bygptomics· 59 skills
Detects differential alternative splicing between conditions using rMATS-turbo (binomial LRT on junction counts), leafcutter (Dirichlet-multinomial GLM on intron clusters), MAJIQ V3 deltapsi/HET (Bayesian posterior on LSVs), SUPPA2 (empirical-null on TPM-derived PSI), or Shiba (junction-imbalance-corrected, 2025 SOTA at low coverage). Reports FDR-corrected significance and delta PSI effect sizes. Tools differ in statistical model, annotation dependence, calibration regime, and replicate-count requirements. Use when comparing splicing patterns between treatment groups, tissues, or disease states.
$npx -y skills add gptomics/bioskills --skill differential-splicingInstalls into the current project.
Run `npx skills use "https://github.com/gptomics/bioskills" --skill "gptomics/bioskills/differential-splicing"` 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/gptomics/bioskills" that are relevant to the current task. Run `npx skills add "https://github.com/gptomics/bioskills"` and select the relevant skills, then follow their instructions.