Reference

Last updated on 2025-11-25 | Edit this page

Here are selected method-specific references covering the major components of RNA-seq analysis, including alignment, quantification, quality control, differential expression, functional interpretation, and batch correction. These papers represent widely used tools and foundational methods in the field.

General RNA-seq references

  1. A beginner’s guide to analysis of RNA sequencing data : K. M. Koch et al. A practical overview of the key steps in a typical RNA-seq analysis: library preparation, QC, alignment, counting, differential expression, and pitfalls.
  2. RNA sequencing (RNA-seq) methods & workflows : Illumina A vendor-agnostic guide describing different RNA-seq workflows, including bulk mRNA-seq, total RNA-seq, strand specificity, and exploratory considerations for experimental design.
  3. RNA sequencing data: Hitchhiker’s guide to expression, interpretation and beyond : K. Van den Berge et al. A detailed review covering design, quantification, differential expression, and emerging types of RNA-seq (e.g., long-read, single-cell).

Core differential expression methods

  1. DESeq2 Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 2014. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-014-0550-8

  2. edgeR Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 2010. https://academic.oup.com/bioinformatics/article/26/1/139/182458

  3. limma-voom Law CW, Chen Y, Shi W, Smyth GK. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biology, 2014. https://genomebiology.biomedcentral.com/articles/10.1186/gb-2014-15-2-r29

  4. Sleuth (for Salmon/Kallisto quantification) Pimentel H, Bray NL, Puente S, Melsted P, Pachter L. Differential analysis of RNA-seq incorporating quantification uncertainty. Nature Methods, 2017. https://www.nature.com/articles/nmeth.4324

Alignment methods

  1. STAR aligner Dobin A et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics, 2013. https://academic.oup.com/bioinformatics/article/29/1/15/272537

  2. HISAT2 Kim D, Langmead B, Salzberg SL. HISAT: a fast spliced aligner with low memory requirements. Nature Methods, 2015. https://www.nature.com/articles/nmeth.3317

  3. TopHat2 (historical, not recommended now but still cited) Kim D et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biology, 2013. https://genomebiology.biomedcentral.com/articles/10.1186/gb-2013-14-4-r36

Quantification methods

  1. Salmon (quasi-mapping and lightweight alignment) Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C. Salmon provides fast and bias-aware quantification of transcript expression. Nature Methods, 2017. https://www.nature.com/articles/nmeth.4197

  2. Kallisto (pseudo-alignment) Bray NL, Pimentel H, Melsted P, Pachter L. Near-optimal probabilistic RNA-seq quantification. Nature Biotechnology, 2016. https://www.nature.com/articles/nbt.3519

  3. RSEM (alignment-based quantification) Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinformatics, 2011. https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-12-323

Quality control and preprocessing

  1. FastQC Andrews S. FastQC: A quality control tool for high throughput sequence data. Babraham Institute, 2010. https://www.bioinformatics.babraham.ac.uk/projects/fastqc/

  2. Trim Galore (cutadapt + FastQC wrapper) Krueger F. Trim Galore. Babraham Institute. https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/

  3. cutadapt Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet Journal, 2011. https://journal.embnet.org/index.php/embnetjournal/article/view/200

  4. MultiQC Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics, 2016. https://academic.oup.com/bioinformatics/article/32/19/3047/2196507 —

Functional analysis and interpretation

  1. GOseq (for GO analysis accounting for transcript length bias) Young MD, Wakefield MJ, Smyth GK, Oshlack A. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biology, 2010. https://genomebiology.biomedcentral.com/articles/10.1186/gb-2010-11-2-r14

  2. clusterProfiler (widely used for GO/KEGG) Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS, 2012. https://www.liebertpub.com/doi/10.1089/omi.2011.0118

  3. fgsea (fast GSEA) Korotkevich G, Sukhov V, Sergushichev A. Fast gene set enrichment analysis. bioRxiv, 2016. https://www.biorxiv.org/content/10.1101/060012v3

  4. KEGG Kanehisa M, Goto S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Research, 2000. https://academic.oup.com/nar/article/28/1/27/2384398

Splicing and isoform analysis (if you want to include optional advanced topics)

  1. DEXSeq (differential exon usage) Anders S, Reyes A, Huber W. Detecting differential usage of exons from RNA-seq data. Genome Research, 2012. https://genome.cshlp.org/content/22/10/2008

  2. SUPPA2 (isoform-level splicing changes) Trincado JL et al. SUPPA2 provides fast, accurate, and uncertainty-aware differential splicing analysis. Genome Biology, 2018. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-018-1417-1

  3. StringTie2 (transcript assembly and quantification) Kovaka S et al. Transcriptome assembly from long-read RNA-seq alignments with StringTie2. Genome Biology, 2019. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1910-1

Batch correction and confounder modeling

  1. ComBat Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics, 2007. https://academic.oup.com/biostatistics/article/8/1/118/252073

    (Still widely used for RNA-seq after variance stabilizing transformation.)

