Quantification of experimentally induced nucleotide conversions in high-throughput sequencing datasets

Area of Science:

• Molecular Biology
• Bioinformatics
• Genomics

Background:

• Nucleic acid modifications are crucial for studying dynamic cellular processes.
• High-throughput sequencing requires specialized bioinformatics for analyzing these modifications.

Purpose of the Study:

• To develop a bioinformatics pipeline for quantifying nucleotide conversions in sequencing data.
• To enable robust and sensitive analysis of RNA labeling experiments.

Main Methods:

• Digital Unmasking of Nucleotide conversions in K-mers (DUNK) pipeline.
• Utilizes Single Nucleotide Polymorphism (SNP) masking to differentiate true SNPs from nucleotide conversions.
• Application as SLAM-DUNK for analyzing SLAMseq data (T>C conversions).

Main Results:

• DUNK maintains constant mapping rates regardless of conversion rates.
• Improves recovery of multimapping reads.
• Facilitates sensitive quantification of nucleotide conversions.
• SLAM-DUNK provides raw counts and normalized fractions of labeled transcripts.

Conclusions:

• DUNK is a broadly applicable strategy for quantifying nucleotide conversions.
• Provides a tool for SLAMseq and related time-resolved RNA sequencing methods.
• Enhances the analysis of dynamic RNA modifications.