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

Shruthi Magesh,Viktor Jonsson,Johan Bengtsson-Palme

doi:10.3897/mbmg.3.36236

Abstract

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).

Highlights

The revolution in sequencing capacity has created an unprecedented ability to glimpse into the functionality of microbial communities, using large-scale shotgun metagenomic techniques (Quince et al 2017)
As a proof-of-concept that our method to identify point mutations in metagenomic sequence data is functional, we used Mumame to quantify the mutations in amplicon data from the gyrA and parC genes
The exact reason for the discrepancies is unknown, but it is likely caused by a taxonomic filtration step that selects for E. coli reads used in the Kraupner et al (2018) study, while Mumame does not perform prior filtering

Summary

Introduction

The revolution in sequencing capacity has created an unprecedented ability to glimpse into the functionality of microbial communities, using large-scale shotgun metagenomic techniques (Quince et al 2017) This has yielded important insights into broad functional patterns of microbial consortia (Yooseph et al 2007; Human Microbiome Project Consortium 2012; Sunagawa et al 2015). Many antibiotic resistance phenotypes are the results of single point mutations rather than acquisition of novel pathways or genes (Johnning et al 2013) This complicates the studies of selection pressures in environmental communities as analysis of such mutations is generally limited to a narrow range of species (Johnning et al 2015a, 2015b; Kraupner et al 2018).

Methods

Results

Discussion

Conclusion