PERFUMES: pipeline to extract RNA functional motifs and exposed structures.
Bioinformatics
; 40(2)2024 02 01.
Article
in En
| MEDLINE
| ID: mdl-38291894
ABSTRACT
MOTIVATION Up to 75% of the human genome encodes RNAs. The function of many non-coding RNAs relies on their ability to fold into 3D structures. Specifically, nucleotides inside secondary structure loops form non-canonical base pairs that help stabilize complex local 3D structures. These RNA 3D motifs can promote specific interactions with other molecules or serve as catalytic sites. RESULTS:
We introduce PERFUMES, a computational pipeline to identify 3D motifs that can be associated with observable features. Given a set of RNA sequences with associated binary experimental measurements, PERFUMES searches for RNA 3D motifs using BayesPairing2 and extracts those that are over-represented in the set of positive sequences. It also conducts a thermodynamics analysis of the structural context that can support the interpretation of the predictions. We illustrate PERFUMES' usage on the SNRPA protein binding site, for which the tool retrieved both previously known binder motifs and new ones. AVAILABILITY AND IMPLEMENTATION PERFUMES is an open-source Python package (https//jwgitlab.cs.mcgill.ca/arnaud_chol/perfumes).
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Perfume
Limits:
Humans
Language:
En
Journal:
Bioinformatics
/
Bioinformatics (Oxford. Online)
Journal subject:
INFORMATICA MEDICA
Year:
2024
Document type:
Article
Affiliation country:
Canada
Country of publication:
United kingdom