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 majiayu000/claude-skill-registry --skill long-read-qc-gptomics-bioskills --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.
# Long-Read Quality Control ## 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 "Sample QC" \ --plots hex dot \ # Plot types --format png pdf \ # Output formats --color darkblue \ --maxlength 50000 \ # Max length for plots --minlength 500 # Min length for plots ``` ## NanoStat - Statistics Only ```bash # Quick statistics (no plots) NanoStat --fastq reads.fastq.gz --threads 4 # From BAM NanoStat --bam aligned.bam --threads 4 # Output to file NanoStat --fastq reads.fastq.gz --threads 4 > qc_stats.txt ``` ## chopper - Filter Reads ```bash # Filter by length and quality gunzip -c reads.fastq.gz | chopper -q 10 -l 1000 | gzip > filtered.fastq.gz # Quality >= 10, length >= 1000bp ``` ## chopper - Common Options ```bash gunzip -c reads.fastq.gz | chopper \ --quality 10 \ # Min quality --minlength 1000 \ # Min length --maxlength 50000 \ # Max le
- 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
- Key Parameters - chopper
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 majiayu000/claude-skill-registry --skill long-read-qc-gptomics-bioskills --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 majiayu000/claude-skill-registry, a repository with 534 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.