The Experts below are selected from a list of 23370 Experts worldwide ranked by ideXlab platform

Jonathan Verneau - One of the best experts on this subject based on the ideXlab platform.

  • mg digger an automated pipeline to search for giant virus related sequences in Metagenomes
    Frontiers in Microbiology, 2016
    Co-Authors: Jonathan Verneau
    Abstract:

    The number of metagenomic studies conducted each year is growing dramatically. Storage and analysis of such big data is difficult and time-consuming. Interestingly, analysis shows that environmental and human Metagenomes include a significant amount of non-annotated sequences, representing a ‘dark matter’. We established a bioinformatics pipeline that automatically detects Metagenome reads matching query sequences from a given set and applied this tool to the detection of sequences matching large and giant DNA viral members of the proposed order Megavirales or virophages. A total of 1,045 environmental and human Metagenomes (≈ 1 Terabase pairs) were collected, processed and stored on our bioinformatics server. In addition, nucleotide and protein sequences from 93 Megavirales representatives, including 19 giant viruses of amoeba, and five virophages, were collected. The pipeline was generated by scripts written in Python language and entitled MG-Digger. Metagenomes previously found to contain megavirus-like sequences were tested as controls. MG-Digger was able to annotate hundreds of Metagenome sequences as best matching those of giant viruses. These sequences were most often found to be similar to phycodnavirus or mimivirus sequences, but included reads related to recently available pandoraviruses, Pithovirus sibericum, and faustoviruses. Compared to other tools, MG-Digger combined stand-alone use on Linux or Windows operating systems through a user-friendly interface, implementation of ready-to-use customized Metagenome databases and query sequence databases, adjustable parameters for BLAST searches, and creation of output files containing selected reads with best match identification. Compared to Metavir 2, a reference tool in viral Metagenome analysis, MG-Digger detected 8% more true positive Megavirales-related reads in a control Metagenome. The present work shows that massive, automated and recurrent analyses of Metagenomes are effective in improving knowledge about the presence and prevalence of giant viruses in the environment and the human body.

  • MG-Digger: An Automated Pipeline to Search for Giant Virus-Related Sequences in Metagenomes
    Frontiers in microbiology, 2016
    Co-Authors: Jonathan Verneau, Anthony Levasseur, Didier Raoult, Bernard La Scola, Philippe Colson
    Abstract:

    The number of metagenomic studies conducted each year is growing dramatically. Storage and analysis of such big data is difficult and time-consuming. Interestingly, analysis shows that environmental and human Metagenomes include a significant amount of non-annotated sequences, representing a 'dark matter.' We established a bioinformatics pipeline that automatically detects Metagenome reads matching query sequences from a given set and applied this tool to the detection of sequences matching large and giant DNA viral members of the proposed order Megavirales or virophages. A total of 1,045 environmental and human Metagenomes (≈ 1 Terabase) were collected, processed, and stored on our bioinformatics server. In addition, nucleotide and protein sequences from 93 Megavirales representatives, including 19 giant viruses of amoeba, and 5 virophages, were collected. The pipeline was generated by scripts written in Python language and entitled MG-Digger. Metagenomes previously found to contain megavirus-like sequences were tested as controls. MG-Digger was able to annotate 100s of Metagenome sequences as best matching those of giant viruses. These sequences were most often found to be similar to phycodnavirus or mimivirus sequences, but included reads related to recently available pandoraviruses, Pithovirus sibericum, and faustoviruses. Compared to other tools, MG-Digger combined stand-alone use on Linux or Windows operating systems through a user-friendly interface, implementation of ready-to-use customized Metagenome databases and query sequence databases, adjustable parameters for BLAST searches, and creation of output files containing selected reads with best match identification. Compared to Metavir 2, a reference tool in viral Metagenome analysis, MG-Digger detected 8% more true positive Megavirales-related reads in a control Metagenome. The present work shows that massive, automated and recurrent analyses of Metagenomes are effective in improving knowledge about the presence and prevalence of giant viruses in the environment and the human body.

