China.|School of Computer Science and Technology, Tianjin University, Tianjin, China.', 'China|China|China|China', 91, 'Algorithms|Breast Neoplasms|Computational Biology|Disease Susceptibility|Female|Genetic Association Studies|Humans|MicroRNAs|Software', 'Journal Article', '10.1093/bioinformatics/bty112', '29490018', 'The identification of disease-related microRNAs (miRNAs) is an essential but challenging task in bioinformatics research. Similarity-based link prediction methods are often used to predict potential associations between miRNAs and diseases. In these methods, all unobserved associations are ranked by their similarity scores. Higher score indicates higher probability of existence. However, most previous studies mainly focus on designing advanced methods to improve the prediction accuracy while neglect to investigate the link predictability of the networks that present the miRNAs and diseases associations. In this work, we construct a bilayer network by integrating the miRNA-disease network, the miRNA similarity network and the disease similarity network. We use structural consistency as an indicator to estimate the link predictability of the related networks. On the basis of the indicator, a derivative algorithm, called structural perturbation method (SPM), is applied to predict potential associations between miRNAs and diseases.'),
(81520, 2018, 'bio', 'ShinyKGode: an interactive application for ODE parameter inference using gradient matching', 'Wandy, Joe|Niu, Mu|Giurghita, Diana|Daly, Rónán|Rogers, Simon|Husmeier, Dirk', 'Glasgow Polyomics, University of Glasgow, Glasgow, UK.|School of Computing, Electronics and Mathematics, University of Plymouth, Devon, UK.|School of Mathematics and Statistics, University of Glasgow, Glasgow, UK.|Glasgow Polyomics, University of Glasgow, Glasgow, UK.|School of Computing Science, University of Glasgow, Glasgow, UK.|School of Mathematics and Statistics, University of Glasgow, Glasgow, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 1, 'Kinetics|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/bty089', '29490021', 'Mathematical modelling based on ordinary differential equations (ODEs) is widely used to describe the dynamics of biological systems, particularly in systems and pathway biology. Often the kinetic parameters of these ODE systems are unknown and have to be inferred from the data. Approximate parameter inference methods based on gradient matching (which do not require performing computationally expensive numerical integration of the ODEs) have been getting popular in recent years, but many implementations are difficult to run without expert knowledge. Here, we introduce ShinyKGode, an interactive web application to perform fast parameter inference on ODEs using gradient matching.'),
(81521, 2018, 'bio', 'Parallelization of MAFFT for large-scale multiple sequence alignments', 'Nakamura, Tsukasa|Yamada, Kazunori D|Tomii, Kentaro|Katoh, Kazutaka', 'Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, Chiba, Japan.|Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan.|Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, Chiba, Japan.|Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan.', 'Japan|Japan|Japan|Japan', 126, 'Algorithms|Computational Biology|Protein Structure, Secondary|Sequence Alignment|Sequence Analysis, Protein|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/bty121', '29506019', 'We report an update for the MAFFT multiple sequence alignment program to enable parallel calculation of large numbers of sequences. The G-INS-1 option of MAFFT was recently reported to have higher accuracy than other methods for large data, but this method has been impractical for most large-scale analyses, due to the requirement of large computational resources. We introduce a scalable variant, G-large-INS-1, which has equivalent accuracy to G-INS-1 and is applicable to 50 000 or more sequences.'),
(81522, 2018, 'bio', 'MIRA: an R package for DNA methylation-based inference of regulatory activity', 'Lawson, John T|Tomazou, Eleni M|Bock, Christoph|Sheffield, Nathan C', 'Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.|Childrens Cancer Research Institute, St. Annas Kinderkrebsforschung, Vienna, Austria.|CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria.|Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.', 'United States|Austria|Austria|United States', 4, 'Biological Ontologies|Computational Biology|DNA Methylation|Epigenomics|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bty083', '29506020', 'DNA methylation contains information about the regulatory state of the cell. MIRA aggregates genome-scale DNA methylation data into a DNA methylation profile for a given region set with shared biological annotation. Using this profile, MIRA infers and scores the collective regulatory activity for the region set. MIRA facilitates regulatory analysis in situations where classical regulatory assays would be difficult and allows public sources of region sets to be leveraged for novel insight into the regulatory state of DNA methylation datasets.'),
(81523, 2018, 'bio', 'Updating the 97% identity threshold for 16S ribosomal RNA OTUs', 'Edgar, Robert C', 'Sonoma, CA, USA.', 'United States', 102, 'Algorithms|Cluster Analysis|Genes, rRNA|Microbiota|RNA, Ribosomal, 16S|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bty113', '29506021', 'The 16S ribosomal RNA (rRNA) gene is widely used to survey microbial communities. Sequences are often clustered into Operational Taxonomic Units (OTUs) as proxies for species. The canonical clustering threshold is 97% identity, which was proposed in 1994 when few 16S rRNA sequences were available, motivating a reassessment on current data.'),
(81524, 2018, 'bio', 'SubRecon: ancestral reconstruction of amino acid substitutions along a branch in a phylogeny', 'Monit, Christopher|Goldstein, Richard A', 'Division of Infection and Immunity, University College London, London, UK.|Division of Infection and Immunity, University College London, London, UK.', 'United Kingdom|United Kingdom', 1, 'Amino Acid Substitution|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/bty101', '29506148', 'Existing ancestral sequence reconstruction techniques are ill-suited to investigating substitutions on a single branch of interest. We present SubRecon, an implementation of a hybrid technique integrating joint and marginal reconstruction for protein sequence data. SubRecon calculates the joint probability of states at adjacent internal nodes in a phylogeny, i.e. how the state has changed along a branch. This does not condition on states at other internal nodes and includes site rate variation. Simulation experiments show the technique to be accurate and powerful. SubRecon has a user-friendly command line interface and produces concise output that is intuitive yet suitable for subsequent parsing in an automated pipeline.'),
(81525, 2018, 'bio', 'Mitigating the adverse impact of batch effects in sample pattern detection', 'Fei, Teng|Zhang, Tengjiao|Shi, Weiyang|Yu, Tianwei', 'Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA.|School of Life Sciences and Technology, Tongji University, Shanghai, China.|Ministry of Education Key Laboratory of Marine Genetics and Breeding, College of Marine Life Sciences, Ocean University of China, Qingdao, China.|Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA.', 'United States|China|China|United States', 0, 'Algorithms|Animals|Artifacts|Cluster Analysis|Humans|Mice|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/bty117', '29506177', 'It is well known that batch effects exist in RNA-seq data and other profiling data. Although some methods do a good job adjusting for batch effects by modifying the data matrices, it is still difficult to remove the batch effects entirely. The remaining batch effect can cause artifacts in the detection of patterns in the data.'),
(81526, 2018, 'bio', 'anexVis: visual analytics framework for analysis of RNA expression', 'Tran, Diem-Trang|Zhang, Tian|Stutsman, Ryan|Might, Matthew|Desai, Umesh R|Kuberan, Balagurunathan', 'Department of Medicinal Chemistry.|School of Computing, University of Utah, Salt Lake City, UT, USA.|School of Computing, University of Utah, Salt Lake City, UT, USA.|Hugh Kaul Personalized Medicine Institute, University of Alabama at Birmingham, Birmingham, AL, USA.|Department of Medicinal Chemistry, Virginia Commonwealth University, Richmond, VA, USA.|Department of Medicinal Chemistry.', 'United States|United States|United States|United States|United States|United States', 0, 'Data Visualization|Gene Expression Profiling|Glycosaminoglycans|Humans|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/bty122', '29506198', 'Although RNA expression data are accumulating at a remarkable speed, gaining insights from them still requires laborious analyses, which hinder many biological and biomedical researchers. This report introduces a visual analytics framework that applies several well-known visualization techniques to leverage understanding of an RNA expression dataset. Our analyses on glycosaminoglycan-related genes have demonstrated the broad application of this tool, anexVis (analysis of RNA expression), to advance the understanding of tissue-specific glycosaminoglycan regulation and functions, and potentially other biological pathways.'),
(81527, 2018, 'bio', 'Extrema-weighted feature extraction for functional data', 'van den Boom, Willem|Mao, Callie|Schroeder, Rebecca A|Dunson, David B', 'Department of Statistical Science, Duke University, Durham, NC, USA.|Department of Statistical Science, Duke University, Durham, NC, USA.|Department of Anesthesiology, Duke University School of Medicine, Durham, NC, USA.|Department of Statistical Science, Duke University, Durham, NC, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Blood Pressure|Computational Biology|Female|Humans|Male|Postoperative Complications|Software|Treatment Outcome', 'Journal Article', '10.1093/bioinformatics/bty120', '29506206', 'Although there is a rich literature on methods for assessing the impact of functional predictors, the focus has been on approaches for dimension reduction that do not suit certain applications. Examples of standard approaches include functional linear models, functional principal components regression and cluster-based approaches, such as latent trajectory analysis. This article is motivated by applications in which the dynamics in a predictor, across times when the value is relatively extreme, are particularly informative about the response. For example, physicians are interested in relating the dynamics of blood pressure changes during surgery to post-surgery adverse outcomes, and it is thought that the dynamics are more important when blood pressure is significantly elevated or lowered.'),
(81528, 2018, 'bio', 'GDCRNATools: an R/Bioconductor package for integrative analysis of lncRNA, miRNA and mRNA data in GDC', 'Li, Ruidong|Qu, Han|Wang, Shibo|Wei, Julong|Zhang, Le|Ma, Renyuan|Lu, Jianming|Zhu, Jianguo|Zhong, Wei-De|Jia, Zhenyu', 'Department of Botany and Plant Sciences.|Department of Botany and Plant Sciences.|Department of Botany and Plant Sciences.|Department of Botany and Plant Sciences.|Department of Botany and Plant Sciences.|Department of Botany and Plant Sciences.|Department of Botany and Plant Sciences.|Department of Urology, Guizhou Provincial Peoples Hospital, Guizhou, China.|Department of Urology, Guangdong Key Laboratory of Clinical Molecular Medicine and Diagnostics, Guangzhou First Peoples Hospital, The Second Affiliated Hospital of South China University of Technology, Guangzhou, China.|Department of Botany and Plant Sciences.', '-|-|-|-|-|-|-|China|China|-', 37, 'Computational Biology|Gene Regulatory Networks|Humans|MicroRNAs|Neoplasms|RNA, Long Noncoding|RNA, Messenger|Software', 'Journal Article', '10.1093/bioinformatics/bty124', '29509844', 'The large-scale multidimensional omics data in the Genomic Data Commons (GDC) provides opportunities to investigate the crosstalk among different RNA species and their regulatory mechanisms in cancers. Easy-to-use bioinformatics pipelines are needed to facilitate such studies.'),
(81529, 2018, 'bio', 'MatrixEpistasis: ultrafast, exhaustive epistasis scan for quantitative traits with covariate adjustment', 'Zhu, Shijia|Fang, Gang', 'Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.|Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.', 'United States|United States', 5, 'Epistasis, Genetic|Genome-Wide Association Study|Humans|Models, Genetic|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/bty094', '29509873', 'For many traits, causal loci uncovered by genetic mapping studies explain only a minority of the heritable contribution to trait variation. Multiple explanations for this missing heritability have been proposed. Single nucleotide polymorphism (SNP)-SNP interaction (epistasis), as one of the compelling models, has been widely studied. However, the genome-wide scan of epistasis, especially for quantitative traits, poses huge computational challenges. Moreover, covariate adjustment is largely ignored in epistasis analysis due to the massive extra computational undertaking.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(81530, 2018, 'bio', 'CarbonylDB: a curated data-resource of protein carbonylation sites', 'Rao, R Shyama Prasad|Zhang, Ning|Xu, Dong|Møller, Ian Max', 'Biostatistics and Bioinformatics Division, Yenepoya Research Center, Yenepoya University, Mangalore, Karnataka, India.|Informatics Institute, University of Missouri-Columbia, MO, USA.|Informatics Institute, University of Missouri-Columbia, MO, USA.|Department of Molecular Biology and Genetics, Aarhus University, Slagelse, Denmark.', 'India|United States|United States|Denmark', 0, 'Animals|Biomarkers|Computational Biology|Humans|Oxidation-Reduction|Oxidative Stress|Protein Carbonylation|Proteins|Rats|Sequence Analysis, Protein|Software|Yeasts', 'Journal Article', '10.1093/bioinformatics/bty123', '29509874', 'Oxidative stress and protein damage have been associated with over 200 human ailments including cancer, stroke, neuro-degenerative diseases and aging. Protein carbonylation, a chemically diverse oxidative post-translational modification, is widely considered as the biomarker for oxidative stress and protein damage. Despite their importance and extensive studies, no database/resource on carbonylated proteins/sites exists. As such information is very useful to research in biology/medicine, we have manually curated a data-resource (CarbonylDB) of experimentally-confirmed carbonylated proteins/sites.'),
(81531, 2018, 'bio', 'GAIT: Gene expression Analysis for Interval Time', 'Kim, Yoojoong|Kang, Yeong Seon|Seok, Junhee', 'School of Electrical Engineering, Korea University, Seoul, South Korea.|Department of Business Administration, University of Seoul, Seoul, South Korea.|School of Electrical Engineering, Korea University, Seoul, South Korea.', '-|-|-', 2, 'Gene Expression|Multivariate Analysis|Software', 'Journal Article', '10.1093/bioinformatics/bty111', '29509896', 'Despite the potential usefulness, the association analysis of gene expression with interval times of two events has been hampered because the occurrence of events can be censored and the conventional survival analysis is not suitable to handle two censored events. However, the recent advances of multivariate survival analysis considering multiple censored events together provide an unprecedented chance for this problem. Based on such advances, we have developed a software tool, GAIT, for the association analysis of gene expression with interval time of two events.'),
(81532, 2018, 'bio', 'FasParser2: a graphical platform for batch manipulation of tremendous amount of sequence data', 'Sun, Yan-Bo', 'State Key Laboratory of Genetic Resources and Evolution, and Center for excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, Yunnan, China.', 'China', 3, 'Data Visualization|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bty126', '29514176', 'FasParser is a graphical platform for manipulating sequences and alignments in a batch mode. It is particularly useful for biologists handling large datasets of sequences, even without an experience in programming. Here, I present an updated version FasParser2, featuring numerous improvements with sets of novel functions that can facilitate sequence manipulation. Its main additional features include (i) re-designed graphical interface which greatly improves its capability for batch processing, (ii) a trimming function to strip poorly-aligned regions in multiple sequence alignments, (iii) a series of functions that can identify as well as remove bad sequence (either too short or too divergent sequences), (iv) an Editor tool for viewing and editing of biological sequences and (v) interfaces (with other programs) for detection of positive selection as well as primer design.'),
(81533, 2018, 'bio', 'AltHapAlignR: improved accuracy of RNA-seq analyses through the use of alternative haplotypes', 'Lee, Wanseon|Plant, Katharine|Humburg, Peter|Knight, Julian C', 'Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.|Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.|Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.|Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 6, 'Alleles|Gene Expression|Gene Expression Profiling|Haplotypes|Humans|Leukocyte Common Antigens|RNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/bty125', '29514179', 'Reliance on mapping to a single reference haplotype currently limits accurate estimation of allele or haplotype-specific expression using RNA-sequencing, notably in highly polymorphic regions such as the major histocompatibility complex.'),
(81534, 2018, 'bio', 'PWMScan: a fast tool for scanning entire genomes with a position-specific weight matrix', 'Ambrosini, Giovanna|Groux, Romain|Bucher, Philipp', 'The Swiss Institute for Experimental Cancer Research (ISREC), Swiss Federal Institute of Technology Lausanne (EPFL).|The Swiss Institute for Experimental Cancer Research (ISREC), Swiss Federal Institute of Technology Lausanne (EPFL).|The Swiss Institute for Experimental Cancer Research (ISREC), Swiss Federal Institute of Technology Lausanne (EPFL).', '-|-|-', 21, 'DNA|Genomics|Humans|Position-Specific Scoring Matrices|Protein Binding|Regulatory Sequences, Nucleic Acid|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/bty127', '29514181', 'Transcription factors regulate gene expression by binding to specific short DNA sequences of 5-20 bp to regulate the rate of transcription of genetic information from DNA to messenger RNA. We present PWMScan, a fast web-based tool to scan server-resident genomes for matches to a user-supplied PWM or transcription factor binding site model from a public database.'),
