Анализ bulk RNA-seq от FASTQ до генных путей

★ 7.0 · research

bulk-rnaseq is a Claude Code skill that orchestrates a complete bulk RNA-seq differential-expression workflow, taking raw FASTQ reads all the way through to enriched pathways and publication figures. The pipeline covers QC and trimming (FastQC, MultiQC, fastp, Trim Galore), alignment and quantification (STAR, Salmon, featureCounts), and gene-level counts matrix assembly via pytximport and a bundled build_counts_matrix.py bridge script, then hands off to the pydeseq2, pathway-enrichment, and scientific-visualization skills. It routes between two upstream paths — nf-core/rnaseq via Nextflow (recommended for reproducibility and scale) and a standalone STAR/Salmon mode for constrained environments — while enforcing experimental design, strandedness detection, and QC gates at every stage. Designed for researchers running tasks like "FASTQ to DESeq2", "configure nf-core/rnaseq", or "build a counts matrix for differential expression"; use the scanpy skill for single-cell data instead.