title: iSeeRNA: identification of long intergenic non-coding RNA transcripts from transcriptome sequencing data.
abstract: BACKGROUND
  Long intergenic non-coding RNAs (lincRNAs) are emerging as a novel class of non-coding RNAs and potent gene regulators. High-throughput RNA-sequencing combined with de novo assembly promises quantity discovery of novel transcripts. However, the identification of lincRNAs from thousands of assembled transcripts is still challenging due to the difficulties of separating them from protein coding transcripts (PCTs).
  
  
  RESULTS
  We have implemented iSeeRNA, a support vector machine (SVM)-based classifier for the identification of lincRNAs. iSeeRNA shows better performance compared to other software. A public available webserver for iSeeRNA is also provided for small size dataset.
  
  
  CONCLUSIONS
  iSeeRNA demonstrates high prediction accuracy and runs several magnitudes faster than other similar programs. It can be integrated into the transcriptome data analysis pipelines or run as a web server, thus offering a valuable tool for lincRNA study.
authors: Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China.
authors: Kun
authors: K
authors: Sun
authors: Xiaona
authors: X
authors: Chen
authors: Peiyong
authors: P
authors: Jiang
authors: Xiaofeng
authors: X
authors: Song
authors: Huating
authors: H
authors: Wang
authors: Hao
authors: H
authors: Sun
mesh_terms: D000818:Animals; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D020407:Internet; D051379:Mice; D062085:RNA, Long Noncoding / Q000145:classification*; D012372:ROC Curve; D012984:Software; D060388:Support Vector Machine
chemical_list: D062085:RNA, Long Noncoding