(81535, 2018, 'bio', 'BPRMeth: a flexible Bioconductor package for modelling methylation profiles', 'Kapourani, Chantriolnt-Andreas|Sanguinetti, Guido', 'School of Informatics, University of Edinburgh, Edinburgh, UK.|School of Informatics, University of Edinburgh, Edinburgh, UK.', 'United Kingdom|United Kingdom', 2, 'DNA Methylation|Epigenomics|Linear Models|Software', 'Journal Article', '10.1093/bioinformatics/bty129', '29522078', 'High-throughput measurements of DNA methylation are increasingly becoming a mainstay of biomedical investigations. While the methylation status of individual cytosines can sometimes be informative, several recent papers have shown that the functional role of DNA methylation is better captured by a quantitative analysis of the spatial variation of methylation across a genomic region.'),
(81536, 2018, 'bio', 'eMolTox: prediction of molecular toxicity with confidence', 'Ji, Changge|Svensson, Fredrik|Zoufir, Azedine|Bender, Andreas', 'Shanghai Engineering Research Center for Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, China.|Department of Chemistry, Center for Molecular Informatics, Cambridge, UK.|Department of Chemistry, Center for Molecular Informatics, Cambridge, UK.|Department of Chemistry, Center for Molecular Informatics, Cambridge, UK.', 'China|United Kingdom|United Kingdom|United Kingdom', 8, 'Animals|Carcinogens|Humans|Mutagens|Software|Toxicology', 'Journal Article', '10.1093/bioinformatics/bty135', '29522123', 'In this work, we present eMolTox, a web server for the prediction of potential toxicity associated with a given molecule. A total of 174 toxicology-related in vitro/vivo experimental datasets were used for model construction and Mondrian conformal prediction was used to estimate the confidence of the resulting predictions. Toxic substructure analysis is also implemented in eMolTox. eMolTox predicts and displays a wealth of information of potential molecular toxicities for safety analysis in drug development.'),
(81537, 2018, 'bio', 'AnaCoDa: analyzing codon data with Bayesian mixture models', 'Landerer, Cedric|Cope, Alexander|Zaretzki, Russell|Gilchrist, Michael A', 'Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, TN, USA.|Genome Science and Technology, University of Tennessee, Knoxville, TN, USA.|National Institute for Mathematical and Biological Synthesis.|Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, TN, USA.', 'United States|United States|United States|United States', 3, 'Algorithms|Bayes Theorem|Codon|Genomics|Models, Genetic|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bty138', '29522124', 'AnaCoDa is an R package for estimating biologically relevant parameters of mixture models, such as selection against translation inefficiency, non-sense errors and ribosome pausing time, from genomic and high throughput datasets. AnaCoDa provides an adaptive Bayesian MCMC algorithm, fully implemented in C++xe2x80x89for high performance with an ergonomic R interface to improve usability. AnaCoDa employs a generic object-oriented design to allow users to extend the framework and implement their own models. Current models implemented in AnaCoDa can accurately estimate biologically relevant parameters given either protein coding sequences or ribosome foot-printing data. Optionally, AnaCoDa can utilize additional data sources, such as gene expression measurements, to aid model fitting and parameter estimation. By utilizing a hierarchical object structure, some parameters can vary between sets of genes while others can be shared. Genes may be assigned to clusters or membership may be estimated by AnaCoDa. This flexibility allows users to estimate the same model parameter under different biological conditions and categorize genes into different sets based on shared model properties embedded within the data. AnaCoDa also allows users to generate simulated data which can be used to aid model development and model analysis as well as evaluate model adequacy. Finally, AnaCoDa contains a set of visualization routines and the ability to revisit or re-initiate previous model fitting, providing researchers with a well rounded easy to use framework to analyze genome scale data.'),
(81538, 2018, 'bio', 'GOLabeler: improving sequence-based large-scale protein function prediction by learning to rank', 'You, Ronghui|Zhang, Zihan|Xiong, Yi|Sun, Fengzhu|Mamitsuka, Hiroshi|Zhu, Shanfeng', 'School of Computer Science and Shanghai Key Lab of Intelligent Information Processing.|School of Computer Science and Shanghai Key Lab of Intelligent Information Processing.|Department of Bioinformatics and Biostatistics, Shanghai Jiaotong University, Shanghai, China.|Center for Computational System Biology, ISTBI, Fudan University, Shanghai, China.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto Prefecture, Japan.|School of Computer Science and Shanghai Key Lab of Intelligent Information Processing.', '-|-|China|China|Japan|-', 27, 'Amino Acid Sequence|Animals|Computational Biology|Eukaryota|Gene Ontology|Humans|Machine Learning|Molecular Sequence Annotation|Protein Structural Elements|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/bty130', '29522145', 'Gene Ontology (GO) has been widely used to annotate functions of proteins and understand their biological roles. Currently only <1% of >70 million proteins in UniProtKB have experimental GO annotations, implying the strong necessity of automated function prediction (AFP) of proteins, where AFP is a hard multilabel classification problem due to one protein with a diverse number of GO terms. Most of these proteins have only sequences as input information, indicating the importance of sequence-based AFP (SAFP: sequences are the only input). Furthermore, homology-based SAFP tools are competitive in AFP competitions, while they do not necessarily work well for so-called difficult proteins, which have <60% sequence identity to proteins with annotations already. Thus, the vital and challenging problem now is how to develop a method for SAFP, particularly for difficult proteins.'),
(81539, 2018, 'bio', 'NeSSie: a tool for the identification of approximate DNA sequence symmetries', 'Berselli, Michele|Lavezzo, Enrico|Toppo, Stefano', 'Department of Molecular Medicine, University of Padova, Padova, Italy.|Department of Molecular Medicine, University of Padova, Padova, Italy.|Department of Molecular Medicine, University of Padova, Padova, Italy.', 'Italy|Italy|Italy', 2, 'DNA|Genome|Genome, Bacterial|Genomics|Mycobacterium bovis|Nucleic Acid Conformation|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bty142', '29522153', 'Non-B DNA conformations play an important role in genomic rearrangements, structural three-dimensional organization and gene regulation. Many non-B DNA structures show symmetrical properties as palindromes and mirrors that can form hairpins, cruciform structures or triplexes. A comprehensive tool, capable to perform a fast genome wide search for exact and degenerate symmetrical patterns, is needed for further investigating nucleotide tracts potentially forming non-B DNA structures.'),
(81540, 2018, 'bio', 'PriLive: privacy-preserving real-time filtering for next-generation sequencing', 'Loka, Tobias P|Tausch, Simon H|Dabrowski, Piotr W|Radonic, Aleksandar|Nitsche, Andreas|Renard, Bernhard Y', 'Bioinformatics Division (MF 1), Department for Methods Development and Research Infrastructure.|Bioinformatics Division (MF 1), Department for Methods Development and Research Infrastructure.|Bioinformatics Division (MF 1), Department for Methods Development and Research Infrastructure.|Centre for Biological Threats and Special Pathogens: Highly Pathogenic Viruses (ZBS 1).|Centre for Biological Threats and Special Pathogens: Highly Pathogenic Viruses (ZBS 1).|Bioinformatics Division (MF 1), Department for Methods Development and Research Infrastructure.', '-|-|-|-|-|-', 0, 'Genetic Privacy|Genomics|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/bty128', '29522157', 'In next-generation sequencing, re-identification of individuals and other privacy-breaching strategies can be applied even for anonymized data. This also holds true for applications in which human DNA is acquired as a by-product, e.g. for viral or metagenomic samples from a human host. Conventional data protection strategies including cryptography and post-hoc filtering are only appropriate for the final and processed sequencing data. This can result in an insufficient level of data protection and a considerable time delay in the further analysis workflow.'),
(81541, 2018, 'bio', 'esATAC: an easy-to-use systematic pipeline for ATAC-seq data analysis', 'Wei, Zheng|Zhang, Wei|Fang, Huan|Li, Yanda|Wang, Xiaowo', 'Ministry of Education Key Laboratory of Bioinformatics, Center for Synthetic and System Biology, BNRist, Department of Automation, Tsinghua University, Beijing, China.|Ministry of Education Key Laboratory of Bioinformatics, Center for Synthetic and System Biology, BNRist, Department of Automation, Tsinghua University, Beijing, China.|Ministry of Education Key Laboratory of Bioinformatics, Center for Synthetic and System Biology, BNRist, Department of Automation, Tsinghua University, Beijing, China.|Ministry of Education Key Laboratory of Bioinformatics, Center for Synthetic and System Biology, BNRist, Department of Automation, Tsinghua University, Beijing, China.|Ministry of Education Key Laboratory of Bioinformatics, Center for Synthetic and System Biology, BNRist, Department of Automation, Tsinghua University, Beijing, China.', 'China|China|China|China|China', 11, 'Animals|Genomics|Humans|Mice|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bty141', '29522192', 'ATAC-seq is rapidly emerging as one of the major experimental approaches to probe chromatin accessibility genome-wide. Here, we present esATAC, a highly integrated easy-to-use R/Bioconductor package, for systematic ATAC-seq data analysis. It covers essential steps for full analyzing procedure, including raw data processing, quality control and downstream statistical analysis such as peak calling, enrichment analysis and transcription factor footprinting. esATAC supports one command line execution for preset pipelines and provides flexible interfaces for building customized pipelines.'),
(81542, 2018, 'bio', 'Optimality and identification of dynamic models in systems biology: an inverse optimal control framework', 'Tsiantis, Nikolaos|Balsa-Canto, Eva|Banga, Julio R', 'Bioprocess Engineering Group, Spanish National Research Council, IIM-CSIC Vigo, Spain.|Bioprocess Engineering Group, Spanish National Research Council, IIM-CSIC Vigo, Spain.|Bioprocess Engineering Group, Spanish National Research Council, IIM-CSIC Vigo, Spain.', 'Spain|Spain|Spain', 0, 'Algorithms|Janus Kinase 2|Models, Biological|STAT5 Transcription Factor|Signal Transduction|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/bty139', '29522196', 'Optimality principles have been used to explain many biological processes and systems. However, the functions being optimized are in general unknown a priori. Here we present an inverse optimal control framework for modeling dynamics in systems biology. The objective is to identify the underlying optimality principle from observed time-series data and simultaneously estimate unmeasured time-dependent inputs and time-invariant model parameters. As a special case, we also consider the problem of optimal simultaneous estimation of inputs and parameters from noisy data. After presenting a general statement of the inverse optimal control problem, and discussing special cases of interest, we outline numerical strategies which are scalable and robust.'),
(81543, 2018, 'bio', 'iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences', 'Chen, Zhen|Zhao, Pei|Li, Fuyi|Leier, André|Marquez-Lago, Tatiana T|Wang, Yanan|Webb, Geoffrey I|Smith, A Ian|Daly, Roger J|Chou, Kuo-Chen|Song, Jiangning', 'School of Basic Medical Science, Qingdao University, 38 Dengzhou Road, Qingdao, China.|State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences (CAAS), Anyang, China.|Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC, Australia.|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.|Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China.|Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC, Australia.|Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC, Australia.|Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC, Australia.|Gordon Life Science Institute, Boston, MA, USA.|Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC, Australia.', 'China|China|Australia|United States|United States|China|Australia|Australia|Australia|United States|Australia', 114, 'Machine Learning|Molecular Sequence Annotation|Peptides|Protein Conformation|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/bty140', '29528364', 'Structural and physiochemical descriptors extracted from sequence data have been widely used to represent sequences and predict structural, functional, expression and interaction profiles of proteins and peptides as well as DNAs/RNAs. Here, we present iFeature, a versatile Python-based toolkit for generating various numerical feature representation schemes for both protein and peptide sequences. iFeature is capable of calculating and extracting a comprehensive spectrum of 18 major sequence encoding schemes that encompass 53 different types of feature descriptors. It also allows users to extract specific amino acid properties from the AAindex database. Furthermore, iFeature integrates 12 different types of commonly used feature clustering, selection and dimensionality reduction algorithms, greatly facilitating training, analysis and benchmarking of machine-learning models. The functionality of iFeature is made freely available via an online web server and a stand-alone toolkit.'),
(81544, 2018, 'bio', 'METHCOMP: a special purpose compression platform for DNA methylation data', 'Peng, Jianhao|Milenkovic, Olgica|Ochoa, Idoia', 'Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA.|Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA.|Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA.', 'United States|United States|United States', 0, 'Algorithms|DNA Methylation|Data Compression|Genomics|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bty143', '29528370', 'DNA methylation is one of the most important epigenetic mechanisms in cells that exhibits a significant role in controlling gene expressions. Abnormal methylation patterns have been associated with cancer, imprinting disorders and repeat-instability diseases. As inexpensive bisulfite sequencing approaches have led to significant efforts in acquiring methylation data, problems of data storage and management have become increasingly important. The de facto compression method for methylation data is gzip, which is a general purpose compression algorithm that does not cater to the special format of methylation files. We propose METHCOMP, a new compression scheme tailor-made for bedMethyl files, which supports random access.'),
(81545, 2018, 'bio', 'Sensitive and robust assessment of ChIP-seq read distribution using a strand-shift profile', 'Nakato, Ryuichiro|Shirahige, Katsuhiko', 'Research Center for Epigenetic Disease, Institute of Molecular and Cellular Biosciences, University of Tokyo, 1-1-1 Yayoi, Bunkyo-Ku, Tokyo, Japan.|Research Center for Epigenetic Disease, Institute of Molecular and Cellular Biosciences, University of Tokyo, 1-1-1 Yayoi, Bunkyo-Ku, Tokyo, Japan.', 'Japan|Japan', 2, 'Animals|Chromatin Immunoprecipitation|DNA|High-Throughput Nucleotide Sequencing|Humans|Protein Binding|Reproducibility of Results|Sequence Analysis, DNA|Software|Transcription Factors|Yeasts', 'Journal Article', '10.1093/bioinformatics/bty137', '29528371', 'Chromatin immunoprecipitation followed by sequencing (ChIP-seq) can detect read-enriched DNA loci for point-source (e.g. transcription factor binding) and broad-source factors (e.g. various histone modifications). Although numerous quality metrics for ChIP-seq data have been developed, the peaks thus obtained are still difficult to assess with respect to signal-to-noise ratio (S/N) and the percentage of false positives.'),
(81546, 2018, 'bio', 'Kernelized rank learning for personalized drug recommendation', 'He, Xiao|Folkman, Lukas|Borgwardt, Karsten', 'Machine Learning and Computational Biology Lab, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.|Machine Learning and Computational Biology Lab, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.|Machine Learning and Computational Biology Lab, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.', 'Switzerland|Switzerland|Switzerland', 0, 'Antineoplastic Agents|Health Planning Guidelines|Humans|Machine Learning|Neoplasms|Precision Medicine', 'Journal Article', '10.1093/bioinformatics/bty132', '29528376', 'Large-scale screenings of cancer cell lines with detailed molecular profiles against libraries of pharmacological compounds are currently being performed in order to gain a better understanding of the genetic component of drug response and to enhance our ability to recommend therapies given a patients molecular profile. These comprehensive screens differ from the clinical setting in which (i) medical records only contain the response of a patient to very few drugs, (ii) drugs are recommended by doctors based on their expert judgment and (iii) selecting the most promising therapy is often more important than accurately predicting the sensitivity to all potential drugs. Current regression models for drug sensitivity prediction fail to account for these three properties.'),
