Campus Units
Genetics, Development and Cell Biology, Bioinformatics and Computational Biology
Document Type
Article
Publication Version
Published Version
Publication Date
2013
Journal or Book Title
Journal of Computer Science and Computational Biology
Volume
6
Issue
4
First Page
182
Last Page
187
DOI
10.4172/jcsb.1000115
Abstract
RNA-protein interactions are important in a wide variety of cellular and developmental processes. Recently, high-throughput experiments have begun to provide valuable information about RNA partners and binding sites for many RNA-binding proteins (RBPs), but these experiments are expensive and time consuming. Thus, computational methods for predicting RNA-Protein interactions (RPIs) can be valuable tools for identifying potential interaction partners of a given protein or RNA, and for identifying likely interfacial residues in RNA-protein complexes. This review focuses on the “partner prediction” problem and summarizes available computational methods, web servers and databases that are devoted to it. New computational tools for addressing the related “interface prediction” problem are also discussed. Together, these computational methods for investigating RNA-protein interactions provide the basis for new strategies for integrating RNA-protein interactions into existing genetic and developmental regulatory networks, an important goal of future research.
Rights
© 2013 Muppirala UK, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Copyright Owner
Muppirala UK, et al.
Copyright Date
2013
Language
en
File Format
application/pdf
Recommended Citation
Muppirala, Usha K.; Lewis, Benjamin A.; and Dobbs, Drena L., "Computational Tools for Investigating RNA-Protein Interaction Partners" (2013). Genetics, Development and Cell Biology Publications. 20.
https://lib.dr.iastate.edu/gdcb_las_pubs/20
Comments
This article is from Journal of Computer Science and Systems Biology 6 (2013): 182–187, doi:10.4172/jcsb.1000115. Posted with permission.