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.
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-longread-qc --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: BioPython 1.83+, numpy 1.26+ Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures - 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. # Long-Read Quality Control **"Check the quality of my Nanopore/PacBio run"** → Generate read length distributions, quality score plots, and summary statistics, then filter reads by length and quality thresholds. - CLI: `NanoPlot --fastq reads.fq.gz -o nanoplot_output/`, `chopper -q 10 -l 1000 < reads.fq > filtered.fq` ## NanoPlot - Visualization ```bash # From FASTQ NanoPlot --fastq reads.fastq.gz -o nanoplot_output -t 4 # From BAM NanoPlot --bam aligned.bam -o nanoplot_output -t 4 # From sequencing summary (fastest) NanoPlot --summary sequencing_summary.txt -o nanoplot_output ``` ## NanoPlot - Common Options ```bash NanoPlot --fastq reads.fastq.gz \ -o nanoplot_output \ -t 8 \ --N50 \ # Show N50 in plots --title "Sampl
- Version Compatibility
- NanoPlot - Visualization
- NanoPlot - Common Options
- NanoStat - Statistics Only
- chopper - Filter Reads
- chopper - Common Options
- NanoFilt - Alternative Filter
- Porechop - Adapter Trimming
- Generate Summary Statistics
- PycoQC - From Basecalling
- Calculate N50
- Parse FASTQ Quality in Python
- NanoPlot Output Files
- Key Parameters - NanoPlot
From FASTQ NanoPlot --fastq reads.fastq.gz -o nanoplot_output -t 4 From BAM NanoPlot --bam aligned.bam -o nanoplot_output -t 4 From sequencing summary (fastest) NanoPlot --summary sequencing_summary.txt -o nanoplot_output NanoPlot --fastq reads.fastq.gz \ Quick statistics (no plots) NanoStat --fastq reads.fastq.gz --threads 4 NanoStat --bam aligned.bam --threads 4
What does the bio-longread-qc skill do?
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.
How do I install it?
Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-longread-qc --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From FreedomIntelligence/OpenClaw-Medical-Skills, a repository with 2,909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.