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Taxator-tk: precise taxonomic assignment of metagenomes by fast approximation of evolutionary neighborhoods

Motivation: Metagenomics characterizes microbial communities by random shotgun sequencing of DNA isolated directly from an environment of interest. An essential step in computational metagenome analysis is taxonomic sequence assignment, which allows identifying the sequenced community members and re...

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Autores principales: Dröge, J., Gregor, I., McHardy, A. C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4380030/
https://www.ncbi.nlm.nih.gov/pubmed/25388150
http://dx.doi.org/10.1093/bioinformatics/btu745
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author Dröge, J.
Gregor, I.
McHardy, A. C.
author_facet Dröge, J.
Gregor, I.
McHardy, A. C.
author_sort Dröge, J.
collection PubMed
description Motivation: Metagenomics characterizes microbial communities by random shotgun sequencing of DNA isolated directly from an environment of interest. An essential step in computational metagenome analysis is taxonomic sequence assignment, which allows identifying the sequenced community members and reconstructing taxonomic bins with sequence data for the individual taxa. For the massive datasets generated by next-generation sequencing technologies, this cannot be performed with de-novo phylogenetic inference methods. We describe an algorithm and the accompanying software, taxator-tk, which performs taxonomic sequence assignment by fast approximate determination of evolutionary neighbors from sequence similarities. Results: Taxator-tk was precise in its taxonomic assignment across all ranks and taxa for a range of evolutionary distances and for short as well as for long sequences. In addition to the taxonomic binning of metagenomes, it is well suited for profiling microbial communities from metagenome samples because it identifies bacterial, archaeal and eukaryotic community members without being affected by varying primer binding strengths, as in marker gene amplification, or copy number variations of marker genes across different taxa. Taxator-tk has an efficient, parallelized implementation that allows the assignment of 6 Gb of sequence data per day on a standard multiprocessor system with 10 CPU cores and microbial RefSeq as the genomic reference data. Availability and implementation: Taxator-tk source and binary program files are publicly available at http://algbio.cs.uni-duesseldorf.de/software/. Contact: Alice.McHardy@uni-duesseldorf.de Supplementary information: Supplementary data are available at Bioinformatics online.
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spelling pubmed-43800302015-04-15 Taxator-tk: precise taxonomic assignment of metagenomes by fast approximation of evolutionary neighborhoods Dröge, J. Gregor, I. McHardy, A. C. Bioinformatics Original Papers Motivation: Metagenomics characterizes microbial communities by random shotgun sequencing of DNA isolated directly from an environment of interest. An essential step in computational metagenome analysis is taxonomic sequence assignment, which allows identifying the sequenced community members and reconstructing taxonomic bins with sequence data for the individual taxa. For the massive datasets generated by next-generation sequencing technologies, this cannot be performed with de-novo phylogenetic inference methods. We describe an algorithm and the accompanying software, taxator-tk, which performs taxonomic sequence assignment by fast approximate determination of evolutionary neighbors from sequence similarities. Results: Taxator-tk was precise in its taxonomic assignment across all ranks and taxa for a range of evolutionary distances and for short as well as for long sequences. In addition to the taxonomic binning of metagenomes, it is well suited for profiling microbial communities from metagenome samples because it identifies bacterial, archaeal and eukaryotic community members without being affected by varying primer binding strengths, as in marker gene amplification, or copy number variations of marker genes across different taxa. Taxator-tk has an efficient, parallelized implementation that allows the assignment of 6 Gb of sequence data per day on a standard multiprocessor system with 10 CPU cores and microbial RefSeq as the genomic reference data. Availability and implementation: Taxator-tk source and binary program files are publicly available at http://algbio.cs.uni-duesseldorf.de/software/. Contact: Alice.McHardy@uni-duesseldorf.de Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2015-03-15 2014-11-10 /pmc/articles/PMC4380030/ /pubmed/25388150 http://dx.doi.org/10.1093/bioinformatics/btu745 Text en © The Author 2014. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Papers
Dröge, J.
Gregor, I.
McHardy, A. C.
Taxator-tk: precise taxonomic assignment of metagenomes by fast approximation of evolutionary neighborhoods
title Taxator-tk: precise taxonomic assignment of metagenomes by fast approximation of evolutionary neighborhoods
title_full Taxator-tk: precise taxonomic assignment of metagenomes by fast approximation of evolutionary neighborhoods
title_fullStr Taxator-tk: precise taxonomic assignment of metagenomes by fast approximation of evolutionary neighborhoods
title_full_unstemmed Taxator-tk: precise taxonomic assignment of metagenomes by fast approximation of evolutionary neighborhoods
title_short Taxator-tk: precise taxonomic assignment of metagenomes by fast approximation of evolutionary neighborhoods
title_sort taxator-tk: precise taxonomic assignment of metagenomes by fast approximation of evolutionary neighborhoods
topic Original Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4380030/
https://www.ncbi.nlm.nih.gov/pubmed/25388150
http://dx.doi.org/10.1093/bioinformatics/btu745
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