Prediction of novel precursor miRNAs using a context-sensitive hidden Markov model (CSHMM)

Background: It has been apparent in the last few years that small non coding RNAs (ncRNA) play a very significant role in biological regulation. Among these microRNAs (miRNAs), 22-23 nucleotide small regulatory RNAs, have been a major object of study as these have been found to be involved in some basic biological processes. So far about 706 miRNAs have been identified in humans alone. However, it is expected that there may be many more miRNAs encoded in the human genome. In this report, a "context-sensitive" Hidden Markov Model (CSHMM) to represent miRNA structures has been proposed and tested extensively. We also demonstrate how this model can be used in conjunction with filters as an ab initio method for miRNA identification. Results: The probabilities of the CSHMM model were estimated using known human miRNA sequences. A classifier for miRNAs based on the likelihood score of this "trained" CSHMM was evaluated by: (a) cross-validation estimates using known human...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Sumeet Agarwal, Candida Vaz, Alok Bhattacharya, Ashwin Srinivasan
• Keywords: BMCBI 2010, Human, Small Regulatory Rnas, Small Rna Sequences