Your browser doesn't support javascript.
loading
OrtSuite: from genomes to prediction of microbial interactions within targeted ecosystem processes.
Saraiva, João Pedro; Bartholomäus, Alexandre; Kallies, René; Gomes, Marta; Bicalho, Marcos; Coelho Kasmanas, Jonas; Vogt, Carsten; Chatzinotas, Antonis; Stadler, Peter; Dias, Oscar; Nunes da Rocha, Ulisses.
Affiliation
  • Saraiva JP; Department of Environmental Microbiology, Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany.
  • Bartholomäus A; GFZ German Research Centre for Geosciences, Section Geomicrobiology, Potsdam, Germany.
  • Kallies R; Department of Environmental Microbiology, Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany.
  • Gomes M; Centre of Biological Engineering, University of Minho, Braga, Portugal.
  • Bicalho M; Department of Environmental Microbiology, Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany.
  • Coelho Kasmanas J; Department of Environmental Microbiology, Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany.
  • Vogt C; Institute of Mathematics and Computer Sciences, University of Sao Paulo, Sao Carlos, Brazil.
  • Chatzinotas A; Department of Computer Science, Bioinformatics Group, Interdisciplinary Center for Bioinformatics, and Competence Center for Scalable Data Services and Solutions Dresden/Leipzig, University of Leipzig, Leipzig, Germany.
  • Stadler P; Department of Environmental Microbiology, Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany.
  • Dias O; Department of Environmental Microbiology, Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany.
  • Nunes da Rocha U; Institute of Biology, Leipzig University, Leipzig, Germany.
Life Sci Alliance ; 4(12)2021 12.
Article in En | MEDLINE | ID: mdl-34580179
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. We evaluated the identification of putative synergistic species interactions using the sequenced genomes of an independent study that 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.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Bacteria / Software / Genome, Bacterial / Ecosystem / Microbial Interactions / Workflow Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: Life Sci Alliance Year: 2021 Document type: Article Affiliation country: Germany

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Bacteria / Software / Genome, Bacterial / Ecosystem / Microbial Interactions / Workflow Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: Life Sci Alliance Year: 2021 Document type: Article Affiliation country: Germany