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EsMeCaTa: Estimating metabolic capabilities from taxonomic affiliations

Summary Predicting the functional potential of microorganisms in environmental samples from cultivation-independent techniques is a major challenge. A persistent difficulty lies in associating taxonomic profiles obtained from metabarcoding experiment with accurate functional profiles, particularly for poorly-resolved taxonomic groups. In this paper, we present EsMeCaTa a python package predicting shared proteins from taxonomic affiliations. EsMeCaTa relies on the UniProt database to retrieve the public proteomes associated with a taxon and then uses MMseqs2 in order to compute the set of proteins shared in the taxon. Finally, Es-MeCaTa extracts the functional annotations of these proteins to provide an accurate estimate of the functional potential associated to taxonomic affiliations. Availability EsMeCaTa is available at: https://github.com/AuReMe/esmecata under the GPL-3 license.