Exploring microbial functional biodiversity at the protein family level-From metagenomic sequence reads to annotated protein clusters

Fotis A Baltoumas; Evangelos Karatzas; David Paez-Espino; Nefeli K Venetsianou; Eleni Aplakidou; Anastasis Oulas; Robert D Finn; Sergey Ovchinnikov; Evangelos Pafilis; Nikos C Kyrpides; Georgios A Pavlopoulos

doi:10.3389/fbinf.2023.1157956

Exploring microbial functional biodiversity at the protein family level-From metagenomic sequence reads to annotated protein clusters

Front Bioinform. 2023 Mar 3:3:1157956. doi: 10.3389/fbinf.2023.1157956. eCollection 2023.

Affiliations

¹ Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece.
² Lawrence Berkeley National Laboratory, DOE Joint Genome Institute, Berkeley, CA, United States.
³ The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus.
⁴ European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, United Kingdom.
⁵ John Harvard Distinguished Science Fellowship Program, Harvard University, Cambridge, MA, United States.
⁶ Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Heraklion, Greece.
⁷ Center of New Biotechnologies and Precision Medicine, Department of Medicine, School of Health Sciences, National and Kapodistrian University of Athens, Athens, Greece.
⁸ Hellenic Army Academy, Vari, Greece.

Abstract

Metagenomics has enabled accessing the genetic repertoire of natural microbial communities. Metagenome shotgun sequencing has become the method of choice for studying and classifying microorganisms from various environments. To this end, several methods have been developed to process and analyze the sequence data from raw reads to end-products such as predicted protein sequences or families. In this article, we provide a thorough review to simplify such processes and discuss the alternative methodologies that can be followed in order to explore biodiversity at the protein family level. We provide details for analysis tools and we comment on their scalability as well as their advantages and disadvantages. Finally, we report the available data repositories and recommend various approaches for protein family annotation related to phylogenetic distribution, structure prediction and metadata enrichment.

Keywords: biodiversity; cluster annotation; metagenomes; metatranscriptomes; microbial dark matter; protein clustering; protein families.

Publication types

Review

Grants and funding

GP, FB, and EK were supported by HFRI (first call of research projects to support faculty members and researchers, Grant: HFRI-FM17-1855-BOLOGNA) and the Fondation Santé. EP was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the “second Call for H.F.R.I. Research Projects to support Faculty Members and Researchers” (Project Number: 2772). DP-E and NK were supported by the U.S. Department of Energy Joint Genome Institute (https://ror.org/04xm1d337), a DOE Office of Science User Facility, supported by the Office of Science of the U.S. Department of Energy operated under Contract No. DE-AC02-05CH11231. FB was also supported by Fondation Santé.