Strain-GeMS: optimized subspecies identification from microbiome data based on accurate variant modeling

Abstract

Motivation Subspecies identification is one of the most critical issues in microbiome studies, as it is directly related to their functions in response to the environmental stress and their feedbacks. However, identification of subspecies remains a challenge largely due to the small variation between different strains within the same species. Accurate identification of subspecies primarily relies on variant identification and categorization through microbiome data. However, current SNP calling and subspecies identification for microbiome data remain underdeveloped. Results In this work, we have proposed Strain-GeMS for subspecies identification from microbiome data, based on solid statistical model for SNP calling, as well as optimized procedure for subspecies identification. Results on simulated, ab initio and in vivo datasets have shown that Strain-GeMS could always generate more accurate results compared with other subspecies identification methods. Availability and implementation Strain-GeMS is available at: https://github.com/HUST-NingKang-Lab/straingems. Supplementary information Supplementary data are available at Bioinformatics online.

Ontology-aware neural network: a general framework for pattern mining from microbiome data

“…It is critical to identify and characterize microbial species in environments and individual human hosts in order to learn about human–microbial interactions. Many bioinformatics computational tools have been developed for the characterization and identification of microorganisms at species or strain levels, such as StrainPhlAn [ 33 ], ConStrains [ 34 ] and Strain-GeMS [ 2 ]. However, most of these traditional tools are based on genomic sequence comparison and marker genes such as 16S rRNA and thus often lack the resolution to reliably capture intraspecific genomic differences.…”

“…Numerous bioinformatics tools have taken advantage of the growing amount of genomic data to identify new species. For example, StrainPhlAn [ 33 ], ConStrains [ 34 ] and Strain-GeMS [ 2 ] are proposed for bacterial identification at the strain level based on genomic information and ArboTyping [ 61 ] for the identification of virus species and genotypes. By considering that species are organized according to the phylogenetic tree of life, the latter can be considered an ontology structure, and the identification of novel species is an ontology-related problem.…”

“…Pattern mining from microbiome data is a broad topic, which can include many types of knowledge to be discovered such as functional gene mining, novel species discovery, dynamic pattern mining and so on. Yet traditionally, microbiome data mining has heavily relied on sequence comparison for gene mining [ 1 ], species mining [ 2 , 3 ] and taxonomic composition comparison for microbial community dynamic pattern discovery [ 4 ]. Machine learning methods have recently been used in a variety of microbiome data mining contexts whereas deep learning approaches are rarely developed in those contexts owing to the high heterogeneity of microbiome samples as well as the critical data involved in these pattern mining processes [ 5 , 6 ].…”

Decoding populations in the ocean microbiome

“…Metagenome-based population genomics studies frequently focus on the microdiversity associated with SNVs, therefore linked to the potential optimization of traits [ 40 ]. Furthermore, there are methods that aim at reconstructing strains or haplotypes from the metagenomic data (e.g., ConStrains [ 110 ], DESMAN [ 111 ], STRONG [ 112 ], InStrain [ 108 ], and Strain-GeMS [ 113 ]). Given the space limitations, below, I will provide a few examples of some of these approaches applied to marine microbes to convey the central message without aiming for a comprehensive review.…”

metaSNV v2: detection of SNVs and subspecies in prokaryotic metagenomes

“…While species-specific microbiome approaches exist ( Karcher et al , 2020 ; Milani et al , 2014 ), no software yet exists to broadly delineate subspecies from metagenomic data. Many tools characterize population-level diversity [MIDAS ( Nayfach et al , 2016 ), metaSNV v1 ( Costea et al , 2017a ), POPGENOM ( Sjöqvist et al , 2021 ), inStrain ( Olm et al, 2021 ), StrainPhlAn ( Truong et al , 2017 )] and/or recover haplotypes or strains from metagenomes [DESMAN ( Quince et al , 2017 ), ConStrains ( Luo et al , 2015 ), InStrain ( Olm et al , 2021 ), strainGEMS ( Tan et al , 2019 )], with varying definitions of strains ( Van Rossum et al , 2020 ). However, none of these tools provide robust clustering of population diversity for data-driven identification of subspecies.…”