edgeR for differential RNA-seq and ChIP-seq analysis: an application to stem cell biology
Olga Nikolayeva1, Mark D Robinson
1Institute of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, CH-8057, Zurich, Switzerland.
Methods in Molecular Biology (Clifton, N.J.)
|April 19, 2014
Summary
The edgeR package provides a statistical framework for analyzing RNA-seq and ChIP-seq data to detect differential gene expression. This guide details its application on human embryonic stem cells for reproducible research.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Analyzing high-throughput sequencing data like RNA-seq and ChIP-seq requires robust statistical methods.
- Identifying differentially expressed genes is crucial for understanding cellular processes and disease mechanisms.
Purpose of the Study:
- To illustrate the application of the edgeR package for differential expression analysis using human embryonic stem cell data.
- To provide a step-by-step guide for analyzing RNA-seq and ChIP-seq data, from raw data to identifying significant genes.
- To demonstrate integrative analysis combining RNA-seq and ChIP-seq data.
Main Methods:
- Utilized the edgeR package, an R-based statistical tool from Bioconductor.
- Performed differential expression analysis on human embryonic stem cell RNA-seq data.
- Conducted integrative analysis with ChIP-seq data.
Main Results:
- Generated a list of putative differentially expressed genes.
- Demonstrated practical steps for data quality checks and the use of positive controls.
- Provided recommendations for reproducible research in genomic data analysis.
Conclusions:
- The edgeR package offers a flexible framework for differential expression analysis of count-based data.
- The chapter provides a practical workflow for analyzing RNA-seq and ChIP-seq data, emphasizing reproducibility.
- Integrative analysis using edgeR can enhance biological insights from combined genomic datasets.