(81547, 2018, 'bio', 'A novel computational approach for drug repurposing using systems biology', 'Peyvandipour, Azam|Saberian, Nafiseh|Shafi, Adib|Donato, Michele|Draghici, Sorin', 'Computer Science, Wayne State University, Detroit, MI, USA.|Computer Science, Wayne State University, Detroit, MI, USA.|Computer Science, Wayne State University, Detroit, MI, USA.|Computer Science, Wayne State University, Detroit, MI, USA.|Computer Science, Wayne State University, Detroit, MI, USA.', 'United States|United States|United States|United States|United States', 17, 'Drug Discovery|Drug Repositioning|Humans|Neoplasms|Systems Biology|Transcriptome', 'Journal Article', '10.1093/bioinformatics/bty133', '29534151', 'Identification of novel therapeutic effects for existing US Food and Drug Administration (FDA)-approved drugs, drug repurposing, is an approach aimed to dramatically shorten the drug discovery process, which is costly, slow and risky. Several computational approaches use transcriptional data to find potential repurposing candidates. The main hypothesis of such approaches is that if gene expression signature of a particular drug is opposite to the gene expression signature of a disease, that drug may have a potential therapeutic effect on the disease. However, this may not be optimal since it fails to consider the different roles of genes and their dependencies at the system level.'),
(81548, 2018, 'bio', 'NGSphy: phylogenomic simulation of next-generation sequencing data', 'Escalona, Merly|Rocha, Sara|Posada, David', 'Department of Biochemistry, Genetics and Immunology.|Department of Biochemistry, Genetics and Immunology.|Department of Biochemistry, Genetics and Immunology.', '-|-|-', 0, 'Biological Evolution|Computer Simulation|Genome|Genomics|High-Throughput Nucleotide Sequencing|Phylogeny|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bty146', '29534152', 'Advances in sequencing technologies have made it feasible to obtain massive datasets for phylogenomic inference, often consisting of large numbers of loci from multiple species and individuals. The phylogenomic analysis of next-generation sequencing (NGS) data requires a complex computational pipeline where multiple technical and methodological decisions are necessary that can influence the final tree obtained, like those related to coverage, assembly, mapping, variant calling and/or phasing.'),
(81549, 2018, 'bio', 'ddPCRclust: an R package and Shiny app for automated analysis of multiplexed ddPCR data', 'Brink, Benedikt G|Meskas, Justin|Brinkman, Ryan R', 'International Research Training Group Computational Methods for the Analysis of the Diversity and Dynamics of Genomes and Biodata Mining Group, Faculty of Technology and Center for Biotechnology, Bielefeld University, Bielefeld, Germany.|Terry Fox Laboratory, BC Cancer Agency, Vancouver, Canada.|Terry Fox Laboratory, BC Cancer Agency, Vancouver, Canada.', 'Germany|Canada|Canada', 0, 'Algorithms|Computational Biology|DNA|Polymerase Chain Reaction|Software', 'Journal Article', '10.1093/bioinformatics/bty136', '29534153', 'Droplet digital PCR (ddPCR) is an emerging technology for quantifying DNA. By partitioning the target DNA into xe2x88xbc20xe2x80x89000 droplets, each serving as its own PCR reaction compartment, a very high sensitivity of DNA quantification can be achieved. However, manual analysis of the data is time consuming and algorithms for automated analysis of non-orthogonal, multiplexed ddPCR data are unavailable, presenting a major bottleneck for the advancement of ddPCR transitioning from low-throughput to high-throughput.'),
(81550, 2018, 'bio', 'Exploiting and assessing multi-source data for supervised biomedical named entity recognition', 'Galea, Dieter|Laponogov, Ivan|Veselkov, Kirill', 'Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK.|Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK.|Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Data Mining|Databases, Factual|Natural Language Processing|PubMed|Supervised Machine Learning', 'Journal Article', '10.1093/bioinformatics/bty152', '29538614', 'Recognition of biomedical entities from scientific text is a critical component of natural language processing and automated information extraction platforms. Modern named entity recognition approaches rely heavily on supervised machine learning techniques, which are critically dependent on annotated training corpora. These approaches have been shown to perform well when trained and tested on the same source. However, in such scenario, the performance and evaluation of these models may be optimistic, as such models may not necessarily generalize to independent corpora, resulting in potential non-optimal entity recognition for large-scale tagging of widely diverse articles in databases such as PubMed.'),
(81551, 2018, 'bio', 'Nimbus: a design-driven analyses suite for amplicon-based NGS data', 'Brouwer, R W W|van den Hout, M C G N|Kockx, C E M|Brosens, E|Eussen, B|de Klein, A|Sleutels, F|van IJcken, W F J', 'Center for Biomics, Department of Cell Biology, Erasmus MC, 3000CA Rotterdam, The Netherlands.|Center for Biomics, Department of Cell Biology, Erasmus MC, 3000CA Rotterdam, The Netherlands.|Center for Biomics, Department of Cell Biology, Erasmus MC, 3000CA Rotterdam, The Netherlands.|Department of Clinical Genetics, Erasmus MC, 3000CA Rotterdam, The Netherlands.|Department of Clinical Genetics, Erasmus MC, 3000CA Rotterdam, The Netherlands.|Department of Clinical Genetics, Erasmus MC, 3000CA Rotterdam, The Netherlands.|Center for Biomics, Department of Cell Biology, Erasmus MC, 3000CA Rotterdam, The Netherlands.|Center for Biomics, Department of Cell Biology, Erasmus MC, 3000CA Rotterdam, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Female|High-Throughput Nucleotide Sequencing|Humans|Male|Polymorphism, Single Nucleotide|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bty145', '29538618', 'PCR-based DNA enrichment followed by massively parallel sequencing is a straightforward and cost effective method to sequence genes up to high depth. The full potential of amplicon-based sequencing assays is currently not achieved as analysis methods do not take into account the source amplicons of the detected variants. Tracking the source amplicons has the potential to identify systematic biases, enhance variant calling and improve the designs of future assays.'),
(81552, 2018, 'bio', 'Repeat-aware evaluation of scaffolding tools', 'Mandric, Igor|Knyazev, Sergey|Zelikovsky, Alex', 'Department of Computer Science, Georgia State University, Atlanta, GA, USA.|Department of Computer Science, Georgia State University, Atlanta, GA, USA.|Department of Computer Science, Georgia State University, Atlanta, GA, USA.', 'Georgia|Georgia|Georgia', 3, 'Algorithms|Bacteria|Contig Mapping|Eukaryota|Genome|Genomics|Humans|Repetitive Sequences, Nucleic Acid|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bty131', '29547882', 'Genomic sequences are assembled into a variable, but large number of contigs that should be scaffolded (ordered and oriented) for facilitating comparative or functional analysis. Finding scaffolding is computationally challenging due to misassemblies, inconsistent coverage across the genome and long repeats. An accurate assessment of scaffolding tools should take into account multiple locations of the same contig on the reference scaffolding rather than matching a repeat to a single best location. This makes mapping of inferred scaffoldings onto the reference a computationally challenging problem. This paper formulates the repeat-aware scaffolding evaluation problem, which is to find a mapping of the inferred scaffolding onto the reference maximizing number of correct links and proposes a scalable algorithm capable of handling large whole-genome datasets. Our novel scaffolding validation framework has been applied to assess the most of state-of-the-art scaffolding tools on the representative subset of Genome Assembly Golden-Standard Evaluations (GAGE) datasets and some novel simulated datasets.'),
(81553, 2018, 'bio', 'REStLESS: automated translation of glycan sequences from residue-based notation to SMILES and atomic coordinates', 'Chernyshov, Ivan Yu|Toukach, Philip V', 'All-Russia Research Institute of Agricultural Biotechnology, Laboratory of Plant Stress Tolerance, Russian Academy of Sciences, Moscow, Russia.|Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Laboratory of Complex and Nano-scaled Catalysts, Moscow, Russia.', '-|-', 0, 'Algorithms|Computational Biology|Databases, Factual|Molecular Conformation|Polysaccharides', 'Journal Article', '10.1093/bioinformatics/bty168', '29547883', 'Glycans and glycoconjugates are usually recorded in dedicated databases in residue-based notations. Only a few of them can be converted into chemical (atom-based) formats highly demanded in conformational and biochemical studies. In this work, we present a tool for translation from a residue-based glycan notation to SMILES.'),
(81554, 2018, 'bio', 'Accurate mapping of tRNA reads', 'Hoffmann, Anne|Fallmann, Jörg|Vilardo, Elisa|Mörl, Mario|Stadler, Peter F|Amman, Fabian', 'Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Laboratory of Complex and Nano-scaled Catalysts, Moscow, Russia.|Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Laboratory of Complex and Nano-scaled Catalysts, Moscow, Russia.|Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Laboratory of Complex and Nano-scaled Catalysts, Moscow, Russia.|Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Laboratory of Complex and Nano-scaled Catalysts, Moscow, Russia.|Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Laboratory of Complex and Nano-scaled Catalysts, Moscow, Russia.|Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Laboratory of Complex and Nano-scaled Catalysts, Moscow, Russia.', '-|-|-|-|-|-', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty118', '29547898', '-'),
(81555, 2018, 'bio', 'SMMB: a stochastic Markov blanket framework strategy for epistasis detection in GWAS', 'Niel, Clément|Sinoquet, Christine|Dina, Christian|Rocheleau, Ghislain', 'Laboratoire des Sciences du Numérique de Nantes (LS2N), Centre National de la recherche Scientifique UMR6004, University of Nantes, Nantes, France.|Laboratoire des Sciences du Numérique de Nantes (LS2N), Centre National de la recherche Scientifique UMR6004, University of Nantes, Nantes, France.|Institut du Thorax, Institut National de la Santé et de la Recherche Médicale UMR 1087, Centre National de la Recherche Scientifique UMR 6291, University of Nantes, Nantes, France.|European Genomic Institute for Diabetes FR3508, Centre National de la Recherche Scientifique UMR 8199, Lille 2 University, Lille, France.', 'France|France|France|France', 4, 'Algorithms|Bayes Theorem|Epistasis, Genetic|Genome-Wide Association Study|Humans|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/bty154', '29547902', 'Large scale genome-wide association studies (GWAS) are tools of choice for discovering associations between genotypes and phenotypes. To date, many studies rely on univariate statistical tests for association between the phenotype and each assayed single nucleotide polymorphism (SNP). However, interaction between SNPs, namely epistasis, must be considered when tackling the complexity of underlying biological mechanisms. Epistasis analysis at large scale entails a prohibitive computational burden when addressing the detection of more than two interacting SNPs. In this paper, we introduce a stochastic causal graph-based method, SMMB, to analyze epistatic patterns in GWAS data.'),
(81556, 2018, 'bio', 'Bastion6: a bioinformatics approach for accurate prediction of type VI secreted effectors', 'Wang, Jiawei|Yang, Bingjiao|Leier, Andrxc3xa9|Marquez-Lago, Tatiana T|Hayashida, Morihiro|Rocker, Andrea|Zhang, Yanju|Akutsu, Tatsuya|Chou, Kuo-Chen|Strugnell, Richard A|Song, Jiangning|Lithgow, Trevor', 'Biomedicine Discovery Institute and Department of Microbiology, Monash University, Clayton, VIC, Australia.|Bioinformatics Group, School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China.|Department of Genetics, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA.|Department of Genetics, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA.|National Institute of Technology, Matsue College, Matsue, Shimane, Japan.|Biomedicine Discovery Institute and Department of Microbiology, Monash University, Clayton, VIC, Australia.|Bioinformatics Group, School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto, Japan.|Gordon Life Science Institute, Boston, MA, USA.|Department of Microbiology and Immunology and Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Parkville, VIC, Australia.|Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology.|Biomedicine Discovery Institute and Department of Microbiology, Monash University, Clayton, VIC, Australia.', 'Australia|China|United States|United States|Japan|Australia|China|Japan|United States|Australia|United States|Australia', 0, 'Amino Acid Sequence|Bacterial Proteins|Computational Biology|Gram-Negative Bacteria|Internet|Machine Learning|Sequence Analysis, DNA|Sequence Analysis, Protein|Software|Type VI Secretion Systems', 'Journal Article', '10.1093/bioinformatics/bty155', '29547915', 'Many Gram-negative bacteria use type VI secretion systems (T6SS) to export effector proteins into adjacent target cells. These secreted effectors (T6SEs) play vital roles in the competitive survival in bacterial populations, as well as pathogenesis of bacteria. Although various computational analyses have been previously applied to identify effectors secreted by certain bacterial species, there is no universal method available to accurately predict T6SS effector proteins from the growing tide of bacterial genome sequence data.'),
(81557, 2017, 'bio', 'ISMB/ECCB 2017 proceedings', 'Beerenwinkel, Niko|Bromberg, Yana', 'ETH Zurich, Basel, Switzerland.|Rutgers University, New Brunswick, NJ, USA.', 'Switzerland|United States', 2, '-', 'Editorial', '10.1093/bioinformatics/btx321', '29547916', '-'),
(81558, 2018, 'bio', 'CoABind: a novel algorithm for Coenzyme A (CoA)- and CoA derivatives-binding residues prediction', 'Meng, Qiaozhen|Peng, Zhenling|Yang, Jianyi', 'Center for Applied Mathematics, Tianjin University, Tianjin, China.|Center for Applied Mathematics, Tianjin University, Tianjin, China.|School of Mathematical Sciences, Nankai University, Tianjin, China.', 'China|China|China', 6, 'Acetyltransferases|Bacterial Proteins|Coenzyme A|Computational Biology|Models, Molecular|Protein Binding|Protein Conformation|Proteins|Sequence Analysis, Protein|Software|Support Vector Machine|Vibrio cholerae', 'Journal Article', '10.1093/bioinformatics/bty162', '29547921', 'Coenzyme A (CoA)-protein binding plays an important role in various cellular functions and metabolic pathways. However, no computational methods can be employed for CoA-binding residues prediction.'),
(81559, 2018, 'bio', 'Network-based integration of multi-omics data for prioritizing cancer genes', 'Dimitrakopoulos, Christos|Hindupur, Sravanth Kumar|Häfliger, Luca|Behr, Jonas|Montazeri, Hesam|Hall, Michael N|Beerenwinkel, Niko', 'Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.|Biozentrum, University of Basel, Basel, Switzerland.|Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.|Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.|Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.|Biozentrum, University of Basel, Basel, Switzerland.|Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 0, 'Chromosome Aberrations|Computational Biology|DNA Methylation|Gene Expression Regulation|Genes, Neoplasm|Genomics|Humans|MicroRNAs|Mutation|Neoplasms|Proteome|Software|Transcriptome', 'Journal Article', '10.1093/bioinformatics/bty148', '29547932', 'Several molecular events are known to be cancer-related, including genomic aberrations, hypermethylation of gene promoter regions and differential expression of microRNAs. These aberration events are very heterogeneous across tumors and it is poorly understood how they affect the molecular makeup of the cell, including the transcriptome and proteome. Protein interaction networks can help decode the functional relationship between aberration events and changes in gene and protein expression.'),
(81560, 2018, 'bio', 'Using combined evidence from replicates to evaluate ChIP-seq peaks', 'Jalili, Vahid|Matteucci, Matteo|Masseroli, Marco|Morelli, Marco J', 'Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.|Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.|Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.|Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty119', '29547940', '-'),
(81561, 2018, 'bio', 'A new method for the high-precision assessment of tumor changes in response to treatment', 'Tar, P D|Thacker, N A|Babur, M|Watson, Y|Cheung, S|Little, R A|Gieling, R G|Williams, K J|OConnor, J P B', 'Division of Informatics, Imaging and Data Science, Manchester Pharmacy School, Manchester, UK.|Division of Informatics, Imaging and Data Science, Manchester Pharmacy School, Manchester, UK.|Division of Pharmacy and Optometry, Manchester Pharmacy School, Manchester, UK.|Division of Informatics, Imaging and Data Science, Manchester Pharmacy School, Manchester, UK.|Division of Informatics, Imaging and Data Science, Manchester Pharmacy School, Manchester, UK.|Division of Informatics, Imaging and Data Science, Manchester Pharmacy School, Manchester, UK.|Division of Pharmacy and Optometry, Manchester Pharmacy School, Manchester, UK.|Division of Pharmacy and Optometry, Manchester Pharmacy School, Manchester, UK.|Division of Cancer Sciences, University of Manchester.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Animals|Cell Line, Tumor|Colorectal Neoplasms|Computational Biology|Female|HCT116 Cells|Humans|Linear Models|Magnetic Resonance Imaging|Mice|Models, Statistical|Neoplasms|Sample Size|Software|Treatment Outcome|Xenograft Model Antitumor Assays', 'Journal Article', '10.1093/bioinformatics/bty115', '29547950', 'Imaging demonstrates that preclinical and human tumors are heterogeneous, i.e. a single tumor can exhibit multiple regions that behave differently during both development and also in response to treatment. The large variations observed in control group, tumors can obscure detection of significant therapeutic effects due to the ambiguity in attributing causes of change. This can hinder development of effective therapies due to limitations in experimental design rather than due to therapeutic failure. An improved method to model biological variation and heterogeneity in imaging signals is described. Specifically, linear Poisson modeling (LPM) evaluates changes in apparent diffusion co-efficient between baseline and 72xe2x80x89h after radiotherapy, in two xenograft models of colorectal cancer. The statistical significance of measured changes is compared to those attainable using a conventional t-test analysis on basic apparent diffusion co-efficient distribution parameters.'),
