bio-metagenomics-strain-tracking

name	bio-metagenomics-strain-tracking
description	Resolves and compares bacterial strains below the species level from shotgun metagenomes with inStrain (popANI/conANI microdiversity), StrainPhlAn (marker-SNV consensus phylogeny and nGD), MIDAS2, metaSNV, and StrainGE, plus genome-vs-genome ANI (skani/fastANI/MASH) for isolate/MAG comparison. Covers why a strain is a threshold not a thing, why ANI answers same-genome while popANI/nGD answer same-population-in-situ, the 99.999% popANI and per-species nGD definitions, the coverage detection limit (absence is not absence), why sharing is not transmission direction, and mapping to the dataset's own dRep MAGs. Use when detecting shared strains, tracking transmission, resolving within-host strain dynamics, or deconvoluting co-occurring strains. For pure-culture isolate outbreak SNP trees see epidemiological-genomics; for MAG assembly see genome-assembly/metagenome-assembly.
tool_type	mixed
primary_tool	inStrain

Version Compatibility

Reference examples tested with: inStrain 1.8+, StrainPhlAn/MetaPhlAn 4.1+, dRep 3.4+, skani 0.2+, Bowtie2 2.5+, samtools 1.19+, pandas 2.2+.

Before using code patterns, verify installed versions match. If versions differ:

• CLI: inStrain profile -h, strainphlan -h, skani dist -h to confirm flags and defaults
• Python: pip show <package> then help(module.function) to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

The mapping REFERENCE defines the answer. Map to genomes present in the sample set (dRep-dereplicated MAGs from this dataset), not generic database genomes - a distant reference inflates apparent SNVs and corrupts popANI. Record the reference set, the popANI/nGD threshold, the minimum coverage and breadth, and the co-detection rate; the "strain" is defined by these, not by nature.

Strain Tracking

"Is the same strain in samples A and B?" -> Compare per-position SNV populations (not consensus genomes) at adequate depth - because a strain is defined by the chosen threshold, and ANI cannot resolve the difference that matters.

• CLI: inStrain profile sample.bam reps.fasta -o sample.IS -s reps.stb -p 8 then inStrain compare

Scope: in-situ strain resolution, sharing, and deconvolution from a community. Pure-culture isolate outbreak SNP/cgMLST trees -> epidemiological-genomics. MAG assembly/binning -> genome-assembly/metagenome-assembly. Species presence/abundance -> kraken-classification, metaphlan-profiling.

The Single Most Important Modern Insight -- A Strain Is a Threshold, Not a Thing

There is no universal definition of a metagenomic strain. A strain is an operational construct fixed by the reference mapped to, the genome fraction comparable at adequate depth, and the cutoff drawn. inStrain's popANI >= 99.999% over >= 50% of the genome IS the strain definition; Valles-Colomer's per-species nGD threshold IS the strain definition. Changing the cutoff changes how many strains exist. Two corollaries:

1. ANI answers "same genome?"; popANI/nGD answer "same population, in situ?" Genome-to-genome ANI (MASH/skani/fastANI) saturates - two genuinely distinct, separately transmissible strains routinely share >99.9% ANI, and ANI's resolution floor sits above the difference that matters. Strain sharing cannot be done with an ANI number; it needs microdiversity-aware, position-level concordance.
2. Detecting a shared strain is a narrow statement: across the genome fraction both samples covered at >= 5x, their SNV populations were >= 99.999% concordant. That is not proof an organism was transmitted between two people - the threshold certifies genomic identity within a coverage window, nothing more.

The Three Tasks (Do Not Conflate)

Task	Question	Tools
Identification	which known reference strain is present?	StrainGST, sourmash gather, MIDAS
Tracking / sharing	is the SAME strain in samples A and B?	inStrain compare, StrainPhlAn, MIDAS2, metaSNV, SameStr
Deconvolution	how many strains coexist in ONE sample, what are their haplotypes?	DESMAN, Strainberry, strainFlye, Strainy

Genome-to-genome ANI (MASH/skani/fastANI) is a fourth, orthogonal task - "are these two assembled genomes the same?" - isolate/MAG comparison and dereplication, NOT in-situ strain resolution.

