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

ABSTRACT:

Background

Methods to read out naturally occurring or experimentally introduced nucleic acid modifications are emerging as powerful tools to study dynamic cellular processes. The recovery, quantification and interpretation of such events in high-throughput sequencing datasets demands specialized bioinformatics approaches.

Results

Here, we present Digital Unmasking of Nucleotide conversions in K-mers (DUNK), a data analysis pipeline enabling the quantification of nucleotide conversions in high-throughput sequencing datasets. We demonstrate using experimentally generated and simulated datasets that DUNK allows constant mapping rates irrespective of nucleotide-conversion rates, promotes the recovery of multimapping reads and employs Single Nucleotide Polymorphism (SNP) masking

SUBMITTER: Neumann T 

PROVIDER: S-EPMC6528199 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature