Poster #100 - Sathyanarayanan Vaidhyanathan

tatDB/tarDB: cancer use cases and machine learning

Sathyanarayanan Vaidhyanathan, Adesupo Adetowubo, Sarthak Chandervanshi, Hameed Sanusi, and Andrey Grigoriev* Department of Biology, Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 08102, USA

Ribosomal RNA-derived fragments (rRFs) and transfer RNA-derived fragments (tRFs) are often neglected as noise while analyzing experimental data like CLASH and CLIP, which capture RNA-RNA interactions loaded into Argonaute protein complexes. The growing field of rRFs is hindered due to a lack of resources to understand rRF functions and target groups. Our previous analysis of experimental data from human HEK293 cells capturing crosslinked Argonaute 1-RNA complexes showed tRFs and rRFs, as a class of emerging post-transcriptional regulators of gene expression, likely bind to the transcripts of target genes (Guan, NAR, 2021, Guan, RNA Biol., 2019 ). We identified chimeric guide-target pairs to identify multiple rRF/tRF isoforms, their targets, and their binding patterns. To expand the domain knowledge about the rRFs/tRFs, targets, and interaction sites, we designed a web-based database, tatDB (tRF-target pairs) and tarDB (rRF-target pairs). The goal is to provide a resource for building users' hypotheses on the potential roles of tRFs/ rRFs for experimental validation.