- cutadapt, for trimming reads
- STAR aligner, for aligning reads to a reference
- htseq-count, for quantifying read counts at genomic features
- Picard tools, including CollectRnaSeqMetrics
- FastQC, for basic FASTQ quality control
- MultiQC, for aggregating quality control reports
- Conda
- Introduction to RNA-Seq using high-performance computing, workshop by Harvard Chan Bioinformatics Core
- Base R
- Advanced R
- Data import (readr)
- Data transformation with ggplot2
- Data visualization
- Regular expressions
- Strings
- DESeq2, original manuscript: Love et al, 2014
- tximport, original manuscript: Soneson et al, 2014
- edgeR, original manuscript: Robinson et al, 2010
- limma, original manuscript: Ritchie et al, 2015
- limma-voom
- Comparison of software packages for detecting differential expression in RNA-seq studies. Seyednasrollah, et al 2013, Genome Biol.
- A comparison of methods for differential expression analysis of RNA-seq data. Soneson, et al 2013, BMC Bioinfo..
- Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data. Rapaport, et al 2013, Genome Biol.
- Systematic comparison and assessment of RNA‑seq procedures for gene expression quantitative analysis. Corchete, et al 2020, Scientific Reports.