Enhancing RNA-seq bias mitigation with the Gaussian self-benchmarking framework: towards unbiased sequencing data.

ABSTRACT:

Background

RNA sequencing is a vital technique for analyzing RNA behavior in cells, but it often suffers from various biases that distort the data. Traditional methods to address these biases are typically empirical and handle them individually, limiting their effectiveness. Our study introduces the Gaussian Self-Benchmarking (GSB) framework, a novel approach that leverages the natural distribution patterns of guanine (G) and cytosine (C) content in RNA to mitigate multiple biases simultaneously. This method is grounded in a theoretical model, organizing k-mers based on their GC content and applying a Gaussian model for alignment to ensure empirical sequencing data closely match their theoretical distribution.

Results

The GSB framework demonstrated superior performance in mi

SUBMITTER: Su Q 

PROVIDER: S-EPMC11441123 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature