Improving gene function predictions using independent transcriptional components.

ABSTRACT: The interpretation of high throughput sequencing data is limited by our incomplete functional understanding of coding and non-coding transcripts. Reliably predicting the function of such transcripts can overcome this limitation. Here we report the use of a consensus independent component analysis and guilt-by-association approach to predict over 23,000 functional groups comprised of over 55,000 coding and non-coding transcripts using publicly available transcriptomic profiles. We show that, compared to using Principal Component Analysis, Independent Component Analysis-derived transcriptional components enable more confident functionality predictions, improve predictions when new members are added to the gene sets, and are less affected by gene multi-functionality. Predictions generated usi

SUBMITTER: Urzua-Traslavina CG 

PROVIDER: S-EPMC7935959 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature