Mumame – Quantifying mutations in metagenomes

Let me get straight to something somewhat besides the point here: summer students can achieve amazing things! One such student I had the pleasure to work with this summer is Shruthi Magesh, and a preprint based on work she did with me at the Wisconsin Institute for Discovery this summer just got published on bioRxiv (1). The preprint describes a software tool called Mumame, which uses database information on mutations in DNA or protein sequences to search metagenomic datasets and quantifies the relative proportion of resistance mutations over wild type sequences.

In the preprint (1), we first of all show that Mumame works on amplicon data where we already knew the true outcome (2). Second, we show that we can detect differences in mutation frequencies in controlled experiments (2,3). Lastly, we use the tool to gain some further information about resistance patterns in sediments from polluted environments in India (4,5). Together these analyses show that one of the most central aspects for Mumame to be able to find mutations is having a very high number of sequenced reads in all libraries (preferably more than 50 million per library), because these mutations are generally rare – even in polluted environments and microcosms exposed to antibiotics. We expect Mumame to be a useful addition to metagenomic studies of e.g. antibiotic resistance, and to increase the detail by which metagenomes can be screened for phenotypically important differences.

While I did write the code for the software (with a lot of input from Viktor Jonsson, who also is a coauthor on the preprint, on the statistical analysis), Shruthi did the software testing and evaluations, and the paper would not have been possible hadn’t she wanted a bioinformatic summer project related to metagenomics, aside from her laboratory work. The resulting preprint is available from bioRxiv and the Mumame software is freely available from this site.

References

1. Magesh S, Jonsson V, Bengtsson-Palme J: Quantifying point-mutations in metagenomic data. bioRxiv, 438572 (2018). doi: 10.1101/438572 [Link]
2. Kraupner N, Ebmeyer S, Bengtsson-Palme J, Fick J, Kristiansson E, Flach C-F, Larsson DGJ: Selective concentration for ciprofloxacin in Escherichia coli grown in complex aquatic bacterial biofilms. Environment International, 116, 255–268 (2018). doi: 10.1016/j.envint.2018.04.029 [Paper link]
3. Lundström S, Östman M, Bengtsson-Palme J, Rutgersson C, Thoudal M, Sircar T, Blanck H, Eriksson KM, Tysklind M, Flach C-F, Larsson DGJ: Minimal selective concentrations of tetracycline in complex aquatic bacterial biofilms. Science of the Total Environment, 553, 587–595 (2016). doi: 10.1016/j.scitotenv.2016.02.103 [Paper link]
4. Bengtsson-Palme J, Boulund F, Fick J, Kristiansson E, Larsson DGJ: Shotgun metagenomics reveals a wide array of antibiotic resistance genes and mobile elements in a polluted lake in India. Frontiers in Microbiology, 5, 648 (2014). doi: 10.3389/fmicb.2014.00648 [Paper link]
5. Kristiansson E, Fick J, Janzon A, Grabic R, Rutgersson C, Weijdegård B, Söderström H, Larsson DGJ: Pyrosequencing of antibiotic-contaminated river sediments reveals high levels of resistance and gene transfer elements. PLoS ONE, Volume 6, e17038 (2011). doi:10.1371/journal.pone.0017038.