iMiRNA-PseDPC: microRNA precursor identification with a pseudo distance-pair composition approach

Abstract

A microRNA (miRNA) is a small non-coding RNA molecule, functioning in transcriptional and post-transcriptional regulation of gene expression. The human genome may encode over 1000 miRNAs. Albeit poorly characterized, miRNAs are widely deemed as important regulators of biological processes. Aberrant expression of miRNAs has been observed in many cancers and other disease states, indicating that they are deeply implicated with these diseases, particularly in carcinogenesis. Therefore, it is important for both basic research and miRNA-based therapy to discriminate the real pre-miRNAs from the false ones (such as hairpin sequences with similar stem-loops). Particularly, with the avalanche of RNA sequences generated in the post-genomic age, it is highly desired to develop computational sequence-based methods for effectively identifying the human pre-miRNAs. Here, we propose a predictor called “iMiRNA-PseDPC”, in which the RNA sequences are formulated by a novel feature vector called “pseudo distance-pair composition” (PseDPC) with 10 types of structure statuses. Rigorous cross-validations on a much larger and more stringent newly constructed benchmark data-set showed that our approach has remarkably outperformed the existing ones in either prediction accuracy or efficiency, indicating the new predictor is quite promising or at least may become a complementary tool to the existing predictors in this area. For the convenience of most experimental scientists, a user-friendly web server for the new predictor has been established at http://bioinformatics.hitsz.edu.cn/iMiRNA-PseDPC/, by which users can easily get their desired results without the need to go through the mathematical details. It is anticipated that the new predictor may become a useful high throughput tool for genome analysis particularly in dealing with large-scale data.

Keywords:

• pre-miRNA
• Chou’s PseAAC approach
• local structure status
• free energy
• iMiRNA-PseDPC
• large-scale analysis

Acknowledgments

The authors are very much indebted to the two anonymous reviewers, whose constructive comments are very helpful for strengthening the presentation of this paper.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant numbers 61300112 and 61272383]; the Scientific Research Innovation Foundation in Harbin Institute of Technology [Project No. HIT.NSRIF.2013103]; the Shanghai Key Laboratory of Intelligent Information Processing, China [grant number IIPL-2012-002], and the Project Sponsored by the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry.