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Open Access Technical Note

AXIOME: automated exploration of microbial diversity

Michael DJ Lynch, Andre P Masella, Michael W Hall, Andrea K Bartram and Josh D Neufeld*

Author Affiliations

Department of Biology, University of Waterloo, 200 University Avenue West, Waterloo, Ontario, N2L 3G1, Canada

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GigaScience 2013, 2:3  doi:10.1186/2047-217X-2-3

Published: 13 March 2013

Abstract

Background

Although high-throughput sequencing of small subunit rRNA genes has revolutionized our understanding of microbial ecosystems, these technologies generate data at depths that benefit from automated analysis. Here we present AXIOME (Automation, eXtension, and Integration Of Microbial Ecology), a highly flexible and extensible management tool for popular microbial ecology analysis packages that promotes reproducibility and customization in microbial research.

Findings

AXIOME streamlines and manages analysis of small subunit (SSU) rRNA marker data in QIIME and mothur. AXIOME also implements features including the PAired-eND Assembler for Illumina sequences (PANDAseq), non-negative matrix factorization (NMF), multi-response permutation procedures (MRPP), exploring and recovering phylogenetic novelty (SSUnique) and indicator species analysis. AXIOME has a companion graphical user interface (GUI) and is designed to be easily extended to facilitate customized research workflows.

Conclusions

AXIOME is an actively developed, open source project written in Vala and available from GitHub (http://neufeld.github.com/axiome webcite) and as a Debian package. Axiometic, a GUI companion tool is also freely available (http://neufeld.github.com/axiometic webcite). Given that data analysis has become an important bottleneck for microbial ecology studies, the development of user-friendly computational tools remains a high priority. AXIOME represents an important step in this direction by automating multi-step bioinformatic analyses and enabling the customization of procedures to suit the diverse research needs of the microbial ecology community.

Keywords:
Microbial ecology; Automation; SSU rRNA; High-throughput sequencing; QIIME; mothur