  • MG-Digger: An Automated Pipeline to Search for Giant Virus-Related Sequences in Metagenomes
    Frontiers in Microbiology, 2016
    Co-Authors: Jonathan Verneau, Anthony Levasseur, Didier Raoult, Bernard La Scola, Philippe Colson
    Abstract:

    The number of metagenomic studies conducted each year is growing dramatically. Storage and analysis of such big data is difficult and time-consuming. Interestingly, analysis shows that environmental and human Metagenomes include a significant amount of non-annotated sequences, representing a `dark matter.' We established a bioinformatics pipeline that automatically detects Metagenome reads matching query sequences from a given set and applied this tool to the detection of sequences matching large and giant DNA viral members of the proposed order Megavirales or virophages. A total of 1,045 environmental and human Metagenomes (approximate to Terabase) were collected, processed, and stored on our bioinformatics server. In addition, nucleotide and protein sequences from 93 Megavirales representatives, including 19 giant viruses of amoeba, and 5 virophages, were collected. The pipeline was generated by scripts written in Python language and entitled MG-Digger. Metagenomes previously found to contain megavirus-like sequences were tested as controls. MG-Digger was able to annotate 100s of Metagenome sequences as best matching those of giant viruses. These sequences were most often found to be similar to phycodnavirus or mimivirus sequences, but included reads related to recently available pandoraviruses, Pithovirus sibericum, and faustoviruses. Compared to other tools, MG-Digger combined stand-alone use on Linux or Windows operating systems through a user-friendly interface, implementation of ready-to-use customized Metagenome databases and query sequence databases, adjustable parameters for BLAST searches, and creation of output files containing selected reads with best match identification. Compared to Metavir 2, a reference tool in viral Metagenome analysis. MG-Digger detected 8% more true positive Megavirales-related reads in a control Metagenome. The present work shows that massive, automated and recurrent analyses of Metagenomes are effective in improving knowledge about the presence and prevalence of giant viruses in the environment and the human body.

Krishna Palaniappan - One of the best experts on this subject based on the ideXlab platform.

  • SSDBM - Maintaining a microbial genome & Metagenome data analysis system in an academic setting
    Proceedings of the 26th International Conference on Scientific and Statistical Database Management - SSDBM '14, 2014
    Co-Authors: I-min A. Chen, Krishna Palaniappan, Ernest Szeto, Victor Markowitz, Ken Chu
    Abstract:

    The Integrated Microbial Genomes (IMG) system integrates microbial community aggregate genomes (Metagenomes) with genomes from all domains of life. IMG provides tools for analyzing and reviewing the structural and functional annotations of Metagenomes and genomes in a comparative context. At the core of the IMG system is a data warehouse that contains genome and Metagenome datasets provided by scientific users, as well as public bacterial, archaeal, eukaryotic, and viral genomes from the US National Center for Biotechnology Information genomic archive and a rich set of engineered, environmental and host associated Metagenomes. Genomes and Metagenome datasets are processed using IMG's microbial genome and Metagenome sequence data processing pipelines and then are integrated into the data warehouse using IMG's data integration toolkit. Microbial genome and Metagenome application specific user interfaces provide access to different subsets of IMG's data and analysis toolkits. Genome and Metagenome analysis is a gene centric iterative process that involves a sequence (composition) of data exploration and comparative analysis operations, with individual operations expected to have rapid response time. From its first release in 2005, IMG has grown from an initial content of about 300 genomes with a total of 2 million genes, to 22,578 bacterial, archaeal, eukaryotic and viral genomes, and 4,188 Metagenome samples, with about 24.6 billion genes as of May 1st, 2014. IMG's database architecture is continuously revised in order to cope with the rapid increase in the number and size of the genome and Metagenome datasets, maintain good query performance, and accommodate new data types. We present in this paper IMG's new database architecture developed over the past three years in the context of limited financial, engineering and data management resources customary to academic database systems. We discuss the alternative commercial and open source database management systems we considered and experimented with and describe the hybrid architecture we devised for sustaining IMG's rapid growth.

