A penalized likelihood approach for robust estimation of isoform expression

TL;DR

This paper presents a penalized likelihood method to improve the accuracy of isoform expression estimates from RNA-Seq data by detecting and correcting systematic biases, enhancing reliability in gene expression analysis.

Contribution

It introduces a novel bias correction model with an efficient algorithm, extending previous methods to better handle sequencing biases and incomplete annotations.

Findings

Improves isoform expression estimates in simulated data

Identifies biases and incomplete annotations in real datasets

Demonstrates robustness against sequencing biases

Abstract

Ultra high-throughput sequencing of transcriptomes (RNA-Seq) has enabled the accurate estimation of gene expression at individual isoform level. However, systematic biases introduced during the sequencing and mapping processes as well as incompleteness of the transcript annotation databases may cause the estimates of isoform abundances to be unreliable, and in some cases, highly inaccurate. This paper introduces a penalized likelihood approach to detect and correct for such biases in a robust manner. Our model extends those previously proposed by introducing bias parameters for reads. An L1 penalty is used for the selection of non-zero bias parameters. We introduce an efficient algorithm for model fitting and analyze the statistical properties of the proposed model. Our experimental studies on both simulated and real datasets suggest that the model has the potential to improve isoform-specific gene expression estimates and identify incompletely annotated gene models.