The Selection of Quantification Pipelines for Illumina RNA-seq Data Using a Subsampling Approach.

Area of Science:

• Bioinformatics
• Genomics
• Computational Biology

Background:

• RNA sequencing (RNA-seq) is crucial for gene and transcript expression analysis.
• Selecting the optimal RNA-seq quantification pipeline is challenging due to numerous options and high computational costs.

Purpose of the Study:

• To develop and validate a computationally efficient method for evaluating RNA-seq quantification pipelines.
• To enable faster selection of accurate gene and transcript expression analysis tools.

Main Methods:

• A subsampling approach was proposed to reduce computational resources needed for pipeline evaluation.
• The method was tested on one simulated and two real-world RNA-seq datasets.

Main Results:

• Expression estimates from subsampled data closely approximated those from full datasets.
• Pipeline rankings based on subsampled data showed high concordance with full data rankings.

Conclusions:

• Subsampling is a valid and efficient strategy for selecting optimal RNA-seq quantification pipelines.
• This approach facilitates faster and more resource-conscious bioinformatics analysis.