On the synergies between ribosomal assembly and machine learning tools for microbial identification
Genome assembly tools are used to reconstruct genomic sequences from raw sequencing data, which are then used for identifying the organisms present in a metagenomic sample. More recently, machine learning approaches have been applied to a variety of bioinformatics problems, and in this paper, we explore their use for organism identification. We start out by evaluating several commonly used metagenomic assembly tools, including PhyloFlash, MEGAHIT, MetaSPAdes, Kraken2, Mothur, UniCycler, and PathRacer, and compare them against state-of-the art deep learning-based machine learning classification approaches represented by DNABERT and DeLUCS, in the context of two synthetic mock community datasets. Our analysis focuses on determining whether ensembling metagenome assembly tools with machine learning tools has the potential to improve identification performance relative to using the tools individually. We find that this is indeed the case, and analyze the level of effectiveness of potential tool ensembling for organisms with different characteristics (based on factors such as repetitiveness, genome size, and GC content). Author SummaryMetagenomic studies focus on the challenging problem of identifying the presence and abundance of different species in a sample. This process typically involves the creation of digital reads from the sample which correspond to small parts of the genome sequence, and then have to be assembled together by a genome assembly tool. More recently, machine learning approaches have been applied to a variety of bioinformatics problems, and in this paper, we explore their use for organism identification, and how they might complement traditional bioinformatics approaches. We conduct experiments with two representative state-of-the-art machine learning approaches and six metagenomic assembly tools in the context of two synthetic datasets. We find that for organisms with certain characteristics (levels of repetitiveness, GC content, and genome size), ensembling metagenome assembly tools with machine learning tools has the potential to improve species identification performance relative to using the tools individually.