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

Chi Zhang¹, Baohong Zhang¹, Lih-Ling Lin²

¹Early Clinical Development, Pfizer Worldwide R&D, Cambridge, MA, 02139, USA.

|August 9, 2017

Summary

Accurate transcript isoform quantification is crucial for understanding health and disease. Alignment-free tools show promise for RNA-sequencing (RNA-seq) data analysis, but gene complexity and sequencing depth impact results.

Keywords:

Data analysis Isoform Kallisto Quantification RNA-seq RSEM Salfish Salmon

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Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Published on: August 4, 2016

Area of Science:

Molecular Biology
Bioinformatics
Genomics

Background:

Alternative splicing generates diverse transcript isoforms in higher eukaryotes.
Isoform expression levels are critical for understanding cellular functions and disease progression.
Current RNA-sequencing (RNA-seq) technologies face limitations in accurate transcript-level quantification due to read length and sequencing depth costs.

Purpose of the Study:

To comprehensively evaluate existing computational tools for transcript isoform quantification.
To identify factors influencing the accuracy of isoform quantification methods.
To guide data analysts in selecting appropriate tools for RNA-seq data.

Main Methods:

Comparative analysis of multiple isoform quantification tools.
Utilized both experimental and simulated RNA-sequencing datasets for evaluation.
Performed simulations using the TP53 gene to assess specific influencing factors.

Main Results:

Recently developed alignment-free tools demonstrated high speed and accuracy.
Quantification accuracy was primarily affected by gene structure complexity.
Short transcripts require careful interpretation of quantification results.
Sequencing depth and relative isoform abundance significantly impact quantification accuracy.

Conclusions:

Comprehensive evaluation provides insights into the performance of various isoform quantification tools.
Alignment-free tools are a viable option for accurate and efficient transcript isoform quantification.
Users must consider gene complexity, transcript length, sequencing depth, and isoform abundance when analyzing RNA-seq data.