The Experts below are selected from a list of 565902 Experts worldwide ranked by ideXlab platform
J. Michael Cherry - One of the best experts on this subject based on the ideXlab platform.
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Correction: AGAPE (Automated Genome Analysis PipelinE) for Pan-Genome Analysis of Saccharomyces cerevisiae.
PloS one, 2015Co-Authors: Giltae Song, Benjamin J. A. Dickins, Janos Demeter, Stacia R. Engel, Jennifer E. G. Gallagher, Kisurb Choe, Barbara Dunn, Michael Snyder, J. Michael CherryAbstract:Jennifer Gallagher, Kisurb Choe, and Michael Snyder are missing from the author list. Please view the correct author order, affiliations, and citation here: Giltae Song 1, Benjamin J. A. Dickins 2, Janos Demeter 1, Stacia Engel 1, Jennifer Gallagher 1, Kisurb Choe 1, Barbara Dunn 1, Michael Snyder 1, J. Michael Cherry 1 1 Department of Genetics, Stanford University School of Medicine, Stanford, California, United States of America, 2 School of Science and Technology, Nottingham Trent University, Nottingham, United Kingdom Song G, Dickins BJA, Demeter J, Engel S, Gallagher J, Choe K, et al. (2015) AGAPE (Automated Genome Analysis PipelinE) for Pan-Genome Analysis of Saccharomyces cerevisiae. PLoS ONE 10(3): e0120671. doi:10.1371/journal.pone.0120671 There are missing Author Contributions. The correct contributions are: Conceived and designed the experiments: GS MS JMC. Performed the experiments: GS JG KC BD. Analyzed the data: GS BJAD JD. Contributed reagents/materials/Analysis tools: GS JG KC BD. Wrote the paper: GS BJAD JD SE BD JMC. There is an omission in the Acknowledgments. The following sentence should be included in the Acknowledgments: We thank SGD Project staff for the creation of the high quality and detailed database of S. cerevisiae genes and their products and Webb Miller for helpful comments. Illumina sequencing services were performed by the Stanford Center for Genomics and Personalized Medicine.
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AGAPE (Automated Genome Analysis PipelinE) for Pan-Genome Analysis of Saccharomyces cerevisiae
PloS one, 2015Co-Authors: Giltae Song, Benjamin J. A. Dickins, Janos Demeter, Stacia R. Engel, Barbara Dunn, J. Michael CherryAbstract:The characterization and public release of Genome sequences from thousands of organisms is expanding the scope for genetic variation studies. However, understanding the phenotypic consequences of genetic variation remains a challenge in eukaryotes due to the complexity of the genotype-phenotype map. One approach to this is the intensive study of model systems for which diverse sources of information can be accumulated and integrated. Saccharomyces cerevisiae is an extensively studied model organism, with well-known protein functions and thoroughly curated phenotype data. To develop and expand the available resources linking genomic variation with function in yeast, we aim to model the pan-Genome of S. cerevisiae. To initiate the yeast pan-Genome, we newly sequenced or re-sequenced the Genomes of 25 strains that are commonly used in the yeast research community using advanced sequencing technology at high quality. We also developed a pipeline for automated pan-Genome Analysis, which integrates the steps of assembly, annotation, and variation calling. To assign strain-specific functional annotations, we identified genes that were not present in the reference Genome. We classified these according to their presence or absence across strains and characterized each group of genes with known functional and phenotypic features. The functional roles of novel genes not found in the reference Genome and associated with strains or groups of strains appear to be consistent with anticipated adaptations in specific lineages. As more S. cerevisiae strain Genomes are released, our Analysis can be used to collate Genome data and relate it to lineage-specific patterns of Genome evolution. Our new tool set will enhance our understanding of genomic and functional evolution in S. cerevisiae, and will be available to the yeast genetics and molecular biology community.
Ira M. Hall - One of the best experts on this subject based on the ideXlab platform.
