NGS Quality Control

Overview

This skill encodes a reproducible QC workflow for Illumina short-read sequencing data across the full lifecycle: raw reads → trimmed reads → aligned BAMs. It produces deterministic metrics suitable for QC gating in WGS, WES, and targeted panel projects.

Pipeline stages:

1. Raw-read QC — FastQC per FASTQ, aggregated with MultiQC.
2. Adapter/quality trimming — fastp with paired-end adapter detection.
3. Post-trim QC — FastQC + MultiQC on trimmed reads.
4. Alignment QC — samtools stats/flagstat, Picard metric suites.
5. Coverage QC — mosdepth (genome windows or target BED).
6. Aggregation — MultiQC over all tool outputs for a single-HTML summary.

Usage

Assume THREADS=8, OUT=qc_out, paired FASTQs sample_R1.fastq.gz / sample_R2.fastq.gz, coordinate-sorted sample.bam with index, reference ref.fasta, and (for WES/panel) target intervals.

1. Raw FASTQ QC — FastQC

mkdir -p $OUT/fastqc_raw
fastqc -t $THREADS -o $OUT/fastqc_raw *.fastq.gz

Key FastQC modules to inspect:

• Per base sequence quality — Q30 drop-off toward 3′ end indicates trimming need.
• Per sequence quality scores — bimodal distribution flags a failing tile/lane.
• Per base sequence content — first ~12 bp bias is normal (random-hexamer priming); large bias elsewhere flags adapter/contamination.
• Per sequence GC content — deviation from expected species GC → contamination or bias.
• Sequence Duplication Levels — high duplication in non-amplicon libraries → library complexity issue.
• Overrepresented sequences — adapter readthrough, rRNA, PhiX carryover.
• Adapter Content — confirms which adapter set to trim.

2. Adapter & Quality Trimming — fastp

mkdir -p $OUT/fastp
fastp \
  -i sample_R1.fastq.gz -I sample_R2.fastq.gz \
  -o $OUT/fastp/sample_R1.trim.fastq.gz -O $OUT/fastp/sample_R2.trim.fastq.gz \
  --detect_adapter_for_pe \
  --thread $THREADS \
  --html $OUT/fastp/sample.fastp.html \
  --json $OUT/fastp/sample.fastp.json

Useful additional flags:

• --qualified_quality_phred 20 — base quality threshold (default 15).
• --length_required 50 — drop reads shorter than 50 bp post-trim.
• --cut_tail --cut_tail_window_size 4 --cut_tail_mean_quality 20 — sliding window 3′ quality trim.
• --dedup — in-memory PCR duplicate removal (read-pair identity; prefer Picard/MarkDuplicates post-alignment for most workflows).

3. Post-trim QC + MultiQC

mkdir -p $OUT/fastqc_trim
fastqc -t $THREADS -o $OUT/fastqc_trim $OUT/fastp/*.trim.fastq.gz

multiqc $OUT -o $OUT/multiqc

MultiQC auto-detects FastQC, fastp, Picard, samtools, and mosdepth outputs in the scanned directory.

4. Alignment QC — samtools

samtools flagstat -@ $THREADS sample.bam > $OUT/samtools/sample.flagstat.txt
samtools stats    -@ $THREADS sample.bam > $OUT/samtools/sample.stats.txt
samtools idxstats sample.bam               > $OUT/samtools/sample.idxstats.txt

Pull from samtools stats: raw total sequences, reads mapped, error rate, insert size average, insert size standard deviation.

5. Picard Metric Suites

Picard requires a sequence dictionary (ref.dict) next to ref.fasta (picard CreateSequenceDictionary R=ref.fasta) and a Picard-format interval list for hybrid-capture metrics (.interval_list, header = BAM @SQ lines; not a plain BED).