  • img m 4 version of the integrated Metagenome comparative analysis system
    Nucleic Acids Research, 2014
    Co-Authors: Victor M. Markowitz, Ken Chu, Ernest Szeto, Krishna Palaniappan, I-min A. Chen, Anna Ratner, Ioanna Pagani, Manoj Pillay, Jinghua Huang, Susannah G. Tringe
    Abstract:

    IMG/M (http://img.jgi.doe.gov/m) provides support for comparative analysis of microbial community aggregate genomes (Metagenomes) in the context of a comprehensive set of reference genomes from all three domains of life, as well as plasmids, viruses and genome fragments. IMG/M's data content and analytical tools have expanded continuously since its first version was released in 2007. Since the last report published in the 2012 NAR Database Issue, IMG/M's database architecture, annotation and data integration pipelines and analysis tools have been extended to copewith the rapid growth in the number and size of Metagenome data sets handled by the system. IMG/M data marts provide support for the analysis of publicly available genomes, expert review of Metagenome annotations (IMG/M ER: http://img.jgi.doe.gov/mer) and Human Microbiome Project (HMP)-specific Metagenome samples (IMG/M HMP: http://img.jgi.doe.gov/imgm_hmp).

  • IMG/M 4 version of the integrated Metagenome comparative analysis system.
    Nucleic acids research, 2013
    Co-Authors: Victor M. Markowitz, Ken Chu, Ernest Szeto, Krishna Palaniappan, I-min A. Chen, Anna Ratner, Ioanna Pagani, Manoj Pillay, Jinghua Huang, Susannah G. Tringe
    Abstract:

    IMG/M (http://img.jgi.doe.gov/m) provides support for comparative analysis of microbial community aggregate genomes (Metagenomes) in the context of a comprehensive set of reference genomes from all three domains of life, as well as plasmids, viruses and genome fragments. IMG/M's data content and analytical tools have expanded continuously since its first version was released in 2007. Since the last report published in the 2012 NAR Database Issue, IMG/M's database architecture, annotation and data integration pipelines and analysis tools have been extended to copewith the rapid growth in the number and size of Metagenome data sets handled by the system. IMG/M data marts provide support for the analysis of publicly available genomes, expert review of Metagenome annotations (IMG/M ER: http://img.jgi.doe.gov/mer) and Human Microbiome Project (HMP)-specific Metagenome samples (IMG/M HMP: http://img.jgi.doe.gov/imgm_hmp).

  • IMG/M-HMP: A Metagenome Comparative Analysis System for the Human Microbiome Project
    PloS one, 2012
    Co-Authors: Victor M. Markowitz, Ken Chu, Ernest Szeto, Krishna Palaniappan, I-min A. Chen, Biju Jacob, Anna Ratner, Konstantinos Liolios, Ioanna Pagani, Marcel Huntemann
    Abstract:

    The Integrated Microbial Genomes and Metagenomes (IMG/M) resource is a data management system that supports the analysis of sequence data from microbial communities in the integrated context of all publicly available draft and complete genomes from the three domains of life as well as a large number of plasmids and viruses. IMG/M currently contains thousands of genomes and Metagenome samples with billions of genes. IMG/M-HMP is an IMG/M data mart serving the US National Institutes of Health (NIH) Human Microbiome Project (HMP), focussed on HMP generated Metagenome datasets, and is one of the central resources provided from the HMP Data Analysis and Coordination Center (DACC). IMG/M-HMP is available at http://www.hmpdacc-resources.org/imgm_hmp/.

  • img m hmp a Metagenome comparative analysis system for the human microbiome project
    PLOS ONE, 2012
    Co-Authors: Victor M. Markowitz, Ken Chu, Ernest Szeto, Krishna Palaniappan, I-min A. Chen, Biju Jacob, Anna Ratner, Konstantinos Liolios, Ioanna Pagani, Marcel Huntemann
    Abstract:

    The Integrated Microbial Genomes and Metagenomes (IMG/M) resource is a data management system that supports the analysis of sequence data from microbial communities in the integrated context of all publicly available draft and complete genomes from the three domains of life as well as a large number of plasmids and viruses. IMG/M currently contains thousands of genomes and Metagenome samples with billions of genes. IMG/M-HMP is an IMG/M data mart serving the US National Institutes of Health (NIH) Human Microbiome Project (HMP), focussed on HMP generated Metagenome datasets, and is one of the central resources provided from the HMP Data Analysis and Coordination Center (DACC). IMG/M-HMP is available at http://www.hmpdacc-resources.org/imgm_hmp/.

