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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
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RNAontheBENCH: computational and empirical resources for benchmarking RNAseq quantification and differential
Pierre-Luc Germain1, Alessandro Vitriolo2, Antonio Adamo1
1European Institute of Oncology, Department of Experimental Oncology, Via Adamello 16, 20139 Milano, Italy.
Nucleic Acids Research
|May 19, 2016
Summary
RNA sequencing (RNAseq) analysis lacks consensus. This study provides a robust benchmark, finding count-based methods best for relative gene quantification and new pseudo-alignment tools efficient and effective.
Area of Science:
- Bioinformatics
- Genomics
- Computational Biology
Background:
- RNA sequencing (RNAseq) is standard for transcriptome analysis.
- Existing RNAseq analysis benchmarks have limitations.
- No consensus exists on optimal RNAseq analysis pipelines.
Purpose of the Study:
- To establish a comprehensive benchmarking resource for RNAseq analysis.
- To compare various RNAseq analysis methods, focusing on relative quantification.
- To evaluate the impact of library preparation on RNAseq results.
Main Methods:
- Utilized two RNAseq datasets with spike-ins and Nanostring measurements.
- Included internal controls and reanalyzed the SEQC dataset.
- Compared alignment, quantification, and differential expression testing methods.
Main Results:
- Absolute quantification methods do not guarantee good relative quantification.
- Count-based methods excel in gene-level relative quantification.
- New pseudo-alignment software offers comparable performance to established methods with reduced computational time.
Conclusions:
- Developed a valuable resource for benchmarking RNAseq analysis tools.
- Count-based methods and pseudo-alignment software are recommended for specific RNAseq analyses.
- A R package and web platform are available for future method benchmarking.
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