title: 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, and discussed the plausible functions of the novel lncRNAs we detected by differential expression analysis. We identified 308 novel lncRNA candidates, which have shorter transcript length, fewer exons, shorter putative open reading frame, compared with known protein-coding transcripts. Of the lncRNAs, 52 large intergenic ncRNAs (lincRNAs) show lower expression level than the protein-coding ones and 13 lncRNAs represent significant differential expression between the wild-type and Klf1 knockout conditions.
  
  
  CONCLUSIONS
  Our method can predict a set of novel lncRNAs from the RNA-Seq data. Some of the lncRNAs are showed differentially expressed between the wild-type and Klf1 knockout strains, suggested that those novel lncRNAs can be given high priority in further functional studies.
authors.affiliation: School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, JiangSu 221008, PR China.
authors.forename: Lei
authors.initials: L
authors.lastname: Sun
authors.forename: Zhihua
authors.initials: Z
authors.lastname: Zhang
authors.forename: Timothy L
authors.initials: TL
authors.lastname: Bailey
authors.forename: Andrew C
authors.initials: AC
authors.lastname: Perkins
authors.forename: Michael R
authors.initials: MR
authors.lastname: Tallack
authors.forename: Zhao
authors.initials: Z
authors.lastname: Xu
authors.forename: Hui
authors.initials: H
authors.lastname: Liu
mesh_terms: D000818:Animals; D001483:Base Sequence; D019295:Computational Biology / Q000379:methods*; D005091:Exons; D016678:Genome; D059014:High-Throughput Nucleotide Sequencing; D051741:Kruppel-Like Transcription Factors / Q000235:genetics; D051379:Mice; D018345:Mice, Knockout; D016366:Open Reading Frames; D062085:RNA, Long Noncoding / Q000235:genetics*; D017423:Sequence Analysis, RNA / Q000379:methods* / Q000706:statistics & numerical data*; D059467:Transcriptome / Q000235:genetics
chemical_list: D051741:Kruppel-Like Transcription Factors; D062085:RNA, Long Noncoding; C080774:erythroid Kruppel-like factor