Enhancing snoRNA family annotations in the Rfam Database

This project aims to enhance the annotation of small nucleolar RNAs (snoRNAs) in the Rfam database, a key resource for classifying non-coding RNAs (ncRNAs). Rfam groups ncRNAs into families based on evolutionary relationships, using initial manual curation followed by a computational pipeline developed by the Rfam team. SnoRNAs are ncRNAs that guide RNA modification and are linked to diseases like cancer when their functions are disrupted. In vertebrates, many snoRNAs exist in multiple copies, potentially acquiring novel targets or functions, highlighting the need for accurate family classification to understand their regulatory mechanisms. Advancements in experimental and computational methods continue to reveal novel snoRNAs, further emphasizing the importance of updating snoRNA family annotations. This project seeks to expand and refine the Rfam database by identifying and organizing previously unannotated snoRNAs. These updates will improve Rfam’s accuracy, better reflecting snoRNA-specific characteristics and providing the ncRNA community with more reliable datasets for studying snoRNA functions and their disease associations. The improved Rfam snoRNA families will specifically benefit the Scott lab (home institution) by providing new Rfam IDs for unannotated snoRNAs, enriching snoDB – a specialized database of human snoRNAs – and supporting further research into snoRNA diversity and function.

Faculty Supervisor:

Michelle Scott

Student:

Partner:

EMBL’s European Bioinformatics Institute

Discipline:

Life Sciences

Sector:

Health and Related Sciences & Technology; Artificial Intelligence

University:

Université de Sherbrooke

Program: