RNA sequencing (RNA-seq) is a widely applied technology that for extractings gene and transcript expression from biological samples. Given numerous quantification pipelines for RNA-seq data, one fundamental challenge is to determine identify a pipeline that can produce the most accurate estimate the most accurate gene and/or transcript expression. Exploring all available pipelines requires tremendous extensive computational resources.
Researchers from Georgia Tech propose to use a subsampling approach that can improve speed up the pipeline evaluation and selection the efficiency process of pipeline performance evaluation for a given RNA-seq dataset. They applied their approach to one simulated and two real RNA-seq datasets and found that expression estimates derived from subsampled data are close surrogates for those derived from original data. In addition, the ranking of quantification pipelines based on the subsampled data was highly correlated concordant with that based on the original data.
(A) The workflow of this study. (B) The detailed workflow of the “Quantification Pipelines” module in Panel (A).