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DeepGOPlus: Improved protein function prediction from sequence

DeepGOPlus is a novel method for predicting protein functions from protein sequences using deep neural networks combined with sequence similarity based predictions.

This repository contains script which were used to build and train the DeepGOPlus model together with the scripts for evaluating the model's performance.

Dependencies

  • The code was developed and tested using python 3.7.
  • To install python dependencies run: pip install -r requirements.txt
  • Install diamond program on your system (diamond command should be available)

Data

  • http://deepgoplus.bio2vec.net/data/ - Here you can find the data used to train and evaluate our method.
  • data.tar.gz - Data required to run predict.sh script
  • data-cafa.tar.gz - CAFA3 challenge dataset
  • data-2016.tar.gz - Dataset which is used to compare DeepGOPlus with GOLabeler and DeepText2GO

Installation

pip install deepgoplus

Running

Scripts

The scripts require GeneOntology in OBO Format.

  • uni2pandas.py - This script is used to convert data from UniProt database format to pandas dataframe.
  • deepgoplus_data.py - This script is used to generate training and testing datasets.
  • deepgoplus.py - This script is used to train the model
  • evaluate_*.py - The scripts are used to compute Fmax, Smin and AUPR

The online version of DeepGOPlus is available at http://deepgoplus.bio2vec.net/

Citation

If you use DeepGOPlus for your research, or incorporate our learning algorithms in your work, please cite: Maxat Kulmanov, Robert Hoehndorf; DeepGOPlus: Improved protein function prediction from sequence, Bioinformatics, https://doi.org/10.1093/bioinformatics/btz595

New version specifications

Current dependencies can be found in the requirements.txt file. The used Python version is 3.7.9. Current version of Tensorflow will require Cuda 10.1 and Cudnn 7.6.5

Updating

The following scripts must be run to update the model using the latest versions of the Gene Ontology (GO) and the SwissProt Database.

  • update.py - This will download new releases of GO and SwissProt and train the model. If there are not new releases, the process will abort.
  • new_evaluation.sh - This will compute Fmax, Smin and AUPR metrics.