README.md


This is a bi-objective integer programming algorithm.
It predicts the secondary structure of a RNA sequence with pieces of 3D information (non-canonical contacts) at some places, 
by identifying zones that can fold like known motifs from the RNA 3D Motif Atlas.


1/ How it works

INPUT:


An RNA sequence (tested with sequences ~100 bases)


THEN


Identifies possible 2D folds with RNAsubopt.
Knowing possible 2D folds, locate every possibly unpaired loop (hairpin loop, internal loop, multiple loop...)
align each unpaired loop to the catalogue of models of known RNA motifs (The 3D Motif Atlas of the BGSU RNA group)
retrieve a list of potential motif-insertion-sites in the RNA sequence. Use them to define the constraints for the IP problem.
Solve a bi-objective IP problem: 


Maximize the expected accuracy of the secondary structure,
Maximize the number and size of motifs inserted in the structure.


OUTPUT:


A set of secondary structures from the pareto front,
The list of known motif inserted in the corresponding structures (and the non-canonical contacts)
(lower score structures from k-Pareto sets, not implemented yet.)


2/ Installation


Download and install RNAsubopt from the ViennaRNA package (https://www.tbi.univie.ac.at/RNA/)
Download and install IBM ILOG Cplex optimization studio (https://www.ibm.com/analytics/cplex-optimizer), free academic account required
Download and install Java runtime (Tested with Java 10)
Download and install the latest JAR3D executable "jar3d_releasedate.jar" and motif models in this folder (http://rna.bgsu.edu/data/jar3d/models/)
Note that for HL and ILs, only the latest version is required (not all the versions provided in the folders).
Download and install a C++ compiler and building dependencies and utilities (g++ or clang, automake, libboost)