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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

Isoform prefiltering improves performance of count-based methods for analysis of differential transcript usage.

Charlotte Soneson^1,2, Katarina L Matthes^3,4, Malgorzata Nowicka^5,6

¹Institute of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, Zurich, 8057, Switzerland. charlotte.soneson@uzh.ch.

|January 28, 2016

Summary

This summary is machine-generated.

Count-based methods are effective for detecting differential transcript usage from RNA-seq data. Prefiltering annotation catalogs improves false discovery rate control, especially in complex transcriptomes.

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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

RNA sequencing (RNA-seq) enables quantitative transcriptome analysis, crucial for identifying differential transcript usage.
Detecting changes in transcript usage, such as alternative splicing, can lead to new therapeutic strategies and improved patient management.
Current analytical approaches include exon-level counting, percentage spliced in analysis, and assembled transcript quantification.

Purpose of the Study:

To compare and contrast state-of-the-art methods for analyzing differential transcript usage from RNA-seq data.
To identify improvements for commonly used RNA-seq analysis workflows.
To evaluate the impact of non-standard counting bin definitions on differential transcript usage detection.

Main Methods:

Performance assessment of RNA-seq analysis workflows using synthetic data.
Evaluation of DEXSeq, a leading inference engine, with canonical and non-canonical counting bin definitions.
Analysis of the effect of incomplete annotation catalogs and isoform-level prefiltering on differential transcript usage detection.

Main Results:

Count-based methods generally outperform event- and assembly-based approaches for differential transcript usage detection.
Canonical counting bins with DEXSeq yielded optimal overall results.
Non-canonical counting approaches demonstrated comparable or superior performance in specific scenarios.
Incomplete annotation catalogs negatively impact detection in transcriptomes with low isoform diversity.
Isoform-level prefiltering significantly enhances false discovery rate control.

Conclusions:

Count-based methods are robust for detecting differential transcript usage.
Controlling the false discovery rate remains challenging, particularly in complex organisms.
Prefiltering the annotation catalog is a key strategy to improve false discovery rate control.