The Experts below are selected from a list of 93 Experts worldwide ranked by ideXlab platform
Jonathan E Allen - One of the best experts on this subject based on the ideXlab platform.
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scalable metagenomic Taxonomy Classification using a reference genome database
Bioinformatics, 2013Co-Authors: Sasha Ames, David Hysom, Shea N Gardner, Scott G Lloyd, Maya Gokhale, Jonathan E AllenAbstract:Motivation: Deep metagenomic sequencing of biological samples has the potential to recover otherwise difficult-to-detect microorganisms and accurately characterize biological samples with limited prior knowledge of sample contents. Existing metagenomic taxonomic Classification algorithms, however, do not scale well to analyze large metagenomic datasets, and balancing Classification accuracy with computational efficiency presents a fundamental challenge. Results: A method is presented to shift computational costs to an off-line computation by creating a Taxonomy/genome index that supports scalable metagenomic Classification. Scalable performance is demonstrated on real and simulated data to show accurate Classification in the presence of novel organisms on samples that include viruses, prokaryotes, fungi and protists. Taxonomic Classification of the previously published 150 giga-base Tyrolean Iceman dataset was found to take <20 h on a single node 40 core large memory machine and provide new insights on the metagenomic contents of the sample. Availability: Software was implemented in C++ and is freely available at http://sourceforge.net/projects/lmat Contact: vog.lnll@99nella Supplementary information: Supplementary data are available at Bioinformatics online.
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Scalable metagenomic Taxonomy Classification using a reference genome database
Bioinformatics, 2013Co-Authors: Sasha Ames, David Hysom, Shea N Gardner, Maya Gokhale, G. Scott Lloyd, Jonathan E AllenAbstract:Motivation: Deep metagenomic sequencing of biological samples has the potential to recover otherwise difficult-to-detect microorganisms and accurately characterize biological samples with limited prior knowledge of sample contents. Existing metagenomic taxonomic Classification algorithms, however, do not scale well to analyze large metagenomic datasets, and balancing Classification accuracy with computational efficiency presents a fundamental challenge. Results: A method is presented to shift computational costs to an off-line computation by creating a Taxonomy/genome index that supports scalable metagenomic Classification. Scalable performance is demonstrated on real and simulated data to show accurate Classification in the presence of novel organisms on samples that include viruses, prokaryotes, fungi and protists. Taxonomic Classification of the previously published 150 giga-base Tyrolean Iceman dataset was found to take
Sasha Ames - One of the best experts on this subject based on the ideXlab platform.
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scalable metagenomic Taxonomy Classification using a reference genome database
Bioinformatics, 2013Co-Authors: Sasha Ames, David Hysom, Shea N Gardner, Scott G Lloyd, Maya Gokhale, Jonathan E AllenAbstract:Motivation: Deep metagenomic sequencing of biological samples has the potential to recover otherwise difficult-to-detect microorganisms and accurately characterize biological samples with limited prior knowledge of sample contents. Existing metagenomic taxonomic Classification algorithms, however, do not scale well to analyze large metagenomic datasets, and balancing Classification accuracy with computational efficiency presents a fundamental challenge. Results: A method is presented to shift computational costs to an off-line computation by creating a Taxonomy/genome index that supports scalable metagenomic Classification. Scalable performance is demonstrated on real and simulated data to show accurate Classification in the presence of novel organisms on samples that include viruses, prokaryotes, fungi and protists. Taxonomic Classification of the previously published 150 giga-base Tyrolean Iceman dataset was found to take <20 h on a single node 40 core large memory machine and provide new insights on the metagenomic contents of the sample. Availability: Software was implemented in C++ and is freely available at http://sourceforge.net/projects/lmat Contact: vog.lnll@99nella Supplementary information: Supplementary data are available at Bioinformatics online.
