Detects contamination in sequencing reads - cross-species (FastQ Screen, Kraken2), vector/PhiX/adapter, rRNA, and same-species cross-sample/index-hopping and sample swaps (SNP fingerprints via verifyBamID2/NGSCheckMate/somalier). Use when suspecting cross-contamination, PDX host reads, microbial carry-over, or sample swaps, and to decide whether to report, filter, or align to a combined reference. For deep taxonomic profiling use metagenomics/kraken-classification.
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Detects contamination in sequencing reads - cross-species (FastQ Screen, Kraken2), vector/PhiX/adapter, rRNA, and same-species cross-sample/index-hopping and sample swaps (SNP fingerprints via verifyBamID2/NGSCheckMate/somalier). Use when suspecting cross-contamination, PDX host reads, microbial carry-over, or sample swaps, and to decide whether to report, filter, or align to a combined reference. For deep taxonomic profiling use metagenomics/kraken-classification.
Before using code patterns, verify installed versions match. If versions differ:
CLI: <tool> --version then <tool> --help to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
Contamination Screening -- a species screen cannot see a same-species swap
Screen reads against a genome panel (FastQ Screen / Kraken2) for foreign ORGANISMS, and against SNP fingerprints for foreign or wrong INDIVIDUALS.
"Check my reads for contamination" -> Map a subsample against multiple references and/or fingerprint sample identity to find foreign DNA and mislabels.
Scope: this skill OWNS contamination detection and the report-vs-filter decision. Deep taxonomic profiling/abundance -> metagenomics/kraken-classification, metagenomics/metaphlan-profiling. rRNA depletion as an RNA prep metric -> read-qc/rnaseq-qc. OUT OF SCOPE: adapter removal (read-qc/adapter-trimming).
The Single Most Important Modern Insight
A SPECIES screen answers "what ORGANISMS are here?"; a SNP FINGERPRINT answers "WHOSE DNA is this, and is it a mixture?" -- and these are orthogonal. A species screen is structurally BLIND to same-species cross-sample contamination and sample swaps: a human-A + human-B mixture, or a mislabeled human file, produces a perfectly clean single-species profile. Most pipelines run only a species screen and declare the data clean. Any human or single-species-cohort pipeline needs BOTH a taxonomic screen AND a SNP-fingerprint identity/contamination check (verifyBamID2, NGSCheckMate, somalier, conpair).
Index hopping is the same-species contamination that lives inside one run, and unique dual indexing (UDI) is the only clean fix. On patterned flowcells (HiSeq X/4000, NovaSeq) ExAmp chemistry lets a free index adapter tag a fragment from another sample, spreading ~0.1-2% of reads into incorrect samples. Irrelevant for germline common variants, CATASTROPHIC for low-VAF work (ctDNA, single-cell, somatic) where hopped reads look like phantom low-frequency variants. Combinatorial indexing cannot detect it; UDI (a unique i7 AND i5 per sample) lets the demultiplexer drop impossible index pairs. UDI is effectively mandatory for ctDNA/plasma, single-cell, and low-input somatic.
Default to SCREEN-AND-REPORT, not filter -- removing reads biases composition. A species screen is a QC gate; if a contaminant is low, aligning to the correct reference simply will not place the foreign reads. Filter only a specific, named contaminant (PhiX before assembly, adapters before alignment) with a precise k-mer remover (BBDuk ref=phix), never "remove anything that hits the screen" (that also discards conserved rRNA/mito reads that belong to the sample). For PDX, align to a COMBINED human+mouse reference and keep human-assigned reads, OR use a dedicated post-alignment classifier (XenofilteR benchmarks above Xenome for variant false-positive rate); both beat hard pre-filtering on one genome, which mis-assigns conserved-region reads.
Deeper trap: reference-genome contamination corrupts the screen itself. A "human" hit can be bacterial sequence mis-deposited inside the human assembly (Conterminator found >2M contaminated GenBank entries). No --confidence setting fixes a wrong database; trust a deconned/curated DB and treat surprising single-source hits as DB artifacts until ruled out.
The Contamination Taxonomy -- five classes, five fixes
XenofilteR > Xenome for SNV FP rate; both beat hard pre-filtering
Remove a NAMED contaminant
BBDuk ref=...
Precise k-mer removal, not "hits the screen"
Strip HUMAN reads before public deposition (non-human library)
hostile / NCBI sra-human-scrubber (HRRT)
Deposition compliance, not a QC gate; a masked T2T reference avoids stripping conserved microbial regions
Default when uncertain: FastQ Screen as the QC gate for organisms, PLUS a SNP-fingerprint check (somalier/NGSCheckMate) for any human cohort.
FastQ Screen
Maps a SUBSAMPLE (--subset, default 100000) with bowtie2 reporting >1 alignment, then classifies each read across the panel. Read the bar chart, not just "% mapped": contamination concentrates in One_hit_one_genome of an UNEXPECTED genome; homology (rRNA, mito, conserved loci) spreads into the *_multiple_genomes categories; high Hit_no_genomes means adapter dimer, a missing reference, or a novel organism (a diagnostic, not a verdict).
