Mumame: a software tool for quantifying gene-specific point-mutations in shotgun metagenomic data

Metagenomics has emerged as a central technique for studying the structure and function of microbial communities. Often the functional analysis is restricted to classification into broad functional categories. However, important phenotypic differences, such as resistance to antibio...

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Main Authors: Shruthi Magesh, Viktor Jonsson, Johan Bengtsson-Palme
Format: Article
Language:English
Published: Pensoft Publishers 2019-09-01
Series:Metabarcoding and Metagenomics
Online Access:https://mbmg.pensoft.net/article/36236/download/pdf/
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spelling doaj-69c1869819d2417980553b2f6470332d2020-11-25T00:46:46ZengPensoft PublishersMetabarcoding and Metagenomics 2534-97082019-09-013596710.3897/mbmg.3.3623636236Mumame: a software tool for quantifying gene-specific point-mutations in shotgun metagenomic dataShruthi Magesh0Viktor Jonsson1Johan Bengtsson-Palme2University of Wisconsin-MadisonChalmers University of TechnologyUniversity of Gothenburg Metagenomics has emerged as a central technique for studying the structure and function of microbial communities. Often the functional analysis is restricted to classification into broad functional categories. However, important phenotypic differences, such as resistance to antibiotics, are often the result of just one or a few point mutations in otherwise identical sequences. Bioinformatic methods for metagenomic analysis have generally been poor at accounting for this fact, resulting in a somewhat limited picture of important aspects of microbial communities. Here, we address this problem by providing a software tool called Mumame, which can distinguish between wildtype and mutated sequences in shotgun metagenomic data and quantify their relative abundances. We demonstrate the utility of the tool by quantifying antibiotic resistance mutations in several publicly available metagenomic data sets. We also identified that sequencing depth is a key factor to detect rare mutations. Therefore, much larger numbers of sequences may be required for reliable detection of mutations than for most other applications of shotgun metagenomics. Mumame is freely available online (http://microbiology.se/software/mumame). https://mbmg.pensoft.net/article/36236/download/pdf/
collection DOAJ
language English
format Article
sources DOAJ
author Shruthi Magesh
Viktor Jonsson
Johan Bengtsson-Palme
spellingShingle Shruthi Magesh
Viktor Jonsson
Johan Bengtsson-Palme
Mumame: a software tool for quantifying gene-specific point-mutations in shotgun metagenomic data
Metabarcoding and Metagenomics
author_facet Shruthi Magesh
Viktor Jonsson
Johan Bengtsson-Palme
author_sort Shruthi Magesh
title Mumame: a software tool for quantifying gene-specific point-mutations in shotgun metagenomic data
title_short Mumame: a software tool for quantifying gene-specific point-mutations in shotgun metagenomic data
title_full Mumame: a software tool for quantifying gene-specific point-mutations in shotgun metagenomic data
title_fullStr Mumame: a software tool for quantifying gene-specific point-mutations in shotgun metagenomic data
title_full_unstemmed Mumame: a software tool for quantifying gene-specific point-mutations in shotgun metagenomic data
title_sort mumame: a software tool for quantifying gene-specific point-mutations in shotgun metagenomic data
publisher Pensoft Publishers
series Metabarcoding and Metagenomics
issn 2534-9708
publishDate 2019-09-01
description Metagenomics has emerged as a central technique for studying the structure and function of microbial communities. Often the functional analysis is restricted to classification into broad functional categories. However, important phenotypic differences, such as resistance to antibiotics, are often the result of just one or a few point mutations in otherwise identical sequences. Bioinformatic methods for metagenomic analysis have generally been poor at accounting for this fact, resulting in a somewhat limited picture of important aspects of microbial communities. Here, we address this problem by providing a software tool called Mumame, which can distinguish between wildtype and mutated sequences in shotgun metagenomic data and quantify their relative abundances. We demonstrate the utility of the tool by quantifying antibiotic resistance mutations in several publicly available metagenomic data sets. We also identified that sequencing depth is a key factor to detect rare mutations. Therefore, much larger numbers of sequences may be required for reliable detection of mutations than for most other applications of shotgun metagenomics. Mumame is freely available online (http://microbiology.se/software/mumame).
url https://mbmg.pensoft.net/article/36236/download/pdf/
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