Sarah Rennie

Ormstown-based baker Carolie Schinck has been selected by the MRC du Haut-Saint-Laurent as the winner of the l…

The Valley came to a standstill around 2:15 p.m. on April 8, as residents looked to the heavens for the start…

It is hard to imagine anybody being bored by the spectacular total solar eclipse that crossed over the Valley…

Members of the team of paramedics at the Huntingdon Paraxion station were on hand earlier this year to present…

A convoy of more than 105 tractors left Saint-Clet for Vaudreuil on April 10, as part of a demonstration stage…

Une Affaire de Famille turns “the big three-O” this year, and a surprise winter storm wasn’t enough to stop re…

RNA-binding proteins and chemical modifications to RNA play vital roles in the co- and post-transcriptional regulation of genes. In order to fully decipher their biological roles, it is an essential task to catalogue their precise target locations along with their preferred contexts and sequence-based determinants. Recently, deep learning approaches have significantly advanced in this field. These methods can predict the presence or absence of modification at specific genomic regions based on diverse features, particularly sequence and secondary structure, allowing us to decipher the highly non-linear sequence patterns and structures that underlie site preferences. This article provides an overview of how deep learning is being applied to this area, with a particular focus on the problem of mRNA-RBP binding, while also considering other types of chemical modification to RNA. It discusses how different types of model can handle sequence-based and/or secondary-structure-based inputs, the process of model training, including choice of negative regions and separating sets for testing and training, and offers recommendations for developing biologically relevant models. Finally, it highlights four key areas that are crucial for advancing the field.

Abstract N6-methyladenosine (m6A) exerts many of its regulatory effects on eukaryotic mRNAs by recruiting cytoplasmic YT521-B homology domain family (YTHDF) proteins.

Arnaud Stigliani Renata Ialchina Jiayi Yao Albin Sandelin Open Access Published: June 28, 2024

The lack of specialty coffee options in town at the time inspired Brunette, who was already working in the cra…