(81562, 2018, 'bio', 'Data mining of effect sizes from PubMed abstracts: a cross-study conceptual replication', 'Monsarrat, Paul|Vergnes, Jean-Noel', 'Department of Anatomical Sciences and Radiology, Dental Faculty, Paul Sabatier University, Toulouse University Hospital (CHU de Toulouse), Toulouse, France.|Department of Epidemiology and Public Health, Dental Faculty, Paul Sabatier University, Toulouse University Hospital (CHU de Toulouse), Toulouse, France.', 'France|France', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty153', '29547980', '-'),
(81563, 2018, 'bio', 'NanoPack: visualizing and processing long-read sequencing data', 'De Coster, Wouter|DHert, Svenn|Schultz, Darrin T|Cruts, Marc|Van Broeckhoven, Christine', 'Neurodegenerative Brain Diseases Group, VIB & University of Antwerp, Antwerp, Belgium.|Bioinformatics, Neuromics Support Facility, Center for Molecular Neurology, VIB & University of Antwerp, Antwerp, Belgium.|Department of Biomolecular Engineering and Bioinformatics, University of California Santa Cruz, Santa Cruz, CA, USA.|Neurodegenerative Brain Diseases Group, VIB & University of Antwerp, Antwerp, Belgium.|Neurodegenerative Brain Diseases Group, VIB & University of Antwerp, Antwerp, Belgium.', 'Belgium|Belgium|United States|Belgium|Belgium', 150, 'Escherichia coli|High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bty149', '29547981', 'Here we describe NanoPack, a set of tools developed for visualization and processing of long-read sequencing data from Oxford Nanopore Technologies and Pacific Biosciences.'),
(81564, 2018, 'bio', 'NMRNet: a deep learning approach to automated peak picking of protein NMR spectra', 'Klukowski, Piotr|Augoff, Michal|Zieba, Maciej|Drwal, Maciej|Gonczarek, Adam|Walczak, Michal J', 'Department of Computer Science, Faculty of Computer Science and Management, Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, Wroclaw, Poland.|Department of Computer Science, Faculty of Computer Science and Management, Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, Wroclaw, Poland.|Department of Computer Science, Faculty of Computer Science and Management, Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, Wroclaw, Poland.|Department of Computer Science, Faculty of Computer Science and Management, Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, Wroclaw, Poland.|Department of Computer Science, Faculty of Computer Science and Management, Wrocxc5x82aw University of Science and Technology, Wybrzeze Wyspianskiego 27, Wrocxc5x82aw, Poland.|Captor Therapeutics Ltd., ul. Dunska 11, Wrocxc5x82aw, Poland.', 'Poland|Poland|Poland|Poland|Poland|Poland', 0, 'Deep Learning|Macromolecular Substances|Nuclear Magnetic Resonance, Biomolecular|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/bty134', '29547986', 'Automated selection of signals in protein NMR spectra, known as peak picking, has been studied for over 20xe2x80x89years, nevertheless existing peak picking methods are still largely deficient. Accurate and precise automated peak picking would accelerate the structure calculation, and analysis of dynamics and interactions of macromolecules. Recent advancement in handling big data, together with an outburst of machine learning techniques, offer an opportunity to tackle the peak picking problem substantially faster than manual picking and on par with human accuracy. In particular, deep learning has proven to systematically achieve human-level performance in various recognition tasks, and thus emerges as an ideal tool to address automated identification of NMR signals.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(81565, 2018, 'bio', 'RapidRMSD: rapid determination of RMSDs corresponding to motions of flexible molecules', 'Neveu, Emilie|Popov, Petr|Hoffmann, Alexandre|Migliosi, Angelo|Besseron, Xavier|Danoy, Grégoire|Bouvry, Pascal|Grudinin, Sergei', 'Inria/Univ. Grenoble Alpes/LJK-CNRS, Grenoble, France.|Inria/Univ. Grenoble Alpes/LJK-CNRS, Grenoble, France.|Inria/Univ. Grenoble Alpes/LJK-CNRS, Grenoble, France.|Faculté des Sciences, de la Technologie et de la Communication, University of Luxembourg, Luxembourg, Luxembourg.|Faculté des Sciences, de la Technologie et de la Communication, University of Luxembourg, Luxembourg, Luxembourg.|Faculté des Sciences, de la Technologie et de la Communication, University of Luxembourg, Luxembourg, Luxembourg.|Faculté des Sciences, de la Technologie et de la Communication, University of Luxembourg, Luxembourg, Luxembourg.|Inria/Univ. Grenoble Alpes/LJK-CNRS, Grenoble, France.', 'France|France|France|Luxembourg|Luxembourg|Luxembourg|Luxembourg|France', 6, 'Algorithms|Computational Biology|Molecular Dynamics Simulation|Motion|Pliability|Principal Component Analysis|Protein Conformation|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/bty160', '29554205', 'The root mean square deviation (RMSD) is one of the most used similarity criteria in structural biology and bioinformatics. Standard computation of the RMSD has a linear complexity with respect to the number of atoms in a molecule, making RMSD calculations time-consuming for the large-scale modeling applications, such as assessment of molecular docking predictions or clustering of spatially proximate molecular conformations. Previously, we introduced the RigidRMSD algorithm to compute the RMSD corresponding to the rigid-body motion of a molecule. In this study, we go beyond the limits of the rigid-body approximation by taking into account conformational flexibility of the molecule. We model the flexibility with a reduced set of collective motions computed with e.g. normal modes or principal component analysis.'),
(81566, 2018, 'bio', 'Arioc: GPU-accelerated alignment of short bisulfite-treated reads', 'Wilton, Richard|Li, Xin|Feinberg, Andrew P|Szalay, Alexander S', 'Department of Physics and Astronomy, Johns Hopkins University, Baltimore, MD, USA.|School of Medicine, Sun Yat-sen University, Guangdong, China.|Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.|Department of Physics and Astronomy, Johns Hopkins University, Baltimore, MD, USA.', 'United States|China|United States|United States', 3, 'Algorithms|Genomics|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Software|Sulfites', 'Journal Article', '10.1093/bioinformatics/bty167', '29554207', 'The alignment of bisulfite-treated DNA sequences (BS-seq reads) to a large genome involves a significant computational burden beyond that required to align non-bisulfite-treated reads. In the analysis of BS-seq data, this can present an important performance bottleneck that can be mitigated by appropriate algorithmic and software-engineering improvements. One strategy is to modify the read-alignment algorithms by integrating the logic related to BS-seq alignment, with the goal of making the software implementation amenable to optimizations that lead to higher speed and greater sensitivity than might otherwise be attainable.'),
(81567, 2018, 'bio', 'SCRAM: a pipeline for fast index-free small RNA read alignment and visualization', 'Fletcher, Stephen J|Boden, Mikael|Mitter, Neena|Carroll, Bernard J', 'School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia, QLD, Australia.|School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia, QLD, Australia.|Centre for Horticultural Science, Queensland Alliance for Agriculture and Food Innovation, St Lucia, QLD, Australia.|School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia, QLD, Australia.', 'Australia|Australia|Australia|Australia', 0, 'Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/bty161', '29554210', 'Small RNAs play key roles in gene regulation, defense against viral pathogens and maintenance of genome stability, though many aspects of their biogenesis and function remain to be elucidated. SCRAM (Small Complementary RNA Mapper) is a novel, simple-to-use short read aligner and visualization suite that enhances exploration of small RNA datasets.'),
(81568, 2018, 'bio', 'DeepSol: a deep learning framework for sequence-based protein solubility prediction', 'Khurana, Sameer|Rawi, Reda|Kunji, Khalid|Chuang, Gwo-Yu|Bensmail, Halima|Mall, Raghvendra', 'Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.|Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institute of Health, Bethesda, MD, USA.|Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.|Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institute of Health, Bethesda, MD, USA.|Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.|Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.', 'United States|United States|Qatar|United States|Qatar|Qatar', 0, 'Amino Acid Sequence|Computational Biology|Computer Simulation|Deep Learning|Proteins|Solubility', 'Journal Article', '10.1093/bioinformatics/bty166', '29554211', 'Protein solubility plays a vital role in pharmaceutical research and production yield. For a given protein, the extent of its solubility can represent the quality of its function, and is ultimately defined by its sequence. Thus, it is imperative to develop novel, highly accurate in silico sequence-based protein solubility predictors. In this work we propose, DeepSol, a novel Deep Learning-based protein solubility predictor. The backbone of our framework is a convolutional neural network that exploits k-mer structure and additional sequence and structural features extracted from the protein sequence.'),
(81569, 2018, 'bio', 'SCOTCH: subtype A coreceptor tropism classification in HIV-1', 'Löchel, Hannah F|Riemenschneider, Mona|Frishman, Dmitrij|Heider, Dominik', 'Department of Mathematics and Computer Science, Philipps-University of Marburg, Marburg, Germany.|Department of Bioinformatics, TUM Campus Straubing, Straubing, Germany.|Department of Genome-Oriented Bioinformatics, Technical University of Munich, Freising, Germany.|Department of Mathematics and Computer Science, Philipps-University of Marburg, Marburg, Germany.', 'Germany|Germany|Germany|Germany', 0, 'CCR5 Receptor Antagonists|Computational Biology|HIV Envelope Protein gp120|HIV Infections|HIV-1|Humans|Receptors, CCR5|Receptors, CXCR4|Sequence Analysis, Protein|Software|Viral Tropism', 'Journal Article', '10.1093/bioinformatics/bty170', '29554213', 'The V3 loop of the gp120 glycoprotein of the Human Immunodeficiency Virus 1 (HIV-1) is considered to be responsible for viral coreceptor tropism. gp120 interacts with the CD4 receptor of the host cell and subsequently V3 binds either CCR5 or CXCR4. Due to the fact that the CCR5 coreceptor is targeted by entry inhibitors, a reliable prediction of the coreceptor usage of HIV-1 is of great interest for antiretroviral therapy. Although several methods for the prediction of coreceptor tropism are available, almost all of them have been developed based on only subtype B sequences, and it has been shown in several studies that the prediction of non-B sequences, in particular subtype A sequences, are less reliable. Thus, the aim of the current study was to develop a reliable prediction model for subtype A viruses.'),
(81570, 2018, 'bio', 'Integrating long-range connectivity information into de Bruijn graphs', 'Turner, Isaac|Garimella, Kiran V|Iqbal, Zamin|McVean, Gil', 'Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.|Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.|Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.|Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 15, 'Algorithms|Data Visualization|Genomics|High-Throughput Nucleotide Sequencing|Humans|Klebsiella pneumoniae|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bty157', '29554215', 'The de Bruijn graph is a simple and efficient data structure that is used in many areas of sequence analysis including genome assembly, read error correction and variant calling. The data structure has a single parameter k, is straightforward to implement and is tractable for large genomes with high sequencing depth. It also enables representation of multiple samples simultaneously to facilitate comparison. However, unlike the string graph, a de Bruijn graph does not retain long range information that is inherent in the read data. For this reason, applications that rely on de Bruijn graphs can produce sub-optimal results given their input data.'),
(81571, 2018, 'bio', 'CGManalyzer: an R package for analyzing continuous glucose monitoring studies', 'Zhang, Xiaohua Douglas|Zhang, Zhaozhi|Wang, Dandan', 'Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.|Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.|Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.', 'United Kingdom|United Kingdom|United Kingdom', 1, '-', 'Journal Article', '10.1093/bioinformatics/bty144', '29554216', '-'),
(81572, 2018, 'bio', 'CMV: visualization for RNA and protein family models and their comparisons', 'Eggenhofer, Florian|Hofacker, Ivo L|Backofen, Rolf|Höner Zu Siederdissen, Christian', 'Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany.|Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria.|Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany.|Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria.', 'Germany|Austria|Germany|Austria', 0, 'Nucleic Acid Conformation|Protein Conformation|RNA|Sequence Alignment|Sequence Analysis, Protein|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/bty158', '29554223', 'A standard method for the identification of novel RNAs or proteins is homology search via probabilistic models. One approach relies on the definition of families, which can be encoded as covariance models (CMs) or Hidden Markov Models (HMMs). While being powerful tools, their complexity makes it tedious to investigate them in their (default) tabulated form. This specifically applies to the interpretation of comparisons between multiple models as in family clans. The Covariance model visualization tools (CMV) visualize CMs or HMMs to: I) Obtain an easily interpretable representation of HMMs and CMs, II) Put them in context with the structural sequence alignments they have been created from, III) Investigate results of model comparisons and highlight regions of interest.'),
(81573, 2018, 'bio', 'ADPredict: ADP-ribosylation site prediction based on physicochemical and structural descriptors', 'Lo Monte, Matteo|Manelfi, Candida|Gemei, Marica|Corda, Daniela|Beccari, Andrea Rosario', 'Institute of Protein Biochemistry, National Research Council, Naples, Italy.|Dompé Farmaceutici SpA, LAquila.|Dompé Farmaceutici SpA, LAquila.|Institute of Protein Biochemistry, National Research Council, Naples, Italy.|Institute of Protein Biochemistry, National Research Council, Naples, Italy.', 'Italy|-|-|Italy|Italy', 7, 'ADP-Ribosylation|Computational Biology|Humans|Machine Learning|Models, Molecular|Protein Structure, Secondary|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/bty159', '29554239', 'ADP-ribosylation is a post-translational modification (PTM) implicated in several crucial cellular processes, ranging from regulation of DNA repair and chromatin structure to cell metabolism and stress responses. To date, a complete understanding of ADP-ribosylation targets and their modification sites in different tissues and disease states is still lacking. Identification of ADP-ribosylation sites is required to discern the molecular mechanisms regulated by this modification. This motivated us to develop a computational tool for the prediction of ADP-ribosylated sites.'),
(81574, 2018, 'bio', 'GenomeDISCO: a concordance score for chromosome conformation capture experiments using random walks on contact map graphs', 'Ursu, Oana|Boley, Nathan|Taranova, Maryna|Wang, Y X Rachel|Yardimci, Galip Gurkan|Stafford Noble, William|Kundaje, Anshul', 'Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.|Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.|Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.|Department of Statistics, Stanford University, Stanford, CA, USA.|Department of Genome Sciences, University of Washington, WA, USA.|Department of Genome Sciences, University of Washington, WA, USA.|Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.', 'United States|United States|United States|United States|United States|United States|United States', 9, 'Cell Line|Chromatin|Computational Biology|Humans|Molecular Conformation|Reproducibility of Results|Software', 'Journal Article', '10.1093/bioinformatics/bty164', '29554289', 'The three-dimensional organization of chromatin plays a critical role in gene regulation and disease. High-throughput chromosome conformation capture experiments such as Hi-C are used to obtain genome-wide maps of three-dimensional chromatin contacts. However, robust estimation of data quality and systematic comparison of these contact maps is challenging due to the multi-scale, hierarchical structure of chromatin contacts and the resulting properties of experimental noise in the data. Measuring concordance of contact maps is important for assessing reproducibility of replicate experiments and for modeling variation between different cellular contexts.'),
(81575, 2018, 'bio', 'ALPHA: a toolkit for Automated Local PHylogenomic Analyses', 'Elworth, R A Leo|Allen, Chabrielle|Benedict, Travis|Dulworth, Peter|Nakhleh, Luay', 'Computer Science, Rice University, Houston, USA.|Computer Science, Rice University, Houston, USA.|Computer Science, Rice University, Houston, USA.|Computer Science, Rice University, Houston, USA.|Computer Science, Rice University, Houston, USA.', 'United States|United States|United States|United States|United States', 0, 'Genealogy and Heraldry|Genome|Genomics|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/bty173', '29562324', 'The evolutionary histories of individual regions across a genomic alignment-called local genealogies-can differ from each other, due to processes such as recombination. Elucidating and analyzing these local genealogies are important for a large number of inference tasks, including those pertaining to species phylogenies, evolutionary processes and trait mapping. In this paper, we present a toolkit for automated local phylogenomic analyses, or ALPHA. The purpose of this toolkit is to provide a wide array of functionalities for automated inference of local genealogies as well as analyses based on these local genealogies. The toolkit uses sliding windows to construct local genealogies and can compute a wide array of local phylogeny based statistics, such as the D-statistic. The toolkit comes with a graphical user interface and several import/export functionalities. Over the last few decades, much emphasis in phylogenomics has been put on developing tools for inferring species phylogenies. This toolkit complements those efforts by emphasizing the local aspect of phylogenomics.'),
