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DiffSegR: an RNA-seq data driven method for differential expression analysis using changepoint detection
Arnaud Liehrmann1,2,3, Etienne Delannoy1,2, Alexandra Launay-Avon1,2
1Institute of Plant Sciences Paris-Saclay (IPS2), Université Paris-Saclay, CNRS, INRAE, Université Evry, Gif sur Yvette, 91190, France.
NAR Genomics and Bioinformatics
|November 13, 2023
Summary
This study introduces DiffSegR, an R package for analyzing RNA-Seq data without prior gene annotation. It identifies transcriptome-wide expression differences, revealing new insights into gene regulation and RNA processing.
Area of Science:
- Transcriptomics
- Bioinformatics
- Molecular Biology
Background:
- Accurate transcriptome analysis is crucial for understanding gene regulation.
- Current RNA-Seq tools often rely on incomplete annotations, leading to differential expression analysis errors.
- A method is needed to analyze transcriptome-wide expression differences without relying on prior annotation.
Purpose of the Study:
- To develop and present DiffSegR, an R package for discovering transcriptome-wide expression differences using RNA-Seq data.
- To enable differential expression analysis independent of gene annotations.
- To provide a tool for uncovering novel biological insights from transcriptomic data.
Main Methods:
- DiffSegR utilizes RNA-Seq data to identify expression differences between biological conditions.
- It employs a multiple changepoints detection algorithm on per-base log2 fold change to find differentially expressed regions.
- The package operates without requiring pre-existing gene annotations.
Main Results:
- DiffSegR accurately identifies transcriptome-wide expression differences.
- The package successfully predicted the roles of chloroplast ribonuclease Mini-III in rRNA maturation and PNPase in RNA degradation.
- It also identified roles in precursor processing and intron accumulation.
Conclusions:
- DiffSegR offers a novel approach to differential expression analysis in transcriptomics.
- The package overcomes limitations of annotation-dependent methods.
- DiffSegR provides valuable insights into gene regulation and RNA processing, benefiting biological research.
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