Modeling bias and variation in the stochastic processes of small RNA sequencing.

ABSTRACT: The use of RNA-seq as the preferred method for the discovery and validation of small RNA biomarkers has been hindered by high quantitative variability and biased sequence counts. In this paper we develop a statistical model for sequence counts that accounts for ligase bias and stochastic variation in sequence counts. This model implies a linear quadratic relation between the mean and variance of sequence counts. Using a large number of sequencing datasets, we demonstrate how one can use the generalized additive models for location, scale and shape (GAMLSS) distributional regression framework to calculate and apply empirical correction factors for ligase bias. Bias correction could remove more than 40% of the bias for miRNAs. Empirical bias correction factors appear to be nearly constant ov

SUBMITTER: Argyropoulos C 

PROVIDER: S-EPMC5499834 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature