OrtSuite - from genomes to prediction of microbial interactions within targeted ecosystem processes

Saraiva, J. P.; Bartholomäus, A.; Kallies, R.; Gomes, M.; Fleming Bicalho, M. V.; Vogt, C.; Chatzinotas, A.; Stadler, P. F.; Dias, O.; Nunes da Rocha, U.

doi:10.1101/2021.06.04.447094

bioRxiv · 10.1101/2021.06.04.447094

OrtSuite - from genomes to prediction of microbial interactions within targeted ecosystem processes

Saraiva, J. P.·Bartholomäus, A.·Kallies, R.·Gomes, M.·Fleming Bicalho, M. V.·Vogt, C.·Chatzinotas, A.·Stadler, P. F.·Dias, O.·Nunes da Rocha, U.

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Abstract

The high complexity found in microbial communities makes the identification of microbial interactions challenging. To address this challenge, we present OrtSuite, a flexible workflow to predict putative microbial interactions based on genomic content of microbial communities and targeted to specific ecosystem processes. The pipeline is composed of three user-friendly bash commands. OrtSuite combines ortholog clustering with genome annotation strategies limited to user-defined sets of functions allowing for hypothesis-driven data analysis such as assessing microbial interactions in specific ecosystems. OrtSuite matched, on average, 96 % of experimentally verified KEGG orthologs involved in benzoate degradation in a known group of benzoate degraders. Identification of putative synergistic species interactions was evaluated using the sequenced genomes of an independent study which had previously proposed potential species interactions in benzoate degradation. OrtSuite is an easy to use workflow that allows for rapid functional annotation based on a user curated database and can easily be extended to ecosystem processes where connections between genes and reactions are known. OrtSuite is an open-source software available at https://github.com/mdsufz/OrtSuite.

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Saraiva, J. P., Bartholomäus, A., Kallies, R., Gomes, M., Fleming Bicalho, M. V., Vogt, C., Chatzinotas, A., Stadler, P. F., Dias, O., Nunes da Rocha, U.. 2021-06-07. OrtSuite - from genomes to prediction of microbial interactions within targeted ecosystem processes. https://doi.org/10.1101/2021.06.04.447094

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