INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(83054, 2019, 'bio', 'MEPSAnd: Minimum Energy Path Surface Analysis over n-dimensional surfaces', 'Marcos-Alcalde, Iñigo|López-Viñas, Eduardo|Gómez-Puertas, Paulino', 'Molecular Modelling Group, Centro de Biología Molecular Severo Ochoa, CBMSO (Consejo Superior de Investigaciones Científicas - Universidad Autónoma de Madrid, CSIC-UAM), CL Nicolás Cabrera, 1, E-28049 Madrid, Spain.|Biosciences Research Institute. School of Experimental Sciences, Universidad Francisco de Vitoria, UFV, Building E, Ctra. M-515 Pozuelo-Majadahonda Km 1,800, Pozuelo de Alarcón, Madrid, Spain.|Molecular Modelling Group, Centro de Biología Molecular Severo Ochoa, CBMSO (Consejo Superior de Investigaciones Científicas - Universidad Autónoma de Madrid, CSIC-UAM), CL Nicolás Cabrera, 1, E-28049 Madrid, Spain.', 'Spain|Spain|Spain', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz649', '31418769', 'n-dimensional energy surfaces are becoming computationally accessible, yet interpreting their information is not straightforward. We present MEPSAnd, an open source GUI-based program that natively calculates minimum energy paths across energy surfaces of any number of dimensions. Among other features, MEPSAnd can compute the path through lowest barriers and automatically provide a set of alternative paths. MEPSAnd offers distinct plotting solutions as well as direct python scripting.'),
(83055, 2019, 'bio', 'baerhunter: An R package for the discovery and analysis of expressed non-coding regions in bacterial RNA-seq data', 'Ozuna, A|Liberto, D|Joyce, R M|Arnvig, K B|Nobeli, I', 'Institute of Structural and Molecular Biology, Department of Biological Sciences, Birkbeck, Malet Street, London, UK.|Institute of Structural and Molecular Biology, Department of Biological Sciences, Birkbeck, Malet Street, London, UK.|Institute of Structural and Molecular Biology, Department of Biological Sciences, Birkbeck, Malet Street, London, UK.|Institute of Structural and Molecular Biology, University College London, London, UK.|Institute of Structural and Molecular Biology, Department of Biological Sciences, Birkbeck, Malet Street, London, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz643', '31418770', 'Standard bioinformatics pipelines for the analysis of bacterial transcriptomic data commonly ignore non-coding but functional elements e.g. small RNAs, long antisense RNAs or untranslated regions (UTRs) of mRNA transcripts. The root of this problem is the use of incomplete genome annotation files. Here, we present baerhunter, a coverage-based method implemented in R, that automates the discovery of expressed non-coding RNAs and UTRs from RNA-seq reads mapped to a reference genome. The core algorithm is part of a pipeline that facilitates downstream analysis of both coding and non-coding features. The method is simple, easy to extend and customize and, in limited tests with simulated and real data, compares favourably against the currently most popular alternative.'),
(83056, 2019, 'bio', 'LightDock goes information-driven', 'Roel-Touris, Jorge|Bonvin, AlexandreM J J|Jiménez-García, Brian', 'Bijvoet Center for Biomolecular Research, Faculty of Science-Chemistry, Utrecht University, Utrecht, The Netherlands.|Bijvoet Center for Biomolecular Research, Faculty of Science-Chemistry, Utrecht University, Utrecht, The Netherlands.|Bijvoet Center for Biomolecular Research, Faculty of Science-Chemistry, Utrecht University, Utrecht, The Netherlands.', 'Netherlands|Netherlands|Netherlands', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz642', '31418773', 'The use of experimental information has been demonstrated to increase the success rate of computational macromolecular docking. Many methods use information to post-filter the simulation output while others drive the simulation based on experimental restraints, which can become problematic for more complex scenarios such as multiple binding interfaces.'),
(83057, 2019, 'bio', 'HiLight-PTM: An Online Application to Aid Matching Peptide Pairs with Isotopically Labelled PTMs', 'Whitwell, Harry J|DiMaggio, Peter', 'Chemical Engineering, Imperial College London.|Chemical Engineering, Imperial College London.', '-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz654', '31424526', 'Database searching of isotopically labelled PTMs can be problematic and we frequently find that only one, or neither in a heavy/light pair are assigned. In such cases, having a pair of MS/MS spectra that differ due to an isotopic label can assist in identifying the relevant m/z values that support the correct peptide annotation or can be used for de novo sequencing.'),
(83058, 2019, 'bio', 'neoepiscope improves neoepitope prediction with multi-variant phasing', 'Wood, Mary A|Nguyen, Austin|Struck, Adam|Ellrott, Kyle|Nellore, Abhinav|Thompson, Reid F', 'Computational Biology Program, Oregon Health & Science University.|Computational Biology Program, Oregon Health & Science University.|Computational Biology Program, Oregon Health & Science University.|Computational Biology Program, Oregon Health & Science University.|Computational Biology Program, Oregon Health & Science University.|Computational Biology Program, Oregon Health & Science University.', '-|-|-|-|-|-', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz653', '31424527', 'The vast majority of tools for neoepitope prediction from DNA sequencing of complementary tumor and normal patient samples do not consider germline context or the potential for co-occurrence of two or more somatic variants on the same mRNA transcript. Without consideration of these phenomena, existing approaches are likely to produce both false positive and false negative results, resulting in an inaccurate and incomplete picture of the cancer neoepitope landscape. We developed neoepiscope chiefly to address this issue for single nucleotide variants (SNVs) and insertions/deletions (indels).'),
(83059, 2019, 'bio', 'Multi-Scale Structural Analysis of Proteins by Deep Semantic Segmentation', 'Eguchi, Raphael R|Huang, Po-Ssu', 'Dept. of Biochemistry, School of Medicine, Stanford University, Shriram Center for Bioengineering and Chemical Engineering, 443 via Ortega, Room 036, Stanford, CA 94305, USA.|Dept. of Bioengineering, Schools of Engineering and Medicine, Stanford University Shriram Center for Bioengineering and Chemical Engineering, 443 via Ortega, Room 036, Stanford, CA 94305, USA.', 'United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz650', '31424530', 'Recent advances in computational methods have facilitated large-scale sampling of protein structures, leading to breakthroughs in protein structural prediction and enabling de novo protein design. Establishing methods to identify candidate structures that can lead to native folds or designable structures remains a challenge, since few existing metrics capture high-level structural features such as architectures, folds, and conformity to conserved structural motifs. Convolutional Neural Networks (CNNs) have been successfully used in semantic segmentation - a subfield of image classification in which a class label is predicted for every pixel. Here, we apply semantic segmentation to protein structures as a novel strategy for fold identification and structure quality assessment.'),
(83060, 2019, 'bio', 'MEXCOwalk: Mutual Exclusion and Coverage Based Random Walk to Identify Cancer Modules', 'Ahmed, Rafsan|Baali, Ilyes|Erten, Cesim|Hoxha, Evis|Kazan, Hilal', 'Electrical and Computer Engineering Graduate Program, Antalya Bilim University, Antalya, Turkey.|Electrical and Computer Engineering Graduate Program, Antalya Bilim University, Antalya, Turkey.|Department of Computer Engineering, Antalya Bilim University, Antalya, Turkey.|Department of Computer Engineering, Antalya Bilim University, Antalya, Turkey.|Department of Computer Engineering, Antalya Bilim University, Antalya, Turkey.', 'Turkey|Turkey|Turkey|Turkey|Turkey', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz655', '31432076', 'Genomic analyses from large cancer cohorts have revealed the mutational heterogeneity problem which hinders the identification of driver genes based only on mutation profiles. One way to tackle this problem is to incorporate the fact that genes act together in functional modules. The connectivity knowledge present in existing protein-protein interaction networks together with mutation frequencies of genes and the mutual exclusivity of cancer mutations can be utilized to increase the accuracy of identifying cancer driver modules.'),
(83061, 2019, 'bio', 'CRDS: Consensus Reverse Docking System for Target Fishing', 'Lee, Aeri|Kim, Dongsup', 'Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea.|Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea.', '-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz656', '31432077', 'Identification of putative drug targets is a critical step for explaining the mechanism of drug action against multiple targets, finding new therapeutic indications for existing drugs, and unveiling the adverse drug reactions. One important approach is to use the molecular docking. However, its widespread utilization has been hindered by the lack of easy-to-use public servers. Therefore, it is vital to develop a streamlined computational tool for target prediction by molecular docking on a large scale.'),
(83062, 2019, 'bio', 'hypeR: An R Package for Geneset Enrichment Workflows', 'Federico, Anthony|Monti, Stefano', 'Bioinformatics Program, Boston University, Boston, MA, USA.|Bioinformatics Program, Boston University, Boston, MA, USA.', 'United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz700', '31498385', 'Geneset enrichment is a popular method for annotating high-throughput sequencing data. Existing tools fall short in providing the flexibility to tackle the varied challenges researchers face in such analyses, particularly when analyzing many signatures across multiple experiments. We present a comprehensive R package for geneset enrichment workflows that offers multiple enrichment, visualization, and sharing methods in addition to novel features such as hierarchical geneset analysis and built-in markdown reporting. hypeR is a one-stop solution to performing geneset enrichment for a wide audience and range of use cases.'),
(83063, 2019, 'bio', 'Spectrum: Fast density-aware spectral clustering for single and multi-omic data', 'John, Christopher R|Watson, David|Barnes, Michael R|Pitzalis, Costantino|Lewis, Myles J', 'Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London, UK.|Oxford Internet Institute, University of Oxford, 1xe2x80x89St. Giles, Oxford, UK.|Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London, UK.|Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London, UK.|Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz704', '31501851', 'Clustering patient omic data is integral to developing precision medicine because it allows the identification of disease subtypes. A current major challenge is the integration multi-omic data to identify a shared structure and reduce noise. Cluster analysis is also increasingly applied on single-omic data, for example, in single cell RNA-seq analysis for clustering the transcriptomes of individual cells. This technology has clinical implications. Our motivation was therefore to develop a flexible and effective spectral clustering tool for both single and multi-omic data.'),
(83064, 2019, 'bio', 'EpiSAFARI: Sensitive detection of valleys in epigenetic signals for enhancing annotations of functional elements', 'Harmanci, Arif|Serin Harmanci, Akdes|Swaminathan, Jyothishmathi|Gopalakrishnan, Vidya', 'School of Biomedical Informatics, Center for Precision Health, University of Texas Health Science Center, Houston, TX, USA.|School of Biomedical Informatics, Center for Systems Medicine, University of Texas Health Science Center, Houston, TX, USA.|Department of Pediatrics, M.D. Anderson Cancer Center, Houston, TX, USA.|Department of Pediatrics, M.D. Anderson Cancer Center, Houston, TX, USA.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz702', '31501853', 'Functional genomics experiments generate genomewide signal profiles that are dense information sources for annotating the regulatory elements. These profiles measure epigenetic activity at the nucleotide resolution and they exhibit distinctive patterns as they fluctuate along the genome. Most notable of these patterns are the valley patterns that are prevalently observed in assays such as ChIP Sequencing and bisulfite sequencing. The genomic positions of valleys pinpoint locations of cis-regulatory elements such as enhancers and insulators. Systematic identification of the valleys provides novel information for delineating the annotation of regulatory elements. Nevertheless, the valleys are not reported by majority of the analysis pipelines.'),
(83065, 2019, 'bio', 'scds: Computational Annotation of Doublets in Single-Cell RNA Sequencing Data', 'Bais, Abha S|Kostka, Dennis', 'Department of Developmental Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.|Department of Developmental Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.', 'United States|United States', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz698', '31501871', 'Single-cell RNA sequencing (scRNA-seq) technologies enable the study of transcriptional heterogeneity at the resolution of individual cells and have an increasing impact on biomedical research. However, it is known that these methods sometimes wrongly consider two or more cells as single cells, and that a number of so-called doublets is present in the output of such experiments. Treating doublets as single cells in downstream analyses can severely bias a studys conclusions, and therefore computational strategies for the identification of doublets are needed.'),
(83066, 2019, 'bio', 'BioBERT: a pre-trained biomedical language representation model for biomedical text mining', 'Lee, Jinhyuk|Yoon, Wonjin|Kim, Sungdong|Kim, Donghyeon|Kim, Sunkyu|So, Chan Ho|Kang, Jaewoo', 'Department of Computer Science and Engineering, Korea University, Seoul, Korea.|Department of Computer Science and Engineering, Korea University, Seoul, Korea.|Clova AI Research, Naver Corp, Seong-Nam, Korea.|Department of Computer Science and Engineering, Korea University, Seoul, Korea.|Department of Computer Science and Engineering, Korea University, Seoul, Korea.|Interdisciplinary Graduate Program in Bioinformatics, Korea University, Seoul, Korea.|Department of Computer Science and Engineering, Korea University, Seoul, Korea.', '-|-|-|-|-|-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz682', '31501885', 'Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows. With the progress in natural language processing, extracting valuable information from biomedical literature has gained popularity among researchers, and deep learning has boosted the development of effective biomedical text mining models. However, directly applying the advancements in natural language processing to biomedical text mining often yields unsatisfactory results due to a word distribution shift from general domain corpora to biomedical corpora. In this paper, we investigate how the recently introduced pre-trained language model BERT can be adapted for biomedical corpora.'),
(83067, 2019, 'bio', 'QUBIC2: A novel and robust biclustering algorithm for analyses and interpretation of large-scale RNA-Seq data', 'Xie, Juan|Ma, Anjun|Zhang, Yu|Liu, Bingqiang|Cao, Sha|Wang, Cankun|Xu, Jennifer|Zhang, Chi|Ma, Qin', 'Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA.|Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA.|Colleges of Computer Science and Technology, Jilin University, Changchun, China.|School of Mathematics, Shandong University, Jinan, China.|Department of Biostatistics, Indiana University, School of Medicine, Indianapolis, IN, USA.|Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA.|Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA.|Department of Medical & Molecular Genetics, Indiana University, School of Medicine, Indianapolis, IN, USA.|Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA.', 'United States|United States|China|China|India|United States|United States|India|United States', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz692', '31503285', 'The biclustering of large-scale gene expression data holds promising potential for detecting condition-specific functional gene modules (i.e., biclusters). However, existing methods do not adequately address a comprehensive detection of all significant bicluster structures and have limited power when applied to expression data generated by RNA-Sequencing (RNA-Seq), especially single-cell RNA-Seq (scRNA-Seq) data, where massive zero and low expression values are observed.'),
(83068, 2019, 'bio', 'Knot_pull-python package for biopolymer smoothing and knot detection', 'Jarmolinska, Aleksandra I|Gambin, Anna|Sulkowska, Joanna I', 'Centre of New Technologies.|Department of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw 02-097, Poland.|Centre of New Technologies.', '-|Poland|-', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz644', '31504154', 'The biggest hurdle in studying topology in biopolymers is the steep learning curve for actually seeing the knots in structure visualization. Knot_pull is a command line utility designed to simplify this process-it presents the user with a smoothing trajectory for provided structures (any number and length of protein, RNA or chromatin chains in PDB, CIF or XYZ format), and calculates the knot type (including presence of any links, and slipknots when a subchain is specified).'),
(83069, 2019, 'bio', 'Improved representation of sequence Bloom trees', 'Harris, Robert S|Medvedev, Paul', 'Department of Biology, The Pennsylvania State University, USA.|Department of Biology, The Pennsylvania State University, USA.', 'United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz662', '31504157', 'Algorithmic solutions to index and search biological databases are a fundamental part of bioinformatics, providing underlying components to many end-user tools. Inexpensive next generation sequencing has filled publicly available databases such as the Sequence Read Archive beyond the capacity of traditional indexing methods. Recently, the Sequence Bloom Tree (SBT) and its derivatives were proposed as a way to efficiently index such data for queries about transcript presence.'),
(83070, 2019, 'bio', 'miComplete: weighted quality evaluation of assembled microbial genomes', 'Hugoson, Eric|Lam, Wai Tin|Guy, Lionel', 'Department of Medical Biochemistry and Microbiology, Science for Life Laboratories, Uppsala University, Uppsala, Sweden.|Department of Microbial Population Biology, Max Planck Institute for Evolutionary Biology, D-24306 Plön, Germany.|Department of Medical Biochemistry and Microbiology, Science for Life Laboratories, Uppsala University, Uppsala, Sweden.', 'Sweden|Germany|Sweden', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz664', '31504158', 'Metagenomics and single-cell genomics have revolutionized the study of microorganisms, increasing our knowledge of microbial genomic diversity by orders of magnitude. A major issue pertaining to metagenome-assembled genomes (MAGs) and single-cell amplified genomes (SAGs) is to estimate their completeness and redundancy. Most approaches rely on counting conserved gene markers. In miComplete, we introduce a weighting strategy, where we normalize the presence/absence of markers by their median distance to the next marker in a set of complete reference genomes. This approach alleviates biases introduced by the presence/absence of shorter DNA pieces containing many markers, e.g. ribosomal protein operons.'),
(83071, 2019, 'bio', 'NACHO: an R package for quality control of NanoString nCounter data', 'Canouil, Mickaël|Bouland, Gerard A|Bonnefond, Amélie|Froguel, Philippe|t Hart, Leen M|Slieker, Roderick C', 'Université de Lille, CNRS, Institut Pasteur de Lille, UMR 8199 - EGID, F-59000 Lille, France.|Department of Cell and Chemical Biology, Leiden University Medical Center, ZC Leiden, The Netherlands.|Université de Lille, CNRS, Institut Pasteur de Lille, UMR 8199 - EGID, F-59000 Lille, France.|Université de Lille, CNRS, Institut Pasteur de Lille, UMR 8199 - EGID, F-59000 Lille, France.|Department of Cell and Chemical Biology, Leiden University Medical Center, ZC Leiden, The Netherlands.|Department of Cell and Chemical Biology, Leiden University Medical Center, ZC Leiden, The Netherlands.', 'France|Netherlands|France|France|Netherlands|Netherlands', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz647', '31504159', 'The NanoStringTM nCounter® is a platform for the targeted quantification of expression data in biofluids and tissues. While software by the manufacturer is available in addition to third parties packages, they do not provide a complete quality control (QC) pipeline. Here, we present NACHO (NAnostring quality Control dasHbOard), a comprehensive QC R-package. The package consists of three subsequent steps: summarize, visualize and normalize. The summarize function collects all the relevant data and stores it in a tidy format, the visualize function initiates a dashboard with plots of the relevant QC outcomes. It contains QC metrics that are measured by default by the manufacturer, but also calculates other insightful measures, including the scaling factors that are needed in the normalization step. In this normalization step, different normalization methods can be chosen to optimally preprocess data. Together, NACHO is a comprehensive method that optimizes insight and preprocessing of nCounter® data.'),
(83072, 2019, 'bio', 'Accurate loop calling for 3D genomic data with cLoops', 'Cao, Yaqiang|Chen, Zhaoxiong|Chen, Xingwei|Ai, Daosheng|Chen, Guoyu|McDermott, Joseph|Huang, Yi|Xiaoxiao, Guo|Han, Jing-Dong J', 'CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, China.|CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, China.|CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, China.|CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, China.|CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, China.|CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, China.|CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, China.|CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, China.|CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, China.', 'China|China|China|China|China|China|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz651', '31504161', 'Sequencing-based 3D genome mapping technologies can identify loops formed by interactions between regulatory elements hundreds of kilobases apart. Existing loop-calling tools are mostly restricted to a single data type, with accuracy dependent on a pre-defined resolution contact matrix or called peaks, and can have prohibitive hardware costs.'),
(83073, 2019, 'bio', 'Automatic local resolution-based sharpening of cryo-EM maps', 'Ramírez-Aportela, Erney|Vilas, Jose Luis|Glukhova, Alisa|Melero, Roberto|Conesa, Pablo|Martínez, Marta|Maluenda, David|Mota, Javier|Jiménez, Amaya|Vargas, Javier|Marabini, Roberto|Sexton, Patrick M|Carazo, Jose Maria|Oscar, Carlos|Sorzano, S', 'Biocomputing Unit, National Center for Biotechnology (CSIC), Darwin 3, Campus Univ. Autónoma de Madrid, Cantoblanco, Madrid, Spain.|Biocomputing Unit, National Center for Biotechnology (CSIC), Darwin 3, Campus Univ. Autónoma de Madrid, Cantoblanco, Madrid, Spain.|Drug Discovery Biology, Monash Institute of Pharmaceutical Sciences, Parkville, Victoria, Australia.|Biocomputing Unit, National Center for Biotechnology (CSIC), Darwin 3, Campus Univ. Autónoma de Madrid, Cantoblanco, Madrid, Spain.|Biocomputing Unit, National Center for Biotechnology (CSIC), Darwin 3, Campus Univ. Autónoma de Madrid, Cantoblanco, Madrid, Spain.|Biocomputing Unit, National Center for Biotechnology (CSIC), Darwin 3, Campus Univ. Autónoma de Madrid, Cantoblanco, Madrid, Spain.|Biocomputing Unit, National Center for Biotechnology (CSIC), Darwin 3, Campus Univ. Autónoma de Madrid, Cantoblanco, Madrid, Spain.|Biocomputing Unit, National Center for Biotechnology (CSIC), Darwin 3, Campus Univ. Autónoma de Madrid, Cantoblanco, Madrid, Spain.|Biocomputing Unit, National Center for Biotechnology (CSIC), Darwin 3, Campus Univ. Autónoma de Madrid, Cantoblanco, Madrid, Spain.|Dept of Anatomy and Cell Biology, McGill University, Rue University, Montreal, Canada.|Univ. Autónoma de Madrid, Campus Univ. Autónoma de Madrid, Cantoblanco, Madrid, Spain.|Drug Discovery Biology, Monash Institute of Pharmaceutical Sciences, Parkville, Victoria, Australia.|Biocomputing Unit, National Center for Biotechnology (CSIC), Darwin 3, Campus Univ. Autónoma de Madrid, Cantoblanco, Madrid, Spain.|Biocomputing Unit, National Center for Biotechnology (CSIC), Darwin 3, Campus Univ. Autónoma de Madrid, Cantoblanco, Madrid, Spain.|Biocomputing Unit, National Center for Biotechnology (CSIC), Darwin 3, Campus Univ. Autónoma de Madrid, Cantoblanco, Madrid, Spain.', 'Spain|Spain|Australia|Spain|Spain|Spain|Spain|Spain|Spain|Canada|Spain|Australia|Spain|Spain|Spain', 9, '-', 'Journal Article', '10.1093/bioinformatics/btz671', '31504163', 'Recent technological advances and computational developments have allowed the reconstruction of cryo-EM maps at near-atomic resolution. On a typical workflow and once the cryo-EM map has been calculated, a sharpening process is usually performed to enhance map visualization, a step that has proven very important in the key task of structural modelling. However, sharpening approaches, in general, neglects the local quality of the map, which is clearly suboptimal.'),
(83074, 2019, 'bio', 'MOOMIN - Mathematical explOration of Omics data on a MetabolIc Network', 'Pusa, Taneli|Ferrarini, Mariana Galvão|Andrade, Ricardo|Mary, Arnaud|Marchetti-Spaccamela, Alberto|Stougie, Leen|Sagot, Marie-France', 'INRIA Grenoble Rhône-Alpes, Montbonnot-Saint-Martin 38334, France.|Laboratoire de Biométrie et Biologie Évolutive, UMR 5558, CNRS, Université de Lyon, Université Lyon 1, Villeurbanne 69622, France.|INRIA Grenoble Rhône-Alpes, Montbonnot-Saint-Martin 38334, France.|INRIA Grenoble Rhône-Alpes, Montbonnot-Saint-Martin 38334, France.|Department of Computer, Automatic and Management Engineering, Sapienza University of Rome, Rome 00185, Italy.|CWI, 1098XG Amsterdam, The Netherlands.|INRIA Grenoble Rhône-Alpes, Montbonnot-Saint-Martin 38334, France.', 'France|France|France|France|Italy|Netherlands|France', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz584', '31504164', 'Analysis of differential expression of genes is often performed to understand how the metabolic activity of an organism is impacted by a perturbation. However, because the system of metabolic regulation is complex and all changes are not directly reflected in the expression levels, interpreting these data can be difficult.'),
(83075, 2019, 'bio', 'COMBAT-TB-NeoDB: fostering Tuberculosis research through integrative analysis using graph database technologies', 'Lose, Thoba|van Heusden, Peter|Christoffels, Alan', 'South African National Bioinformatics Institute, South African MRC Bioinformatics Unit, University of the Western Cape, Bellville, South Africa.|South African National Bioinformatics Institute, South African MRC Bioinformatics Unit, University of the Western Cape, Bellville, South Africa.|South African National Bioinformatics Institute, South African MRC Bioinformatics Unit, University of the Western Cape, Bellville, South Africa.', 'South Africa|South Africa|South Africa', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz658', '31504165', 'Recent advancements in genomic technologies have enabled high throughput cost-effective generation of omics data from M.tuberculosis (M.tb) isolates, which then gets shared via a number of heterogeneous publicly available biological data resources. Albeit useful, fragmented curation negatively impacts the researchers ability to leverage the data via federated queries.'),
(83076, 2019, 'bio', 'A likelihood method for estimating present-day human contamination in ancient male samples using low-depth X-chromosome data', 'Moreno-Mayar, J Víctor|Korneliussen, Thorfinn Sand|Dalal, Jyoti|Renaud, Gabriel|Albrechtsen, Anders|Nielsen, Rasmus|Malaspinas, Anna-Sapfo', 'Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.|Centre for Geogenetics, University of Copenhagen, Copenhagen, Denmark.|Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.|Centre for Geogenetics, University of Copenhagen, Copenhagen, Denmark.|The Bioinformatics Centre, Department of Biology, University of Copenhagen, Copenhagen, Denmark.|Department of Statistics, University of California, Berkeley, CA, USA.|Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.', 'Switzerland|Denmark|Switzerland|Denmark|Denmark|United States|Switzerland', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz660', '31504166', 'The presence of present-day human contaminating DNA fragments is one of the challenges defining ancient DNA (aDNA) research. This is especially relevant to the ancient human DNA field where it is difficult to distinguish endogenous molecules from human contaminants due to their genetic similarity. Recently, with the advent of high-throughput sequencing and new aDNA protocols, hundreds of ancient human genomes have become available. Contamination in those genomes has been measured with computational methods often developed specifically for these empirical studies. Consequently, some of these methods have not been implemented and tested for general use while few are aimed at low-depth nuclear data, a common feature in aDNA datasets.'),
(83077, 2019, 'bio', 'Regulatory annotation of genomic intervals based on tissue-specific expression QTLs', 'Xu, Tianlei|Jin, Peng|Qin, Zhaohui', 'Department of Mathematics and Computer Science, Emory University, Atlanta, GA, USA.|Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA.|Department of Biostatics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA.', 'United States|United States|United States', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz669', '31504167', 'Annotating a given genomic locus or a set of genomic loci is an important yet challenging task. This is especially true for the non-coding part of the genome which is enormous yet poorly understood. Since gene set enrichment analyses have demonstrated to be effective approach to annotate a set of genes, the same idea can be extended to explore the enrichment of functional elements or features in a set of genomic intervals to reveal potential functional connections.'),
(83078, 2019, 'bio', 'CROSSalive: a web server for predicting the in vivo structure of RNA molecules', 'Delli Ponti, Riccardo|Armaos, Alexandros|Vandelli, Andrea|Tartaglia, Gian Gaetano', 'Gene Function and Evolution, Centre for Genomic Regulation (CRG), Dr. Aiguader 88, Barcelona, Spain.|Gene Function and Evolution, Centre for Genomic Regulation (CRG), Dr. Aiguader 88, Barcelona, Spain.|Gene Function and Evolution, Centre for Genomic Regulation (CRG), Dr. Aiguader 88, Barcelona, Spain.|Gene Function and Evolution, Centre for Genomic Regulation (CRG), Dr. Aiguader 88, Barcelona, Spain.', 'Spain|Spain|Spain|Spain', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz666', '31504168', 'RNA structure is difficult to predict in vivo due to interactions with enzymes and other molecules. Here we introduce CROSSalive, an algorithm to predict the single- and double-stranded regions of RNAs in vivo using predictions of protein interactions.'),
(83079, 2019, 'bio', 'A Gaussian process-based definition reveals new and bona fide genetic interactions com-pared to a multiplicative model in the Gram-negative Escherichia coli', 'Kumar, Ashwani|Hosseinnia, Ali|Gagarinova, Alla|Phanse, Sadhna|Kim, Sunyoung|Aly, Khaled A|Zilles, Sandra|Babu, Mohan', 'Department of Computer Science, University of Regina, 3737 Wascana Parkway, Regina, SK S4S 0A2, Canada.|Department of Biochemistry, University of Regina, 3737 Wascana Parkway, Regina, SK S4S 0A2, Canada.|Department of Biochemistry, University of Saskatchewan, Saskatoon, SK S7N 5E5, Canada.|Department of Biochemistry, University of Regina, 3737 Wascana Parkway, Regina, SK S4S 0A2, Canada.|Department of Biochemistry, University of Regina, 3737 Wascana Parkway, Regina, SK S4S 0A2, Canada.|Department of Biochemistry, University of Regina, 3737 Wascana Parkway, Regina, SK S4S 0A2, Canada.|Department of Computer Science, University of Regina, 3737 Wascana Parkway, Regina, SK S4S 0A2, Canada.|Department of Biochemistry, University of Regina, 3737 Wascana Parkway, Regina, SK S4S 0A2, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz673', '31504172', 'A digenic genetic interaction (GI) is observed when mutations in two genes within the same organism yield a phenotype that is different from the expected, given each mutations individual effects. While multiplicative scoring is widely applied to define GIs, revealing underlying gene functions, it remains unclear if it is the most suitable choice for scoring GIs in Escherichia coli. Here, we assess many different definitions, including the multiplicative model, for mapping functional links between genes and pathways in E. coli.'),
(83080, 2019, 'bio', 'pCRM1exportome: database of predicted CRM1-dependent Nuclear Export Signal (NES) motifs in cancer-related genes', 'Lee, Yoonji|Baumhardt, Jordan M|Pei, Jimin|Chook, Yuh Min|Grishin, Nick V', 'Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA.|Department of Pharmacology, University of Texas Southwestern Medical Center, Dallas, TX, USA.|Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, Dallas, TX, USA.|Department of Pharmacology, University of Texas Southwestern Medical Center, Dallas, TX, USA.|Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA.', 'United States|United States|United States|United States|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz657', '31504173', 'The consensus pattern of Nuclear Export Signal (NES) is a short sequence motif that is commonly identified in protein sequences, whether the motif acts as an NES (true positive) or not (false positive). Finding more plausible NES functioning regions among the vast array of consensus-matching segments would provide an interesting resource for further experimental validation. Better defined NES should also allow meaningful mapping of cancer-related mutation positions, leading to plausible explanations for the relationship between nuclear export and disease.'),
(83081, 2019, 'bio', 'A powerful and flexible weighted distance-based method incorporating interactions between DNA methylation and environmental factors on health outcomes', 'Wang, Ya|Qian, Min|Tang, Deliang|Herbstman, Julie|Perera, Frederica|Wang, Shuang', 'Department of Biostatistics.|Department of Biostatistics.|Columbia Center for Childrens Environmental Health, Department of Environmental Health Science, Mailman School of Public Health, Columbia University, New York, NY 10032, USA.|Columbia Center for Childrens Environmental Health, Department of Environmental Health Science, Mailman School of Public Health, Columbia University, New York, NY 10032, USA.|Columbia Center for Childrens Environmental Health, Department of Environmental Health Science, Mailman School of Public Health, Columbia University, New York, NY 10032, USA.|Department of Biostatistics.', 'United States|United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz630', '31504174', 'Deoxyribonucleic acid (DNA) methylation plays a crucial role in human health. Studies have demonstrated associations between DNA methylation and environmental factors with evidence also supporting the idea that DNA methylation may modify the risk of environmental factors on health outcomes. However, due to high dimensionality and low study power, current studies usually focus on finding differential methylation on health outcomes at CpG level or gene level combining multiple CpGs and/or finding environmental effects on health outcomes but ignoring their interactions on health outcomes. Here we introduce the idea of a pseudo-data matrix constructed with cross-product terms between CpGs and environmental factors that are able to capture their interactions. We then develop a powerful and flexible weighted distance-based method with the pseudo-data matrix where association strength was used as weights on CpGs, environmental factors and their interactions to up-weight signals and down-weight noises in distance calculations.'),
(83082, 2019, 'bio', 'NExUS: Bayesian simultaneous network estimation across unequal sample sizes', 'Das, Priyam|Peterson, Christine B|Do, Kim-Anh|Akbani, Rehan|Baladandayuthapani, Veerabhadran', 'Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.|Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.|Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.|Department of Bioinformatics & Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.|Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.', 'United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz636', '31504175', 'Network-based analyses of high-throughput genomics data provide a holistic, systems-level understanding of various biological mechanisms for a common population. However, when estimating multiple networks across heterogeneous sub-populations, varying sample sizes pose a challenge in the estimation and inference, as network differences may be driven by differences in power. We are particularly interested in addressing this challenge in the context of proteomic networks for related cancers, as the number of subjects available for rare cancer (sub-)types is often limited.'),
(83083, 2019, 'bio', 'breakpointR: an R/Bioconductor package to localize strand state changes in Strand-seq data', 'Porubsky, David|Sanders, Ashley D|Taudt, Aaron|Colomé-Tatché, Maria|Lansdorp, Peter M|Guryev, Victor', 'European Research Institute for the Biology of Ageing, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.|Terry Fox Laboratory, BC Cancer Agency, Vancouver, BC, Canada.|European Research Institute for the Biology of Ageing, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.|European Research Institute for the Biology of Ageing, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.|European Research Institute for the Biology of Ageing, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.|European Research Institute for the Biology of Ageing, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.', 'Netherlands|Canada|Netherlands|Netherlands|Netherlands|Netherlands', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz681', '31504176', 'Strand-seq is a specialized single-cell DNA sequencing technique centered around the directionality of single-stranded DNA. Computational tools for Strand-seq analyses must capture the strand-specific information embedded in these data.'),
(83084, 2019, 'bio', 'ChaperISM: improved chaperone binding prediction using position-independent scoring matrices', 'Gutierres, M B B|Bonorino, C B C|Rigo, M M', 'Laboratório de Imunoterapia, Universidade Federal de Ciências da Saúde de Porto Alegre, Porto Alegre, Brazil.|Laboratório de Imunoterapia, Universidade Federal de Ciências da Saúde de Porto Alegre, Porto Alegre, Brazil.|Escola de Medicina, Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre, Brazil.', 'Brazil|Brazil|Brazil', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz670', '31504177', 'Understanding the mechanisms of client protein interaction with Hsp70 chaperones is essential to analyze the complex dynamics in the context of normal or dysregulated metabolism. Because Hsp70 can bind millions of proteins, including key molecules involved in processes of stemness, tumorigenesis, and survival, in silico prediction of Hsp70 interactions has great value in validating possible new clients. Currently, two algorithms are available to predict binding to DnaK - the bacterial Hsp70 - but both are based on amino acid sequence and energy calculations of qualitative information - binders and non-binders.'),
(83085, 2019, 'bio', 'Partition: a surjective mapping approach for dimensionality reduction', 'Millstein, Joshua|Battaglin, Francesca|Barrett, Malcolm|Cao, Shu|Zhang, Wu|Stintzing, Sebastian|Heinemann, Volker|Lenz, Heinz-Josef', 'Department of Preventive Medicine, Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.|Department of Medicine, Division of Medical Oncology, Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.|Department of Preventive Medicine, Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.|Department of Preventive Medicine, Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.|Department of Medicine, Division of Medical Oncology, Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.|Medical Department, Division of Oncology and Hematology, Charité Universitaetsmedizin Berlin, Berlin, Germany.|Department of Medicine III, University Hospital Munich, Munich, Germany.|Department of Medicine, Division of Medical Oncology, Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.', 'United States|United States|United States|United States|United States|Germany|Germany|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz661', '31504178', 'Large amounts of information generated by genomic technologies are accompanied by statistical and computational challenges due to redundancy, badly behaved data and noise. Dimensionality reduction (DR) methods have been developed to mitigate these challenges. However, many approaches are not scalable to large dimensions or result in excessive information loss.'),
(83086, 2019, 'bio', 'Accurate estimation of microbial sequence diversity with Distanced', 'Hackmann, Timothy J', 'Department of Animal Science, University of California, Davis, CA, USA.', 'United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz668', '31504180', 'Microbes are the most diverse organisms on the planet. Deep sequencing of ribosomal DNA suggests thousands of different microbes may be present in a single sample. However, errors in sequencing have made any estimate of within-sample (alpha) diversity uncertain.'),
(83087, 2019, 'bio', 'Analysis of several key factors influencing deep learning-based inter-residue contact prediction', 'Wu, Tianqi|Hou, Jie|Adhikari, Badri|Cheng, Jianlin', 'Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA.|Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA.|Department of Mathematics and Computer Science, University of Missouri, St. Louis, MO, USA.|Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz679', '31504181', 'Deep learning has become the dominant technology for protein contact prediction. However, the factors that affect the performance of deep learning in contact prediction have not been systematically investigated.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(83088, 2019, 'bio', 'Gene relevance based on multiple evidences in complex networks', 'Nanni, Noemi Di|Gnocchi, Matteo|Moscatelli, Marco|Milanesi, Luciano|Mosca, Ettore', 'Institute of Biomedical Technologies, Italian National Research Council, Viale Fratelli Cervi 93, Segrate (MI), Italy.|Institute of Biomedical Technologies, Italian National Research Council, Viale Fratelli Cervi 93, Segrate (MI), Italy.|Institute of Biomedical Technologies, Italian National Research Council, Viale Fratelli Cervi 93, Segrate (MI), Italy.|Institute of Biomedical Technologies, Italian National Research Council, Viale Fratelli Cervi 93, Segrate (MI), Italy.|Institute of Biomedical Technologies, Italian National Research Council, Viale Fratelli Cervi 93, Segrate (MI), Italy.', 'Italy|Italy|Italy|Italy|Italy', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz652', '31504182', 'Multi-omics approaches offer the opportunity to reconstruct a more complete picture of the molecular events associated with human diseases, but pose challenges in data analysis. Network-based methods for the analysis of multi-omics leverage the complex web of macromolecular interactions occurring within cells to extract significant patterns of molecular alterations involving molecular systems. Existing network-based approaches typically address specific combinations of omics and are limited in terms of the number of layers that can be jointly analysed. In this study, we investigate the application of network diffusion to quantify gene relevance on the basis of multiple evidences (layers).'),
(83089, 2019, 'bio', 'EasyModel: user-friendly tool for building and analysis of simple mathematical models in systems biology', 'Bartolome, Jordi|Alves, Rui|Solsona, Francesc|Teixido, Ivan', 'Dept. of Computer Science and INSPIRES, Universitat de Lleida, Lleida, Spain.|Dept. of Basic Medical Sciences and IRBLleida, Universitat de Lleida, Lleida, Spain.|Dept. of Computer Science and INSPIRES, Universitat de Lleida, Lleida, Spain.|Dept. of Computer Science and INSPIRES, Universitat de Lleida, Lleida, Spain.', 'Spain|Spain|Spain|Spain', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz659', '31504183', 'EasyModel is a new user-friendly web application that contains ready-for-simulation versions of the BioModels Database, and allows for the intuitive creation of new models. Its main target audience is the experimental biologist and students of bioinformatics or systems biology without programming skills. Expert users can also benefit from it by implementing basic models quickly and downloading the code for further tailoring.'),
(83090, 2019, 'bio', 'A Latent Unknown Clustering Integrating Multi-Omics Data (LUCID) with Phenotypic Traits', 'Peng, Cheng|Wang, Jun|Asante, Isaac|Louie, Stan|Jin, Ran|Chatzi, Lida|Casey, Graham|Thomas, Duncan C|Conti, David V', 'Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.|Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.|Department of Clinical Pharmacy, School of Pharmacy, University of Southern California, Los Angeles, CA, USA.|Department of Clinical Pharmacy, School of Pharmacy, University of Southern California, Los Angeles, CA, USA.|Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.|Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.|Department of Clinical Pharmacy, School of Pharmacy, University of Southern California, Los Angeles, CA, USA.|Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.|Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz667', '31504184', 'Epidemiologic, clinical, and translational studies are increasingly generating multiplatform omics data. Methods that can integrate across multiple high-dimensional data types while accounting for differential patterns are critical for uncovering novel associations and underlying relevant subgroups.'),
