Table 1 Tools for strain identification in community amplicon and shotgun metagenomic sequencing. Methods and brief summaries of their algorithms for detecting and quantifying strains (by various definitions) from 16S rRNA gene amplicon or shotgun metagenomic sequencing. These are currently the two most prevalent assays for culture-independent strain detection within microbial communities. Note that we have excluded other experimental protocols from this summary, including single-cell, long-read, and synthetic long-read sequencing, since they generally require more than application of a specific software pipeline. These alternatives, and non-sequencing-based approaches, are described in more detail in the text
From: Strain-level epidemiology of microbial communities and the human microbiome
Method | Platform | Authors’ description | Reference |
|---|---|---|---|
Oligotyping | 16S rRNA gene amplicon | “oligotyping... Focus [es] on the variable sites revealed by the entropy analysis to identify highly refined taxonomic units” | [112] |
Sub-OTU clustering | 16S rRNA gene amplicon | “we combine error-model-based denoising and systematic cross-sample comparisons to resolve the fine (sub-OTU) structure of moderate-to-high-abundance community members” | [113] |
MED | 16S rRNA gene amplicon | “MED uses information uncertainty among sequence reads to iteratively decompose a dataset until the maximum entropy criterion is satisfied for each final unit” | [114] |
DADA2 | 16S rRNA gene amplicon | “DADA2 implements a new quality-aware model of Illumina amplicon errors. Sample composition is inferred by dividing amplicon reads into partitions consistent with the error model.” | [115] |
Deblur | 16S rRNA gene amplicon | “Deblur … compares sequence-to-sequence Hamming distances within a sample to an upper-bound error profile combined with a greedy algorithm to obtain single-nucleotide resolution.” | [116] |
UNOISE2 | 16S rRNA gene amplicon | “UNOISE2... Cluster [s] the unique sequences in the reads. A cluster has a centroid sequence with higher abundance plus similar sequences having lower abundances.” | [117] |
PathoScope | Shotgun metagenomic | “PathoID … reassign [s] ambiguously aligned sequencing reads and accurately estimate [s] read proportions from each genome in the sample.” | [118] |
LSA | Shotgun metagenomic | “LSA... separates reads into biologically informed partitions and thereby enables assembly of individual genomes.” | [119] |
PanPhlAn | Shotgun metagenomic | “PanPhlAn identifies which genes are present or absent within different strains of a species, based on the entire gene set of the species’ pangenome.” | [66] |
MetaMLST | Shotgun metagenomic | “MetaMLST performs an in silico consensus sequence reconstruction of the allelic profile of the microbial strains in a metagenomics sample.” | [120] |
MIDAS | Shotgun metagenomic | “MIDAS … is a computational pipeline that quantifies bacterial species abundance and intra-species genomic variation from shotgun metagenomes.” | [37] |
ConStrains | Shotgun metagenomic | “ConStrains … exploits the polymorphism patterns in a set of universal bacterial and archaeal genes to infer strain-level structures in species populations.” | [121] |
StrainPhlAn | Shotgun metagenomic | “StrainPhlAn … is based on reconstructing consensus sequence variants within species-specific marker genes and using them to estimate strain-level phylogenies.” | [8] |
metaSNV | Shotgun metagenomic | “metaSNV … performs SNV calling for individual samples and across the whole data set, and generates various statistics for individual species” | [102] |
DESMAN | Shotgun metagenomic | “DESMAN identifies variants in core genes and uses co-occurrence across samples to link variants into haplotypes and abundance profiles.” | [122] |