A comprehensive comparison of general RNA-RNA interaction prediction methods.

Nucleic Acids Res

Centre for High-Throughput Biology, Department of Computer Science and Department of Medical Genetics, University of British Columbia, Vancouver V6T 1Z4, Canada

Published: April 2016

AI Article Synopsis

  • * Researchers developed computational methods to predict these interactions, comparing 14 different prediction methods using a dataset of confirmed interactions from fungal and bacterial species.
  • * The study found that non-comparative energy-based tools perform best for short interactions, but maintaining accuracy across diverse datasets and longer sequences presents ongoing challenges, leading to implications for future research efforts.

Article Abstract

RNA-RNA interactions are fast emerging as a major functional component in many newly discovered non-coding RNAs. Basepairing is believed to be a major contributor to the stability of these intermolecular interactions, much like intramolecular basepairs formed in RNA secondary structure. As such, using algorithms similar to those for predicting RNA secondary structure, computational methods have been recently developed for the prediction of RNA-RNA interactions. We provide the first comprehensive comparison comprising 14 methods that predict general intermolecular basepairs. To evaluate these, we compile an extensive data set of 54 experimentally confirmed fungal snoRNA-rRNA interactions and 102 bacterial sRNA-mRNA interactions. We test the performance accuracy of all methods, evaluating the effects of tool settings, sequence length, and multiple sequence alignment usage and quality. Our results show that-unlike for RNA secondary structure prediction--the overall best performing tools are non-comparative energy-based tools utilizing accessibility information that predict short interactions on this data set. Furthermore, we find that maintaining high accuracy across biologically different data sets and increasing input lengths remains a huge challenge, causing implications for de novo transcriptome-wide searches. Finally, we make our interaction data set publicly available for future development and benchmarking efforts.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4838349PMC
http://dx.doi.org/10.1093/nar/gkv1477DOI Listing

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