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Ray Meta: scalable de novo metagenome assembly and profiling
Voluminous parallel sequencing datasets, especially metagenomic experiments, require distributed computing for de novo assembly and taxonomic profiling. Ray Meta is a massively distributed metagenome assembler that is coupled with Ray Communities, which profiles microbiomes based on uniquely-colored...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4056372/ https://www.ncbi.nlm.nih.gov/pubmed/23259615 http://dx.doi.org/10.1186/gb-2012-13-12-r122 |
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author | Boisvert, Sébastien Raymond, Frédéric Godzaridis, Élénie Laviolette, François Corbeil, Jacques |
author_facet | Boisvert, Sébastien Raymond, Frédéric Godzaridis, Élénie Laviolette, François Corbeil, Jacques |
author_sort | Boisvert, Sébastien |
collection | PubMed |
description | Voluminous parallel sequencing datasets, especially metagenomic experiments, require distributed computing for de novo assembly and taxonomic profiling. Ray Meta is a massively distributed metagenome assembler that is coupled with Ray Communities, which profiles microbiomes based on uniquely-colored k-mers. It can accurately assemble and profile a three billion read metagenomic experiment representing 1,000 bacterial genomes of uneven proportions in 15 hours with 1,024 processor cores, using only 1.5 GB per core. The software will facilitate the processing of large and complex datasets, and will help in generating biological insights for specific environments. Ray Meta is open source and available at http://denovoassembler.sf.net. |
format | Online Article Text |
id | pubmed-4056372 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-40563722014-06-13 Ray Meta: scalable de novo metagenome assembly and profiling Boisvert, Sébastien Raymond, Frédéric Godzaridis, Élénie Laviolette, François Corbeil, Jacques Genome Biol Method Voluminous parallel sequencing datasets, especially metagenomic experiments, require distributed computing for de novo assembly and taxonomic profiling. Ray Meta is a massively distributed metagenome assembler that is coupled with Ray Communities, which profiles microbiomes based on uniquely-colored k-mers. It can accurately assemble and profile a three billion read metagenomic experiment representing 1,000 bacterial genomes of uneven proportions in 15 hours with 1,024 processor cores, using only 1.5 GB per core. The software will facilitate the processing of large and complex datasets, and will help in generating biological insights for specific environments. Ray Meta is open source and available at http://denovoassembler.sf.net. BioMed Central 2012 2012-12-22 /pmc/articles/PMC4056372/ /pubmed/23259615 http://dx.doi.org/10.1186/gb-2012-13-12-r122 Text en Copyright © 2012 Boisvert et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Method Boisvert, Sébastien Raymond, Frédéric Godzaridis, Élénie Laviolette, François Corbeil, Jacques Ray Meta: scalable de novo metagenome assembly and profiling |
title | Ray Meta: scalable de novo metagenome assembly and profiling |
title_full | Ray Meta: scalable de novo metagenome assembly and profiling |
title_fullStr | Ray Meta: scalable de novo metagenome assembly and profiling |
title_full_unstemmed | Ray Meta: scalable de novo metagenome assembly and profiling |
title_short | Ray Meta: scalable de novo metagenome assembly and profiling |
title_sort | ray meta: scalable de novo metagenome assembly and profiling |
topic | Method |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4056372/ https://www.ncbi.nlm.nih.gov/pubmed/23259615 http://dx.doi.org/10.1186/gb-2012-13-12-r122 |
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