Ernest Szeto - One of the best experts on this subject based on the ideXlab platform.

  • SSDBM - Maintaining a microbial genome & Metagenome data analysis system in an academic setting
    Proceedings of the 26th International Conference on Scientific and Statistical Database Management - SSDBM '14, 2014
    Co-Authors: I-min A. Chen, Krishna Palaniappan, Ernest Szeto, Victor Markowitz, Ken Chu
    Abstract:

    The Integrated Microbial Genomes (IMG) system integrates microbial community aggregate genomes (Metagenomes) with genomes from all domains of life. IMG provides tools for analyzing and reviewing the structural and functional annotations of Metagenomes and genomes in a comparative context. At the core of the IMG system is a data warehouse that contains genome and Metagenome datasets provided by scientific users, as well as public bacterial, archaeal, eukaryotic, and viral genomes from the US National Center for Biotechnology Information genomic archive and a rich set of engineered, environmental and host associated Metagenomes. Genomes and Metagenome datasets are processed using IMG's microbial genome and Metagenome sequence data processing pipelines and then are integrated into the data warehouse using IMG's data integration toolkit. Microbial genome and Metagenome application specific user interfaces provide access to different subsets of IMG's data and analysis toolkits. Genome and Metagenome analysis is a gene centric iterative process that involves a sequence (composition) of data exploration and comparative analysis operations, with individual operations expected to have rapid response time. From its first release in 2005, IMG has grown from an initial content of about 300 genomes with a total of 2 million genes, to 22,578 bacterial, archaeal, eukaryotic and viral genomes, and 4,188 Metagenome samples, with about 24.6 billion genes as of May 1st, 2014. IMG's database architecture is continuously revised in order to cope with the rapid increase in the number and size of the genome and Metagenome datasets, maintain good query performance, and accommodate new data types. We present in this paper IMG's new database architecture developed over the past three years in the context of limited financial, engineering and data management resources customary to academic database systems. We discuss the alternative commercial and open source database management systems we considered and experimented with and describe the hybrid architecture we devised for sustaining IMG's rapid growth.

  • img m 4 version of the integrated Metagenome comparative analysis system
    Nucleic Acids Research, 2014
    Co-Authors: Victor M. Markowitz, Ken Chu, Ernest Szeto, Krishna Palaniappan, I-min A. Chen, Anna Ratner, Ioanna Pagani, Manoj Pillay, Jinghua Huang, Susannah G. Tringe
    Abstract:

    IMG/M (http://img.jgi.doe.gov/m) provides support for comparative analysis of microbial community aggregate genomes (Metagenomes) in the context of a comprehensive set of reference genomes from all three domains of life, as well as plasmids, viruses and genome fragments. IMG/M's data content and analytical tools have expanded continuously since its first version was released in 2007. Since the last report published in the 2012 NAR Database Issue, IMG/M's database architecture, annotation and data integration pipelines and analysis tools have been extended to copewith the rapid growth in the number and size of Metagenome data sets handled by the system. IMG/M data marts provide support for the analysis of publicly available genomes, expert review of Metagenome annotations (IMG/M ER: http://img.jgi.doe.gov/mer) and Human Microbiome Project (HMP)-specific Metagenome samples (IMG/M HMP: http://img.jgi.doe.gov/imgm_hmp).

