Signal to Sequence Attention-Based Multiple Instance Network for Segmentation Free Inference of RNA Modifications

Christopher Hendra, Alexandre H. Thiery, Jonathan Goeke

Keywords: Multiple Instance Learning, Deep Learning, RNA Modification, Computational Biology

Abstract: Direct RNA sequencing technology works by allowing long RNA molecules to pass through tiny pores, generating electrical current, called squiggle, that are interpreted as a series of RNA nucleotides through the use of Deep Learning algorithms. The platform has also facilitated computational detection of RNA modifications via machine learning and statistical approaches as they cause detectable shift in the current generated as the modified nucleotides pass through the pores. Nevertheless, since modifications only occur in a handful of positions along the molecules, existing techniques require segmentation of the long squiggle in order to filter off irrelevant signals and this step produces large computational and storage overhead. Inspired by the recent work in vector similarity search, we introduce a segmentation-free approach by utilizing scaled-dot product attention to perform implicit segmentation and feature extraction of raw signals that correspond to sites of interest. We further demonstrate the feasibility of our approach by achieving significant speedup while maintaining competitive performance in m6A detection against existing state-of-the-art methods.

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