# Convert BED → interval_list (required for CollectHsMetrics)
picard BedToIntervalList \
  I=targets.bed O=targets.interval_list SD=ref.dict

# General alignment metrics (all library types)
picard CollectAlignmentSummaryMetrics \
  R=ref.fasta I=sample.bam O=$OUT/picard/sample.alignment_metrics.txt

# Insert size distribution (paired-end)
picard CollectInsertSizeMetrics \
  I=sample.bam O=$OUT/picard/sample.insert_size.txt \
  H=$OUT/picard/sample.insert_size.pdf

# WGS coverage metrics
picard CollectWgsMetrics \
  R=ref.fasta I=sample.bam O=$OUT/picard/sample.wgs_metrics.txt

# WES / targeted panel hybrid-selection metrics
picard CollectHsMetrics \
  R=ref.fasta I=sample.bam O=$OUT/picard/sample.hs_metrics.txt \
  BAIT_INTERVALS=baits.interval_list \
  TARGET_INTERVALS=targets.interval_list

6. Coverage — mosdepth

# WGS: 500 bp fixed windows
mosdepth --by 500 --threads $THREADS --fast-mode --no-per-base \
  $OUT/mosdepth/sample_wgs sample.bam

# WES / panel: per-target coverage
mosdepth --by targets.bed --threads $THREADS --no-per-base \
  $OUT/mosdepth/sample_wes sample.bam

Outputs of interest:

• *.mosdepth.summary.txt — mean depth per chromosome and total.
• *.mosdepth.region.dist.txt — cumulative fraction of regions at ≥X coverage (use for % targets ≥20x).
• *.regions.bed.gz — per-window/per-target mean depth.

Add --quantize 0:1:10:30: for stratified coverage bins, or --thresholds 10,20,30,50 to emit per-region pass counts at those depths.

Response Format

• Lead with the command or code the user needs — explain after
• Structure as: confirm inputs → working code → key parameters explained → gotchas
• One complete working example per task; do not show every alternative
• Keep code comments minimal and functional (what, not why-it-exists)
• Target: 50-100 lines of code with brief surrounding explanation

Core Concepts

QC Tool Selection by Sequencing Type

All types: FastQC (raw + trimmed) → fastp (trim) → samtools flagstat → MultiQC (aggregate).

Quality encoding detection

Modern Illumina (CASAVA ≥1.8, 2011+) emits Phred+33 (Sanger). Legacy data (Illumina 1.3–1.7, Solexa) used Phred+64. Aligners expect Phred+33.

• FastQC reports encoding in Basic Statistics → Encoding. Look for Sanger / Illumina 1.9.
• Quick CLI check — inspect the quality-line ASCII range:

  zcat sample_R1.fastq.gz | head -4000 \
    | awk 'NR%4==0' \
    | od -An -c | tr -s ' \n' '\n' \
    | awk 'length==1' | sort -u
  

  
  Characters in the !–I range → Phred+33; characters @–h → Phred+64.
• Convert legacy data: seqtk seq -Q64 -V in.fq > out.fq (shifts +64 → +33).

Trim parameter intent

• Adapter trimming is for read-through into the adapter (insert < read length). --detect_adapter_for_pe uses read overlap, which is more reliable than sequence-based detection for paired-end.
• Quality trimming is for 3′ quality decay on 2-channel chemistry (NextSeq/NovaSeq emit poly-G at dark cycles; use --trim_poly_g for those platforms — fastp auto-enables it by platform detection).
• Do not hard-clip fixed lengths unless you know the library design (UMIs, inline barcodes).

Duplication: optical vs PCR

• samtools flagstat "duplicates" reflects whatever flag is set in the BAM (usually by Picard MarkDuplicates or samtools markdup). Run MarkDuplicates after alignment, not on trimmed FASTQs.
• CollectWgsMetrics / CollectHsMetrics report PCT_EXC_DUPE — fraction of bases excluded due to duplication. This is the canonical dup rate for coverage-based QC.

