Visualized computational predictions of transcriptional effects by intronic endogenous retroviruses.

ABSTRACT: When endogenous retroviruses (ERVs) or other transposable elements (TEs) insert into an intron, the consequence on gene transcription can range from negligible to a complete ablation of normal transcripts. With the advance of sequencing technology, more and more insertionally polymorphic or private TE insertions are being identified in humans and mice, of which some could have a significant impact on host gene expression. Nevertheless, an efficient and low cost approach to prioritize their potential effect on gene transcription has been lacking. By building a computational model based on artificial neural networks (ANN), we demonstrate the feasibility of using machine-learning approaches to predict the likelihood that intronic ERV insertions will have major effects on gene transcription, f

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC3735543 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature