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BugSeq: a highly accurate cloud platform for long-read metagenomic analyses

BACKGROUND: As the use of nanopore sequencing for metagenomic analysis increases, tools capable of performing long-read taxonomic classification (ie. determining the composition of a sample) in a fast and accurate manner are needed. Existing tools were either designed for short-read data (eg. Centri...

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Autores principales: Fan, Jeremy, Huang, Steven, Chorlton, Samuel D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7993542/
https://www.ncbi.nlm.nih.gov/pubmed/33765910
http://dx.doi.org/10.1186/s12859-021-04089-5
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author Fan, Jeremy
Huang, Steven
Chorlton, Samuel D.
author_facet Fan, Jeremy
Huang, Steven
Chorlton, Samuel D.
author_sort Fan, Jeremy
collection PubMed
description BACKGROUND: As the use of nanopore sequencing for metagenomic analysis increases, tools capable of performing long-read taxonomic classification (ie. determining the composition of a sample) in a fast and accurate manner are needed. Existing tools were either designed for short-read data (eg. Centrifuge), take days to analyse modern sequencer outputs (eg. MetaMaps) or suffer from suboptimal accuracy (eg. CDKAM). Additionally, all tools require command line expertise and do not scale in the cloud. RESULTS: We present BugSeq, a novel, highly accurate metagenomic classifier for nanopore reads. We evaluate BugSeq on simulated data, mock microbial communities and real clinical samples. On the ZymoBIOMICS Even and Log communities, BugSeq (F1 = 0.95 at species level) offers better read classification than MetaMaps (F1 = 0.89–0.94) in a fraction of the time. BugSeq significantly improves on the accuracy of Centrifuge (F1 = 0.79–0.93) and CDKAM (F1 = 0.91–0.94) while offering competitive run times. When applied to 41 samples from patients with lower respiratory tract infections, BugSeq produces greater concordance with microbiological culture and qPCR compared with “What’s In My Pot” analysis. CONCLUSION: BugSeq is deployed to the cloud for easy and scalable long-read metagenomic analyses. BugSeq is freely available for non-commercial use at https://bugseq.com/free. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-021-04089-5.
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spelling pubmed-79935422021-03-26 BugSeq: a highly accurate cloud platform for long-read metagenomic analyses Fan, Jeremy Huang, Steven Chorlton, Samuel D. BMC Bioinformatics Software BACKGROUND: As the use of nanopore sequencing for metagenomic analysis increases, tools capable of performing long-read taxonomic classification (ie. determining the composition of a sample) in a fast and accurate manner are needed. Existing tools were either designed for short-read data (eg. Centrifuge), take days to analyse modern sequencer outputs (eg. MetaMaps) or suffer from suboptimal accuracy (eg. CDKAM). Additionally, all tools require command line expertise and do not scale in the cloud. RESULTS: We present BugSeq, a novel, highly accurate metagenomic classifier for nanopore reads. We evaluate BugSeq on simulated data, mock microbial communities and real clinical samples. On the ZymoBIOMICS Even and Log communities, BugSeq (F1 = 0.95 at species level) offers better read classification than MetaMaps (F1 = 0.89–0.94) in a fraction of the time. BugSeq significantly improves on the accuracy of Centrifuge (F1 = 0.79–0.93) and CDKAM (F1 = 0.91–0.94) while offering competitive run times. When applied to 41 samples from patients with lower respiratory tract infections, BugSeq produces greater concordance with microbiological culture and qPCR compared with “What’s In My Pot” analysis. CONCLUSION: BugSeq is deployed to the cloud for easy and scalable long-read metagenomic analyses. BugSeq is freely available for non-commercial use at https://bugseq.com/free. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-021-04089-5. BioMed Central 2021-03-25 /pmc/articles/PMC7993542/ /pubmed/33765910 http://dx.doi.org/10.1186/s12859-021-04089-5 Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.
spellingShingle Software
Fan, Jeremy
Huang, Steven
Chorlton, Samuel D.
BugSeq: a highly accurate cloud platform for long-read metagenomic analyses
title BugSeq: a highly accurate cloud platform for long-read metagenomic analyses
title_full BugSeq: a highly accurate cloud platform for long-read metagenomic analyses
title_fullStr BugSeq: a highly accurate cloud platform for long-read metagenomic analyses
title_full_unstemmed BugSeq: a highly accurate cloud platform for long-read metagenomic analyses
title_short BugSeq: a highly accurate cloud platform for long-read metagenomic analyses
title_sort bugseq: a highly accurate cloud platform for long-read metagenomic analyses
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7993542/
https://www.ncbi.nlm.nih.gov/pubmed/33765910
http://dx.doi.org/10.1186/s12859-021-04089-5
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