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SpeedSeq: ultra-fast personal Genome Analysis and interpretation
Nature methods, 2015Co-Authors: Colby Chiang, Ryan M. Layer, Gregory G. Faust, Michael R. Lindberg, David B Rose, Erik Garrison, Gabor T. Marth, Aaron R. Quinlan, Ira M. HallAbstract:SpeedSeq is an open-source Genome Analysis platform that accomplishes alignment, variant detection and functional annotation of a 50× human Genome in 13 h on a low-cost server and alleviates a bioinformatics bottleneck that typically demands weeks of computation with extensive hands-on expert involvement. SpeedSeq offers performance competitive with or superior to current methods for detecting germline and somatic single-nucleotide variants, structural variants, insertions and deletions, and it includes novel functionality for streamlined interpretation.
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SpeedSeq: Ultra-fast personal Genome Analysis and interpretation
2014Co-Authors: Colby Chiang, Ryan M. Layer, Gregory G. Faust, Michael R. Lindberg, David B Rose, Erik Garrison, Gabor T. Marth, Aaron R. Quinlan, Ira M. HallAbstract:Comprehensive interpretation of human Genome sequencing data is a challenging bioinformatic problem that typically requires weeks of Analysis, with extensive hands-on expert involvement. This informatics bottleneck inflates Genome sequencing costs, poses a computational burden for large-scale projects, and impedes the adoption of time-critical clinical applications such as personalized cancer profiling and newborn disease diagnosis, where the actionable timeframe can measure in hours or days. We developed SpeedSeq, an open-source Genome Analysis platform that vastly reduces computing time. SpeedSeq accomplishes read alignment, duplicate removal, variant detection and functional annotation of a 50X human Genome in
Natalia Maltsev - One of the best experts on this subject based on the ideXlab platform.
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GNARE: automated system for high-throughput Genome Analysis with grid computational backend.
Journal of Clinical Monitoring and Computing, 2005Co-Authors: Dinanath Sulakhe, Ian Foster, Alex Rodriguez, Veronika Nefedova, Mark D'souza, Michael Wilde, Natalia MaltsevAbstract:Recent progress in genomics and experimental biology has brought exponential growth of the biological information available for computational Analysis in public genomics databases. However, applying the potentially enormous scientific value of this information to the understanding of biological systems requires computing and data storage technology of an unprecedented scale. The Grid, with its aggregated and distributed computational and storage infrastructure, offers an ideal platform for high-throughput bioinformatics Analysis. To leverage this we have developed the Genome Analysis Research Environment (GNARE) – a scalable computational system for the high-throughput Analysis of Genomes, which provides an integrated database and computational backend for data-driven bioinformatics applications. GNARE efficiently automates the major steps of Genome Analysis including acquisition of data from multiple genomic databases; data Analysis by a diverse set of bioinformatics tools; and storage of results and annotations.
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CCGRID - GNARE: an environment for grid-based high-throughput Genome Analysis
CCGrid 2005. IEEE International Symposium on Cluster Computing and the Grid 2005., 2005Co-Authors: Dinanath Sulakhe, Ian Foster, Alex Rodriguez, Veronika Nefedova, Mark D'souza, Michael Wilde, Natalia MaltsevAbstract:Recent progress in genomics and experimental biology has brought exponential growth of the biological information available for computational Analysis in public genomics databases. However, applying the potentially enormous scientific value of this information to the understanding of biological systems requires computing and data storage technology of an unprecedented scale. The grid, with its aggregated and distributed computational and storage infrastructure, offers an ideal platform for high-throughput bioinformatics Analysis. To leverage this we have developed the Genome Analysis Research Environment (GNARE) - a scalable computational system for the high-throughput Analysis of Genomes, which provides an integrated database and computational backend for data-driven bioinformatics applications. GNARE efficiently automates the major steps of Genome Analysis including acquisition of data from multiple genomic databases; data Analysis by a diverse set of bioinformatics tools; and storage of results and annotations. High-throughput computations in GNARE are performed using distributed heterogeneous grid computing resources such as Grid2003, TeraGrid, and the DOE science grid. Multi-step Genome Analysis workflows involving massive data processing, the use of application-specific toots and algorithms and updating of an integrated database to provide interactive Web access to results are all expressed and controlled by a "virtual data" model which transparently maps computational workflows to distributed grid resources. This paper describes how Grid technologies such as Globus, Condor, and the Gryphyn virtual data system were applied in the development of GNARE. It focuses on our approach to Grid resource allocation and to the use of GNARE as a computational framework for the development of bioinformatics applications.