Coverage: mean vs uniformity

Mean depth alone is insufficient. Report both:

• Mean target coverage (MEAN_TARGET_COVERAGE from HsMetrics; MEAN_COVERAGE from WgsMetrics).
• Coverage breadth — % targets ≥20x / % genome ≥20x (PCT_TARGET_BASES_20X, PCT_20X in Picard; mosdepth region.dist.txt).

A panel with mean 200x but only 70% of targets at 20x has a coverage-uniformity problem (capture efficiency, GC bias).

Quick Reference

Quality thresholds (typical gating)

One-liners

# Full raw → trimmed → FastQC → MultiQC
fastqc -t 8 -o qc/raw *.fastq.gz && \
fastp -i R1.fq.gz -I R2.fq.gz -o R1.trim.fq.gz -O R2.trim.fq.gz \
      --detect_adapter_for_pe --thread 8 --html fastp.html --json fastp.json && \
fastqc -t 8 -o qc/trim *.trim.fq.gz && \
multiqc qc -o qc/multiqc

# BAM QC bundle
samtools flagstat sample.bam > qc/sample.flagstat.txt
picard CollectAlignmentSummaryMetrics R=ref.fasta I=sample.bam O=qc/aln.txt
picard CollectInsertSizeMetrics I=sample.bam O=qc/ins.txt H=qc/ins.pdf
mosdepth --by 500 --threads 8 --fast-mode --no-per-base qc/sample sample.bam

Tool roles at a glance

Common Mistakes

• Wrong: Running FastQC only after trimming Right: Run FastQC on both raw and trimmed reads Why: Pre-trim QC diagnoses the library; post-trim QC confirms trimming worked without over-clipping
• Wrong: Passing a raw BED file to Picard CollectHsMetrics Right: Convert to Picard-format .interval_list via picard BedToIntervalList I=x.bed O=x.interval_list SD=ref.dictWhy: BAIT_INTERVALS/TARGET_INTERVALS require .interval_list with a full @SQ header; a raw BED will fail or silently produce wrong metrics
• Wrong: Using interval-list files with mismatched contig names or sort order Right: Ensure contig names (chr1 vs 1) and @SQ ordering match the BAM header exactly Why: Mismatches cause silent metric errors or tool failures
• Wrong: Running MarkDuplicates on trimmed FASTQs or name-sorted BAMs Right: Run MarkDuplicates only on coordinate-sorted, aligned BAMs Why: MarkDuplicates requires coordinate-sorted aligned reads to identify optical and PCR duplicates
• Wrong: Reporting only mean coverage as the QC metric Right: Always pair mean depth with breadth (% ≥20x) and duplicate rate Why: A high mean with poor uniformity hides failing targets
• Wrong: Trusting auto-encoding detection on pre-2012 FASTQ data Right: Verify Phred encoding explicitly before alignment for ancient data Why: Phred+64 data passed to a modern aligner produces silently wrong base qualities
• Wrong: Merging lanes/samples before running per-lane QC Right: Run FastQC per lane/FASTQ first; merge only after per-lane QC passes Why: A single bad lane becomes unidentifiable once merged
• Wrong: Using --dedup in fastp as the primary deduplication step Right: Use Picard MarkDuplicates or samtools markdup post-alignment for deduplication Why: fastp --dedup only catches exact-sequence duplicates pre-alignment, missing alignment-based duplicates
• Wrong: Running mosdepth on large genomes without --no-per-baseRight: Always use --no-per-base for QC on large genomes Why: Per-base output is enormous and rarely needed; windowed/region output is sufficient for QC

References

• FastQC: https://www.bioinformatics.babraham.ac.uk/projects/fastqc/
• fastp: Chen et al. Bioinformatics 2018, https://doi.org/10.1093/bioinformatics/bty560
• Picard: https://broadinstitute.github.io/picard/
• mosdepth: Pedersen & Quinlan, Bioinformatics 2018, https://doi.org/10.1093/bioinformatics/btx699
• Coverage recommendations: Sims et al. Nat Rev Genet 2014, https://doi.org/10.1038/nrg3642