  • IMG/M 4 version of the integrated Metagenome comparative analysis system.
    Nucleic acids research, 2013
    Co-Authors: Victor M. Markowitz, Ken Chu, Ernest Szeto, Krishna Palaniappan, I-min A. Chen, Anna Ratner, Ioanna Pagani, Manoj Pillay, Jinghua Huang, Susannah G. Tringe
    Abstract:

    IMG/M (http://img.jgi.doe.gov/m) provides support for comparative analysis of microbial community aggregate genomes (Metagenomes) in the context of a comprehensive set of reference genomes from all three domains of life, as well as plasmids, viruses and genome fragments. IMG/M's data content and analytical tools have expanded continuously since its first version was released in 2007. Since the last report published in the 2012 NAR Database Issue, IMG/M's database architecture, annotation and data integration pipelines and analysis tools have been extended to copewith the rapid growth in the number and size of Metagenome data sets handled by the system. IMG/M data marts provide support for the analysis of publicly available genomes, expert review of Metagenome annotations (IMG/M ER: http://img.jgi.doe.gov/mer) and Human Microbiome Project (HMP)-specific Metagenome samples (IMG/M HMP: http://img.jgi.doe.gov/imgm_hmp).

  • IMG/M-HMP: A Metagenome Comparative Analysis System for the Human Microbiome Project
    PloS one, 2012
    Co-Authors: Victor M. Markowitz, Ken Chu, Ernest Szeto, Krishna Palaniappan, I-min A. Chen, Biju Jacob, Anna Ratner, Konstantinos Liolios, Ioanna Pagani, Marcel Huntemann
    Abstract:

    The Integrated Microbial Genomes and Metagenomes (IMG/M) resource is a data management system that supports the analysis of sequence data from microbial communities in the integrated context of all publicly available draft and complete genomes from the three domains of life as well as a large number of plasmids and viruses. IMG/M currently contains thousands of genomes and Metagenome samples with billions of genes. IMG/M-HMP is an IMG/M data mart serving the US National Institutes of Health (NIH) Human Microbiome Project (HMP), focussed on HMP generated Metagenome datasets, and is one of the central resources provided from the HMP Data Analysis and Coordination Center (DACC). IMG/M-HMP is available at http://www.hmpdacc-resources.org/imgm_hmp/.

  • img m hmp a Metagenome comparative analysis system for the human microbiome project
    PLOS ONE, 2012
    Co-Authors: Victor M. Markowitz, Ken Chu, Ernest Szeto, Krishna Palaniappan, I-min A. Chen, Biju Jacob, Anna Ratner, Konstantinos Liolios, Ioanna Pagani, Marcel Huntemann
    Abstract:

    The Integrated Microbial Genomes and Metagenomes (IMG/M) resource is a data management system that supports the analysis of sequence data from microbial communities in the integrated context of all publicly available draft and complete genomes from the three domains of life as well as a large number of plasmids and viruses. IMG/M currently contains thousands of genomes and Metagenome samples with billions of genes. IMG/M-HMP is an IMG/M data mart serving the US National Institutes of Health (NIH) Human Microbiome Project (HMP), focussed on HMP generated Metagenome datasets, and is one of the central resources provided from the HMP Data Analysis and Coordination Center (DACC). IMG/M-HMP is available at http://www.hmpdacc-resources.org/imgm_hmp/.

Philippe Colson - One of the best experts on this subject based on the ideXlab platform.

  • MG-Digger: An Automated Pipeline to Search for Giant Virus-Related Sequences in Metagenomes
    Frontiers in microbiology, 2016
    Co-Authors: Jonathan Verneau, Anthony Levasseur, Didier Raoult, Bernard La Scola, Philippe Colson
    Abstract:

    The number of metagenomic studies conducted each year is growing dramatically. Storage and analysis of such big data is difficult and time-consuming. Interestingly, analysis shows that environmental and human Metagenomes include a significant amount of non-annotated sequences, representing a 'dark matter.' We established a bioinformatics pipeline that automatically detects Metagenome reads matching query sequences from a given set and applied this tool to the detection of sequences matching large and giant DNA viral members of the proposed order Megavirales or virophages. A total of 1,045 environmental and human Metagenomes (≈ 1 Terabase) were collected, processed, and stored on our bioinformatics server. In addition, nucleotide and protein sequences from 93 Megavirales representatives, including 19 giant viruses of amoeba, and 5 virophages, were collected. The pipeline was generated by scripts written in Python language and entitled MG-Digger. Metagenomes previously found to contain megavirus-like sequences were tested as controls. MG-Digger was able to annotate 100s of Metagenome sequences as best matching those of giant viruses. These sequences were most often found to be similar to phycodnavirus or mimivirus sequences, but included reads related to recently available pandoraviruses, Pithovirus sibericum, and faustoviruses. Compared to other tools, MG-Digger combined stand-alone use on Linux or Windows operating systems through a user-friendly interface, implementation of ready-to-use customized Metagenome databases and query sequence databases, adjustable parameters for BLAST searches, and creation of output files containing selected reads with best match identification. Compared to Metavir 2, a reference tool in viral Metagenome analysis, MG-Digger detected 8% more true positive Megavirales-related reads in a control Metagenome. The present work shows that massive, automated and recurrent analyses of Metagenomes are effective in improving knowledge about the presence and prevalence of giant viruses in the environment and the human body.