# Config: aligner binary + DATABASE lines (bowtie2 index prefixes)cat > fastq_screen.conf <<'EOF'
BOWTIE2 /usr/local/bin/bowtie2
THREADS 8
DATABASE Human /refs/GRCh38_bt2/GRCh38
DATABASE Mouse /refs/GRCm39_bt2/GRCm39
DATABASE Ecoli /refs/Ecoli_bt2/Ecoli
DATABASE PhiX /refs/phix_bt2/phix
DATABASE rRNA /refs/rRNA_bt2/rRNA
EOF
fastq_screen --conf fastq_screen.conf --threads 8 --outdir screen/ *.fastq.gz
multiqc screen/ # MultiQC parses *_screen.txt across samples# Tag every read with a per-genome status, then extract a subset by pattern
fastq_screen --conf fastq_screen.conf --tag --filter 10000 sample.fastq.gz # maps only to genome 1
fastq_screen --conf fastq_screen.conf --nohits sample.fastq.gz # reads hitting nothing
--filter digits (one per genome, config order): 0=no map, 1=unique, 2=multi, 3=maps, 4=pass 0 or 1, 5=pass 0 or 2, -=ignore. --subset 0 screens the whole file; --bisulfite uses Bismark.
Kraken2 + Bracken (read-level taxonomy)
Default --confidence 0.0 over-reports a long tail of spurious low-abundance species (a few shared k-mers suffice); raise to 0.05-0.1 and keep --minimum-hit-groups 2 (or 3 for custom DBs). Kraken2 gives CLASSIFICATION; Bracken redistributes higher-rank reads to species for ABUNDANCE.
# verifyBamID2: FREEMIX = contamination fraction (action threshold ~0.02);# FREEMIX~0 with CHIPMIX~1 indicates a SWAP, not contamination.# --SVDPrefix points to the panel resource that ships with verifyBamID2 (resource/1000g.phase3...).
verifyBamID2 --SVDPrefix /res/1000g.phase3.100k.b38.vcf.gz.dat --BamFile sample.bam --Reference ref.fa
# somalier: extract genome sketches, then relate to find swaps / identity across a cohort.# --sites = somalier's released sites.<build>.vcf.gz (github releases), not a custom panel.
somalier extract -d sites/ --sites sites.hg38.vcf.gz -f ref.fa sample.bam
somalier relate sites/*.somalier # off-diagonal identity flags swaps# conpair (tumor-normal): concordance + independent per-sample contamination
Index hopping is mitigated at demultiplexing with UDI (drop impossible i7,i5 pairs); residual contamination is then quantified by the SNP-fingerprint tools above.
Common Errors
Symptom
Cause
Solution
"Single species, data is clean" but a swap is suspected
Species screen is blind to same-species swaps
Run somalier / NGSCheckMate on SNP fingerprints
Phantom low-VAF variants in ctDNA/single-cell
Index hopping on patterned flowcell
Use UDI; quantify residual with verifyBamID2/conpair
Kraken2 reports dozens of trace species
Default confidence 0.0 over-reports
Raise --confidence to 0.05-0.1; raise hit-groups
A "human" Kraken hit on a microbial isolate
Reference/DB contamination
Use a deconned DB; treat as artifact until confirmed
Filtering "contaminant" reads skews composition
Removed conserved rRNA/mito too
Remove a NAMED contaminant with BBDuk, not "hits the screen"
PDX human counts look biased
Hard pre-filtering of ambiguous reads
Align to combined human+mouse reference instead
References
Wingett SW, Andrews S. 2018. FastQ Screen: a tool for multi-genome mapping and quality control. F1000Research 7:1338.
Wood DE, Lu J, Langmead B. 2019. Improved metagenomic analysis with Kraken 2. Genome Biology 20:257.
Lu J, Breitwieser FP, Thielen P, Salzberg SL. 2017. Bracken: estimating species abundance in metagenomics data. PeerJ Computer Science 3:e104.
Steinegger M, Salzberg SL. 2020. Terminating contamination: large-scale search identifies more than 2,000,000 contaminated entries in GenBank. Genome Biology 21:115.
Conway T, Wazny J, Bromage A, et al. 2012. Xenome - a tool for classifying reads from xenograft samples. Bioinformatics 28(12):i172-i178.
Costello M, Fleharty M, Abreu J, et al. 2018. Characterization and remediation of sample index swaps by non-redundant dual indexing. BMC Genomics 19:332.
Zhang F, Flickinger M, Taliun SAG, et al. 2020. Ancestry-agnostic estimation of DNA sample contamination from sequence reads. Genome Research 30(2):185-194.
Lee S, Lee S, Ouellette S, Park WY, Lee EA, Park PJ. 2017. NGSCheckMate: software for validating sample identity in next-generation sequencing studies within and across data types. Nucleic Acids Research 45(11):e103.
Pedersen BS, Bhetariya PJ, Brown J, et al. 2020. Somalier: rapid relatedness estimation for cancer and germline studies using efficient genome sketches. Genome Medicine 12:62.
Related Skills
read-qc/quality-reports - Bimodal GC and overrepresented sequences flag contamination
read-qc/adapter-trimming - Remove adapter contamination
read-qc/rnaseq-qc - rRNA fraction as a prep-efficiency metric
metagenomics/kraken-classification - Deeper taxonomic classification and profiling
variant-calling/joint-calling - Where SNP-fingerprint sample swaps do the most damage
