bio-metagenomics-amr-detection

name	bio-metagenomics-amr-detection
description	Profiles the antimicrobial-resistance gene content (resistome) of shotgun metagenomes - read-based quantification with RGI bwt, AMR++/MEGARes, ARGs-OAP/SARG, deepARG, or GROOT, and presence calling with AMRFinderPlus/ABRicate on assembled contigs or MAGs. Covers why an ARG hit is a sequence match not a phenotype, why a metagenomic ARG has no host and no genomic context until assembly (and assembly breaks at ARGs), per-gene curated thresholds vs a flat 80/80, gene-fraction false-positive control, and cross-study normalization pitfalls. Use when quantifying a community resistome, normalizing ARG abundance, or calling ARGs from metagenome contigs. For pure-culture isolate AMR, point mutations, and phenotype/MIC prediction see epidemiological-genomics/amr-surveillance.
tool_type	cli
primary_tool	AMRFinderPlus

Version Compatibility

Reference examples tested with: AMRFinderPlus 3.12+, RGI 6+ (CARD 3.2+), ABRicate 1.0+, pandas 2.2+.

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

• CLI: amrfinder -V (reports software AND database version), rgi main --version, abricate --list to confirm DB snapshots
• 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 AMR reference DATABASE is versioned and updated roughly monthly; amrfinder -V reports both software and database version, and ABRicate ships pinned database snapshots so two labs on different versions get different calls. Record the tool version, the database version, and (for read-based work) the normalization unit and sequencing depth - none are recoverable later.

AMR Detection (Community Resistome)

"What resistance genes are in my community, and how abundant?" -> Match reads or contigs to a curated ARG database - reporting that ARG sequences are present at some relative abundance, never that the sample is resistant, because a metagenomic hit has no host and no expression.

• CLI (reads): rgi bwt -1 R1.fq.gz -2 R2.fq.gz -a kma -n 16 -o sample --local
• CLI (contigs/MAGs): amrfinder -n contigs.fasta --plus -o amr.tsv

Scope: community/metagenomic resistome - read-based quantification and contig/MAG presence calling. Pure-culture isolate AMR, point-mutation resistance, in-silico antibiogram, MLST/clone/outbreak context, and GLASS reporting -> epidemiological-genomics/amr-surveillance. Assembly/binning and ARG-host linkage mechanics -> genome-assembly/metagenome-assembly. General gene-family/pathway abundance -> functional-profiling.

The Single Most Important Modern Insight -- An ARG Hit Is a Sequence Match, Not a Phenotype

An ARG hit is a match against a reference database, not a measured resistance phenotype - and in a metagenome it is a match with no host and no genomic context until assembly. Three inferences a naive pipeline silently makes, all wrong:

1. match -> gene - defeated by partial hits (a 30 bp conserved-domain hit to a 1 kb ARG is not a gene); guarded by gene-fraction / breadth-of-coverage.
2. gene -> resistance - defeated by expression and regulation: silent sul2 below ECOFF, ampC/blaOXA driven only when an IS lands in the promoter, efflux that needs overexpression, truncations that still score a partial hit, point mutations where only the SNP matters.
3. gene -> who carries it / is it mobile - defeated by read shortness: a short read cannot see its neighbors, so host and plasmid context need assembly, long reads, or Hi-C.

The honest deliverable is "these ARG sequences are present at this relative abundance in this community," never "this sample is resistant to drug X." The moment a report says resistant, it has smuggled in a host, an expression assumption, and a clinical breakpoint the data never contained. On a pure culture the organism can be grown and an MIC measured - that is a different skill (epidemiological-genomics/amr-surveillance).

Read-Based vs Assembly-Based: the Core Metagenomic Tradeoff

Axis	Read-based (RGI bwt, AMR++, ARGs-OAP, deepARG, GROOT)	Assembly-based (AMRFinderPlus/RGI main/ABRicate on contigs/MAGs)
Low-abundance sensitivity	high - every read counts, below assembly coverage	low - ARGs at low coverage do not assemble
Quantification	yes - abundance + normalization	presence/absence per contig
Host / MGE context	none without binning	possible via contig taxonomy / MAG
Point-mutation resistance	weak/unreliable (RGI bwt cannot screen the SNP)	yes, with organism/model
False positives	partial hits unless gene-fraction filtered	chimeric contigs, but vettable

The assembly paradox: metagenomic assemblies preferentially break exactly at ARG/MGE boundaries, recovering only a small fraction of true ARG genomic contexts and underestimating the resistome (Abramova 2024 BMC Genomics 25:959). So "assemble to get host" is necessary but not sufficient - long reads (Nanopore/PacBio) and Hi-C metagenomics are the real remedy for ARG-host/MGE linkage.

