metaSNV v2: detection of SNVs and subspecies in prokaryotic metagenomes

Abstract

Summary Taxonomic analysis of microbial communities is well supported at the level of species and strains. However, species can contain significant phenotypic diversity and strains are rarely widely shared across global populations. Stratifying the diversity between species and strains can identify ‘subspecies’, which are a useful intermediary. High-throughput identification and profiling of subspecies is not yet supported in the microbiome field. Here, we use an operational definition of subspecies based on single nucleotide variant (SNV) patterns within species to identify and profile subspecies in metagenomes, along with their distinctive SNVs and genes. We incorporate this method into metaSNV v2, which extends existing SNV-calling software to support further SNV interpretation for population genetics. These new features support microbiome analyses to link SNV profiles with host phenotype or environment and niche-specificity. We demonstrate subspecies identification in marine and fecal metagenomes. In the latter, we analyze 70 species in 7524 adult and infant subjects, supporting a common subspecies population structure in the human gut microbiome and illustrating some limits in subspecies calling. Availability and implementation Source code, documentation, tutorials and test data are available at https://github.com/metasnv-tool/metaSNV and https://metasnv.embl.de. Supplementary information Supplementary data are available at Bioinformatics online.

MIDAS2: Metagenomic Intra-species Diversity Analysis System

“…Since metaSNV v2 was previously shown to be efficient enough to metagenotype thousands of samples (Van Rossum et al 2021), we assessed the scalability of MIDAS2 compared to metaSNV v2 on 1,097 samples from the PREDICT study (NCBI accession: PRJEB39223), using MIDAS DB UHGG with both tools (Supplementary 4 Note). Despite the same species selection criteria, MIDAS2 metagenotyped many more species (44 versus 14 for metaSNV v2) (Supplementary Note).…”

“…Whole-genomes are used as the reference for most pipelines (MIDAS2, metaSNV v2 (Van Rossum et al 2021), InStrain (Olm et al 2021)), while species-specific marker genes are used by StrainPhlan (Truong et al 2017). MIDAS2 is the only pipeline providing precomputed reference genome databases: namely MIDAS DB UHGG and MIDAS DB GTDB.…”

“…The other tools leave database and alignment to the user. InStrain (Olm et al 2021) starts from a BAM file and performs post-alignment filtering as a first step, whereas metaSNV v2 (Van Rossum et al 2021) starts from a BAM file that has already been filtered. By integrating and automating all steps of the metagenotyping process, MIDAS2 helps to promote reproducible research.…”

Pitfalls of genotyping microbial communities with rapidly growing genome collections

“…Conserved nucleotides may represent recently acquired mobile elements, sites with strong negative selection, or closely related lineages (when genome-wide). Some metagenotyping tools merge alleles of SNVs detected within a set of samples to enable across-sample genetic analyses and to identify population SNVs detected in more than one sample (Olm et al, 2021; Schloissnig et al, 2013; Van Rossum et al, 2021; Zhao et al, 2022). In other cases, users need to write customized scripts for cross-sample metagenotype analysis (Shi et al, 2022).…”

“…It is important to keep in mind that new genomes are only helpful to metagenotyping tools if their quality is high enough to generate accurate genotypes. When reference genomes are fragmented, incomplete or contaminated with sequences from off-target species, the number of reads that can be correctly mapped is reduced and fewer sites can be metagenotyped (Olm et al, 2021; Shi et al, 2022; Van Rossum et al, 2021; Zhao et al, 2022).…”