(83091, 2019, 'bio', 'Tissue-specific deconvolution of immune cell composition by integrating bulk and single-cell transcriptomes', 'Chen, Ziyi|Ji, Chengyang|Shen, Qin|Liu, Wei|Qin, F Xiao-Feng|Wu, Aiping', 'Center for Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China, Suzhou Institute of Systems Medicine, Suzhou, China.|Center for Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China, Suzhou Institute of Systems Medicine, Suzhou, China.|Center for Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China, Suzhou Institute of Systems Medicine, Suzhou, China.|Center for Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China, Suzhou Institute of Systems Medicine, Suzhou, China.|Center for Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China, Suzhou Institute of Systems Medicine, Suzhou, China.|Center for Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China, Suzhou Institute of Systems Medicine, Suzhou, China.', 'China|China|China|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz672', '31504185', 'Many methods have been developed to estimate immune cell composition from tissue transcriptomes. One common characteristic of these methods is that they are trained using a set of general immune cell transcriptomes that ignores tissue specificities. However, as immune cells are localized in different tissues, they may have distinct expression profiles. Hence, calculations that use general signature matrices may hinder the deconvolution accuracy.'),
(83092, 2019, 'bio', 'DeepCOP - Deep Learning-Based Approach to Predict Gene Regulating Effects of Small Molecules', 'Woo, Godwin|Fernandez, Michael|Hsing, Michael|Lack, Nathan A|Cavga, Ayse Derya|Cherkasov, Artem', 'Vancouver Prostate Centre, Department of Urologic Sciences, Faculty of Medicine, University of British Columbia, 2660 Oak Street, Vancouver, British Columbia, Canada.|Vancouver Prostate Centre, Department of Urologic Sciences, Faculty of Medicine, University of British Columbia, 2660 Oak Street, Vancouver, British Columbia, Canada.|Vancouver Prostate Centre, Department of Urologic Sciences, Faculty of Medicine, University of British Columbia, 2660 Oak Street, Vancouver, British Columbia, Canada.|Vancouver Prostate Centre, Department of Urologic Sciences, Faculty of Medicine, University of British Columbia, 2660 Oak Street, Vancouver, British Columbia, Canada.|Chemical and Biological Engineering, Koxc3xa7 University, Rumelifeneri Yolu, Sariyer, Istanbul, Turkey.|Vancouver Prostate Centre, Department of Urologic Sciences, Faculty of Medicine, University of British Columbia, 2660 Oak Street, Vancouver, British Columbia, Canada.', 'Canada|Canada|Canada|Canada|Turkey|Canada', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz645', '31504186', 'Recent advances in the areas of bioinformatics and chemogenomics are poised to accelerate the discovery of small-molecule regulators of cell development. Combining large genomics and molecular data sources with powerful deep learning techniques has the potential to revolutionize predictive biology. In this study, we present Deep Compound Profiler (DeepCOP), a deep learning based model that can predict gene regulating effects of low-molecular weight compounds. This model can be used for direct identification of a drug candidate causing a desired gene expression response, without utilizing any information on its interactions with protein target(s).'),
(83093, 2019, 'bio', 'SimkaMin: fast and resource frugal de novo comparative metagenomics', 'Benoit, Gaetan|Mariadassou, Mahendra|Robin, Stxc3xa9phane|Schbath, Sophie|Peterlongo, Pierre|Lemaitre, Claire', 'Univ Rennes, Inria, CNRS, IRISA, F-35000 Rennes, France.|MaIAGE, INRA, Universitxc3xa9 Paris-Saclay, Jouy-en-Josas, France.|UMR MIA-Paris, AgroParisTech, INRA, Universitxc3xa9 Paris-Saclay, Paris, France.|MaIAGE, INRA, Universitxc3xa9 Paris-Saclay, Jouy-en-Josas, France.|Univ Rennes, Inria, CNRS, IRISA, F-35000 Rennes, France.|Univ Rennes, Inria, CNRS, IRISA, F-35000 Rennes, France.', 'France|France|France|France|France|France', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz685', '31504187', 'De novo comparative metagenomics is one of the most straightforward ways to analyze large sets of metagenomics data. Latest methods use the fraction of shared k-mers to estimate genomic similarity between read sets. However, those methods, while extremely efficient, are still limited by computational needs for practical usage outside of large computing facilities.'),
(83094, 2019, 'bio', 'Phylogenetic Tree-based Microbiome Association Test', 'Kim, Kang Jin|Park, Jaehyun|Park, Sang-Chul|Won, Sungho', 'Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, South Korea.|Interdisciplinary Program for Bioinformatics, College of Natural Science, Seoul National University, Seoul, South Korea.|Institute of Health and Environment, Seoul National University, Seoul, South Korea.|Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, South Korea.', '-|-|-|-', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz686', '31504188', 'Ecological patterns of the human microbiota exhibit high inter-subject variation, with few operational taxonomic units (OTUs) shared across individuals. To overcome these issues, non-parametric approaches, such as the Mann-Whitney U-test and Wilcoxon rank-sum test, have often been used to identify OTUs associated with host diseases. However, these approaches only use the ranks of observed relative abundances, leading to information loss, and are associated with high false-negative rates. In this study, we propose a phylogenetic tree-based microbiome association test (TMAT) to analyze the associations between microbiome OTU abundances and disease phenotypes. Phylogenetic trees illustrate patterns of similarity among different OTUs, and TMAT provides an efficient method for utilizing such information for association analyses. The proposed TMAT provides test statistics for each node, which are combined to identify mutations associated with host diseases.'),
(83095, 2019, 'bio', 'MeLAD: an integrated resource for metalloenzyme-ligand associations', 'Li, Gen|Su, Yu|Yan, Yu-Hang|Peng, Jia-Yi|Dai, Qing-Qing|Ning, Xiang-Li|Zhu, Cheng-Long|Fu, Chen|McDonough, Michael A|Schofield, Christopher J|Huang, Cheng|Li, Guo-Bo', 'Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education, West China School of Pharmacy, Sichuan University, Chengdu, Sichuan, China.|College of Cybersecurity, Sichuan University, Chengdu, Sichuan, China.|Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education, West China School of Pharmacy, Sichuan University, Chengdu, Sichuan, China.|College of Cybersecurity, Sichuan University, Chengdu, Sichuan, China.|Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education, West China School of Pharmacy, Sichuan University, Chengdu, Sichuan, China.|Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education, West China School of Pharmacy, Sichuan University, Chengdu, Sichuan, China.|Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education, West China School of Pharmacy, Sichuan University, Chengdu, Sichuan, China.|Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education, West China School of Pharmacy, Sichuan University, Chengdu, Sichuan, China.|Department of Chemistry, University of Oxford, 12 Mansfield Road, Oxford, OX1 3TA, United Kingdom.|Department of Chemistry, University of Oxford, 12 Mansfield Road, Oxford, OX1 3TA, United Kingdom.|College of Cybersecurity, Sichuan University, Chengdu, Sichuan, China.|Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education, West China School of Pharmacy, Sichuan University, Chengdu, Sichuan, China.', 'China|China|China|China|China|China|China|China|United Kingdom|United Kingdom|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz648', '31504189', 'Metalloenzymes have emerged as attractive targets for therapeutic intervention owing to their central roles in various biological processes and pathological situations. The fast-growing body of structural data on metalloenzyme-ligand interactions is facilitating efficient drug discovery targeting metalloenzymes. However, there remains a shortage of specific databases that can provide centralized, interconnected information exclusive to metalloenzyme-ligand associations.'),
(83096, 2019, 'bio', 'DDAP: docking domain affinity and biosynthetic pathway prediction tool for type I polyketide synthases', 'Li, Tingyang|Tripathi, Ashootosh|Yu, Fengan|Sherman, David H|Rao, Arvind', 'Department of Computational Medicine and Bioinformatics, Ann Arbor, Michigan, USA.|Life Sciences Institute, Ann Arbor, Michigan, USA.|Life Sciences Institute, Ann Arbor, Michigan, USA.|Life Sciences Institute, Ann Arbor, Michigan, USA.|Department of Computational Medicine and Bioinformatics, Ann Arbor, Michigan, USA.', 'United States|United States|United States|United States|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz677', '31504190', 'DDAP is a tool for predicting the biosynthetic pathways of the products of type I modular polyketide synthase (PKS) with the focus on providing a more accurate prediction of the ordering of proteins and substrates in the pathway. In this study, the module docking domain (DD) affinity prediction performance on a hold-out testing data set reached 0.88 as measured by the area under the receiver operating characteristic (ROC) curve (AUC), the Mean Reciprocal Ranking (MRR) of pathway prediction reached 0.67. DDAP has advantages compared to previous informatics tools in several aspects: (i) it does not rely on large databases, making it a high efficiency tool, (ii) the predicted DD affinity is represented by a probability (0 to 1), which is more intuitive than raw scores, (iii) its performance is competitive compared to the current popular rule-based algorithm. DDAP is so far the first machine learning based algorithm for type I PKS DD affinity and pathway prediction. We also established the first database of type I modular PKSs, featuring a comprehensive annotation of available docking domains information in bacterial biosynthetic pathways.'),
(83097, 2019, 'bio', 'Non-homogeneous dynamic Bayesian networks with edge-wise sequentially coupled parameters', 'Kamalabad, Mahdi Shafiee|Grzegorczyk, Marco', 'Bernoulli Institute, Faculty of Science and Engineering, Groningen University, Groningen, 9747 AG, Netherlands.|Bernoulli Institute, Faculty of Science and Engineering, Groningen University, Groningen, 9747 AG, Netherlands.', 'Netherlands|Netherlands', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz690', '31504191', 'Non-homogeneous dynamic Bayesian networks (NH-DBNs) are a popular tool for learning networks with time-varying interaction parameters. A multiple changepoint process is used to divide the data into disjoint segments and the network interaction parameters are assumed to be segment-specific. The objective is to infer the network structure along with the segmentation and the segment-specific parameters from the data. The conventional (uncoupled) NH-DBNs do not allow for information exchange among segments, and the interaction parameters have to be learned separately for each segment. More advanced coupled NH-DBN models allow the interaction parameters to vary but enforce them to stay similar over time. As the enforced similarity of the network parameters can have counter-productive effects, we propose a new consensus NH-DBN model that combines features of the uncoupled and the coupled NH-DBN. The new model infers for each individual edge whether its interaction parameter stays similar over time (and should be coupled) or if it changes from segment to segment (and should stay uncoupled).'),
(83098, 2019, 'bio', 'A Reinforcement-Learning-Based Approach to Enhance Exhaustive Protein Loop Sampling', 'Barozet, Amélie|Molloy, Kevin|Vaisset, Marc|Siméon, Thierry|Cortés, Juan', 'LAAS-CNRS, Université de Toulouse, CNRS, Toulouse, France.|James Madison University, Harrisonburg, Virginia, USA.|LAAS-CNRS, Université de Toulouse, CNRS, Toulouse, France.|LAAS-CNRS, Université de Toulouse, CNRS, Toulouse, France.|LAAS-CNRS, Université de Toulouse, CNRS, Toulouse, France.', 'France|United States|France|France|France', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz684', '31504192', 'Loop portions in proteins are involved in many molecular interaction processes. They often exhibit a high degree of flexibility, which can be essential for their function. However, molecular modeling approaches usually represent loops using a single conformation. Although this conformation may correspond to a (meta-)stable state, it does not always provide a realistic representation.'),
(83099, 2019, 'bio', 'Identifying Molecular Recognition Features in Intrinsically Disordered Regions of Proteins by Transfer Learning', 'Hanson, Jack|Litfin, Thomas|Paliwal, Kuldip|Zhou, Yaoqi', 'Signal Processing Laboratory, Griffith University, Brisbane, Australia.|Institute for Glycomics, School of Information and Communication Technology, Griffith University, Southport, Australia.|Signal Processing Laboratory, Griffith University, Brisbane, Australia.|Institute for Glycomics, School of Information and Communication Technology, Griffith University, Southport, Australia.', 'Australia|Australia|Australia|Australia', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz691', '31504193', 'Protein intrinsic disorder describes the tendency of sequence residues to not fold into a rigid three-dimensional shape by themselves. However, some of these disordered regions can transition from disorder to order when interacting with another molecule in segments known as Molecular Recognition Features (MoRFs). Previous analysis has shown that these MoRF regions are indirectly encoded within the prediction of residue disorder as low-confidence predictions (ie. in a semi-disordered state P(D)xe2x89x880.5). Thus, what has been learned for disorder prediction may be transferable to MoRF prediction. Transferring the internal characterization of protein disorder for the prediction of MoRF residues would allow us to take advantage of the large training set available for disorder prediction, enabling the training of larger analytical models than is currently feasible on the small number of currently-available annotated MoRF proteins. In this paper we propose a new method for MoRF prediction by transfer learning from the SPOT-Disorder2 ensemble models built for disorder prediction.'),
(83100, 2019, 'bio', 'dbgap2x: An R package to explore and extract data from the database of Genotypes and Phenotypes (dbGaP)', 'Versmée, Grégoire|Versmée, Laura|Dusenne, Mikaël|Jalali, Niloofar|Avillach, Paul', 'Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.|Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.|Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.|Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.|Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.', 'United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz680', '31504194', 'Based on the Genomic Data Sharing Policy issued in August 2007, the National Institutes of Health (NIH) has supported several repositories such as the database of Genotypes and Phenotypes (dbGaP). dbGaP is an online repository that provides access to large-scale genetic and phenotypic datasets with more than 1,000 studies. However, navigating the website and understanding the relationship between the studies are not easy tasks. Moreover, the decryption of the files is a complex procedure. In this study we propose the dbgap2x R package that covers a broad range of functions for searching dbGaP studies, exploring the characteristics of a study and easily decrypting the files from dbGaP.'),
(83101, 2019, 'bio', 'VisFeature: a stand-alone program for visualizing and analyzing statistical features of biological sequences', 'Wang, Jun|Du, Pu-Feng|Xue, Xin-Yu|Li, Guang-Ping|Zhou, Yuan-Ke|Zhao, Wei|Lin, Hao|Chen, Wei', 'College of Intelligence and Computing, Tianjin University, Tianjin, China.|College of Intelligence and Computing, Tianjin University, Tianjin, China.|College of Intelligence and Computing, Tianjin University, Tianjin, China.|College of Intelligence and Computing, Tianjin University, Tianjin, China.|College of Intelligence and Computing, Tianjin University, Tianjin, China.|College of Intelligence and Computing, Tianjin University, Tianjin, China.|Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.|Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China.', 'China|China|China|China|China|China|China|China', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz689', '31504195', 'Many efforts have been made in developing bioinformatics algorithms to predict functional attributes of genes and proteins from their primary sequences. One challenge in this process is to intuitively analyze and to understand the statistical features that have been selected by heuristic or iterative methods. In this paper, we developed VisFeature, which aims to be a helpful software tool that allows the users to intuitively visualize and analyze statistical features of all types of biological sequence, including DNA, RNA and proteins. VisFeature also integrates sequence data retrieval, multiple sequence alignments, and statistical feature generation functions.'),
(83102, 2019, 'bio', 'Unification of miRNA and isomiR research: the mirGFF3 format and the mirtop API', 'Desvignes, Thomas|Loher, Phillipe|Eilbeck, Karen|Ma, Jeffery|Urgese, Gianvito|Fromm, Bastian|Sydes, Jason|Aparicio-Puerta, Ernesto|Barrera, Victor|Espxc3xadn, Roderic|Thibord, Florian|Ros, Xavier Bofill-De|Londin, Eric|Telonis, Aristeidis G|Ficarra, Elisa|Friedlxc3xa4nder, Marc R|Postlethwait, John H|Rigoutsos, Isidore|Hackenberg, Michael|Vlachos, Ioannis S|Halushka, Marc K|Pantano, Lorena', 'Institute of Neuroscience, University of Oregon, Eugene OR, USA.|Computational Medicine Center, Thomas Jefferson University, Philadelphia, PA, USA.|University of Utah, Biomedical Informatics, UT, USA.|Computational Medicine Center, Thomas Jefferson University, Philadelphia, PA, USA.|Department of Control and Computer Engineering, Politecnico di Torino, Italy.|Science for Life Laboratory, Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, Stockholm, Sweden.|Institute of Neuroscience, University of Oregon, Eugene OR, USA.|Computational Epigenomics Lab., Genetics Department & Biotechnology Institute & Biosanitary Institute, University of Granada, Spain.|Department of Biostatistics, Harvard T.H. Chan School of Public Health. Boston, MA, USA.|Universitat Oberta de Catalunya. Spain.|Sorbonne Universitxc3xa9, Pierre Louis Doctoral School of Public Health, Paris, France.|RNA Mediated Gene Regulation Section, RNA Biology Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD.|Computational Medicine Center, Thomas Jefferson University, Philadelphia, PA, USA.|Computational Medicine Center, Thomas Jefferson University, Philadelphia, PA, USA.|Department of Control and Computer Engineering, Politecnico di Torino, Italy.|Science for Life Laboratory, Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, Stockholm, Sweden.|Institute of Neuroscience, University of Oregon, Eugene OR, USA.|Computational Medicine Center, Thomas Jefferson University, Philadelphia, PA, USA.|Computational Epigenomics Lab., Genetics Department & Biotechnology Institute & Biosanitary Institute, University of Granada, Spain.|Non-coding Research Lab, Cancer Research Institute | Harvard Medical School Initiative for RNA Medicine, Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, USA.|Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.|Bioinformatics Core, The Picower Institute for Learning and Memory, MA, USA.', 'United States|United States|United States|United States|Italy|Sweden|United States|Spain|United States|Spain|France|United States|United States|United States|Italy|Sweden|United States|United States|Spain|United States|Israel|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz675', '31504201', 'MicroRNAs (miRNAs) are small RNA molecules (xe2x88xbc22 nucleotide long) involved in post-transcriptional gene regulation. Advances in high-throughput sequencing technologies led to the discovery of isomiRs, which are miRNA sequence variants. While many miRNA-seq analysis tools exist, the diversity of output formats hinders accurate comparisons between tools and precludes data sharing and the development of common downstream analysis methods.'),
(83103, 2019, 'bio', 'Semi-blind sparse affine spectral unmixing of autofluorescence-contaminated micrographs', 'Rossetti, Blair J|Wilbert, Steven A|Mark Welch, Jessica L|Borisy, Gary G|Nagy, James G', 'Department of Computer Science, Emory University, Atlanta, GA, USA.|Department of Microbiology, Forsyth Institute, Cambridge, MA, USA.|Josephine Bay Paul Center, Marine Biological Laboratory, Woods Hole, MA, USA.|Department of Microbiology, Forsyth Institute, Cambridge, MA, USA.|Department of Mathematics, Emory University, Atlanta, GA, USA.', 'United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz674', '31504202', 'Spectral unmixing methods attempt to determine the concentrations of different fluorophores present at each pixel location in an image by analyzing a set of measured emission spectra. Unmixing algorithms have shown great promise for applications where samples contain many fluorescent labels, however, existing methods perform poorly when confronted with autofluorescence-contaminated images.'),
(83104, 2019, 'bio', 'Association rule mining to identify transcription factor interactions in genomic regions', 'Ceddia, Gaia|Martino, Liuba Nausicaa|Parodi, Alice|Secchi, Piercesare|Campaner, Stefano|Masseroli, Marco', 'Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy.|MOX - Dipartimento di Matematica, Politecnico di Milano, Milan, Italy.|MOX - Dipartimento di Matematica, Politecnico di Milano, Milan, Italy.|MOX - Dipartimento di Matematica, Politecnico di Milano, Milan, Italy.|Center for Genomic Science of IIT@SEMM, Istituto Italiano di Tecnologia (IIT), Milan, Italy.|Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz687', '31504203', 'Genome regulatory networks have different layers and ways to modulate cellular processes, such as cell differentiation, proliferation and adaptation to external stimuli. Transcription factors and other chromatin associated proteins act as combinatorial protein complexes that control gene transcription. Thus, identifying functional interaction networks among these proteins is a fundamental task to understand the genome regulation framework.'),
(83105, 2019, 'bio', 'Applying Citizen Science to Gene, Drug, and Disease Relationship Extraction from Biomedical Abstracts', 'Tsueng, Ginger|Nanis, Max|Fouquier, Jennifer T|Mayers, Michael|Good, Benjamin M|Su, Andrew I', 'Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA.|Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA.|Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA.|Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA.|Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA.|Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA.', 'United States|United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz678', '31504205', 'Biomedical literature is growing at a rate that outpaces our ability to harness the knowledge contained therein. To mine valuable inferences from the large volume of literature, many researchers use information extraction algorithms to harvest information in biomedical texts. Information extraction is usually accomplished via a combination of manual expert curation and computational methods. Advances in computational methods usually depends on the time-consuming generation of gold standards by a limited number of expert curators. Citizen science is public participation in scientific research. We previously found that citizen scientists are willing and capable of performing named entity recognition of disease mentions in biomedical abstracts, but did not know if this was true with relationship extraction.'),
(83106, 2019, 'bio', 'Fast and Accurate Correction of Optical Mapping Data via Spaced Seeds', 'Salmela, Leena|Mukherjee, Kingshuk|Puglisi, Simon J|Muggli, Martin D|Boucher, Christina', 'Department of Computer Science, Helsinki Institute for Information Technology HIIT, University of Helsinki, Helsinki, Finland.|Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, USA.|Department of Computer Science, Helsinki Institute for Information Technology HIIT, University of Helsinki, Helsinki, Finland.|Department of Computer Science, Colorado State University, Fort Collins, CO, USA.|Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, USA.', 'Finland|United States|Finland|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz663', '31504206', 'Optical mapping data is used in many core genomics applications, including structural variation detection, scaffolding assembled contigs, and mis-assembly detection. However, the pervasiveness of spurious and deleted cut sites in the raw data, which are called Rmaps, make assembly and alignment of them challenging. Although there exists another method to error correct Rmap data, named cOMet (Mukherjee et al., GigaScience, 7(6), 2018), it is unable to scale to even moderately-large sized genomes. The challenge faced in error correction is in determining pairs of Rmaps that originate from the same region of the same genome.'),
(83107, 2019, 'bio', 'Accurate and Efficient Cell Lineage Tree Inference from Noisy Single Cell Data: the Maximum Likelihood Perfect Phylogeny Approach', 'Wu, Yufeng', 'Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA.', 'United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz676', '31504211', 'Cells in an organism share a common evolutionary history, called cell lineage tree. Cell lineage tree can be inferred from single cell genotypes at genomic variation sites. Cell lineage tree inference from noisy single cell data is a challenging computational problem. Most existing methods for cell lineage tree inference assume uniform uncertainty in genotypes. A key missing aspect is that real single cell data usually has non-uniform uncertainty in individual genotypes. Moreover, existing methods are often sampling-based and can be very slow for large data.'),
(83108, 2019, 'bio', 'CroP - Coordinated Panel Visualization for Biological Networks Analysis', 'Cruz, António|Machado, Penousal|Arrais, Joel P', 'CISUC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal.|CISUC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal.|CISUC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal.', 'Portugal|Portugal|Portugal', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz688', '31504214', 'CroP is a data visualization application that focuses on the analysis of relational data that changes over time. While it was specifically designed for addressing the preeminent need to interpret large scale time series from gene expression studies, CroP is prepared to analyze datasets from multiple contexts. Multiple datasets can be uploaded simultaneously and viewed through dynamic visualization models, which are contained within flexible panels that allow users to adapt the workspace to their data. Through clustering and the time curve visualization it is possible to quickly identify groups of data points with similar proprieties or behaviours, as well as temporal patterns across all points, such as periodic waves of expression. Additionally, it integrates a public biomedical database for gene annotation. CroP will be of major interest to biologists who seek to extract relations from complex sets of data.'),
(83109, 2017, 'bio', 'ISMB/ECCB 2017 PROCEEDINGS PAPERS COMMITTEE', '-', '-', '-', 0, '-', 'Journal Article', '10.1093/bioinformatics/btx331', '31505536', '-'),
(83110, 2019, 'bio', 'Comprehensive evaluation of deep learning architectures for prediction of DNA/RNA sequence binding specificities', 'Trabelsi, Ameni|Chaabane, Mohamed|Ben-Hur, Asa', 'Department of Computer Science, Colorado State University, Fort Collins, CO, USA.|Department of Computer Science, Colorado State University, Fort Collins, CO, USA.|Department of Computer Science, Colorado State University, Fort Collins, CO, USA.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz339', '31510640', 'Deep learning architectures have recently demonstrated their power in predicting DNA- and RNA-binding specificity. Existing methods fall into three classes: Some are based on convolutional neural networks (CNNs), others use recurrent neural networks (RNNs) and others rely on hybrid architectures combining CNNs and RNNs. However, based on existing studies the relative merit of the various architectures remains unclear.'),
(83111, 2019, 'bio', 'ADAPTIVE: leArning DAta-dePendenT, concIse molecular VEctors for fast, accurate metabolite identification from tandem mass spectra', 'Nguyen, Dai Hai|Nguyen, Canh Hao|Mamitsuka, Hiroshi', 'Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Japan.', 'Japan|Japan|Japan', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz319', '31510641', 'Metabolite identification is an important task in metabolomics to enhance the knowledge of biological systems. There have been a number of machine learning-based methods proposed for this task, which predict a chemical structure of a given spectrum through an intermediate (chemical structure) representation called molecular fingerprints. They usually have two steps: (i) predicting fingerprints from spectra, (ii) searching chemical compounds (in database) corresponding to the predicted fingerprints. Fingerprints are feature vectors, which are usually very large to cover all possible substructures and chemical properties, and therefore heavily redundant, in the sense of having many molecular (sub)structures irrelevant to the task, causing limited predictive performance and slow prediction.'),
(83112, 2019, 'bio', 'cloudSPAdes: assembly of synthetic long reads using de Bruijn graphs', 'Tolstoganov, Ivan|Bankevich, Anton|Chen, Zhoutao|Pevzner, Pavel A', 'Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia.|Department of Computer Science and Engineering, University of California at San Diego, La Jolla, CA, USA.|Universal Sequencing Technology Corporation, Carlsbad, CA, USA.|Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz349', '31510642', 'The recently developed barcoding-based synthetic long read (SLR) technologies have already found many applications in genome assembly and analysis. However, although some new barcoding protocols are emerging and the range of SLR applications is being expanded, the existing SLR assemblers are optimized for a narrow range of parameters and are not easily extendable to new barcoding technologies and new applications such as metagenomics or hybrid assembly.'),
(83113, 2019, 'bio', 'Modeling clinical and molecular covariates of mutational process activity in cancer', 'Robinson, Welles|Sharan, Roded|Leiserson, Mark D M', 'Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA.|Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.|Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA.', 'United States|Israel|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz340', '31510643', 'Somatic mutations result from processes related to DNA replication or environmental/lifestyle exposures. Knowing the activity of mutational processes in a tumor can inform personalized therapies, early detection, and understanding of tumorigenesis. Computational methods have revealed 30 validated signatures of mutational processes active in human cancers, where each signature is a pattern of single base substitutions. However, half of these signatures have no known etiology, and some similar signatures have distinct etiologies, making patterns of mutation signature activity hard to interpret. Existing mutation signature detection methods do not consider tumor-level clinical/demographic (e.g. smoking history) or molecular features (e.g. inactivations to DNA damage repair genes).'),
(83114, 2019, 'bio', 'Model-based optimization of subgroup weights for survival analysis', 'Richter, Jakob|Madjar, Katrin|Rahnenführer, Jörg', 'Department of Statistics, TU Dortmund University, Dortmund, Germany.|Department of Statistics, TU Dortmund University, Dortmund, Germany.|Department of Statistics, TU Dortmund University, Dortmund, Germany.', 'Germany|Germany|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz361', '31510644', 'To obtain a reliable prediction model for a specific cancer subgroup or cohort is often difficult due to limited sample size and, in survival analysis, due to potentially high censoring rates. Sometimes similar data from other patient subgroups are available, e.g. from other clinical centers. Simple pooling of all subgroups can decrease the variance of the predicted parameters of the prediction models, but also increase the bias due to heterogeneity between the cohorts. A promising compromise is to identify those subgroups with a similar relationship between covariates and target variable and then include only these for model building.'),
(83115, 2019, 'bio', 'Identifying progressive imaging genetic patterns via multi-task sparse canonical correlation analysis: a longitudinal study of the ADNI cohort', 'Du, Lei|Liu, Kefei|Zhu, Lei|Yao, Xiaohui|Risacher, Shannon L|Guo, Lei|Saykin, Andrew J|Shen, Li|Alzheimer’s Disease Neuroimaging Initiative', 'School of Automation, Northwestern Polytechnical University, Xian, China.|Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.|School of Computer Science and Engineering, Xian University of Technology, Xian, China.|Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.|Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA.|School of Automation, Northwestern Polytechnical University, Xian, China.|Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA.|Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.|Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.', 'China|United States|China|United States|India|China|India|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz320', '31510645', 'Identifying the genetic basis of the brain structure, function and disorder by using the imaging quantitative traits (QTs) as endophenotypes is an important task in brain science. Brain QTs often change over time while the disorder progresses and thus understanding how the genetic factors play roles on the progressive brain QT changes is of great importance and meaning. Most existing imaging genetics methods only analyze the baseline neuroimaging data, and thus those longitudinal imaging data across multiple time points containing important disease progression information are omitted.'),
(83116, 2019, 'bio', 'Integrating read-based and population-based phasing for dense and accurate haplotyping of individual genomes', 'Bansal, Vikas', 'Department of Pediatrics, School of Medicine, University of California, San Diego, La Jolla, CA, USA.', 'United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz329', '31510646', 'Reconstruction of haplotypes for human genomes is an important problem in medical and population genetics. Hi-C sequencing generates read pairs with long-range haplotype information that can be computationally assembled to generate chromosome-spanning haplotypes. However, the haplotypes have limited completeness and low accuracy. Haplotype information from population reference panels can potentially be used to improve the completeness and accuracy of Hi-C haplotyping.'),
(83117, 2019, 'bio', 'Building large updatable colored de Bruijn graphs via merging', 'Muggli, Martin D|Alipanahi, Bahar|Boucher, Christina', 'Department of Computer Science, Colorado State University, Fort Collins, CO, USA.|Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, USA.|Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, USA.', 'United States|United States|United States', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz350', '31510647', 'There exist several large genomic and metagenomic data collection efforts, including GenomeTrakr and MetaSub, which are routinely updated with new data. To analyze such datasets, memory-efficient methods to construct and store the colored de Bruijn graph were developed. Yet, a problem that has not been considered is constructing the colored de Bruijn graph in a scalable manner that allows new data to be added without reconstruction. This problem is important for large public datasets as scalability is needed but also the ability to update the construction is also needed.'),
(83118, 2019, 'bio', 'Controlling large Boolean networks with single-step perturbations', 'Baudin, Alexis|Paul, Soumya|Su, Cui|Pang, Jun', 'Department of Computer Science, École Normale Supérieure Paris-Saclay, Cachan, France.|Faculty of Science, Technology and Communication, University of Luxembourg, Esch-sur-Alzette, Luxembourg.|Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Luxembourg, Luxembourg.|Faculty of Science, Technology and Communication, University of Luxembourg, Esch-sur-Alzette, Luxembourg.', 'France|Luxembourg|Luxembourg|Luxembourg', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz371', '31510648', 'The control of Boolean networks has traditionally focussed on strategies where the perturbations are applied to the nodes of the network for an extended period of time. In this work, we study if and how a Boolean network can be controlled by perturbing a minimal set of nodes for a single-step and letting the system evolve afterwards according to its original dynamics. More precisely, given a Boolean network (BN), we compute a minimal subset Cmin of the nodes such that BN can be driven from any initial state in an attractor to another desired attractor by perturbing some or all of the nodes of Cmin for a single-step. Such kind of control is attractive for biological systems because they are less time consuming than the traditional strategies for control while also being financially more viable. However, due to the phenomenon of state-space explosion, computing such a minimal subset is computationally inefficient and an approach that deals with the entire network in one-go, does not scale well for large networks.'),
(83119, 2019, 'bio', 'Minnow: a principled framework for rapid simulation of dscRNA-seq data at the read level', 'Sarkar, Hirak|Srivastava, Avi|Patro, Rob', 'Department of Computer Science, Stony Brook University, Stony Brook, NY, USA.|Department of Computer Science, Stony Brook University, Stony Brook, NY, USA.|Department of Computer Science, Stony Brook University, Stony Brook, NY, USA.', 'United States|United States|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz351', '31510649', 'With the advancements of high-throughput single-cell RNA-sequencing protocols, there has been a rapid increase in the tools available to perform an array of analyses on the gene expression data that results from such studies. For example, there exist methods for pseudo-time series analysis, differential cell usage, cell-type detection RNA-velocity in single cells, etc. Most analysis pipelines validate their results using known marker genes (which are not widely available for all types of analysis) and by using simulated data from gene-count-level simulators. Typically, the impact of using different read-alignment or unique molecular identifier (UMI) deduplication methods has not been widely explored. Assessments based on simulation tend to start at the level of assuming a simulated count matrix, ignoring the effect that different approaches for resolving UMI counts from the raw read data may produce. Here, we present minnow, a comprehensive sequence-level droplet-based single-cell RNA-sequencing (dscRNA-seq) experiment simulation framework. Minnow accounts for important sequence-level characteristics of experimental scRNA-seq datasets and models effects such as polymerase chain reaction amplification, cellular barcodes (CB) and UMI selection and sequence fragmentation and sequencing. It also closely matches the gene-level ambiguity characteristics that are observed in real scRNA-seq experiments. Using minnow, we explore the performance of some common processing pipelines to produce gene-by-cell count matrices from droplet-bases scRNA-seq data, demonstrate the effect that realistic levels of gene-level sequence ambiguity can have on accurate quantification and show a typical use-case of minnow in assessing the output generated by different quantification pipelines on the simulated experiment.'),
(83120, 2019, 'bio', 'Fully-sensitive seed finding in sequence graphs using a hybrid index', 'Ghaffaari, Ali|Marschall, Tobias', 'Center for Bioinformatics, Saarland University, Saarbrücken, Germany.|Center for Bioinformatics, Saarland University, Saarbrücken, Germany.', 'Germany|Germany', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz341', '31510650', 'Sequence graphs are versatile data structures that are, for instance, able to represent the genetic variation found in a population and to facilitate genome assembly. Read mapping to sequence graphs constitutes an important step for many applications and is usually done by first finding exact seed matches, which are then extended by alignment. Existing methods for finding seed hits prune the graph in complex regions, leading to a loss of information especially in highly polymorphic regions of the genome. While such complex graph structures can indeed lead to a combinatorial explosion of possible alleles, the query set of reads from a diploid individual realizes only two alleles per locus-a property that is not exploited by extant methods.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(83121, 2019, 'bio', 'DeepLigand: accurate prediction of MHC class I ligands using peptide embedding', 'Zeng, Haoyang|Gifford, David K', 'Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.|Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.', 'United States|United States', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz330', '31510651', 'The computational modeling of peptide display by class I major histocompatibility complexes (MHCs) is essential for peptide-based therapeutics design. Existing computational methods for peptide-display focus on modeling the peptide-MHC-binding affinity. However, such models are not able to characterize the sequence features for the other cellular processes in the peptide display pathway that determines MHC ligand selection.'),
(83122, 2019, 'bio', 'A statistical simulator scDesign for rational scRNA-seq experimental design', 'Li, Wei Vivian|Li, Jingyi Jessica', 'Department of Statistics, University of California, Los Angeles, CA, USA.|Department of Statistics, University of California, Los Angeles, CA, USA.', 'United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz321', '31510652', 'Single-cell RNA sequencing (scRNA-seq) has revolutionized biological sciences by revealing genome-wide gene expression levels within individual cells. However, a critical challenge faced by researchers is how to optimize the choices of sequencing platforms, sequencing depths and cell numbers in designing scRNA-seq experiments, so as to balance the exploration of the depth and breadth of transcriptome information.'),
(83123, 2019, 'bio', 'Selfish: discovery of differential chromatin interactions via a self-similarity measure', 'Ardakany, Abbas Roayaei|Ay, Ferhat|Lonardi, Stefano', 'Department of Computer Science and Engineering, University of California, Riverside, Riverside, CA, USA.|Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, UC San Diego, La Jolla, CA, USA.|Department of Computer Science and Engineering, University of California, Riverside, Riverside, CA, USA.', 'United States|United States|United States', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz362', '31510653', 'High-throughput conformation capture experiments, such as Hi-C provide genome-wide maps of chromatin interactions, enabling life scientists to investigate the role of the three-dimensional structure of genomes in gene regulation and other essential cellular functions. A fundamental problem in the analysis of Hi-C data is how to compare two contact maps derived from Hi-C experiments. Detecting similarities and differences between contact maps are critical in evaluating the reproducibility of replicate experiments and for identifying differential genomic regions with biological significance. Due to the complexity of chromatin conformations and the presence of technology-driven and sequence-specific biases, the comparative analysis of Hi-C data is analytically and computationally challenging.'),
(83124, 2019, 'bio', 'PRECISE: a domain adaptation approach to transfer predictors of drug response from pre-clinical models to tumors', 'Mourragui, Soufiane|Loog, Marco|van de Wiel, Mark A|Reinders, Marcel J T|Wessels, Lodewyk F A', 'Computational Cancer Biology, Division of Molecular Carcinogenesis, Oncode Institute, the Netherlands Cancer Institute, Amsterdam CX, The Netherlands.|Department of Intelligent Systems, Faculty of EEMCS, Delft University of Technology, Delft CD, The Netherlands.|Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, Amsterdam University Medical Centers, Amsterdam, The Netherlands.|Department of Intelligent Systems, Faculty of EEMCS, Delft University of Technology, Delft CD, The Netherlands.|Computational Cancer Biology, Division of Molecular Carcinogenesis, Oncode Institute, the Netherlands Cancer Institute, Amsterdam CX, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz372', '31510654', 'Cell lines and patient-derived xenografts (PDXs) have been used extensively to understand the molecular underpinnings of cancer. While core biological processes are typically conserved, these models also show important differences compared to human tumors, hampering the translation of findings from pre-clinical models to the human setting. In particular, employing drug response predictors generated on data derived from pre-clinical models to predict patient response remains a challenging task. As very large drug response datasets have been collected for pre-clinical models, and patient drug response data are often lacking, there is an urgent need for methods that efficiently transfer drug response predictors from pre-clinical models to the human setting.'),
