Appendix A — Supporting Materials

Published

Jun 2026

  • ID: RNASEQ-999
  • Type: Appendix
  • Audience: Students, biologists, bioinformaticians, data scientists, researchers, and practitioners
  • Theme: Reference resources and supporting materials for the RNA-Seq System

Introduction

This appendix provides selected references, software resources, databases, and learning materials that support the concepts presented throughout the RNA-Seq System.

Differential Expression Methods

DESeq2

The DESeq2 framework is one of the most widely used approaches for RNA-Seq differential expression analysis (Love, Huber, and Anders 2014).

edgeR

edgeR provides statistical methods for differential expression analysis of count-based transcriptomic data (Robinson, McCarthy, and Smyth 2010).

limma-voom

The limma-voom framework extends linear modeling approaches to RNA-Seq count data (Law et al. 2014).

Quantification Resources

Salmon

Salmon provides efficient transcript quantification using lightweight alignment methods (Patro et al. 2017).

kallisto

kallisto provides rapid transcript abundance estimation using pseudoalignment approaches (Bray et al. 2016).

tximport

The tximport workflow supports importing transcript-level abundance estimates for downstream gene-level analyses (Soneson, Love, and Robinson 2015).

Quality Control Resources

FastQC

FastQC is a widely used tool for assessing sequencing read quality (Andrews 2010).

MultiQC

MultiQC aggregates quality control reports from multiple samples and tools into a single summary report (Ewels et al. 2016).

Functional Interpretation Resources

Gene Ontology

Gene Ontology provides a structured framework for describing biological processes, molecular functions, and cellular components (Ashburner et al. 2000).

KEGG

KEGG provides pathway-based biological knowledge useful for functional interpretation (Kanehisa and Goto 2000).

clusterProfiler

clusterProfiler supports enrichment analysis and biological interpretation of gene lists (Yu et al. 2012).

Reproducible Analysis Resources

Bioconductor

Bioconductor provides an extensive ecosystem of packages for genomic and transcriptomic analysis (Huber et al. 2015).

Quarto

Quarto supports reproducible reporting by combining narrative text, code, figures, and interpretation into a single workflow.

Additional Learning Resources

Recommended areas for continued study include:

  • Advanced differential expression analysis
  • Transcript-level analysis
  • Alternative splicing analysis
  • Single-cell RNA-Seq
  • Multiomics integration
  • Reproducible research practices
  • Scientific communication

CDI Perspective

The RNA-Seq System is built around enduring analytical principles rather than specific software tools.

Biological Question
        ↓
Study Design
        ↓
Data Quality
        ↓
Expression Measurement
        ↓
Statistical Evidence
        ↓
Biological Interpretation
        ↓
Reproducible Reporting

Software evolves over time, but these principles remain central to reliable transcriptomic analysis.