We developed a Markov model to characterize position-wise pairing patterns of miRNA-target interactions. We further integrated this model as a scoring method and developed a dynamic programming (DP) algorithm, MDPS (Markov model-scored Dynamic Programming algorithm for miRNA target site Selection) that can screen putative target sites of miRNA-target binding. The MDPS algorithm thus can take into account both the dependency of neighboring pairing positions and the global pairing information. Based on the trained Markov models from both miRNA specific and general datasets, we discovered that the position-wise binding information specific to a given miRNA would benefit its target prediction. We also found that miRNAs maintain region-wise similarity in their target binding patterns. Combining MDPS with existing methods significantly improves their precision while only slightly reduces their recall. Therefore, position-wise pairing patterns have the promise to improve target prediction if incorporated into existing software tools.
The source code of MDPS is available here.
The manual for running the program is available here.
Amlan Talukder1, Xiaoman Li2 and Haiyan Hu1
1Department of Electrical Engineering and Computer Science, University Of Central Florida, Orlando, FL 32826, USA.
2Burnett School of Biomedical Science, University Of Central Florida, Orlando, FL 32826, USA.