pysster: a Sequence-STructure classifiER Build Status Build status License: MIT

Learning Sequence And Structure Motifs In Biological Sequences Using Convolutional Neural Networks

pysster is a Python package for training and interpretation of convolutional neural networks on biological sequence data. Sequences are classified by learning sequence (and optionally structure) motifs and the package offers sensible default parameters, a hyper-parameter optimization procedure and options to visualize learned motifs. The main features of the package are:

If you found our tool useful for your work, please cite the accompanying Bioinformatics paper (link). If you run into bugs, missing documentation or if you have a feature request, feel free to open an issue.

Installation

pysster is compatible with Python 3.5+ and can be installed from PyPI or GitHub.

Install latest version from GitHub:

git clone https://github.com/budach/pysster.git
cd pysster
pip3 install .

Install from PyPI:

pip3 install pysster

Using the GPU

pysster depends on TensorFlow and by default the CPU version of TensorFlow will be installed. If you want to use your NVIDIA GPU (which is recommended for large data sets or grid searchs) make sure that your CUDA and cuDNN drivers are correctly installed and then install the GPU version of TensorFlow:

pip3 uninstall tensorflow
pip3 install tensorflow-gpu

At the time of writing the most recent TensorFlow version is 1.14 and the pre-built binary requires CUDA 10 and cuDNN 7.4. You can always check the required versions in the TensorFlow GPU support notes.

Right now, we only support TensorFlow 1.x. TensorFlow 2 has recently been released and we plan switching to it and its integrated tf.keras in the future.

Documentation

Tutorials

API documentation

Changelog

v1.2.2 - 22. October 2019 (PyPI)

v1.2.1 - 28. February 2019 (PyPI)

v1.2.0 - 6. December 2018 (PyPI)

v1.1.4 - 17. July 2018 (PyPI)

v1.1.3 - 19. March 2018 (PyPI)