(81576, 2018, 'bio', 'accuMUlate: a mutation caller designed for mutation accumulation experiments', 'Winter, David J|Wu, Steven H|Howell, Abigail A|Azevedo, Ricardo B R|Zufall, Rebecca A|Cartwright, Reed A', 'The Biodesign Institute, Arizona State University, Tempe, AZ, USA.|The Biodesign Institute, Arizona State University, Tempe, AZ, USA.|The Biodesign Institute, Arizona State University, Tempe, AZ, USA.|Department of Biology and Biochemistry, University of Houston, Houston, TX, USA.|Department of Biology and Biochemistry, University of Houston, Houston, TX, USA.|The Biodesign Institute, Arizona State University, Tempe, AZ, USA.', 'United States|United States|United States|United States|United States|United States', 1, 'Arabidopsis|Computational Biology|Genomics|Mutation Accumulation|Software|Whole Genome Sequencing', 'Journal Article', '10.1093/bioinformatics/bty165', '29566129', 'Mutation accumulation (MA) is the most widely used method for directly studying the effects of mutation. By sequencing whole genomes from MA lines, researchers can directly study the rate and molecular spectra of spontaneous mutations and use these results to understand how mutation contributes to biological processes. At present there is no software designed specifically for identifying mutations from MA lines. Here we describe accuMUlate, a probabilistic mutation caller that reflects the design of a typical MA experiment while being flexible enough to accommodate properties unique to any particular experiment. Availability and implementation accuMUlate is available from https://github.com/dwinter/accuMUlate.'),
(81577, 2018, 'bio', 'QuimP: analyzing transmembrane signalling in highly deformable cells', 'Baniukiewicz, Piotr|Collier, Sharon|Bretschneider, Till', 'Department of Computer Science & Zeeman Institute, University of Warwick, Coventry, UK.|Department of Computer Science & Zeeman Institute, University of Warwick, Coventry, UK.|Department of Computer Science & Zeeman Institute, University of Warwick, Coventry, UK.', 'United Kingdom|United Kingdom|United Kingdom', 4, 'Animals|Cell Membrane|Computational Biology|Humans|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/bty169', '29566132', 'Transmembrane signalling plays important physiological roles, with G protein-coupled cell surface receptors being particularly important therapeutic targets. Fluorescent proteins are widely used to study signalling, but analyses of image time series can be challenging, in particular when cells change shape. QuimP software semi-automatically tracks spatio-temporal patterns of fluorescence at the cell membrane at high spatial resolution. This makes it a unique tool for studying transmembrane signalling, particularly during cell migration in immune or cancer cells for example.'),
(81578, 2018, 'bio', 'EWAS: epigenome-wide association study software 20', 'Xu, Jing|Zhao, Linna|Liu, Di|Hu, Simeng|Song, Xiuling|Li, Jin|Lv, Hongchao|Duan, Lian|Zhang, Mingming|Jiang, Qinghua|Liu, Guiyou|Jin, Shuilin|Liao, Mingzhi|Zhang, Meng|Feng, Rennan|Kong, Fanwu|Xu, Liangde|Jiang, Yongshuai', 'College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China.|Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China.|Department of Mathematics, Harbin Institute of Technology, Harbin, China.|College of Life Science, Northwest A&F University, Yangling, Shaanxi, China.|Training Center for Students Innovation and Entrepreneurship Education, Harbin Medical University, Harbin, China.|Training Center for Students Innovation and Entrepreneurship Education, Harbin Medical University, Harbin, China.|Department of Nephrology, The Second Affiliated Hospital, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.', 'China|China|China|China|China|China|China|China|China|China|China|China|China|China|China|China|Chin', 0, 'DNA Methylation|Epigenesis, Genetic|Epigenomics|Genome-Wide Association Study|Phenotype|Software', 'Journal Article', '10.1093/bioinformatics/bty163', '29566144', 'With the development of biotechnology, DNA methylation data showed exponential growth. Epigenome-wide association study (EWAS) provide a systematic approach to uncovering epigenetic variants underlying common diseases/phenotypes. But the EWAS software has lagged behind compared with genome-wide association study (GWAS). To meet the requirements of users, we developed a convenient and useful software, EWAS2.0.'),
(81579, 2018, 'bio', 'iterClust: a statistical framework for iterative clustering analysis', 'Ding, Hongxu|Wang, Wanxin|Califano, Andrea', 'Department of Systems Biology, Columbia University, New York, NY, USA.|Department of Bioengineering, Stanford University, Stanford, CA, USA.|Department of Systems Biology, Columbia University, New York, NY, USA.', 'United States|United States|United States', 0, 'Cluster Analysis|Software', 'Journal Article', '10.1093/bioinformatics/bty176', '29579153', 'In a scenario where populations A, B1 and B2 (subpopulations of B) exist, pronounced differences between A and B may mask subtle differences between B1 and B2.'),
(81580, 2018, 'bio', 'A Bayesian framework for multiple trait colocalization from summary association statistics', 'Giambartolomei, Claudia|Zhenli Liu, Jimmy|Zhang, Wen|Hauberg, Mads|Shi, Huwenbo|Boocock, James|Pickrell, Joe|Jaffe, Andrew E|CommonMind Consortium|Pasaniuc, Bogdan|Roussos, Panos', 'Department of Pathology and Laboratory Medicine, University of California, Los Angeles, Los Angeles, CA, USA.|New York Genome Center, New York, NY, USA.|Department of Genetics and Genomic Science and Institute for Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.|Department of Genetics and Genomic Science and Institute for Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.|Bioinformatics Interdepartmental Program, University of California, Los Angeles, CA, USA.|Department of Pathology and Laboratory Medicine, University of California, Los Angeles, Los Angeles, CA, USA.|New York Genome Center, New York, NY, USA.|Departments of Mental Health and Biostatistics, Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.|Departments of Mental Health and Biostatistics, Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.|Department of Pathology and Laboratory Medicine, University of California, Los Angeles, Los Angeles, CA, USA.|Department of Genetics and Genomic Science and Institute for Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 30, 'Bayes Theorem|Brain|Chromosome Mapping|DNA Methylation|Epigenesis, Genetic|Epigenomics|Gene Expression Profiling|Genome-Wide Association Study|Genomics|Humans|Quantitative Trait Loci|Schizophrenia|Software|Transcriptome', 'Journal Article', '10.1093/bioinformatics/bty147', '29579179', 'Most genetic variants implicated in complex diseases by genome-wide association studies (GWAS) are non-coding, making it challenging to understand the causative genes involved in disease. Integrating external information such as quantitative trait locus (QTL) mapping of molecular traits (e.g. expression, methylation) is a powerful approach to identify the subset of GWAS signals explained by regulatory effects. In particular, expression QTLs (eQTLs) help pinpoint the responsible gene among the GWAS regions that harbor many genes, while methylation QTLs (mQTLs) help identify the epigenetic mechanisms that impact gene expression which in turn affect disease risk. In this work, we propose multiple-trait-coloc (moloc), a Bayesian statistical framework that integrates GWAS summary data with multiple molecular QTL data to identify regulatory effects at GWAS risk loci.'),
(81581, 2018, 'bio', 'A comprehensive evaluation of alignment software for reduced representation bisulfite sequencing data', 'Sun, Xiwei|Han, Yi|Zhou, Liyuan|Chen, Enguo|Lu, Bingjian|Liu, Yong|Pan, Xiaoqing|Cowley, Allen W|Liang, Mingyu|Wu, Qingbiao|Lu, Yan|Liu, Pengyuan', 'Department of Respiratory Medicine, Sir Run Run Shaw Hospital and Institute of Translational Medicine.|Department of Respiratory Medicine, Sir Run Run Shaw Hospital and Institute of Translational Medicine.|Department of Respiratory Medicine, Sir Run Run Shaw Hospital and Institute of Translational Medicine.|Department of Respiratory Medicine, Sir Run Run Shaw Hospital and Institute of Translational Medicine.|Center for Uterine Cancer Diagnosis & Therapy Research of Zhejiang Province, Womens Reproductive Health Key Laboratory of Zhejiang Province, Department of Gynecologic Oncology, Womens Hospital and Institute of Translational Medicine, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.|Department of Physiology, Center of Systems Molecular Medicine, Medical College of Wisconsin, Milwaukee, USA.|Department of Physiology, Center of Systems Molecular Medicine, Medical College of Wisconsin, Milwaukee, USA.|Department of Physiology, Center of Systems Molecular Medicine, Medical College of Wisconsin, Milwaukee, USA.|Department of Physiology, Center of Systems Molecular Medicine, Medical College of Wisconsin, Milwaukee, USA.|Department of Mathematics, Zhejiang University, Hangzhou, Zhejiang, China.|Center for Uterine Cancer Diagnosis & Therapy Research of Zhejiang Province, Womens Reproductive Health Key Laboratory of Zhejiang Province, Department of Gynecologic Oncology, Womens Hospital and Institute of Translational Medicine, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.|Department of Respiratory Medicine, Sir Run Run Shaw Hospital and Institute of Translational Medicine.', 'United States|United States|United States|United States|China|United States|United States|United States|United States|China|China|United States', 5, 'Algorithms|CpG Islands|DNA Methylation|Epigenomics|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Software|Sulfites', 'Comparative Study', '10.1093/bioinformatics/bty174', '29579198', 'The rapid development of next-generation sequencing technology provides an opportunity to study genome-wide DNA methylation at single-base resolution. However, depletion of unmethylated cytosines brings challenges for aligning bisulfite-converted sequencing reads to a large reference. Software tools for aligning methylation reads have not yet been comprehensively evaluated, especially for the widely used reduced representation bisulfite sequencing (RRBS) that involves enrichment for CpG islands (CGIs).'),
(81582, 2018, 'bio', 'Learned protein embeddings for machine learning', 'Yang, Kevin K|Wu, Zachary|Bedbrook, Claire N|Arnold, Frances H', 'Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, USA.|Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, USA.|Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.|Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, USA.', 'United States|United States|United States|United States', 25, 'Amino Acid Sequence|Bacteria|Computational Biology|Eukaryota|Humans|Machine Learning|Models, Biological|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/bty178', '29584811', 'Machine-learning models trained on protein sequences and their measured functions can infer biological properties of unseen sequences without requiring an understanding of the underlying physical or biological mechanisms. Such models enable the prediction and discovery of sequences with optimal properties. Machine-learning models generally require that their inputs be vectors, and the conversion from a protein sequence to a vector representation affects the models ability to learn. We propose to learn embedded representations of protein sequences that take advantage of the vast quantity of unmeasured protein sequence data available. These embeddings are low-dimensional and can greatly simplify downstream modeling.'),
(81583, 2018, 'bio', 'Leveraging known genomic variants to improve detection of variants, especially close-by Indels', 'Vo, Nam S|Phan, Vinhthuy', 'Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.|Department of Computer Science, The University of Memphis, Memphis, TN, USA.', 'United States|United States', 0, 'Algorithms|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|INDEL Mutation', 'Journal Article', '10.1093/bioinformatics/bty183', '29590294', 'The detection of genomic variants has great significance in genomics, bioinformatics, biomedical research and its applications. However, despite a lot of effort, Indels and structural variants are still under-characterized compared to SNPs. Current approaches based on next-generation sequencing data usually require large numbers of reads (high coverage) to be able to detect such types of variants accurately. However Indels, especially those close to each other, are still hard to detect accurately.'),
(81584, 2018, 'bio', 'Functional annotation of genomic variants in studies of late-onset Alzheimers disease', 'Butkiewicz, Mariusz|Blue, Elizabeth E|Leung, Yuk Yee|Jian, Xueqiu|Marcora, Edoardo|Renton, Alan E|Kuzma, Amanda|Wang, Li-San|Koboldt, Daniel C|Haines, Jonathan L|Bush, William S', 'Department of Population and Quantitative Health Sciences, Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA.|Division of Medical Genetics, University of Washington, Seattle, WA, USA.|Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA.|Division of Epidemiology, Human Genetics and Environmental Sciences, University of Texas Health Science Center, Houston, TX, USA.|Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA.|Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA.|Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA.|Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA.|Nationwide Childrens Hospital, Columbus, OH, USA.|Department of Population and Quantitative Health Sciences, Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA.|Department of Population and Quantitative Health Sciences, Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 8, 'Alzheimer Disease|Databases, Genetic|Genetic Predisposition to Disease|Genome|Genomics|Humans|Molecular Sequence Annotation|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bty177', '29590295', 'Annotation of genomic variants is an increasingly important and complex part of the analysis of sequence-based genomic analyses. Computational predictions of variant function are routinely incorporated into gene-based analyses of rare-variants, though to date most studies use limited information for assessing variant function that is often agnostic of the disease being studied.'),
(81585, 2018, 'bio', 'Deep learning improves antimicrobial peptide recognition', 'Veltri, Daniel|Kamath, Uday|Shehu, Amarda', 'Bioinformatics and Computational Biosciences Branch, Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, U.S. National Institutes of Health, Rockville, MD, USA.|Digital Reasoning, McLean, VA, USA.|Department of Computer Science, George Mason University, Fairfax, VA, USA.', 'United States|United States|United States', 37, 'Anti-Infective Agents|Computational Biology|Deep Learning|Peptides|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/bty179', '29590297', 'Bacterial resistance to antibiotics is a growing concern. Antimicrobial peptides (AMPs), natural components of innate immunity, are popular targets for developing new drugs. Machine learning methods are now commonly adopted by wet-laboratory researchers to screen for promising candidates.'),
(81586, 2018, 'bio', 'GENEASE: real time bioinformatics tool for multi-omics and disease ontology exploration, analysis and visualization', 'Ghandikota, Sudhir|Hershey, Gurjit K Khurana|Mersha, Tesfaye B', 'Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH, USA.|Department of Pediatrics, Cincinnati Childrens Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA.|Department of Pediatrics, Cincinnati Childrens Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA.', 'United States|United States|United States', 4, '-', 'Journal Article', '10.1093/bioinformatics/bty182', '29590301', 'Advances in high-throughput sequencing technologies have made it possible to generate multiple omics data at an unprecedented rate and scale. The accumulation of these omics data far outpaces the rate at which biologists can mine and generate new hypothesis to test experimentally. There is an urgent need to develop a myriad of powerful tools to efficiently and effectively search and filter these resources to address specific post-GWAS functional genomics questions. However, to date, these resources are scattered across several databases and often lack a unified portal for data annotation and analytics. In addition, existing tools to analyze and visualize these databases are highly fragmented, resulting researchers to access multiple applications and manual interventions for each gene or variant in an ad hoc fashion until all the questions are answered.'),
(81587, 2018, 'bio', 'SPV: a JavaScript Signaling Pathway Visualizer', 'Calderone, Alberto|Cesareni, Gianni', 'Bioinformatics and Computational Biology Unit, Department of Biology, University of Rome Tor Vergata, Rome, Italy.|Bioinformatics and Computational Biology Unit, Department of Biology, University of Rome Tor Vergata, Rome, Italy.', 'Italy|Italy', 6, 'Computational Biology|Protein Interaction Maps|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/bty188', '29590303', 'The visualization of molecular interactions annotated in web resources is useful to offer to users such information in a clear intuitive layout. These interactions are frequently represented as binary interactions that are laid out in free space where, different entities, cellular compartments and interaction types are hardly distinguishable. Signaling Pathway Visualizer is a free open source JavaScript library, which offers a series of pre-defined elements, compartments and interaction types meant to facilitate the representation of signaling pathways consisting of causal interactions without neglecting simple protein-protein interaction networks.'),
