/ReactionMiner

Primary LanguageC++

ReactionMiner

ReactionMiner is a tool to predict (bio)chemical reactions using graph mining. It currently is built on the KEGG database.

Getting Started

  1. Clone this repository to your computer using git, or download the entire repository and decompress it.
  2. Unzip data.zip in its current (root) directory
  3. jbliss needs to be installed, by using make command. In the Makefile, edit the path of the JNI_H and JNI_MD_H libraries. Also change libjbliss.dylib to libjbliss.so while compiling, if on a Linux system.

Using ReactionMiner to predict paths

  1. Use ReactionMiner/reactionMiner.sh script to use the ReactionMiner prediction algorithm to predict pathways between a pair of molecules.
  2. For using an unknown molecule (not present in the KEGG database) as query, add the mol file to the data/molStereo_Hadded_PH_CoA/ directory and use the name of the mol file in the query.
  3. By default, pathway prediction will happen in the KEGG Universe. To predict paths in a different organism, an extra command line argument needs to be provided with the organism ID in the Path2Models database.

Usage

cd ReactionMiner/ 
bash reactionMiner.sh -org_id BMID000000142681 -source C00118 -target C00022 -paths 10

In this case, paths will be predicted between C00118 and C00022 in E. coli

Authors

License

  1. By using the software enclosed in this package (ReactionMiner), you agree to become bound by the terms of this license. 
  2. This software is for your internal use only. Please DO NOT redistribute it without the permission from the authors.
  3. This software is for academic use only. No other usage is allowed without a written permission from the authors. It cannot be used for any commercial interest.
  4. The authors appreciate it if you can send us your feedback including any bug report.
  5. The authors do not hold any responsibility for the correctness of this software, though we cross-checked all experimental results.

Citation

This work has been published in - Bioinformatics 2017. Please cite the paper if you use it for research.

Acknowledgments

This work was supported by the Indian Institute of Technology Madras grant CSE/14-15/5643/NFSC/SAYN to Sayan Ranu and the Initiative for Biological Systems Engineering (IBSE) at IIT Madras.