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taxMaps: comprehensive and highly accurate taxonomic classification of short-read data in reasonable time

High-throughput sequencing is a revolutionary technology for the analysis of metagenomic samples. However, querying large volumes of reads against comprehensive DNA/RNA databases in a sensitive manner can be compute-intensive. Here, we present taxMaps, a highly efficient, sensitive, and fully scalab...

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Detalles Bibliográficos
Autores principales: Corvelo, André, Clarke, Wayne E., Robine, Nicolas, Zody, Michael C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory Press 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5932614/
https://www.ncbi.nlm.nih.gov/pubmed/29588360
http://dx.doi.org/10.1101/gr.225276.117
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author Corvelo, André
Clarke, Wayne E.
Robine, Nicolas
Zody, Michael C.
author_facet Corvelo, André
Clarke, Wayne E.
Robine, Nicolas
Zody, Michael C.
author_sort Corvelo, André
collection PubMed
description High-throughput sequencing is a revolutionary technology for the analysis of metagenomic samples. However, querying large volumes of reads against comprehensive DNA/RNA databases in a sensitive manner can be compute-intensive. Here, we present taxMaps, a highly efficient, sensitive, and fully scalable taxonomic classification tool. Using a combination of simulated and real metagenomics data sets, we demonstrate that taxMaps is more sensitive and more precise than widely used taxonomic classifiers and is capable of delivering classification accuracy comparable to that of BLASTN, but at up to three orders of magnitude less computational cost.
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spelling pubmed-59326142018-11-01 taxMaps: comprehensive and highly accurate taxonomic classification of short-read data in reasonable time Corvelo, André Clarke, Wayne E. Robine, Nicolas Zody, Michael C. Genome Res Method High-throughput sequencing is a revolutionary technology for the analysis of metagenomic samples. However, querying large volumes of reads against comprehensive DNA/RNA databases in a sensitive manner can be compute-intensive. Here, we present taxMaps, a highly efficient, sensitive, and fully scalable taxonomic classification tool. Using a combination of simulated and real metagenomics data sets, we demonstrate that taxMaps is more sensitive and more precise than widely used taxonomic classifiers and is capable of delivering classification accuracy comparable to that of BLASTN, but at up to three orders of magnitude less computational cost. Cold Spring Harbor Laboratory Press 2018-05 /pmc/articles/PMC5932614/ /pubmed/29588360 http://dx.doi.org/10.1101/gr.225276.117 Text en © 2018 Corvelo et al.; Published by Cold Spring Harbor Laboratory Press http://creativecommons.org/licenses/by-nc/4.0/ This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the first six months after the full-issue publication date (see http://genome.cshlp.org/site/misc/terms.xhtml). After six months, it is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.
spellingShingle Method
Corvelo, André
Clarke, Wayne E.
Robine, Nicolas
Zody, Michael C.
taxMaps: comprehensive and highly accurate taxonomic classification of short-read data in reasonable time
title taxMaps: comprehensive and highly accurate taxonomic classification of short-read data in reasonable time
title_full taxMaps: comprehensive and highly accurate taxonomic classification of short-read data in reasonable time
title_fullStr taxMaps: comprehensive and highly accurate taxonomic classification of short-read data in reasonable time
title_full_unstemmed taxMaps: comprehensive and highly accurate taxonomic classification of short-read data in reasonable time
title_short taxMaps: comprehensive and highly accurate taxonomic classification of short-read data in reasonable time
title_sort taxmaps: comprehensive and highly accurate taxonomic classification of short-read data in reasonable time
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5932614/
https://www.ncbi.nlm.nih.gov/pubmed/29588360
http://dx.doi.org/10.1101/gr.225276.117
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