Computational identification of extra coding RNAs related to HIV-1 infection

Abstract

A few studies suggest that many protein coding loci also transcribe extra-coding RNAs (ecRNAs), a distinct class of long non-coding RNAs. These ecRNAs span over and beyond mRNAs originating from the same loci, are much less abundant than mRNAs, and lack polyadenylation. Previously, ecRNAs were found to regulate the transcription of mRNAs from the same loci epigenetically. However, further investigation of ecRNAs is impeded by the lack of accurate ecRNA annotation and quantification. Here, to uncover these mRNA-overlapping ecRNAs, we first depleted polyadenylated RNAs, i.e. mRNAs, from total RNAs and performed deep sequencing analysis of the remaining total RNAs. Next, we devised a computational strategy to identify ecRNA transcribing loci from this unique sequencing data. Our approach uses a combination of features including the ratio of intronic to exonic read counts, the percent read coverage of introns, exons, and flanking regions, and the ratio of flanking to intronic read counts. In this pilot study of HIV-1 infected CD4+ T cells, we observed transcriptional signatures of ecRNAs from 880 (∼11%) of 7,690 expressed coding genes. We expect these custom computational developments will enable a better understanding of the involvement of ecRNAs in regulating host response to HIV infection and in other conditions.