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GNARE: automated system for high-throughput Genome Analysis with grid computational backend.
Journal of clinical monitoring and computing, 2005Co-Authors: Dinanath Sulakhe, Ian Foster, Alex Rodriguez, Veronika Nefedova, Mark D'souza, Michael Wilde, Natalia MaltsevAbstract:Recent progress in genomics and experimental biology has brought exponential growth of the biological information available for computational Analysis in public genomics databases. However, applying the potentially enormous scientific value of this information to the understanding of biological systems requires computing and data storage technology of an unprecedented scale. The Grid, with its aggregated and distributed computational and storage infrastructure, offers an ideal platform for high-throughput bioinformatics Analysis. To leverage this we have developed the Genome Analysis Research Environment (GNARE)--a scalable computational system for the high-throughput Analysis of Genomes, which provides an integrated database and computational backend for data-driven bioinformatics applications. GNARE efficiently automates the major steps of Genome Analysis including acquisition of data from multiple genomic databases; data Analysis by a diverse set of bioinformatics tools; and storage of results and annotations. High-throughput computations in GNARE are performed using distributed heterogeneous Grid computing resources such as Grid2003, TeraGrid, and the DOE Science Grid. Multi-step Genome Analysis workflows involving massive data processing, the use of application-specific tools and algorithms and updating of an integrated database to provide interactive web access to results are all expressed and controlled by a "virtual data" model which transparently maps computational workflows to distributed Grid resources. This paper describes how Grid technologies such as Globus, Condor, and the Gryphyn Virtual Data System were applied in the development of GNARE. It focuses on our approach to Grid resource allocation and to the use of GNARE as a computational framework for the development of bioinformatics applications.
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GADU – Genome Analysis and Database Update Pipeline
2003Co-Authors: Alex Rodriguez, Dinanath Sulakhe, Elizabeth Marland, Veronika Nefedova, Natalia MaltsevAbstract:Realizing the enormous scientific potential of exponentially growing biological information requires the development of high-throughput automated computational environments that integrate large amounts of genomic and experimental data, and powerful tools for knowledge discovery and data mining. To assist high-throughput Analysis of the Genomes, we have developed the Genome Analysis and Databases Update system. GADU efficiently automates major steps of Genome Analysis: data acquisition and data Analysis by a variety of tools and algorithms, as well as data storage and annotation. We are developing a TeraGrid technologybased backend for large-scale computations using GADU. GADU can function in either an automated or interactive mode via a Web-based user interface. Programs monitor every operation in GADU and report the status of the process. This architecture ensures GADU’s robust performance and allows simultaneous processing of a large number of sequenced Genomes regardless of their size.
Giltae Song - One of the best experts on this subject based on the ideXlab platform.
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Correction: AGAPE (Automated Genome Analysis PipelinE) for Pan-Genome Analysis of Saccharomyces cerevisiae.
PloS one, 2015Co-Authors: Giltae Song, Benjamin J. A. Dickins, Janos Demeter, Stacia R. Engel, Jennifer E. G. Gallagher, Kisurb Choe, Barbara Dunn, Michael Snyder, J. Michael CherryAbstract:Jennifer Gallagher, Kisurb Choe, and Michael Snyder are missing from the author list. Please view the correct author order, affiliations, and citation here: Giltae Song 1, Benjamin J. A. Dickins 2, Janos Demeter 1, Stacia Engel 1, Jennifer Gallagher 1, Kisurb Choe 1, Barbara Dunn 1, Michael Snyder 1, J. Michael Cherry 1 1 Department of Genetics, Stanford University School of Medicine, Stanford, California, United States of America, 2 School of Science and Technology, Nottingham Trent University, Nottingham, United Kingdom Song G, Dickins BJA, Demeter J, Engel S, Gallagher J, Choe K, et al. (2015) AGAPE (Automated Genome Analysis PipelinE) for Pan-Genome Analysis of Saccharomyces cerevisiae. PLoS ONE 10(3): e0120671. doi:10.1371/journal.pone.0120671 There are missing Author Contributions. The correct contributions are: Conceived and designed the experiments: GS MS JMC. Performed the experiments: GS JG KC BD. Analyzed the data: GS BJAD JD. Contributed reagents/materials/Analysis tools: GS JG KC BD. Wrote the paper: GS BJAD JD SE BD JMC. There is an omission in the Acknowledgments. The following sentence should be included in the Acknowledgments: We thank SGD Project staff for the creation of the high quality and detailed database of S. cerevisiae genes and their products and Webb Miller for helpful comments. Illumina sequencing services were performed by the Stanford Center for Genomics and Personalized Medicine.