Tool Taxonomy

Tool	Citation	Role	When
AMRFinderPlus	Feldgarden 2021 Sci Rep 11:12728	NCBI Reference Gene Catalog; per-gene curated cutoffs + HMMs	contig/MAG presence calling; the default contig caller
RGI bwt	Alcock 2023 Nucleic Acids Res 51:D690	CARD homolog-model read mapping (KMA/bowtie2/bwa)	read-based resistome with coverage/depth per allele
AMR++ / MEGARes 3.0	Bonin & Doster 2023 Nucleic Acids Res 51:D744	BWA-MEM + gene-fraction filter + rarefaction	quantitative resistome with built-in partial-hit control
ARGs-OAP / SARG	Yin 2023 Engineering 27:234	two-stage read annotation + 16S/cell normalization	copies-ARG-per-16S / per-cell units
deepARG	Arango-Argoty 2018 Microbiome 6:23	deep-NN over dissimilarity features	catches divergent ARGs best-hit BLAST misses
GROOT	Rowe & Winn 2018 Bioinformatics 34:3601	variation-graph alignment	types SNP-bearing alleles that flat references conflate
ABRicate	Seemann (no paper)	flat 80/80 BLASTn, bundled DB snapshots	quick contig screen; acquired genes only, no point mutations

Decision Tree by Scenario

Scenario	Recommended	Why
Quantitative resistome from reads	RGI bwt or AMR++ or ARGs-OAP	abundance + normalization; no host/context
Divergent / novel ARGs from reads	deepARG (confirm surprising calls)	dissimilarity features beat top-hit BLAST
Type a specific high-similarity allele	GROOT	graph carries SNP-bearing variants
Presence per contig / MAG	AMRFinderPlus (--plus) on contigs	curated per-gene cutoffs; possible host via binning
Quick multi-DB contig screen	ABRicate	fast; but flat 80/80, no point mutations
Is the ARG mobile / in a pathogen?	assemble+bin, long read, or Hi-C	short reads cannot link ARG to host
Pure culture / phenotype / MIC	-> epidemiological-genomics/amr-surveillance	isolate AMR is a different skill
Cross-study abundance comparison	within-study only, same DB+normalization+depth	"total ARG abundance" is rarely comparable

Read-Based Resistome Quantification

# CARD read mapping (homolog models). RGI bwt CANNOT screen point-mutation SNPs, so this is for
# acquired/homolog ARGs only - never report a gyrA read hit as fluoroquinolone resistance.
rgi bwt -1 reads_R1.fq.gz -2 reads_R2.fq.gz \
    -a kma -n 16 \
    -o sample_resistome --local
# Outputs *.gene_mapping_data.txt with percent coverage and depth per gene.

# AMR++/MEGARes applies the gene-fraction filter (default 80%): the minimum proportion of a
# reference covered by >=1 read for "present" - the read-based analog of breadth-of-coverage.

ARGs-OAP/SARG normalizes to copies-of-ARG-per-16S or per-cell; report the unit. Gene fraction (breadth) is the single most important false-positive guard - without it a conserved-domain fragment counts as a present gene.

Contig / MAG Presence Calling

amrfinder -n contigs.fasta \
    --plus \                  # also report biocide/metal (STRESS) and virulence elements
    --threads 8 -o amr.tsv
# --ident_min default -1 = use the per-gene CURATED cutoffs; overriding with a global value is usually a mistake.
# Point mutations require --organism (a single known species) - inappropriate for a mixed community;
# use it only on a taxonomically resolved MAG, and defer isolate point-mutation work to amr-surveillance.

AMRFinderPlus uses manually curated per-gene BLAST cutoffs (plus HMM cutoffs with protein), not a flat 80/80 - catching divergent real variants while rejecting partial housekeeping homologs. ABRicate, by contrast, is flat 80/80 and acquired-genes-only; it will never report a point mutation.

Per-Method Failure Modes

ARG presence reported as resistance

Trigger: an output column or summary that says "resistant." Mechanism: presence is not expression and not a host-linked MIC. Symptom: a sewage metagenome described as "resistant to carbapenems." Fix: report "ARG detected at abundance X"; reserve phenotype claims for isolates (amr-surveillance).

--organism on a mixed community

Trigger: amrfinder --organism Escherichia on community contigs. Mechanism: organism mode assumes a single known species and calls organism-specific point mutations/intrinsic genes. Symptom: spurious point-mutation calls; filtered "intrinsic" genes wrong for the community. Fix: run organism mode only on a taxonomically resolved MAG; otherwise omit it.

