Transcriptomics on Pi Stack

Self-Hosted RNA-seq Alignment and Quantification: STAR vs Kallisto vs Salmon vs StringTie

Sat, 13 Jun 2026 00:00:00 +0000

Introduction

RNA sequencing (RNA-seq) has revolutionized our understanding of gene expression, transcript structure, and RNA biology. But the raw output from a sequencer — millions of short nucleotide reads — is useless without computational processing. Two critical steps transform raw reads into biologically meaningful data: alignment (mapping reads to a reference genome) and quantification (estimating transcript abundance).

Self-Hosted Transcriptomics: DESeq2 vs edgeR vs limma for Differential Expression Analysis

Sat, 13 Jun 2026 00:00:00 +0000

Introduction

RNA sequencing (RNA-seq) has become the standard method for measuring gene expression across the entire transcriptome. A single RNA-seq experiment generates count data for 20,000-60,000 genes across multiple experimental conditions, and the core computational challenge is differential expression analysis — identifying which genes show statistically significant changes between conditions.