SLIMM: Species level identification of microorganisms from metagenomes

Abstract

Identification and quantification of microorganisms is an important step in studying the alpha and beta diversities within and between microbial communities respectively. Both, identification and quantification of a given microbial community can be carried out using whole genome shotgun sequences with less bias than using 16S-rRNA sequences. However, shared regions of DNA among reference genomes and taxonomic units pose a significant challenge in assigning reads correctly to their true origins. The existing microbial community profiling tools commonly deal with this problem by either preparing signature-based unique references or assigning an ambiguous read to its least common ancestor in a taxonomic tree. The former method is limited to making use of the reads which can be mapped to the curated regions, while the later suffer from the lack of uniquely-mapped reads at higher (more specific) taxonomic ranks. Moreover, even if the tools exhibited generally good performance in calling the organisms present in a sample, there is room for improvement in calling the correct relative abundance of the organisms. We present a new method Species Level Identification of Microorganisms from Metagenomes (SLIMM) which addresses the above issues by using coverage information of reference genomes to remove unlikely genomes from the analysis and subsequently gain more uniquely-mapped reads to assign at higher ranks of a taxonomic tree. SLIMM is based on a few, seemingly easy steps which lead to a tool that outperforms state-of-the- art tools in run-time and/or memory usage while being on par or better in computing quantitative and qualitative information at the species level.