pysster: A Python repository from tilii

pysster: a Sequence-STructure classifiER

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:

multi-class and single-label or multi-label classifications
hyper-parameter tuning (grid search)
interpretation of learned motifs in terms of positional and class enrichment and motif co-occurrence
support of input strings over user-defined alphabets (e.g. applicable to DNA, RNA, protein data)
optional use of structure information, handcrafted features and recurrent layers
seamless CPU or GPU computation

The corresponding Bioinformatics paper can be found here.

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 via pip or github.

Install via pip:

pip3 install pysster

Install latest version via github:

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

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

By the time of writing the most recent TensorFlow version is 1.7 and requires CUDA 9 and cuDNN 7. You can always check the required versions in the TensorFlow release notes.

Documentation

Tutorials

Example workflow (data loading, model training via grid search, model evaluation + motif visualization showcased using an RNA editing data set)
Visualization by optimization of all network layers (an alternative visualization method showcased using an artifical data set)
Limitations of Neural Networks (some critical thoughts on networks applied to sequence data)

API documentation

Data objects (handling of input data)
Model objects (training and interpretation of networks)
Grid_Search objects (hyperparameter tuning)
Motif objects (motif representation of a PWM)
utils functions (save/load Data/Model objects, predict/annotate secondary structures, further processing, etc.)

Changelog

v1.1.3 - 19. March 2018 (PyPI)