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MeCorS: Metagenome-enabled error correction of single cell sequencing reads

Summary: We present a new tool, MeCorS, to correct chimeric reads and sequencing errors in Illumina data generated from single amplified genomes (SAGs). It uses sequence information derived from accompanying metagenome sequencing to accurately correct errors in SAG reads, even from ultra-low coverag...

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Detalles Bibliográficos
Autores principales: Bremges, Andreas, Singer, Esther, Woyke, Tanja, Sczyrba, Alexander
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4937190/
https://www.ncbi.nlm.nih.gov/pubmed/27153586
http://dx.doi.org/10.1093/bioinformatics/btw144
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author Bremges, Andreas
Singer, Esther
Woyke, Tanja
Sczyrba, Alexander
author_facet Bremges, Andreas
Singer, Esther
Woyke, Tanja
Sczyrba, Alexander
author_sort Bremges, Andreas
collection PubMed
description Summary: We present a new tool, MeCorS, to correct chimeric reads and sequencing errors in Illumina data generated from single amplified genomes (SAGs). It uses sequence information derived from accompanying metagenome sequencing to accurately correct errors in SAG reads, even from ultra-low coverage regions. In evaluations on real data, we show that MeCorS outperforms BayesHammer, the most widely used state-of-the-art approach. MeCorS performs particularly well in correcting chimeric reads, which greatly improves both accuracy and contiguity of de novo SAG assemblies. Availability and implementation: https://github.com/metagenomics/MeCorS Contact: abremges@cebitec.uni-bielefeld.de Supplementary information: Supplementary data are available at Bioinformatics online.
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spelling pubmed-49371902016-07-11 MeCorS: Metagenome-enabled error correction of single cell sequencing reads Bremges, Andreas Singer, Esther Woyke, Tanja Sczyrba, Alexander Bioinformatics Applications Notes Summary: We present a new tool, MeCorS, to correct chimeric reads and sequencing errors in Illumina data generated from single amplified genomes (SAGs). It uses sequence information derived from accompanying metagenome sequencing to accurately correct errors in SAG reads, even from ultra-low coverage regions. In evaluations on real data, we show that MeCorS outperforms BayesHammer, the most widely used state-of-the-art approach. MeCorS performs particularly well in correcting chimeric reads, which greatly improves both accuracy and contiguity of de novo SAG assemblies. Availability and implementation: https://github.com/metagenomics/MeCorS Contact: abremges@cebitec.uni-bielefeld.de Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2016-07-15 2016-03-15 /pmc/articles/PMC4937190/ /pubmed/27153586 http://dx.doi.org/10.1093/bioinformatics/btw144 Text en © The Author 2016. 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 Applications Notes
Bremges, Andreas
Singer, Esther
Woyke, Tanja
Sczyrba, Alexander
MeCorS: Metagenome-enabled error correction of single cell sequencing reads
title MeCorS: Metagenome-enabled error correction of single cell sequencing reads
title_full MeCorS: Metagenome-enabled error correction of single cell sequencing reads
title_fullStr MeCorS: Metagenome-enabled error correction of single cell sequencing reads
title_full_unstemmed MeCorS: Metagenome-enabled error correction of single cell sequencing reads
title_short MeCorS: Metagenome-enabled error correction of single cell sequencing reads
title_sort mecors: metagenome-enabled error correction of single cell sequencing reads
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4937190/
https://www.ncbi.nlm.nih.gov/pubmed/27153586
http://dx.doi.org/10.1093/bioinformatics/btw144
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