Bio Long Read Sequencing Long Read Qc

What it does

When your agent uses it

• Judging a long-read run
• Computing read N50
• Percent identity
• Filtering reads before assembly

Example prompts

• “Use the bio-long-read-sequencing-long-read-qc skill to assess Oxford Nanopore and PacBio long-read quality with NanoPlot, cramino, NanoComp…”
• “/bio-long-read-sequencing-long-read-qc”

Requirements

• A Bash shell

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

1. 1Per-read Qscore is an uncalibrated basecaller posterior, not an empirical error rate. It is the Phred of the mean per-base error…
2. 2The sequencing_summary.txt is the run-health layer, and it is not in the FASTQ. Pore/channel activity, yield-over-time, translocation…
3. 3The right filter depends on intent, not a fixed cutoff. Assembly wants the long reads (which are the lowest-Q) and small replicons…

What it can do on your machine

Read from SKILL.md and the folder at commit d91ed3d. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Ships script files (Shell), which the agent can run.

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  No URLs in SKILL.md.

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Bio Long Read Sequencing Long Read Qc loads about 2.7k tokens when it runs. Until then it costs about 195 tokens; SKILL.md has 1,165 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~195

When it runs · the whole SKILL.md, loaded when a task matches

~2.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from GPTomics/bioSkills at commit d91ed3d, republished under its MIT licence (© GPTomics). 1,165 words, ~2,667 tokens.

name

bio-long-read-sequencing-long-read-qc

description

Assesses Oxford Nanopore and PacBio long-read quality with NanoPlot, cramino, NanoComp, pycoQC/toulligQC, and seqkit, and filters reads with chopper/Filtlong for the downstream goal. Covers why read-only Qscore is an uncalibrated posterior (real accuracy needs a reference BAM), why the sequencing_summary.txt is required for run-health metrics, intent-conditioned filtering (preserve long reads and small replicons for assembly, filter almost nothing for variant calling), the chimera/internal-adapter trap that fabricates SVs, and PacBio rq-based HiFi QC. Use when judging a long-read run, computing read N50 or percent identity, filtering reads before assembly or variant calling, comparing barcodes/runs, or reading run-health red flags.

tool_type

cli

primary_tool

nanoplot

# 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

© GPTomics, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files in long-read-sequencing/long-read-qc of GPTomics/bioSkills.

• SKILL.md
• examples/qc_workflow.sh
• usage-guide.md

Open the folder on GitHubat commit d91ed3d

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in GPTomics/bioSkills, which our catalogue first saw on October 7, 2026.

• PKU-YuanGroup/OpenAI4Sskills/bioskills/bio-long-read-sequencing-long-read-qc