A computational pipeline to infer alternative poly-adenylation from 3' sequencing data

Area of Science:

• Molecular Biology
• Genomics
• Bioinformatics

Background:

• Alternative polyadenylation (APA) is a crucial gene regulation mechanism altering 3' untranslated region (UTR) length and mRNA termini.
• APA plays a significant role in various disease contexts, driving the development of specialized sequencing techniques.
• Existing 3' sequencing methods face limitations in read coverage and computational tools for precise polyadenylation site analysis.

Purpose of the Study:

• To provide an updated computational protocol, PolyA-miner, for analyzing 3' sequencing datasets, specifically Poly(A)-ClickSeq (PAC-seq).
• To evaluate the impact of sequencing depth on the detection of polyadenylation sites and APA changes.
• To enhance the protocol for handling unique molecular identifiers (UMIs) to mitigate PCR duplication in PAC-seq data.

Main Methods:

• Utilized click-chemistry based Poly(A)-ClickSeq (PAC-seq) for 3' end sequencing.
• Developed and updated the PolyA-miner computational protocol for analyzing 3'-Seq data.
• Assessed the influence of varying sequencing depths on polyadenylation site detection and APA quantification.

Main Results:

• The updated PolyA-miner protocol effectively analyzes PAC-seq and other 3'-Seq datasets.
• Sequencing depth significantly influences the number of detected polyadenylation sites and APA variations.
• The protocol now accommodates UMIs, improving accuracy by addressing PCR duplication.

Conclusions:

• The updated PolyA-miner protocol offers a robust computational solution for analyzing 3' sequencing data, including PAC-seq.
• Understanding the impact of sequencing depth is critical for accurate APA site identification and quantification.
• This protocol facilitates more precise analysis of gene regulation through alternative polyadenylation.