Tool Taxonomy

Tool	Citation	Mechanism / role	When
inStrain	Olm 2021 Nat Biotechnol 39:727	popANI/conANI microdiversity from reads mapped to MAGs	the reference standard for shared-strain detection
StrainPhlAn	Truong 2017 Genome Res 27:626	marker-SNV consensus -> phylogeny -> nGD	large cross-sample marker surveys, no assembly needed
MIDAS2	Zhao 2023 Bioinformatics 39:btac713	UHGG pan-genome SNV + gene CNV	accessory-genome strain signal at scale
StrainGE	van Dijk 2022 Genome Biol 23:74	k-mer search + low-coverage variant calling	low-abundance strains down to 0.5x coverage
metaSNV v2	Van Rossum 2022 Bioinformatics 38:1162	SNV distances + subspecies clustering	subspecies structure across samples
skani	Shaw 2023 Nat Methods 20:1661	sparse-chaining ANI	genome-vs-genome ANI; robust on fragmented MAGs (prefer over fastANI)
Strainberry / strainFlye	Vicedomini 2021 Nat Commun 12:4485; Fedarko 2022 Genome Res 32:2119	long-read haplotype separation	deconvolute co-occurring strains (-> genome-assembly)

Decision Tree by Scenario

Scenario	Recommended	Why
Is a strain shared between two metagenomes?	inStrain compare (popANI)	microdiversity-aware; the field standard
Cross-sample transmission survey, many samples	StrainPhlAn (per-species nGD)	marker-based, scalable, no assembly
Low-abundance pathogen (< 1% / < 5x)	StrainGE	detects/compares down to 0.5x
Accessory-genome / pan-genome strain signal	MIDAS2	adds gene-content axis SNV tools miss
Separate co-occurring strains into haplotypes	DESMAN (many samples) or long-read Strainberry/strainFlye	SNV tools do not partition a mixture
Compare two assembled genomes / dereplicate	skani (or fastANI)	genome-vs-genome ANI, not in-situ strains
Pure-culture isolate outbreak tree	-> epidemiological-genomics	cgMLST/SNP-distance on one genome per sample

inStrain: popANI vs conANI

Goal: Decide whether two metagenomes share a strain without being fooled by which allele happens to be the majority.

Approach: dRep the dataset's MAGs into representative genomes, map reads to the concatenated references, profile each sample, then compare on popANI (microdiversity-aware) over the co-covered genome fraction.

# 1. dRep -> representative genomes (97-99% ANI); concatenate; build scaffold-to-bin (.stb).
# 2. Map reads to the concatenated reps - your OWN MAGs, not database genomes.
bowtie2 -x reps -1 r1.fq.gz -2 r2.fq.gz | samtools sort -o sampleA.bam
inStrain profile sampleA.bam reps.fasta -o sampleA.IS -s reps.stb -g genes.fna -p 8
inStrain profile sampleB.bam reps.fasta -o sampleB.IS -s reps.stb -g genes.fna -p 8
inStrain compare -i sampleA.IS sampleB.IS -o compare.out -s reps.stb -p 8

conANI calls a difference whenever the consensus base differs - confounded by within-sample microdiversity (a minor-allele flip fakes a difference). popANI calls a difference only if the two samples share NO alleles at all, including minor ones, so popANI >= conANI always and is what detects shared strains consensus tools miss. Read genome-level calls from genomeWide_compare.tsv (breadth column percent_compared); the per-scaffold comparisonsTable.tsv uses percent_genome_compared.

StrainPhlAn: Marker SNVs and nGD

metaphlan sample.fq.gz --input_type fastq -s sample.sam.bz2 --bowtie2out sample.bz2 -o profile.tsv  # need the SAM (-s)
sample2markers.py -i sams/*.sam.bz2 -o consensus_markers -n 8
extract_markers.py -c t__SGB1877 -o clade_markers/
strainphlan -s consensus_markers/*.json -m clade_markers/t__SGB1877.fna \
    -r reference_genomes/*.fna.bz2 -o output -c t__SGB1877 \
    --marker_in_n_samples_perc 80 --sample_with_n_markers 20 --nproc 8  # 4.0 named this --marker_in_n_samples

The output tree gives a pairwise nGD (normalized genetic distance). There is no universal nGD strain cutoff - derive a per-species threshold from the data (same-individual-different-timepoint pairs fall below it, unrelated pairs above), as in Valles-Colomer 2023. Low coverage means too few markers pass the filters and the sample is dropped from the species tree silently - so a missing shared-strain call is not evidence of no shared strain.

Genome-vs-Genome ANI (Isolate/MAG Comparison, NOT In-Situ Strains)

skani dist genomeA.fasta genomeB.fasta   # prefer skani over fastANI: robust on fragmented MAGs

~95% ANI is the species boundary (Jain 2018 Nat Commun 9:5114). ANI saturates above that and cannot resolve same-vs-different strain - use it to compare isolates/MAGs and to dereplicate, never to call transmission.

Per-Method Failure Modes

ANI distance reported as strain resolution

Trigger: "MASH distance < 0.001 = same strain" or "fastANI > 99% = same strain." Mechanism: ANI operates on consensus genomes, saturates above 99.9%, and ignores microdiversity. Symptom: distinct transmissible strains called identical; transmission inferred from an ANI number. Fix: use ANI for isolate/MAG comparison; use inStrain popANI / StrainPhlAn nGD for strain sharing.