(81588, 2018, 'bio', 'Generalizing biomedical relation classification with neural adversarial domain adaptation', 'Rios, Anthony|Kavuluru, Ramakanth|Lu, Zhiyong', 'National Library of Medicine (NLM), National Center for Biotechnology Information (NCBI), National Institutes of Health (NIH), Bethesda, MD, USA.|Department of Computer Science, University of Kentucky, Lexington, KY, USA.|National Library of Medicine (NLM), National Center for Biotechnology Information (NCBI), National Institutes of Health (NIH), Bethesda, MD, USA.', 'United States|United States|United States', 13, 'Algorithms|Biomedical Research|Databases, Factual|Neural Networks (Computer)|Proteins', 'Journal Article', '10.1093/bioinformatics/bty190', '29590309', 'Creating large datasets for biomedical relation classification can be prohibitively expensive. While some datasets have been curated to extract protein-protein and drug-drug interactions (PPIs and DDIs) from text, we are also interested in other interactions including gene-disease and chemical-protein connections. Also, many biomedical researchers have begun to explore ternary relationships. Even when annotated data are available, many datasets used for relation classification are inherently biased. For example, issues such as sample selection bias typically prevent models from generalizing in the wild. To address the problem of cross-corpora generalization, we present a novel adversarial learning algorithm for unsupervised domain adaptation tasks where no labeled data are available in the target domain. Instead, our method takes advantage of unlabeled data to improve biased classifiers through learning domain-invariant features via an adversarial process. Finally, our method is built upon recent advances in neural network (NN) methods.'),
(81589, 2018, 'bio', 'POPdemog: visualizing population demographic history from simulation scripts', 'Zhou, Ying|Tian, Xiaowen|Browning, Brian L|Browning, Sharon R', 'Department of Biostatistics, University of Washington, WA, USA.|Department of Biostatistics, University of Washington, WA, USA.|Department of Biostatistics, University of Washington, WA, USA.|Department of Biostatistics, University of Washington, WA, USA.', 'United States|United States|United States|United States', 1, 'Demography|Genetics, Population|Software', 'Journal Article', '10.1093/bioinformatics/bty184', '29590339', 'We present POPdemog, an R package which converts coalescent simulation program input parameters into a visual representation of the demographic model. This package is useful for preparing figures, for checking that demographic simulation parameters have been correctly specified, and for understanding demographic models that other researchers have used to simulate genetic data. The POPdemog package supports the ms, msa, msHot, MaCS, msprime, scrm and Cosi2 programs, and includes options for customizing the output figures.'),
(81590, 2018, 'bio', 'SimPhospho: a software tool enabling confident phosphosite assignment', 'Suni, Veronika|Suomi, Tomi|Tsubosaka, Tomoya|Imanishi, Susumu Y|Elo, Laura L|Corthals, Garry L', 'TUCS - Turku Centre for Computer Science, FI-20500 Turku, Finland.|Bioinformatics Unit, Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Tykistökatu 6, FI-20520 Turku, Finland.|Faculty of Pharmacy, Meijo University, Nagoya, Japan.|Faculty of Pharmacy, Meijo University, Nagoya, Japan.|Bioinformatics Unit, Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Tykistökatu 6, FI-20520 Turku, Finland.|Vant Hoff Institute of Molecular Sciences, 1090 GS Amsterdam, The Netherlands.', 'Finland|Finland|Japan|Japan|Finland|Netherlands', 2, 'Databases, Protein|Phosphopeptides|Phosphorylation|Protein Processing, Post-Translational|Proteomics|Software|Tandem Mass Spectrometry', 'Journal Article', '10.1093/bioinformatics/bty151', '29596608', 'Mass spectrometry combined with enrichment strategies for phosphorylated peptides has been successfully employed for two decades to identify sites of phosphorylation. However, unambiguous phosphosite assignment is considered challenging. Given that site-specific phosphorylation events function as different molecular switches, validation of phosphorylation sites is of utmost importance. In our earlier study we developed a method based on simulated phosphopeptide spectral libraries, which enables highly sensitive and accurate phosphosite assignments. To promote more widespread use of this method, we here introduce a software implementation with improved usability and performance.'),
(81591, 2018, 'bio', 'ONETOOL for the analysis of family-based big data', 'Song, Yeunjoo E|Lee, Sungyoung|Park, Kyungtaek|Elston, Robert C|Yang, Hyeon-Jong|Won, Sungho', 'Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA.|Interdisciplinary Program of Bioinformatics, Seoul National University, Seoul, Korea.|Interdisciplinary Program of Bioinformatics, Seoul National University, Seoul, Korea.|Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA.|SCH Biomedical Informatics Research Unit, Soonchunhyang University Hospital, Seoul, Korea.|Interdisciplinary Program of Bioinformatics, Seoul National University, Seoul, Korea.', 'United States|United States|United States|United States|United States|United States', 6, 'Big Data|Databases, Factual|Software', 'Journal Article', '10.1093/bioinformatics/bty180', '29596615', 'Despite the need for separate tools to analyze family-based data, there are only a handful of tools optimized for family-based big data compared to the number of tools available for analyzing population-based data.'),
(81592, 2018, 'bio', 'MetaboCraft: building a Minecraft plugin for metabolomics', 'Megalios, Anargyros|Daly, Rónán|Burgess, Karl', 'Glasgow Polyomics, University of Glasgow, Glasgow, UK.|Institute of Infection, Immunity and Inflammation, College of Medical Veterinary and Life Sciences, University of Glasgow, Glasgow, UK.|Glasgow Polyomics, University of Glasgow, Glasgow, UK.', 'United Kingdom|United Kingdom|United Kingdom', 2, 'Data Visualization|Humans|Metabolic Networks and Pathways|Metabolomics|Software', 'Journal Article', '10.1093/bioinformatics/bty102', '29608638', 'The rapid advances in metabolomics pose a significant challenge in presentation and interpretation of results. Development of new, engaging visual aids is crucial to advancing our understanding of new findings.'),
(81593, 2018, 'bio', 'LSMM: a statistical approach to integrating functional annotations with genome-wide association studies', 'Ming, Jingsi|Dai, Mingwei|Cai, Mingxuan|Wan, Xiang|Liu, Jin|Yang, Can', 'Department of Mathematics, Hong Kong Baptist University, Hong Kong.|School of Mathematics and Statistics, Xian Jiaotong University, Xian, China.|Department of Mathematics, Hong Kong Baptist University, Hong Kong.|Shenzhen Research Institute of Big Data, Shenzhen, China.|Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore.|Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong.', 'Hong Kong|China|Hong Kong|China|Singapore|Hong Kong', 4, 'Algorithms|Genome-Wide Association Study|Molecular Sequence Annotation|Phenotype|Software', 'Journal Article', '10.1093/bioinformatics/bty187', '29608640', 'Thousands of risk variants underlying complex phenotypes (quantitative traits and diseases) have been identified in genome-wide association studies (GWAS). However, there are still two major challenges towards deepening our understanding of the genetic architectures of complex phenotypes. First, the majority of GWAS hits are in non-coding region and their biological interpretation is still unclear. Second, accumulating evidence from GWAS suggests the polygenicity of complex traits, i.e. a complex trait is often affected by many variants with small or moderate effects, whereas a large proportion of risk variants with small effects remain unknown.'),
(81594, 2018, 'bio', 'BART: a transcription factor prediction tool with query gene sets or epigenomic profiles', 'Wang, Zhenjia|Civelek, Mete|Miller, Clint L|Sheffield, Nathan C|Guertin, Michael J|Zang, Chongzhi', 'Center for Public Health Genomics, Charlottesville, VA, USA.|Center for Public Health Genomics, Charlottesville, VA, USA.|Center for Public Health Genomics, Charlottesville, VA, USA.|Center for Public Health Genomics, Charlottesville, VA, USA.|Center for Public Health Genomics, Charlottesville, VA, USA.|Center for Public Health Genomics, Charlottesville, VA, USA.', 'United States|United States|United States|United States|United States|United States', 14, 'Animals|Databases, Genetic|Epigenomics|Gene Expression Regulation|Humans|Mice|Sequence Analysis, DNA|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/bty194', '29608647', 'Identification of functional transcription factors that regulate a given gene set is an important problem in gene regulation studies. Conventional approaches for identifying transcription factors, such as DNA sequence motif analysis, are unable to predict functional binding of specific factors and not sensitive enough to detect factors binding at distal enhancers. Here, we present binding analysis for regulation of transcription (BART), a novel computational method and software package for predicting functional transcription factors that regulate a query gene set or associate with a query genomic profile, based on more than 6000 existing ChIP-seq datasets for over 400 factors in human or mouse. This method demonstrates the advantage of utilizing publicly available data for functional genomics research.'),
(81595, 2018, 'bio', 'Understanding sequencing data as compositions: an outlook and review', 'Quinn, Thomas P|Erb, Ionas|Richardson, Mark F|Crowley, Tamsyn M', 'Bioinformatics Core Research Group, Deakin University, Geelong, Australia.|Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain.|Bioinformatics Core Research Group, Deakin University, Geelong, Australia.|Bioinformatics Core Research Group, Deakin University, Geelong, Australia.', 'Australia|Spain|Australia|Australia', 0, 'Gene Library|Humans|Models, Statistical|Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/bty175', '29608657', 'Although seldom acknowledged explicitly, count data generated by sequencing platforms exist as compositions for which the abundance of each component (e.g. gene or transcript) is only coherently interpretable relative to other components within that sample. This property arises from the assay technology itself, whereby the number of counts recorded for each sample is constrained by an arbitrary total sum (i.e. library size). Consequently, sequencing data, as compositional data, exist in a non-Euclidean space that, without normalization or transformation, renders invalid many conventional analyses, including distance measures, correlation coefficients and multivariate statistical models.'),
(81596, 2018, 'bio', 'pyNBS: a Python implementation for network-based stratification of tumor mutations', 'Huang, Justin K|Jia, Tongqiu|Carlin, Daniel E|Ideker, Trey', 'Bioinformatics and Systems Biology Program, Bioengineering Department, UC San Diego, La Jolla, CA, USA.|Department of Medicine, UC San Diego, La Jolla, CA, USA.|Department of Medicine, UC San Diego, La Jolla, CA, USA.|Bioinformatics and Systems Biology Program, Bioengineering Department, UC San Diego, La Jolla, CA, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Humans|Mutation|Neoplasms|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bty186', '29608663', 'We present pyNBS: a modularized Python 2.7 implementation of the network-based stratification (NBS) algorithm for stratifying tumor somatic mutation profiles into molecularly and clinically relevant subtypes. In addition to release of the software, we benchmark its key parameters and provide a compact cancer reference network that increases the significance of tumor stratification using the NBS algorithm. The structure of the code exposes key steps of the algorithm to foster further collaborative development.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(81597, 2018, 'bio', 'scanPAV: a pipeline for extracting presence-absence variations in genome pairs', 'Giordano, Francesca|Stammnitz, Maximilian R|Murchison, Elizabeth P|Ning, Zemin', 'The Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton.|Transmissible Cancer Group, Department of Veterinary Medicine, University of Cambridge, Cambridge, UK.|Transmissible Cancer Group, Department of Veterinary Medicine, University of Cambridge, Cambridge, UK.|The Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 3, 'Animals|Chromosome Mapping|Genome|Genomics|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bty189', '29608694', 'The recent technological advances in genome sequencing techniques have resulted in an exponential increase in the number of sequenced human and non-human genomes. The ever increasing number of assemblies generated by novel de novo pipelines and strategies demands the development of new software to evaluate assembly quality and completeness. One way to determine the completeness of an assembly is by detecting its Presence-Absence variations (PAV) with respect to a reference, where PAVs between two assemblies are defined as the sequences present in one assembly but entirely missing in the other one. Beyond assembly error or technology bias, PAVs can also reveal real genome polymorphism, consequence of species or individual evolution, or horizontal transfer from viruses and bacteria.'),
(81598, 2018, 'bio', 'Genome-by-genome approach for fast bacterial genealogical relationship evaluation', 'Zhang, Ji|Xiong, Yanwen|Rogers, Lynn|Carter, Glen P|French, Nigel', 'mEpiLab, New Zealand Food Safety Science & Research Centre, Institute of Veterinary, Animal and Biomedical Sciences, Massey University, Palmerston North, New Zealand.|State Key Laboratory of Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, China CDC, Beijing, China.|mEpiLab, New Zealand Food Safety Science & Research Centre, Institute of Veterinary, Animal and Biomedical Sciences, Massey University, Palmerston North, New Zealand.|Microbiological Diagnostic Unit Public Health Laboratory, University of Melbourne, Melbourne, Australia.|mEpiLab, New Zealand Food Safety Science & Research Centre, Institute of Veterinary, Animal and Biomedical Sciences, Massey University, Palmerston North, New Zealand.', 'New Zealand|China|New Zealand|Australia|New Zealand', 5, 'Algorithms|Bacteria|Genome|Pedigree|Software|Whole Genome Sequencing', 'Journal Article', '10.1093/bioinformatics/bty195', '29608746', 'Large-scale whole-genome sequencing dataset-based studies are becoming increasingly common in pathogen surveillance and outbreak investigations. A highly discriminative and time-efficient bioinformatics tool is needed to transform large amounts of sequencing data into usable biological information. To replace the intuitive, yet inefficient, way of gene-by-gene allele calling algorithm, a new algorithm using genome-by-genome approach was developed.'),
(81599, 2018, 'bio', 'Predicting serious rare adverse reactions of novel chemicals', 'Poleksic, Aleksandar|Xie, Lei', 'Department of Computer Science, University of Northern Iowa, Cedar Falls, IA, USA.|Department of Computer Science, Hunter College, The Graduate Center, The City University of New York, New York, NY, USA.', 'United States|United States', 4, 'Databases, Factual|Drug Discovery|Drug-Related Side Effects and Adverse Reactions', 'Journal Article', '10.1093/bioinformatics/bty193', '29617731', 'Adverse drug reactions (ADRs) are one of the main causes of death and a major financial burden on the worlds economy. Due to the limitations of the animal model, computational prediction of serious and rare ADRs is invaluable. However, current state-of-the-art computational methods do not yield significantly better predictions of rare ADRs than random guessing.'),
(81600, 2018, 'bio', 'Building online genomics applications using BioPyramid', 'Stephenson, Liam|Wakeham, Yoshua|Seidenman, Nick|Choi, Jarny', 'Division of Molecular Medicine, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia.|Division of Molecular Medicine, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia.|Division of Molecular Medicine, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia.|Division of Molecular Medicine, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia.', 'Australia|Australia|Australia|Australia', 1, 'Gene Expression|Genomics|Internet|Molecular Sequence Annotation|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/bty207', '29617932', 'BioPyramid is a Python package, which serves as a scaffold for building an online application for the exploration of gene expression data. It is designed for bioinformaticians wishing to quickly share transformed data and interactive analyses with collaborators. Current R-based tools similarly address the need to quickly share omics-data in an exploratory format, but these are generally small-scale, single-dataset solutions. Biopyramid is written in Python pyramid framework and scalable to address longer-term or more complex projects. It contains a number of components designed to reduce the time and effort in building such an application from scratch, including gene annotation, dataset models and visualization tools.'),
(81601, 2018, 'bio', 'De novo haplotype reconstruction in viral quasispecies using paired-end read guided path finding', 'Chen, Jiao|Zhao, Yingchao|Sun, Yanni', 'Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA.|School of Computing and Information Sciences, Caritas Institute of Higher Education, Hong Kong, China.|Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA.', 'United States|China|United States', 4, 'Haplotypes|High-Throughput Nucleotide Sequencing|Quasispecies|RNA Viruses|Software', 'Journal Article', '10.1093/bioinformatics/bty202', '29617936', 'RNA virus populations contain different but genetically related strains, all infecting an individual host. Reconstruction of the viral haplotypes is a fundamental step to characterize the virus population, predict their viral phenotypes and finally provide important information for clinical treatment and prevention. Advances of the next-generation sequencing technologies open up new opportunities to assemble full-length haplotypes. However, error-prone short reads, high similarities between related strains, an unknown number of haplotypes pose computational challenges for reference-free haplotype reconstruction. There is still much room to improve the performance of existing haplotype assembly tools.'),
(81602, 2018, 'bio', 'Efficient analysis of large-scale genome-wide data with two R packages: bigstatsr and bigsnpr', 'Privxc3xa9, Florian|Aschard, Hugues|Ziyatdinov, Andrey|Blum, Michael G B', 'Laboratoire TIMC-IMAG, UMR 5525, CNRS, Universitxc3xa9 Grenoble Alpes, Grenoble, France.|Centre de Bioinformatique, Biostatistique et Biologie Intxc3xa9grative (C3BI), Institut Pasteur, Paris, France.|Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.|Laboratoire TIMC-IMAG, UMR 5525, CNRS, Universitxc3xa9 Grenoble Alpes, Grenoble, France.', 'France|France|United States|France', 19, 'Algorithms|Genome, Human|Genomics|Humans|Multifactorial Inheritance|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/bty185', '29617937', 'Genome-wide datasets produced for association studies have dramatically increased in size over the past few years, with modern datasets commonly including millions of variants measured in dozens of thousands of individuals. This increase in data size is a major challenge severely slowing down genomic analyses, leading to some software becoming obsolete and researchers having limited access to diverse analysis tools.'),