(83125, 2019, 'bio', 'Integrating regulatory DNA sequence and gene expression to predict genome-wide chromatin accessibility across cellular contexts', 'Nair, Surag|Kim, Daniel S|Perricone, Jacob|Kundaje, Anshul', 'Department of Computer Science, Stanford University, Stanford, CA, USA.|Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA.|Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA.|Department of Computer Science, Stanford University, Stanford, CA, USA.', 'United States|United States|United States|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz352', '31510655', 'Genome-wide profiles of chromatin accessibility and gene expression in diverse cellular contexts are critical to decipher the dynamics of transcriptional regulation. Recently, convolutional neural networks have been used to learn predictive cis-regulatory DNA sequence models of context-specific chromatin accessibility landscapes. However, these context-specific regulatory sequence models cannot generalize predictions across cell types.'),
(83126, 2019, 'bio', 'Deep learning with multimodal representation for pancancer prognosis prediction', 'Cheerla, Anika|Gevaert, Olivier', 'Monta Vista High School, Cupertino, CA, USA.|Department of Medicine and Biomedical Data Science, Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.', 'United States|United States', 9, '-', 'Journal Article', '10.1093/bioinformatics/btz342', '31510656', 'Estimating the future course of patients with cancer lesions is invaluable to physicians, however, current clinical methods fail to effectively use the vast amount of multimodal data that is available for cancer patients. To tackle this problem, we constructed a multimodal neural network-based model to predict the survival of patients for 20 different cancer types using clinical data, mRNA expression data, microRNA expression data and histopathology whole slide images (WSIs). We developed an unsupervised encoder to compress these four data modalities into a single feature vector for each patient, handling missing data through a resilient, multimodal dropout method. Encoding methods were tailored to each data type-using deep highway networks to extract features from clinical and genomic data, and convolutional neural networks to extract features from WSIs.'),
(83127, 2019, 'bio', 'Summarizing the solution space in tumor phylogeny inference by multiple consensus trees', 'Aguse, Nuraini|Qi, Yuanyuan|El-Kebir, Mohammed', 'Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA.|Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA.|Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz312', '31510657', 'Cancer phylogenies are key to studying tumorigenesis and have clinical implications. Due to the heterogeneous nature of cancer and limitations in current sequencing technology, current cancer phylogeny inference methods identify a large solution space of plausible phylogenies. To facilitate further downstream analyses, methods that accurately summarize such a set T of cancer phylogenies are imperative. However, current summary methods are limited to a single consensus tree or graph and may miss important topological features that are present in different subsets of candidate trees.'),
(83128, 2019, 'bio', 'Drug repositioning based on bounded nuclear norm regularization', 'Yang, Mengyun|Luo, Huimin|Li, Yaohang|Wang, Jianxin', 'School of Computer Science and Engineering, Central South University, Changsha.|School of Computer Science and Engineering, Central South University, Changsha.|Department of Computer Science, Old Dominion University, Norfolk, VA, USA.|School of Computer Science and Engineering, Central South University, Changsha.', 'United States|United States|United States|United States', 9, '-', 'Journal Article', '10.1093/bioinformatics/btz331', '31510658', 'Computational drug repositioning is a cost-effective strategy to identify novel indications for existing drugs. Drug repositioning is often modeled as a recommendation system problem. Taking advantage of the known drug-disease associations, the objective of the recommendation system is to identify new treatments by filling out the unknown entries in the drug-disease association matrix, which is known as matrix completion. Underpinned by the fact that common molecular pathways contribute to many different diseases, the recommendation system assumes that the underlying latent factors determining drug-disease associations are highly correlated. In other words, the drug-disease matrix to be completed is low-rank. Accordingly, matrix completion algorithms efficiently constructing low-rank drug-disease matrix approximations consistent with known associations can be of immense help in discovering the novel drug-disease associations.'),
(83129, 2019, 'bio', 'Representation transfer for differentially private drug sensitivity prediction', 'Niinimäki, Teppo|Heikkilä, Mikko A|Honkela, Antti|Kaski, Samuel', 'Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Espoo, Finland.|Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.|Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.|Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Espoo, Finland.', 'Finland|Finland|Finland|Finland', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz373', '31510659', 'Human genomic datasets often contain sensitive information that limits use and sharing of the data. In particular, simple anonymization strategies fail to provide sufficient level of protection for genomic data, because the data are inherently identifiable. Differentially private machine learning can help by guaranteeing that the published results do not leak too much information about any individual data point. Recent research has reached promising results on differentially private drug sensitivity prediction using gene expression data. Differentially private learning with genomic data is challenging because it is more difficult to guarantee privacy in high dimensions. Dimensionality reduction can help, but if the dimension reduction mapping is learned from the data, then it needs to be differentially private too, which can carry a significant privacy cost. Furthermore, the selection of any hyperparameters (such as the target dimensionality) needs to also avoid leaking private information.'),
(83130, 2019, 'bio', 'Comprehensive evaluation of transcriptome-based cell-type quantification methods for immuno-oncology', 'Sturm, Gregor|Finotello, Francesca|Petitprez, Florent|Zhang, Jitao David|Baumbach, Jan|Fridman, Wolf H|List, Markus|Aneichyk, Tatsiana', 'Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany.|Biocenter, Division of Bioinformatics, Medical University of Innsbruck, Innsbruck, Austria.|Cordeliers Research Centre, UMRS_1138, INSERM, University Paris-Descartes, Sorbonne University, Paris, France.|Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland.|Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany.|Cordeliers Research Centre, UMRS_1138, INSERM, University Paris-Descartes, Sorbonne University, Paris, France.|Big Data in BioMedicine Group, Chair of Experimental Bioinformatis, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany.|Pieris Pharmaceuticals GmbH, Freising, Germany.', 'Germany|Austria|France|Switzerland|Germany|France|Germany|Germany', 28, '-', 'Journal Article', '10.1093/bioinformatics/btz363', '31510660', 'The composition and density of immune cells in the tumor microenvironment (TME) profoundly influence tumor progression and success of anti-cancer therapies. Flow cytometry, immunohistochemistry staining or single-cell sequencing are often unavailable such that we rely on computational methods to estimate the immune-cell composition from bulk RNA-sequencing (RNA-seq) data. Various methods have been proposed recently, yet their capabilities and limitations have not been evaluated systematically. A general guideline leading the research community through cell type deconvolution is missing.'),
(83131, 2019, 'bio', 'GkmExplain: fast and accurate interpretation of nonlinear gapped k-mer SVMs', 'Shrikumar, Avanti|Prakash, Eva|Kundaje, Anshul', 'Department of Computer Science, Stanford University, Stanford, CA, USA.|Computer Science, BASIS Independent Silicon Valley, San Jose, CA, USA.|Department of Computer Science, Stanford University, Stanford, CA, USA.', 'United States|United States|United States', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz322', '31510661', 'Support Vector Machines with gapped k-mer kernels (gkm-SVMs) have been used to learn predictive models of regulatory DNA sequence. However, interpreting predictive sequence patterns learned by gkm-SVMs can be challenging. Existing interpretation methods such as deltaSVM, in-silico mutagenesis (ISM) or SHAP either do not scale well or make limiting assumptions about the model that can produce misleading results when the gkm kernel is combined with nonlinear kernels. Here, we propose GkmExplain: a computationally efficient feature attribution method for interpreting predictive sequence patterns from gkm-SVM models that has theoretical connections to the method of Integrated Gradients. Using simulated regulatory DNA sequences, we show that GkmExplain identifies predictive patterns with high accuracy while avoiding pitfalls of deltaSVM and ISM and being orders of magnitude more computationally efficient than SHAP. By applying GkmExplain and a recently developed motif discovery method called TF-MoDISco to gkm-SVM models trained on in vivo transcription factor (TF) binding data, we recover consolidated, non-redundant TF motifs. Mutation impact scores derived using GkmExplain consistently outperform deltaSVM and ISM at identifying regulatory genetic variants from gkm-SVM models of chromatin accessibility in lymphoblastoid cell-lines.'),
(83132, 2019, 'bio', 'Rotation equivariant and invariant neural networks for microscopy image analysis', 'Chidester, Benjamin|Zhou, Tianming|Do, Minh N|Ma, Jian', 'Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.|Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.|Department of Electrical and Computer Engineering, University of Illinois, Urbana, IL, USA.|Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.', 'United States|United States|United States|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz353', '31510662', 'Neural networks have been widely used to analyze high-throughput microscopy images. However, the performance of neural networks can be significantly improved by encoding known invariance for particular tasks. Highly relevant to the goal of automated cell phenotyping from microscopy image data is rotation invariance. Here we consider the application of two schemes for encoding rotation equivariance and invariance in a convolutional neural network, namely, the group-equivariant CNN (G-CNN), and a new architecture with simple, efficient conic convolution, for classifying microscopy images. We additionally integrate the 2D-discrete-Fourier transform (2D-DFT) as an effective means for encoding global rotational invariance. We call our new method the Conic Convolution and DFT Network (CFNet).'),
(83133, 2019, 'bio', 'Predicting drug-induced transcriptome responses of a wide range of human cell lines by a novel tensor-train decomposition algorithm', 'Iwata, Michio|Yuan, Longhao|Zhao, Qibin|Tabei, Yasuo|Berenger, Francois|Sawada, Ryusuke|Akiyoshi, Sayaka|Hamano, Momoko|Yamanishi, Yoshihiro', 'Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan.|Graduate School of Engineering, Saitama Institute of Technology, Fukaya, Saitama, Japan.|RIKEN Center for Advanced Intelligence Project, Chuo-ku, Tokyo, Japan.|RIKEN Center for Advanced Intelligence Project, Chuo-ku, Tokyo, Japan.|Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan.|Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan.|Medical Institute of Bioregulation, Kyushu University, Higashi-ku, Fukuoka, Japan.|Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan.|Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan.', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz313', '31510663', 'Genome-wide identification of the transcriptomic responses of human cell lines to drug treatments is a challenging issue in medical and pharmaceutical research. However, drug-induced gene expression profiles are largely unknown and unobserved for all combinations of drugs and human cell lines, which is a serious obstacle in practical applications.'),
(83134, 2019, 'bio', 'Large-scale mammalian genome rearrangements coincide with chromatin interactions', 'Swenson, Krister M|Blanchette, Mathieu', 'Laboratoire dInformatique, de Robotique, et de Microelectronique de Montpellier (LIRMM), Universitxc3xa9 Montpellier, Montpellier, France.|School of Computer Science, McGill University, Montrxc3xa9al, QC, Canada.', 'France|Canada', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz343', '31510664', 'Genome rearrangements drastically change gene order along great stretches of a chromosome. There has been initial evidence that these apparently non-local events in the 1D sense may have breakpoints that are close in the 3D sense. We harness the power of the Double Cut and Join model of genome rearrangement, along with Hi-C chromosome conformation capture data to test this hypothesis between human and mouse.'),
(83135, 2019, 'bio', 'Estimating the predictability of cancer evolution', 'Hosseini, Sayed-Rzgar|Diaz-Uriarte, Ramon|Markowetz, Florian|Beerenwinkel, Niko', 'Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.|Department of Biochemistry, Universidad Autónoma de Madrid, Instituto de Investigaciones Biomédicas Alberto Sols (UAM-CSIC), Madrid, Spain.|Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK.|Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.', 'Switzerland|Spain|United Kingdom|Switzerland', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz332', '31510665', 'How predictable is the evolution of cancer? This fundamental question is of immense relevance for the diagnosis, prognosis and treatment of cancer. Evolutionary biologists have approached the question of predictability based on the underlying fitness landscape. However, empirical fitness landscapes of tumor cells are impossible to determine in vivo. Thus, in order to quantify the predictability of cancer evolution, alternative approaches are required that circumvent the need for fitness landscapes.'),
(83136, 2019, 'bio', 'NPS: scoring and evaluating the statistical significance of peptidic natural product-spectrum matches', 'Tagirdzhanov, Azat M|Shlemov, Alexander|Gurevich, Alexey', 'Center for Algorithmic Biotechnology, St. Petersburg State University, St. Petersburg, Russia.|Center for Algorithmic Biotechnology, St. Petersburg State University, St. Petersburg, Russia.|Center for Algorithmic Biotechnology, St. Petersburg State University, St. Petersburg, Russia.', '-|-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz374', '31510666', 'Peptidic natural products (PNPs) are considered a promising compound class that has many applications in medicine. Recently developed mass spectrometry-based pipelines are transforming PNP discovery into a high-throughput technology. However, the current computational methods for PNP identification via database search of mass spectra are still in their infancy and could be substantially improved.'),
(83137, 2019, 'bio', 'Locality-sensitive hashing for the edit distance', 'Marçais, Guillaume|DeBlasio, Dan|Pandey, Prashant|Kingsford, Carl', 'Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA.|Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA.|Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA.|Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA.', 'United States|United States|United States|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz354', '31510667', 'Sequence alignment is a central operation in bioinformatics pipeline and, despite many improvements, remains a computationally challenging problem. Locality-sensitive hashing (LSH) is one method used to estimate the likelihood of two sequences to have a proper alignment. Using an LSH, it is possible to separate, with high probability and relatively low computation, the pairs of sequences that do not have high-quality alignment from those that may. Therefore, an LSH reduces the overall computational requirement while not introducing many false negatives (i.e. omitting to report a valid alignment). However, current LSH methods treat sequences as a bag of k-mers and do not take into account the relative ordering of k-mers in sequences. In addition, due to the lack of a practical LSH method for edit distance, in practice, LSH methods for Jaccard similarity or Hamming similarity are used as a proxy.'),
(83138, 2019, 'bio', 'TreeMerge: a new method for improving the scalability of species tree estimation methods', 'Molloy, Erin K|Warnow, Tandy', 'Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA.|Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA.', 'United States|United States', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz344', '31510668', 'At RECOMB-CG 2018, we presented NJMerge and showed that it could be used within a divide-and-conquer framework to scale computationally intensive methods for species tree estimation to larger datasets. However, NJMerge has two significant limitations: it can fail to return a tree and, when used within the proposed divide-and-conquer framework, has O(n5) running time for datasets with n species.'),
(83139, 2019, 'bio', 'A joint method for marker-free alignment of tilt series in electron tomography', 'Han, Renmin|Bao, Zhipeng|Zeng, Xiangrui|Niu, Tongxin|Zhang, Fa|Xu, Min|Gao, Xin', 'Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.|Department of Electronic Engineering, Tsinghua University, Beijing, China.|Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.|National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.|High Performance Computer Research Center, Chinese Academy of Sciences, Beijing, China.|Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.|Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.', 'Saudi Arabia|China|United States|China|China|United States|Saudi Arabia', 4, '-', 'Journal Article', '10.1093/bioinformatics/btz323', '31510669', 'Electron tomography (ET) is a widely used technology for 3D macro-molecular structure reconstruction. To obtain a satisfiable tomogram reconstruction, several key processes are involved, one of which is the calibration of projection parameters of the tilt series. Although fiducial marker-based alignment for tilt series has been well studied, marker-free alignment remains a challenge, which requires identifying and tracking the identical objects (landmarks) through different projections. However, the tracking of these landmarks is usually affected by the pixel density (intensity) change caused by the geometry difference in different views. The tracked landmarks will be used to determine the projection parameters. Meanwhile, different projection parameters will also affect the localization of landmarks. Currently, there is no alignment method that takes interrelationship between the projection parameters and the landmarks.'),
(83140, 2019, 'bio', 'Large-scale inference of competing endogenous RNA networks with sparse partial correlation', 'List, Markus|Dehghani Amirabad, Azim|Kostka, Dennis|Schulz, Marcel H', 'Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany.|Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany.|Department of Developmental Biology, Department of Computational & Systems Biology, Pittsburgh Center for Evolutionary Biology and Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.|Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany.', 'Germany|Germany|United States|Germany', 4, '-', 'Journal Article', '10.1093/bioinformatics/btz314', '31510670', 'MicroRNAs (miRNAs) are important non-coding post-transcriptional regulators that are involved in many biological processes and human diseases. Individual miRNAs may regulate hundreds of genes, giving rise to a complex gene regulatory network in which transcripts carrying miRNA binding sites act as competing endogenous RNAs (ceRNAs). Several methods for the analysis of ceRNA interactions exist, but these do often not adjust for statistical confounders or address the problem that more than one miRNA interacts with a target transcript.'),
(83141, 2019, 'bio', 'Block HSIC Lasso: model-free biomarker detection for ultra-high dimensional data', 'Climente-González, Héctor|Azencott, Chloé-Agathe|Kaski, Samuel|Yamada, Makoto', 'Institut Curie, PSL Research University, Paris, France.|Institut Curie, PSL Research University, Paris, France.|Department of Computer Science, Aalto University, Espoo, Finland.|RIKEN AIP, Tokyo, Japan.', 'France|France|Finland|Japan', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz333', '31510671', 'Finding non-linear relationships between biomolecules and a biological outcome is computationally expensive and statistically challenging. Existing methods have important drawbacks, including among others lack of parsimony, non-convexity and computational overhead. Here we propose block HSIC Lasso, a non-linear feature selector that does not present the previous drawbacks.'),
(83142, 2019, 'bio', 'LinearFold: linear-time approximate RNA folding by 5-to-3 dynamic programming and beam search', 'Huang, Liang|Zhang, He|Deng, Dezhong|Zhao, Kai|Liu, Kaibo|Hendrix, David A|Mathews, David H', 'School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR, USA.|Baidu Research USA, Sunnyvale, CA, USA.|School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR, USA.|School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR, USA.|School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR, USA.|School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR, USA.|Department of Biochemistry & Biophysics, University of Rochester Medical Center, Rochester, NY, USA.', 'United States|United States|United States|United States|United States|United States|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz375', '31510672', 'Predicting the secondary structure of an ribonucleic acid (RNA) sequence is useful in many applications. Existing algorithms [based on dynamic programming] suffer from a major limitation: their runtimes scale cubically with the RNA length, and this slowness limits their use in genome-wide applications.'),
(83143, 2019, 'bio', 'Adversarial domain adaptation for cross data source macromolecule in situ structural classification in cellular electron cryo-tomograms', 'Lin, Ruogu|Zeng, Xiangrui|Kitani, Kris|Xu, Min', 'Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA.|Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA.|Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA.|Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA.', 'United States|United States|United States|United States', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz364', '31510673', 'Since 2017, an increasing amount of attention has been paid to the supervised deep learning-based macromolecule in situ structural classification (i.e. subtomogram classification) in cellular electron cryo-tomography (CECT) due to the substantially higher scalability of deep learning. However, the success of such supervised approach relies heavily on the availability of large amounts of labeled training data. For CECT, creating valid training data from the same data source as prediction data is usually laborious and computationally intensive. It would be beneficial to have training data from a separate data source where the annotation is readily available or can be performed in a high-throughput fashion. However, the cross data source prediction is often biased due to the different image intensity distributions (a.k.a. domain shift).'),
(83144, 2019, 'bio', 'Collaborative intra-tumor heterogeneity detection', 'Khakabimamaghani, Sahand|Malikic, Salem|Tang, Jeffrey|Ding, Dujian|Morin, Ryan|Chindelevitch, Leonid|Ester, Martin', 'School of Computing Science, Simon Fraser University, Burnaby, BC.|School of Computing Science, Simon Fraser University, Burnaby, BC.|Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC.|School of Computing Science, Simon Fraser University, Burnaby, BC.|Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC.|School of Computing Science, Simon Fraser University, Burnaby, BC.|School of Computing Science, Simon Fraser University, Burnaby, BC.', '-|-|-|-|-|-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz355', '31510674', 'Despite the remarkable advances in sequencing and computational techniques, noise in the data and complexity of the underlying biological mechanisms render deconvolution of the phylogenetic relationships between cancer mutations difficult. Besides that, the majority of the existing datasets consist of bulk sequencing data of single tumor sample of an individual. Accurate inference of the phylogenetic order of mutations is particularly challenging in these cases and the existing methods are faced with several theoretical limitations. To overcome these limitations, new methods are required for integrating and harnessing the full potential of the existing data.'),
(83145, 2019, 'bio', 'Unsupervised segmentation of mass spectrometric ion images characterizes morphology of tissues', 'Guo, Dan|Bemis, Kylie|Rawlins, Catherine|Agar, Jeffrey|Vitek, Olga', 'Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA.|Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA.|Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA, USA.|Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA, USA.|Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA.', 'United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz345', '31510675', 'Mass spectrometry imaging (MSI) characterizes the spatial distribution of ions in complex biological samples such as tissues. Since many tissues have complex morphology, treatments and conditions often affect the spatial distribution of the ions in morphology-specific ways. Evaluating the selectivity and the specificity of ion localization and regulation across morphology types is biologically important. However, MSI lacks algorithms for segmenting images at both single-ion and spatial resolution.'),
(83146, 2019, 'bio', 'Bayesian metabolic flux analysis reveals intracellular flux couplings', 'Heinonen, Markus|Osmala, Maria|Mannerström, Henrik|Wallenius, Janne|Kaski, Samuel|Rousu, Juho|Lähdesmäki, Harri', 'Department of Computer Science, Aalto University, Espoo, Finland.|Department of Computer Science, Aalto University, Espoo, Finland.|Department of Computer Science, Aalto University, Espoo, Finland.|Institute for Molecular Medicine Finland, Helsinki, Finland.|Department of Computer Science, Aalto University, Espoo, Finland.|Department of Computer Science, Aalto University, Espoo, Finland.|Department of Computer Science, Aalto University, Espoo, Finland.', 'Finland|Finland|Finland|Finland|Finland|Finland|Finland', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz315', '31510676', 'Metabolic flux balance analysis (FBA) is a standard tool in analyzing metabolic reaction rates compatible with measurements, steady-state and the metabolic reaction network stoichiometry. Flux analysis methods commonly place model assumptions on fluxes due to the convenience of formulating the problem as a linear programing model, while many methods do not consider the inherent uncertainty in flux estimates.'),
(83147, 2019, 'bio', 'TideHunter: efficient and sensitive tandem repeat detection from noisy long-reads using seed-and-chain', 'Gao, Yan|Liu, Bo|Wang, Yadong|Xing, Yi', 'Department of Computer Science and Technology, Center for Bioinformatics Harbin Institute of Technology, Harbin, Heilongjiang, China.|Department of Computer Science and Technology, Center for Bioinformatics Harbin Institute of Technology, Harbin, Heilongjiang, China.|Department of Computer Science and Technology, Center for Bioinformatics Harbin Institute of Technology, Harbin, Heilongjiang, China.|Center for Computational and Genomic Medicine, Childrens Hospital of Philadelphia, Philadelphia, PA, USA.', 'China|China|China|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz376', '31510677', 'Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) sequencing technologies can produce long-reads up to tens of kilobases, but with high error rates. In order to reduce sequencing error, Rolling Circle Amplification (RCA) has been used to improve library preparation by amplifying circularized template molecules. Linear products of the RCA contain multiple tandem copies of the template molecule. By integrating additional in silico processing steps, these tandem sequences can be collapsed into a consensus sequence with a higher accuracy than the original raw reads. Existing pipelines using alignment-based methods to discover the tandem repeat patterns from the long-reads are either inefficient or lack sensitivity.'),
(83148, 2019, 'bio', 'Learning signaling networks from combinatorial perturbations by exploiting siRNA off-target effects', 'Tiuryn, Jerzy|Szczurek, Ewa', 'Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland.|Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland.', 'Poland|Poland', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz334', '31510678', 'Perturbation experiments constitute the central means to study cellular networks. Several confounding factors complicate computational modeling of signaling networks from this data. First, the technique of RNA interference (RNAi), designed and commonly used to knock-down specific genes, suffers from off-target effects. As a result, each experiment is a combinatorial perturbation of multiple genes. Second, the perturbations propagate along unknown connections in the signaling network. Once the signal is blocked by perturbation, proteins downstream of the targeted proteins also become inactivated. Finally, all perturbed network members, either directly targeted by the experiment, or by propagation in the network, contribute to the observed effect, either in a positive or negative manner. One of the key questions of computational inference of signaling networks from such data are, how many and what combinations of perturbations are required to uniquely and accurately infer the model?'),
(83149, 2019, 'bio', 'SCRIBER: accurate and partner type-specific prediction of protein-binding residues from proteins sequences', 'Zhang, Jian|Kurgan, Lukasz', 'School of Computer and Information Technology, Xinyang Normal University, Xinyang, China.|Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA.', 'China|United States', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz324', '31510679', 'Accurate predictions of protein-binding residues (PBRs) enhances understanding of molecular-level rules governing protein-protein interactions, helps protein-protein docking and facilitates annotation of protein functions. Recent studies show that current sequence-based predictors of PBRs severely cross-predict residues that interact with other types of protein partners (e.g. RNA and DNA) as PBRs. Moreover, these methods are relatively slow, prohibiting genome-scale use.'),
(83150, 2019, 'bio', 'Identifying and ranking potential driver genes of Alzheimers disease using multiview evidence aggregation', 'Mukherjee, Sumit|Perumal, Thanneer M|Daily, Kenneth|Sieberts, Solveig K|Omberg, Larsson|Preuss, Christoph|Carter, Gregory W|Mangravite, Lara M|Logsdon, Benjamin A', 'Sage Bionetworks, Seattle, WA, USA.|Sage Bionetworks, Seattle, WA, USA.|Sage Bionetworks, Seattle, WA, USA.|Sage Bionetworks, Seattle, WA, USA.|Sage Bionetworks, Seattle, WA, USA.|The Jackson Laboratory for Mammalian Genetics, Bar Harbor, ME, USA.|The Jackson Laboratory for Mammalian Genetics, Bar Harbor, ME, USA.|Sage Bionetworks, Seattle, WA, USA.|Sage Bionetworks, Seattle, WA, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz365', '31510680', 'Late onset Alzheimers disease is currently a disease with no known effective treatment options. To better understand disease, new multi-omic data-sets have recently been generated with the goal of identifying molecular causes of disease. However, most analytic studies using these datasets focus on uni-modal analysis of the data. Here, we propose a data driven approach to integrate multiple data types and analytic outcomes to aggregate evidences to support the hypothesis that a gene is a genetic driver of the disease. The main algorithmic contributions of our article are: (i) a general machine learning framework to learn the key characteristics of a few known driver genes from multiple feature sets and identifying other potential driver genes which have similar feature representations, and (ii) A flexible ranking scheme with the ability to integrate external validation in the form of Genome Wide Association Study summary statistics. While we currently focus on demonstrating the effectiveness of the approach using different analytic outcomes from RNA-Seq studies, this method is easily generalizable to other data modalities and analysis types.'),
(83151, 2019, 'bio', 'Alignment-free filtering for cfNA fusion fragments', 'Yang, Xiao|Saito, Yasushi|Rao, Arjun|Kim, Hyunsung John|Singh, Pranav|Scott, Eric|Larson, Matthew|Pan, Wenying|Desai, Mohini|Hubbell, Earl', 'Grail, Inc, Menlo Park, CA, USA.|Grail, Inc, Menlo Park, CA, USA.|Grail, Inc, Menlo Park, CA, USA.|Grail, Inc, Menlo Park, CA, USA.|Grail, Inc, Menlo Park, CA, USA.|Grail, Inc, Menlo Park, CA, USA.|Grail, Inc, Menlo Park, CA, USA.|Grail, Inc, Menlo Park, CA, USA.|Grail, Inc, Menlo Park, CA, USA.|Grail, Inc, Menlo Park, CA, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz346', '31510681', 'Cell-free nucleic acid (cfNA) sequencing data require improvements to existing fusion detection methods along multiple axes: high depth of sequencing, low allele fractions, short fragment lengths and specialized barcodes, such as unique molecular identifiers.'),
(83152, 2019, 'bio', 'Large scale microbiome profiling in the cloud', 'Valdes, Camilo|Stebliankin, Vitalii|Narasimhan, Giri', 'Bioinformatics Research Group (BioRG), School of Computing and Information Sciences, Florida International University, Miami, FL, USA.|Bioinformatics Research Group (BioRG), School of Computing and Information Sciences, Florida International University, Miami, FL, USA.|Bioinformatics Research Group (BioRG), School of Computing and Information Sciences, Florida International University, Miami, FL, USA.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz356', '31510682', 'Bacterial metagenomics profiling for metagenomic whole sequencing (mWGS) usually starts by aligning sequencing reads to a collection of reference genomes. Current profiling tools are designed to work against a small representative collection of genomes, and do not scale very well to larger reference genome collections. However, large reference genome collections are capable of providing a more complete and accurate profile of the bacterial population in a metagenomics dataset. In this paper, we discuss a scalable, efficient and affordable approach to this problem, bringing big data solutions within the reach of laboratories with modest resources.'),
(83153, 2019, 'bio', 'Efficient merging of genome profile alignments', 'Hennig, André|Nieselt, Kay', 'Center for Bioinformatics (ZBIT), Integrative Transcriptomics, Eberhard Karls University of Tübingen, Tübingen, Germany.|Center for Bioinformatics (ZBIT), Integrative Transcriptomics, Eberhard Karls University of Tübingen, Tübingen, Germany.', 'Germany|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz377', '31510683', 'Whole-genome alignment (WGA) methods show insufficient scalability toward the generation of large-scale WGAs. Profile alignment-based approaches revolutionized the fields of multiple sequence alignment construction methods by significantly reducing computational complexity and runtime. However, WGAs need to consider genomic rearrangements between genomes, which make the profile-based extension of several whole-genomes challenging. Currently, none of the available methods offer the possibility to align or extend WGA profiles.'),
(83154, 2019, 'bio', 'Enhancing the drug discovery process: Bayesian inference for the analysis and comparison of dose-response experiments', 'Labelle, Caroline|Marinier, Anne|Lemieux, Sébastien', 'Institute for Research in Immunology and Cancer, Université de Montréal, Montréal, QC, Canada.|Institute for Research in Immunology and Cancer, Université de Montréal, Montréal, QC, Canada.|Institute for Research in Immunology and Cancer, Université de Montréal, Montréal, QC, Canada.', 'Canada|Canada|Canada', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz335', '31510684', 'The efficacy of a chemical compound is often tested through dose-response experiments from which efficacy metrics, such as the IC50, can be derived. The Marquardt-Levenberg algorithm (non-linear regression) is commonly used to compute estimations for these metrics. The analysis are however limited and can lead to biased conclusions. The approach does not evaluate the certainty (or uncertainty) of the estimates nor does it allow for the statistical comparison of two datasets. To compensate for these shortcomings, intuition plays an important role in the interpretation of results and the formulations of conclusions. We here propose a Bayesian inference methodology for the analysis and comparison of dose-response experiments.'),
(83155, 2019, 'bio', 'FunDMDeep-m6A: identification and prioritization of functional differential m6A methylation genes', 'Zhang, Song-Yao|Zhang, Shao-Wu|Fan, Xiao-Nan|Zhang, Teng|Meng, Jia|Huang, Yufei', 'Key Laboratory of Information Fusion Technology of Ministry of Education, Department of intelligent science and technology, School of Automation, Northwestern Polytechnical University, Xían, China.|Key Laboratory of Information Fusion Technology of Ministry of Education, Department of intelligent science and technology, School of Automation, Northwestern Polytechnical University, Xían, China.|Key Laboratory of Information Fusion Technology of Ministry of Education, Department of intelligent science and technology, School of Automation, Northwestern Polytechnical University, Xían, China.|Key Laboratory of Information Fusion Technology of Ministry of Education, Department of intelligent science and technology, School of Automation, Northwestern Polytechnical University, Xían, China.|Department of Biological Sciences, HRINU, SUERI, Xian Jiaotong-Liverpool University, Suzhou, Jiangsu, China.|Department of Electrical and Computer Engineering, The University of Texas at San Antonio, San Antonio, TX, USA.', 'China|China|China|China|China|United States', 4, '-', 'Journal Article', '10.1093/bioinformatics/btz316', '31510685', 'As the most abundant mammalian mRNA methylation, N6-methyladenosine (m6A) exists in >25% of human mRNAs and is involved in regulating many different aspects of mRNA metabolism, stem cell differentiation and diseases like cancer. However, our current knowledge about dynamic changes of m6A levels and how the change of m6A levels for a specific gene can play a role in certain biological processes like stem cell differentiation and diseases like cancer is largely elusive.'),
(83156, 2019, 'bio', 'Inferring signalling dynamics by integrating interventional with observational data', 'Cardner, Mathias|Meyer-Schaller, Nathalie|Christofori, Gerhard|Beerenwinkel, Niko', 'Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.|Department of Biomedicine, University of Basel, Basel, Switzerland.|Department of Biomedicine, University of Basel, Basel, Switzerland.|Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz325', '31510686', 'In order to infer a cell signalling network, we generally need interventional data from perturbation experiments. If the perturbation experiments are time-resolved, then signal progression through the network can be inferred. However, such designs are infeasible for large signalling networks, where it is more common to have steady-state perturbation data on the one hand, and a non-interventional time series on the other. Such was the design in a recent experiment investigating the coordination of epithelial-mesenchymal transition (EMT) in murine mammary gland cells. We aimed to infer the underlying signalling network of transcription factors and microRNAs coordinating EMT, as well as the signal progression during EMT.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(83157, 2019, 'bio', 'pNovo 3: precise de novo peptide sequencing using a learning-to-rank framework', 'Yang, Hao|Chi, Hao|Zeng, Wen-Feng|Zhou, Wen-Jing|He, Si-Min', 'Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing. Technology, Chinese Academy of Sciences, Beijing, China.|Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing. Technology, Chinese Academy of Sciences, Beijing, China.|Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing. Technology, Chinese Academy of Sciences, Beijing, China.|Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing. Technology, Chinese Academy of Sciences, Beijing, China.|Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing. Technology, Chinese Academy of Sciences, Beijing, China.', 'China|China|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz366', '31510687', 'De novo peptide sequencing based on tandem mass spectrometry data is the key technology of shotgun proteomics for identifying peptides without any database and assembling unknown proteins. However, owing to the low ion coverage in tandem mass spectra, the order of certain consecutive amino acids cannot be determined if all of their supporting fragment ions are missing, which results in the low precision of de novo sequencing.'),
(83158, 2019, 'bio', 'A divide-and-conquer method for scalable phylogenetic network inference from multilocus data', 'Zhu, Jiafan|Liu, Xinhao|Ogilvie, Huw A|Nakhleh, Luay K', 'Department of Computer Science, Rice University, Houston, TX, USA.|Department of Computer Science, Rice University, Houston, TX, USA.|Department of Computer Science, Rice University, Houston, TX, USA.|Department of Computer Science, Rice University, Houston, TX, USA.', 'United States|United States|United States|United States', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz359', '31510688', 'Reticulate evolutionary histories, such as those arising in the presence of hybridization, are best modeled as phylogenetic networks. Recently developed methods allow for statistical inference of phylogenetic networks while also accounting for other processes, such as incomplete lineage sorting. However, these methods can only handle a small number of loci from a handful of genomes.'),
(83159, 2019, 'bio', 'Efficient haplotype matching between a query and a panel for genealogical search', 'Naseri, Ardalan|Holzhauser, Erwin|Zhi, Degui|Zhang, Shaojie', 'Department of Computer Science, University of Central Florida, Orlando, FL, USA.|Department of Computer Science, University of Central Florida, Orlando, FL, USA.|School of Biomedical Informatics and School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA.|Department of Computer Science, University of Central Florida, Orlando, FL, USA.', 'United States|United States|United States|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz347', '31510689', 'With the wide availability of whole-genome genotype data, there is an increasing need for conducting genetic genealogical searches efficiently. Computationally, this task amounts to identifying shared DNA segments between a query individual and a very large panel containing millions of haplotypes. The celebrated Positional Burrows-Wheeler Transform (PBWT) data structure is a pre-computed index of the panel that enables constant time matching at each position between one haplotype and an arbitrarily large panel. However, the existing algorithm (Durbins Algorithm 5) can only identify set-maximal matches, the longest matches ending at any location in a panel, while in real genealogical search scenarios, multiple good enough matches are desired.'),
(83160, 2019, 'bio', 'Inheritance and variability of kinetic gene expression parameters in microbial cells: modeling and inference from lineage tree data', 'Marguet, Aline|Lavielle, Marc|Cinquemani, Eugenio', 'Univ. Grenoble Alpes, Inria, Grenoble, France.|Inria Saclay & Ecole Polytechnique, Palaiseau, France.|Univ. Grenoble Alpes, Inria, Grenoble, France.', 'France|France|France', 4, '-', 'Journal Article', '10.1093/bioinformatics/btz378', '31510690', 'Modern experimental technologies enable monitoring of gene expression dynamics in individual cells and quantification of its variability in isogenic microbial populations. Among the sources of this variability is the randomness that affects inheritance of gene expression factors at cell division. Known parental relationships among individually observed cells provide invaluable information for the characterization of this extrinsic source of gene expression noise. Despite this fact, most existing methods to infer stochastic gene expression models from single-cell data dedicate little attention to the reconstruction of mother-daughter inheritance dynamics.'),
