TACO – a computational method to reconstruct a consensus transcriptome from multiple RNA-seq data sets

Accurate transcript structure and abundance inference from RNA sequencing (RNA-seq) data is foundational for molecular discovery. Here researchers from the University of Michigan present a new meta-assembly method, Transcriptome Assemblies Combined into One (TACO), as a robust solution for leveraging the vast RNA-seq data landscape for transcript structure prediction. To prepare data for TACO, RNA-seq analysis protocols such as the Tuxedo suite can be used. In short, sequence reads are aligned to a reference genome by any spliced alignment tool such as STAR or HISAT. Then, genome-guided transcript assembly with tools such as Cufflinks or StringTie is performed, and these transcript assemblies serve as input to TACO. TACO employs novel change-point detection to demarcate transcript start and end sites, leading to improved reconstruction accuracy compared with other tools in its class.

Schematic detailing the transcriptome meta-assembly workflow for TACO

Availability – TACO is available at: http://tacorna.github.io