Research Initiation Award: Uncovering and Extracting Biological Information from Nanopore Long-read Sequencing Data with Machine Learning and Mathematical Approaches

Tougaloo College, Tougaloo MS

Investigators

Abstract

HBCU-UP’s Research Initiation Awards provide support for STEM faculty to pursue research activities to further their research capabilities and effectiveness and help enhance research and teaching at HBCUs. This award to Tougaloo College seeks to uncover crucial insights into gene expression and cellular differentiation. It will advance the field of genomics by utilizing innovative technology and computational tools. Through the use of long-read sequencing, the project aims to make contributions in understanding the control of gene expression in general, with specific application understanding diseases. Additionally, it promotes education and diversity by providing undergraduate students, particularly those from historically marginalized communities, through valuable research experiences. By integrating this research into the curriculum, Tougaloo College aims to transform STEM pedagogies and enrich the learning of students in various disciplines. The potential benefits of this project are wide-reaching. It will not only enhance the research capabilities of faculty members, but also foster the development of future STEM professionals. By engaging students in authentic research, it will prepare them for successful careers and contributes to their retention in STEM fields. The overarching goal of this project is to utilize high-throughput genomic and transcriptome data sets, primarily based on Nanopore long-read sequencing, to investigate the influence of DNA methylation on RNA velocity trajectories in diverse biological systems and diseases. The project will develop innovative computational tools to accurately estimate RNA velocity and detect DNA methylation patterns, including rare or novel modifications. Leveraging the unique advantages of Nanopore long-read sequencing, such as rich signal-level information, the project will advance the field by providing a comprehensive understanding of the interplay between DNA methylation and RNA velocity. This research's technical approach involves the development of a computational toolbox for RNA velocity estimation and a transformer-based machine learning tool for direct DNA methylation detection. Through the integration of RNA velocity and DNA methylation data, the project will shed light on the regulatory mechanisms underlying gene expression and cellular differentiation. The anticipated outcomes have the potential to impact various sectors, including healthcare, by enabling more accurate diagnostics, personalized therapies, and transformative advancements in genomic medicine. 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.