On the limits of inferring biophysical parameters of RBP-RNA interactions from in vitro RNA Bind'n Seq data.
Bayesian statistics
RNA Bind'n'Seq
RNA binding proteins
Systems biology
bioinformatics
computational biology
machine learning
maximum entropy method
Journal
F1000Research
ISSN: 2046-1402
Titre abrégé: F1000Res
Pays: England
ID NLM: 101594320
Informations de publication
Date de publication:
2023
2023
Historique:
accepted:
13
06
2023
medline:
26
6
2023
pubmed:
26
6
2023
entrez:
26
7
2024
Statut:
epublish
Résumé
We develop a thermodynamic model describing the binding of RNA binding proteins (RBP) to oligomers in vitro. We apply expectation-maximization to infer the specificity of RBPs, represented as position-specific weight matrices (PWMs), by maximizing the likelihood of RNA Bind'n Seq data from the ENCODE project. We demonstrate that the model can reproduce known specificities for well-studied proteins and that in some cases we predict novel, longer binding motifs. However, the model does not recover all the motifs that are in principle known, indicating that the data is not well explained by a single underlying biophysical model. Our code is publicly available.
Identifiants
pubmed: 39056095
doi: 10.12688/f1000research.135164.1
pmc: PMC11269977
doi:
Substances chimiques
RNA-Binding Proteins
0
RNA
63231-63-0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
742Informations de copyright
Copyright: © 2023 Schlusser N and Zavolan M.
Déclaration de conflit d'intérêts
No competing interests were disclosed.