Lightweight compositional analysis of metagenomes with FracMinHash and minimum metagenome covers

Abstract

The identification of reference genomes and taxonomic labels from metagenome data underlies many microbiome studies. Here we describe two algorithms for compositional analysis of metagenome sequencing data. We first investigate the FracMinHash sketching technique, a derivative of modulo hash that supports Jaccard containment estimation between sets of different sizes. We implement FracMinHash in the sourmash software, evaluate its accuracy, and demonstrate large-scale containment searches of metagenomes using 700,000 microbial reference genomes. We next frame shotgun metagenome compositional analysis as the problem of finding a minimum collection of reference genomes that "cover" the known k-mers in a metagenome, a minimum set cover problem. We implement a greedy approximate solution using FracMinHash sketches, and evaluate its accuracy for taxonomic assignment using a CAMI community benchmark. Finally, we show that the minimum metagenome cover can be used to guide the selection of reference genomes for read mapping. sourmash is available as open source software under the BSD 3-Clause license at github.com/dib-lab/sourmash/.

Meta-analysis of metagenomes via machine learning and assembly graphs reveals strain switches in Crohn’s disease

“…We refer to FracMinHash sketches as sketches or k-mer abundance profiles, and for simplicity, continue referring to the sub-sampled k-mers in a sketch as k-mers . Retaining only k-mers associated with IBD, we used a minimum set cover approach to identify the genomes that best encompassed these k-mers [21].…”

“…Using these trimmed reads, we generated FracMinHash signatures for each library using sourmash (k-size 31, scaled 2000, abundance tracking on) [57]. FracMinHash sketching produces compressed representations of k-mers in a metagenome while retaining the sequence diversity in a sample [21,23]. This approach creates a consistent set of k-mers across samples by retaining the same k-mers when the same k-mers were observed.…”

Protein k-mers enable assembly-free microbial metapangenomics

“…We binned the resultant assemblies using metabat2 with parameter [60]. We assigned GTDB species to each bin using sourmash (DNA, k = 31, scaled = 2000) against the GTDB rs202 database, selecting the species of the best match [19]. We decontaminated each bin with charcoal using default parameters [61].…”

“…The ability to distinguish between species without alignment or assembly have popularized the use of k-mers for metagenome analysis, primarily through lightweight sketching and compact de Bruijn assembly graphs (cDBGs). Lightweight sketching facilitates fast and accurate sequence comparisons between potentially large data sets through random but consistent sub-sampling [18,19]. cDBGs maintain connectivity between k-mers and organize them into species-specific neighborhoods [20,21].…”

KMCP: accurate metagenomic profiling of both prokaryotic and viral populations by pseudo-mapping

“…Sourmash (Irber et al, 2022) outperformed other tools in the Critical Assessment of Metagenome Interpretation (CAMI) based on mouse gut datasets (Meyer et al, 2019) using FracMinHash sketches with a scale of 10,000 calculated from all 141,677 prokaryotic genomes in a RefSeq snapshot. The sketching algorithm uses a small number of signatures from whole-genome sequences; therefore, it can index a large number of reference genomes.…”