Motif models for RNA-binding proteins

Current Opinion in Structural Biology
Alexander SasseQuaid D Morris

Abstract

Identifying the binding preferences of RNA-binding proteins (RBPs) is important in understanding their contribution to post-transcriptional regulation. Here, we review the current state-of-the art of RNA motif identification tools for RBPs. New in vivo and in vitro data sets provide sufficient statistical power to enable detection of relatively long and complex sequence and sequence-structure binding preferences, and recent computational methods are geared towards quantitative identification of these patterns. We classify methods by their motif model's representational power and describe the underlying considerations for RNA-protein interactions. All classical motif identification algorithms apply physically motivated architectures, consisting of a motif and an occupancy model, we call these explicit motif models. Recent methods, such as convolutional neural networks and support vector machines, abandon the classical architecture and implicitly model RNA binding without defining a motif model. Although they achieve high accuracy on held-out data they may be unsuitable to solve the ultimate goal of the field, using motifs trained on in vitro data to predict in vivo binding sites. For this task methods need to separate intrinsic ...Continue Reading

Citations

May 9, 2019·Wiley Interdisciplinary Reviews. RNA·Xiaoyong PanHong-Bin Shen
Jun 5, 2019·Nucleic Acids Research·Marta AdinolfiManuela Helmer-Citterich
Nov 22, 2020·Theory in Biosciences = Theorie in Den Biowissenschaften·Rituparno SenPeter F Stadler
Jul 23, 2020·Cell Reports·Nele Merret HollmannJanosch Hennig
May 27, 2021·Nucleic Acids Research·Andrea GuarracinoManuela Helmer-Citterich
May 26, 2021·Molecular Diversity·Joel Markus Vaz, S Balaji

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