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Assembling metagenomes, one community at a time
BACKGROUND: Metagenomics allows unprecedented access to uncultured environmental microorganisms. The analysis of metagenomic sequences facilitates gene prediction and annotation, and enables the assembly of draft genomes, including uncultured members of a community. However, while several platforms...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5502489/ https://www.ncbi.nlm.nih.gov/pubmed/28693474 http://dx.doi.org/10.1186/s12864-017-3918-9 |
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author | van der Walt, Andries Johannes van Goethem, Marc Warwick Ramond, Jean-Baptiste Makhalanyane, Thulani Peter Reva, Oleg Cowan, Don Arthur |
author_facet | van der Walt, Andries Johannes van Goethem, Marc Warwick Ramond, Jean-Baptiste Makhalanyane, Thulani Peter Reva, Oleg Cowan, Don Arthur |
author_sort | van der Walt, Andries Johannes |
collection | PubMed |
description | BACKGROUND: Metagenomics allows unprecedented access to uncultured environmental microorganisms. The analysis of metagenomic sequences facilitates gene prediction and annotation, and enables the assembly of draft genomes, including uncultured members of a community. However, while several platforms have been developed for this critical step, there is currently no clear framework for the assembly of metagenomic sequence data. RESULTS: To assist with selection of an appropriate metagenome assembler we evaluated the capabilities of nine prominent assembly tools on nine publicly-available environmental metagenomes, as well as three simulated datasets. Overall, we found that SPAdes provided the largest contigs and highest N50 values across 6 of the 9 environmental datasets, followed by MEGAHIT and metaSPAdes. MEGAHIT emerged as a computationally inexpensive alternative to SPAdes, assembling the most complex dataset using less than 500 GB of RAM and within 10 hours. CONCLUSIONS: We found that assembler choice ultimately depends on the scientific question, the available resources and the bioinformatic competence of the researcher. We provide a concise workflow for the selection of the best assembly tool. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-017-3918-9) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5502489 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-55024892017-07-12 Assembling metagenomes, one community at a time van der Walt, Andries Johannes van Goethem, Marc Warwick Ramond, Jean-Baptiste Makhalanyane, Thulani Peter Reva, Oleg Cowan, Don Arthur BMC Genomics Research Article BACKGROUND: Metagenomics allows unprecedented access to uncultured environmental microorganisms. The analysis of metagenomic sequences facilitates gene prediction and annotation, and enables the assembly of draft genomes, including uncultured members of a community. However, while several platforms have been developed for this critical step, there is currently no clear framework for the assembly of metagenomic sequence data. RESULTS: To assist with selection of an appropriate metagenome assembler we evaluated the capabilities of nine prominent assembly tools on nine publicly-available environmental metagenomes, as well as three simulated datasets. Overall, we found that SPAdes provided the largest contigs and highest N50 values across 6 of the 9 environmental datasets, followed by MEGAHIT and metaSPAdes. MEGAHIT emerged as a computationally inexpensive alternative to SPAdes, assembling the most complex dataset using less than 500 GB of RAM and within 10 hours. CONCLUSIONS: We found that assembler choice ultimately depends on the scientific question, the available resources and the bioinformatic competence of the researcher. We provide a concise workflow for the selection of the best assembly tool. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-017-3918-9) contains supplementary material, which is available to authorized users. BioMed Central 2017-07-10 /pmc/articles/PMC5502489/ /pubmed/28693474 http://dx.doi.org/10.1186/s12864-017-3918-9 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article van der Walt, Andries Johannes van Goethem, Marc Warwick Ramond, Jean-Baptiste Makhalanyane, Thulani Peter Reva, Oleg Cowan, Don Arthur Assembling metagenomes, one community at a time |
title | Assembling metagenomes, one community at a time |
title_full | Assembling metagenomes, one community at a time |
title_fullStr | Assembling metagenomes, one community at a time |
title_full_unstemmed | Assembling metagenomes, one community at a time |
title_short | Assembling metagenomes, one community at a time |
title_sort | assembling metagenomes, one community at a time |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5502489/ https://www.ncbi.nlm.nih.gov/pubmed/28693474 http://dx.doi.org/10.1186/s12864-017-3918-9 |
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