KEMET - A python tool for KEGG Module evaluation and microbial genome annotation expansion

The rapid accumulation of sequencing data from metagenomic studies is enabling the generation of huge collections of microbial genomes, with new challenges for mapping their functional potential. In particular, metagenome-assembled genomes are typically incomplete and harbor partial gene sequences that can limit their annotation from traditional tools. New scalable solutions are thus needed to facilitate the evaluation of functional potential in microbial genomes.

To resolve annotation gaps in microbial genomes, we developed KEMET, an open-source Python library devised for the analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) functional units. KEMET focuses on the in-depth analysis of metabolic reaction networks to identify missing orthologs through hidden Markov model profiles.

We evaluate the potential of KEMET for expanding functional annotations by simulating the effect of assembly issues on real gene sequences and showing that our approach can identify missing KEGG orthologs. Additionally, we show that recovered gene annotations can sensibly increase the quality of draft genome-scale metabolic models obtained from metagenome-assembled genomes, in some cases reaching the accuracy of models generated from complete genomes.

KEMET therefore allows expanding genome annotations by targeted searches for orthologous sequences, enabling a better qualitative and quantitative assessment of metabolic capabilities in novel microbial organisms.