Partial hit counted as a gene

Trigger: read mapping or BLAST with no breadth filter. Mechanism: a short conserved-domain match to a long ARG passes an identity threshold. Symptom: inflated ARG counts dominated by fragments. Fix: require gene-fraction / breadth-of-coverage (AMR++ default 80%); inspect coverage, not just identity.

Cross-study abundance comparison

Trigger: comparing "total ARG abundance" across papers. Mechanism: different databases (CARD/MEGARes/SARG/ResFinder), normalization units, aligners, and depth all change the number. Symptom: apparent resistome differences that are pipeline artifacts. Fix: compare only within a study with one pipeline; report DB version, tool version, normalization unit, and depth; hAMRonization harmonizes format, not the metric.

Loose/Discovery hits reported as ARGs

Trigger: CARD-RGI --include_loose in a surveillance report. Mechanism: Loose is below the curated bit-score cutoff - discovery only. Symptom: a flood of low-similarity false positives. Fix: report Perfect/Strict; reserve Loose for novel-variant discovery with manual curation.

Quantitative Thresholds

Threshold	Source	Rationale
AMRFinderPlus --ident_min -1 (use curated)	Feldgarden 2021 Sci Rep 11:12728	per-gene curated cutoffs beat a global 80/80; overriding is usually wrong
AMRFinderPlus --coverage_min 0.5	AMRFinderPlus docs	minimum reference coverage for a call
AMR++ gene fraction 80%	Bonin & Doster 2023 Nucleic Acids Res 51:D744	breadth guard against partial-hit false positives
ResFinder acquired 0.80 id / 0.60 cov	Bortolaia 2020 J Antimicrob Chemother 75:3491	the documented default (not 0.90)
deepARG --min-prob 0.8	Arango-Argoty 2018 Microbiome 6:23	category probability cutoff; confirm surprising calls
CARD Loose tier = discovery only	Alcock 2023 Nucleic Acids Res 51:D690	below curated bit-score; not for surveillance

Common Errors

Error / symptom	Cause	Solution
No point mutations reported	ABRicate or read-based homolog mapper used	those cannot call SNPs; use AMRFinderPlus --organism on a MAG / defer to amr-surveillance
AMRFinderPlus calls changed silently	stale reference database	amrfinder -u; record amrfinder -V software + DB version
ABRicate results differ between labs	pinned DB snapshot version differs	record abricate --list versions; update with abricate-get_db
Inflated ARG abundance	no gene-fraction/breadth filter	apply breadth-of-coverage; inspect coverage
gyrA "hit" from read-based RGI	--include_other_models reports the gene, not the SNP	do not call resistance; SNP screening needs an isolate/organism

References

• Feldgarden M, Brover V, Gonzalez-Escalona N, et al. 2021. AMRFinderPlus and the Reference Gene Catalog facilitate examination of the genomic links among antimicrobial resistance, stress response, and virulence. Sci Rep 11:12728.
• Alcock BP, Huynh W, Chalil R, et al. 2023. CARD 2023: expanded curation, support for machine learning, and resistome prediction at the Comprehensive Antibiotic Resistance Database. Nucleic Acids Res 51:D690-D699.
• Bortolaia V, Kaas RS, Ruppe E, et al. 2020. ResFinder 4.0 for predictions of phenotypes from genotypes. J Antimicrob Chemother 75:3491-3500.
• Bonin N, Doster E, Worley H, et al. 2023. MEGARes and AMR++, v3.0: an updated comprehensive database of antimicrobial resistance determinants and an improved software pipeline. Nucleic Acids Res 51:D744-D752.
• Yin X, Zheng X, Li L, et al. 2023. ARGs-OAP v3.0: antibiotic-resistance gene database curation and analysis pipeline optimization. Engineering 27:234-241.
• Arango-Argoty G, Garner E, Pruden A, et al. 2018. DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data. Microbiome 6:23.
• Rowe WPM, Winn MD. 2018. Indexed variation graphs for efficient and accurate resistome profiling. Bioinformatics 34:3601-3608.
• Abramova A, Karkman A, Bengtsson-Palme J. 2024. Metagenomic assemblies tend to break around antibiotic resistance genes. BMC Genomics 25:959.

Related Skills

• epidemiological-genomics/amr-surveillance - Isolate AMR, point mutations, phenotype/MIC, typing, GLASS
• functional-profiling - General gene-family/pathway abundance (HUMAnN can surface ARG families)
• kraken-classification - Taxonomic context for the community
• genome-assembly/metagenome-assembly - Assembly/binning and ARG-host linkage
• contamination-controls - Host depletion before resistome profiling
• workflows/metagenomics-pipeline - End-to-end shotgun analysis