Improvement of eukaryotic protein predictions from soil metagenomes

Abstract

During the last decades, metagenomics has highlighted the diversity of microorganisms from environmental or host-associated samples. Most metagenomics public repositories use annotation pipelines tailored for prokaryotes regardless of the taxonomic origin of contigs. Consequently, eukaryotic contigs with intrinsically different gene features, are not optimally annotated. Using a bioinformatics pipeline, we have filtered 7.9 billion contigs from 6,872 soil metagenomes in the JGI’s IMG/M database to identify eukaryotic contigs. We have re-annotated genes using eukaryote-tailored methods, yielding 8 million eukaryotic proteins and over 300,000 orphan proteins lacking homology in public databases. Comparing the gene predictions we made with initial JGI ones on the same contigs, we confirmed our pipeline improves eukaryotic proteins completeness and contiguity in soil metagenomes. The improved quality of eukaryotic proteins combined with a more comprehensive assignment method yielded more reliable taxonomic annotation. This dataset of eukaryotic soil proteins with improved completeness, quality and taxonomic annotation reliability is of interest for any scientist aiming at studying the composition, biological functions and gene flux in soil communities involving eukaryotes.

The endohyphal microbiome: current progress and challenges for scaling down integrative multi-omic microbiome research

“…Sequences from metagenomic datasets can be assembled, taxonomically classified, and assessed for quality using a number of established and taxa-specific approaches [ 104 , 105 ]. Tools have recently been developed to selectively identify eukaryotic and viral contigs from shotgun metagenomics data to aid in the separation and subsequent analyses of certain contigs [ 106 – 108 ]. More specific tools have also shown promise for these analyses: the Spore-associated Symbiotic Microbes (SeSaMe) bioinformatics tool was specifically developed for the sequence classification of microbial associates of arbuscular mycorrhizal fungi from metagenomic sequencing data [ 109 ].…”