Evaluation and comparison of computational tools for RNA-seq isoform quantification

TLDR

Higher eukaryotes exhibit widespread alternative splicing, and isoform expression is crucial for normal function and disease, but current RNA‑seq technologies struggle to quantify transcripts accurately due to limited read length and sequencing cost. The study aims to comprehensively evaluate RNA‑seq isoform quantification tools using experimental and simulated datasets. We assessed a large number of tools, comparing their performance on both experimental and simulated RNA‑seq data. Alignment‑free tools proved fast and accurate, but overall accuracy depends on gene structure complexity, short transcript interpretation caution, and is affected by sequencing depth and isoform abundance, guiding analysts in tool selection.

Abstract

Alternatively spliced transcript isoforms are commonly observed in higher eukaryotes. The expression levels of these isoforms are key for understanding normal functions in healthy tissues and the progression of disease states. However, accurate quantification of expression at the transcript level is limited with current RNA-seq technologies because of, for example, limited read length and the cost of deep sequencing. A large number of tools have been developed to tackle this problem, and we performed a comprehensive evaluation of these tools using both experimental and simulated RNA-seq datasets. We found that recently developed alignment-free tools are both fast and accurate. The accuracy of all methods was mainly influenced by the complexity of gene structures and caution must be taken when interpreting quantification results for short transcripts. Using TP53 gene simulation, we discovered that both sequencing depth and the relative abundance of different isoforms affect quantification accuracy Our comprehensive evaluation helps data analysts to make informed choice when selecting computational tools for isoform quantification.