Unifying the known and unknown microbial coding sequence space

Abstract

<jats:title>Abstract</jats:title><jats:p>Bridging the gap between the known and the unknown coding sequence space is one of the biggest challenges in molecular biology today. This challenge is especially extreme in microbiome analyses where between 40% to 60% of the coding sequences detected are of unknown function, and ignoring this fraction limits our understanding of microbial systems. Discarding the uncharacterized fraction is not an option anymore. Here, we present an in-depth exploration of the microbial unknown fraction through the lenses of a conceptual framework and a computational workflow we developed to unify the microbial known and unknown coding sequence space. Our approach partitions the coding sequence space in gene clusters and contextualizes them with genomic and environmental information. We analyzed 415,971,742 genes predicted from 1,749 metagenomes and 28,941 bacterial and archaeal genomes putting into perspective the extent of the unknown fraction, its diversity, and its relevance in a genomic and environmental context. With the identification of a target gene of unknown function for antibiotic resistance, we demonstrate how a contextualized unknown coding sequence space provides a robust framework for the generation of hypotheses that can be used to augment experimental data.</jats:p>

Functional repertoire convergence of distantly related eukaryotic plankton lineages revealed by genome-resolved metagenomics

“…Our current lack of understanding of many eukaryotic functional genes even within the scope of model organisms 58 can explain the limits of referencebased approaches to study the gene content of eukaryotic plankton. Thus, to gain further insights and overcome these limitations, we partitioned and categorized the eukaryotic gene content with AGNOSTOS 59 . AGNOSTOS grouped 5.4 million genes in 424,837 groups of genes sharing remote homologies, adding 2.3 million genes left uncharacterized by the EggNOG annotation.…”

“…Eukaryotic SMAGs integration in the AGNOSTOS-DB. We used the AGNOSTOS workflow to integrate the protein coding genes predicted from the SMAG into a variant of the AGNOSTOS-DB that contains 1,829 metagenomes from the marine and human microbiomes, 28,941 archaeal and bacterial genomes from the Genome Taxonomy Database (GTDB) and 3,243 nucleocytoplasmic large DNA viruses (NCLDV) metagenome assembled genomes (MAGs) 59 .…”

“…AGNOSTOS functional aggregation inference. AGNOSTOS partitioned protein coding genes from the SMAGs in groups connected by remote homologies, and categorized those groups as members of the known or unknown coding sequence space based on the workflow described in Vanni et al 2020 59 . To combine the results from AGNOSTOS and the EggNOG classification we identified those groups of genes in the known space that contain genes annotated with an EggNOG and we inferred a consensus annotation using a quorum majority voting approach.…”

“…To combine the results from AGNOSTOS and the EggNOG classification we identified those groups of genes in the known space that contain genes annotated with an EggNOG and we inferred a consensus annotation using a quorum majority voting approach. AGNOSTOS produces groups of genes with low functional entropy in terms of EggNOG annotations as shown in Vanni et al 2020 59 allowing us to combine both sources of information. We merged the groups of genes that shared the same consensus EggNOG annotations and we integrated them with the rest of AGNOSTOS groups of genes, mostly representing the unknown coding sequence space.…”

Protein Structural Alignments From Sequence

“…Unfortunately, over half of all proteins do not have detectable homologs in standard sequence databases due to their distant evolutionary relationships [13]. Detecting these remote homologs would help us better understand mutagenesis [14], aid protein design [15], predict protein function [16], predict protein structure [17,18,19] and model evolution [20].…”