This page explains how to use the RPISeq.
RNA-protein interactions (RPIs) play important roles in a wide variety of cellular processes. High throughput experiments to identify RNA-protein interactions are beginning to provide valuable information about the complexity of RNA-protein interaction networks, but are expensive and time consuming. RPISeq was developed to address the need for reliable computational methods for predicting RNA-protein interactions.
RPISeq predictions are based on Random Forest (RF) or Support Vector Machine (SVM) classifiers trained and tested on 2 non-redundant benchmark datasets of RNA-protein interactions, RPI2241 and RPI369, extracted from PRIDB, a comprehensive database of RNA-protein complexes extracted from the PDB.
RPIntDB contains a total of 30,056 RNA-Protein interactions (5694 unique RNA chains and 1702 unique protein chains). The datasets can be downloaded from here.
PRIDB - RNA-protein interactions extracted from PRIDB, a database of RNA protein interfaces.
High-throughput experimental data - These interactions are extracted from available literature. Links to the corresponding publications are provided in the 'Source' column.
The output displays the sequences submitted and the probabilities ofinteraction predicted using Random Forest and SVM classifiers. Figure 2 shows the output using the sample protein and RNA sequences.
The output page displays the protein hits and interacting RNAs and reference for the interaction. Links to SwissProt, PRIDB, NPInter and other available literature from where these interactions are obtained are also provided.