miRAW: A deep learning-based approach to predict microRNA targets by analyzing whole microRNA transcripts

We present a novel approach for miRNA target prediction based on Deep Learning (DL) which, rather than in corporating any knowledge (such as seed regions), investigates the entire miRNA and 3’TR mRNA nucleotides to learn a uninhibited set of feature descriptors related to the targeting process. We collected more than 150,000 experimentally validated homo sapiens miRNA:gene targets and cross referenced them with different CLIP-Seq, CLASH and iPAR-CLIP datasets to obtain ∼20,000 validated miRNA:gene exact target sites. Using this data, we implemented and trained a deep neural network—composed of autoencoders and a feed-forward network—able to automatically learn features describing miRNA-mRN A interactions and assess functionality. Predictions were then refined using information such as site location or site accessibility energy. In a comparison using independent datasets, our DL approach consistently outperformed existing prediction methods, recognizing the seed region as a common feat ure in the targeting process, but also identifying the role of pairings outside this region. Thermodynamic analysis also suggests that site accessibility plays a role in targeting but that it cannot be used as a sole indicator for functionality. Data and source code available at: https://bitbucket.o rg/account/user/bipous/projects/MIRAW.
Source: PLoS Computational Biology - Category: Biology Authors: Source Type: research