ProtGauss

This repository is the official implementation of Machine learning predicts nucleosome binding modes of transcription factors.

Block diagram

Requirements

The codebase is implemented in Python 3.6.9 and the packages used for developments are mentioned below.

argparse 		1.1
gensim			3.8.1
numpy			1.16.1
pandas			1.0.0
pickle 			4.0
prettytable		0.7.2
pyfasta			0.5.2
scikit-learn		0.23.1
texttable		1.6.2
tqdm			4.40.2

Datasets

Protein sequences from UniProt (release 2019_11) is used to train ProtVec model. To model and evaluate nucleosome-binding modes of transcription factors, we use seven nucleosome-TF interaction patters for195 TFs as our training dataset. These datasets (raw and preprocessed) are provided in data folder. More details are provided inside data folder.

Options

Training of ProtGauss is handled by main.py which provides following arguments:

--seqtype	STR		Type of sequences: eDBD or full 	Default  is eDBD.
--len		INT		Length of subsequence (3, 4, 5, 6)	Default is 3.	
--binding	INT		Binding mode to use for training 	Default is 0.

For binding parameters, we use following mapping:

0 		End binding
1 		Periodic
2 		Groove
3 		Dyad
4 		Gyre spanning
5 		Oriented
6 		Nucleosome_stability
-1 		All

Running ProtGauss

• Make sure you check the README in data folder before running the model. Before training the model, main.py runs ProtVec and also preprocess data if the processed files are not already available.

• Run the model with default parameters

python3 main.py

• Run the model on full sequences

python3 main.py --seqtype full

• Run the model with subsequence length 4

python3 main.py --len 4

• Run the model with subsequence length 3 and sequence type eDBD

python3 main.py --len 3 --seqtype eDBD

• Run the model for specific binding modes

python3 main.py --binding 0

• Run the model for all binding modes

python3 main.py --binding -1

Results

Tables reporting model's performance are saved inside results folder with naming convention as: {binding_mode}_{subsequence_length}.csv.

For example,

python3 main.py --binding 0 --seqtype eDBD --len 3

The results will be saved inside results/eDBD/ as end_preference_3.csv.

Predictions

Model's prediction for test sequences in folder data/yeast/ will be saved in the same folder. Note that the folder name should be set in line 63 of main.py as organism =['yeast']. You can run the model and predict on multiple sequences by defining organism =['ecoli', 'yeast'].

For example, if we train the model with organism =['yeast'] in line 63 of main.py,

python3 main.py --binding 0 --seqtype eDBD --len 3

The predictions will be saved inside data/yeast/ as yeast_prediction_eDBD3.csv. The name for prediction file is set as {organism}_prediction_{sequence_type}{subsequence_length}.csv.

Acknowledgement

We are thankful to kyu99 for biovec implementation link.