NuPose: Nucleosome positioning based on DNA sequence

The standalone version of ResNuc

The web platform supports at most 500 DNA fragments for the SNPP and 700-bp input for the MPP due to high computational complexities. If your input size is larger than the described input sizes, you can utilize the standalone version of ResNuc. For this purpose, please ensure that you have all the requirements listed as follows:
  • Python version 3.x programming language
  • Tensorflow machine learning library
  • Keras machine learning library

Python programming language can be downloaded from python.org. It is suggested that the above-mentioned software packages be installed in a virtual environment which helps properly manage the files and outputs in terms of reducing error and enhancing security. To this end, the following commands should be considered:

python3 -m venv MyEnvironment
source MyEnvironment/bin/activate

If you are installing a virtual environment, it should be activated before running ResNuc (using the source command). After installing the programming language, the TensorFlow and Keras machine learning libraries can be installed and tested using the following commands:

pip3 install tensorflow
pip3 install keras
pip3 show keras

ResNuc is downloadable by clicking on this link and easily can be executed using two bellow commands for SNPP and MPP, respectively:

python3 ResNuc.py SNPP MyInput.txt MyOutput.txt
python3 ResNuc.py MNPP MyInput.txt MyOutput.txt

When confronting a technical issue, please feel free to contact us. We will gladly help you.



About Panchenko's lab

We study the associations between key components in the epigenome to understand how its perturbation can lead to cancer. Our team works to identify factors contributing to cancer mutation occurrence in DNA, to discover molecular mechanisms of how mutations and covalent modifications affect nucleosomes and chromatin, their interactions, stability and dynamics.

Go to Panchenko lab