Metaxa2 Diversity Tools: Easing microbial community analysis with Metaxa2
Artikel i vetenskaplig tidskrift, 2016

DNA sequencing has become an integrated part of microbial ecology, and taxonomic marker genes such as the SSU and LSU rRNA are frequently used to assess community structure. One solution for taxonomic community analysis based on shotgun metagenomic data is the Metaxa2 software, which can extract and classify sequence fragments belonging to the rRNA genes. This paper describes the Metaxa2 Diversity Tools, a set of new open-source software programs that extends the capabilities of the Metaxa2 software. These tools allow for better handling of data from multiple samples, improved species classifications, rarefaction analysis accounting for unclassified entries, and determination of significant differences in community composition of different samples. We demonstrate the performance of the software tools on rRNA data extracted from different shotgun metagenomes, and find the tools to streamline and improve the assessments of community diversity, particularly for samples from environments for which few reference genomes are available. Finally, we establish that our resampling algorithm for determining community dissimilarity is robust to differences in coverage depth, suggesting that it forms a complement to multidimensional visualization approaches for finding differences between communities. The Metaxa2 Diversity Tools are included in recent versions (2.1 and later) of Metaxa2 (http://microbiology.se/software/metaxa2/) and facilitate implementation of Metaxa2 within software pipelines for taxonomic analysis of environmental communities.

rRNA

Metagenomics

Rarefaction analysis

Community similarity

Microbial communities

Diversity assessment

Författare

Johan Bengtsson-Palme

Göteborgs universitet

Christian Wurzbacher

Göteborgs universitet

Åsa Sjöling

R. Henrik Nilsson

Göteborgs universitet

Ecological Informatics

1574-9541 (ISSN)

Vol. 33 45-50

Ämneskategorier

Ekologi

Mikrobiologi

Bioinformatik och systembiologi

DOI

10.1016/j.ecoinf.2016.04.004