MuStARD: Deep Learning for intra- and inter-species scanning of functional genomic patterns

Regions of the genome that produce different classes of functional elements also exhibit different patterns in their sequence, secondary structure, and evolutionary conservation. Deep Learning is a family of Machine Learning algorithms recently applied to a variety of pattern recognition problems. Here we present MuStARD (gitlab.com/RBP_Bioinformatics/mustard) a Deep Learning framework that can learn and combine sequence, structure, and conservation patterns in sets of functional regions, and accurately identify additional members of the given set over wide genomic areas. MuStARD is designed with general use in mind, and has sophisticated iterative fully-automated background selection capability. We demonstrate that MuStARD can be trained without changes on different classes of human small RNA loci (pre-microRNAs and snoRNAs) and accurately build prediction models for both, outperforming state of the art methods specifically designed for each specific class. Furthermore, we demonstrate the ability of MuStARD for inter-species identification of functional elements by predicting mouse small RNAs using human trained models. MuStARD is easy to deploy and extend to a variety of genomic classification questions.

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