alterlab-rnaseq-quant

# RNA-seq Quantification — salmon & kallisto Transcript Abundance The command-line quantification entry point for bulk RNA-seq: take raw FASTQ reads plus a reference transcriptome and produce transcript-level abundance estimates (counts + TPM) with **salmon** (selective alignment) or **kallisto** (pseudoalignment via kb-python), then aggregate to the gene level with `tximport`/`tximeta` and hand off to `alterlab-pydeseq2` for differential expression. It is the raw-data-to-count-matrix pipeline that the repo's Python analysis skills assume already ran. ## Quick Start ``` Quantify these RNA-seq FASTQs with salmon and a decoy-aware index Build a salmon gentrome index from this transcriptome + genome Run kallisto / kb count on my paired-end reads Turn my salmon quant.sf files into a gene-level count matrix for DESeq2 ``` → Build a **decoy-aware** index once, run `salmon quant` (or `kb count`) per sample, then run `scripts/build_tx2gene.py` + `scripts/import_quant.py` to make the `tximport` gene matrix and route it to `alterlab-pydeseq2`. --- ## When to Use This Skill Use this skill when the request is about **getting from FASTQ to transcript or gene abundance** with a lightweight quantifier: - "Quantify my RNA-seq with salmon / kallisto." - "Build a decoy-aware salmon index (gentrome + decoys.txt)." - "Run selective alignment with `--gcBias`." / "Use salmon's `--sketch` mode." - "I have `quant.sf` files — make me a gene-level count matrix for DESeq2." - "Set up `tximport`...