  • MG-Digger: An Automated Pipeline to Search for Giant Virus-Related Sequences in Metagenomes
    Frontiers in Microbiology, 2016
    Co-Authors: Jonathan Verneau, Anthony Levasseur, Didier Raoult, Bernard La Scola, Philippe Colson
    Abstract:

    The number of metagenomic studies conducted each year is growing dramatically. Storage and analysis of such big data is difficult and time-consuming. Interestingly, analysis shows that environmental and human Metagenomes include a significant amount of non-annotated sequences, representing a `dark matter.' We established a bioinformatics pipeline that automatically detects Metagenome reads matching query sequences from a given set and applied this tool to the detection of sequences matching large and giant DNA viral members of the proposed order Megavirales or virophages. A total of 1,045 environmental and human Metagenomes (approximate to Terabase) were collected, processed, and stored on our bioinformatics server. In addition, nucleotide and protein sequences from 93 Megavirales representatives, including 19 giant viruses of amoeba, and 5 virophages, were collected. The pipeline was generated by scripts written in Python language and entitled MG-Digger. Metagenomes previously found to contain megavirus-like sequences were tested as controls. MG-Digger was able to annotate 100s of Metagenome sequences as best matching those of giant viruses. These sequences were most often found to be similar to phycodnavirus or mimivirus sequences, but included reads related to recently available pandoraviruses, Pithovirus sibericum, and faustoviruses. Compared to other tools, MG-Digger combined stand-alone use on Linux or Windows operating systems through a user-friendly interface, implementation of ready-to-use customized Metagenome databases and query sequence databases, adjustable parameters for BLAST searches, and creation of output files containing selected reads with best match identification. Compared to Metavir 2, a reference tool in viral Metagenome analysis. MG-Digger detected 8% more true positive Megavirales-related reads in a control Metagenome. The present work shows that massive, automated and recurrent analyses of Metagenomes are effective in improving knowledge about the presence and prevalence of giant viruses in the environment and the human body.

Victor M. Markowitz - One of the best experts on this subject based on the ideXlab platform.

  • img m 4 version of the integrated Metagenome comparative analysis system
    Nucleic Acids Research, 2014
    Co-Authors: Victor M. Markowitz, Ken Chu, Ernest Szeto, Krishna Palaniappan, I-min A. Chen, Anna Ratner, Ioanna Pagani, Manoj Pillay, Jinghua Huang, Susannah G. Tringe
    Abstract:

    IMG/M (http://img.jgi.doe.gov/m) provides support for comparative analysis of microbial community aggregate genomes (Metagenomes) in the context of a comprehensive set of reference genomes from all three domains of life, as well as plasmids, viruses and genome fragments. IMG/M's data content and analytical tools have expanded continuously since its first version was released in 2007. Since the last report published in the 2012 NAR Database Issue, IMG/M's database architecture, annotation and data integration pipelines and analysis tools have been extended to copewith the rapid growth in the number and size of Metagenome data sets handled by the system. IMG/M data marts provide support for the analysis of publicly available genomes, expert review of Metagenome annotations (IMG/M ER: http://img.jgi.doe.gov/mer) and Human Microbiome Project (HMP)-specific Metagenome samples (IMG/M HMP: http://img.jgi.doe.gov/imgm_hmp).

