SASeq: A Selective and Adaptive Shrinkage Approach to Detect and Quantify Active Transcripts using RNA-Seq

TL;DR

SASeq is a novel computational method that improves the detection and quantification of condition-specific transcripts in RNA-Seq data by filtering unsupported transcripts and applying an informed shrinkage model, outperforming existing methods.

Contribution

It introduces a new approach combining filtering of unsupported transcripts with an adaptive shrinkage model for more accurate transcript quantification.

Findings

Outperforms competing methods in accuracy

Reduces model overfitting by filtering unsupported transcripts

Faster and more accurate in real-world data

Abstract

Identification and quantification of condition-specific transcripts using RNA-Seq is vital in transcriptomics research. While initial efforts using mathematical or statistical modeling of read counts or per-base exonic signal have been successful, they may suffer from model overfitting since not all the reference transcripts in a database are expressed under a specific biological condition. Standard shrinkage approaches, such as Lasso, shrink all the transcript abundances to zero in a non-discriminative manner. Thus it does not necessarily yield the set of condition-specific transcripts. Informed shrinkage approaches, using the observed exonic coverage signal, are thus desirable. Motivated by ubiquitous uncovered exonic regions in RNA-Seq data, termed as "naked exons", we propose a new computational approach that first filters out the reference transcripts not supported by splicing and paired-end reads, then followed by fitting a new mathematical model of per-base exonic coverage signal and the underlying transcripts structure. We introduce a tuning parameter to penalize the specific regions of the selected transcripts that were not supported by the naked exons. Our approach compares favorably with the selected competing methods in terms of both time complexity and accuracy using simulated and real-world data. Our method is implemented in SAMMate, a GUI software suite freely available from http://sammate.sourceforge.net