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AGAPE (Automated Genome Analysis PipelinE) for Pan-Genome Analysis of Saccharomyces cerevisiae
PloS one, 2015Co-Authors: Giltae Song, Benjamin J. A. Dickins, Janos Demeter, Stacia R. Engel, Barbara Dunn, J. Michael CherryAbstract:The characterization and public release of Genome sequences from thousands of organisms is expanding the scope for genetic variation studies. However, understanding the phenotypic consequences of genetic variation remains a challenge in eukaryotes due to the complexity of the genotype-phenotype map. One approach to this is the intensive study of model systems for which diverse sources of information can be accumulated and integrated. Saccharomyces cerevisiae is an extensively studied model organism, with well-known protein functions and thoroughly curated phenotype data. To develop and expand the available resources linking genomic variation with function in yeast, we aim to model the pan-Genome of S. cerevisiae. To initiate the yeast pan-Genome, we newly sequenced or re-sequenced the Genomes of 25 strains that are commonly used in the yeast research community using advanced sequencing technology at high quality. We also developed a pipeline for automated pan-Genome Analysis, which integrates the steps of assembly, annotation, and variation calling. To assign strain-specific functional annotations, we identified genes that were not present in the reference Genome. We classified these according to their presence or absence across strains and characterized each group of genes with known functional and phenotypic features. The functional roles of novel genes not found in the reference Genome and associated with strains or groups of strains appear to be consistent with anticipated adaptations in specific lineages. As more S. cerevisiae strain Genomes are released, our Analysis can be used to collate Genome data and relate it to lineage-specific patterns of Genome evolution. Our new tool set will enhance our understanding of genomic and functional evolution in S. cerevisiae, and will be available to the yeast genetics and molecular biology community.
Colby Chiang - One of the best experts on this subject based on the ideXlab platform.
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SpeedSeq: ultra-fast personal Genome Analysis and interpretation
Nature methods, 2015Co-Authors: Colby Chiang, Ryan M. Layer, Gregory G. Faust, Michael R. Lindberg, David B Rose, Erik Garrison, Gabor T. Marth, Aaron R. Quinlan, Ira M. HallAbstract:SpeedSeq is an open-source Genome Analysis platform that accomplishes alignment, variant detection and functional annotation of a 50× human Genome in 13 h on a low-cost server and alleviates a bioinformatics bottleneck that typically demands weeks of computation with extensive hands-on expert involvement. SpeedSeq offers performance competitive with or superior to current methods for detecting germline and somatic single-nucleotide variants, structural variants, insertions and deletions, and it includes novel functionality for streamlined interpretation.
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SpeedSeq: Ultra-fast personal Genome Analysis and interpretation
2014Co-Authors: Colby Chiang, Ryan M. Layer, Gregory G. Faust, Michael R. Lindberg, David B Rose, Erik Garrison, Gabor T. Marth, Aaron R. Quinlan, Ira M. HallAbstract:Comprehensive interpretation of human Genome sequencing data is a challenging bioinformatic problem that typically requires weeks of Analysis, with extensive hands-on expert involvement. This informatics bottleneck inflates Genome sequencing costs, poses a computational burden for large-scale projects, and impedes the adoption of time-critical clinical applications such as personalized cancer profiling and newborn disease diagnosis, where the actionable timeframe can measure in hours or days. We developed SpeedSeq, an open-source Genome Analysis platform that vastly reduces computing time. SpeedSeq accomplishes read alignment, duplicate removal, variant detection and functional annotation of a 50X human Genome in