(81603, 2018, 'bio', 'Identification and function of FAR protein family genes from a transcriptome analysis of Aphelenchoides besseyi', 'Wang, Dong-Wei|Xu, Chun-Ling|Ding, Shan-Wen|Huang, Xin|Cheng, Xi|Zhang, Chao|Chen, Chun|Xie, Hui', 'Laboratory of Plant Nematology and Research Center of Nematodes of Plant Quarantine, Department of Plant Pathology/Guangdong Province Key Laboratory of Microbial Signals and Disease Control, College of Agriculture, South China Agricultural University, Guangzhou, Peoples Republic of China.|Laboratory of Plant Nematology and Research Center of Nematodes of Plant Quarantine, Department of Plant Pathology/Guangdong Province Key Laboratory of Microbial Signals and Disease Control, College of Agriculture, South China Agricultural University, Guangzhou, Peoples Republic of China.|Laboratory of Plant Nematology and Research Center of Nematodes of Plant Quarantine, Department of Plant Pathology/Guangdong Province Key Laboratory of Microbial Signals and Disease Control, College of Agriculture, South China Agricultural University, Guangzhou, Peoples Republic of China.|Laboratory of Plant Nematology and Research Center of Nematodes of Plant Quarantine, Department of Plant Pathology/Guangdong Province Key Laboratory of Microbial Signals and Disease Control, College of Agriculture, South China Agricultural University, Guangzhou, Peoples Republic of China.|Key Laboratory of Biopesticide and Chemical Biology, Ministry of Education, College of plant Protection, Fujian Agriculture and Forestry University, Fuzhou, Peoples Republic of China.|Institute of Genetic Engineering, Department of biochemistry, College of Basic Medicine, Southern Medical University, Guangzhou, Peoples Republic of China.|Laboratory of Plant Nematology and Research Center of Nematodes of Plant Quarantine, Department of Plant Pathology/Guangdong Province Key Laboratory of Microbial Signals and Disease Control, College of Agriculture, South China Agricultural University, Guangzhou, Peoples Republic of China.|Laboratory of Plant Nematology and Research Center of Nematodes of Plant Quarantine, Department of Plant Pathology/Guangdong Province Key Laboratory of Microbial Signals and Disease Control, College of Agriculture, South China Agricultural University, Guangzhou, Peoples Republic of China.', 'China|China|China|China|China|China|China|China', 4, 'Animals|Gene Expression Profiling|Helminth Proteins|High-Throughput Nucleotide Sequencing|Oryza|Transcriptome|Tylenchida', 'Journal Article', '10.1093/bioinformatics/bty209', '29617938', 'The rice white tip nematode (RWTN) Aphelenchoides besseyi is a migratory plant parasitic nematode that infects the aboveground parts of plants. Fatty acid- and retinoid-binding (FAR) proteins are nematode-specific proteins that are involved in many important biological processes. Genes encoding FAR proteins have been identified in many species of nematodes, which indicated that nematodes may produce more than one type of FAR protein. The main goal of this study is to find new molecular targets including new far genes that will help control RWTN, and reduce the economic damage caused by RWTN.'),
(81604, 2018, 'bio', 'FaStore: a space-saving solution for raw sequencing data', 'Roguski, Lukasz|Ochoa, Idoia|Hernaez, Mikel|Deorowicz, Sebastian', 'Centro Nacional de Análisis Genómico-Centre for Genomic Regulation, Barcelona Institute of Science and Technology (CNAG-CRG), Barcelona, Spain.|Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, IL, USA.|Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, IL, USA.|Institute of Informatics, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Gliwice, Poland.', 'Spain|United States|United States|Poland', 13, 'Algorithms|Data Compression|Genomics|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bty205', '29617939', 'The affordability of DNA sequencing has led to the generation of unprecedented volumes of raw sequencing data. These data must be stored, processed and transmitted, which poses significant challenges. To facilitate this effort, we introduce FaStore, a specialized compressor for FASTQ files. FaStore does not use any reference sequences for compression and permits the user to choose from several lossy modes to improve the overall compression ratio, depending on the specific needs.'),
(81605, 2018, 'bio', 'A reference peptide database for proteome quantification based on experimental mass spectrum response curves', 'Liu, Wanlin|Wei, Lai|Sun, Jianan|Feng, Jinwen|Guo, Gaigai|Liang, Lizhu|Fu, Tianyi|Liu, Mingwei|Li, Kai|Huang, Yin|Zhu, Weimin|Zhen, Bei|Wang, Yi|Ding, Chen|Qin, Jun', 'State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China.|State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China.|State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China.|State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China.|State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China.|State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China.|State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China.|State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China.|State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China.|State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China.|State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China.|State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China.|State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China.|State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China.|State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China.', 'China|China|China|China|China|China|China|China|China|China|China|China|China|China|China', 0, 'Databases, Protein|HEK293 Cells|HeLa Cells|Humans|Mass Spectrometry|Peptides|Proteomics', 'Journal Article', '10.1093/bioinformatics/bty201', '29617941', 'Mass spectrometry (MS) based quantification of proteins/peptides has become a powerful tool in biological research with high sensitivity and throughput. The accuracy of quantification, however, has been problematic as not all peptides are suitable for quantification. Several methods and tools have been developed to identify peptides that response well in mass spectrometry and they are mainly based on predictive models, and rarely consider the linearity of the response curve, limiting the accuracy and applicability of the methods. An alternative solution is to select empirically superior peptides that offer satisfactory MS response intensity and linearity in a wide dynamic range of peptide concentration.'),
(81606, 2018, 'bio', 'Efficient simulation of intrinsic, extrinsic and external noise in biochemical systems', 'Pischel, Dennis|Sundmacher, Kai|Flassig, Robert J', 'State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China.|State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China.|State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China.', 'China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/bty171', '29617943', '-'),
(81607, 2018, 'bio', 'PhenotypeSimulator: A comprehensive framework for simulating multi-trait, multi-locus genotype to phenotype relationships', 'Meyer, Hannah Verena|Birney, Ewan', 'European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.', 'United Kingdom|United Kingdom', 3, 'Genetic Loci|Genotype|Phenotype|Software', 'Journal Article', '10.1093/bioinformatics/bty197', '29617944', 'Simulation is a critical part of method development and assessment. With the increasing sophistication of multi-trait and multi-locus genetic analysis techniques, it is important that the community has flexible simulation tools to challenge and explore the properties of these methods.'),
(81608, 2018, 'bio', 'Reactome Pengine: a web-logic API to the Homo sapiens reactome', 'Neaves, Samuel R|Tsoka, Sophia|Millard, Louise A C', 'Department of Informatics, Kings College London, Strand, London, UK.|Department of Informatics, Kings College London, Strand, London, UK.|Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.', 'United Kingdom|United Kingdom|United Kingdom', 3, 'Databases, Factual|Humans|Logic|Software', 'Journal Article', '10.1093/bioinformatics/bty181', '29617950', 'Existing ways of accessing data from the Reactome database are limited. Either a researcher is restricted to particular queries defined by a web application programming interface (API) or they have to download the whole database. Reactome Pengine is a web service providing a logic programming-based API to the human reactome. This gives researchers greater flexibility in data access than existing APIs, as users can send their own small programs (alongside queries) to Reactome Pengine.'),
(81609, 2018, 'bio', 'AGORA: organellar genome annotation from the amino acid and nucleotide references', 'Jung, Jaehee|Kim, Jong Im|Jeong, Young-Sik|Yi, Gangman', 'Department of General Education, Hongik University, Seoul, Korea.|Department of Biology, Chungnam National University, Daejeon, Korea.|Department of Multimedia Engineering, Dongguk University, Seoul, Korea.|Department of Multimedia Engineering, Dongguk University, Seoul, Korea.', '-|-|-|-', 10, 'Animals|Eukaryota|Genome, Chloroplast|Genome, Mitochondrial|High-Throughput Nucleotide Sequencing|Molecular Sequence Annotation|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bty196', '29617954', 'Next-generation sequencing (NGS) technologies have led to the accumulation of high-throughput sequence data from various organisms in biology. To apply gene annotation of organellar genomes for various organisms, more optimized tools for functional gene annotation are required. Almost all gene annotation tools are mainly focused on the chloroplast genome of land plants or the mitochondrial genome of animals. We have developed a web application AGORA for the fast, user-friendly and improved annotations of organellar genomes. Annotator for Genes of Organelle from the Reference sequence Analysis (AGORA) annotates genes based on a basic local alignment search tool (BLAST)-based homology search and clustering with selected reference sequences from the NCBI database or user-defined uploaded data. AGORA can annotate the functional genes in almost all mitochondrion and plastid genomes of eukaryotes. The gene annotation of a genome with an exon-intron structure within a gene or inverted repeat region is also available. It provides information of start and end positions of each gene, BLAST results compared with the reference sequence and visualization of gene map by OGDRAW.'),
(81610, 2018, 'bio', 'Detecting hidden batch factors through data-adaptive adjustment for biological effects', 'Yi, Haidong|Raman, Ayush T|Zhang, Han|Allen, Genevera I|Liu, Zhandong', 'College of Computer and Control Engineering, Nankai University, Tianjin 300350, China.|Graduate Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine, Houston, TX 77030, USA.|College of Computer and Control Engineering, Nankai University, Tianjin 300350, China.|Department of Statistics, Rice University, Houston, TX, 77030 USA.|Graduate Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine, Houston, TX 77030, USA.', 'China|United States|China|United States|United States', 3, 'Algorithms|Gene Expression Profiling|Glioblastoma|Humans|Quality Control|Research Design|Sequence Analysis, RNA|Topotecan', 'Journal Article', '10.1093/bioinformatics/btx635', '29617963', 'Batch effects are one of the major source of technical variations that affect the measurements in high-throughput studies such as RNA sequencing. It has been well established that batch effects can be caused by different experimental platforms, laboratory conditions, different sources of samples and personnel differences. These differences can confound the outcomes of interest and lead to spurious results. A critical input for batch correction algorithms is the knowledge of batch factors, which in many cases are unknown or inaccurate. Hence, the primary motivation of our paper is to detect hidden batch factors that can be used in standard techniques to accurately capture the relationship between gene expression and other modeled variables of interest.'),
(81611, 2018, 'bio', 'MechRNA: prediction of lncRNA mechanisms from RNA-RNA and RNA-protein interactions', 'Gawronski, Alexander R|Uhl, Michael|Zhang, Yajia|Lin, Yen-Yi|Niknafs, Yashar S|Ramnarine, Varune R|Malik, Rohit|Feng, Felix|Chinnaiyan, Arul M|Collins, Colin C|Sahinalp, S Cenk|Backofen, Rolf', 'Computing Science, Simon Fraser University, Burnaby BC, Canada.|Centre for Biological Signalling Studies, University of Freiburg, Freiburg im Breisgau, Germany.|Department of Pathology, University of Michigan, Ann Arbor, MI, USA.|Computing Science, Simon Fraser University, Burnaby BC, Canada.|Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, USA.|Vancouver Prostate Centre, Vancouver, BC, Canada.|Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, USA.|Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, USA.|Department of Pathology, University of Michigan, Ann Arbor, MI, USA.|Vancouver Prostate Centre, Vancouver, BC, Canada.|Vancouver Prostate Centre, Vancouver, BC, Canada.|Centre for Biological Signalling Studies, University of Freiburg, Freiburg im Breisgau, Germany.', 'Canada|Germany|United States|Canada|United States|Canada|United States|United States|United States|Canada|Canada|Germany', 14, 'Biochemical Phenomena|Proteins|RNA, Long Noncoding|Software|Transcriptome', 'Journal Article', '10.1093/bioinformatics/bty208', '29617966', 'Long non-coding RNAs (lncRNAs) are defined as transcripts longer than 200 nt that do not get translated into proteins. Often these transcripts are processed (spliced, capped and polyadenylated) and some are known to have important biological functions. However, most lncRNAs have unknown or poorly understood functions. Nevertheless, because of their potential role in cancer, lncRNAs are receiving a lot of attention, and the need for computational tools to predict their possible mechanisms of action is more than ever. Fundamentally, most of the known lncRNA mechanisms involve RNA-RNA and/or RNA-protein interactions. Through accurate predictions of each kind of interaction and integration of these predictions, it is possible to elucidate potential mechanisms for a given lncRNA.'),
(81612, 2018, 'bio', 'Constructing lightweight and flexible pipelines using Plugin-Based Microbiome Analysis (PluMA)', 'Cickovski, Trevor|Narasimhan, Giri', 'Centre for Biological Signalling Studies, University of Freiburg, Freiburg im Breisgau, Germany.|Centre for Biological Signalling Studies, University of Freiburg, Freiburg im Breisgau, Germany.', 'Germany|Germany', 0, 'Algorithms|Animals|Gastrointestinal Microbiome|Mice|Microbiota|Software', 'Journal Article', '10.1093/bioinformatics/bty198', '29618009', 'Software pipelines have become almost standardized tools for microbiome analysis. Currently many pipelines are available, often sharing some of the same algorithms as stages. This is largely because each pipeline has its own source language and file formats, making it typically more economical to reinvent the wheel than to learn and interface to an existing package. We present Plugin-Based Microbiome Analysis (PluMA), which addresses this problem by providing a lightweight back end that can be infinitely extended using dynamically loaded plugin extensions. These can be written in one of many compiled or scripting languages. With PluMA and its online plugin pool, algorithm designers can easily plug-and-play existing pipeline stages with no knowledge of their underlying implementation, allowing them to efficiently test a new algorithm alongside these stages or combine them in a new and creative way.'),
(81613, 2018, 'bio', 'Pleiotropic mapping and annotation selection in genome-wide association studies with penalized Gaussian mixture models', 'Zeng, Ping|Hao, Xingjie|Zhou, Xiang', 'Department of Epidemiology and Biostatistics, Xuzhou Medical University, Xuzhou, Jiangsu, China.|Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.|Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.', 'China|United States|United States', 3, 'Algorithms|Genome-Wide Association Study|Normal Distribution|Phenotype', 'Journal Article', '10.1093/bioinformatics/bty204', '29635306', 'Genome-wide association studies (GWASs) have identified many genetic loci associated with complex traits. A substantial fraction of these identified loci is associated with multiple traits-a phenomena known as pleiotropy. Identification of pleiotropic associations can help characterize the genetic relationship among complex traits and can facilitate our understanding of disease etiology. Effective pleiotropic association mapping requires the development of statistical methods that can jointly model multiple traits with genome-wide single nucleic polymorphisms (SNPs) together.'),
(81614, 2018, 'bio', '3DClusterViSu: 3D clustering analysis of super-resolution microscopy data by 3D Voronoi tessellations', 'Andronov, Leonid|Michalon, Jonathan|Ouararhni, Khalid|Orlov, Igor|Hamiche, Ali|Vonesch, Jean-Luc|Klaholz, Bruno P', 'Centre for Integrative Biology (CBI), Department of Integrated Structural Biology, IGBMC, CNRS, Inserm, Université de Strasbourg, 1 rue Laurent Fries, Illkirch, France.|Centre for Integrative Biology (CBI), Department of Integrated Structural Biology, IGBMC, CNRS, Inserm, Université de Strasbourg, 1 rue Laurent Fries, Illkirch, France.|Centre for Integrative Biology (CBI), Department of Integrated Structural Biology, IGBMC, CNRS, Inserm, Université de Strasbourg, 1 rue Laurent Fries, Illkirch, France.|Centre for Integrative Biology (CBI), Department of Integrated Structural Biology, IGBMC, CNRS, Inserm, Université de Strasbourg, 1 rue Laurent Fries, Illkirch, France.|Centre for Integrative Biology (CBI), Department of Integrated Structural Biology, IGBMC, CNRS, Inserm, Université de Strasbourg, 1 rue Laurent Fries, Illkirch, France.|Centre for Integrative Biology (CBI), Department of Integrated Structural Biology, IGBMC, CNRS, Inserm, Université de Strasbourg, 1 rue Laurent Fries, Illkirch, France.|Centre for Integrative Biology (CBI), Department of Integrated Structural Biology, IGBMC, CNRS, Inserm, Université de Strasbourg, 1 rue Laurent Fries, Illkirch, France.', 'France|France|France|France|France|France|France', 9, 'Centromere Protein A|Cluster Analysis|Histones|Humans|Imaging, Three-Dimensional|Single Molecule Imaging|Software', 'Journal Article', '10.1093/bioinformatics/bty200', '29635310', 'Single-molecule localization microscopy (SMLM) can play an important role in integrated structural biology approaches to identify, localize and determine the 3D structure of cellular structures. While many tools exist for the 3D analysis and visualization of crystal or cryo-EM structures little exists for 3D SMLM data, which can provide unique insights but are particularly challenging to analyze in three dimensions especially in a dense cellular context.'),
