Mining mammalian transcript data for functional long non-coding RNAs.

ABSTRACT:

Background

The role of long non-coding RNAs (lncRNAs) in controlling gene expression has garnered increased interest in recent years. Sequencing projects, such as Fantom3 for mouse and H-InvDB for human, have generated abundant data on transcribed components of mammalian cells, the majority of which appear not to be protein-coding. However, much of the non-protein-coding transcriptome could merely be a consequence of 'transcription noise'. It is therefore essential to use bioinformatic approaches to identify the likely functional candidates in a high throughput manner.

Principal findings

We derived a scheme for classifying and annotating likely functional lncRNAs in mammals. Using the available experimental full-length cDNA data sets for human and mouse, we identified 78 lnc

SUBMITTER: Khachane AN 

PROVIDER: S-EPMC2859052 | biostudies-literature | 2010 Apr

REPOSITORIES: biostudies-literature