Prediction of novel long non-coding RNAs based on RNA-Seq data of mouse Klf1 knockout study.

ABSTRACT:

Background

Study on long non-coding RNAs (lncRNAs) has been promoted by high-throughput RNA sequencing (RNA-Seq). However, it is still not trivial to identify lncRNAs from the RNA-Seq data and it remains a challenge to uncover their functions.

Results

We present a computational pipeline for detecting novel lncRNAs from the RNA-Seq data. First, the genome-guided transcriptome reconstruction is used to generate initially assembled transcripts. The possible partial transcripts and artefacts are filtered according to the quantified expression level. After that, novel lncRNAs are detected by further filtering known transcripts and those with high protein coding potential, using a newly developed program called lncRScan. We applied our pipeline to a mouse Klf1 knockout dataset, an

SUBMITTER: Sun L 

PROVIDER: S-EPMC3577497 | biostudies-literature | 2012 Dec

REPOSITORIES: biostudies-literature