(83161, 2019, 'bio', 'Precise modelling and interpretation of bioactivities of ligands targeting G protein-coupled receptors', 'Wu, Jiansheng|Liu, Ben|Chan, Wallace K B|Wu, Weijian|Pang, Tao|Hu, Haifeng|Yan, Shancheng|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.|Department of Pharmacology, University of Michigan, Ann Arbor, MI, USA.|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.|School of Geographic and Biological Information, Nanjing University of Posts and Telecommunications, Nanjing, China.|Child Mental Health Research Center, Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China.|Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.', 'China|China|United States|China|China|China|China|China|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz336', '31510691', 'Accurate prediction and interpretation of ligand bioactivities are essential for virtual screening and drug discovery. Unfortunately, many important drug targets lack experimental data about the ligand bioactivities, this is particularly true for G protein-coupled receptors (GPCRs), which account for the targets of about a third of drugs currently on the market. Computational approaches with the potential of precise assessment of ligand bioactivities and determination of key substructural features which determine ligand bioactivities are needed to address this issue.'),
(83162, 2019, 'bio', 'Robust network inference using response logic', 'Gross, Torsten|Wongchenko, Matthew J|Yan, Yibing|Blüthgen, Nils', 'Institut für Pathologie, Charité-Universitätsmedizin, Berlin.|Oncology Biomarker Development, Genentech Inc., South San Francisco, CA, USA.|Oncology Biomarker Development, Genentech Inc., South San Francisco, CA, USA.|Institut für Pathologie, Charité-Universitätsmedizin, Berlin.', 'United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz326', '31510692', 'A major challenge in molecular and cellular biology is to map out the regulatory networks of cells. As regulatory interactions can typically not be directly observed experimentally, various computational methods have been proposed to disentangling direct and indirect effects. Most of these rely on assumptions that are rarely met or cannot be adapted to a given context.'),
(83163, 2019, 'bio', 'hicGAN infers super resolution Hi-C data with generative adversarial networks', 'Liu, Qiao|Lv, Hairong|Jiang, Rui', 'Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic and Systems Biology, Beijing National Research Center for Information Science and Technology, Department of Automation, Tsinghua University, Beijing, China.|Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic and Systems Biology, Beijing National Research Center for Information Science and Technology, Department of Automation, Tsinghua University, Beijing, China.|Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic and Systems Biology, Beijing National Research Center for Information Science and Technology, Department of Automation, Tsinghua University, Beijing, China.', 'China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz317', '31510693', 'Hi-C is a genome-wide technology for investigating 3D chromatin conformation by measuring physical contacts between pairs of genomic regions. The resolution of Hi-C data directly impacts the effectiveness and accuracy of downstream analysis such as identifying topologically associating domains (TADs) and meaningful chromatin loops. High resolution Hi-C data are valuable resources which implicate the relationship between 3D genome conformation and function, especially linking distal regulatory elements to their target genes. However, high resolution Hi-C data across various tissues and cell types are not always available due to the high sequencing cost. It is therefore indispensable to develop computational approaches for enhancing the resolution of Hi-C data.'),
(83164, 2019, 'bio', 'Reconstructing signaling pathways using regular language constrained paths', 'Wagner, Mitchell J|Pratapa, Aditya|Murali, T M', 'Department of Computer Science, Virginia Tech, Blacksburg, VA, USA.|Department of Computer Science, Virginia Tech, Blacksburg, VA, USA.|Department of Computer Science, Virginia Tech, Blacksburg, VA, USA.', 'United States|United States|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz360', '31510694', 'High-quality curation of the proteins and interactions in signaling pathways is slow and painstaking. As a result, many experimentally detected interactions are not annotated to any pathways. A natural question that arises is whether or not it is possible to automatically leverage existing pathway annotations to identify new interactions for inclusion in a given pathway.'),
(83165, 2019, 'bio', 'scOrange-a tool for hands-on training of concepts from single-cell data analytics', 'Straxc5xbear, Martin|xc5xbdagar, Lan|Kokoxc5xa1ar, Jaka|Tanko, Vesna|Erjavec, Alexc5xa1|Polixc4x8dar, Pavlin G|Starixc4x8d, Anxc5xbee|Demxc5xa1ar, Janez|Shaulsky, Gad|Menon, Vilas|Lemire, Andrew|Parikh, Anup|Zupan, Blaxc5xbe', 'Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.|Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.|Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.|Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.|Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.|Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.|Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.|Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.|Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.|Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.|Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.|Naringi, San Francisco, CA, USA.|Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.', 'Slovenia|Slovenia|Slovenia|Slovenia|Slovenia|Slovenia|Slovenia|Slovenia|United States|United States|United States|United States|Slovenia', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz348', '31510695', 'Single-cell RNA sequencing allows us to simultaneously profile the transcriptomes of thousands of cells and to indulge in exploring cell diversity, development and discovery of new molecular mechanisms. Analysis of scRNA data involves a combination of non-trivial steps from statistics, data visualization, bioinformatics and machine learning. Training molecular biologists in single-cell data analysis and empowering them to review and analyze their data can be challenging, both because of the complexity of the methods and the steep learning curve.'),
(83166, 2019, 'bio', 'Inference of clonal selection in cancer populations using single-cell sequencing data', 'Skums, Pavel|Tsyvina, Viachaslau|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', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz392', '31510696', 'Intra-tumor heterogeneity is one of the major factors influencing cancer progression and treatment outcome. However, evolutionary dynamics of cancer clone populations remain poorly understood. Quantification of clonal selection and inference of fitness landscapes of tumors is a key step to understanding evolutionary mechanisms driving cancer. These problems could be addressed using single-cell sequencing (scSeq), which provides an unprecedented insight into intra-tumor heterogeneity allowing to study and quantify selective advantages of individual clones. Here, we present Single Cell Inference of FItness Landscape (SCIFIL), a computational tool for inference of fitness landscapes of heterogeneous cancer clone populations from scSeq data. SCIFIL allows to estimate maximum likelihood fitnesses of clone variants, measure their selective advantages and order of appearance by fitting an evolutionary model into the tumor phylogeny. We demonstrate the accuracy our approach, and show how it could be applied to experimental tumor data to study clonal selection and infer evolutionary history. SCIFIL can be used to provide new insight into the evolutionary dynamics of cancer.'),
(83167, 2019, 'bio', 'PRISM: methylation pattern-based, reference-free inference of subclonal makeup', 'Lee, Dohoon|Lee, Sangseon|Kim, Sun', 'Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea.|Department of Computer Science and Engineering, Seoul National University, Seoul, Korea.|Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea.', '-|-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz327', '31510697', 'Characterizing cancer subclones is crucial for the ultimate conquest of cancer. Thus, a number of bioinformatic tools have been developed to infer heterogeneous tumor populations based on genomic signatures such as mutations and copy number variations. Despite accumulating evidence for the significance of global DNA methylation reprogramming in certain cancer types including myeloid malignancies, none of the bioinformatic tools are designed to exploit subclonally reprogrammed methylation patterns to reveal constituent populations of a tumor. In accordance with the notion of global methylation reprogramming, our preliminary observations on acute myeloid leukemia (AML) samples implied the existence of subclonally occurring focal methylation aberrance throughout the genome.'),
(83168, 2019, 'bio', 'Prediction of mRNA subcellular localization using deep recurrent neural networks', 'Yan, Zichao|Lécuyer, Eric|Blanchette, Mathieu', 'School of Computer Science, McGill University, Montreal, QC, Canada.|Department of Biochemistry, University of Montreal, Montreal, QC, Canada.|School of Computer Science, McGill University, Montreal, QC, Canada.', 'Canada|Canada|Canada', 4, '-', 'Journal Article', '10.1093/bioinformatics/btz337', '31510698', 'Messenger RNA subcellular localization mechanisms play a crucial role in post-transcriptional gene regulation. This trafficking is mediated by trans-acting RNA-binding proteins interacting with cis-regulatory elements called zipcodes. While new sequencing-based technologies allow the high-throughput identification of RNAs localized to specific subcellular compartments, the precise mechanisms at play, and their dependency on specific sequence elements, remain poorly understood.'),
(83169, 2019, 'bio', 'DIFFUSE: predicting isoform functions from sequences and expression profiles via deep learning', 'Chen, Hao|Shaw, Dipan|Zeng, Jianyang|Bu, Dongbo|Jiang, Tao', 'Department of Compute Science and Engineering, University of California, Riverside, CA, USA.|Department of Compute Science and Engineering, University of California, Riverside, CA, USA.|Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China.|Key Lab of Intelligent Information Process, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.|Department of Compute Science and Engineering, University of California, Riverside, CA, USA.', 'United States|United States|China|China|United States', 3, '-', 'Journal Article', '10.1093/bioinformatics/btz367', '31510699', 'Alternative splicing generates multiple isoforms from a single gene, greatly increasing the functional diversity of a genome. Although gene functions have been well studied, little is known about the specific functions of isoforms, making accurate prediction of isoform functions highly desirable. However, the existing approaches to predicting isoform functions are far from satisfactory due to at least two reasons: (i) unlike genes, isoform-level functional annotations are scarce. (ii) The information of isoform functions is concealed in various types of data including isoform sequences, co-expression relationship among isoforms, etc.'),
(83170, 2019, 'bio', 'MOLI: multi-omics late integration with deep neural networks for drug response prediction', 'Sharifi-Noghabi, Hossein|Zolotareva, Olga|Collins, Colin C|Ester, Martin', 'School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.|International Research Training Group Computational Methods for the Analysis of the Diversity and Dynamics of Genomes and Genome Informatics, Faculty of Technology and Center for Biotechnology, Bielefeld University, Germany.|Vancouver Prostate Centre, Vancouver, BC, Canada.|School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.', 'Canada|Germany|Canada|Canada', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz318', '31510700', 'Historically, gene expression has been shown to be the most informative data for drug response prediction. Recent evidence suggests that integrating additional omics can improve the prediction accuracy which raises the question of how to integrate the additional omics. Regardless of the integration strategy, clinical utility and translatability are crucial. Thus, we reasoned a multi-omics approach combined with clinical datasets would improve drug response prediction and clinical relevance.'),
(83171, 2019, 'bio', 'TADA: phylogenetic augmentation of microbiome samples enhances phenotype classification', 'Sayyari, Erfan|Kawas, Ban|Mirarab, Siavash', 'Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, USA.|IBM Research-Almaden Research Center, San Jose, CA, USA.|Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, USA.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz394', '31510701', 'Learning associations of traits with the microbial composition of a set of samples is a fundamental goal in microbiome studies. Recently, machine learning methods have been explored for this goal, with some promise. However, in comparison to other fields, microbiome data are high-dimensional and not abundant, leading to a high-dimensional low-sample-size under-determined system. Moreover, microbiome data are often unbalanced and biased. Given such training data, machine learning methods often fail to perform a classification task with sufficient accuracy. Lack of signal is especially problematic when classes are represented in an unbalanced way in the training data, with some classes under-represented. The presence of inter-correlations among subsets of observations further compounds these issues. As a result, machine learning methods have had only limited success in predicting many traits from microbiome. Data augmentation consists of building synthetic samples and adding them to the training data and is a technique that has proved helpful for many machine learning tasks.'),
(83172, 2019, 'bio', 'MCS2: minimal coordinated supports for fast enumeration of minimal cut sets in metabolic networks', 'Miraskarshahi, Reza|Zabeti, Hooman|Stephen, Tamon|Chindelevitch, Leonid', 'School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.|School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.|Department of Mathematics, Simon Fraser University, Burnaby, BC, Canada.|School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.', 'Canada|Canada|Canada|Canada', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz393', '31510702', 'Constraint-based modeling of metabolic networks helps researchers gain insight into the metabolic processes of many organisms, both prokaryotic and eukaryotic. Minimal cut sets (MCSs) are minimal sets of reactions whose inhibition blocks a target reaction in a metabolic network. Most approaches for finding the MCSs in constrained-based models require, either as an intermediate step or as a byproduct of the calculation, the computation of the set of elementary flux modes (EFMs), a convex basis for the valid flux vectors in the network. Recently, Ballerstein et al. proposed a method for computing the MCSs of a network without first computing its EFMs, by creating a dual network whose EFMs are a superset of the MCSs of the original network. However, their dual network is always larger than the original network and depends on the target reaction. Here we propose the construction of a different dual network, which is typically smaller than the original network and is independent of the target reaction, for the same purpose. We prove the correctness of our approach, minimal coordinated support (MCS2), and describe how it can be modified to compute the few smallest MCSs for a given target reaction.'),
(83173, 2019, 'bio', 'Statistical compression of protein sequences and inference of marginal probability landscapes over competing alignments using finite state models and Dirichlet priors', 'Sumanaweera, Dinithi|Allison, Lloyd|Konagurthu, Arun S', 'Faculty of Information Technology, Monash University, Clayton, VIC, Australia.|Faculty of Information Technology, Monash University, Clayton, VIC, Australia.|Faculty of Information Technology, Monash University, Clayton, VIC, Australia.', 'Australia|Australia|Australia', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz368', '31510703', 'The information criterion of minimum message length (MML) provides a powerful statistical framework for inductive reasoning from observed data. We apply MML to the problem of protein sequence comparison using finite state models with Dirichlet distributions. The resulting framework allows us to supersede the ad hoc cost functions commonly used in the field, by systematically addressing the problem of arbitrariness in alignment parameters, and the disconnect between substitution scores and gap costs. Furthermore, our framework enables the generation of marginal probability landscapes over all possible alignment hypotheses, with potential to facilitate the users to simultaneously rationalize and assess competing alignment relationships between protein sequences, beyond simply reporting a single (best) alignment. We demonstrate the performance of our program on benchmarks containing distantly related protein sequences.'),
(83174, 2019, 'bio', 'Weighted elastic net for unsupervised domain adaptation with application to age prediction from DNA methylation data', 'Handl, Lisa|Jalali, Adrin|Scherer, Michael|Eggeling, Ralf|Pfeifer, Nico', 'Department for Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarbrücken, Germany.|Department for Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarbrücken, Germany.|Department for Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarbrücken, Germany.|Department of Computer Science, University of Tübingen, Tübingen, Germany.|Department for Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarbrücken, Germany.', 'Germany|Germany|Germany|Germany|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz338', '31510704', 'Predictive models are a powerful tool for solving complex problems in computational biology. They are typically designed to predict or classify data coming from the same unknown distribution as the training data. In many real-world settings, however, uncontrolled biological or technical factors can lead to a distribution mismatch between datasets acquired at different times, causing model performance to deteriorate on new data. A common additional obstacle in computational biology is scarce data with many more features than samples. To address these problems, we propose a method for unsupervised domain adaptation that is based on a weighted elastic net. The key idea of our approach is to compare dependencies between inputs in training and test data and to increase the cost of differently behaving features in the elastic net regularization term. In doing so, we encourage the model to assign a higher importance to features that are robust and behave similarly across domains.'),
(83175, 2019, 'bio', 'Multifaceted protein-protein interaction prediction based on Siamese residual RCNN', 'Chen, Muhao|Ju, Chelsea J-T|Zhou, Guangyu|Chen, Xuelu|Zhang, Tianran|Chang, Kai-Wei|Zaniolo, Carlo|Wang, Wei', 'Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA.|Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA.|Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA.|Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA.|Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA.|Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA.|Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA.|Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz328', '31510705', 'Sequence-based protein-protein interaction (PPI) prediction represents a fundamental computational biology problem. To address this problem, extensive research efforts have been made to extract predefined features from the sequences. Based on these features, statistical algorithms are learned to classify the PPIs. However, such explicit features are usually costly to extract, and typically have limited coverage on the PPI information.'),
(83176, 2019, 'bio', 'Using the structure of genome data in the design of deep neural networks for predicting amyotrophic lateral sclerosis from genotype', 'Yin, Bojian|Balvert, Marleen|van der Spek, Rick A A|Dutilh, Bas E|Bohté, Sander|Veldink, Jan|Schönhuth, Alexander', 'Centrum Wiskunde & Informatica, Life Sciences & Health, XG Amsterdam, The Netherlands.|Centrum Wiskunde & Informatica, Life Sciences & Health, XG Amsterdam, The Netherlands.|Department of Neurology, Brain Center Rudolf Magnus University Medical Center Utrecht, Utrecht, The Netherlands.|Theoretical Biology & Bioinformatics, Utrecht University, JE Utrecht, The Netherlands.|Centrum Wiskunde & Informatica, Life Sciences & Health, XG Amsterdam, The Netherlands.|Department of Neurology, Brain Center Rudolf Magnus University Medical Center Utrecht, Utrecht, The Netherlands.|Centrum Wiskunde & Informatica, Life Sciences & Health, XG Amsterdam, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz369', '31510706', 'Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease caused by aberrations in the genome. While several disease-causing variants have been identified, a major part of heritability remains unexplained. ALS is believed to have a complex genetic basis where non-additive combinations of variants constitute disease, which cannot be picked up using the linear models employed in classical genotype-phenotype association studies. Deep learning on the other hand is highly promising for identifying such complex relations. We therefore developed a deep-learning based approach for the classification of ALS patients versus healthy individuals from the Dutch cohort of the Project MinE dataset. Based on recent insight that regulatory regions harbor the majority of disease-associated variants, we employ a two-step approach: first promoter regions that are likely associated to ALS are identified, and second individuals are classified based on their genotype in the selected genomic regions. Both steps employ a deep convolutional neural network. The network architecture accounts for the structure of genome data by applying convolution only to parts of the data where this makes sense from a genomics perspective.'),
(83177, 2019, 'bio', 'ShaKer: RNA SHAPE prediction using graph kernel', 'Mautner, Stefan|Montaseri, Soheila|Miladi, Milad|Raden, Martin|Costa, Fabrizio|Backofen, Rolf', 'Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany.|Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany.|Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany.|Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany.|Department Computer Science, University of Exeter, Exeter, UK.|Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany.', 'Germany|Germany|Germany|Germany|United Kingdom|Germany', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz395', '31510707', 'SHAPE experiments are used to probe the structure of RNA molecules. We present ShaKer to predict SHAPE data for RNA using a graph-kernel-based machine learning approach that is trained on experimental SHAPE information. While other available methods require a manually curated reference structure, ShaKer predicts reactivity data based on sequence input only and by sampling the ensemble of possible structures. Thus, ShaKer is well placed to enable experiment-driven, transcriptome-wide SHAPE data prediction to enable the study of RNA structuredness and to improve RNA structure and RNA-RNA interaction prediction. For performance evaluation, we use accuracy and accessibility comparing to experimental SHAPE data and competing methods. We can show that Shaker outperforms its competitors and is able to predict high quality SHAPE annotations even when no reference structure is provided.'),
(83178, 2019, 'bio', 'ISMB/ECCB 2019 Proceedings', 'Bromberg, Yana|El-Mabrouk, Nadia|Radivojac, Predrag', 'Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ, USA.|Department of Computer Science (DIRO), Université de Montréal, QC, Canada.|Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA.', 'United States|Canada|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz439', '31510708', '-'),
(83179, 2019, 'bio', 'Learning a mixture of microbial networks using minorization-maximization', 'Tavakoli, Sahar|Yooseph, Shibu', 'Department of Computer Science, Genomics and Bioinformatics Cluster, University of Central Florida, Orlando, FL, USA.|Department of Computer Science, Genomics and Bioinformatics Cluster, University of Central Florida, Orlando, FL, USA.', 'United States|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz370', '31510709', 'The interactions among the constituent members of a microbial community play a major role in determining the overall behavior of the community and the abundance levels of its members. These interactions can be modeled using a network whose nodes represent microbial taxa and edges represent pairwise interactions. A microbial network is typically constructed from a sample-taxa count matrix that is obtained by sequencing multiple biological samples and identifying taxa counts. From large-scale microbiome studies, it is evident that microbial community compositions and interactions are impacted by environmental and/or host factors. Thus, it is not unreasonable to expect that a sample-taxa matrix generated as part of a large study involving multiple environmental or clinical parameters can be associated with more than one microbial network. However, to our knowledge, microbial network inference methods proposed thus far assume that the sample-taxa matrix is associated with a single network.'),
(83180, 2019, 'bio', 'ISMB/ECCB 2019 PROCEEDINGS PAPERS COMMITTEE', '-', '-', '-', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz494', '31510710', '-'),
(83181, 2019, 'bio', 'Subtype-Specific Transcriptional Regulators in Breast Tumors Subjected to Genetic and Epigenetic Alterations', 'Zhu, Qian|Tekpli, Xavier|Troyanskaya, Olga G|Kristensen, Vessela N', 'Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 360 Longwood Ave, Boston, MA.|Department of Genetics, Institute for Cancer Research, Oslo University Hospital, Radiumhospitalet, Oslo, Norway.|Department of Computer Science, Princeton University, Princeton, NJ, USA.|Department of Genetics, Institute for Cancer Research, Oslo University Hospital, Radiumhospitalet, Oslo, Norway.', 'United States|Norway|United States|Norway', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz709', '31529022', 'Breast cancer consists of multiple distinct tumor subtypes, and results from epigenetic and genetic aberrations that give rise to distinct transcriptional profiles. Despite previous efforts to understand transcriptional deregulation through transcription factor networks, the transcriptional mechanisms leading to subtypes of the disease remain poorly understood.'),
(83182, 2019, 'bio', 'DeepMSPeptide: peptide detectability prediction using deep learning', 'Serrano, Guillermo|Guruceaga, Elizabeth|Segura, Victor', 'Bioinformatics Platform, Center for Applied Medical Research, University of Navarra, Pamplona, Spain.|Bioinformatics Platform, Center for Applied Medical Research, University of Navarra, Pamplona, Spain.|Bioinformatics Platform, Center for Applied Medical Research, University of Navarra, Pamplona, Spain.', 'Spain|Spain|Spain', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz708', '31529040', 'The protein detection and quantification using high-throughput proteomic technologies is still challenging due to the stochastic nature of the peptide selection in the mass spectrometer, the difficulties in the statistical analysis of the results and the presence of degenerated peptides. However, considering in the analysis only those peptides that could be detected by mass spectrometry (MS), also called proteotypic peptides, increases the accuracy of the results. Several approaches have been applied to predict peptide detectability based on the physicochemical properties of the peptides. In this manuscript we present DeepMSPeptide, a bioinformatic tool that uses a deep learning method to predict proteotypic peptides exclusively based on the peptide amino acid sequences.'),
(83183, 2019, 'bio', 'PPNID: a reference database and molecular identification pipeline for plant-parasitic nematodes', 'Qing, Xue|Wang, Meng|Karssen, Gerrit|Bucki, Patricia|Bert, Wim|Braun-Miyara, Sigal', 'Department of Entomology, Nematology and Chemistry units, Agricultural Research Organization (ARO), the Volcani Center, Rishon LeZion, P.O. Box 15159, Israel.|Department of Entomology, Nematology and Chemistry units, Agricultural Research Organization (ARO), the Volcani Center, Rishon LeZion, P.O. Box 15159, Israel.|National Plant Protection Organization, Wageningen Nematode Collection, P.O. Box 9102, 6700 HC, Wageningen, The Netherlands.|Department of Entomology, Nematology and Chemistry units, Agricultural Research Organization (ARO), the Volcani Center, Rishon LeZion, P.O. Box 15159, Israel.|Nematology Research Unit, Department of Biology, Ghent University, K.L. Ledeganckstraat 35, 9000 Ghent, Belgium.|Department of Entomology, Nematology and Chemistry units, Agricultural Research Organization (ARO), the Volcani Center, Rishon LeZion, P.O. Box 15159, Israel.', 'Israel|Israel|Netherlands|Israel|Belgium|Israel', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz707', '31529041', 'The phylum Nematoda comprises the most cosmopolitan and abundant metazoans on Earth and plant-parasitic nematodes represent one of the most significant nematode groups, causing severe losses in agriculture. Practically, the demands for accurate nematode identification are high for ecological, agricultural, taxonomic and phylogenetic researches. Despite their importance, the morphological diagnosis is often a difficult task due to phenotypic plasticity and the absence of clear diagnostic characters while molecular identification is very difficult due to the problematic database and complex genetic background.'),
(83184, 2019, 'bio', 'Biogenesis mechanisms of circular RNA can be categorized through feature extraction of a machine learning model', 'Liu, Chengyu|Liu, Yu-Chen|Huang, Hsien-Da|Wang, Wei', 'Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla,CA, USA.|Institute of Engineering in Medicine, University of California, San Diego, La Jolla,CA, USA.|Arieh Warshel Institute of Computational Biology, Chinese University of Hong Kong, Shenzhen.|Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla,CA, USA.', 'United States|United States|Hong Kong|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz705', '31529043', 'In recent years, multiple circular RNAs biogenesis mechanisms have been discovered. While each reported mechanism has been experimentally verified in different circular RNAs, no single biogenesis mechanism has been proposed that can universally explain the biogenesis of all tens of thousands of discovered circular RNAs. Under the hypothesis that human circular RNAs can be categorized according to different biogenesis mechanisms, we designed a contextual regression model trained to predict the formation of circular RNA from a random genomic locus on human genome, with potential biogenesis factors of circular RNA as the features of the training data.'),
(83185, 2019, 'bio', 'AssessORF: combining evolutionary conservation and proteomics to assess prokaryotic gene predictions', 'Korandla, Deepank R|Wozniak, Jacob M|Campeau, Anaamika|Gonzalez, David J|Wright, Erik S', 'Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA.|Department of Pharmacology, University of California San Diego, La Jolla, CA.|Department of Pharmacology, University of California San Diego, La Jolla, CA.|Department of Pharmacology, University of California San Diego, La Jolla, CA.|Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA.', '-|-|-|-|-', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz714', '31532487', 'A core task of genomics is to identify the boundaries of protein coding genes, which may cover over 90% of a prokaryotes genome. Several programs are available for gene finding, yet it is currently unclear how well these programs perform and whether any offers superior accuracy. This is in part because there is no universal benchmark for gene finding and, therefore, most developers select their own benchmarking strategy.'),
(83186, 2019, 'bio', 'MUFold-SSW: A new web server for predicting protein secondary structures, torsion angles, and turns', 'Fang, Chao|Li, Zhaoyu|Xu, Dong|Shang, Yi', 'Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA.|Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA.|Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA.|Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA.', 'United States|United States|United States|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz712', '31532508', 'Protein secondary structure and backbone torsion angle prediction can provide important information for predicting protein 3D structures and protein functions. Our new methods MUFold-SS, MUFold-Angle, MUFold-BetaTurn, and MUFold-GammaTurn, developed based on advanced deep neural networks, achieved state-of-the-art performance for predicting secondary structures, backbone torsion angles, beta-turns, and gamma-turns, respectively. An easy-to-use web service will provide the community a convenient way to use these methods for research and development.'),
(83187, 2019, 'bio', 'XMAn v2-a database of Homo sapiens mutated peptides', 'Flores, Marcela Aguilera|Lazar, Iulia M', 'Genomics, Bioinformatics and Computational Biology.|Department of Biological Sciences, Virginia Tech, Blacksburg, VA, USA.', 'United States|United States', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz693', '31539018', 'The Unknown Mutation Analysis (XMAn) database is a compilation of Homo sapiens mutated peptides in FASTA format, that was constructed for facilitating the identification of protein sequence alterations by tandem mass spectrometry detection. The database comprises 2 539 031 non-redundant mutated entries from 17 599 proteins, of which 2 377 103 are missense and 161 928 are nonsense mutations. It can be used in conjunction with search engines that seek the identification of peptide amino acid sequences by matching experimental tandem mass spectrometry data to theoretical sequences from a database.'),
(83188, 2019, 'bio', 'Chordomics: a visualisation tool for linking function to phylogeny in microbiomes', 'McDonnell, Kevin|Waters, Nicholas|Howley, Enda|Abram, Florence', 'Functional Environmental Microbiology, School of Natural Sciences, Ryan Institute, National University of Ireland Galway, Ireland.|Functional Environmental Microbiology, School of Natural Sciences, Ryan Institute, National University of Ireland Galway, Ireland.|Department of Information Technology, School of Computer Science, National University of Ireland Galway, Ireland.|Functional Environmental Microbiology, School of Natural Sciences, Ryan Institute, National University of Ireland Galway, Ireland.', 'Ireland|Ireland|Ireland|Ireland', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz711', '31539022', 'The overarching aim of microbiome analysis is to uncover the links between microbial phylogeny and function in order to access ecosystem functioning. This can be done using several experimental strategies targeting different biomolecules, including DNA (metagenomics), RNA (metatranscriptomics) and proteins (metaproteomics). Despite the importance of linking microbial function to phylogeny there are currently no visualisation tools that effectively integrate this information. Chordomics is a Shiny-based application for linked -omics data analysis, allowing users to visualise microbial function and phylogeny on a single plot and compare datasets across time and environments.'),
(83189, 2019, 'bio', 'EpiGraph: an open-source platform to quantify epithelial organization', 'Vicente-Munuera, Pablo|Gómez-Gálvez, Pedro|Tetley, Robert J|Forja, Cristina|Tagua, Antonio|Letrán, Marta|Tozluoglu, Melda|Mao, Yanlan|Escudero, Luis M', 'Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla and Departamento de Biología Celular, Universidad de Sevilla, Seville, Spain.|Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla and Departamento de Biología Celular, Universidad de Sevilla, Seville, Spain.|MRC Laboratory for Molecular Cell Biology, University College London, London, UK.|Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla and Departamento de Biología Celular, Universidad de Sevilla, Seville, Spain.|Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla and Departamento de Biología Celular, Universidad de Sevilla, Seville, Spain.|Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla and Departamento de Biología Celular, Universidad de Sevilla, Seville, Spain.|MRC Laboratory for Molecular Cell Biology, University College London, London, UK.|MRC Laboratory for Molecular Cell Biology, University College London, London, UK.|Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla and Departamento de Biología Celular, Universidad de Sevilla, Seville, Spain.', 'Spain|Spain|United Kingdom|Spain|Spain|Spain|United Kingdom|United Kingdom|Spain', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz683', '31544932', 'Here we present EpiGraph, an image analysis tool that quantifies epithelial organization. Our method combines computational geometry and graph theory to measure the degree of order of any packed tissue. EpiGraph goes beyond the traditional polygon distribution analysis, capturing other organizational traits that improve the characterization of epithelia. EpiGraph can objectively compare the rearrangements of epithelial cells during development and homeostasis to quantify how the global ensemble is affected. Importantly, it has been implemented in the open-access platform Fiji. This makes EpiGraph very user friendly, with no programming skills required.'),
(83190, 2019, 'bio', 'Pavian: Interactive analysis of metagenomics data for microbiome studies and pathogen identification', 'Breitwieser, Florian P|Salzberg, Steven L', 'Center for Computational Biology, Johns Hopkins University, Baltimore, MD, United States.|Departments of Biomedical Engineering, Computer Science and Biostatistics, Johns Hopkins University, Baltimore, MD, United States.', 'United States|United States', 9, '-', 'Journal Article', '10.1093/bioinformatics/btz715', '31553437', 'Pavian is a web application for exploring classification results from metagenomics experiments. With Pavian, researchers can analyze, visualize, and transform results from various classifiers - such as Kraken, Centrifuge, and MethaPhlAn - using interactive data tables, heatmaps and Sankey flow diagrams. An interactive alignment coverage viewer can help in the validation of matches to a particular genome, which can be crucial when using metagenomics experiments for pathogen detection.'),
(83191, 2019, 'bio', 'scDAPA: detection and visualization of dynamic alternative polyadenylation from single cell RNA-seq data', 'Ye, Congting|Zhou, Qian|Wu, Xiaohui|Yu, Chen|Ji, Guoli|Saban, Daniel R|Li, Qingshun Q', 'Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Xiamen, Fujian 361102, 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.|Department of Ophthalmology, Duke University, Durham, NC 27710, USA.|Department of Automation, Xiamen University, Xiamen, Fujian 361005, China.|Department of Ophthalmology, Duke University, Durham, NC 27710, USA.|Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Xiamen, Fujian 361102, China.', 'China|China|China|United States|China|United States|China', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz701', '31557285', 'Alternative polyadenylation (APA) plays a key post-transcriptional regulatory role in mRNA stability and functions in eukaryotes. Single cell RNA-seq (scRNA-seq) is a powerful tool to discover cellular heterogeneity at gene expression level. Given 3 enriched strategy in library construction, the most commonly used scRNA-seq protocol-10× Genomics enables us to improve the study resolution of APA to the single cell level. However, currently there is no computational tool available for investigating APA profiles from scRNA-seq data.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(83192, 2019, 'bio', 'EvolClust: automated inference of evolutionary conserved gene clusters in eukaryotes', 'Marcet-Houben, Marina|Gabaldón, Toni', 'Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona, Spain.|Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona, Spain.', 'Spain|Spain', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz706', '31560365', 'The evolution and role of gene clusters in eukaryotes is poorly understood. Currently, most studies and computational prediction programs limit their focus to specific types of clusters, such as those involved in secondary metabolism.'),
(83193, 2019, 'bio', 'Metric Learning on Expression Data for Gene Function Prediction', 'Makrodimitris, Stavros|Reinders, Marcel J T|van Ham, Roeland C H J', 'Delft Bioinformatics Lab, Delft University of Technology, Van Mourik Broekmanweg 6, Delft, the Netherlands.|Delft Bioinformatics Lab, Delft University of Technology, Van Mourik Broekmanweg 6, Delft, the Netherlands.|Delft Bioinformatics Lab, Delft University of Technology, Van Mourik Broekmanweg 6, Delft, the Netherlands.', 'Netherlands|Netherlands|Netherlands', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz731', '31562759', 'Co-expression of two genes across different conditions is indicative of their involvement in the same biological process. However, when using RNA-Seq datasets with many experimental conditions from diverse sources, only a subset of the experimental conditions is expected to be relevant for finding genes related to a particular Gene Ontology (GO) term. Therefore, we hypothesize that when the purpose is to find similarly functioning genes, the co-expression of genes should not be determined on all samples but only on those samples informative for the GO term of interest.'),
(83194, 2019, 'bio', 'Hexa-Longin domain scaffolds for inter-Rab signalling', 'Sanchez-Pulido, Luis|Ponting, Chris P', 'Medical Research Council Human Genetics Unit, IGMM, University of Edinburgh, Edinburgh EH4 2XU, UK.|Medical Research Council Human Genetics Unit, IGMM, University of Edinburgh, Edinburgh EH4 2XU, UK.', 'United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz739', '31562761', 'CPLANE is a protein complex required for assembly and maintenance of primary cilia. It contains several proteins, such as INTU, FUZ, WDPCP, JBTS17, and RSG1 (REM2- and RAB-like small GTPase 1), whose genes are mutated in ciliopathies. Using two contrasting evolutionary analyses, coevolution-based contact prediction and sequence conservation, we first identified the INTU/FUZ heterodimer as a novel member of homologous HerMon (Hermansky-Pudlak syndrome and MON1-CCZ1) complexes. Subsequently, we identified homologous Longin domains that are triplicated in each of these six proteins (MON1A, CCZ1, HPS1, HPS4, INTU and FUZ). HerMon complexes are known to be Rab effectors and Rab GEFs (Guanine nucleotide Exchange Factors) that regulate vesicular trafficking. Consequently, INTU/FUZ, their homologous complex, is likely to act as a GEF during activation of Rab GTPases involved in ciliogenesis.'),
(83195, 2019, 'bio', 'The CroCo cross-link converter: a user-centred tool to convert results from crosslinking mass spectrometry experiments', 'Bender, Julian|Schmidt, Carla', 'Interdisciplinary Research Center HALOmem, Charles Tanford Protein Center, Institute for Biochemistry and Biotechnology, Martin Luther University Halle-Wittenberg, Kurt-Mothes-Str. 3a, Halle (Saale), Germany.|Interdisciplinary Research Center HALOmem, Charles Tanford Protein Center, Institute for Biochemistry and Biotechnology, Martin Luther University Halle-Wittenberg, Kurt-Mothes-Str. 3a, Halle (Saale), Germany.', 'Germany|Germany', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz732', '31562766', 'A variety of search engines exists for the identification of peptide spectrum matches after cross-linking mass spectrometry experiments. The resulting diversity in output formats complicates data validation and visualisation as well as exchange with collaborators, particularly from other research areas.'),
(83196, 2019, 'bio', 'Zombi: A phylogenetic simulator of trees, genomes and sequences that accounts for dead linages', 'Davxc3xadn, Adrixc3xa1n A|Tricou, Thxc3xa9o|Tannier, Eric|de Vienne, Damien M|Szxc3xb6llxc5x91si, Gergely J', 'MTA-ELTE Lendxc3xbclet Evolutionary Genomics Research Group, Budapest, Hungary.|Univ Lyon, Universitxc3xa9 Lyon 1, CNRS, Laboratoire de Biomxc3xa9trie et Biologie xc3x89volutive UMR5558, F-69622 Villeurbanne, France.|Univ Lyon, Universitxc3xa9 Lyon 1, CNRS, Laboratoire de Biomxc3xa9trie et Biologie xc3x89volutive UMR5558, F-69622 Villeurbanne, France.|Univ Lyon, Universitxc3xa9 Lyon 1, CNRS, Laboratoire de Biomxc3xa9trie et Biologie xc3x89volutive UMR5558, F-69622 Villeurbanne, France.|MTA-ELTE Lendxc3xbclet Evolutionary Genomics Research Group, Budapest, Hungary.', 'Hungary|France|France|France|Hungary', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz710', '31566657', 'Here we present Zombi, a tool to simulate the evolution of species, genomes and sequences in silico, that considers for the first time the evolution of genomes in extinct lineages. It also incorporates various features that have not to date been combined in a single simulator, such as the possibility of generating species trees with a pre-defined variation of speciation and extinction rates through time, simulating explicitly intergenic sequences of variable length and outputting gene tree - species tree reconciliations.'),