(81615, 2018, 'bio', 'The latent geometry of the human protein interaction network', 'Alanis-Lobato, Gregorio|Mier, Pablo|Andrade-Navarro, Miguel', 'Institute of Organismic and Molecular Evolution, Faculty of Biology, Johannes Gutenberg Universität, Mainz, Germany.|Institute of Organismic and Molecular Evolution, Faculty of Biology, Johannes Gutenberg Universität, Mainz, Germany.|Institute of Organismic and Molecular Evolution, Faculty of Biology, Johannes Gutenberg Universität, Mainz, Germany.', 'Germany|Germany|Germany', 5, 'Algorithms|Humans|Protein Interaction Maps|Proteins|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/bty206', '29635317', 'A series of recently introduced algorithms and models advocates for the existence of a hyperbolic geometry underlying the network representation of complex systems. Since the human protein interaction network (hPIN) has a complex architecture, we hypothesized that uncovering its latent geometry could ease challenging problems in systems biology, translating them into measuring distances between proteins.'),
(81616, 2018, 'bio', 'VSClust: feature-based variance-sensitive clustering of omics data', 'Schwämmle, Veit|Jensen, Ole N', 'Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense M, Denmark.|Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense M, Denmark.', 'Denmark|Denmark', 6, 'Algorithms|Cluster Analysis|Genomics|Metabolomics|Software', 'Journal Article', '10.1093/bioinformatics/bty224', '29635359', 'Data clustering is indispensable for identifying biologically relevant molecular features in large-scale omics experiments with thousands of measurements at multiple conditions. Optimal clustering results yield groups of functionally related features that may include genes, proteins and metabolites in biological processes and molecular networks. Omics experiments typically include replicated measurements of each feature within a given condition to statistically assess feature-specific variation. Current clustering approaches ignore this variation by averaging, which often leads to incorrect cluster assignments.'),
(81617, 2018, 'bio', 'A comprehensive web tool for toehold switch design', 'To, Andrew Ching-Yuet|Chu, David Ho-Ting|Wang, Angela Ruoning|Li, Frances Cheuk-Yau|Chiu, Alan Wai-On|Gao, Daisy Yuwei|Choi, Chung Hang Jonathan|Kong, Siu-Kai|Chan, Ting-Fung|Chan, King-Ming|Yip, Kevin Y', 'School of Life Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.|School of Life Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.|School of Life Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.|Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.|Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.|Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.|Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.|School of Life Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.|School of Life Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.|School of Life Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.|Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.', 'Hong Kong|Hong Kong|Hong Kong|Hong Kong|Hong Kong|Hong Kong|Hong Kong|Hong Kong|Hong Kong|Hong Kong|', 4, 'Binding Sites|Nucleic Acid Conformation|RNA|Ribosomes|Software Design', 'Journal Article', '10.1093/bioinformatics/bty216', '29648573', 'Toehold switches are a class of RNAs with a hairpin loop that can be unfolded upon binding a trigger RNA, thereby exposing a ribosome binding site (RBS) and permitting translation of the reporter protein. They have been shown very useful in detecting a variety of targets including RNAs from Zika and Ebola viruses. The base complementation between the toehold switch and the trigger RNA also makes it sensitive to sequence variations. Design of toehold switches involves a series of considerations related to their sequence properties, structures and specificities.'),
(81618, 2018, 'bio', 'Inference of the human polyadenylation code', 'Leung, Michael K K|Delong, Andrew|Frey, Brendan J', 'Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada.|Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada.|Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada.', 'Canada|Canada|Canada', 7, '3 Untranslated Regions|Gene Expression Regulation|Genome, Human|Genomics|Humans|Poly A|Polyadenylation', 'Journal Article', '10.1093/bioinformatics/bty211', '29648582', 'Processing of transcripts at the 3-end involves cleavage at a polyadenylation site followed by the addition of a poly(A)-tail. By selecting which site is cleaved, the process of alternative polyadenylation enables genes to produce transcript isoforms with different 3-ends. To facilitate the identification and treatment of disease-causing mutations that affect polyadenylation and to understand the sequence determinants underlying this regulatory process, a computational model that can accurately predict polyadenylation patterns from genomic features is desirable.'),
(81619, 2018, 'bio', 'LipidPedia: a comprehensive lipid knowledgebase', 'Kuo, Tien-Chueh|Tseng, Yufeng Jane', 'Graduate Institute of Biomedical Engineering and Bioinformatics, National Taiwan University, Taipei, Taiwan.|Graduate Institute of Biomedical Engineering and Bioinformatics, National Taiwan University, Taipei, Taiwan.', '-|-', 6, 'Databases, Factual|Knowledge Bases|Lipids', 'Journal Article', '10.1093/bioinformatics/bty213', '29648583', 'Lipids are divided into fatty acyls, glycerolipids, glycerophospholipids, sphingolipids, saccharolipids, sterols, prenol lipids and polyketides. Fatty acyls and glycerolipids are commonly used as energy storage, whereas glycerophospholipids, sphingolipids, sterols and saccharolipids are common used as components of cell membranes. Lipids in fatty acyls, glycerophospholipids, sphingolipids and sterols classes play important roles in signaling. Although more than 36 million lipids can be identified or computationally generated, no single lipid database provides comprehensive information on lipids. Furthermore, the complex systematic or common names of lipids make the discovery of related information challenging.'),
(81620, 2018, 'bio', 'DeepSimulator: a deep simulator for Nanopore sequencing', 'Li, Yu|Han, Renmin|Bi, Chongwei|Li, Mo|Wang, Sheng|Gao, Xin', 'Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal, Saudi Arabia.|Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal, Saudi Arabia.|Biological and Environmental Sciences and Engineering (BESE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.|Biological and Environmental Sciences and Engineering (BESE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.|Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal, Saudi Arabia.|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|Saudi Arabia|Saudi Arabia', 14, 'Genome|High-Throughput Nucleotide Sequencing|Humans|Nanopores|Software', 'Journal Article', '10.1093/bioinformatics/bty223', '29659695', 'Oxford Nanopore sequencing is a rapidly developed sequencing technology in recent years. To keep pace with the explosion of the downstream data analytical tools, a versatile Nanopore sequencing simulator is needed to complement the experimental data as well as to benchmark those newly developed tools. However, all the currently available simulators are based on simple statistics of the produced reads, which have difficulty in capturing the complex nature of the Nanopore sequencing procedure, the main task of which is the generation of raw electrical current signals.'),
(81621, 2018, 'bio', 'Ribopeaks: a web tool for bacterial classification through m/z data from ribosomal proteins', 'Tomachewski, Douglas|Galvão, Carolina Weigert|de Campos Júnior, Arion|Guimarães, Alaine Margarete|Ferreira da Rocha, José Carlos|Etto, Rafael Mazer', 'Postgraduate Program in Applied Computing, Department of Computer Science, State University of Ponta Grossa, PR, Brazil.|Microbial Molecular Biology Laboratory, Sector of Biological and Health Sciences, State University of Ponta Grossa, PR, Brazil.|Postgraduate Program in Applied Computing, Department of Computer Science, State University of Ponta Grossa, PR, Brazil.|Postgraduate Program in Applied Computing, Department of Computer Science, State University of Ponta Grossa, PR, Brazil.|Postgraduate Program in Applied Computing, Department of Computer Science, State University of Ponta Grossa, PR, Brazil.|Postgraduate Program in Applied Computing, Department of Computer Science, State University of Ponta Grossa, PR, Brazil.', 'Brazil|Brazil|Brazil|Brazil|Brazil|Brazil', 2, 'Bacteria|Bacterial Proteins|Ribosomal Proteins|Software|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Journal Article', '10.1093/bioinformatics/bty215', '29659702', 'MALDI-TOF MS is a rapid, sensitive and economic tool for bacterial identification. Highly abundant bacterial proteins are detected by this technique, including ribosomal proteins (r-protein), and the generated mass spectra are compared with a MALDI-TOF MS spectra database. Currently, it allows mainly the classification of clinical bacteria due to the limited number of environmental bacteria included in the spectra database. We present a wide-ranging bacterium classifier tool, called Ribopeaks, which was created based on r-protein data from the Genbank. The Ribopeaks database has more than 28 500 bacterial taxonomic records. It compares the incoming m/z data from MALDI-TOF MS analysis with models stored in the Ribopeaks database created by machine learning and then taxonomically classifies the bacteria.'),
(81622, 2018, 'bio', 'B-cell receptor reconstruction from single-cell RNA-seq with VDJPuzzle', 'Rizzetto, Simone|Koppstein, David N P|Samir, Jerome|Singh, Mandeep|Reed, Joanne H|Cai, Curtis H|Lloyd, Andrew R|Eltahla, Auda A|Goodnow, Christopher C|Luciani, Fabio', 'Kirby Institute for Infection and Immunity, Sydney, Australia.|Kirby Institute for Infection and Immunity, Sydney, Australia.|Kirby Institute for Infection and Immunity, Sydney, Australia.|Immunogenomics, Garvan Institute of Medical Research, Sydney, Australia.|Immunogenomics, Garvan Institute of Medical Research, Sydney, Australia.|Kirby Institute for Infection and Immunity, Sydney, Australia.|School of Medical Sciences, UNSW, Sydney, Australia.|Kirby Institute for Infection and Immunity, Sydney, Australia.|Immunogenomics, Garvan Institute of Medical Research, Sydney, Australia.|Kirby Institute for Infection and Immunity, Sydney, Australia.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia', 24, 'Animals|Computational Biology|Humans|Mice|RNA|Receptors, Antigen, B-Cell|Sequence Analysis, RNA|Single-Cell Analysis|Transcriptome', 'Journal Article', '10.1093/bioinformatics/bty203', '29659703', 'The B-cell receptor (BCR) performs essential functions for the adaptive immune system including recognition of pathogen-derived antigens. The vast repertoire and adaptive variation of BCR sequences due to V(D)J recombination and somatic hypermutation necessitates single-cell characterization of BCR sequences. Single-cell RNA sequencing presents the opportunity for simultaneous capture of paired BCR heavy and light chains and the transcriptomic signature.'),
(81623, 2018, 'bio', 'IRscope: an online program to visualize the junction sites of chloroplast genomes', 'Amiryousefi, Ali|Hyvönen, Jaakko|Poczai, Peter', 'Organismal Evolutionary Biology Research Program, Faculty of Biology and Environmental Sciences, Viikki Plant Science Centre.|Organismal Evolutionary Biology Research Program, Faculty of Biology and Environmental Sciences, Viikki Plant Science Centre.|Finnish Museum of Natural History, University of Helsinki, FI-00014 Helsinki, Finland.', '-|-|Finland', 60, 'Genome, Chloroplast|Internet|Software', 'Journal Article', '10.1093/bioinformatics/bty220', '29659705', 'Genome plotting is performed using a wide range of visualizations tools each with emphasis on a different informative dimension of the genome. These tools can provide a deeper insight into the genomic structure of the organism.'),
(81624, 2018, 'bio', 'PhyloProfile: dynamic visualization and exploration of multi-layered phylogenetic profiles', 'Tran, Ngoc-Vinh|Greshake Tzovaras, Bastian|Ebersberger, Ingo', 'Department for Applied Bioinformatics, Institute of Cell Biology and Neuroscience, Goethe University, Frankfurt am Main, Germany.|Department for Applied Bioinformatics, Institute of Cell Biology and Neuroscience, Goethe University, Frankfurt am Main, Germany.|Department for Applied Bioinformatics, Institute of Cell Biology and Neuroscience, Goethe University, Frankfurt am Main, Germany.', 'Germany|Germany|Germany', 2, 'Phylogeny|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/bty225', '29659708', 'Phylogenetic profiles form the basis for tracing proteins and their functions across species and through time. Novel genome sequences nowadays often represent species from the remotest corner of the tree of life. Thus, phylogenetic profiling becomes increasingly important for functionally annotating this data and to integrate it into a comprehensive view on organismal evolution. To strengthen the link between the sharing of a gene across species and of the corresponding function, it is meanwhile common to complement phylogenetic profiles with additional information, such as domain architecture similarities between orthologs, or pairwise similarities of other protein features. However, there are few visualization tools that facilitate an intuitive integration of these various information layers. Here, we present PhyloProfile, an R-based tool to visualize, explore and analyze multi-layered phylogenetic profiles.'),
(81625, 2018, 'bio', 'SnapperDB: a database solution for routine sequencing analysis of bacterial isolates', 'Dallman, Timothy|Ashton, Philip|Schafer, Ulf|Jironkin, Aleksey|Painset, Anais|Shaaban, Sharif|Hartman, Hassan|Myers, Richard|Underwood, Anthony|Jenkins, Claire|Grant, Kathie', 'Gastrointestinal Bacteria Reference Unit, National Infections Service, Public Health England, London, UK.|Gastrointestinal Bacteria Reference Unit, National Infections Service, Public Health England, London, UK.|Infectious Disease Informatics, National Infections Service, Public Health England, London, UK.|Infectious Disease Informatics, National Infections Service, Public Health England, London, UK.|Gastrointestinal Bacteria Reference Unit, National Infections Service, Public Health England, London, UK.|Roslin Institute, University of Edinburgh, Scotland, UK.|Gastrointestinal Bacteria Reference Unit, National Infections Service, Public Health England, London, UK.|Infectious Disease Informatics, National Infections Service, Public Health England, London, UK.|Infectious Disease Informatics, National Infections Service, Public Health England, London, UK.|Gastrointestinal Bacteria Reference Unit, National Infections Service, Public Health England, London, UK.|Gastrointestinal Bacteria Reference Unit, National Infections Service, Public Health England, London, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 34, 'Databases, Factual|Genome|Sequence Analysis|Software|Whole Genome Sequencing', 'Journal Article', '10.1093/bioinformatics/bty212', '29659710', 'Real-time surveillance of infectious disease using whole genome sequencing data poses challenges in both result generation and communication. SnapperDB represents a set of tools to store bacterial variant data and facilitate reproducible and scalable analysis of bacterial populations. We also introduce the SNP address nomenclature to describe the relationship between isolates in a population to the single nucleotide resolution. We announce the release of SnapperDB v1.0 a program for scalable routine SNP analysis and storage of microbial populations.'),
(81626, 2018, 'bio', 'Computation and application of tissue-specific gene set weights', 'Frost, H Robert', 'Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH, USA.', 'United States', 1, 'Computational Biology|Humans|Physiological Phenomena|Proteins', 'Journal Article', '10.1093/bioinformatics/bty217', '29659714', 'Gene set testing, or pathway analysis, has become a critical tool for the analysis of high-dimensional genomic data. Although the function and activity of many genes and higher-level processes is tissue-specific, gene set testing is typically performed in a tissue agnostic fashion, which impacts statistical power and the interpretation and replication of results.'),
(81627, 2018, 'bio', 'A sequence family database built on ECOD structural domains', 'Liao, Yuxing|Schaeffer, R Dustin|Pei, Jimin|Grishin, Nick V', 'Department of Biophysics and Biochemistry, University of Texas Southwestern Medical Center, Dallas, TX, USA.|Department of Biophysics and Biochemistry, University of Texas Southwestern Medical Center, Dallas, TX, USA.|Department of Biophysics and Biochemistry, University of Texas Southwestern Medical Center, Dallas, TX, USA.|Department of Biophysics and Biochemistry, University of Texas Southwestern Medical Center, Dallas, TX, USA.', 'United States|United States|United States|United States', 3, 'Databases, Protein|Protein Domains|Proteins|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/bty214', '29659718', 'The ECOD database classifies protein domains based on their evolutionary relationships, considering both remote and close homology. The family group in ECOD provides classification of domains that are closely related to each other based on sequence similarity. Due to different perspectives on domain definition, direct application of existing sequence domain databases, such as Pfam, to ECOD struggles with several shortcomings.');
