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bio-longread-qc

Quality control for long-read sequencing data using NanoPlot, NanoStat, and chopper. Generate QC reports, filter reads by length and quality, and visualize read characteristics. Use when assessing ONT or PacBio run quality or filtering reads before assembly or alignment.

personAuthor: jakexiaohubgithub
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Version Compatibility

Reference examples tested with: NanoPlot 1.42+ (NanoPack2), cramino 0.14+, chopper 0.7+, Filtlong 0.2+, seqkit 2.5+, pycoQC 2.5+.

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

  • CLI: <tool> --version then <tool> --help to confirm flags (chopper/cramino are fast-moving Rust tools)

Inputs that determine what QC is even possible - record them:

  • sequencing_summary.txt is produced by the basecaller (Dorado/Guppy), not the FASTQ. pycoQC/toulligQC REQUIRE it for pore activity, yield-over-time, and translocation speed. FASTQ-only hand-off permanently loses the run-health layer.
  • Percent identity requires a reference BAM (NanoPlot --bam / cramino); it cannot come from FASTQ.

If code throws an error, introspect the installed tool (NanoPlot --help, cramino --help) and adapt the example to the actual API rather than retrying.

Long-Read QC

"Is my long-read run any good?" -> Read length N50 and yield from FASTQ, real percent identity from a reference BAM, run-health from the sequencing_summary, then filter for the downstream goal.

  • CLI: NanoPlot --fastq reads.fq.gz -o qc/ (overview), cramino aln.bam (fast BAM stats + identity), pycoQC -f sequencing_summary.txt -o run.html (run health)

The Single Most Important Modern Insight -- Read-Only Qscore Is a Self-Graded Posterior; Real Accuracy and the Failures That Sink a Run Are Only Visible Against a BAM and the Summary

Three corrections a naive long-read QC misses:

  1. Per-read Qscore is an uncalibrated basecaller posterior, not an empirical error rate. It is the Phred of the mean per-base error probability (NOT the arithmetic mean of Q values), assigned by the basecaller to its own output. ONT's own data: bases labeled Q20 are empirically ~Q12.5 on older chemistries; R10 sup and HiFi are better calibrated but read-only Q still overstates accuracy. Real accuracy is gap-compressed identity from a reference BAM (cramino, NanoPlot --bam). Treat Q thresholds as relative knobs, not accuracy guarantees.
  2. The sequencing_summary.txt is the run-health layer, and it is not in the FASTQ. Pore/channel activity, yield-over-time, translocation speed, and barcode breakdown come from the basecaller's summary TSV. Hand a collaborator only FASTQ and that layer is gone (re-basecalling from POD5 can regenerate it; FASTQ cannot).
  3. The right filter depends on intent, not a fixed cutoff. Assembly wants the long reads (which are the lowest-Q) and small replicons preserved - subsample by quality, never hard-length-cut. Variant calling wants depth - filter almost nothing and let the caller model per-base Q. HiFi is already Q20+ - do not Phred-filter it like noisy CLR.

Tool Roles

| Tool | Input | Reports | |------|-------|---------| | NanoPlot | FASTQ / BAM / summary | length dist, length-vs-quality, yield; --bam adds percent identity | | cramino | BAM/CRAM | fast N50, yield, gap-compressed identity, --phased block N50, --karyotype | | NanoComp | multiple FASTQ/BAM/summaries | compare runs/barcodes (length, quality, identity) | | pycoQC / toulligQC | sequencing_summary.txt | run health: pore activity, mux map, yield/speed over time, barcodes | | seqkit stats -a | FASTA/FASTQ | N50, quartiles, total bases, GC | | chopper | FASTQ (stdin) | filter/trim by mean Q and length | | Filtlong | FASTQ | keep best reads by length x identity; subsample to a target depth |

Read N50 = the length where 50% of total bases are in reads at least that long (length-weighted, far above the median); it predicts assembly contiguity. NanoFilt and the rrwick Porechop are deprecated/unmaintained (use chopper and Porechop_ABI).

Intent-Conditioned Filtering Decision Tree

| Goal | Filter | Why | |------|--------|-----| | Bacterial / small-genome assembly | light Q/length, then subsample by quality to ~50-100x (filtlong --target_bases) | a hard 10 kb length cut erases small plasmids; quality-subsampling beats length filtering | | Eukaryotic / large-genome assembly | minimal; keep the long tail | the longest (lowest-Q) reads span repeats; over-filtering loses N50 | | SV calling | light Q only; trim chimeras | chimeras fabricate SVs; trimming matters more than Q filtering | | SNV / small-variant calling | almost nothing (chopper -q 10) | callers model per-base Q and want depth | | PacBio HiFi | rq >= 0.99 only | already Q20+; Phred filtering adds nothing | | cDNA / direct RNA | orient/trim (pychopper), no hard length cut | transcript length is biology; a length cut biases the expression matrix |

Core Commands

# Overview from FASTQ (length + posterior quality only - not real accuracy)
NanoPlot --fastq reads.fq.gz -o qc_fastq/ --N50
seqkit stats -a reads.fq.gz                     # N50 + quartiles, fast

# Real accuracy: fast BAM stats incl. gap-compressed identity (needs a reference BAM)
cramino aln.bam
NanoPlot --bam aln.bam -o qc_bam/               # percent identity scatter