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Scalable metagenomic Taxonomy Classification using a reference genome database
Bioinformatics, 2013Co-Authors: Sasha Ames, David Hysom, Shea N Gardner, Maya Gokhale, G. Scott Lloyd, Jonathan E AllenAbstract:Motivation: Deep metagenomic sequencing of biological samples has the potential to recover otherwise difficult-to-detect microorganisms and accurately characterize biological samples with limited prior knowledge of sample contents. Existing metagenomic taxonomic Classification algorithms, however, do not scale well to analyze large metagenomic datasets, and balancing Classification accuracy with computational efficiency presents a fundamental challenge. Results: A method is presented to shift computational costs to an off-line computation by creating a Taxonomy/genome index that supports scalable metagenomic Classification. Scalable performance is demonstrated on real and simulated data to show accurate Classification in the presence of novel organisms on samples that include viruses, prokaryotes, fungi and protists. Taxonomic Classification of the previously published 150 giga-base Tyrolean Iceman dataset was found to take
David Hysom - One of the best experts on this subject based on the ideXlab platform.
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scalable metagenomic Taxonomy Classification using a reference genome database
Bioinformatics, 2013Co-Authors: Sasha Ames, David Hysom, Shea N Gardner, Scott G Lloyd, Maya Gokhale, Jonathan E AllenAbstract:Motivation: Deep metagenomic sequencing of biological samples has the potential to recover otherwise difficult-to-detect microorganisms and accurately characterize biological samples with limited prior knowledge of sample contents. Existing metagenomic taxonomic Classification algorithms, however, do not scale well to analyze large metagenomic datasets, and balancing Classification accuracy with computational efficiency presents a fundamental challenge. Results: A method is presented to shift computational costs to an off-line computation by creating a Taxonomy/genome index that supports scalable metagenomic Classification. Scalable performance is demonstrated on real and simulated data to show accurate Classification in the presence of novel organisms on samples that include viruses, prokaryotes, fungi and protists. Taxonomic Classification of the previously published 150 giga-base Tyrolean Iceman dataset was found to take <20 h on a single node 40 core large memory machine and provide new insights on the metagenomic contents of the sample. Availability: Software was implemented in C++ and is freely available at http://sourceforge.net/projects/lmat Contact: vog.lnll@99nella Supplementary information: Supplementary data are available at Bioinformatics online.
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Scalable metagenomic Taxonomy Classification using a reference genome database
Bioinformatics, 2013Co-Authors: Sasha Ames, David Hysom, Shea N Gardner, Maya Gokhale, G. Scott Lloyd, Jonathan E AllenAbstract:Motivation: Deep metagenomic sequencing of biological samples has the potential to recover otherwise difficult-to-detect microorganisms and accurately characterize biological samples with limited prior knowledge of sample contents. Existing metagenomic taxonomic Classification algorithms, however, do not scale well to analyze large metagenomic datasets, and balancing Classification accuracy with computational efficiency presents a fundamental challenge. Results: A method is presented to shift computational costs to an off-line computation by creating a Taxonomy/genome index that supports scalable metagenomic Classification. Scalable performance is demonstrated on real and simulated data to show accurate Classification in the presence of novel organisms on samples that include viruses, prokaryotes, fungi and protists. Taxonomic Classification of the previously published 150 giga-base Tyrolean Iceman dataset was found to take
Shea N Gardner - One of the best experts on this subject based on the ideXlab platform.
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scalable metagenomic Taxonomy Classification using a reference genome database
Bioinformatics, 2013Co-Authors: Sasha Ames, David Hysom, Shea N Gardner, Scott G Lloyd, Maya Gokhale, Jonathan E AllenAbstract:Motivation: Deep metagenomic sequencing of biological samples has the potential to recover otherwise difficult-to-detect microorganisms and accurately characterize biological samples with limited prior knowledge of sample contents. Existing metagenomic taxonomic Classification algorithms, however, do not scale well to analyze large metagenomic datasets, and balancing Classification accuracy with computational efficiency presents a fundamental challenge. Results: A method is presented to shift computational costs to an off-line computation by creating a Taxonomy/genome index that supports scalable metagenomic Classification. Scalable performance is demonstrated on real and simulated data to show accurate Classification in the presence of novel organisms on samples that include viruses, prokaryotes, fungi and protists. Taxonomic Classification of the previously published 150 giga-base Tyrolean Iceman dataset was found to take <20 h on a single node 40 core large memory machine and provide new insights on the metagenomic contents of the sample. Availability: Software was implemented in C++ and is freely available at http://sourceforge.net/projects/lmat Contact: vog.lnll@99nella Supplementary information: Supplementary data are available at Bioinformatics online.
