A multi-sample approach increases the accuracy of transcript assembly

Abstract

Transcript assembly from RNA-seq reads is a critical step in gene expression and subsequent functional analyses. Here we present PsiCLASS, an accurate and efficient transcript assembler based on an approach that simultaneously analyzes multiple RNA-seq samples. PsiCLASS combines mixture statistical models for exonic feature selection across multiple samples with splice graph based dynamic programming algorithms and a weighted voting scheme for transcript selection. PsiCLASS achieves significantly better sensitivity-precision tradeoff, and renders precision up to 2-3 fold higher than the StringTie system and Scallop plus TACO, the two best current approaches. PsiCLASS is efficient and scalable, assembling 667 GEUVADIS samples in 9 h, and has robust accuracy with large numbers of samples.

Abstract

Transcript assembly is an important step in analysis of RNA-seq data whose accuracy influences downstream quantification, detection and characterization of alternative splice variants. Here, the authors develop PsiCLASS, a transcript assembler leveraging simultaneous analysis of multiple RNA-seq samples.