RCK: accurate and efficient inference of sequence- and structure-based protein–RNA binding models from RNAcompete data
Name
btw259.pdf
Size
1016.53 KB
Format
Adobe PDF
Checksum (MD5)
937a2b84ac10049c6b6e6022339a67dc
Author(s)
Orenstein, Yaron
Wang, Yuhao
Berger Leighton, Bonnie
Date Issued
June 2016
Journal
Bioinformatics
Publisher
Oxford University Press (OUP)
Citation
Orenstein, Yaron, et al. “RCK: Accurate and Efficient Inference of Sequence- and Structure-Based Protein–RNA Binding Models from RNAcompete Data.” Bioinformatics, vol. 32, no. 12, June 2016, pp. i351–59.
Version
Final published version
Abstract
Motivation: Protein-RNA interactions, which play vital roles in many processes, are mediated through both RNA sequence and structure. CLIP-based methods, which measure protein-RNA binding in vivo, suffer from experimental noise and systematic biases, whereas in vitro experiments capture a clearer signal of protein RNA-binding. Among them, RNAcompete provides binding affinities of a specific protein to more than 240 000 unstructured RNA probes in one experiment. The computational challenge is to infer RNA structure- and sequence-based binding models from these data. The state-of-the-art in sequence models, Deepbind, does not model structural preferences. RNAcontext models both sequence and structure preferences, but is outperformed by GraphProt. Unfortunately, GraphProt cannot detect structural preferences from RNAcompete data due to the unstructured nature of the data, as noted by its developers, nor can it be tractably run on the full RNACompete dataset. Results: We develop RCK, an efficient, scalable algorithm that infers both sequence and structure preferences based on a new k-mer based model. Remarkably, even though RNAcompete data is designed to be unstructured, RCK can still learn structural preferences from it. RCK significantly outperforms both RNAcontext and Deepbind in in vitro binding prediction for 244 RNAcompete experiments. Moreover, RCK is also faster and uses less memory, which enables scalability. While currently on par with existing methods in in vivo binding prediction on a small scale test, we demonstrate that RCK will increasingly benefit from experimentally measured RNA structure profiles as compared to computationally predicted ones. By running RCK on the entire RNAcompete dataset, we generate and provide as a resource a set of protein-RNA structure-based models on an unprecedented scale.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Mathematics
Terms of Use
Creative Commons Attribution-NonCommercial 4.0 International
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1093/BIOINFORMATICS/BTW259