(83197, 2019, 'bio', 'ChIPseqSpikeInFree: A ChIP-seq normalization approach to reveal global changes in histone modifications without spike-in', 'Jin, Hongjian|Kasper, Lawryn H|Larson, Jon D|Wu, Gang|Baker, Suzanne J|Zhang, Jinghui|Fan, Yiping', 'Department of Computational Biology, St. Jude Childrens Research Hospital, Memphis, TN, USA.|Department of Developmental Neurobiology, St. Jude Childrens Research Hospital, Memphis, TN, USA.|Department of Developmental Neurobiology, St. Jude Childrens Research Hospital, Memphis, TN, USA.|Department of Computational Biology, St. Jude Childrens Research Hospital, Memphis, TN, USA.|Department of Developmental Neurobiology, St. Jude Childrens Research Hospital, Memphis, TN, USA.|Department of Computational Biology, St. Jude Childrens Research Hospital, Memphis, TN, USA.|Department of Computational Biology, St. Jude Childrens Research Hospital, Memphis, TN, USA.', 'United States|United States|United States|United States|United States|United States|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz720', '31566663', 'The traditional reads per million normalization method is inappropriate for the evaluation of ChIP-seq data when treatments or mutations have global effects. Changes in global levels of histone modifications can be detected with exogenous reference spike-in controls. However, most ChIP-seq studies overlook the normalization that must be corrected with spike-in. A method that retrospectively renormalizes datasets without spike-in is lacking.'),
(83198, 2019, 'bio', 'DeepCleave: a deep learning predictor for caspase and matrix metalloprotease substrates and cleavage sites', 'Li, Fuyi|Chen, Jinxiang|Leier, André|Marquez-Lago, Tatiana|Liu, Quanzhong|Wang, Yanze|Revote, Jerico|Smith, A Ian|Akutsu, Tatsuya|Webb, Geoffrey I|Kurgan, Lukasz|Song, Jiangning', 'Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC, Australia.|Monash 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, USA.|Department of Genetics, School of Medicine, University of Alabama at Birmingham, USA.|College of Information Engineering, Northwest A&F University, Yangling, China.|College of Information Engineering, Northwest A&F University, Yangling, China.|Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC, Australia.|Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC, Australia.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto, Japan.|Monash Centre for Data Science, Monash University, Melbourne, VIC, Australia.|Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA.|Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC, Australia.', 'Australia|Australia|United States|United States|China|China|Australia|Australia|Japan|Australia|United States|Australia', 10, '-', 'Journal Article', '10.1093/bioinformatics/btz721', '31566664', 'Proteases are enzymes that cleave target substrate proteins by catalyzing the hydrolysis of peptide bonds between specific amino acids. While the functional proteolysis regulated by proteases plays a central role in the life and death process of proteins, many of the corresponding substrates and their cleavage sites were not found yet. Availability of accurate predictors of the substrates and cleavage sites would facilitate understanding of proteases functions and physiological roles. Deep learning is a promising approach for the development of accurate predictors of substrate cleavage events.'),
(83199, 2019, 'bio', 'MitoTrack, a user-friendly semi-automatic software for lineage tracking in living embryos', 'Trullo, A|Dufourt, J|Lagha, M', 'Institut de Génétique Moléculaire de Montpellier, University of Montpellier, CNRS, Montpellier, France.|Institut de Génétique Moléculaire de Montpellier, University of Montpellier, CNRS, Montpellier, France.|Institut de Génétique Moléculaire de Montpellier, University of Montpellier, CNRS, Montpellier, France.', 'France|France|France', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz717', '31580394', 'During development, progenitor cells undergo multiple rounds of cellular divisions during which transcriptional programs must be faithfully propagated. Investigating the timing of transcriptional activation, which is a highly stochastic phenomenon, requires the analysis of large amounts of data. In order to perform automatic image analysis of transcriptional activation, we developed a software that segments and tracks both small and large objects, leading the user from raw data up to the results in their final form.'),
(83200, 2019, 'bio', 'MetaQUBIC: a computational pipeline for gene-level functional profiling of metagenome and metatranscriptome', 'Ma, Anjun|Sun, Minxuan|McDermaid, Adam|Liu, Bingqiang|Ma, Qin', 'Institut de Génétique Moléculaire de Montpellier, University of Montpellier, CNRS, Montpellier, France.|Institut de Génétique Moléculaire de Montpellier, University of Montpellier, CNRS, Montpellier, France.|Institut de Génétique Moléculaire de Montpellier, University of Montpellier, CNRS, Montpellier, France.|Institut de Génétique Moléculaire de Montpellier, University of Montpellier, CNRS, Montpellier, France.|Institut de Génétique Moléculaire de Montpellier, University of Montpellier, CNRS, Montpellier, France.', 'France|France|France|France|France', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz696', '31580397', '-'),
(83201, 2019, 'bio', 'VikNGS: A Cxe2x80x89++ Variant Integration Kit for Next Generation Sequencing Association Analysis', 'Baskurt, Zeynep|Mastromatteo, Scott|Gong, Jiafen|Wintle, Richard F|Scherer, Stephen W|Strug, Lisa J', 'Program in Genetics and Genome Biology, Research Institute, The Hospital for Sick Children, Toronto, Ontario, M5G0A4, Canada.|Program in Genetics and Genome Biology, Research Institute, The Hospital for Sick Children, Toronto, Ontario, M5G0A4, Canada.|Program in Genetics and Genome Biology, Research Institute, The Hospital for Sick Children, Toronto, Ontario, M5G0A4, Canada.|Program in Genetics and Genome Biology, Research Institute, The Hospital for Sick Children, Toronto, Ontario, M5G0A4, Canada.|Program in Genetics and Genome Biology, Research Institute, The Hospital for Sick Children, Toronto, Ontario, M5G0A4, Canada.|Program in Genetics and Genome Biology, Research Institute, The Hospital for Sick Children, Toronto, Ontario, M5G0A4, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz716', '31580400', 'Integration of next generation sequencing data (NGS) across different research studies can improve the power of genetic association testing by increasing sample size and can obviate the need for sequencing controls. If differential genotype uncertainty across studies is not accounted for, combining data sets can produce spurious association results. We developed the Variant Integration Kit for NGS (VikNGS), a fast cross-platform software package, to enable aggregation of several data sets for rare and common variant genetic association analysis of quantitative and binary traits with covariate adjustment. VikNGS also includes a graphical user interface, power simulation functionality and data visualization tools.'),
(83202, 2019, 'bio', 'T-lex3: an accurate tool to genotype and estimate population frequencies of transposable elements using the latest short-read whole genome sequencing data', 'Bogaerts-Mxc3xa1rquez, Marxc3xada|Barrxc3xb3n, Maite G|Fiston-Lavier, Anna-Sophie|Vendrell-Mir, Pol|Castanera, Raxc3xbal|Casacuberta, Josep M|Gonzxc3xa1lez, Josefa', 'Institute of Evolutionary Biology (CSIC-Universitat Pompeu Fabra).|Institute of Evolutionary Biology (CSIC-Universitat Pompeu Fabra).|Institut des Sciences de lEvolution de Montpellier (UMR 5554, CNRS-UM-IRD-EPHE), 11 Universitxc3xa9 de Montpellier, Place Eugxc3xa8ne Bataillon, Montpellier, France.|Center for Research in Agricultural Genomics, CRAG (CSIC-IRTA-UAB-UB), Campus UAB, Cerdanyola del Vallxc3xa8s, Barcelona, Spain.|Center for Research in Agricultural Genomics, CRAG (CSIC-IRTA-UAB-UB), Campus UAB, Cerdanyola del Vallxc3xa8s, Barcelona, Spain.|Center for Research in Agricultural Genomics, CRAG (CSIC-IRTA-UAB-UB), Campus UAB, Cerdanyola del Vallxc3xa8s, Barcelona, Spain.|Institute of Evolutionary Biology (CSIC-Universitat Pompeu Fabra).', '-|-|France|Spain|Spain|Spain|-', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz727', '31580402', 'Transposable elements (TEs) constitute a significant proportion of the majority of genomes sequenced to date. TEs are responsible for a considerable fraction of the genetic variation within and among species. Accurate genotyping of TEs in genomes is therefore crucial for a complete identification of the genetic differences among individuals, populations, and species.'),
(83203, 2019, 'bio', 'Towards next-generation diagnostics for tuberculosis: identification of novel molecular targets by large-scale comparative genomics', 'Goig, Galo A|Torres-Puente, Manuela|Mariner-Llicer, Carla|Villamayor, Luis M|Chiner-Oms, Álvaro|Gil-Brusola, Ana|Borrás, Rafael|Comas, Iñaki', 'Institute of Biomedicine of Valencia (CSIC), Valencia, Spain.|Institute of Biomedicine of Valencia (CSIC), Valencia, Spain.|Institute of Biomedicine of Valencia (CSIC), Valencia, Spain.|FISABIO Public Health (CSISP), Valencia, Spain.|Institute of Biomedicine of Valencia (CSIC), Valencia, Spain.|La Fe University and Polytechnic Hospital, Valencia, Spain.|University Clinic Hospital, Valencia, Spain.|Institute of Biomedicine of Valencia (CSIC), Valencia, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz729', '31580405', 'Tuberculosis remains one of the main causes of death worldwide. The long and cumbersome process of culturing Mycobacterium tuberculosis complex (MTBC) bacteria has encouraged the development of specific molecular tools for detecting the pathogen. Most of these tools aim to become novel tuberculosis diagnostics, and big efforts and resources are invested in their development, looking for the endorsement of the main public health agencies. Surprisingly, no study had been conducted where the vast amount of genomic data available is used to identify the best MTBC diagnostic markers.'),
(83204, 2019, 'bio', 'simurg: simulate bacterial pangenomes in R', 'Ferrés, Ignacio|Fresia, Pablo|Iraola, Gregorio', 'Microbial Genomics Laboratory, Institut Pasteur Montevideo, Montevideo, Uruguay.|Microbial Genomics Laboratory, Institut Pasteur Montevideo, Montevideo, Uruguay.|Microbial Genomics Laboratory, Institut Pasteur Montevideo, Montevideo, Uruguay.', 'Uruguay|Uruguay|Uruguay', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz735', '31584605', 'The pangenome concept describes genetic variability as the union of genes shared in a set of genomes and constitutes the current paradigm for comparative analysis of bacterial populations. However, there is a lack of tools to simulate pangenome variability and structure using defined evolutionary models.'),
(83205, 2019, 'bio', 'bayNorm: Bayesian gene expression recovery, imputation and normalisation for single cell RNA-sequencing data', 'Tang, Wenhao|Bertaux, François|Thomas, Philipp|Stefanelli, Claire|Saint, Malika|Marguerat, Samuel|Shahrezaei, Vahid', 'Department of Mathematics, Faculty of Natural Sciences, Imperial College, London SW7 2AZ, UK.|Department of Mathematics, Faculty of Natural Sciences, Imperial College, London SW7 2AZ, UK.|Department of Mathematics, Faculty of Natural Sciences, Imperial College, London SW7 2AZ, UK.|Department of Mathematics, Faculty of Natural Sciences, Imperial College, London SW7 2AZ, UK.|MRC London Institute of Medical Sciences (LMS), Du Cane Road, London W12 0NN, UK.|MRC London Institute of Medical Sciences (LMS), Du Cane Road, London W12 0NN, UK.|Department of Mathematics, Faculty of Natural Sciences, Imperial College, London SW7 2AZ, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz726', '31584606', 'Normalisation of single cell RNA sequencing (scRNA-seq) data is a prerequisite to their interpretation. The marked technical variability, high amounts of missing observations and batch effect typical of scRNA-seq datasets make this task particularly challenging. There is a need for an efficient and unified approach for normalisation, imputation and batch effect correction.'),
(83206, 2019, 'bio', 'Thermodynamically Consistent Estimation of Gibbs Free Energy from Data: Data Reconciliation Approach', 'Salike, Saman|Bhatt, Nirav', 'Department of Chemical Engineering, National Institute of Technology, Tiruchirappalli, India.|Department of Biotechnology, Chennai, India.', 'India|India', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz741', '31584610', 'Thermodynamic analysis of biological reaction networks requires the availability of accurate and consistent values of Gibbs free energies of reaction and formation. These Gibbs energies can be measured directly via the careful design of experiments or can be computed from the curated Gibbs free energy databases. However, the computed Gibbs free energies of reactions and formations do not satisfy the thermodynamic constraints due to the compounding effect of measurement errors in the experimental data. The propagation of these errors can lead to a false prediction of pathway feasibility and uncertainty in the estimation of thermodynamic parameters.'),
(83207, 2019, 'bio', 'Evaluation of Bottom-up and Top-down Mass Spectrum Identifications with Different Customized Protein Sequences Databases', 'Li, Ziwei|He, Bo|Feng, Weixing', 'Department of Biotechnology, Chennai, India.|College of Automation, Harbin Engineering University 145, Nan Tong Street, Harbin, Heilongjiang, China.|College of Automation, Harbin Engineering University 145, Nan Tong Street, Harbin, Heilongjiang, China.', 'India|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz733', '31584612', 'Generally, bottom-up and top-down are two complementary approaches for proteoforms identification. The inference of proteoforms relies on searching mass spectra against an accurate proteoform sequence database. A customized protein sequence database derived by RNA-Seq data can be used to better identify the proteoform existed in a studied species. However, the quality of sequences in customized databases which constructed by different strategies affect the performances of mass spectrometry identification. Additionally, performances of identifications between bottom-up and top-down using customized databases are also needed to be evaluated.'),
(83208, 2019, 'bio', 'SCSsim: an integrated tool for simulating single-cell genome sequencing data', 'Yu, Zhenhua|Du, Fang|Sun, Xuehong|Li, Ao', 'Department of Software Engineering, Ningxia University, Yinchuan, China.|Department of Software Engineering, Ningxia University, Yinchuan, China.|Department of Software Engineering, Ningxia University, Yinchuan, China.|Department of Electronic Science and Technology, University of Science and Technology of China, Hefei, China.', 'China|China|China|China', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz713', '31584615', 'Allele dropout (ADO) and unbalanced amplification of alleles are main technical issues of single-cell sequencing (SCS), and effectively emulating these issues is necessary for reliably benchmarking SCS-based bioinformatics tools. Unfortunately, currently available sequencing simulators are free of whole-genome amplification involved in SCS technique and therefore not suited for generating SCS datasets. We develop a new software package (SCSsim) that can efficiently simulate SCS datasets in a parallel fashion with minimal user intervention. SCSsim first constructs the genome sequence of single cell by mimicking a complement of genomic variations under user-controlled manner, and then amplifies the genome according to MALBAC technique and finally yields sequencing reads from the amplified products based on inferred sequencing profiles. Comprehensive evaluation in simulating different ADO rates, variation detection efficiency and genome coverage demonstrates that SCSsim is a very useful tool in mimicking single-cell sequencing data with high efficiency.'),
(83209, 2019, 'bio', 'A Fast and Memory Efficient MLCS Algorithm by Character Merging for DNA Sequences Alignment', 'Liu, Sen|Wang, Yuping|Tong, Wuning|Wei, Shiwei', 'School of Computer Science and Technology, Xidian University, Xian, China.|School of Computer Science and Technology, Xidian University, Xian, China.|School of Computer Science and Technology, Xidian University, Xian, China.|School of Computer Science and Technology, Xidian University, Xian, China.', 'China|China|China|China', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz725', '31584616', 'Multiple Longest Common Subsequence (MLCS) problem is searching all longest common subsequences of multiple character sequences. It appears in many fields such as data mining, DNA alignment, bioinformatics, text editing and so on. With the increasing of sequence length and number of sequences, the existing dynamic programming algorithms and the dominant point based algorithms become ineffective and inefficient, especially for large-scale MLCS problems.'),
(83210, 2019, 'bio', 'Deep-learning with synthetic data enables automated picking of cryo-EM particle images of biological macromolecules', 'Yao, Ruijie|Qian, Jiaqiang|Huang, Qiang', 'State Key Laboratory of Genetic Engineering, MOE Engineering Research Center of Gene Technology, School of Life Sciences, Fudan University, Shanghai.|State Key Laboratory of Genetic Engineering, MOE Engineering Research Center of Gene Technology, School of Life Sciences, Fudan University, Shanghai.|State Key Laboratory of Genetic Engineering, MOE Engineering Research Center of Gene Technology, School of Life Sciences, Fudan University, Shanghai.', '-|-|-', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz728', '31584618', 'Single-particle cryo-electron microscopy (cryo-EM) has become a powerful technique for determining three-dimensional structures of biological macromolecules at near-atomic resolution. However, this approach requires picking huge numbers of macromolecular particle images from thousands of low-contrast, high-noisy electron micrographs. Although machine-learning methods were developed to get rid of this bottleneck, it still lacks universal methods that could automatically picking the noisy cryo-EM particles of various macromolecules.'),
(83211, 2019, 'bio', 'Meltos: Multi-Sample Tumor Phylogeny Reconstruction for Structural Variants', 'Ricketts, Camir|Seidman, Daniel|Popic, Victoria|Hormozdiari, Fereydoun|Batzoglou, Serafim|Hajirasouliha, Iman', 'Tri-I Computational Biology & Medicine Graduate Program, Cornell University, NY, USA.|Tri-I Computational Biology & Medicine Graduate Program, Cornell University, NY, USA.|Department of Computer Science, Stanford University, Stanford, CA, USA.|Department of Biochemistry and Molecular Medicine, MIND Institute and Genome Center, University of California, Davis, CA, USA.|Department of Computer Science, Stanford University, Stanford, CA, USA.|Institute for Computational Biomedicine, Englander Institute for Precision Medicine, The Meyer Cancer Center, Department of Physiology and Biophysics, Weill Cornell Medicine of Cornell University, NY, USA.', 'United States|United States|United States|United States|United States|United States', 1, '-', 'Journal Article', '10.1093/bioinformatics/btz737', '31584621', 'We propose Meltos, a novel computational framework to address the challenging problem of building tumor phylogeny trees using somatic structural variants (SVs) among multiple samples. Meltos leverages the tumor phylogeny tree built on somatic single nucleotide variants (SNVs) to identify high confidence SVs and produce a comprehensive tumor lineage tree, using a novel optimization formulation. While we do not assume the evolutionary progression of SVs is necessarily the same as SNVs, we show that a tumor phylogeny tree using high-quality somatic SNVs can act as a guide for calling and assigning somatic SVs on a tree. Meltos utilizes multiple genomic read signals for potential SV breakpoints in whole genome sequencing data and proposes a probabilistic formulation for estimating variant allele fractions of SV events.'),
(83212, 2019, 'bio', 'Proteome-level assessment of origin, prevalence and function of Leucine-Aspartic Acid (LD) motifs', 'Alam, Tanvir|Alazmi, Meshari|Naser, Rayan|Huser, Franceline|Momin, Afaque A|Astro, Veronica|Hong, SeungBeom|Walkiewicz, Katarzyna W|Canlas, Christian G|Huser, Raphaxc3xabl|Ali, Amal J|Merzaban, Jasmeen|Adamo, Antonio|Jaremko, Mariusz|Jaremko, xc5x81ukasz|Bajic, Vladimir B|Gao, Xin|Arold, Stefan T', 'King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Division of Computer, Electrical and Mathematical Sciences & Engineering (CEMSE), Thuwal, Saudi Arabia.|King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Division of Computer, Electrical and Mathematical Sciences & Engineering (CEMSE), Thuwal, Saudi Arabia.|King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Division of Biological and Environmental Sciences and Engineering (BESE), Thuwal, Saudi Arabia.|King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Division of Biological and Environmental Sciences and Engineering (BESE), Thuwal, Saudi Arabia.|King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Division of Biological and Environmental Sciences and Engineering (BESE), Thuwal, Saudi Arabia.|King Abdullah University of Science and Technology (KAUST), Division of Biological and Environmental Sciences and Engineering (BESE), Thuwal, Saudi Arabia.|King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Division of Biological and Environmental Sciences and Engineering (BESE), Thuwal, Saudi Arabia.|King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Division of Biological and Environmental Sciences and Engineering (BESE), Thuwal, Saudi Arabia.|King Abdullah University of Science and Technology (KAUST), Core Labs, Thuwal, Saudi Arabia.|King Abdullah University of Science and Technology (KAUST), Division of Computer, Electrical and Mathematical Sciences & Engineering (CEMSE), Thuwal, Saudi Arabia.|King Abdullah University of Science and Technology (KAUST), Division of Biological and Environmental Sciences and Engineering (BESE), Thuwal, Saudi Arabia.|King Abdullah University of Science and Technology (KAUST), Division of Biological and Environmental Sciences and Engineering (BESE), Thuwal, Saudi Arabia.|King Abdullah University of Science and Technology (KAUST), Division of Biological and Environmental Sciences and Engineering (BESE), Thuwal, Saudi Arabia.|King Abdullah University of Science and Technology (KAUST), Division of Biological and Environmental Sciences and Engineering (BESE), Thuwal, Saudi Arabia.|King Abdullah University of Science and Technology (KAUST), Division of Biological and Environmental Sciences and Engineering (BESE), Thuwal, Saudi Arabia.|King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Division of Computer, Electrical and Mathematical Sciences & Engineering (CEMSE), Thuwal, Saudi Arabia.|King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Division of Computer, Electrical and Mathematical Sciences & Engineering (CEMSE), Thuwal, Saudi Arabia.|King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Division of Biological and Environmental Sciences and Engineering (BESE), Thuwal, Saudi Arabia.', 'Saudi Arabia|Saudi Arabia|Saudi Arabia|Saudi Arabia|Saudi Arabia|Saudi Arabia|Saudi Arabia|Saudi Ara', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz703', '31584626', 'Leucine-aspartic acid (LD) motifs are short linear interaction motifs (SLiMs) that link paxillin family proteins to factors controlling cell adhesion, motility and survival. The existence and importance of LD motifs beyond the paxillin family is poorly understood.'),
(83213, 2019, 'bio', 'Fast and Flexible Coarse-grained Prediction of Protein Folding Routes using Ensemble Modelling and Evolutionary Sequence Variation', 'Becerra, David|Butyaev, Alexander|Waldispühl, Jérôme', 'School of Computer Science, McGill University, Montréal QC, Canada.|School of Computer Science, McGill University, Montréal QC, Canada.|School of Computer Science, McGill University, Montréal QC, Canada.', 'Canada|Canada|Canada', 0, '-', 'Journal Article', '10.1093/bioinformatics/btz743', '31584628', 'Protein folding is a dynamic process through which polypeptide chains reach their native 3D structures. Although the importance of this mechanism is widely acknowledged, very few high-throughput computational methods have been developed to study it.'),
(83214, 2019, 'bio', 'L1EM: A tool for accurate locus specific LINE-1 RNA quantification', 'McKerrow, Wilson|Fenyö, David', 'Institute for Systems Genetics, NYU School of Medicine, 550 1st Ave, New York, NY, USA.|Institute for Systems Genetics, NYU School of Medicine, 550 1st Ave, New York, NY, USA.', 'United States|United States', 2, '-', 'Journal Article', '10.1093/bioinformatics/btz724', '31584629', 'LINE-1 elements are retrotransposons that are capable of copying their sequence to new genomic loci. LINE-1 derepression is associated with a number of disease states, and has the potential to cause significant cellular damage. Because LINE-1 elements are repetitive, it is difficult to quantify LINE-1 RNA at specific loci and to separate transcripts with protein coding capability from other sources of LINE-1 RNA.'),
(83215, 2019, 'bio', 'Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations', 'Yue, Xiang|Wang, Zhen|Huang, Jingong|Parthasarathy, Srinivasan|Moosavinasab, Soheil|Huang, Yungui|Lin, Simon M|Zhang, Wen|Zhang, Ping|Sun, Huan', 'Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA.|Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA.|Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH, USA.|Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA.|Research Information Solutions and Innovation, The Research Institute at Nationwide Childrens Hospital, Columbus, OH, USA.|Research Information Solutions and Innovation, The Research Institute at Nationwide Childrens Hospital, Columbus, OH, USA.|Research Information Solutions and Innovation, The Research Institute at Nationwide Childrens Hospital, Columbus, OH, USA.|College of Informatics, Huazhong Agricultural University, Wuhan, Hubei, China.|Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA.|Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA.', 'United States|United States|United States|United States|United States|United States|United States|China|United States|United States', 9, '-', 'Journal Article', '10.1093/bioinformatics/btz718', '31584634', 'Graph embedding learning which aims to automatically learn low-dimensional node representations, has drawn increasing attention in recent years. To date, most recent graph embedding methods are evaluated on social and information networks and are not comprehensively studied on biomedical networks under systematic experiments and analyses. On the other hand, for a variety of biomedical network analysis tasks, traditional techniques such as matrix factorization (which can be seen as a type of graph embedding methods) have shown promising results, and hence there is a need to systematically evaluate the more recent graph embedding methods (e.g., random walk-based and neural network-based) in terms of their usability and potential to further the state-of-the-art.'),
(83216, 1998, 'bio', 'The Merck Gene Index browser: an extensible data integration system for gene finding, gene characterization and EST data mining', 'Eckman, B A|Aaronson, J S|Borkowski, J A|Bailey, W J|Elliston, K O|Williamson, A R|Blevins, R A', 'Department of Bioinformatics, Merck Research Laboratories, West Point, PA, USA. barbara_eckman@sbphrd.com|Department of Bioinformatics, Merck Research Laboratories, West Point, PA, USA. barbara_eckman@sbphrd.com|Department of Bioinformatics, Merck Research Laboratories, West Point, PA, USA. barbara_eckman@sbphrd.com|Department of Bioinformatics, Merck Research Laboratories, West Point, PA, USA. barbara_eckman@sbphrd.com|Department of Bioinformatics, Merck Research Laboratories, West Point, PA, USA. barbara_eckman@sbphrd.com|Department of Bioinformatics, Merck Research Laboratories, West Point, PA, USA. barbara_eckman@sbphrd.com|Department of Bioinformatics, Merck Research Laboratories, West Point, PA, USA. barbara_eckman@sbphrd.com', 'United States|United States|United States|United States|United States|United States|United States', 23, 'Abstracting and Indexing as Topic|Algorithms|Computer Communication Networks|Computer Systems|DNA, Complementary|Database Management Systems|Gene Expression Regulation|Genes|Humans|Sequence Homology, Amino Acid|Sequence Homology, Nucleic Acid|Software', 'Journal Article', '10.1093/bioinformatics/14.1.2', '9520496', 'To make effective use of the vast amounts of expressed sequence tag (EST) sequence data generated by the Merck-sponsored EST project and other similar efforts, sequences must be organized into gene classes, and scientists must be able to mine the gene class data in the context of related genomic data.'),
(83217, 1998, 'bio', 'Algorithms and software for support of gene identification experiments', 'Sze, S H|Roytberg, M A|Gelfand, M S|Mironov, A A|Astakhova, T V|Pevzner, P A', 'Department of Computer Science, University of Southern California, Los Angeles 90089-1113, USA.|Department of Computer Science, University of Southern California, Los Angeles 90089-1113, USA.|Department of Computer Science, University of Southern California, Los Angeles 90089-1113, USA.|Department of Computer Science, University of Southern California, Los Angeles 90089-1113, USA.|Department of Computer Science, University of Southern California, Los Angeles 90089-1113, USA.|Department of Computer Science, University of Southern California, Los Angeles 90089-1113, USA.', 'United States|United States|United States|United States|United States|United States', 15, 'Algorithms|Arabidopsis|DNA Primers|Genes|Humans|Open Reading Frames|Polymerase Chain Reaction|Software', 'Journal Article', '10.1093/bioinformatics/14.1.14', '9520497', 'Gene annotation is the final goal of gene prediction algorithms. However, these algorithms frequently make mistakes and therefore the use of gene predictions for sequence annotation is hardly possible. As a result, biologists are forced to conduct time-consuming gene identification experiments by designing appropriate PCR primers to test cDNA libraries or applying RT-PCR, exon trapping/amplification, or other techniques. This process frequently amounts to guessing PCR primers on top of unreliable gene predictions and frequently leads to wasting of experimental efforts.'),
(83218, 1998, 'bio', 'GenXref VI: Automatic generation of links between two heterogeneous databases', 'Achard, F|Dessen, P', 'GIS Infobiogen, Villejuif, France.|GIS Infobiogen, Villejuif, France.', 'France|France', 5, 'Abstracting and Indexing as Topic|Databases as Topic|Genetics|Humans|Information Storage and Retrieval|Information Systems', 'Journal Article', '10.1093/bioinformatics/14.1.20', '9520498', 'A large proportion of the information found in public databases is not sufficiently cross-referenced. We developed genXref, an automated system for link inference, because embarking on a manual cross-referencing of genome data would require too much expensive human expertise. It uses information retrieval technology to generate links between objects of heterogeneous databases.'),
(83219, 1998, 'bio', 'Bayesian adaptive sequence alignment algorithms', 'Zhu, J|Liu, J S|Lawrence, C E', 'Wadsworth Center for Laboratories and Research, Albany, NY, USA.|Wadsworth Center for Laboratories and Research, Albany, NY, USA.|Wadsworth Center for Laboratories and Research, Albany, NY, USA.', 'United States|United States|United States', 90, 'Adenylate Kinase|Algorithms|Amino Acid Sequence|Bayes Theorem|Computational Biology|Databases, Factual|Guanylate Kinases|Models, Molecular|Molecular Sequence Data|Nucleoside-Phosphate Kinase|Peptide Elongation Factor G|Peptide Elongation Factor Tu|Peptide Elongation Factors|Sequence Alignment', 'Journal Article', '10.1093/bioinformatics/14.1.25', '9520499', 'The selection of a scoring matrix and gap penalty parameters continues to be an important problem in sequence alignment. We describe here an algorithm, the Bayes block aligner, which bypasses this requirement. Instead of requiring a fixed set of parameter settings, this algorithm returns the Bayesian posterior probability for the number of gaps and for the scoring matrices in any series of interest. Furthermore, instead of returning the single best alignment for the chosen parameter settings, this algorithm returns the posterior distribution of all alignments considering the full range of gapping and scoring matrices selected, weighing each in proportion to its probability based on the data. We compared the Bayes aligner with the popular Smith-Waterman algorithm with parameter settings from the literature which had been optimized for the identification of structural neighbors, and found that the Bayes aligner correctly identified more structural neighbors. In a detailed examination of the alignment of a pair of kinase and a pair of GTPase sequences, we illustrate the algorithms potential to identify subsequences that are conserved to different degrees. In addition, this example shows that the Bayes aligner returns an alignment-free assessment of the distance between a pair of sequences.'),
(83220, 1998, 'bio', 'Comparative accuracy of methods for protein sequence similarity search', 'Agarwal, P|States, D J', 'Institute for Biomedical Computing, Washington University, St Louis, MO 63110, USA.|Institute for Biomedical Computing, Washington University, St Louis, MO 63110, USA.', 'United States|United States', 0, 'Databases, Factual|Information Storage and Retrieval|Mathematical Computing|Proteins|Sequence Alignment|Sequence Homology, Amino Acid', 'Comparative Study', '10.1093/bioinformatics/14.1.40', '9520500', 'Searching a protein sequence database for homologs is a powerful tool for discovering the structure and function of a sequence. Two new methods for searching sequence databases have recently been described: Probabilistic Smith-Waterman (PSW), which is based on Hidden Markov models for a single sequence using a standard scoring matrix, and a new version of BLAST (WU-BLAST2), which uses Sum statistics for gapped alignments.'),
(83221, 1998, 'bio', 'Combining evidence using p-values: application to sequence homology searches', 'Bailey, T L|Gribskov, M', 'San Diego Supercomputer Center, CA 92186-9784, USA.|San Diego Supercomputer Center, CA 92186-9784, USA.', 'United States|United States', 826, 'Algorithms|DNA|Mathematical Computing|Proteins|Sequence Homology, Amino Acid|Sequence Homology, Nucleic Acid', 'Journal Article', '10.1093/bioinformatics/14.1.48', '9520501', 'To illustrate an intuitive and statistically valid method for combining independent sources of evidence that yields a p-value for the complete evidence, and to apply it to the problem of detecting simultaneous matches to multiple patterns in sequence homology searches.'),
(83222, 1998, 'bio', 'Combinatorial pattern discovery in biological sequences: The TEIRESIAS algorithm', 'Rigoutsos, I|Floratos, A', 'Computational Biology Center, IBM Thomas J. Watson Research Center, York Town Heights, NY 10598, USA.|Computational Biology Center, IBM Thomas J. Watson Research Center, York Town Heights, NY 10598, USA.', 'United States|United States', 0, 'Algorithms|Amino Acid Sequence|Animals|Conserved Sequence|Evaluation Studies as Topic|Histones|Humans|Leghemoglobin|Molecular Sequence Data|Pattern Recognition, Automated|Sequence Alignment|Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/14.1.55', '9520502', 'The discovery of motifs in biological sequences is an important problem.'),
(83223, 1998, 'bio', 'SplitsTree: analyzing and visualizing evolutionary data', 'Huson, D H', 'FSPM, University of Bielefeld, Germany.', 'Germany', 1013, 'Biological Evolution|DNA, Ribosomal|Data Interpretation, Statistical|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/14.1.68', '9520503', 'Real evolutionary data often contain a number of different and sometimes conflicting phylogenetic signals, and thus do not always clearly support a unique tree. To address this problem, Bandelt and Dress (Adv. Math., 92, 47-05, 1992) developed the method of split decomposition. For ideal data, this method gives rise to a tree, whereas less ideal data are represented by a tree-like network that may indicate evidence for different and conflicting phylogenies.'),
(83224, 1998, 'bio', 'Xlandscape: the graphical display of word frequencies in sequences', 'Levy, S|Compagnoni, L|Myers, E W|Stormo, G D', 'University of Colorado, Boulder 80309-0347, USA.|University of Colorado, Boulder 80309-0347, USA.|University of Colorado, Boulder 80309-0347, USA.|University of Colorado, Boulder 80309-0347, USA.', 'United States|United States|United States|United States', 13, 'Algorithms|Base Sequence|Computer Graphics|DNA|Humans|Huntington Disease|Information Storage and Retrieval|Molecular Sequence Data|Repetitive Sequences, Nucleic Acid|Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/14.1.74', '9520504', 'To provide a graphical interface for the generation, display and manipulation of a sequence landscape that will run on all X-windows-based Unix workstations.'),
(83225, 1998, 'bio', 'Design and implementation of a qualitative simulation model of lambda phage infection', 'Heidtke, K R|Schulze-Kremer, S', 'Max-Planck-Institute for Molecular Genetics, Department Lehrach, Berlin, Germany.|Max-Planck-Institute for Molecular Genetics, Department Lehrach, Berlin, Germany.', 'Germany|Germany', 23, 'Algorithms|Bacteriophage lambda|Computer Simulation|Escherichia coli|Lysogeny', 'Journal Article', '10.1093/bioinformatics/14.1.81', '9520505', 'Molecular biology databases hold a large number of empirical facts about many different aspects of biological entities. That data is static in the sense that one cannot ask a database What effect has protein A on gene B? or Do gene A and gene B interact, and if so, how?. Those questions require an explicit model of the target organism. Traditionally, biochemical systems are modelled using kinetics and differential equations in a quantitative simulator. For many biological processes however, detailed quantitative information is not available, only qualitative or fuzzy statements about the nature of interactions.'),
(83226, 1998, 'bio', 'Trace alignment and some of its applications', 'Mott, R', 'Informatics Group, Sanger Centre, Cambridge, UK.', 'United Kingdom', 5, 'Algorithms|Automation|DNA|DNA, Complementary|Mathematical Computing|Mutation|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/14.1.92', '9520506', 'Extra useful information can be extracted from a DNA chromatogram trace, over that contained in the base-called DNA sequence. Many sequencing applications can benefit from examination of these traces.'),
(83227, 1998, 'bio', 'Spectrum: spectral analysis of phylogenetic data', 'Charleston, M A', 'Department of Zoology, University of Oxford, UK.', 'United Kingdom', 39, 'Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/14.1.98', '9520507', 'Spectrum is a new Macintosh and Microsoft Windows program designed to read in phylogenetic four-state or binary data in NEXUS format, and output the corresponding bipartition spectra. It can be used to find the tree whose expected spectrum is closest to the observed spectrum (the closest tree, Hendy, Discr, Math., 96, 51-58,. 1991).'),
(83228, 1998, 'bio', 'Mapper: an intelligent restriction mapping tool', 'Inglehart, J A|Nelson, P C|Zou, Y', 'Artificial Intelligence Laboratory, Department of Electrical Engineering and Computer Science, University of Illinois at Chicago, 60607-7053, USA.|Artificial Intelligence Laboratory, Department of Electrical Engineering and Computer Science, University of Illinois at Chicago, 60607-7053, USA.|Artificial Intelligence Laboratory, Department of Electrical Engineering and Computer Science, University of Illinois at Chicago, 60607-7053, USA.', 'United States|United States|United States', 1, 'Algorithms|Artificial Intelligence|Computational Biology|Evaluation Studies as Topic|Restriction Mapping|Software', 'Comparative Study', '10.1093/bioinformatics/14.2.101', '9545441', 'To determine the most powerful artificial intelligence techniques for automated restriction mapping, and use them to create a powerful multiple-enzyme restriction mapping tool.'),
(83229, 1998, 'bio', 'Design and implementation of a CORBA-based genome mapping system prototype', 'Hu, J|Mungall, C|Nicholson, D|Archibald, A L', 'Roslin Institute (Edinburgh), Midlothian, UK.|Roslin Institute (Edinburgh), Midlothian, UK.|Roslin Institute (Edinburgh), Midlothian, UK.|Roslin Institute (Edinburgh), Midlothian, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 19, 'Chromosome Mapping|Computational Biology|Computer Systems|Genome|Software Design|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/14.2.112', '9545442', 'CORBA (Common Object Request Broker Architecture), as an open standard, is considered to be a good solution for the development and deployment of applications in distributed heterogeneous environments. This technology can be applied in the bioinformatics area to enhance utilization, management and interoperation between biological resources.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(83230, 1998, 'bio', 'Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network', 'Brusic, V|Rudy, G|Honeyman, G|Hammer, J|Harrison, L', 'The Walter and Eliza Hall Institute of Medical Research, PO Royal Melbourne Hospital, Victoria, Australia.|The Walter and Eliza Hall Institute of Medical Research, PO Royal Melbourne Hospital, Victoria, Australia.|The Walter and Eliza Hall Institute of Medical Research, PO Royal Melbourne Hospital, Victoria, Australia.|The Walter and Eliza Hall Institute of Medical Research, PO Royal Melbourne Hospital, Victoria, Australia.|The Walter and Eliza Hall Institute of Medical Research, PO Royal Melbourne Hospital, Victoria, Australia.', 'Australia|Australia|Australia|Australia|Australia', 0, 'Algorithms|Amino Acid Sequence|Computational Biology|Computer Simulation|Directed Molecular Evolution|Epitopes, T-Lymphocyte|Histocompatibility Antigens Class II|Humans|Molecular Sequence Data|Neural Networks (Computer)|Peptides|Protein Binding|Reproducibility of Results|Software', 'Journal Article', '10.1093/bioinformatics/14.2.121', '9545443', 'Prediction methods for identifying binding peptides could minimize the number of peptides required to be synthesized and assayed, and thereby facilitate the identification of potential T-cell epitopes. We developed a bioinformatic method for the prediction of peptide binding to MHC class II molecules.'),
(83231, 1998, 'bio', 'Analysis of DNA curvature using artificial neural networks', 'Parbhane, R V|Tambe, S S|Kulkarni, B D', 'Chemical Engineering Division, National Chemical Laboratory, Pune, India.|Chemical Engineering Division, National Chemical Laboratory, Pune, India.|Chemical Engineering Division, National Chemical Laboratory, Pune, India.', 'India|India|India', 6, 'Base Sequence|Computational Biology|Computer Simulation|DNA|Neural Networks (Computer)|Nucleic Acid Conformation', 'Journal Article', '10.1093/bioinformatics/14.2.131', '9545444', 'Our aim is to utilize an artificial neural network (ANN) for the prediction of DNA curvature in terms of retardation anomaly.'),
(83232, 1998, 'bio', 'Feature selection for genetic sequence classification', 'Chuzhanova, N A|Jones, A J|Margetts, S', 'Institute of Mathematics, Siberian Branch of Russian Academy of Science, Novosibirsk, Russia.|Institute of Mathematics, Siberian Branch of Russian Academy of Science, Novosibirsk, Russia.|Institute of Mathematics, Siberian Branch of Russian Academy of Science, Novosibirsk, Russia.', '-|-|-', 69, 'Algorithms|Animals|Computational Biology|Databases, Factual|Models, Genetic|Phylogeny|RNA, Ribosomal|Sequence Alignment|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/14.2.139', '9545445', 'Most of the existing methods for genetic sequence classification are based on a computer search for homologies in nucleotide or amino acid sequences. The standard sequence alignment programs scale very poorly as the number of sequences increases or the degree of sequence identity is <30%. Some new computationally inexpensive methods based on nucleotide or amino acid compositional analysis have been proposed, but prediction results are still unsatisfactory and depend on the features chosen to represent the sequences.'),
(83233, 1998, 'bio', 'DIVCLUS: an automatic method in the GEANFAMMER package that finds homologous domains in single- and multi-domain proteins', 'Park, J|Teichmann, S A', 'MRC Laboratory of Molecular Biology, Cambridge, UK.|MRC Laboratory of Molecular Biology, Cambridge, UK.', 'United Kingdom|United Kingdom', 46, 'Algorithms|Amino Acid Sequence|Cluster Analysis|Computational Biology|Databases, Factual|Molecular Sequence Data|Multigene Family|Proteins|Sequence Alignment|Sequence Homology, Amino Acid|Software|Software Design', 'Journal Article', '10.1093/bioinformatics/14.2.144', '9545446', 'Large-scale determination of relationships between the proteins produced by genome sequences is now common. All protein sequences are matched and those that have high match scores are clustered into families. In cases where the proteins are built of several domains or duplication modules, this can lead to misleading results. Consider the very simple example of three proteins: 1, formed by duplication modules A and B, 2, formed by duplication modules B and C, and 3, formed by duplication modules C and D. Duplication modules B and B are homologous, as are C and C. Matching the sequences of 1, 2 and 3 followed by simple single-linkage clustering would put all three in the same family, even though proteins 1 and 3 are not related. This is because the different parts of 2 match 1 and 3. This paper describes a procedure, DIVCLUS, that divides such complex clusters of partially related sequences into simple clusters that contain only related duplication modules. In the example just given, it would produce two groups of sequences: the first with domains B of sequence 1 and B of sequence 2, and the second with domain C of sequence 2 and C of sequence 3. DIVCLUS is part of a package called GEANFAMMER, for GEnome ANalysis and protein FAMily MakER. The package automates the detection of families of duplication modules from a protein sequence database.'),
