Visual abstract

TBA

Abstract

Messenger RNA (mRNA) decay is crucial in regulating gene expression and influencing cellular functions and organismal phenotypes. Precise identification of mRNA decay sites will help to understand the post-transcriptional control mechanisms that affect mRNA stability. In this presentation, we will discuss the prediction of mRNA decay sites using a DeBERTa transformer model. Our model is trained on sequences derived from direct RNA sequencing of polyadenylated RNA from HeLa cells, focusing on subsequences within mRNA coding regions to determine the presence of decay sites. Based on our model, we also analyze the role of single nucleotide variants in mRNA decay.

Slides

TBA

Lecture Recordings

Readings

1. Determinants of Functional MicroRNA Targeting
   https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9880601/
   
2. miRBind: A Deep Learning Method for miRNA Binding Classification
   https://www.mdpi.com/2073-4425/13/12/2323
3. Using Attribution Sequence Alignment to Interpret Deep Learning Models for
   miRNA Binding Site Prediction https://www.mdpi.com/2079-7737/12/3/369
   

Catering

Water