# Run health (requires the basecaller's summary)
pycoQC -f sequencing_summary.txt -o run_qc.html

# Compare barcodes / runs
NanoComp --bam s1.bam s2.bam s3.bam --names s1 s2 s3 -o compare/

# Filter for VARIANT calling: light quality only
chopper -q 10 -i reads.fq.gz | gzip > q10.fq.gz

# Subsample for ASSEMBLY: by quality to ~100x of a 5 Mb genome (never a hard length cut)
filtlong --target_bases 500000000 reads.fq.gz | gzip > subsampled.fq.gz

Per-Method Failure Modes

Trusting FASTQ Qscore as accuracy

Trigger: judging a run from NanoStat --fastq mean Q. Mechanism: Q is an uncalibrated posterior. Symptom: "Q20 reads" that are ~94% accurate. Fix: align and read gap-compressed identity (cramino / NanoPlot --bam).

QC without the summary

Trigger: only FASTQ/BAM at hand-off. Mechanism: run-health metrics live in sequencing_summary.txt. Symptom: cannot see pore death, mux map, or yield-over-time. Fix: obtain the summary (or re-basecall from POD5 to regenerate it).

Over-filtering erases assembly value

Trigger: a blunt -q 15 or hard 10 kb length cut before assembly. Mechanism: the longest reads are the lowest-Q; small plasmids fall under a length floor. Symptom: worse N50; missing plasmids. Fix: subsample by quality (Filtlong --target_bases), keep the long tail, never length-floor above the smallest replicon.

Chimeras masquerade as SVs

Trigger: undetected internal adapters (two molecules ligated as one read). Mechanism: the read's halves map to different loci. Symptom: phantom translocations/insertions in the SV VCF. Fix: check whether Dorado already trimmed/split; use Porechop_ABI for unknown adapters; suspect a biologically implausible long-read spike.

Re-filtering HiFi like CLR

Trigger: Phred-quality-filtering PacBio HiFi. Mechanism: HiFi is Q20+ consensus already. Symptom: wasted reads, no accuracy gain. Fix: filter on rq >= 0.99 only.

Quantitative Thresholds

| Threshold | Source | Rationale | |-----------|--------|-----------| | Q20-labeled bases ~Q12.5 empirically | ONT EPI2ME | read-only Q overstates accuracy; verify by alignment | | Subsample assembly data to ~50-100x | Wick 2026 | >100x slows assemblers and can propagate systematic errors | | Pore occupancy <~70% in hour 1 rarely recovers | ONT guidance | run-health red flag for early pore death | | Translocation ~400 b/s (R10 DNA) | ONT chemistry | drift off target correlates with falling basecall Q | | HiFi rq >= 0.99 (Q20); >= 0.999 for Q30 | PacBio CCS | the canonical HiFi accuracy filter | | -q 10 as a light QC floor | convention | a relative knob, not a 90%-accuracy guarantee |

Common Errors

| Error / symptom | Cause | Solution | |-----------------|-------|----------| | NanoPlot gives no percent identity | run on FASTQ | use --bam (identity needs alignment) | | pycoQC errors / empty | no sequencing_summary.txt | supply the basecaller summary | | cramino fails on FASTQ | cramino is BAM/CRAM only | give it the aligned BAM | | Assembly N50 dropped after filtering | hard length/quality cut removed long reads | subsample by quality instead | | Missing small plasmids | length floor above the replicon size | lower/remove the length floor | | Phantom SVs in the VCF | chimeric reads | trim/split internal adapters |

References

  • De Coster W, D'Hert S, Schultz DT, Cruts M, Van Broeckhoven C. 2018. NanoPack: visualizing and processing long-read sequencing data. Bioinformatics 34(15):2666-2669.
  • De Coster W, Rademakers R. 2023. NanoPack2: population-scale evaluation of long-read sequencing data (cramino, chopper). Bioinformatics 39(5):btad311.
  • Leger A, Leonardi T. 2019. pycoQC, interactive quality control for Oxford Nanopore Sequencing. J Open Source Softw 4(34):1236.
  • Steinig E, Coin L. 2022. Nanoq: ultra-fast quality control for nanopore reads. J Open Source Softw 7(69):2991.
  • Bonenfant Q, Noé L, Touzet H. 2023. Porechop_ABI: discovering unknown adapters in Oxford Nanopore sequencing reads. Bioinform Adv 3(1):vbac085.
  • Shen W, Le S, Li Y, Hu F. 2016. SeqKit: a cross-platform and ultrafast toolkit for FASTA/Q file manipulation. PLoS ONE 11(10):e0163962.

Related Skills

  • basecalling - Produces the reads and the sequencing_summary.txt this QC needs
  • long-read-alignment - Produces the BAM required for real percent identity
  • structural-variants - Chimeras flagged here fabricate SVs there
  • medaka-polishing - QC/subsample reads before polishing
  • genome-assembly/long-read-assembly - Subsample by quality before assembling
  • genome-assembly/genome-profiling - K-mer ploidy/size estimate alongside read QC
  • read-qc/quality-reports - General (short-read-oriented) read QC
  • sequence-io/sequence-statistics - FASTA/FASTQ summary statistics