Coverage detection limit (absence is not absence)

Trigger: concluding "no transmission" or "strain turnover." Mechanism: a shared strain can only be called for a species detected at adequate depth in BOTH samples (inStrain >= 5x and >= 50% breadth; StrainPhlAn enough markers). Symptom: a coverage dropout misread as biological absence; sharing rates biased to abundant taxa. Fix: report co-detection rates alongside sharing rates; use StrainGE for low-abundance targets.

Sharing read as transmission direction

Trigger: narrating "A infected B." Mechanism: a shared strain is an undirected edge. Symptom: directionality claimed from one cross-sectional comparison. Fix: direction comes from timepoints, contact metadata, or a known index case - the published landscapes infer it from study design, not the genomic comparison.

Wrong reference genome

Trigger: mapping to a generic database genome. Mechanism: a distant reference inflates apparent SNVs. Symptom: corrupted popANI; spurious differences. Fix: map to dRep-dereplicated MAGs from the sample set.

Asking a SNV tool to deconvolute a mixture

Trigger: "what are the two strains here?" from inStrain. Mechanism: SNV/marker tools characterize population diversity; they do not partition it into haplotypes. Symptom: a category error. Fix: use DESMAN (many samples) or long-read Strainberry/strainFlye/Strainy for haplotype separation.

Quantitative Thresholds

Threshold	Source	Rationale
popANI >= 99.999% same strain	Olm 2021 Nat Biotechnol 39:727	empirical shared-strain cutoff; IS the operational definition
percent_compared >= 50% (genome-level breadth)	Olm 2021 Nat Biotechnol 39:727	a genome below 50% breadth is not confidently present
min_cov 5x	Olm 2021 Nat Biotechnol 39:727	lowest coverage at which sub-50% minor alleles are reliable
StrainGE detection ~0.5x	van Dijk 2022 Genome Biol 23:74	tracks low-abundance strains below the inStrain floor
Per-species nGD threshold (derive it)	Valles-Colomer 2023 Nature 614:125	no universal cutoff; separate within-host timepoints from unrelated
~95% ANI species boundary	Jain 2018 Nat Commun 9:5114	ANI saturates above this; cannot resolve strains

Common Errors

Error / symptom	Cause	Solution
Everything looks like one strain	ANI/MASH used for strain calls	switch to inStrain popANI / StrainPhlAn nGD
Sample missing from the StrainPhlAn tree	too few markers passed filters at low coverage	report co-detection; do not read absence as no-sharing
popANI implausibly low across the board	mapped to a distant database reference	map to dRep MAGs from the dataset
inStrain compare gives no genomes	< 50% breadth or < 5x in one sample	deepen sequencing or use StrainGE for that taxon
"Who infected whom" asked of one timepoint	sharing is undirected	need longitudinal/epi design for direction

References

• Olm MR, Crits-Christoph A, Bouma-Gregson K, et al. 2021. inStrain profiles population microdiversity from metagenomic data and sensitively detects shared microbial strains. Nat Biotechnol 39:727-736.
• Truong DT, Tett A, Pasolli E, Huttenhower C, Segata N. 2017. Microbial strain-level population structure and genetic diversity from metagenomes. Genome Res 27:626-638.
• Zhao C, Dimitrov B, Goldman M, Nayfach S, Pollard KS. 2023. MIDAS2: Metagenomic Intra-species Diversity Analysis System. Bioinformatics 39:btac713.
• Van Rossum T, Costea PI, Paoli L, et al. 2022. metaSNV v2: detection of SNVs and subspecies in prokaryotic metagenomes. Bioinformatics 38:1162-1164.
• van Dijk LR, Walker BJ, Straub TJ, et al. 2022. StrainGE: a toolkit to track and characterize low-abundance strains in complex microbial communities. Genome Biol 23:74.
• Vicedomini R, Quince C, Darling AE, Chikhi R. 2021. Strainberry: automated strain separation in low-complexity metagenomes using long reads. Nat Commun 12:4485.
• Valles-Colomer M, Blanco-Miguez A, Manghi P, et al. 2023. The person-to-person transmission landscape of the gut and oral microbiomes. Nature 614:125-135.
• Shaw J, Yu YW. 2023. Fast and robust metagenomic sequence comparison through sparse chaining with skani. Nat Methods 20:1661-1665.
• Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. 2018. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun 9:5114.

Related Skills

• metaphlan-profiling - StrainPhlAn builds on MetaPhlAn markers; profile species first
• kraken-classification - Species presence before strain resolution
• genome-assembly/metagenome-assembly - dRep MAGs to map against; long-read deconvolution
• epidemiological-genomics/amr-surveillance - Isolate outbreak SNP/cgMLST trees from pure cultures
• workflows/metagenomics-pipeline - End-to-end shotgun analysis