Louis BECQUEY / biorseo

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INSTALL.md 4.95 KB

Installation

CLONING

  • Clone this git repository : git clone https://github.com/persalteas/biorseo.git and cd biorseo.
  • Create folders for the modules you will use: mkdir -p data/modules/. If you plan to use several module sources, add subdirectories : mkdir -p data/modules/DESC and mkdir -p data/modules/BGSU

RNA3DMOTIFS DATA

If you use Rna3Dmotifs, you need to get RNA-MoIP's .DESC dataset: download it from GitHub. Put all the .desc from the Non_Redundant_DESC folder into ./data/modules/DESC. Otherwise, you also can run Rna3Dmotifs' catalog program to get your own DESC modules collection from updated 3D data (download Rna3Dmotifs). You also need to move the final DESC files into ./data/modules/DESC.

THE RNA 3D MOTIF ATLAS DATA

If not done during the installation of JAR3D, get the latest version of the HL and IL module models from the BGSU website and extract the Zip files. Put the HL and IL folders from inside the Zip files into ./data/modules/BGSU. Note that only the latest Zip is required.

DEPENDENCIES

  • Make sure you have Python 3.5+, Cmake, and a C++ compiler installed on your distribution. Please, it's 2019, use a recent one, we use the 2017 C++ standard. The compilation will not work with Ubuntu 16's GCC 5.4 for example. Tested with libstdc++-dev >= 6.0, so use GCC >=6.0 or Clang >= 6.0.
  • Install automake, libboost-program-options and libboost-filesystem.
  • Download and install IBM ILOG Cplex optimization studio, an academic account is required. The free version is too limited, you must register as academic. This is also free.
  • Download and install Eigen: Get the latest Eigen archive from http://eigen.tuxfamily.org. Unpack it, and install it. bash wget http://bitbucket.org/eigen/eigen/get/3.3.7.tar.gz -O eigen_src.tar.gz tar -xf eigen_src.tar.gz cd eigen-eigen-323c052e1731 mkdir build cd build cmake .. sudo make install
  • Download and install NUPACK: Register on Nupack's website, download the source, unpack it, build it, and install it: bash wget http://www.nupack.org/downloads/serve_file/nupack3.2.2.tar.gz tar -xf nupack3.2.2.tar.gz cd nupack3.2.2 mkdir build cd build cmake .. make -j4 sudo make install You will notice that the installation process is not complete, some of the headers are not well copied to /usr/local. Solve it manually: sudo cp nupack3.2.2/src/thermo/*.h /usr/local/include/nupack/thermo/ ### OPTIONAL DEPENDENCIES FOR USE OF JAR3D
  • Download and install RNAsubopt from the ViennaRNA package.
  • Download and install Java runtime (Tested with Java 10)
  • Download the latest JAR3D executable "jar3d_releasedate.jar" from the BGSU website.

OPTIONAL DEPENDENCIES FOR USE OF BAYESPAIRING

  • Download and install RNAfold from the ViennaRNA package (if not already done at the previous step).
  • Make sure you have Python 3.5+ with packages networkx, numpy, regex, wrapt and biopython. You can install them with pip, you will need the python3-dev package to build them.
  • Clone the latest BayesPairing Git repo, and install it : git clone http://jwgitlab.cs.mcgill.ca/sarrazin/rnabayespairing.git BayesPairing cd BayesPairing pip install .

BUILDING

  • Edit the file EditMe to set the paths of the above dependencies and data. Fields that you will not use can be ignored (ex: bypdir if you do not use BayesPairing). Example of my setup:
    • CPLEXDir="/opt/ibm/ILOG/CPLEX_Studio128_Student"
    • IEIGEN="/usr/local/include/eigen3"
    • INUPACK="/usr/local/include/nupack"
    • jar3dexec="/nhome/siniac/lbecquey/Software/jar3dbin/jar3d_2014-12-11.jar"
    • bypdir="/nhome/siniac/lbecquey/Software/BayesPairing/bayespairing/src"
    • biorseoDir="/nhome/siniac/lbecquey/Software/biorseo"
  • You might want to edit Makefile if you are not using clang as compiler. For example, if you use g++, replace clang++ by g++.
  • Build it: make -j4
  • Check if the executable file exists: ./bin/biorseo --version.

BAYESPAIRING USERS: PREPARE BAYESIAN NETWORKS

We run an example job for it to build the bayesian networks of our modules.

cd rnabayespairing/src
python3 parse_sequences.py -d rna3dmotif -seq ACACGGGGUAAGAGCUGAACGCAUCUAAGCUCGAAACCCACUUGGAAAAGAGACACCGCCGAGGUCCCGCGUACAAGACGCGGUCGAUAGACUCGGGGUGUGCGCGUCGAGGUAACGAGACGUUAAGCCCACGAGCACUAACAGACCAAAGCCAUCAU -ss ".................................................................((...............)xxxx(...................................................)xxx).............."

Use -d rna3dmotif or -d 3dmotifatlas depending on the module source you are planning to use. This is a quite long step, but the bayesian networks will be ready for all the future uses.