MirTime: identifying condition-specific targets of microRNA in time-series transcript data using Gaussian process model and spherical vector clustering.

Abstract

MicroRNAs, small noncoding RNAs, are conserved in many species, and they are key regulators that mediate post-transcriptional gene silencing. Since biologists cannot perform experiments for each of target genes of thousands of microRNAs in numerous specific conditions, prediction on microRNA target genes has been extensively investigated. A general framework is a two-step process of selecting target candidates based on sequence and binding energy features and then predicting targets based on negative correlation of microRNAs and their targets. However, there are few methods that are designed for target predictions using time-series gene expression data. In this article, we propose a new pipeline, mirTime, that predicts microRNA targets by integrating sequence features and time-series expression profiles in a specific experimental condition. The most important feature of mirTime is that it uses the Gaussian process regression model to measure data at unobserved or unpaired time points. In experiments with two datasets in different experimental conditions and cell types, condition-specific target modules reported in the original papers were successfully predicted with our pipeline. The context specificity of target modules was assessed with three (correlation-based, target gene-based and network-based) evaluation criteria. mirTime showed better performance than existing expression-based microRNA target prediction methods in all three criteria. mirTime is available at https://github.com/mirTime/mirtime. Supplementary data are available at Bioinformatics online.