  2. RUVSeq Risso D, Ngai J, Speed TP, Dudoit S. Normalization of RNA-seq data using factor analysis of control genes or samples. Nature Biotechnology, 2014. https://www.nature.com/articles/nbt.2931 —

Glossary

Experimental concepts

adapter trimming Removal of sequencing adapters and low-quality bases from raw reads before alignment, typically using tools such as fastp or Trim Galore.

alignment (read mapping) Assigning sequencing reads to a reference genome or transcriptome using aligners like HISAT2 or STAR.

biological replicate Independent samples from the same biological condition, used to estimate natural biological variation.

batch effects Confounding technical variation introduced by differences in library prep, sequencing runs, or operators; must be accounted for in experimental design and statistical models.

bulk RNA-seq Conventional RNA-seq performed on pooled cells or tissues, as opposed to single-cell or spatial RNA-seq.

cDNA Complementary DNA synthesized from RNA templates during library preparation.

library preparation Experimental conversion of RNA into a sequencing-ready library, including fragmentation, adapter ligation, and amplification.

paired-end sequencing Sequencing both ends of DNA/cDNA fragments to improve mapping accuracy and detection of splice junctions.

read depth / sequencing depth The total number of reads generated per sample; higher depth increases power to detect lowly expressed or differentially expressed genes.

stranded library / strand specificity Preservation of the transcriptional strand of origin during library prep, allowing direction-specific quantification.

File formats and data organization

FASTQ A text format containing raw sequencing reads with per-base quality scores.

GTF / GFF Annotation file formats describing gene and transcript coordinates, essential for read counting and feature quantification.

counts table A matrix of raw read counts (rows = genes or transcripts, columns = samples) used for downstream differential expression analysis.

Quantification and normalization

featureCounts A program that summarizes aligned reads to annotated genomic features, producing a counts table for DE analysis.

gene-level vs. transcript-level quantification Gene-level quantification collapses isoforms into a single total count per gene; transcript-level quantification measures individual isoform expression, critical for splicing or isoform-specific analyses.

normalization Adjusting for library size and composition so samples are comparable. Common methods include TMM (edgeR), median-ratio (DESeq2), and TPM for within-sample comparisons. Use raw counts with DESeq2/edgeR for statistical modeling—FPKM and TPM are not appropriate for DE testing.

FPKM / TPM FPKM = Fragments Per Kilobase per Million; TPM = Transcripts Per Million. TPM normalizes read counts across transcript lengths and library size and is preferred for within-sample comparisons, but not for differential expression across samples.

pseudo-alignment Lightweight transcript quantification without full alignment, used by tools such as Salmon or Kallisto.

reference-based vs. de novo RNA-seq Reference-based workflows align reads to a known genome or transcriptome. De novo workflows assemble transcripts directly from reads when no reference exists.

transcriptome assembly Reconstruction of transcripts from RNA-seq reads, either de novo or guided by a reference.

Differential expression and statistics

differential expression (DE) Statistical identification of genes or transcripts with significant changes in expression between experimental conditions.

dispersion A parameter in negative-binomial count models representing variability beyond Poisson noise; estimated in DESeq2 and edgeR.

log₂ fold change (LFC) A measure of change in expression between two conditions on a log₂ scale; values > 1 or < –1 typically indicate meaningful differences.

FDR / adjusted p-value The false discovery rate controls for multiple testing (Benjamini–Hochberg correction). Genes are typically considered significant at FDR < 0.05.

normalization factor Scaling coefficient applied during model fitting to account for library size or composition bias (internally computed by DESeq2/edgeR).

Interpretation and downstream analysis

functional enrichment analysis Identification of Gene Ontology (GO) terms, pathways, or functional categories overrepresented among differentially expressed genes.

gene ontology (GO) A controlled vocabulary describing gene functions in three domains: biological process, molecular function, and cellular component.

KEGG The Kyoto Encyclopedia of Genes and Genomes; maps genes to metabolic and signaling pathways.

visualization Graphical summaries such as PCA plots, volcano plots, or heatmaps used to assess overall patterns and highlight DE genes.

workflow reproducibility Ensuring analyses can be replicated by others using automated, version-controlled workflows (e.g., Snakemake, Nextflow, Apptainer).