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Scalable metagenomic Taxonomy Classification using a reference genome database
Bioinformatics, 2013Co-Authors: Sasha Ames, David Hysom, Shea N Gardner, Maya Gokhale, G. Scott Lloyd, Jonathan E AllenAbstract:Motivation: Deep metagenomic sequencing of biological samples has the potential to recover otherwise difficult-to-detect microorganisms and accurately characterize biological samples with limited prior knowledge of sample contents. Existing metagenomic taxonomic Classification algorithms, however, do not scale well to analyze large metagenomic datasets, and balancing Classification accuracy with computational efficiency presents a fundamental challenge. Results: A method is presented to shift computational costs to an off-line computation by creating a Taxonomy/genome index that supports scalable metagenomic Classification. Scalable performance is demonstrated on real and simulated data to show accurate Classification in the presence of novel organisms on samples that include viruses, prokaryotes, fungi and protists. Taxonomic Classification of the previously published 150 giga-base Tyrolean Iceman dataset was found to take
Maya Gokhale - One of the best experts on this subject based on the ideXlab platform.
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scalable metagenomic Taxonomy Classification using a reference genome database
Bioinformatics, 2013Co-Authors: Sasha Ames, David Hysom, Shea N Gardner, Scott G Lloyd, Maya Gokhale, Jonathan E AllenAbstract:Motivation: Deep metagenomic sequencing of biological samples has the potential to recover otherwise difficult-to-detect microorganisms and accurately characterize biological samples with limited prior knowledge of sample contents. Existing metagenomic taxonomic Classification algorithms, however, do not scale well to analyze large metagenomic datasets, and balancing Classification accuracy with computational efficiency presents a fundamental challenge. Results: A method is presented to shift computational costs to an off-line computation by creating a Taxonomy/genome index that supports scalable metagenomic Classification. Scalable performance is demonstrated on real and simulated data to show accurate Classification in the presence of novel organisms on samples that include viruses, prokaryotes, fungi and protists. Taxonomic Classification of the previously published 150 giga-base Tyrolean Iceman dataset was found to take <20 h on a single node 40 core large memory machine and provide new insights on the metagenomic contents of the sample. Availability: Software was implemented in C++ and is freely available at http://sourceforge.net/projects/lmat Contact: vog.lnll@99nella Supplementary information: Supplementary data are available at Bioinformatics online.
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Scalable metagenomic Taxonomy Classification using a reference genome database
Bioinformatics, 2013Co-Authors: Sasha Ames, David Hysom, Shea N Gardner, Maya Gokhale, G. Scott Lloyd, Jonathan E AllenAbstract:Motivation: Deep metagenomic sequencing of biological samples has the potential to recover otherwise difficult-to-detect microorganisms and accurately characterize biological samples with limited prior knowledge of sample contents. Existing metagenomic taxonomic Classification algorithms, however, do not scale well to analyze large metagenomic datasets, and balancing Classification accuracy with computational efficiency presents a fundamental challenge. Results: A method is presented to shift computational costs to an off-line computation by creating a Taxonomy/genome index that supports scalable metagenomic Classification. Scalable performance is demonstrated on real and simulated data to show accurate Classification in the presence of novel organisms on samples that include viruses, prokaryotes, fungi and protists. Taxonomic Classification of the previously published 150 giga-base Tyrolean Iceman dataset was found to take
