Collaborative Research: MFB: Deciphering RNA-based regulatory logic with interpretable machine learning

Cornell University, Ithaca NY

Investigators

Abstract

RNA transcripts, single-stranded ribonucleic acid products synthesized by transcription of DNA, contain multiple, complex, and overlapping codes that dictate their biochemical processing. Two RNA processes, RNA splicing and 5’UTR regulation, play key roles in the fundamental transfer of information from DNA to functional RNA and protein products. Understanding the regulatory logic of these RNA-based codes is required for the rational design of RNA transcripts in biotechnology. Despite decades of genetics, biochemistry, and bioinformatics research, understanding RNA-based regulatory logic remains elusive. Recent applications of “off-the-shelf” machine learning methods to limited datasets have provided limited insights into the underlying regulatory logic, hindering rational RNA transcript design. In this collaborative project, “interpretable-by-design” machine learning algorithms that can explain how they arrive at their predictions will be designed, deployed, and trained on massively parallel reporter assays and interpretability will be demonstrated by experimental validation. The project will have broader impacts through the development of generalizable experimental and machine learning approaches that can be applied to other biomolecular systems, its potential impact for biotechnology, recruitment, participation, and professional development for trainees, with an emphasis on supporting students and researchers from diverse backgrounds underrepresented in the sciences, and development of curricula for undergraduate students in computer science and biology. This Molecular Foundations of Biotechnology (MFB) project is focused on two applications: (1) A comprehensive understanding of the splicing code in determining exon skipping. During RNA splicing, introns are removed, and exons are ligated to form the mature RNA transcript. It is currently unknown how exactly an exon’s sequence determines whether it would be included or not. Using a massively parallel reporter assay dataset with >350,000 constructs and analyzed using an interpretable neural network the investigators have derived multiple insights into the splicing code. In this project, experimental RNA binding protein binding data will be incorporated to capture non-additive effects among sequence features and elucidate the effect of secondary structure. (2) A comprehensive understanding of the role of the 5’UTR code in translation initiation and RNA stability. The 5’UTR sequence plays important roles in regulating translation and stability, yet our understanding of this 5’UTR code is far from complete. In this project, interpretable neural networks will be designed to decipher the 5’UTR code, based on preliminary analysis, as well as information about microRNAs, and bidirectional 5’UTR scanning. This project will demonstrate that interpretable machine learning can be used to decipher RNA-based regulatory logic, a critical step forward for basic research with direct and generalizable implications for biotechnology applications. This project is jointly supported by the Division of Molecular and Cellular Biosciences (MCB), the Division of Chemistry (CHE), and the Division of Information and Intelligent Systems (IIS). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.