The DeepRNA project modelled the human RNA-protein interactome using experimental data and predictions.
A pilot RNA-protein interaction network was generated from current ENCODE eCLIP protein–RNA interaction data for 119 proteins. Additional RBP-RNA interactions covering the entire human proteome were then predicted using the “catRAPID” method, as published by the host group.
To prioritise interactions of interest using human coding and non-coding variants that affect the network, the pilot RNA-protein interaction network was then enhanced by overlaying information and trans- and cis-eQTLs (expression quantitative trait loci).
The pilot RNA-protein interaction network was then further enhanced by integrating human disease-associated and natural variation data to test the robustness and disease relevance of specific interactions using prediction methods.
A final full-coverage RNA-protein interaction network, integrating additional experimental data and systematic prediction method refinements, was completed and a database interface web server was developed (
https://rnact.crg.eu(odnośnik otworzy się w nowym oknie)). This website is intended to be the first easily accessible resource for high-quality human RNA-protein interaction data.
Additionally, machine learning was intended to be applied to newly identify interactions of potential medical relevance to arrive at a prioritised list of likely disease-relevant protein-RNA interactions, to be followed up within the host group by experimental validation. A short secondment at the Kundaje lab, a genomic machine learning group at Stanford University, allowed me to initiate a collaboration aiming to develop a deep neural network classifier trained to identify potential disease-relevant variants within the human RNA-protein interactome. However, due to the immense technical challenge of this, this project is currently still in its starting phase.
Two articles were published relating to the project, both in separate Nucleic Acids Research special database issues: a database interface web server was developed (
https://rnact.crg.eu(odnośnik otworzy się w nowym oknie)). This web server now provides easy access to human and mouse protein–RNA interaction data generated by the ENCODE Project, the largest and most consistent such effort to date. It is aimed at experts and non-experts alike. RNAct is also now linked out to by the authoritative UniProt protein database as a cross-referenced resource, which greatly increases its reach and visibility. The second is BacFITBase, a database collating information on the essentiality of bacterial genes during host infection in various vertebrate species, accessible at
http://www.tartaglialab.com/bacfitbase(odnośnik otworzy się w nowym oknie).
