Bio Metagenomics Contamination Controls

Version Compatibility

Reference examples tested with: decontam 1.22+, Hostile 1.1+, Bowtie2 2.5+, Nonpareil 3.4+, pandas 2.2+, R 4.3+.

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

• R: packageVersion('decontam') then ?isContaminant to verify parameters
• CLI: hostile --version, nonpareil -h to confirm flags and indexes
• 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 controls define the result: an extraction blank defines the kitome for its batch/lot, a mock community defines the limit of detection and the extraction-lysis bias, and the host reference (prefer T2T-CHM13 over GRCh38) defines what host is removed. Record the extraction kit and lot, the host index, the blanks, the mock version (whole-cell vs DNA), and the reads removed at each step.

Contamination Controls

"Is this signal real, or did my pipeline create it?" -> Remove host reads, define the kitome with blanks, validate with a mock, and confirm depth - because a low-biomass community can be entirely reagent contamination.

• R: decontam::isContaminant(seqtab, conc=, neg=, method='combined') on the classifier output table
• CLI: hostile clean --fastq1 R1.fq.gz --fastq2 R2.fq.gz --index human-t2t-hla

Scope: sample-level pre-analysis cleanup and controls - host depletion, kitome/blank/mock controls, decontam, depth adequacy. Adapter/quality trimming mechanics -> read-qc/adapter-trimming, read-qc/quality-filtering. MAG-level decontamination (CheckM2/GUNC chimerism, FCS-GX foreign sequence) -> genome-assembly/metagenome-assembly - a different, genome-level problem. Classification -> kraken-classification, metaphlan-profiling.

The Single Most Important Modern Insight -- A Metagenomic Result Is a Position in a Choice-Chain

A metagenomic profile is the product of a chain of choices - extraction, host/contaminant depletion, depth, read-vs-assembly, classifier, database, normalization - and each link silently sets what is observable. The community is never observed directly; the report is the community as refracted by this pipeline. Three consequences a newcomer misses:

1. There is no raw truth to recover. Even the input DNA is already a biased sample of the cells (lysis bias). Cleaning the data does not get closer to the community; it changes which lens dominates. The honest framing is relative-within-a-consistent-pipeline.
2. Absence means not-detectable-by-this-chain. A zero is below the depth detection limit, OR not in the database, OR lost in extraction, OR removed by depletion - almost never simple biological absence. Force the question "which link is responsible?" before interpreting absence.
3. The pipeline can manufacture the result. In low biomass the entire community can BE the kitome; with the wrong database the profile is the database's bias; over-aggressive depletion deletes real taxa. Controls plus a consistent chain plus explicit reporting are what convert an uninterpretable number into a defensible measurement.

Extraction Is the Experiment

Lysis efficiency is taxon-dependent: tough-walled Gram-positives (Firmicutes, Staphylococcus, Enterococcus), endospores (Bacillus, Clostridium), acid-fast Mycobacterium, and fungi/archaea resist lysis and are under-represented unless bead-beating is used. Gentle/enzymatic kits inflate easy-to-lyse Gram-negatives - so a Firmicutes:Bacteroidetes shift can be an extraction artifact. Extraction had the largest effect on observed composition across 21 protocols (Costea 2017 Nat Biotechnol 35:1069). Use bead-beating, hold one method constant across a study, validate lysis with a whole-cell mock, and report kit and lot. Note the tradeoff: aggressive bead-beating shears DNA and hurts long-read assembly, so the best extraction depends on the read-vs-assembly choice.

Decision Tree by Scenario

Host-Read Depletion

# Hostile removes >99.5% of human reads while discarding far fewer microbial reads than naive mapping.
# Prefer the T2T-CHM13-based index over GRCh38; high-sensitivity Bowtie2 drives removal more than the reference.
hostile clean --fastq1 sample_R1.fq.gz --fastq2 sample_R2.fq.gz \
    --index human-t2t-hla --aligner bowtie2
# Report the reads removed - it is a QC metric, not a footnote. For long reads use --aligner minimap2.

Remove host for two reasons: analytical (depth, false positives, runtime) and ethical (raw human-associated reads carry identifiable host genotype; depleting before deposit is increasingly required). Wet-lab depletion (saponin/DNase, methyl-CpG capture) saves sequencing but adds its own bias - a genuine tradeoff.

Identify Reagent Contaminants with decontam

Goal: Separate real low-abundance taxa from the kitome using blanks and DNA concentration.

Approach: Run decontam on the classifier output table (taxa x samples) using the frequency signal (contaminants scale inversely with input DNA) and the prevalence signal (contaminants are enriched in blanks); raise the prevalence threshold for low biomass.

library(decontam)
# seqtab: samples x taxa from the Bracken/MetaPhlAn table; conc: per-sample DNA concentration; neg: TRUE for blanks.
contam <- isContaminant(seqtab, conc = dna_conc, neg = is_blank, method = 'combined', threshold = 0.1)
# Low-biomass studies: use the prevalence method at the more aggressive threshold 0.5, and inspect the calls.
contam_lowbio <- isContaminant(seqtab, neg = is_blank, method = 'prevalence', threshold = 0.5, batch = batch_id)
seqtab_clean <- seqtab[, !contam$contaminant]

decontam runs per batch (batch=) because the kitome differs by lot/run. Always inspect the called contaminants against the canonical kitome genera (Bradyrhizobium, Ralstonia, Burkholderia, Pseudomonas, Acinetobacter, Sphingomonas, Methylobacterium, Stenotrophomonas) rather than applying blindly - over-aggressive removal deletes real taxa.

Depth Adequacy

# Nonpareil estimates how much of the community's sequence space you have sampled, without assembly or a DB.
nonpareil -s reads.fasta -T kmer -f fasta -b sample_np
# Plot the coverage-vs-effort curve in R (Nonpareil.curve); a non-detection below the implied limit is meaningless.

Depth is set by the question: dominant taxa need a few million reads, rare-pathogen detection sets a limit of detection, and strain SNVs need high per-genome coverage. Host depletion can silently halve usable depth - budget for it.

Per-Method Failure Modes