DPAC: A Tool for Differential Poly(A)-Cluster Usage from Poly(A)-Targeted RNAseq Data

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Poly(A)-tail targeted RNA sequencing (RNAseq) offers advantages over random-primed RNAseq for studying polyadenylated RNAs.
• These methods focus sequencing on RNA 3' ends, enabling poly(A)-site identification and usage analysis.
• They provide comparable gene expression data with reduced sequencing volume and simpler library preparation.

Purpose of the Study:

• To present DPAC (Differential Poly(A)-clustering), a streamlined bioinformatics pipeline.
• To enable comprehensive analysis of poly(A)-tail targeted RNAseq data.
• To facilitate the identification and quantification of differential poly(A)-cluster usage.

Main Methods:

• DPAC pipeline for preprocessing poly(A)-tail targeted RNAseq data.
• Mapping and clustering of poly(A)-sites.
• Annotation of poly(A)-clusters.
• Differential analysis of poly(A)-cluster usage using DESeq2.

Main Results:

• DPAC effectively preprocesses and analyzes poly(A)-tail targeted RNAseq data.
• The pipeline identifies and clusters poly(A)-sites.
• Differential poly(A)-cluster usage analysis simultaneously reports gene expression, terminal exon usage, and alternative polyadenylation (APA).

Conclusions:

• DPAC provides a streamlined approach for analyzing poly(A)-tail targeted RNAseq data.
• It enables robust identification of differential poly(A)-cluster usage.
• This method integrates APA analysis with gene expression and terminal exon usage determination.