bymims-harvard· 57 skills
FASTQ quality control and adapter/quality-trimming decisions with local NGS tools — run FastQC on raw reads, summarize a project with MultiQC, interpret per-base sequence quality, per-base N content, adapter content, overrepresented sequences, sequence duplication and GC content, and decide whether (and how) to trim with fastp / Cutadapt before downstream analysis. seqkit for read counts/stats/subsampling. Use when someone asks "run QC on my FASTQs", "are my reads good quality?", "do I need to trim adapters?", "interpret this FastQC report", "what does this WARN/FAIL mean", "why are overrepresented sequences flagged", "should I quality-trim before alignment", "make a MultiQC summary", or "clean up these reads with fastp". NOT for differential expression / DEG analysis (use tooluniverse-rnaseq-deseq2), NOT for read alignment, coverage, or variant calling (use tooluniverse-variant-analysis / tooluniverse-sequence-analysis). Honest: shells out to real local binaries; if a tool is missing it emits an install plan and stops rather than inventing QC numbers, and it never auto-trims or overwrites raw FASTQs.
$npx -y skills add mims-harvard/tooluniverse --skill tooluniverse-fastq-qcInstalls into the current project.
Run `npx skills use "https://github.com/mims-harvard/tooluniverse" --skill "mims-harvard/tooluniverse/tooluniverse-fastq-qc"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/mims-harvard/tooluniverse" that are relevant to the current task. Run `npx skills add "https://github.com/mims-harvard/tooluniverse"` and select the relevant skills, then follow their instructions.