  • IMG/M 4 version of the integrated Metagenome comparative analysis system.
    Nucleic acids research, 2013
    Co-Authors: Victor M. Markowitz, Ken Chu, Ernest Szeto, Krishna Palaniappan, I-min A. Chen, Anna Ratner, Ioanna Pagani, Manoj Pillay, Jinghua Huang, Susannah G. Tringe
    Abstract:

    IMG/M (http://img.jgi.doe.gov/m) provides support for comparative analysis of microbial community aggregate genomes (Metagenomes) in the context of a comprehensive set of reference genomes from all three domains of life, as well as plasmids, viruses and genome fragments. IMG/M's data content and analytical tools have expanded continuously since its first version was released in 2007. Since the last report published in the 2012 NAR Database Issue, IMG/M's database architecture, annotation and data integration pipelines and analysis tools have been extended to copewith the rapid growth in the number and size of Metagenome data sets handled by the system. IMG/M data marts provide support for the analysis of publicly available genomes, expert review of Metagenome annotations (IMG/M ER: http://img.jgi.doe.gov/mer) and Human Microbiome Project (HMP)-specific Metagenome samples (IMG/M HMP: http://img.jgi.doe.gov/imgm_hmp).

  • IMG/M-HMP: A Metagenome Comparative Analysis System for the Human Microbiome Project
    PloS one, 2012
    Co-Authors: Victor M. Markowitz, Ken Chu, Ernest Szeto, Krishna Palaniappan, I-min A. Chen, Biju Jacob, Anna Ratner, Konstantinos Liolios, Ioanna Pagani, Marcel Huntemann
    Abstract:

    The Integrated Microbial Genomes and Metagenomes (IMG/M) resource is a data management system that supports the analysis of sequence data from microbial communities in the integrated context of all publicly available draft and complete genomes from the three domains of life as well as a large number of plasmids and viruses. IMG/M currently contains thousands of genomes and Metagenome samples with billions of genes. IMG/M-HMP is an IMG/M data mart serving the US National Institutes of Health (NIH) Human Microbiome Project (HMP), focussed on HMP generated Metagenome datasets, and is one of the central resources provided from the HMP Data Analysis and Coordination Center (DACC). IMG/M-HMP is available at http://www.hmpdacc-resources.org/imgm_hmp/.

  • img m hmp a Metagenome comparative analysis system for the human microbiome project
    PLOS ONE, 2012
    Co-Authors: Victor M. Markowitz, Ken Chu, Ernest Szeto, Krishna Palaniappan, I-min A. Chen, Biju Jacob, Anna Ratner, Konstantinos Liolios, Ioanna Pagani, Marcel Huntemann
    Abstract:

    The Integrated Microbial Genomes and Metagenomes (IMG/M) resource is a data management system that supports the analysis of sequence data from microbial communities in the integrated context of all publicly available draft and complete genomes from the three domains of life as well as a large number of plasmids and viruses. IMG/M currently contains thousands of genomes and Metagenome samples with billions of genes. IMG/M-HMP is an IMG/M data mart serving the US National Institutes of Health (NIH) Human Microbiome Project (HMP), focussed on HMP generated Metagenome datasets, and is one of the central resources provided from the HMP Data Analysis and Coordination Center (DACC). IMG/M-HMP is available at http://www.hmpdacc-resources.org/imgm_hmp/.

  • IMG/M: A data management and analysis system for Metagenomes
    Nucleic Acids Research, 2008
    Co-Authors: Victor M. Markowitz, I. M.a. Chen, Ken Chu, Yuri Grechkin, Daniel Dalevi, Natalia N. Ivanova, Ernest Szeto, Krishna Palaniappan, Inna Dubchak, Iain Anderson
    Abstract:

    IMG/M is a data management and analysis system for microbial community genomes (Metagenomes) hosted at the Department of Energy's (DOE) Joint Genome Institute (JGI). IMG/M consists of Metagenome data integrated with isolate microbial genomes from the Integrated Microbial Genomes (IMG) system. IMG/M provides IMG's comparative data analysis tools extended to handle Metagenome data, together with Metagenome-specific analysis tools. IMG/M is available at http://img.jgi.doe.gov/m.