(83234, 1998, 'bio', 'A major component approach to presenting consensus sequences', 'Smith, D K|Xue, H', 'Biochemistry Department, Hong Kong University of Science and Technology, Kowloon, Hong Kong.|Biochemistry Department, Hong Kong University of Science and Technology, Kowloon, Hong Kong.', 'Hong Kong|Hong Kong', 0, 'Amino Acid Sequence|Base Sequence|Computational Biology|Consensus Sequence|Endoribonucleases|Immunoglobulin Variable Region|Immunoglobulin lambda-Chains|Molecular Sequence Data|RNA|RNA, Catalytic|Receptors, GABA|Receptors, Nicotinic|Ribonuclease P|Sequence Alignment|Software', 'Comparative Study', '10.1093/bioinformatics/14.2.151', '9545447', 'Summarizing and displaying the information contained in a set of aligned sequences is an important aid to identifying patterns within the sequences. A variety of forms of consensus sequences have been used previously to provide this information. However, these methods can cause a loss of information or introduce ambiguities into the consensus sequence, and some graphical approaches may become difficult to interpret due to visual distortion.'),
(83235, 1998, 'bio', 'Rose: generating sequence families', 'Stoye, J|Evers, D|Meyer, F', 'Research Center for Interdisciplinary Studies on Structure Formation (FSPM), University of Bielefeld, Postfach, Germany.|Research Center for Interdisciplinary Studies on Structure Formation (FSPM), University of Bielefeld, Postfach, Germany.|Research Center for Interdisciplinary Studies on Structure Formation (FSPM), University of Bielefeld, Postfach, Germany.', 'Germany|Germany|Germany', 201, 'Algorithms|Amino Acid Sequence|Base Sequence|Computational Biology|DNA|Evolution, Molecular|Humans|Models, Genetic|Models, Statistical|Molecular Sequence Data|Mutation|Phylogeny|Proteins|RNA|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/14.2.157', '9545448', 'We present a new probabilistic model of the evolution of RNA-, DNA-, or protein-like sequences and a software tool, Rose, that implements this model. Guided by an evolutionary tree, a family of related sequences is created from a common ancestor sequence by insertion, deletion and substitution of characters. During this artificial evolutionary process, the true history is logged and the correct multiple sequence alignment is created simultaneously. The model also allows for varying rates of mutation within the sequences, making it possible to establish so-called sequence motifs.'),
(83236, 1998, 'bio', 'Automated protein sequence database classification I Integration of compositional similarity search, local similarity search, and multiple sequence alignment', 'Gracy, J|Argos, P', 'European Molecular Biology Laboratory, Heidelberg, Germany.|European Molecular Biology Laboratory, Heidelberg, Germany.', 'Germany|Germany', 54, 'Algorithms|Computational Biology|Databases, Factual|Proteins|Sequence Alignment', 'Journal Article', '10.1093/bioinformatics/14.2.164', '9545449', 'Genome sequencing projects require the periodic application of analysis tools that can classify and multiply align related protein sequence domains. Full automation of this task requires an efficient integration of similarity and alignment techniques.'),
(83237, 1998, 'bio', 'Automated protein sequence database classification II Delineation Of domain boundaries from sequence similarities', 'Gracy, J|Argos, P', 'European Molecular Biology Laboratory, Heidelberg, Germany.|European Molecular Biology Laboratory, Heidelberg, Germany.', 'Germany|Germany', 0, 'Algorithms|Amino Acid Sequence|Ankyrins|Calcium-Binding Proteins|Cluster Analysis|Computational Biology|Databases, Factual|Epidermal Growth Factor|Molecular Sequence Data|Proteins|Quality Control|Repetitive Sequences, Nucleic Acid|Sequence Alignment|Sequence Homology, Amino Acid', 'Journal Article', '10.1093/bioinformatics/14.2.174', '9545450', 'Decomposing each protein into modular domains is a basic prerequisite to classify accurately structural units in biological molecules. Boundaries between domains are indicated by two similar amino acid sequence segments located within the same protein (repeats) or within homologous proteins at notably different distances from their respective N- or C-termini.'),
(83238, 1998, 'bio', '3DinSight: an integrated relational database and search tool for the structure, function and properties of biomolecules', 'An, J|Nakama, T|Kubota, Y|Sarai, A', 'Tsukuba Life Science Center, The Institute of Physical & Chemical Research (RIKEN), Ibaraki, Japan.|Tsukuba Life Science Center, The Institute of Physical & Chemical Research (RIKEN), Ibaraki, Japan.|Tsukuba Life Science Center, The Institute of Physical & Chemical Research (RIKEN), Ibaraki, Japan.|Tsukuba Life Science Center, The Institute of Physical & Chemical Research (RIKEN), Ibaraki, Japan.', 'Japan|Japan|Japan|Japan', 24, 'Computer Communication Networks|Computer Simulation|Databases, Factual|Models, Molecular|Molecular Structure|Proteins|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/14.2.188', '9545451', 'Although a large amount of information on the structure, function and properties of biomolecules is becoming available, it is difficult to understand the relationship between them. Thus, we have attempted to create an integrated relational database, search and visualization tool, 3DinSight, to help researchers to gain insight into their relationship.'),
(83239, 1998, 'bio', 'Estimation and filtering of potential protein-protein docking positions', 'Ackermann, F|Herrmann, G|Posch, S|Sagerer, G', 'Bielefeld University, Technical Faculty, Germany.|Bielefeld University, Technical Faculty, Germany.|Bielefeld University, Technical Faculty, Germany.|Bielefeld University, Technical Faculty, Germany.', 'Germany|Germany|Germany|Germany', 17, 'Algorithms|Binding Sites|Computational Biology|Databases, Factual|Protein Binding|Proteins|Software|Software Design|Thermodynamics', 'Journal Article', '10.1093/bioinformatics/14.2.196', '9545452', 'Software systems predicting automatically whether and how two proteins may interact are highly desirable, both for understanding biological processes and for the rational design of new proteins. As a part of a future complete solution to this problem, a bundle of programs is presented designed (i) to estimate initial docking positions for a given pair of docking candidates, (ii) to adjust them, and (iii) to filter them, thus preparing more detailed computations of free energies.'),
(83240, 1998, 'bio', 'Incremental threading optimization (TITO) to help alignment and modelling of remote homologues', 'Labesse, G|Mornon, J', 'Systèmes Moléculaires et Biologie Structurale, Laboratoire de Minéralogie-Cristallographie, Universités Paris, Cedex, France.|Systèmes Moléculaires et Biologie Structurale, Laboratoire de Minéralogie-Cristallographie, Universités Paris, Cedex, France.', 'France|France', 63, 'Algorithms|Amino Acid Sequence|Computational Biology|Models, Molecular|Molecular Sequence Data|Protein Conformation|Proteins|Sequence Alignment|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1093/bioinformatics/14.2.206', '9545453', 'Protein sequence comparison has become a major tool for biologists. Various methods have been designed in order to reveal even remote homologies among proteins. Sequence analyses are powerful, but the lower the identity score, the more skill and time are required to perform them accurately. While necessary to confirm the sequence comparison through the potential of mean force, sequence alignment and molecular modelling are still tedious and time-consuming tasks.'),
(83241, 1998, 'bio', 'Fast computation of 3D radon transform via a direct Fourier method', 'Lanzavecchia, S|Luigi Bellon, P', 'Dipartimento di Chimica Strutturale e Stereochimica Inorganica, Università degli Studi, Milano, Italy.|Dipartimento di Chimica Strutturale e Stereochimica Inorganica, Università degli Studi, Milano, Italy.', 'Italy|Italy', 17, 'Algorithms|Computational Biology|Fourier Analysis|Tomography', 'Journal Article', '10.1093/bioinformatics/14.2.212', '9545454', 'Arrays of three-dimensional (3D) data are ubiquitous in structural biology, biomedicine and clinical imaging. The Radon transform can be implied in their manipulation mainly for the solution of the inverse tomographic problem, since experimental data are often collected as projections or as samples of the Radon space. In electron tomography, new applications of the transform may become convenient if a fast and accurate transformation algorithm is adopted.'),
(83242, 1998, 'bio', 'Automated management of gene discovery projects', 'Taylor, S|Hodgman, T C|Littler, C B|Sayle, R A|Gill, R W|Oxer, M D|ODonnell, C H|Lyall, A', 'Advanced Technology and Informatics, Glaxo Wellcome Research and Development, Stevenage, Herts, UK.|Advanced Technology and Informatics, Glaxo Wellcome Research and Development, Stevenage, Herts, UK.|Advanced Technology and Informatics, Glaxo Wellcome Research and Development, Stevenage, Herts, UK.|Advanced Technology and Informatics, Glaxo Wellcome Research and Development, Stevenage, Herts, UK.|Advanced Technology and Informatics, Glaxo Wellcome Research and Development, Stevenage, Herts, UK.|Advanced Technology and Informatics, Glaxo Wellcome Research and Development, Stevenage, Herts, UK.|Advanced Technology and Informatics, Glaxo Wellcome Research and Development, Stevenage, Herts, UK.|Advanced Technology and Informatics, Glaxo Wellcome Research and Development, Stevenage, Herts, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Computational Biology|Database Management Systems|Databases, Factual|Genetic Techniques|Human Genome Project|Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/14.2.217', '9545455', 'The paper reports on programs, organization and Hypertext Mark-up Language (HTML) presentation provided by the Hierarchical Automated Gene Identification System (HAGIS) designed for managing sequencing projects.'),
(83243, 1998, 'bio', 'TOPAL: recombination detection in DNA and protein sequences', 'McGuire, G|Wright, F', 'Biomathematics and Statistics Scotland, Kings Buildings, Edinburgh, UK.|Biomathematics and Statistics Scotland, Kings Buildings, Edinburgh, UK.', 'United Kingdom|United Kingdom', 20, 'Computational Biology|DNA|Hepatitis B virus|Phylogeny|Proteins|Recombination, Genetic|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/14.2.219', '9545456', 'TOPAL scans a multiple sequence alignment for evidence of recombinant sequences, prior to phylogenetic analysis.'),
(83244, 1998, 'bio', 'STRIRED: graphical analysis of string repeats', 'Iazzetti, G|Chiusano, M L|Calogero, R A', 'Dipartimento di Genetica, Biologia Generale e Molecolare, Napoli, Italy.|Dipartimento di Genetica, Biologia Generale e Molecolare, Napoli, Italy.|Dipartimento di Genetica, Biologia Generale e Molecolare, Napoli, Italy.', 'Italy|Italy|Italy', 0, 'Base Sequence|Computational Biology|Computer Graphics|Databases, Factual|Humans|RNA, Messenger|Repetitive Sequences, Nucleic Acid|Sequence Homology, Nucleic Acid|Software', 'Journal Article', '10.1093/bioinformatics/14.2.221', '9545457', 'STRIRED is a toolkit to generate a graphical picture of the distribution of 4- to 6-mer repeats in a set of user-defined nucleic acid sequences.'),
(83245, 1998, 'bio', 'GeneFIND web server for protein family identification and information retrieval', 'Wu, C H|Shivakumar, S|Shivakumar, C V|Chen, S C', 'Department of Epidemiology/Biomathematics, The University of Texas Health Center at Tyler 75710, USA.|Department of Epidemiology/Biomathematics, The University of Texas Health Center at Tyler 75710, USA.|Department of Epidemiology/Biomathematics, The University of Texas Health Center at Tyler 75710, USA.|Department of Epidemiology/Biomathematics, The University of Texas Health Center at Tyler 75710, USA.', 'United States|United States|United States|United States', 15, 'Amino Acid Sequence|Computational Biology|Computer Communication Networks|Database Management Systems|Databases, Factual|Proteins|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/14.2.223', '9545458', 'An integrated database and search system has been developed for protein family identification and information retrieval, as an approach to undertake the highly complex, genomic-scale problem of molecular sequence database search and organization.'),
(83246, 1998, 'bio', 'MEICPS: substitution mutations to engineer intracellular protein stability', 'Reddy, B V|Ramesh, P|Tiwari, S', 'Centre for Cellular and Molecular Biology, Hyderabad, India.|Centre for Cellular and Molecular Biology, Hyderabad, India.|Centre for Cellular and Molecular Biology, Hyderabad, India.', 'India|India|India', 5, 'Computational Biology|Databases, Factual|Drug Stability|Mutation|Protein Engineering|Proteins|Software|Software Design', 'Journal Article', '10.1093/bioinformatics/14.2.225', '9545459', 'In MEICPS, results from earlier analyses are utilized to suggest possible substitution point mutations to engineer intracellular stability using a given sequence or structure of the protein.'),
(83247, 1998, 'bio', 'An EXCEL template for calculation of enzyme kinetic parameters by non-linear regression', 'Hernández, A|Ruiz, M T', 'Katholieke Universiteit Leuven, Laboratorium voor Moleculaire Celbiologie, Belgium.|Katholieke Universiteit Leuven, Laboratorium voor Moleculaire Celbiologie, Belgium.', 'Belgium|Belgium', 65, 'Computational Biology|Enzymes|Kinetics|Nonlinear Dynamics|Regression Analysis|Software', 'Journal Article', '10.1093/bioinformatics/14.2.227', '9545460', 'An EXCEL template has been developed for the calculation of enzyme kinetic parameters by non-linear regression techniques. The tool is accurate, inexpensive, as well as easy to use and modify.'),
(83248, 1998, 'bio', 'GeneGenerator--a flexible algorithm for gene prediction and its application to maize sequences', 'Kleffe, J|Hermann, K|Vahrson, W|Wittig, B|Brendel, V', 'Freie Universität Berlin, Abteilung Molekularbiologie und Bioinformatik, Institut für Molekularbiologie und Biochemie, Arnimallee 22, 14195 Berlin, Germany. jkleffe@euler.grumed.fu-berlin-de|Freie Universität Berlin, Abteilung Molekularbiologie und Bioinformatik, Institut für Molekularbiologie und Biochemie, Arnimallee 22, 14195 Berlin, Germany. jkleffe@euler.grumed.fu-berlin-de|Freie Universität Berlin, Abteilung Molekularbiologie und Bioinformatik, Institut für Molekularbiologie und Biochemie, Arnimallee 22, 14195 Berlin, Germany. jkleffe@euler.grumed.fu-berlin-de|Freie Universität Berlin, Abteilung Molekularbiologie und Bioinformatik, Institut für Molekularbiologie und Biochemie, Arnimallee 22, 14195 Berlin, Germany. jkleffe@euler.grumed.fu-berlin-de|Freie Universität Berlin, Abteilung Molekularbiologie und Bioinformatik, Institut für Molekularbiologie und Biochemie, Arnimallee 22, 14195 Berlin, Germany. jkleffe@euler.grumed.fu-berlin-de', 'Germany|Germany|Germany|Germany|Germany', 13, 'Algorithms|Computational Biology|DNA-Binding Proteins|Exons|Genes, Plant|Glucosyltransferases|Introns|Leucine Zippers|Logistic Models|Markov Chains|Models, Genetic|Plant Proteins|Sequence Analysis, DNA|Software|Software Validation|Transcription Factors|Zea mays', 'Journal Article', '10.1093/bioinformatics/14.3.232', '9614266', 'We developed GeneGenerator because of the need for a tool to predict gene structure without knowing in advance how to score potential exons and introns in order to obtain the best results, pertinent in particular to less well-studied organisms for which suitable training sets are small. GeneGenerator is a very flexible algorithm which for a given genomic sequence generates a number of feasible gene structures satisfying user-defined constraints. The specific implementation described in detail requires minimum scoring for translation start and donor and acceptor splice sites according to previously trained logitlinear models. In addition, potential exons and introns are required to exceed specified minimal lengths and threshold scores for coding or non-coding potential derived as log-likelihood ratios of appropriate Markov sequence models.'),
(83249, 1998, 'bio', 'Transcription regulatory region analysis using signal detection and fuzzy clustering', 'Pickert, L|Reuter, I|Klawonn, F|Wingender, E', 'Gesellschaft für Biotechnologische Forschung mbH, Mascheroder Weg 1, D-38124 Braunschweig, Germany.|Gesellschaft für Biotechnologische Forschung mbH, Mascheroder Weg 1, D-38124 Braunschweig, Germany.|Gesellschaft für Biotechnologische Forschung mbH, Mascheroder Weg 1, D-38124 Braunschweig, Germany.|Gesellschaft für Biotechnologische Forschung mbH, Mascheroder Weg 1, D-38124 Braunschweig, Germany.', 'Germany|Germany|Germany|Germany', 45, 'Algorithms|Binding Sites|Cluster Analysis|Computational Biology|Databases as Topic|False Positive Reactions|Fuzzy Logic|Models, Genetic|Protein Binding|Regulatory Sequences, Nucleic Acid|Sequence Analysis, DNA|Signal Processing, Computer-Assisted|Software Validation|Transcription Factors|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/14.3.244', '9614267', 'Presently available programs for the recognition of potential transcription factor binding sites in genomic sequences generally yield a huge amount of output. These output lists have to be filtered to obtain biologically significant elements, which is highly laborious work to be done manually.'),
(83250, 1998, 'bio', 'A distributed environment for physical map construction', 'Grigoriev, A|Levin, A|Lehrach, H', 'Max-Planck-Institute for Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany.|Max-Planck-Institute for Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany.|Max-Planck-Institute for Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany.', 'Germany|Germany|Germany', 7, 'Algorithms|Chromosome Mapping|Computational Biology|Computer Communication Networks|Computer Systems|Contig Mapping|Database Management Systems|Databases, Factual|Internet|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/14.3.252', '9614268', 'With the main focus of the Human Genome Project shifting to sequencing, bioinformatics support for constructing large-scale genomic maps of other organisms is still required. We attempt to provide for this with our work, aimed at the delivery of robust and user-friendly contig-building software on the WWW.'),
(83251, 1998, 'bio', 'A genetic algorithm for designing gene family-specific oligonucleotide sets used for hybridization: the G protein-coupled receptor protein superfamily', 'Kel, A|Ptitsyn, A|Babenko, V|Meier-Ewert, S|Lehrach, H', 'Institute of Cytology and Genetics, pr.Lavrentyeva 10, 630090, Novosibirsk, Russia. kel@bionet.nsc.ru|Institute of Cytology and Genetics, pr.Lavrentyeva 10, 630090, Novosibirsk, Russia. kel@bionet.nsc.ru|Institute of Cytology and Genetics, pr.Lavrentyeva 10, 630090, Novosibirsk, Russia. kel@bionet.nsc.ru|Institute of Cytology and Genetics, pr.Lavrentyeva 10, 630090, Novosibirsk, Russia. kel@bionet.nsc.ru|Institute of Cytology and Genetics, pr.Lavrentyeva 10, 630090, Novosibirsk, Russia. kel@bionet.nsc.ru', '-|-|-|-|-', 8, 'Algorithms|Base Sequence|Computational Biology|DNA Primers|GTP-Binding Proteins|Humans|Models, Genetic|Molecular Sequence Data|Multigene Family|Nucleic Acid Hybridization|Receptors, Cell Surface|Receptors, Thrombin|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Signal Transduction|Software Validation', 'Journal Article', '10.1093/bioinformatics/14.3.259', '9614269', 'Massive oligonucleotide hybridization is one of the most promising technologies of functional genome analysis. The critical point is to design appropriate sets of oligonucleotides that can be used effectively in identification by hybridization.'),
(83252, 1998, 'bio', 'Systematic genomic screening and analysis of mRNA in untranslated regions and mRNA precursors: combining experimental and computational approaches', 'Dandekar, T|Beyer, K|Bork, P|Kenealy, M R|Pantopoulos, K|Hentze, M|Sonntag-Buck, V|Flouriot, G|Gannon, F|Schreiber, S', 'European Molecular Biology Laboratory, Postfach 102209, D-69012 Heidelberg, Germany. dandekar@embl-heidelberg.de|European Molecular Biology Laboratory, Postfach 102209, D-69012 Heidelberg, Germany. dandekar@embl-heidelberg.de|European Molecular Biology Laboratory, Postfach 102209, D-69012 Heidelberg, Germany. dandekar@embl-heidelberg.de|European Molecular Biology Laboratory, Postfach 102209, D-69012 Heidelberg, Germany. dandekar@embl-heidelberg.de|European Molecular Biology Laboratory, Postfach 102209, D-69012 Heidelberg, Germany. dandekar@embl-heidelberg.de|European Molecular Biology Laboratory, Postfach 102209, D-69012 Heidelberg, Germany. dandekar@embl-heidelberg.de|European Molecular Biology Laboratory, Postfach 102209, D-69012 Heidelberg, Germany. dandekar@embl-heidelberg.de|European Molecular Biology Laboratory, Postfach 102209, D-69012 Heidelberg, Germany. dandekar@embl-heidelberg.de|European Molecular Biology Laboratory, Postfach 102209, D-69012 Heidelberg, Germany. dandekar@embl-heidelberg.de|European Molecular Biology Laboratory, Postfach 102209, D-69012 Heidelberg, Germany. dandekar@embl-heidelberg.de', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 25, '3 Untranslated Regions|Chromosome Mapping|Computational Biology|Escherichia coli|Humans|Iron|Nucleic Acid Conformation|RNA, Messenger|Receptors, Estrogen|Regulatory Sequences, Nucleic Acid|Sequence Analysis, RNA|Software Validation', 'Journal Article', '10.1093/bioinformatics/14.3.271', '9614270', 'The untranslated regions (UTRs) of mRNA upstream (5UTR) and downstream (3UTR) of the open reading frame, as well as the mRNA precursor, carry important regulatory sequences. To reveal unidentified regulatory signals, we combine information from experiments with computational approaches. Depending on available knowledge, three different strategies are employed.'),
(83253, 1998, 'bio', 'Statistics of large-scale sequence searching', 'Spang, R|Vingron, M', 'Deutsches Krebsforschungszentrum (DKFZ), Theoretische Bioinformatik, Im Neuenheimer Feld 280, D-69120 Heidelberg, Germany.|Deutsches Krebsforschungszentrum (DKFZ), Theoretische Bioinformatik, Im Neuenheimer Feld 280, D-69120 Heidelberg, Germany.', 'Germany|Germany', 0, 'Computational Biology|Computer Simulation|Databases, Factual|Models, Genetic|Sample Size|Sequence Analysis, DNA|Software Validation|Statistics as Topic|Time Factors', 'Journal Article', '10.1093/bioinformatics/14.3.279', '9614271', 'Database search programs such as FASTA, BLAST or a rigorous Smith-Waterman algorithm produce lists of database entries, which are assumed to be related to the query. The computation of statistical significance of similarity scores is well established for single pairs of sequences and using purely random models. However, the multi-trial context of a database search poses new problems. The credibility of a certain score obtained in a database search decreases with the amount of data that is compared. To improve p-value computation for database search experiments, statistical properties of the databases, such as the distribution of sequence length and effects induced by frequently repeated sequence patterns, need to be taken into account.'),
(83254, 1998, 'bio', 'Towards detection of orthologues in sequence databases', 'Yuan, Y P|Eulenstein, O|Vingron, M|Bork, P', 'Biocomputing, European Molecular Biology Laboratory (EMBL), Meyerhofstr. 1, 69012 Heidelberg, Germany.|Biocomputing, European Molecular Biology Laboratory (EMBL), Meyerhofstr. 1, 69012 Heidelberg, Germany.|Biocomputing, European Molecular Biology Laboratory (EMBL), Meyerhofstr. 1, 69012 Heidelberg, Germany.|Biocomputing, European Molecular Biology Laboratory (EMBL), Meyerhofstr. 1, 69012 Heidelberg, Germany.', 'Germany|Germany|Germany|Germany', 59, 'Computational Biology|Databases, Factual|Evolution, Molecular|Internet|Phylogeny|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Software Validation', 'Journal Article', '10.1093/bioinformatics/14.3.285', '9614272', 'Numerous homologous sequences from diverse species can be retrieved from databases using programs such as BLAST. However, due to multigene families, evolutionary relationship often cannot be easily determined and proper functional assignment becomes difficult. Thus, discrimination between orthologues and paralogues within BLAST output lists of homologous sequences becomes more and more important.'),
(83255, 1998, 'bio', 'DIALIGN: finding local similarities by multiple sequence alignment', 'Morgenstern, B|Frech, K|Dress, A|Werner, T', 'GSF-National Research Center for Environment and Health, Institute of Biomathematics and Biometry, Institute of Mammalian Genetics, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany.|GSF-National Research Center for Environment and Health, Institute of Biomathematics and Biometry, Institute of Mammalian Genetics, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany.|GSF-National Research Center for Environment and Health, Institute of Biomathematics and Biometry, Institute of Mammalian Genetics, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany.|GSF-National Research Center for Environment and Health, Institute of Biomathematics and Biometry, Institute of Mammalian Genetics, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany.', 'Germany|Germany|Germany|Germany', 68, 'Algorithms|Amino Acid Sequence|Computational Biology|Conserved Sequence|Molecular Sequence Data|Sequence Alignment|Sequence Analysis|Sequence Analysis, DNA|Sequence Homology, Amino Acid|Software Validation', 'Journal Article', '10.1093/bioinformatics/14.3.290', '9614273', 'DIALIGN is a new method for pairwise as well as multiple alignment of nucleic acid and protein sequences. While standard alignment programs rely on comparing single residues and imposing gap penalties, DIALIGN constructs alignments by comparing whole segments of the sequences. No gap penalty is employed. This point of view is especially adequate if sequences are not globally related, but share only local similarities, as is the case in genomic DNA sequences and in many protein families.'),
(83256, 1998, 'bio', 'New scoring schemes for protein fold recognition based on Voronoi contacts', 'Zimmer, R|Wöhler, M|Thiele, R', 'Institute for Algorithms and Scientific Computing (SCAI), German National Research Center for Information Technology (GMD), 53754 Sankt Augustin, Germany.|Institute for Algorithms and Scientific Computing (SCAI), German National Research Center for Information Technology (GMD), 53754 Sankt Augustin, Germany.|Institute for Algorithms and Scientific Computing (SCAI), German National Research Center for Information Technology (GMD), 53754 Sankt Augustin, Germany.', 'Germany|Germany|Germany', 0, 'Algorithms|Amino Acids|Computational Biology|Computer Simulation|Models, Biological|Protein Folding|Protein Structure, Tertiary|Sequence Analysis|Software Validation', 'Journal Article', '10.1093/bioinformatics/14.3.295', '9614274', 'The genome projects produce a wealth of protein sequences. Theoretical methods to predict possible structures and functions are needed for screening purposes, large-scale comparisons and in-depth analysis to identify worthwhile targets for further experimental research. Sequence-structure alignment is a basic tool for the identification of model folds for protein sequences and the construction of crude structural models. Empirical contact potentials (potentials of mean force) are used to optimize and evaluate such alignments.'),
(83257, 1998, 'bio', 'Mapmerge: merge genomic maps', 'Nadkarni, P', 'Center for Medical Informatics and Department of Anesthesiology, Yale University School of Medicine, 333 Cedar Street, TMP-3, New Haven, CT 06510, USA. Prakash.Nadkarni@yale.edu', 'United States', 3, 'Algorithms|Chromosome Mapping|Computer Graphics|Data Display|Software|Software Design', 'Journal Article', '10.1093/bioinformatics/14.4.310', '9632825', 'When two or more genomic maps of a chromosomal region are available, it is useful to be able to synthesize them to create a merged map.'),
(83258, 1998, 'bio', 'Automatic extraction of motifs represented in the hidden Markov model from a number of DNA sequences', 'Yada, T|Totoki, Y|Ishikawa, M|Asai, K|Nakai, K', 'Japan Science and Technology Corporation (JST), 5-3 Yonbancho, Chiyoda-ku, Tokyo, 102. yada@tokyo.jst.go.jp|Japan Science and Technology Corporation (JST), 5-3 Yonbancho, Chiyoda-ku, Tokyo, 102. yada@tokyo.jst.go.jp|Japan Science and Technology Corporation (JST), 5-3 Yonbancho, Chiyoda-ku, Tokyo, 102. yada@tokyo.jst.go.jp|Japan Science and Technology Corporation (JST), 5-3 Yonbancho, Chiyoda-ku, Tokyo, 102. yada@tokyo.jst.go.jp|Japan Science and Technology Corporation (JST), 5-3 Yonbancho, Chiyoda-ku, Tokyo, 102. yada@tokyo.jst.go.jp', 'Japan|Japan|Japan|Japan|Japan', 0, 'Algorithms|Base Sequence|Benchmarking|Binding Sites|Chi-Square Distribution|Gene Library|Humans|Information Storage and Retrieval|Markov Chains|Models, Genetic|Models, Statistical|Promoter Regions, Genetic|Software|Software Design|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/14.4.317', '9632826', 'Automatic extraction of motifs that occur frequently on a set of unaligned DNA sequences is useful for predicting the binding sites of unknown transcription factors. Several programs for this purpose have been released. However, in our opinion, they are not practical enough to be applied to a large number of upstream sequences.'),
(83259, 1998, 'bio', 'DIAMOD: display and modeling of DNA bending', 'Dlakic, M|Harrington, R E', 'Department of Biochemistry 330, University of Nevada at Reno, Reno, NV 89557-0014, USA. mensur@umich.edu|Department of Biochemistry 330, University of Nevada at Reno, Reno, NV 89557-0014, USA. mensur@umich.edu', 'United States|United States', 0, 'Algorithms|Computer Graphics|DNA|Data Display|Models, Genetic|Nucleic Acid Conformation|Repetitive Sequences, Nucleic Acid|Software', 'Journal Article', '10.1093/bioinformatics/14.4.326', '9632827', 'DIAMOD (Displayandmodeling ofDNA) was created as a user-friendly software for exploring and better understanding DNA structural variations, particularly DNA bending. It was intended to be as open as possible so that any of the existing or future predictive models can be used with it.'),
(83260, 1998, 'bio', 'Optimization of ribosomal RNA profile alignments', 'OBrien, E A|Notredame, C|Higgins, D G', 'Department of Biochemistry, University College, Cork, Ireland and EMBL-European Bioinformatics Institute, Hinxton, Cambridge CB10 1RQ, UK. emmet@chah.ucc.ie|Department of Biochemistry, University College, Cork, Ireland and EMBL-European Bioinformatics Institute, Hinxton, Cambridge CB10 1RQ, UK. emmet@chah.ucc.ie|Department of Biochemistry, University College, Cork, Ireland and EMBL-European Bioinformatics Institute, Hinxton, Cambridge CB10 1RQ, UK. emmet@chah.ucc.ie', 'Ireland|Ireland|Ireland', 4, 'Animals|Humans|Protein Structure, Secondary|RNA, Ribosomal|Sequence Alignment|Software', 'Comparative Study', '10.1093/bioinformatics/14.4.332', '9632828', 'Large alignments of ribosomal RNA sequences are maintained at various sites. New sequences are added to these alignments using a combination of manual and automatic methods. We examine the use of profile alignment methods for rRNA alignment and try to optimize the choice of parameters and sequence weights.'),
(83261, 1998, 'bio', 'The new Virgil database: a service of rich links', 'Achard, F|Cussat-Blanc, C|Viara, E|Barillot, E', 'GIS Infobiogen, 7 rue Guy Môquet, 94801 Villejuif, France and Sysra informatique, 7 rue de Bievres, 92140 Clamart, France. Frederic.Achard@infobiogen.fr|GIS Infobiogen, 7 rue Guy Môquet, 94801 Villejuif, France and Sysra informatique, 7 rue de Bievres, 92140 Clamart, France. Frederic.Achard@infobiogen.fr|GIS Infobiogen, 7 rue Guy Môquet, 94801 Villejuif, France and Sysra informatique, 7 rue de Bievres, 92140 Clamart, France. Frederic.Achard@infobiogen.fr|GIS Infobiogen, 7 rue Guy Môquet, 94801 Villejuif, France and Sysra informatique, 7 rue de Bievres, 92140 Clamart, France. Frederic.Achard@infobiogen.fr', 'France|France|France|France', 0, 'Computer Communication Networks|Data Collection|Database Management Systems|Databases, Factual|Gene Library|Human Genome Project|Software Design', 'Journal Article', '10.1093/bioinformatics/14.4.342', '9632829', 'Links between biological objects are frequently used by researchers in biology. However, many of the links found in public databases are insufficiently documented and difficult to retrieve. Virgil introduces the idea of a rich link, i.e. the link itself and the related pieces of information. Virgil was developed to collect, manage and distribute such links.'),
(83262, 1998, 'bio', 'Searching DNA databases for similarities to DNA sequences: when is a match significant?', 'Anderson, I|Brass, A', 'School of Biological Sciences, University of Manchester, 2.205 Stopford Building, Oxford Road, Manchester M13 9PT, UK. I.Anderson@stud.man.ac.uk|School of Biological Sciences, University of Manchester, 2.205 Stopford Building, Oxford Road, Manchester M13 9PT, UK. I.Anderson@stud.man.ac.uk', 'United Kingdom|United Kingdom', 58, 'Algorithms|Base Sequence|Databases, Factual|Information Storage and Retrieval|Predictive Value of Tests|ROC Curve|Sensitivity and Specificity|Software', 'Journal Article', '10.1093/bioinformatics/14.4.349', '9632830', 'Searching DNA sequences against a DNA database is an essential element of sequence analysis. However, few systematic studies have been carried out to determine when a match between two DNA sequences has biological significance and this is limiting the use that can be made of DNA searching algorithms.'),
(83263, 1998, 'bio', 'Comparative analysis of seven multiple protein sequence alignment servers: clues to enhance reliability of predictions', 'Briffeuil, P|Baudoux, G|Lambert, C|De Bolle, X|Vinals, C|Feytmans, E|Depiereux, E', 'Laboratory of Molecular & Structural Biology, The University of Namur, 61 rue de Bruxelles, B-5000 Namur, Belgium.|Laboratory of Molecular & Structural Biology, The University of Namur, 61 rue de Bruxelles, B-5000 Namur, Belgium.|Laboratory of Molecular & Structural Biology, The University of Namur, 61 rue de Bruxelles, B-5000 Namur, Belgium.|Laboratory of Molecular & Structural Biology, The University of Namur, 61 rue de Bruxelles, B-5000 Namur, Belgium.|Laboratory of Molecular & Structural Biology, The University of Namur, 61 rue de Bruxelles, B-5000 Namur, Belgium.|Laboratory of Molecular & Structural Biology, The University of Namur, 61 rue de Bruxelles, B-5000 Namur, Belgium.|Laboratory of Molecular & Structural Biology, The University of Namur, 61 rue de Bruxelles, B-5000 Namur, Belgium.', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 30, 'Computer Communication Networks|Decision Making|Evaluation Studies as Topic|Online Systems|Repetitive Sequences, Nucleic Acid|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment', 'Comparative Study', '10.1093/bioinformatics/14.4.357', '9632831', 'The prediction reliability of seven multiple alignment servers currently available on the Internet (ClustalW, MAP, PIMA, Block Maker, MSA, MEME and Match-Box) has been evaluated in terms of power (sensitivity) and confidence (selectivity). Therefore, the alignments obtained have been respectively compared to refined structural alignments for 20 families of related proteins with low levels of identity.'),
(83264, 1998, 'bio', 'Frame: detection of genomic sequencing errors', 'Brown, N P|Sander, C|Bork, P', '1European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, CB10 1SD, UK. brown@ebi.ac.uk|1European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, CB10 1SD, UK. brown@ebi.ac.uk|1European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, CB10 1SD, UK. brown@ebi.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 14, 'Amino Acid Sequence|Computer Communication Networks|DNA|DNA, Bacterial|Databases, Factual|Genome|Information Storage and Retrieval|Open Reading Frames|Repetitive Sequences, Nucleic Acid|Sequence Homology|Software|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/14.4.367', '9632832', 'The underlying error rate for genomic sequencing sometimes results in the introduction of artificial frameshifts and in-frame stop codons into putative protein encoding genes. Severe errors are then introduced into the inferred transcripts through mis-translation or premature termination.'),
(83265, 1998, 'bio', 'GCUA: general codon usage analysis', 'McInerney, J O', 'Department of Zoology, The Natural History Museum, Cromwell Road, London SW7 5BD, UK. J.McInerney@nhm.ac.uk', 'United Kingdom', 124, 'Amino Acid Sequence|Codon|Database Management Systems|Multivariate Analysis', 'Journal Article', '10.1093/bioinformatics/14.4.372', '9632833', 'The program General Codon Usage Analysis (GCUA) has been developed for analysing codon and amino acid usage patterns.'),
(83266, 1998, 'bio', 'POLINA: detection and evaluation of single amino acid substitutions in protein superfamilies', 'Levin, S|Satir, B H', 'Department of Anatomy and Structural Biology, Albert Einstein College of Medicine, Bronx, NY 10461, USA. slevin@aecom.yu.edu|Department of Anatomy and Structural Biology, Albert Einstein College of Medicine, Bronx, NY 10461, USA. slevin@aecom.yu.edu', 'United States|United States', 10, 'Algorithms|Amino Acid Sequence|Amino Acid Substitution|Computer Graphics|Conserved Sequence|False Positive Reactions|Hemoglobins|Humans|Point Mutation|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/14.4.374', '9632834', 'An algorithm is described for the quick identification and evaluation of amino acid substitutions in multiple protein sequence alignment. The strategy is based on the calculation of a relative conservation index for an amino acid at each position of the alignment. The algorithm is implemented in the computer program POLINA (Protein Oriented LINear Analysis) which provides a summary of analysis in a format suitable for import into a graphing program.'),
(83267, 1998, 'bio', 'Self-organizing tree growing network for classifying amino acids', 'Wang, H C|Dopazo, J|Carazo, J M', 'Centro Nacional de Biotecnologia-CSIC, Universidad Autonoma, 28049 Madrid, Spain.|Centro Nacional de Biotecnologia-CSIC, Universidad Autonoma, 28049 Madrid, Spain.|Centro Nacional de Biotecnologia-CSIC, Universidad Autonoma, 28049 Madrid, Spain.', 'Spain|Spain|Spain', 22, 'Algorithms|Amino Acids|Neural Networks (Computer)|Software', 'Journal Article', '10.1093/bioinformatics/14.4.376', '9632835', 'A self-organizing tree growing neural network was applied to classify amino acids and amino acid exchange matrices.'),
(83268, 1998, 'bio', 'SOSUI: classification and secondary structure prediction system for membrane proteins', 'Hirokawa, T|Boon-Chieng, S|Mitaku, S', 'Department of Biotechnology, Tokyo University of Agriculture and Technology, Nakacho, Koganei, Tokyo 184, Japan.|Department of Biotechnology, Tokyo University of Agriculture and Technology, Nakacho, Koganei, Tokyo 184, Japan.|Department of Biotechnology, Tokyo University of Agriculture and Technology, Nakacho, Koganei, Tokyo 184, Japan.', 'Japan|Japan|Japan', 1453, 'Computer Communication Networks|Data Display|Membrane Proteins|Protein Structure, Secondary|Software', 'Journal Article', '10.1093/bioinformatics/14.4.378', '9632836', 'The system SOSUI for the discrimination of membrane proteins and soluble ones together with the prediction of transmembrane helices was developed, in which the accuracy of the classification of proteins was 99% and the corresponding value for the transmembrane helix prediction was 97%.'),
(83269, 1998, 'bio', 'MView: a web-compatible database search or multiple alignment viewer', 'Brown, N P|Leroy, C|Sander, C', 'European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge CB10 1SD, UK. brown@ebi.ac.uk|European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge CB10 1SD, UK. brown@ebi.ac.uk|European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge CB10 1SD, UK. brown@ebi.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 190, 'Computer Communication Networks|Information Storage and Retrieval|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/14.4.380', '9632837', 'MView is a tool for converting the results of a sequence database search into the form of a coloured multiple alignment of hits stacked against the query. Alternatively, an existing multiple alignment can be processed. In either case, the output is simply HTML, so the result is platform independent and does not require a separate application or applet to be loaded.'),
(83270, 1998, 'bio', 'Assessing protein coding region integrity in cDNA sequencing projects', 'Salamov, A A|Nishikawa, T|Swindells, M B', 'Helix Research Institute, 1532-3 Yana, Kisarazu-shi, Chiba-ken 292, Japan.|Helix Research Institute, 1532-3 Yana, Kisarazu-shi, Chiba-ken 292, Japan.|Helix Research Institute, 1532-3 Yana, Kisarazu-shi, Chiba-ken 292, Japan.', 'Japan|Japan|Japan', 127, 'Base Sequence|Codon, Initiator|Computational Biology|DNA, Complementary|Databases, Factual|Humans|Open Reading Frames|Proteins|RNA, Messenger|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/14.5.384', '9682051', 'In cDNA sequencing projects, it is vital to know whether the protein coding region of a sequence is complete, or whether errors have occurred during library construction. Here we present a linear discriminant approach that predicts this completeness by estimating the probability of each ATG being the initiation codon.'),
(83271, 1998, 'bio', 'Prediction of transcriptional regulatory sites in the complete genome sequence of Escherichia coli K-12', 'Thieffry, D|Salgado, H|Huerta, A M|Collado-Vides, J', 'Centro de Investigación sobre Fijación de Nitrógeno, Universidad Nacional Autónoma de México, AP 565-A, Cuernavaca, Morelos 62100, México.|Centro de Investigación sobre Fijación de Nitrógeno, Universidad Nacional Autónoma de México, AP 565-A, Cuernavaca, Morelos 62100, México.|Centro de Investigación sobre Fijación de Nitrógeno, Universidad Nacional Autónoma de México, AP 565-A, Cuernavaca, Morelos 62100, México.|Centro de Investigación sobre Fijación de Nitrógeno, Universidad Nacional Autónoma de México, AP 565-A, Cuernavaca, Morelos 62100, México.', '-|-|-|-', 75, 'Bacterial Proteins|Binding Sites|Computational Biology|Databases, Factual|Escherichia coli|Genes, Regulator|Genome, Bacterial|Regulon', 'Journal Article', '10.1093/bioinformatics/14.5.391', '9682052', 'As one of the best-characterized free-living organisms, Escherichia coli and its recently completed genomic sequence offer a special opportunity to exploit systematically the variety of regulatory data available in the literature in order to make a comprehensive set of regulatory predictions in the whole genome.'),
(83272, 1998, 'bio', 'Reduced space hidden Markov model training', 'Tarnas, C|Hughey, R', 'Department of Computer Engineering, Jack Baskin School of Engineering, University of California, Santa Cruz, CA 95064, USA.|Department of Computer Engineering, Jack Baskin School of Engineering, University of California, Santa Cruz, CA 95064, USA.', 'United States|United States', 25, 'Algorithms|Computational Biology|Markov Chains|Sequence Alignment|Software|Time Factors', 'Journal Article', '10.1093/bioinformatics/14.5.401', '9682053', 'Complete forward-backward (Baum-Welch) hidden Markov model training cannot take advantage of the linear space, divide-and-conquer sequence alignment algorithms because of the examination of all possible paths rather than the single best path.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(83273, 1998, 'bio', 'COFFEE: an objective function for multiple sequence alignments', 'Notredame, C|Holm, L|Higgins, D G', 'EMBL Outstation-The European Bioinformatics Institute, Hinxton Hall, Hinxton, Cambridge CB10 1SD, UK.|EMBL Outstation-The European Bioinformatics Institute, Hinxton Hall, Hinxton, Cambridge CB10 1SD, UK.|EMBL Outstation-The European Bioinformatics Institute, Hinxton Hall, Hinxton, Cambridge CB10 1SD, UK.', 'United Kingdom|United Kingdom|United Kingdom', 172, 'Algorithms|Amino Acid Sequence|Computational Biology|Databases, Factual|Evaluation Studies as Topic|Fibronectins|Models, Genetic|Molecular Sequence Data|Sequence Alignment|Sequence Homology, Amino Acid|Software', 'Comparative Study', '10.1093/bioinformatics/14.5.407', '9682054', 'In order to increase the accuracy of multiple sequence alignments, we designed a new strategy for optimizing multiple sequence alignments by genetic algorithm. We named it COFFEE (Consistency based Objective Function For alignmEnt Evaluation). The COFFEE score reflects the level of consistency between a multiple sequence alignment and a library containing pairwise alignments of the same sequences.'),
(83274, 1998, 'bio', 'Removing near-neighbour redundancy from large protein sequence collections', 'Holm, L|Sander, C', 'EMBL-EBI, Cambridge CB10 1SD, UK.|EMBL-EBI, Cambridge CB10 1SD, UK.', 'United Kingdom|United Kingdom', 218, 'Algorithms|Animals|Computational Biology|Connectin|Databases, Factual|Fungal Proteins|Genome, Fungal|Humans|Muscle Proteins|Protein Kinases|Proteins|Saccharomyces cerevisiae|Sequence Alignment|Software', 'Comparative Study', '10.1093/bioinformatics/14.5.423', '9682055', 'To maximize the chances of biological discovery, homology searching must use an up-to-date collection of sequences. However, the available sequence databases are growing rapidly and are partially redundant in content. This leads to increasing strain on CPU resources and decreasing density of first-hand annotation.'),
(83275, 1998, 'bio', 'A set-theoretic approach to database searching and clustering', 'Krause, A|Vingron, M', 'Deutsches Krebsforschungszentrum (DKFZ), Theoretische Bioinformatik, Im Neuenheimer Feld 280, D-69120 Heidelberg, Germany. a.krause@dkfz-heidelberg.de|Deutsches Krebsforschungszentrum (DKFZ), Theoretische Bioinformatik, Im Neuenheimer Feld 280, D-69120 Heidelberg, Germany. a.krause@dkfz-heidelberg.de', 'Germany|Germany', 42, 'Algorithms|Cluster Analysis|Computational Biology|Databases, Factual|Proteins|Sequence Alignment|Software', 'Comparative Study', '10.1093/bioinformatics/14.5.430', '9682056', 'In this paper, we introduce an iterative method of database searching and apply it to design a database clustering algorithm applicable to an entire protein database. The clustering procedure relies on the quality of the database searching routine and further improves its results based on a set-theoretic analysis of a highly redundant yet efficient to generate cluster system.'),
