Giter Site home page Giter Site logo

aertslab / deepbrain Goto Github PK

View Code? Open in Web Editor NEW
4.0 4.0 0.0 2.48 MB

DeepBrain: a collection of vertebrate sequence-based enhancer models aimed at understanding brain cell type enhancer code across and within species

License: Other

Jupyter Notebook 92.73% Python 7.02% Shell 0.25%
brain deeplearning enhancer evolution genomics single-cell

deepbrain's Introduction

DeepBrain

DeepBrain contains an example of using our enhancer models to score and understand any region in any genome for the cell types in our datasets.

Installation and usage

1. Clone the repo

git clone https://github.com/aertslab/DeepBrain.git

2. Install libraries

Create and activate conda environment:

conda env create -f environment.yml
conda activate DeepBrain

If the installation fails for some reason, another option is to run the steps in the following script for a manual installation of all packages and the environment:

./install.sh

If you are using a GPU (recommended), and it is not found after installation, a potential fix may be to link an installed libcusolver.so.11 to the correct path:

#Define CUDA_INSTALL_PATH depending on where it is installed on the local machine
ln -s $CUDA_INSTALL_PATH/CUDA/11.3.1/lib64/libcusolver.so.11 $(python -c "import tensorflow.python as x; print(x.__path__[0])")/libcusolver.so.10

3. Download the DeepBrain models

The weights of the models are stored using Git Large File Storage (LFS). To download them, you will need to have installed Git LFS (https://git-lfs.com/). On Linux, you can install Git LFS with the following command if it was not installed yet:

sudo apt-get install git-lfs 

Then the following commands are required after installation to retrieve the model weights:

git lfs install
git lfs pull

If Git LFS does not work, you can also download the model weights from Zenodo: https://zenodo.org/records/10868679

4. Usage

Run the notebook DeepBrain_example.ipynb for example usage for predicting on genomic regions, getting contribution scores and calculating correlation between cell types. If you are running JupyterLab, you can make the environment visible by running:

ipython kernel install --user --name DeepBrain --display-name "DeepBrain"

Citation

If the models or accompanying files are helpful for your research please cite the following publication:

Enhancer-driven cell type comparison reveals similarities between the mammalian and bird pallium

Nikolai Hecker*, Niklas Kempynck*, David Mauduit, Darina Abaffyová, Roel Vandepoel, Sam Dieltiens, Ioannis Sarropoulus, Carmen Bravo González-Blas, Elke Leysen, Rani Moors, Gert Hulselmans, Lynette Lim, Joris De Wit, Valerie Christiaens, Suresh Poovathingal, Stein Aerts

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.