From RNA-seq reads to differential expression results

2010/01/01 by Alicia Oshlack, Mark D. Robinson, Matthew D. Young · 4 citations
Biochemistry, Genetics and Molecular Biology · #RNA modifications and cancer #RNA Research and Splicing #Cancer-related molecular mechanisms research

paper · pdf · doi:10.1186/gb-2010-11-12-220

openalex publication_date 2010/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29

Abstract

Many methods and tools are available for preprocessing high-throughput RNA sequencing data and detecting differential expression.

Citations

• Gene Ontology: tool for the unification of biology
• Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

Cited by

• Methods to Study Splicing from High-Throughput RNA Sequencing Data
• Tool Choice Matters: Evaluating edgeR vs. DESeq2 for Sensitivity, Robustness, and Cross-Study Performance
• Computational and analytical challenges in single-cell transcriptomics
• Biostatistics [wikipedia]

Related

• Comprehensive evaluation of differential expression analysis methods for\n RNA-seq data
• RNA‐Seq methods for transcriptome analysis
• RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis
• A broad introduction to RNA-Seq
• Evaluation of tools for differential gene expression analysis by RNA-seq on a 48 biological replicate experiment