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This article is part of the supplement: Selected articles from ISCB-Asia 2012

Open Access Research

iSeeRNA: identification of long intergenic non-coding RNA transcripts from transcriptome sequencing data

Kun Sun12, Xiaona Chen13, Peiyong Jiang12, Xiaofeng Song4*, Huating Wang13* and Hao Sun12*

Author Affiliations

1 Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China

2 Departments of Chemical Pathology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China

3 Department of Obstetrics and Gynaecology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China

4 Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

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BMC Genomics 2013, 14(Suppl 2):S7  doi:10.1186/1471-2164-14-S2-S7

Published: 15 February 2013

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.