An assessment of genome annotation coverage across the bacterial tree of life

Abstract

Although gene-finding in bacterial genomes is relatively straightforward, the automated assignment of gene function is still challenging, resulting in a vast quantity of hypothetical sequences of unknown function. But how prevalent are hypothetical sequences across bacteria, what proportion of genes in different bacterial genomes remain unannotated, and what factors affect annotation completeness? To address these questions, we surveyed over 27 000 bacterial genomes from the Genome Taxonomy Database, and measured genome annotation completeness as a function of annotation method, taxonomy, genome size, 'research bias' and publication date. Our analysis revealed that 52 and 79 % of the average bacterial proteome could be functionally annotated based on protein and domain-based homology searches, respectively. Annotation coverage using protein homology search varied significantly from as low as 14 % in some species to as high as 98 % in others. We found that taxonomy is a major factor influencing annotation completeness, with distinct trends observed across the microbial tree (e.g. the lowest level of completeness was found in the Patescibacteria lineage). Most lineages showed a significant association between genome size and annotation incompleteness, likely reflecting a greater degree of uncharacterized sequences in 'accessory' proteomes than in 'core' proteomes. Finally, research bias, as measured by publication volume, was also an important factor influencing genome annotation completeness, with early model organisms showing high completeness levels relative to other genomes in their own taxonomic lineages. Our work highlights the disparity in annotation coverage across the bacterial tree of life and emphasizes a need for more experimental characterization of accessory proteomes as well as understudied lineages.

4-Methylcytosine DNA modification is critical for global epigenetic regulation and virulence in the human pathogenLeptospira interrogans

“…This suggests that the root cause(s) of the phenotypes observed in the lomA mutant remain(s) elusive, but are likely due to the preferentially impacted COGs (signaling, motility, and envelope biogenesis). This complexity is compounded by the fact that Leptospira , and, more generally, all spirochetes, possess many putative genes that code for proteins of unknown function with little to no sequence identity with characterized proteins from model organisms ( 66 ).…”

Shedding Light on Microbial Dark Matter with A Universal Language of Life

“…The LookingGlass 'universal language of life' creates representations of DNA sequences that capture their functional and evolutionary relevance, independent of whether the sequence is contained in reference databases. The vast majority of microbial diversity is uncultured and unannotated [4][5][6] . LookingGlass opens the door to .…”

“…The microbial world is dominated by "microbial dark matter" -the majority of microbial genomes remain to be sequenced 4,5 , while the molecular functions of many genes in microbial genomes are unknown 6 . In microbial communities (microbiomes), the combination of these factors compounds this limitation.…”

“…Microbial communities are a subject of great interest for developing natural61 and synthetic 62 products for bioengineering applications, but our ability to describe, model, and manipulate the systems-level functions of these microbiomes is limited.The LookingGlass 'universal language of life' creates representations of DNA sequences that capture their functional and evolutionary relevance, independent of whether the sequence is contained in reference databases. The vast majority of microbial diversity is uncultured and unannotated[4][5][6] . LookingGlass opens the door to harnessing the potential of this "microbial dark matter" to improve our understanding of, and ability to manipulate, microbial systems.…”

Protein homeostasis imprinting across evolution

“…On the other hand, the vast majority of organisms has been sketchily annotated, only through electronic inference based on homology associations (Gaudet et al, 2011). This causes inconsistencies regarding the semantic network profile describing biological processes across species, and even taxonomically related species could have divergence in annotation coverage (Lobb et al, 2020). All these predicate upon a standardized version of GO, suitable for comparative, evolutionary analyses.…”