(83276, 1998, 'bio', 'Computational space reduction and parallelization of a new clustering approach for large groups of sequences', 'Trelles, O|Andrade, M A|Valencia, A|Zapata, E L|Carazo, J M', 'Computer Architecture Department, University of Malaga, 29017 Malaga, Spain. ots@ac.uma.es|Computer Architecture Department, University of Malaga, 29017 Malaga, Spain. ots@ac.uma.es|Computer Architecture Department, University of Malaga, 29017 Malaga, Spain. ots@ac.uma.es|Computer Architecture Department, University of Malaga, 29017 Malaga, Spain. ots@ac.uma.es|Computer Architecture Department, University of Malaga, 29017 Malaga, Spain. ots@ac.uma.es', 'Spain|Spain|Spain|Spain|Spain', 0, 'Algorithms|Cluster Analysis|Computational Biology|Databases, Factual|Evaluation Studies as Topic|Genome|Proteins|Sequence Alignment', 'Comparative Study', '10.1093/bioinformatics/14.5.439', '9682057', 'The explosive growth of the biological sequences databases stimulated by genome projects has modified the framework of several applications in the biological sequence analysis area. In most cases, this new scenario is characterized by studies on large sets of sequences, suggesting the need for effective and automatic methods for their clustering. A more effective clustering of the database could be followed by the application of common family analysis schemes to the groups so formed.'),
(83277, 1998, 'bio', 'W2H: WWW interface to the GCG sequence analysis package', 'Senger, M|Flores, T|Glatting, K|Ernst, P|Hotz-Wagenblatt, A|Suhai, S', 'DKFZ - German Cancer Research Center, Department of Molecular Biophysics, Im Neuenheimer Feld 280, D-69120 Heidelberg, Germany. m.senger@ebi.ac.uk|DKFZ - German Cancer Research Center, Department of Molecular Biophysics, Im Neuenheimer Feld 280, D-69120 Heidelberg, Germany. m.senger@ebi.ac.uk|DKFZ - German Cancer Research Center, Department of Molecular Biophysics, Im Neuenheimer Feld 280, D-69120 Heidelberg, Germany. m.senger@ebi.ac.uk|DKFZ - German Cancer Research Center, Department of Molecular Biophysics, Im Neuenheimer Feld 280, D-69120 Heidelberg, Germany. m.senger@ebi.ac.uk|DKFZ - German Cancer Research Center, Department of Molecular Biophysics, Im Neuenheimer Feld 280, D-69120 Heidelberg, Germany. m.senger@ebi.ac.uk|DKFZ - German Cancer Research Center, Department of Molecular Biophysics, Im Neuenheimer Feld 280, D-69120 Heidelberg, Germany. m.senger@ebi.ac.uk', 'Germany|Germany|Germany|Germany|Germany|Germany', 46, 'Computational Biology|Computer Communication Networks|Computer Security|Computer Systems|Databases, Factual|Sequence Analysis|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/14.5.452', '9682058', 'The user-friendly, graphical X-windows interface (WPI) to the GCG sequence analysis package can often not be used due to the lack of an X-server on PC or Macintosh computers. Because Web browsers like Netscape are much more common on those platforms, we decided to develop W2H, a WWW interface to the GCG Sequence Analysis Software Package with nearly the same functionality as the X-windows interface WPI.'),
(83278, 1998, 'bio', 'POPSIM: a general population simulation program', 'Hampe, J|Wienker, T|Schreiber, S|Nürnberg, P', 'IVth Department of Medicine, Charite Medical School Berlin, Germany. J.Hampe@mucosa.de|IVth Department of Medicine, Charite Medical School Berlin, Germany. J.Hampe@mucosa.de|IVth Department of Medicine, Charite Medical School Berlin, Germany. J.Hampe@mucosa.de|IVth Department of Medicine, Charite Medical School Berlin, Germany. J.Hampe@mucosa.de', 'Germany|Germany|Germany|Germany', 0, 'Computational Biology|Computer Simulation|Female|Genetic Diseases, Inborn|Genetics, Medical|Genetics, Population|Humans|Linkage Disequilibrium|Male|Meiosis|Models, Genetic|Software', 'Journal Article', '10.1093/bioinformatics/14.5.458', '9682059', 'The investigation of common disorders of polygenic inheritance using both population-based association designs and non-parametric linkage analysis within families is gaining increasing importance. We have created a program that allows for the flexible simulation of populations as a tool to investigate the properties of population-based mapping approaches.'),
(83279, 1998, 'bio', 'CloneIt: finding cloning strategies, in-frame deletions and frameshifts', 'Lindenbaum, P', 'Laboratoire de Biologie Moléculaire des Rotavirus, Virologie et Immunologie Moléculaires, Institut National de la Recherche Agronomique, 78350 Jouy-en-Josas Cedex, France. lindenb@biotec.jouy.inra.fr', 'France', 3, 'Algorithms|Cloning, Molecular|Computational Biology|DNA Restriction Enzymes|Databases, Factual|Frameshift Mutation|Genetic Vectors|Plasmids|Sequence Deletion|Software', 'Journal Article', '10.1093/bioinformatics/14.5.465', '9682060', 'The CloneIt program searches for sub-cloning strategies, in-frame deletions and frameshifts within a plasmid sequence.'),
(83280, 1998, 'bio', 'SWAN: sliding window analysis of nucleotide sequence variability', 'Proutski, V|Holmes, E', 'The Wellcome Trust Centre for the Epidemiology of Infectious Disease, Department of Zoology, University of Oxford, South Parks Road Oxford OX1 3PS, UK. Vitali.Proutski@zoology.oxford.ac.uk|The Wellcome Trust Centre for the Epidemiology of Infectious Disease, Department of Zoology, University of Oxford, South Parks Road Oxford OX1 3PS, UK. Vitali.Proutski@zoology.oxford.ac.uk', 'United Kingdom|United Kingdom', 27, 'Algorithms|Base Sequence|Computational Biology|Computer Graphics|Genetic Variation|Sequence Alignment|Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/14.5.467', '9682061', 'This paper describes a new program which reveals, analyses and graphically represents patterns of variability along nucleotide sequences.'),
(83281, 1998, 'bio', 'The BioCatalog', 'Rodriguez-Tomé, P', 'EMBL Outstation-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.', 'United Kingdom', 9, 'Computational Biology|Computer Communication Networks|Databases, Factual|Molecular Biology|Software', 'Journal Article', '10.1093/bioinformatics/14.5.469', '9682062', 'The BioCatalog is a database of information on software of interest in molecular biology and genetics. The programs are grouped by domain of interest.'),
(83282, 1998, 'bio', 'Grand challenges in bioinformatics', 'Kanehisa, M', 'EMBL Outstation-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.', 'United Kingdom', 5, 'Algorithms|Computational Biology|Protein Folding|Protein Structure, Secondary|Thermodynamics', 'Editorial', '10.1093/bioinformatics/14.4.309', '9687209', '-'),
(83283, 1998, 'bio', 'Formal design and implementation of an improved DDBJ DNA database with a new schema and object-oriented library', 'Okayama, T|Tamura, T|Gojobori, T|Tateno, Y|Ikeo, K|Miyazaki, S|Fukami-Kobayashi, K|Sugawara, H', 'Center for Information Biology, National Institute of Genetics, Mishima 411, Japan.|Center for Information Biology, National Institute of Genetics, Mishima 411, Japan.|Center for Information Biology, National Institute of Genetics, Mishima 411, Japan.|Center for Information Biology, National Institute of Genetics, Mishima 411, Japan.|Center for Information Biology, National Institute of Genetics, Mishima 411, Japan.|Center for Information Biology, National Institute of Genetics, Mishima 411, Japan.|Center for Information Biology, National Institute of Genetics, Mishima 411, Japan.|Center for Information Biology, National Institute of Genetics, Mishima 411, Japan.', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan', 14, 'Base Sequence|Computational Biology|Computer Systems|DNA|Databases, Factual|Internet|Japan|Software Design', 'Journal Article', '10.1093/bioinformatics/14.6.472', '9694985', 'The DNA Data Bank of Japan (DDBJ) has developed a new DNA database system with a new schema design to accommodate rapid change and growth of requirements on the system.'),
(83284, 1998, 'bio', 'The bio-objects project Part I: the object data model core elements', 'Maltchenko, S Z', 'Genetic Systems Department, Hitachi Software Engineering America, Ltd, 601 Gateway Blvd, Suite 500, So. San Francisco, CA 94080-7025, USA. smaltch@msn.com', 'United States', 1, 'Biotechnology|Computational Biology|Computer Simulation|Computer Systems|Databases, Factual|Software', 'Journal Article', '10.1093/bioinformatics/14.6.479', '9694986', 'Scientific data reside in a number of different data sources, both public and commercial, and are accessible through various software applications. Data and software interoperability is becoming a critical issue in the research community. In our opinion, the need to develop a unified object model for biotechnology must be recognized. Herein, we present a prototype and core elements for the Object Data Model, a central part of our future commercial product development.'),
(83285, 1998, 'bio', 'A metadata approach to query interoperation between molecular biology databases', 'Cheung, K H|Nadkarni, P M|Shin, D G', 'Center for Medical Informatics, Yale University School of Medicine, 333 Cedar Street, TMP-3, New Haven, CT 06510, USA. kei.cheung@yale.edu|Center for Medical Informatics, Yale University School of Medicine, 333 Cedar Street, TMP-3, New Haven, CT 06510, USA. kei.cheung@yale.edu|Center for Medical Informatics, Yale University School of Medicine, 333 Cedar Street, TMP-3, New Haven, CT 06510, USA. kei.cheung@yale.edu', 'United States|United States|United States', 9, 'Chromosome Mapping|Computational Biology|Computer Simulation|Database Management Systems|Databases, Factual|Molecular Biology', 'Journal Article', '10.1093/bioinformatics/14.6.486', '9694987', 'Molecular biology databases have been proliferating rapidly. Their heterogeneity and complexity pose a great challenge to efforts in database interoperation. To minimize the efforts of interoperating heterogeneous databases, it is useful to develop a system that lets a user of a particular genomic database access another related database as if the latter is structurally similar to the former.'),
(83286, 1998, 'bio', 'Detecting periodic patterns in biological sequences', 'Coward, E|Drabløs, F', 'Department of Mathematical Sciences, Norwegian University of Science and Technology, Norway. evindc@math.ntnu.no|Department of Mathematical Sciences, Norwegian University of Science and Technology, Norway. evindc@math.ntnu.no', 'Norway|Norway', 52, 'Algorithms|Animals|Base Sequence|Computational Biology|DNA|Enzyme Inhibitors|Models, Molecular|Molecular Sequence Data|Pattern Recognition, Automated|Protein Conformation|Proteins|Repetitive Sequences, Amino Acid|Repetitive Sequences, Nucleic Acid|Ribonucleases|Sequence Alignment|Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/14.6.498', '9694988', 'The search for repeated patterns in DNA and protein sequences is important in sequence analysis. The rapid increase in available sequences, in particular from large-scale genome sequencing projects, makes it relevant to develop sensitive automatic methods for the identification of repeats.'),
(83287, 1998, 'bio', 'SSMAL: similarity searching with alignment graphs', 'Nicodème, P', 'INRIA-Rocquencourt and LIX Ecole Polytechnique, France, Present address: DKFZ, Abt. Theoretische Bioinformatik, INF 280, D-69120 Heidelberg, Germany. p.nicodeme@DKFZ-Heidelberg.de', 'Germany', 5, 'Amino Acid Sequence|Animals|Computational Biology|Conserved Sequence|Databases, Factual|Globins|Humans|Models, Theoretical|Molecular Sequence Data|Proteins|Sequence Alignment|Software', 'Comparative Study', '10.1093/bioinformatics/14.6.508', '9694989', 'We want to provide biologists with a fast and sensitive scanning tool for searching local alignments of a protein query sequence against databases of protein multiple alignments, such as ProDom. Conversely, we want to provide a tool for locally aligning a protein multiple alignment query against a protein database such as SWISSPROT.'),
(83288, 1998, 'bio', 'A geometric algorithm to find small but highly similar 3D substructures in proteins', 'Pennec, X|Ayache, N', 'INRIA, BP 93, 2004 route des Lucioles, 06902 Sophia Antipolis Cedex, France. Xavier.Pennec@sophia.inria.fr|INRIA, BP 93, 2004 route des Lucioles, 06902 Sophia Antipolis Cedex, France. Xavier.Pennec@sophia.inria.fr', 'France|France', 66, 'Algorithms|Amino Acid Sequence|Bacterial Proteins|Bacteriophages|Cluster Analysis|Computational Biology|DNA-Binding Proteins|Escherichia coli|Helix-Turn-Helix Motifs|Models, Molecular|Molecular Sequence Data|Protein Conformation|Proteins|Repressor Proteins|Software|Viral Proteins|Viral Regulatory and Accessory Proteins', 'Comparative Study', '10.1093/bioinformatics/14.6.516', '9694990', 'Most biological actions of proteins depend on some typical parts of their three-dimensional structure, called 3D motifs. It is desirable to find automatically common geometric substructures between proteins to discover similarities in new structures or to model precisely a particular motif. Most algorithms for structural comparison of proteins deal with large (fold) similarities. Here, we focus on small but precise similarities.'),
(83289, 1998, 'bio', 'Homology modeling, model and software evaluation: three related resources', 'Rodriguez, R|Chinea, G|Lopez, N|Pons, T|Vriend, G', 'Center for Genetic Engineering and Biotechnology, Havana, Cuba and 2BIOcomputing, EMBL, Heidelberg, Germany.|Center for Genetic Engineering and Biotechnology, Havana, Cuba and 2BIOcomputing, EMBL, Heidelberg, Germany.|Center for Genetic Engineering and Biotechnology, Havana, Cuba and 2BIOcomputing, EMBL, Heidelberg, Germany.|Center for Genetic Engineering and Biotechnology, Havana, Cuba and 2BIOcomputing, EMBL, Heidelberg, Germany.|Center for Genetic Engineering and Biotechnology, Havana, Cuba and 2BIOcomputing, EMBL, Heidelberg, Germany.', 'Cuba|Cuba|Cuba|Cuba|Cuba', 298, 'Computational Biology|Databases, Factual|Internet|Models, Molecular|Proteins|Reproducibility of Results|Sequence Homology, Amino Acid|Software', 'Comparative Study', '10.1093/bioinformatics/14.6.523', '9694991', 'Homology modeling is rapidly becoming the method of choice for obtaining three-dimensional coordinates for proteins because genome projects produce sequences at a much higher rate than NMR and X-ray laboratories can solve the three-dimensional structures. The quality of protein models will not be immediately clear to novices and support with the evaluation seems to be needed. Expert users are sometimes interested in evaluating the quality of modeling programs rather than the quality of the models themselves.'),
(83290, 1998, 'bio', 'GeneNet: a gene network database and its automated visualization', 'Kolpakov, F A|Ananko, E A|Kolesov, G B|Kolchanov, N A', 'Laboratory of Theoretical Molecular Genetics, Institute of Cytology & Genetics, 633090, Lavrentieva 10, Novosibirsk, Russia. fedor@bionet.nsc.ru|Laboratory of Theoretical Molecular Genetics, Institute of Cytology & Genetics, 633090, Lavrentieva 10, Novosibirsk, Russia. fedor@bionet.nsc.ru|Laboratory of Theoretical Molecular Genetics, Institute of Cytology & Genetics, 633090, Lavrentieva 10, Novosibirsk, Russia. fedor@bionet.nsc.ru|Laboratory of Theoretical Molecular Genetics, Institute of Cytology & Genetics, 633090, Lavrentieva 10, Novosibirsk, Russia. fedor@bionet.nsc.ru', '-|-|-|-', 61, 'Computational Biology|Computer Graphics|Databases, Factual|Gene Expression Regulation|Internet|Signal Transduction|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/14.6.529', '9694992', 'Gene networks that provide the regulation of physiological processes are the basic feature of organisms. Information regarding the regulation of gene expression and signal transduction pathways is increasing rapidly. However, the information is hard to formalize and systematize. Ways and means for automated visualization of the gene networks based on their formalized description are needed.'),
(83291, 1998, 'bio', 'Z extensions to the RHMAPPER package', 'Soderlund, C|Lau, T|Deloukas, P', 'The Sanger Centre, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK. cari@sanger.ac.uk|The Sanger Centre, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK. cari@sanger.ac.uk|The Sanger Centre, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK. cari@sanger.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 10, 'Animals|Chromosome Mapping|Computational Biology|Computer Graphics|Genetic Markers|Humans|Hybrid Cells|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/14.6.538', '9694993', 'Extensions have been made to the RHMAPPER-1.1 package. One set of extensions computes the totally linked markers and uses the results as input to the salient RHMAPPER functions. The second set of extensions uses TKperl to provide an interactive interface for ease of querying the database and displaying maps.'),
(83292, 1998, 'bio', 'TLCQA: quantitative study of thin-layer chromatography', 'Chau, F T|Chan, T P|Wang, J', 'Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong. bftchau@poly.edu.hk|Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong. bftchau@poly.edu.hk|Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong. bftchau@poly.edu.hk', 'Hong Kong|Hong Kong|Hong Kong', 17, 'Chromatography, Thin Layer|Computational Biology|Image Processing, Computer-Assisted|Software', 'Journal Article', '10.1093/bioinformatics/14.6.540', '9694994', 'An accurate, simple and low-cost image-analysis system has been set up for capturing thin-layer chromatography images, and a software package, TLCQA, has also been developed for both qualitative and quantitative analyses using the acquired image.'),
(83293, 1998, 'bio', 'EUCLID: automatic classification of proteins in functional classes by their database annotations', 'Tamames, J|Ouzounis, C|Casari, G|Sander, C|Valencia, A', 'Protein Design Group, CNB-CSIC, Campus U. Autonoma, Cantoblanco, E-28049 Madrid, Spain, 2EMBL-EBI, Cambridge CB10 1SD, UK and 3Lion-AG, Heidelberg, Germany.|Protein Design Group, CNB-CSIC, Campus U. Autonoma, Cantoblanco, E-28049 Madrid, Spain, 2EMBL-EBI, Cambridge CB10 1SD, UK and 3Lion-AG, Heidelberg, Germany.|Protein Design Group, CNB-CSIC, Campus U. Autonoma, Cantoblanco, E-28049 Madrid, Spain, 2EMBL-EBI, Cambridge CB10 1SD, UK and 3Lion-AG, Heidelberg, Germany.|Protein Design Group, CNB-CSIC, Campus U. Autonoma, Cantoblanco, E-28049 Madrid, Spain, 2EMBL-EBI, Cambridge CB10 1SD, UK and 3Lion-AG, Heidelberg, Germany.|Protein Design Group, CNB-CSIC, Campus U. Autonoma, Cantoblanco, E-28049 Madrid, Spain, 2EMBL-EBI, Cambridge CB10 1SD, UK and 3Lion-AG, Heidelberg, Germany.', 'Germany|Germany|Germany|Germany|Germany', 53, 'Computational Biology|Databases, Factual|Expert Systems|Genome|Humans|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/14.6.542', '9694995', 'A tool is described for the automatic classification of sequences in functional classes using their database annotations. The Euclid system is based on a simple learning procedure from examples provided by human experts.'),
(83294, 1998, 'bio', 'New phylogenetic venues opened by a novel implementation of the DNAml algorithm', 'Trelles, O|Ceron, C|Wang, H C|Dopazo, J|Carazo, J M', 'Computer Architecture Department, University of Malaga, 29071 Malaga, Spain. ots@ac.uma.es|Computer Architecture Department, University of Malaga, 29071 Malaga, Spain. ots@ac.uma.es|Computer Architecture Department, University of Malaga, 29071 Malaga, Spain. ots@ac.uma.es|Computer Architecture Department, University of Malaga, 29071 Malaga, Spain. ots@ac.uma.es|Computer Architecture Department, University of Malaga, 29071 Malaga, Spain. ots@ac.uma.es', 'Spain|Spain|Spain|Spain|Spain', 8, 'Algorithms|Computational Biology|DNA|Databases, Factual|Likelihood Functions|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/14.6.544', '9694996', 'This note describes the parallelization of a key biological application, such as the construction of phylogenetic trees, using the DNAml program. A careful run-time analysis allowed us to propose an efficient parallelization that scaled very well to large problem sizes.'),
(83295, 1998, 'bio', 'Graphical interface to the genetic network database GeNet', 'Serov, V N|Spirov, A V|Samsonova, M G', 'Institute of High Performance Computing and Data Bases, PO Box 71,St. Petersburg, 194291, Russia.|Institute of High Performance Computing and Data Bases, PO Box 71,St. Petersburg, 194291, Russia.|Institute of High Performance Computing and Data Bases, PO Box 71,St. Petersburg, 194291, Russia.', '-|-|-', 19, 'Computational Biology|Computer Graphics|Databases, Factual|Gene Expression Regulation|Internet|Programming Languages|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/14.6.546', '9694997', 'We designed a Java applet which enables the visualization of genetic networks and can be used as a Web publishing tool by molecular biologists studying the mechanisms of gene interactions.'),
(83296, 1998, 'bio', 'Comprehensive, comprehensible, distributed and intelligent databases: current status', 'Frishman, D|Heumann, K|Lesk, A|Mewes, H W', 'Munich Information Center for Protein Sequences of the German National Research Center for Environment and Health, Am Klopferspitz 18a, 82152 Martinsried, Germany. frishman@mips.biochem.mpg.de|Munich Information Center for Protein Sequences of the German National Research Center for Environment and Health, Am Klopferspitz 18a, 82152 Martinsried, Germany. frishman@mips.biochem.mpg.de|Munich Information Center for Protein Sequences of the German National Research Center for Environment and Health, Am Klopferspitz 18a, 82152 Martinsried, Germany. frishman@mips.biochem.mpg.de|Munich Information Center for Protein Sequences of the German National Research Center for Environment and Health, Am Klopferspitz 18a, 82152 Martinsried, Germany. frishman@mips.biochem.mpg.de', 'Germany|Germany|Germany|Germany', 30, 'Computational Biology|Database Management Systems|Databases as Topic|Internet|Quality Control', 'Journal Article', '10.1093/bioinformatics/14.7.551', '9730920', 'It is only a matter of time until a user will see not many but one integrated database of information for molecular biology. Is this true? Is it a good thing? Why will it happen? Where are we now? What developments are fostering and what developments are impeding progress towards this end?'),
(83297, 1998, 'bio', 'The LabBase system for data management in large scale biology research laboratories', 'Goodman, N|Rozen, S|Stein, L D|Smith, A G', 'The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609-1500, USA, Whitehead Institute for Biomedical Research, Cambridge, MA, USA. nat@jax.org|The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609-1500, USA, Whitehead Institute for Biomedical Research, Cambridge, MA, USA. nat@jax.org|The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609-1500, USA, Whitehead Institute for Biomedical Research, Cambridge, MA, USA. nat@jax.org|The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609-1500, USA, Whitehead Institute for Biomedical Research, Cambridge, MA, USA. nat@jax.org', 'United States|United States|United States|United States', 24, 'Computational Biology|Database Management Systems|Laboratories', 'Journal Article', '10.1093/bioinformatics/14.7.562', '9730921', 'The development of laboratory information management systems (LIMSs) for large scale biology research projects can be a challenging problem. Many such projects generate complex datasets via complex procedures that undergo continuous refinement. A key software challenge is to simplify the database-development task so that databases can be built and modified quickly enough to keep pace with changing project-requirements.'),
(83298, 1998, 'bio', 'A model system for studying the integration of molecular biology databases', 'Macauley, J|Wang, H|Goodman, N', 'The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA.|The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA.|The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA.', 'United States|United States|United States', 12, 'Animals|Computational Biology|Computer Simulation|Database Management Systems|Databases, Factual|Genome|Humans|Mice', 'Journal Article', '10.1093/bioinformatics/14.7.575', '9730922', 'Integration of molecular biology databases remains limited in practice despite its practical importance and considerable research effort. The complexity of the problem is such that an experimental approach is mandatory, yet this very complexity makes it hard to design definitive experiments. This dilemma is common in science, and one tried-and-true strategy is to work with model systems. We propose a model system for this problem, namely a database of genes integrating diverse data across organisms, and describe an experiment using this model.'),
(83299, 1998, 'bio', 'Issues in developing integrated genomic databases and application to the human X chromosome', 'Leser, U|Lehrach, H|Roest Crollius, H', 'Max-Planck-Institute für Molekulare Genetik, Ihnestrasse 73, D-14195 Berlin, Germany.|Max-Planck-Institute für Molekulare Genetik, Ihnestrasse 73, D-14195 Berlin, Germany.|Max-Planck-Institute für Molekulare Genetik, Ihnestrasse 73, D-14195 Berlin, Germany.', 'Germany|Germany|Germany', 6, 'Database Management Systems|Databases, Factual|Humans|X Chromosome', 'Journal Article', '10.1093/bioinformatics/14.7.583', '9730923', 'In the past decade, a vast amount of mapping data has been generated on the human X chromosome, without a mechanism which would provide a global view of exactly what has been achieved. Large datasets are available electronically, but in heterogeneous formats and with incompatible access modes. In addition, relationships between objects in different datasets are often not specified.'),
(83300, 1998, 'bio', 'LIGAND: chemical database for enzyme reactions', 'Goto, S|Nishioka, T|Kanehisa, M', 'Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan. goto@kuicr.kyoto-u.ac.jp|Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan. goto@kuicr.kyoto-u.ac.jp|Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan. goto@kuicr.kyoto-u.ac.jp', 'Japan|Japan|Japan', 20, 'Computational Biology|Databases, Factual|Enzymes', 'Journal Article', '10.1093/bioinformatics/14.7.591', '9730924', 'The existing molecular biology databases focus on the sequence and structural aspects of biological macromolecules, i.e. DNAs, RNAs and proteins. However, in order to understand the functional aspects, it is essential to computerize the interaction of these molecules. Furthermore, living cells contain additional molecules, such as metabolic compounds and metal ions, that may also be considered as parts of the basic building blocks of life, but are not well organized in public databases. LIGAND chemical database is our attempt to solve these problems, at least for enzymatic reactions.'),
(83301, 1998, 'bio', 'Automatic extraction of keywords from scientific text: application to the knowledge domain of protein families', 'Andrade, M A|Valencia, A', 'Protein Design Group, CNB-CSIC, Cantoblanco, E-28049 Madrid, Spain.|Protein Design Group, CNB-CSIC, Cantoblanco, E-28049 Madrid, Spain.', 'Spain|Spain', 27, 'Abstracting and Indexing as Topic|Algorithms|Automation|Forecasting|Internet|MEDLINE|Mathematical Computing|Proteins', 'Journal Article', '10.1093/bioinformatics/14.7.600', '9730925', 'Annotation of the biological function of different protein sequences is a time-consuming process currently performed by human experts. Genome analysis tools encounter great difficulty in performing this task. Database curators, developers of genome analysis tools and biologists in general could benefit from access to tools able to suggest functional annotations and facilitate access to functional information.'),
(83302, 1998, 'bio', 'The effect of evenly spaced constant sites on the distribution of the random division of a molecular sequence', 'Sneath, P H', 'Department of Microbiology and Immunology, Leicester University, Leicester LE1 9HN, UK. phas1@le.ac.uk', 'United Kingdom', 2, 'Algorithms|Animals|Coleoptera|Mathematical Computing|Neisseria|Sequence Alignment', 'Journal Article', '10.1093/bioinformatics/14.7.608', '9730926', 'A modified Sherman statistic can be used to test whether the differences between two aligned sequences are distributed at random along the sequences, or whether they are clustered, which suggests anomalies of evolution such as partial gene recombination or functional constraints. The presence of evenly spaced constant sites (such as constancy at the second codon position in genes coding for proteins) lowers the statistic and makes the significance less than it should be.'),
(83303, 1998, 'bio', 'JOY: protein sequence-structure representation and analysis', 'Mizuguchi, K|Deane, C M|Blundell, T L|Johnson, M S|Overington, J P', 'Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge CB2 1GA, UK. kenji@cryst.bioc.cam.ac.uk|Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge CB2 1GA, UK. kenji@cryst.bioc.cam.ac.uk|Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge CB2 1GA, UK. kenji@cryst.bioc.cam.ac.uk|Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge CB2 1GA, UK. kenji@cryst.bioc.cam.ac.uk|Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge CB2 1GA, UK. kenji@cryst.bioc.cam.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 345, 'Amino Acid Sequence|Hydrogen Bonding|Molecular Sequence Data|Protein Conformation|Protein Structure, Secondary|Proteins|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/14.7.617', '9730927', 'JOY is a program to annotate protein sequence alignments with three-dimensional (3D) structural features. It was developed to display 3D structural information in a sequence alignment and to help understand the conservation of amino acids in their specific local environments.'),
(83304, 1998, 'bio', 'An NMR assignment module implemented in the Gifa NMR processing program', 'Malliavin, T E|Pons, J L|Delsuc, M A', 'Centre de Biochimie Structurale CNRS-UMR9955 INSERM-U414, Faculté de Pharmacie, 15 avenue Charles Flahault, 34060 Montpellier, France.|Centre de Biochimie Structurale CNRS-UMR9955 INSERM-U414, Faculté de Pharmacie, 15 avenue Charles Flahault, 34060 Montpellier, France.|Centre de Biochimie Structurale CNRS-UMR9955 INSERM-U414, Faculté de Pharmacie, 15 avenue Charles Flahault, 34060 Montpellier, France.', 'France|France|France', 21, 'Database Management Systems|Nuclear Magnetic Resonance, Biomolecular|Software', 'Journal Article', '10.1093/bioinformatics/14.7.624', '9730928', 'Peptide and protein structures are determined daily using NMR spectroscopy. Assignment of the NMR spectra is an important step within the procedure and is usually the limiting one. Computer-aided assignment tools should be user friendly with open architecture to communicate with other programs involved in the structure determination.'),
(83305, 1998, 'bio', 'QU-GENE: a simulation platform for quantitative analysis of genetic models', 'Podlich, D W|Cooper, M', 'School of Land and Food, The University of Queensland, Brisbane, Queensland 4072, Australia.|School of Land and Food, The University of Queensland, Brisbane, Queensland 4072, Australia.', 'Australia|Australia', 13, 'Computer Simulation|Epistasis, Genetic|Genotype|Models, Genetic|Software', 'Journal Article', '10.1093/bioinformatics/14.7.632', '9730929', 'Classical quantitative genetics theory makes a number of simplifying assumptions in order to develop mathematical expressions that describe the mean and variation (genetic and phenotypic) within and among populations, and to predict how these are expected to change under the influence of external forces. These assumptions are often necessary to render the development of many aspects of the theory mathematically tractable. The availability of high-speed computers today provides opportunity for the use of computer simulation methodology to investigate the implications of relaxing many of the assumptions that are commonly made.'),
(83306, 1998, 'bio', 'The Delphic boat or what the genomic texts tell us', 'Danchin, A', 'School of Land and Food, The University of Queensland, Brisbane, Queensland 4072, Australia.', 'Australia', 8, 'Computational Biology|Databases, Factual|Genome|Genomic Library', 'Editorial', '10.1093/bioinformatics/14.5.383', '9734989', '-'),
(83307, 1998, 'bio', 'GeneCards: a novel functional genomics compendium with automated data mining and query reformulation support', 'Rebhan, M|Chalifa-Caspi, V|Prilusky, J|Lancet, D', '1Department of Molecular Genetics, 2Department of Biological Services (Bioinformatics Unit) and 3The Genome Center, Weizmann Institute of Science, 76100 Rehovot, Israel.|1Department of Molecular Genetics, 2Department of Biological Services (Bioinformatics Unit) and 3The Genome Center, Weizmann Institute of Science, 76100 Rehovot, Israel.|1Department of Molecular Genetics, 2Department of Biological Services (Bioinformatics Unit) and 3The Genome Center, Weizmann Institute of Science, 76100 Rehovot, Israel.|1Department of Molecular Genetics, 2Department of Biological Services (Bioinformatics Unit) and 3The Genome Center, Weizmann Institute of Science, 76100 Rehovot, Israel.', 'Israel|Israel|Israel|Israel', 242, 'Algorithms|Databases, Factual|Genetics, Medical|Humans|Internet|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/14.8.656', '9789091', 'Modern biology is shifting from the one gene one postdoc approach to genomic analyses that include the simultaneous monitoring of thousands of genes. The importance of efficient access to concise and integrated biomedical information to support data analysis and decision making is therefore increasing rapidly, in both academic and industrial research. However, knowledge discovery in the widely scattered resources relevant for biomedical research is often a cumbersome and non-trivial task, one that requires a significant amount of training and effort.'),
(83308, 1998, 'bio', 'Gene recognition by combination of several gene-finding programs', 'Murakami, K|Takagi, T', '1Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokane-dai Minato-ku, Tokyo 108-8639 and 2Central Research Laboratory, Hitachi Ltd, 1-280 Higashi-Koigakubo, Kokubunji-shi, Tokyo 185-8601, Japan.|1Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokane-dai Minato-ku, Tokyo 108-8639 and 2Central Research Laboratory, Hitachi Ltd, 1-280 Higashi-Koigakubo, Kokubunji-shi, Tokyo 185-8601, Japan.', 'Japan|Japan', 45, 'Algorithms|DNA|Genes|Internet|Software', 'Journal Article', '10.1093/bioinformatics/14.8.665', '9789092', 'A number of programs have been developed to predict the eukaryotic gene structures in DNA sequences. However, gene finding is still a challenging problem.'),
(83309, 1998, 'bio', 'Recognition of splice junctions on DNA sequences by BRAIN learning algorithm', 'Rampone, S', 'Dpt of Scienze Fisiche E.R.Caianiello, Università di Salerno, Via S. Allende, I-84081 Baronissi (Sa), Italy.', 'Italy', 37, 'Algorithms|DNA|Discriminant Analysis|Evaluation Studies as Topic|Exons|Humans|Introns|Neural Networks (Computer)|RNA Splicing', 'Journal Article', '10.1093/bioinformatics/14.8.676', '9789093', 'The problem addressed in this paper is the prediction of splice site locations in human DNA. The aims of the proposed approach are explicit splicing rule description, high recognition quality, and robust and stable one shot data processing.'),
(83310, 1998, 'bio', 'A new fourier transform approach for protein coding measure based on the format of the Z curve', 'Yan, M|Lin, Z S|Zhang, C T', 'Department of Physics, Tianjin University, Tianjin 300072, China.|Department of Physics, Tianjin University, Tianjin 300072, China.|Department of Physics, Tianjin University, Tianjin 300072, China.', 'China|China|China', 60, 'Algorithms|DNA|Evaluation Studies as Topic|Fourier Analysis|Nucleic Acid Conformation|Proteins', 'Journal Article', '10.1093/bioinformatics/14.8.685', '9789094', 'At the core of most protein gene-finding algorithms are the coding measures used to make a decision on coding/non-coding. Of the protein coding measures, the Fourier measure is one of the most important. However, due to the limited length of the windows usually used, the accuracy of the measure is not satisfactory. This paper is devoted to improving the accuracy by lengthening the sequence to amplify the periodicity of 3 in the coding regions.'),
(83311, 1998, 'bio', 'An RNA folding method capable of identifying pseudoknots and base triples', 'Tabaska, J E|Cary, R B|Gabow, H N|Stormo, G D', '1Department of Molecular, Cellular and Developmental Biology and 2Department of Computer Science, University of Colorado, Boulder, CO 80309, USA.|1Department of Molecular, Cellular and Developmental Biology and 2Department of Computer Science, University of Colorado, Boulder, CO 80309, USA.|1Department of Molecular, Cellular and Developmental Biology and 2Department of Computer Science, University of Colorado, Boulder, CO 80309, USA.|1Department of Molecular, Cellular and Developmental Biology and 2Department of Computer Science, University of Colorado, Boulder, CO 80309, USA.', 'United States|United States|United States|United States', 120, 'Algorithms|Bacillus subtilis|Base Sequence|Escherichia coli|Molecular Sequence Data|Nucleic Acid Conformation|Phylogeny|RNA|RNA, Bacterial|Thermodynamics', 'Journal Article', '10.1093/bioinformatics/14.8.691', '9789095', 'Recently, we described a Maximum Weighted Matching (MWM) method for RNA structure prediction. The MWM method is capable of detecting pseudoknots and other tertiary base-pairing interactions in a computationally efficient manner (Cary and Stormo, Proceedings of the Third International Conference on Intelligent Systems for Molecular Biology, pp. 75-80, 1995). Here we report on the results of our efforts to improve the MWM methods predictive accuracy, and show how the method can be extended to detect base interactions formerly inaccessible to automated RNA modeling techniques.'),
(83312, 1998, 'bio', 'Measurement of the effectiveness of transitive sequence comparison, through a third intermediate sequence', 'Gerstein, M', 'Department of Molecular Biophysics and Biochemistry, 266 Whitney Avenue, Yale University, PO Box 208114, New Haven, CT 06520, USA. Mark.Gerstein@yale.ed', 'United States', 48, 'Amino Acid Sequence|Databases, Factual|Protein Conformation|Proteins|Sequence Alignment', 'Journal Article', '10.1093/bioinformatics/14.8.707', '9789096', 'Transitive sequence matching expands the scope of sequence comparison by re-running the results of a given query against the databank as a new query. This sometimes results in the initial query sequence (Q) being related to a final match (M) indirectly, through a third, intermediate sequence (Q --> I --> M ). This approach has often been suggested as providing greater sensitivity in sequence comparison, however, it has not yet been possible to gauge its improvement precisely.'),
(83313, 1998, 'bio', 'A strategy for finding regions of similarity in complete genome sequences', 'Vincens, P|Buffat, L|André, C|Chevrolat, J P|Boisvieux, J F|Hazout2, S', '1Département de Biologie (FR 36), Ecole Normale Supérieure, 46 rue dUlm, 75230 Paris Cedex 05, France.|1Département de Biologie (FR 36), Ecole Normale Supérieure, 46 rue dUlm, 75230 Paris Cedex 05, France.|1Département de Biologie (FR 36), Ecole Normale Supérieure, 46 rue dUlm, 75230 Paris Cedex 05, France.|1Département de Biologie (FR 36), Ecole Normale Supérieure, 46 rue dUlm, 75230 Paris Cedex 05, France.|1Département de Biologie (FR 36), Ecole Normale Supérieure, 46 rue dUlm, 75230 Paris Cedex 05, France.|1Département de Biologie (FR 36), Ecole Normale Supérieure, 46 rue dUlm, 75230 Paris Cedex 05, France.', 'France|France|France|France|France|France', 19, 'Algorithms|Base Sequence|DNA|Genome|Sequence Alignment', 'Comparative Study', '10.1093/bioinformatics/14.8.715', '9789097', 'Complete genomic sequences will become available in the future. New methods to deal with very large sequences (sizes beyond 100 kb) efficiently are required. One of the main aims of such work is to increase our understanding of genome organization and evolution. This requires studies of the locations of regions of similarity.'),
(83314, 1998, 'bio', 'PASSML: combining evolutionary inference and protein secondary structure prediction', 'Liò, P|Goldman, N|Thorne, J L|Jones3, D T', 'Department of Genetics, University of Cambridge, Cambridge, UK. University of Warwick, UK.|Department of Genetics, University of Cambridge, Cambridge, UK. University of Warwick, UK.|Department of Genetics, University of Cambridge, Cambridge, UK. University of Warwick, UK.|Department of Genetics, University of Cambridge, Cambridge, UK. University of Warwick, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 37, 'Adenylate Kinase|Algorithms|Amino Acid Sequence|Animals|Cattle|Databases, Factual|Escherichia coli|Evolution, Molecular|Humans|Neisseria meningitidis|Phylogeny|Protein Structure, Secondary|Rats|Saccharomyces cerevisiae|Schizosaccharomyces|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/14.8.726', '9789098', 'Evolutionary models of amino acid sequences can be adapted to incorporate structure information, protein structure biologists can use phylogenetic relationships among species to improve prediction accuracy. Results : A computer program called PASSML (Phylogeny and Secondary Structure using Maximum Likelihood) has been developed to implement an evolutionary model that combines protein secondary structure and amino acid replacement. The model is related to that of Dayhoff and co-workers, but we distinguish eight categories of structural environment: alpha helix, beta sheet, turn and coil, each further classified according to solvent accessibility, i.e. buried or exposed. The model of sequence evolution for each of the eight categories is a Markov process with discrete states in continuous time, and the organization of structure along protein sequences is described by a hidden Markov model. This paper describes the PASSML software and illustrates how it allows both the reconstruction of phylogenies and prediction of secondary structure from aligned amino acid sequences.'),
(83315, 1998, 'bio', 'Mapplet: a CORBA-based genome map viewer', 'Jungfer, K|Rodriguez-Tomé, P', 'EMBL Outstation-Hinxton, The European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.|EMBL Outstation-Hinxton, The European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.', 'United Kingdom|United Kingdom', 8, 'Chromosome Mapping|Genome|Information Storage and Retrieval|Internet|Software', 'Journal Article', '10.1093/bioinformatics/14.8.734', '9789099', 'There are a large number of genetic and physical maps, distributed at many sites. Each site offers different kinds of access methods and viewers. CORBA, the de facto standard for distributed object-oriented computing, offers new opportunities to unify the view on these maps through standard interfaces. A collaboration of Infobiogen and the EBI proposes a common IDL for maps.'),
(83316, 1998, 'bio', 'A visual environment for the manipulation and integration of JAVA beans', 'Boyle, J', 'School of Computer and Mathematical Sciences, The Robert Gordon University, Aberdeen, UK.', 'United Kingdom', 5, 'Computational Biology|Computer Graphics|Programming Languages|Software|Software Design', 'Journal Article', '10.1093/oxfordjournals.bioinformatics.a011053', '9789100', 'Visual programming has the potential to allow non-programmers to redesign and rebuild applications to suit their individual needs. We have built such a visual programming environment, which allows non-programmers to interrogate and combine software components graphically to form new applications. As the needs of the biological community grow, so too will the need for more powerful and easy to use software tools. Intelligent visual programming environments will allow users to design and develop applications easily, so that they can concentrate on the application they wish to build rather than how it is to be done.'),
(83317, 1998, 'bio', 'A web server to locate periodicities in a sequence ', '-', '-', '-', 16, '-', 'Journal Article', '10.1093/oxfordjournals.bioinformatics.a011054', '9789101', 'Summary : FT is a tool written in C++, which implements the Fourier analysis method to locate periodicities in aminoacid or DNA sequences. It is provided for free public use on a WWW server with a Java interface. Availability : The server address is http://o2.db. uoa.gr/FT Contact : shamodr@atlas.uoa.gr'),
(83318, 1998, 'bio', 'PPCMatrix: a PowerPC dotmatrix program to compare large genomic sequences against protein sequences ', '-', '-', '-', 12, '-', 'Journal Article', '10.1093/oxfordjournals.bioinformatics.a011055', '9789102', 'Summary : An interactive dotmatrix program for the MacOS was designed that allows comparison of DNA to protein sequences using nested 3-frame translations. Availability : Shareware, available at http://copan.bioz.unibas.ch/software/ Contact : burglin@ubaclu. unibas.ch');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(83319, 1998, 'bio', 'Prediction of RNA secondary structure based on helical regions distribution', 'WuJu, L|JiaJin, W', 'Laboratory of Bioinformation Engineering, Institute of Basic Medical Sciences, P.O. Box 130(3), Beijing 100850, China.|Laboratory of Bioinformation Engineering, Institute of Basic Medical Sciences, P.O. Box 130(3), Beijing 100850, China.', 'China|China', 13, 'Algorithms|Animals|Base Sequence|Molecular Sequence Data|Monte Carlo Method|Nucleic Acid Conformation|RNA|RNA Precursors|RNA, Protozoan|RNA, Ribosomal|RNA, Transfer, Phe|RNA, Transfer, Tyr|Tetrahymena', 'Journal Article', '10.1093/bioinformatics/14.8.700', '9790689', 'RNAs play an important role in many biological processes and knowing their structure is important in understanding their function. Due to difficulties in the experimental determination of RNA secondary structure, the methods of theoretical prediction for known sequences are often used. Although many different algorithms for such predictions have been developed, this problem has not yet been solved. It is thus necessary to develop new methods for predicting RNA secondary structure. The most-used at present is Zukers algorithm which can be used to determine the minimum free energy secondary structure. However many RNA secondary structures verified by experiments are not consistent with the minimum free energy secondary structures. In order to solve this problem, a method used to search a group of secondary structures whose free energy is close to the global minimum free energy was developed by Zuker in 1989. When considering a group of secondary structures, if there is no experimental data, we cannot tell which one is better than the others. This case also occurs in combinatorial and heuristic methods. These two kinds of methods have several weaknesses. Here we show how the central limit theorem can be used to solve these problems.'),
