Introduction

In this study we aim to analyse the effect that padding the amino acid sequences has on the performance of deep learning models. Specifically, we have chosen a hierarchical EC number prediction task to carry out the analysis. We use three different architectures (only_denses, 1_conv, stack_conv) to check if they affect the padding effect.

The data used in this study consists on all the reviewed protein sequences of the taxonomy Archaea from Uniprot version 2019_05.

The project is almost entirely coded in Python 3.6.7. R version 3.4.4 has been used for building the explanatory linear models (notebooks 07 and 09).

The findings of this analysis are explained in the following article: Lopez-del Rio, A., Martin, M., Perera-Lluna, A. et al. Effect of sequence padding on the performance of deep learning models in archaeal protein functional prediction. Sci Rep 10, 14634 (2020). https://doi.org/10.1038/s41598-020-71450-8.

Structure

• Functions are defined in the src/ folder (.py and .R files)
• The workflow of the analysis is applied through Jupyter Notebooks in the notebooks/ folder. The files are preceded by a number that indicates the chronological order of their execution.
• raw_data/ folder contains the CSV file with reviewed Uniprot entries for Archaea.
• data/ is the folder where intermediate and final results will be stored when running the notebooks.

Workflow (notebooks)

1. Data preprocessing

• 00_creating_data.ipynb
• 01_pre_statistics.ipynb

2. Models training and results processing

• 02_task1_training.ipynb
• 03_task1_comparison.ipynb
• 04_task2_training.ipynb
• 05_task2_comparison.ipynb

3. Performance metrics and graphical representation

• 06_comparing_architectures.ipynb
• 07_linear_models_metrics.ipynb
• 08_activations_sequences_pca.ipynb
• 09_linear_models_pcs.ipynb

System requirements

The runs have been executed on the following hardware from the B2SLab (Universitat Politecnica de Catalunya):

• tob: 8 threads, 32GB RAM, NVIDIA TITAN Xp GeForce GTX 1070

• lapsus: 12 threads, 32GB RAM, 2 x NVIDIA GeForce GTX 1070

Considerations

• The absPath variable at the beginning of each notebook and src file should be changed once the repository is cloned to the correct path in each case.
• Notebooks 02-04 are implemented to analyse only one architecture each time. The block of variables/parameters in the second cell corresponding to the studied architecture should be uncommented (and the rest, commented) prior to running the notebook.
• In src/Target, all the different types of padding tested in this study are implemented.
• Execution without GPU of notebooks 02-05 and 08 may require considerable time and it is not recommended.