Assay

Bulk RNA-seq: sanity checks and pitfalls

Reads are aligned (STAR, HISAT2) or pseudo-aligned (salmon, kallisto) to produce a gene-by-sample count matrix, then modeled with a negative binomial GLM (DESeq2, edgeR) or voom-transformed for limma. The recurring failures are design-level, not tool-level: batch confounded with condition, TPM fed into DESeq2, samples swapped at the sequencing core, or a PCA nobody looked at before running the differential test. Depth is usually 20-40M reads per sample and replicates matter far more than depth.

Who this is for: Wet-lab biologists with a handful of conditions and 3-6 replicates each, asking which genes change and whether the change survives correction. Usually the first analysis someone runs after leaving the bench, and the one most often analyzed with defaults that do not match the design.