(83320, 1998, 'bio', 'A curriculum for bioinformatics: the time is ripe', 'Altman, R B', 'Laboratory of Bioinformation Engineering, Institute of Basic Medical Sciences, P.O. Box 130(3), Beijing 100850, China.', 'China', 68, 'Computational Biology|Curriculum', 'Editorial', '10.1093/bioinformatics/14.7.549', '9841111', '-'),
(83321, 1998, 'bio', 'Profile hidden Markov models', 'Eddy, S R', 'Department of Genetics, Washington University School of Medicine, 4566 Scott Avenue, St Louis, MO 63110, USA. eddy@genetics.wustl.edu', 'United States', 0, 'Humans|Markov Chains|Peptide Library|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/14.9.755', '9918945', 'The recent literature on profile hidden Markov model (profile HMM) methods and software is reviewed. Profile HMMs turn a multiple sequence alignment into a position-specific scoring system suitable for searching databases for remotely homologous sequences. Profile HMM analyses complement standard pairwise comparison methods for large-scale sequence analysis. Several software implementations and two large libraries of profile HMMs of common protein domains are available. HMM methods performed comparably to threading methods in the CASP2 structure prediction exercise.'),
(83322, 1998, 'bio', 'Molecules to maps: tools for visualization and interaction in support of computational biology', 'Kraemer, E T|Ferrin, T E', 'Department of Computer Science, The University of Georgia, Athens, GA 30602, USA. eileen@cs.wustl.edu|Department of Computer Science, The University of Georgia, Athens, GA 30602, USA. eileen@cs.wustl.edu', 'Georgia|Georgia', 7, 'Computational Biology|Genome, Human|Humans|Proteins', 'Journal Article', '10.1093/bioinformatics/14.9.764', '9918946', 'The volume of data produced by genome projects, X-ray crystallography, NMR spectroscopy, and electron and confocal microscopy present the bioinformatics community with new challenges for analyzing, understanding, and exchanging this data. At the 1998 Pacific Symposium on Biocomputing, a track entitled Molecules to Maps: Tools for Visualization and Interaction in Computational Biology provided tool developers and users with the opportunity to discuss advances in tools and techniques to assist scientists in evaluating, absorbing, navigating, and correlating this sea of information, through visualization and user interaction. In this paper we present these advances and discuss some of the challenges that remain to be solved.'),
(83323, 1998, 'bio', 'Weighting hidden Markov models for maximum discrimination', 'Karchin, R|Hughey, R', 'Department of Computer Engineering, Jack Baskin School of Engineering, University of California, Santa Cruz, CA 95064, USA. rph@cse.ucsc.edu|Department of Computer Engineering, Jack Baskin School of Engineering, University of California, Santa Cruz, CA 95064, USA. rph@cse.ucsc.edu', 'United States|United States', 31, 'Algorithms|Markov Chains|Proteins|Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/14.9.772', '9918947', 'Hidden Markov models can efficiently and automatically build statistical representations of related sequences. Unfortunately, training sets are frequently biased toward one subgroup of sequences, leading to an insufficiently general model. This work evaluates sequence weighting methods based on the maximum-discrimination idea.'),
(83324, 1998, 'bio', 'Domain identification by clustering sequence alignments', 'Guan, X|Du, L', 'Glaxo Wellcome Research and Development, Five Moore Drive, Research Triangle Park, NC 27709, USA. xg42498@glaxowellcome.com|Glaxo Wellcome Research and Development, Five Moore Drive, Research Triangle Park, NC 27709, USA. xg42498@glaxowellcome.com', 'United States|United States', 0, 'Cluster Analysis|Databases, Factual|Sequence Alignment|Sequence Homology', 'Comparative Study', '10.1093/bioinformatics/14.9.783', '9918948', 'As sequence databases grow rapidly, results from sequence comparison searches using fast search methods such as BLAST and FASTA tend to be long and difficult to digest.'),
(83325, 1998, 'bio', 'A method for comparing circular genomes from gene locations: application to mitochondrial genomes', 'Horimoto, K|Suyama, M|Toh, H|Mori, K|Otsuka, J', 'Laboratory of Mathematics, Saga Medical School, 5-1-1 Nabeshima, Saga, Saga 849-8501, Japan. horimoto@ged.saga-med.ac.jp|Laboratory of Mathematics, Saga Medical School, 5-1-1 Nabeshima, Saga, Saga 849-8501, Japan. horimoto@ged.saga-med.ac.jp|Laboratory of Mathematics, Saga Medical School, 5-1-1 Nabeshima, Saga, Saga 849-8501, Japan. horimoto@ged.saga-med.ac.jp|Laboratory of Mathematics, Saga Medical School, 5-1-1 Nabeshima, Saga, Saga 849-8501, Japan. horimoto@ged.saga-med.ac.jp|Laboratory of Mathematics, Saga Medical School, 5-1-1 Nabeshima, Saga, Saga 849-8501, Japan. horimoto@ged.saga-med.ac.jp', 'Japan|Japan|Japan|Japan|Japan', 5, 'Animals|Chromosome Mapping|DNA, Mitochondrial|Genes|Genome|Humans', 'Comparative Study', '10.1093/bioinformatics/14.9.789', '9918949', 'Data on the entire structures of organelle and bacterial genomes, most of which are known to be circular, have accumulated at a rapid pace. This information enables us to utilize the locations of homologous gene pairs for measuring the dissimilarity between complete genomic structures.'),
(83326, 1998, 'bio', 'Representing metabolic pathway information: an object-oriented approach', 'Ellis, L B|Speedie, S M|McLeish, R', 'Center for Biodegradation Research and Informatics and Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, Box 609 Mayo Building, 420 SE Delaware Street, Minneapolis, MN 55455, USA. lynda@email.labmed.umn.edu|Center for Biodegradation Research and Informatics and Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, Box 609 Mayo Building, 420 SE Delaware Street, Minneapolis, MN 55455, USA. lynda@email.labmed.umn.edu|Center for Biodegradation Research and Informatics and Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, Box 609 Mayo Building, 420 SE Delaware Street, Minneapolis, MN 55455, USA. lynda@email.labmed.umn.edu', 'United States|United States|United States', 16, 'Databases, Factual|Enzymes|Internet|Metabolism|Microbiology|Minnesota|Online Systems|Universities', 'Journal Article', '10.1093/bioinformatics/14.9.803', '9918950', 'The University of Minnesota Biocatalysis/Biodegradation Database (UM-BBD) is a website providing information and dynamic links for microbial metabolic pathways, enzyme reactions, and their substrates and products. The Compound, Organism, Reaction and Enzyme (CORE) object-oriented database management system was developed to contain and serve this information.'),
(83327, 1998, 'bio', 'Zomit: biological data visualization and browsing', 'Pook, S|Vaysseix, G|Barillot, E', 'GIS Infobiogen, 7 rue Guy Môquet - BP 8, 94801 Villejuif cedex, France. stuart@infobiogen.fr|GIS Infobiogen, 7 rue Guy Môquet - BP 8, 94801 Villejuif cedex, France. stuart@infobiogen.fr|GIS Infobiogen, 7 rue Guy Môquet - BP 8, 94801 Villejuif cedex, France. stuart@infobiogen.fr', 'France|France|France', 0, 'Biological Science Disciplines|Databases, Factual|Humans|Metaphor|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/14.9.807', '9918951', 'The problems caused by the difficulty in visualizing and browsing biological databases have become crucial. Scientists can no longer interact directly with the huge amount of available data. However, future breakthroughs in biology depend on this interaction. We propose a new metaphor for biological data visualization and browsing that allows navigation in very large databases in an intuitive way. The concepts underlying our approach are based on navigation and visualization with zooming, semantic zooming and portals, and on data transformation via magic lenses. We think that these new visualization and navigation techniques should be applied globally to a federation of biological databases.'),
(83328, 1998, 'bio', 'VIRTLAB: a virtual molecular biology laboratory', 'Iazzetti, G|Santini, G|Rau, M|Bucci, E|Calogero, R A', 'Dipartimento di Genetica, Biologia Generale e Molecolare, via Mezzocannone 8, 80134 Napoli, Italy. calogero@biol.dgbm.unina.it|Dipartimento di Genetica, Biologia Generale e Molecolare, via Mezzocannone 8, 80134 Napoli, Italy. calogero@biol.dgbm.unina.it|Dipartimento di Genetica, Biologia Generale e Molecolare, via Mezzocannone 8, 80134 Napoli, Italy. calogero@biol.dgbm.unina.it|Dipartimento di Genetica, Biologia Generale e Molecolare, via Mezzocannone 8, 80134 Napoli, Italy. calogero@biol.dgbm.unina.it|Dipartimento di Genetica, Biologia Generale e Molecolare, via Mezzocannone 8, 80134 Napoli, Italy. calogero@biol.dgbm.unina.it', 'Italy|Italy|Italy|Italy|Italy', 2, 'Clinical Laboratory Techniques|Internet|Molecular Biology|Online Systems|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/14.9.815', '9918952', 'VIRTLAB is a self-training program based on PBL (Problem-Based-Learning Pathway) built to simulate a molecular biology laboratory. It has been designed to stimulate students in the biological sciences to analyse and solve molecular biology problems using standard laboratory techniques (e.g. restriction enzyme digestions, analytical and preparative agarose gels, DNA cloning and sequencing, etc.) and can thus be viewed as a teaching aid.'),
(83329, 1998, 'bio', 'MODELTEST: testing the model of DNA substitution', 'Posada, D|Crandall, K A', 'Department of Zoology, Brigham Young University, 574 WIDB, Provo, UT 84602-5255, USA. dp47@email.byu.edu|Department of Zoology, Brigham Young University, 574 WIDB, Provo, UT 84602-5255, USA. dp47@email.byu.edu', 'United States|United States', 0, 'Algorithms|DNA|Evolution, Molecular|Internet|Models, Biological|Online Systems|Software', 'Journal Article', '10.1093/bioinformatics/14.9.817', '9918953', 'The program MODELTEST uses log likelihood scores to establish the model of DNA evolution that best fits the data.'),
(83330, 1998, 'bio', 'GeneTree: comparing gene and species phylogenies using reconciled trees', 'Page, R D', 'Division of Environmental and Evolutionary Biology, Institute of Biomedical and Life Sciences, Graham Kerr Building, University of Glasgow, Glasgow, G12 8QQ, UK. r.page@bio.gla.ac.uk', 'United Kingdom', 189, 'Algorithms|Classification|Genes|Online Systems|Phylogeny|Software|Species Specificity', 'Comparative Study', '10.1093/bioinformatics/14.9.819', '9918954', 'GeneTree is a program for comparing gene and species trees using reconciled trees. The program can compute the cost of embedding a gene tree within a species tree, visually display the location and number of gene duplications and losses, and search for optimal species trees.'),
(83331, 1998, 'bio', 'JavaShade: multiple sequence alignment box-and-shading on the World Wide Web', 'Southern, M R|Lewis, A P', 'Bioinformatics, Research Information Systems, GlaxoWellcome Medicines Research Centre, Stevenage, Herts SG1 2NY, UK.|Bioinformatics, Research Information Systems, GlaxoWellcome Medicines Research Centre, Stevenage, Herts SG1 2NY, UK.', 'United Kingdom|United Kingdom', 0, 'Algorithms|Amino Acid Sequence|CD8 Antigens|Internet|Molecular Sequence Data|Online Systems|Protein Isoforms|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/14.9.821', '9918955', 'JavaShade is a multiple sequence alignment box-and-shade tool for generating high quality printed output that uses a variety of methods for boxing and shading, allowing the most appropriate functions to be chosen for displaying the most meaningful positions in an alignment.'),
(83332, 1998, 'bio', 'SPEM: a parser for EMBL style flat file database entries', 'Pocock, M R|Hubbard, T|Birney, E', 'Informatics, The Sanger Centre, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK. mrp@sanger.ac.uk|Informatics, The Sanger Centre, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK. mrp@sanger.ac.uk|Informatics, The Sanger Centre, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK. mrp@sanger.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Databases, Factual|Electronic Data Processing|Internet|Molecular Sequence Data|Online Systems|Software', 'Journal Article', '10.1093/bioinformatics/14.9.823', '9918956', 'We present a set of Perl modules for the flexible and robust parsing and editing of EMBL/SWISS-PROT databases.'),
(83333, 1998, 'bio', 'WEBMAP: radiation hybrid mapping on the WWW', 'Newell, W|Sanseau, P|Riley, J|Lyall, A', 'Oxford Molecular Ltd, The Medawar Centre, Oxford Science Park, Oxford OX4 4GA, UK. William.Newell@ariad.com|Oxford Molecular Ltd, The Medawar Centre, Oxford Science Park, Oxford OX4 4GA, UK. William.Newell@ariad.com|Oxford Molecular Ltd, The Medawar Centre, Oxford Science Park, Oxford OX4 4GA, UK. William.Newell@ariad.com|Oxford Molecular Ltd, The Medawar Centre, Oxford Science Park, Oxford OX4 4GA, UK. William.Newell@ariad.com', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Chimera|Chromosome Mapping|Databases, Factual|Internet|Online Systems|Radiation|Software', 'Journal Article', '10.1093/bioinformatics/14.9.825', '9918957', 'A Java interface to radiation hybrid (RH) mapping software is described which enables users to build and interactively refine RH maps over the web.'),
(83334, 1998, 'bio', 'An Internet linkage and mutation database for the complex phenotype asthma', 'Wjst, M|Immervoll, T', 'GSF National Research Center for Environment and Health, P.O.B. 1129, D-85758 Oberschleissheim, Germany. wjst@gsf.de|GSF National Research Center for Environment and Health, P.O.B. 1129, D-85758 Oberschleissheim, Germany. wjst@gsf.de', 'Germany|Germany', 43, 'Asthma|Databases, Factual|Genetic Linkage|Humans|Hypersensitivity|Internet|Mutation|Phenotype', 'Journal Article', '10.1093/bioinformatics/14.9.827', '9918958', 'The paper presents details of database construction, website installation and server architecture of the asthma and allergy gene database.'),
(83335, 1998, 'bio', 'Empirical estimation of the reliability of ribosomal RNA alignments', 'OBrien, E A|Higgins, D G', 'Department of Biochemistry, University College, Cork, Ireland. emmet@chah.ucc.ie|Department of Biochemistry, University College, Cork, Ireland. emmet@chah.ucc.ie', 'Ireland|Ireland', 1, 'Algorithms|Animals|Base Sequence|Computational Biology|Humans|RNA, Fungal|RNA, Ribosomal|Reproducibility of Results|Saccharomyces cerevisiae|Sequence Alignment|Sequence Homology, Nucleic Acid', 'Comparative Study', '10.1093/bioinformatics/14.10.830', '9927711', 'The automatic alignment of rRNA sequences can reproduce manual expert alignments with high, but not perfect, fidelity. We examine the use of empirical methods for the identification of regions of an alignment of a new sequence with an existing large alignment which can confidently be predicted to be correctly aligned.'),
(83336, 1998, 'bio', 'SALSA: improved protein database searching by a new algorithm for assembly of sequence fragments into gapped alignments', 'Rognes, T|Seeberg, E', 'Institute of Medical Microbiology, University of Oslo, The National Hospital, N-0027 Oslo, Norway.|Institute of Medical Microbiology, University of Oslo, The National Hospital, N-0027 Oslo, Norway.', 'Norway|Norway', 11, 'Algorithms|Amino Acid Sequence|Animals|Computational Biology|Databases, Factual|Deoxyribonuclease (Pyrimidine Dimer)|Endodeoxyribonucleases|Escherichia coli Proteins|Evaluation Studies as Topic|Globins|Molecular Sequence Data|Peptide Fragments|Proteins|Sensitivity and Specificity|Sequence Alignment|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1093/bioinformatics/14.10.839', '9927712', 'Optimal sequence alignment based on the Smith-Waterman algorithm is usually too computationally demanding to be practical for searching large sequence databases. Heuristic programs like FASTA and BLAST have been developed which run much faster, but at the expense of sensitivity.'),
(83337, 1998, 'bio', 'Hidden Markov models for detecting remote protein homologies', 'Karplus, K|Barrett, C|Hughey, R', 'Department of Computer Engineering, Jack Baskin School of Engineering, University of California, Santa Cruz, CA 95064, USA.|Department of Computer Engineering, Jack Baskin School of Engineering, University of California, Santa Cruz, CA 95064, USA.|Department of Computer Engineering, Jack Baskin School of Engineering, University of California, Santa Cruz, CA 95064, USA.', 'United States|United States|United States', 876, 'Algorithms|Computational Biology|Databases, Factual|Evaluation Studies as Topic|Markov Chains|Proteins|Sequence Alignment|Sequence Homology, Amino Acid|Software', 'Comparative Study', '10.1093/bioinformatics/14.10.846', '9927713', 'A new hidden Markov model method (SAM-T98) for finding remote homologs of protein sequences is described and evaluated. The method begins with a single target sequence and iteratively builds a hidden Markov model (HMM) from the sequence and homologs found using the HMM for database search. SAM-T98 is also used to construct model libraries automatically from sequences in structural databases.'),
(83338, 1998, 'bio', 'A new criterion to classify globular proteins based on their secondary structure contents', 'Zhang, C T|Zhang, R', 'Department of Physics, Tianjin University, Tianjin 300072, China and Department of Epidemiology and Biostatistics, Tianjin Cancer Institute and Hospital, Tianjin 300060, China. ctzhang@tju.edu.cn|Department of Physics, Tianjin University, Tianjin 300072, China and Department of Epidemiology and Biostatistics, Tianjin Cancer Institute and Hospital, Tianjin 300060, China. ctzhang@tju.edu.cn', 'China|China', 8, 'Algorithms|Computational Biology|Databases, Factual|Evaluation Studies as Topic|Protein Structure, Secondary|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/14.10.857', '9927714', 'With the enlargement of protein structure databases, it is hoped that a method to classify proteins automatically will be developed. Although the classification criterion proposed by Nakashima et al. ( J. Biochem., 1986, 99, 153-162) was widely used in the literature, it leads to some inconsistencies with the classification databases currently available in the class assignment of protein structures. To improve their work, a new classification criterion is proposed relying on statistical analysis of the secondary structure contents of more than 200 proteins with well-known structural classes. The Fisher linear discriminant algorithm is used to derive the new classification criterion.'),
(83339, 1998, 'bio', 'VHMPT: a graphical viewer and editor for helical membrane protein topologies', 'Lin, W J|Hwang, M J', 'Institute of Biomedical Sciences, Academia Sinica, Taipei and Department of Computer and Information Science, National Chiao-Tung University, Hsin Chu, Taiwan.|Institute of Biomedical Sciences, Academia Sinica, Taipei and Department of Computer and Information Science, National Chiao-Tung University, Hsin Chu, Taiwan.', '-|-', 9, 'Computational Biology|Computer Graphics|Conserved Sequence|Databases, Factual|Membrane Proteins|Models, Molecular|Potassium Channels|Protein Structure, Secondary|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/14.10.866', '9927715', 'Lacking structures resolved at atomic resolution, the great majority of membrane proteins have typically been depicted in a schematic two-dimensional (2D) topology consisting of putative transmembrane domains predicted from hydropathy plots. As more and more sequences of membrane proteins become available from genome projects, there is a need to automate the process of generating the schematic topology while allowing important information, such as the individual amino acid and the extent to which it is conserved in evolution, to be conveniently inspected. We addressed this need by developing a program called VHMPT.'),
(83340, 1998, 'bio', 'Non-linear optimization of biochemical pathways: applications to metabolic engineering and parameter estimation', 'Mendes, P|Kell, D', 'Institute of Biological Sciences, University of Wales Aberystwyth, Aberystwyth, Ceredigion SY23 3DD, UK. prm@aber.ac.uk|Institute of Biological Sciences, University of Wales Aberystwyth, Aberystwyth, Ceredigion SY23 3DD, UK. prm@aber.ac.uk', 'United Kingdom|United Kingdom', 521, 'Biochemistry|Biomedical Engineering|Computational Biology|Computer Simulation|Kinetics|Metabolism|Models, Biological|Nonlinear Dynamics|Software', 'Journal Article', '10.1093/bioinformatics/14.10.869', '9927716', 'The simulation of biochemical kinetic systems is a powerful approach that can be used for: (i) checking the consistency of a postulated model with a set of experimental measurements, (ii) answering what if? questions and (iii) exploring possible behaviours of a model. Here we describe a generic approach to combine numerical optimization methods with biochemical kinetic simulations, which is suitable for use in the rational design of improved metabolic pathways with industrial significance (metabolic engineering) and for solving the inverse problem of metabolic pathways, i.e. the estimation of parameters from measured variables.'),
(83341, 1998, 'bio', 'GeneDn: for high-level expression design of heterologous genes in a prokaryotic system', 'Ju, L W|Xing, L H|Hong, P W|Jin, W J', 'Laboratory of Bioinformation Engineering, Institute of Basic Medical Sciences, P.O. Box 130 (3), Beijing 100850, China.|Laboratory of Bioinformation Engineering, Institute of Basic Medical Sciences, P.O. Box 130 (3), Beijing 100850, China.|Laboratory of Bioinformation Engineering, Institute of Basic Medical Sciences, P.O. Box 130 (3), Beijing 100850, China.|Laboratory of Bioinformation Engineering, Institute of Basic Medical Sciences, P.O. Box 130 (3), Beijing 100850, China.', 'China|China|China|China', 0, 'Algorithms|Computational Biology|Escherichia coli|Gene Expression|Genetic Vectors|Humans|Models, Genetic|Recombinant Proteins|Software', 'Journal Article', '10.1093/bioinformatics/14.10.884', '9927717', 'Based on the mathematical model of high-level expression of heterologous genes in prokaryotic vector pBV220, we developed a program GeneDn for high-level expression design of natural and synthetic genes. AVAILIBILITY: The program is written in Turbo Pascal 7.0. The source code and related material are available upon request.'),
(83342, 1998, 'bio', 'HIV-1 genotypic resistance: automated sequence analysis and interpretation', 'Goujon, C P|Schneider, V M|Jacomet, C|Nicolas, J C', 'Service de Microbiologie, Institut Fédératif Saint Antoine de Recherche sur la Santé, Hôpital Rothschild, 33 Bd de Picpus, 75571, Paris, France. goujon@infobiogen.fr|Service de Microbiologie, Institut Fédératif Saint Antoine de Recherche sur la Santé, Hôpital Rothschild, 33 Bd de Picpus, 75571, Paris, France. goujon@infobiogen.fr|Service de Microbiologie, Institut Fédératif Saint Antoine de Recherche sur la Santé, Hôpital Rothschild, 33 Bd de Picpus, 75571, Paris, France. goujon@infobiogen.fr|Service de Microbiologie, Institut Fédératif Saint Antoine de Recherche sur la Santé, Hôpital Rothschild, 33 Bd de Picpus, 75571, Paris, France. goujon@infobiogen.fr', 'France|France|France|France', 0, 'Anti-HIV Agents|Computational Biology|DNA Mutational Analysis|Drug Resistance, Microbial|Genotype|HIV-1|Humans|Mutation|Software', 'Journal Article', '10.1093/bioinformatics/14.10.886', '9927718', 'We present here GRANT ( G enotypic R esistance An alysis T ool), a program that automatically detects resistance-related mutations in HIV-1 protein sequences, and comments on the degree of resistance conferred when possible. This program is easily user-customizable, allowing immediate updates as new resistance mutations are discovered, or use with an organism other than HIV.'),
(83343, 1998, 'bio', 'BioABACUS: a database of abbreviations and acronyms in biotechnology and computer science', 'Rimer, M|OConnell, M', 'Molecular Biology Program, Department of Agronomy and Horticulture, MSC 3Q, New Mexico State University, P.O. Box 30003, Las Cruces, NM 88003, USA.|Molecular Biology Program, Department of Agronomy and Horticulture, MSC 3Q, New Mexico State University, P.O. Box 30003, Las Cruces, NM 88003, USA.', 'Mexico|Mexico', 0, 'Abbreviations as Topic|Biotechnology|Computational Biology|Databases, Factual|Internet', 'Journal Article', '10.1093/bioinformatics/14.10.888', '9927719', 'BioABACUS (Biotechnology AB breviation and A cronym U ncovering Service) a new, searchable, cross-referenced, database of abbreviations and acronyms in biotechnology and computer science is described.'),
(83344, 1998, 'bio', 'BEAUTY-X: enhanced BLAST searches for DNA queries', 'Worley, K C|Culpepper, P|Wiese, B A|Smith, R F', 'Human Genome Center, Department of Molecular and Human Genetics, Baylor College of Medicine, N-1519.08, One Baylor Plaza, Houston, TX 77030, USA. kworley,culpep@bcm.tmc.edu|Human Genome Center, Department of Molecular and Human Genetics, Baylor College of Medicine, N-1519.08, One Baylor Plaza, Houston, TX 77030, USA. kworley,culpep@bcm.tmc.edu|Human Genome Center, Department of Molecular and Human Genetics, Baylor College of Medicine, N-1519.08, One Baylor Plaza, Houston, TX 77030, USA. kworley,culpep@bcm.tmc.edu|Human Genome Center, Department of Molecular and Human Genetics, Baylor College of Medicine, N-1519.08, One Baylor Plaza, Houston, TX 77030, USA. kworley,culpep@bcm.tmc.edu', 'United States|United States|United States|United States', 40, 'Amino Acid Sequence|Computational Biology|DNA|Databases, Factual|Molecular Sequence Data|Proteins|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/14.10.890', '9927720', 'BEAUTY (BLAST Enhanced Alignment Utility) is an enhanced version of the BLAST database search tool that facilitates identification of the functions of matched sequences. Three recent improvements to the BEAUTY program described here make the enhanced output (1) available for DNA queries, (2) available for searches of any protein database, and (3) more up-to-date, with periodic updates of the domain information.'),
(83345, 1998, 'bio', 'JPred: a consensus secondary structure prediction server', 'Cuff, J A|Clamp, M E|Siddiqui, A S|Finlay, M|Barton, G J', 'Laboratory of Molecular Biophysics, Rex Richards Building, South Parks Road, Oxford OX1 3QU, UK.|Laboratory of Molecular Biophysics, Rex Richards Building, South Parks Road, Oxford OX1 3QU, UK.|Laboratory of Molecular Biophysics, Rex Richards Building, South Parks Road, Oxford OX1 3QU, UK.|Laboratory of Molecular Biophysics, Rex Richards Building, South Parks Road, Oxford OX1 3QU, UK.|Laboratory of Molecular Biophysics, Rex Richards Building, South Parks Road, Oxford OX1 3QU, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 887, 'Algorithms|Computational Biology|Consensus Sequence|Internet|Protein Structure, Secondary|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/14.10.892', '9927721', 'An interactive protein secondary structure prediction Internet server is presented. The server allows a single sequence or multiple alignment to be submitted, and returns predictions from six secondary structure prediction algorithms that exploit evolutionary information from multiple sequences. A consensus prediction is also returned which improves the average Q3 accuracy of prediction by 1% to 72.9%. The server simplifies the use of current prediction algorithms and allows conservation patterns important to structure and function to be identified.'),
(83346, 2005, 'plos', 'Ab initio prediction of transcription factor targets using structural knowledge', 'Kaplan, Tommy|Friedman, Nir|Margalit, Hanah', 'School of Computer Science and Engineering, The Hebrew University, Jerusalem, Israel.|School of Computer Science and Engineering, The Hebrew University, Jerusalem, Israel.|School of Computer Science and Engineering, The Hebrew University, Jerusalem, Israel.', 'Israel|Israel|Israel', 0, '-', 'Journal Article', '10.1371/journal.pcbi.0010001', '16103898', 'Current approaches for identification and detection of transcription factor binding sites rely on an extensive set of known target genes. Here we describe a novel structure-based approach applicable to transcription factors with no prior binding data. Our approach combines sequence data and structural information to infer context-specific amino acid-nucleotide recognition preferences. These are used to predict binding sites for novel transcription factors from the same structural family. We demonstrate our approach on the Cys(2)His(2) Zinc Finger protein family, and show that the learned DNA-recognition preferences are compatible with experimental results. We use these preferences to perform a genome-wide scan for direct targets of Drosophila melanogaster Cys(2)His(2) transcription factors. By analyzing the predicted targets along with gene annotation and expression data we infer the function and activity of these proteins.'),
(83347, 2005, 'plos', 'Extraction of transcript diversity from scientific literature', 'Shah, Parantu K|Jensen, Lars J|Boué, Stéphanie|Bork, Peer', 'Structural and Computational Biology Program, European Molecular Biology Laboratory, Heidelberg, Germany.|Structural and Computational Biology Program, European Molecular Biology Laboratory, Heidelberg, Germany.|Structural and Computational Biology Program, European Molecular Biology Laboratory, Heidelberg, Germany.|Structural and Computational Biology Program, European Molecular Biology Laboratory, Heidelberg, Germany.', 'Germany|Germany|Germany|Germany', 0, '-', 'Journal Article', '10.1371/journal.pcbi.0010010', '16103899', 'Transcript diversity generated by alternative splicing and associated mechanisms contributes heavily to the functional complexity of biological systems. The numerous examples of the mechanisms and functional implications of these events are scattered throughout the scientific literature. Thus, it is crucial to have a tool that can automatically extract the relevant facts and collect them in a knowledge base that can aid the interpretation of data from high-throughput methods. We have developed and applied a composite text-mining method for extracting information on transcript diversity from the entire MEDLINE database in order to create a database of genes with alternative transcripts. It contains information on tissue specificity, number of isoforms, causative mechanisms, functional implications, and experimental methods used for detection. We have mined this resource to identify 959 instances of tissue-specific splicing. Our results in combination with those from EST-based methods suggest that alternative splicing is the preferred mechanism for generating transcript diversity in the nervous system. We provide new annotations for 1,860 genes with the potential for generating transcript diversity. We assign the MeSH term alternative splicing to 1,536 additional abstracts in the MEDLINE database and suggest new MeSH terms for other events. We have successfully extracted information about transcript diversity and semiautomatically generated a database, LSAT, that can provide a quantitative understanding of the mechanisms behind tissue-specific gene expression. LSAT (Literature Support for Alternative Transcripts) is publicly available at http://www.bork.embl.de/LSAT/.'),
(83348, 2005, 'plos', 'A phenomenological theory of spatially structured local synaptic connectivity', 'Amirikian, Bagrat', 'Brain Sciences Center, Veterans Affairs Medical Center, Minneapolis, Minnesota, United States of America. amiri001@umn.edu', 'United States', 16, '-', 'Journal Article', '10.1371/journal.pcbi.0010011', '16103900', 'The structure of local synaptic circuits is the key to understanding cortical function and how neuronal functional modules such as cortical columns are formed. The central problem in deciphering cortical microcircuits is the quantification of synaptic connectivity between neuron pairs. I present a theoretical model that accounts for the axon and dendrite morphologies of pre- and postsynaptic cells and provides the average number of synaptic contacts formed between them as a function of their relative locations in three-dimensional space. An important aspect of the current approach is the representation of a complex structure of an axonal/dendritic arbor as a superposition of basic structures-synaptic clouds. Each cloud has three structural parameters that can be directly estimated from two-dimensional drawings of the underlying arbor. Using empirical data available in literature, I applied this theory to three morphologically different types of cell pairs. I found that, within a wide range of cell separations, the theory is in very good agreement with empirical data on (i) axonal-dendritic contacts of pyramidal cells and (ii) somatic synapses formed by the axons of inhibitory interneurons. Since for many types of neurons plane arborization drawings are available from literature, this theory can provide a practical means for quantitatively deriving local synaptic circuits based on the actual observed densities of specific types of neurons and their morphologies. It can also have significant implications for computational models of cortical networks by making it possible to wire up simulated neural networks in a realistic fashion.'),
(83349, 2005, 'plos', 'The genomic pattern of tDNA operon expression in E coli', 'Ardell, David H|Kirsebom, Leif A', 'Department of Cell and Molecular Biology, Biomedical Center, Uppsala University, Uppsala, Sweden. dave.ardell@lcb.uu.se|Department of Cell and Molecular Biology, Biomedical Center, Uppsala University, Uppsala, Sweden. dave.ardell@lcb.uu.se', 'Sweden|Sweden', 19, '-', 'Journal Article', '10.1371/journal.pcbi.0010012', '16103901', 'In fast-growing microorganisms, a tRNA concentration profile enriched in major isoacceptors selects for the biased usage of cognate codons. This optimizes translational rate for the least mass invested in the translational apparatus. Such translational streamlining is thought to be growth-regulated, but its genetic basis is poorly understood. First, we found in reanalysis of the E. coli tRNA profile that the degree to which it is translationally streamlined is nearly invariant with growth rate. Then, using least squares multiple regression, we partitioned tRNA isoacceptor pools to predicted tDNA operons from the E. coli K12 genome. Co-expression of tDNAs in operons explains the tRNA profile significantly better than tDNA gene dosage alone. Also, operon expression increases significantly with proximity to the origin of replication, oriC, at all growth rates. Genome location explains about 15% of expression variation in a form, at a given growth rate, that is consistent with replication-dependent gene concentration effects. Yet the change in the tRNA profile with growth rate is less than would be expected from such effects. We estimated per-copy expression rates for all tDNA operons that were consistent with independent estimates for rDNA operons. We also found that tDNA operon location, and the location dependence of expression, were significantly different in the leading and lagging strands. The operonic organization and genomic location of tDNA operons are significant factors influencing their expression. Nonrandom patterns of location and strandedness shown by tDNA operons in E. coli suggest that their genomic architecture may be under selection to satisfy physiological demand for tRNA expression at high growth rates.'),
(83350, 2005, 'plos', 'microRNA target predictions across seven Drosophila species and comparison to mammalian targets', 'Grün, Dominic|Wang, Yi-Lu|Langenberger, David|Gunsalus, Kristin C|Rajewsky, Nikolaus', 'Center for Comparative Functional Genomics, Department of Biology, New York University, NY, USA.|Center for Comparative Functional Genomics, Department of Biology, New York University, NY, USA.|Center for Comparative Functional Genomics, Department of Biology, New York University, NY, USA.|Center for Comparative Functional Genomics, Department of Biology, New York University, NY, USA.|Center for Comparative Functional Genomics, Department of Biology, New York University, NY, USA.', 'United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1371/journal.pcbi.0010013', '16103902', 'microRNAs are small noncoding genes that regulate the protein production of genes by binding to partially complementary sites in the mRNAs of targeted genes. Here, using our algorithm PicTar, we exploit cross-species comparisons to predict, on average, 54 targeted genes per microRNA above noise in Drosophila melanogaster. Analysis of the functional annotation of target genes furthermore suggests specific biological functions for many microRNAs. We also predict combinatorial targets for clustered microRNAs and find that some clustered microRNAs are likely to coordinately regulate target genes. Furthermore, we compare microRNA regulation between insects and vertebrates. We find that the widespread extent of gene regulation by microRNAs is comparable between flies and mammals but that certain microRNAs may function in clade-specific modes of gene regulation. One of these microRNAs (miR-210) is predicted to contribute to the regulation of fly oogenesis. We also list specific regulatory relationships that appear to be conserved between flies and mammals. Our findings provide the most extensive microRNA target predictions in Drosophila to date, suggest specific functional roles for most microRNAs, indicate the existence of coordinate gene regulation executed by clustered microRNAs, and shed light on the evolution of microRNA function across large evolutionary distances. All predictions are freely accessible at our searchable Web site http://pictar.bio.nyu.edu.'),
(83351, 2005, 'plos', 'What makes ribosome-mediated transcriptional attenuation sensitive to amino acid limitation?', 'Elf, Johan|Ehrenberg, Måns', 'Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden. Johan.Elf@icm.uu.se|Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden. Johan.Elf@icm.uu.se', 'Sweden|Sweden', 0, '-', 'Journal Article', '10.1371/journal.pcbi.0010002', '16103903', 'Ribosome-mediated transcriptional attenuation mechanisms are commonly used to control amino acid biosynthetic operons in bacteria. The mRNA leader of such an operon contains an open reading frame with regulatory codons, cognate to the amino acid that is synthesized by the enzymes encoded by the operon. When the amino acid is in short supply, translation of the regulatory codons is slow, which allows transcription to continue into the structural genes of the operon. When amino acid supply is in excess, translation of regulatory codons is rapid, which leads to termination of transcription. We use a discrete master equation approach to formulate a probabilistic model for the positioning of the RNA polymerase and the ribosome in the attenuator leader sequence. The model describes how the current rate of amino acid supply compared to the demand in protein synthesis (signal) determines the expression of the amino acid biosynthetic operon (response). The focus of our analysis is on the sensitivity of operon expression to a change in the amino acid supply. We show that attenuation of transcription can be hyper-sensitive for two main reasons. The first is that its response depends on the outcome of a race between two multi-step mechanisms with synchronized starts: transcription of the leader of the operon, and translation of its regulatory codons. The relative change in the probability that transcription is aborted (attenuated) can therefore be much larger than the relative change in the time it takes for the ribosome to read a regulatory codon. The second is that the general usage frequencies of codons of the type used in attenuation control are small. A small percentage decrease in the rate of supply of the controlled amino acid can therefore lead to a much larger percentage decrease in the rate of reading a regulatory codon. We show that high sensitivity further requires a particular choice of regulatory codon among several synonymous codons for the same amino acid. We demonstrate the importance of a high fraction of regulatory codons in the control region. Finally, our integrated model explains how differences in leader sequence design of the trp and his operons of Escherichia coli and Salmonella typhimurium lead to high basal expression and low sensitivity in the former case, and to large dynamic range and high sensitivity in the latter. The model clarifies how mechanistic and systems biological aspects of the attenuation mechanism contribute to its overall sensitivity. It also explains structural differences between the leader sequences of the trp and his operons in terms of their different functions.'),
(83352, 2005, 'plos', 'Predicting functional gene links from phylogenetic-statistical analyses of whole genomes', 'Barker, Daniel|Pagel, Mark', 'School of Animal and Microbial Sciences, University of Reading, United Kingdom.|School of Animal and Microbial Sciences, University of Reading, United Kingdom.', 'United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1371/journal.pcbi.0010003', '16103904', 'An important element of the developing field of proteomics is to understand protein-protein interactions and other functional links amongst genes. Across-species correlation methods for detecting functional links work on the premise that functionally linked proteins will tend to show a common pattern of presence and absence across a range of genomes. We describe a maximum likelihood statistical model for predicting functional gene linkages. The method detects independent instances of the correlated gain or loss of pairs of proteins on phylogenetic trees, reducing the high rates of false positives observed in conventional across-species methods that do not explicitly incorporate a phylogeny. We show, in a dataset of 10,551 protein pairs, that the phylogenetic method improves by up to 35% on across-species analyses at identifying known functionally linked proteins. The method shows that protein pairs with at least two to three correlated events of gain or loss are almost certainly functionally linked. Contingent evolution, in which one genes presence or absence depends upon the presence of another, can also be detected phylogenetically, and may identify genes whose functional significance depends upon its interaction with other genes. Incorporating phylogenetic information improves the prediction of functional linkages. The improvement derives from having a lower rate of false positives and from detecting trends that across-species analyses miss. Phylogenetic methods can easily be incorporated into the screening of large-scale bioinformatics datasets to identify sets of protein links and to characterise gene networks.'),
(83353, 2005, 'plos', 'PLoS Computational Biology: a new community journal', 'Bourne, Philip E|Brenner, Steven E|Eisen, Michael B', 'School of Animal and Microbial Sciences, University of Reading, United Kingdom.|School of Animal and Microbial Sciences, University of Reading, United Kingdom.|School of Animal and Microbial Sciences, University of Reading, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom', 3, 'Computational Biology|Periodicals as Topic', 'Editorial', '10.1371/journal.pcbi.0010004', '16103905', '-'),
(83354, 2005, 'plos', 'An open forum for computational biology', 'Gribskov, Michael', 'International Society for Computational Biology. gribskov@purdue.edu', '-', 0, '-', 'Journal Article', '10.1371/journal.pcbi.0010005', '16103906', '-'),
(83355, 2005, 'plos', 'Antedisciplinary science', 'Eddy, Sean R', 'Howard Hughes Medical Institute, Department of Genetics at Washington University School of Medicine, Saint Louis, Missouri, USA. eddy@genetics.wustl.edu', 'United States', 0, '-', 'Journal Article', '10.1371/journal.pcbi.0010006', '16103907', '-'),
(83356, 2005, 'plos', 'Susceptibility to superhelically driven DNA duplex destabilization: a highly conserved property of yeast replication origins', 'Ak, Prashanth|Benham, Craig J', 'UC Davis Genome Center, University of California, Davis, USA. prashanth@ucdavis.edu|UC Davis Genome Center, University of California, Davis, USA. prashanth@ucdavis.edu', 'United States|United States', 0, '-', 'Journal Article', '10.1371/journal.pcbi.0010007', '16103908', 'Strand separation is obligatory for several DNA functions, including replication. However, local DNA properties such as A+T content or thermodynamic stability alone do not determine the susceptibility to this transition in vivo. Rather, superhelical stresses provide long-range coupling among the transition behaviors of all base pairs within a topologically constrained domain. We have developed methods to analyze superhelically induced duplex destabilization (SIDD) in genomic DNA that take into account both this long-range stress-induced coupling and sequence-dependent local thermodynamic stability. Here we apply this approach to examine the SIDD properties of 39 experimentally well-characterized autonomously replicating DNA sequences (ARS elements), which function as replication origins in the yeast Saccharomyces cerevisiae. We find that these ARS elements have a strikingly increased susceptibility to SIDD relative to their surrounding sequences. On average, these ARS elements require 4.78 kcal/mol less free energy to separate than do their immediately surrounding sequences, making them more than 2,000 times easier to open. Statistical analysis shows that the probability of this strong an association between SIDD sites and ARS elements arising by chance is approximately 4 x 10(-10). This local enhancement of the propensity to separate to single strands under superhelical stress has obvious implications for origin function. SIDD properties also could be used, in conjunction with other known origin attributes, to identify putative replication origins in yeast, and possibly in other metazoan genomes.');
