A Computational Pipeline for Functional Gene Discovery

Abstract

Many computational pipelines exist for the detection of differentially expressed genes. However, computational pipelines for functional gene detection are rarely exist. We developed a new computational pipeline for functional gene identification from transcriptome profiling data. Key features of the pipeline include clustering optimization by gap statistics, gene ontology analysis for each cluster, and literature analysis for functional gene discovery. By leveraging this pipeline on RNA-seq datasets of mouse retinal development studies, we identified 14 candidate genes involved in the formation of the photoreceptor outer segment. The expression of top three candidate genes (Pde8b, Laptm4b, and Nr1h4) in the outer segment of the developing mouse retina were experimentally validated by immunohistochemical analysis. This computational pipeline can accurately predict novel functional gene for a specific biological process, e.g., the outer segment development of the photoreceptor cells in the mouse retina. This pipeline is also applicable to functional gene discovery for any other biological processes and in any other organs and tissues.