oubounyt / deepromoter

Pytorch implementation of DeePromoter Active sequence detection for promoter(DNA subsequence regulates transcription initiation of the gene by controlling the binding of RNA polymerase)

DeePromoter

Pytorch implementation of DeePromoter Active sequence detection for promoter(DNA subsequence regulates transcription initiation of the gene by controlling the binding of RNA polymerase)

Updates

• 2021-07-08 : Finish training and testing scripts for DeePromoter

Requirements

• Please install torch==1.9 from https://pytorch.org

• You can install others Python dependencies with

  pip3 install -r requirements.txt

Dataset

Current supported dataset is:

• EPDnew : A collection of experimentally validated promoters for selected model organisms. Evidence comes from TSS-mapping from high-throughput expreriments such as CAGE and Oligocapping

Preprocessing

Dataset for Human and Mouse had been processed and stored in ./data

Procedure for create negative dataset as described in paper:

• Step 1: Break the protein sequence to N part(20 as in the paper)

• Step 2: Random choose M part of the original protein to keep it, and random initialize the rest

• Step 3: For every training step mix the positive batch with negative batch and perform training

##Training

python3 train.py -d data/human/nonTATA/hs_pos_nonTATA.txt --experiment_name human_nonTATA

Early stop had been implement and train will automatically stop when Mathews correlation coefficient is saturated

The results will be saved in to ./output/experiment_name

You can do continue training by pass the path to weight by flag -w or --weight

Inference

Prepare your dataset in txt format with each DNA sequence(length 300) on a line

Run inference by

python3 test.py -d data/human/nonTATA/hs_pos_nonTATA.txt -w path_to_weight

Output will be save into file infer_results.txt in the main folder

Implementation Issues

Negative sampling

1. In addition to using negative sampling as in the paper described(see Preprocessing) I added a random dataset to help the model generalize.

Parallel convolution

1. The author use grid search to find optimal parameters for the network. I used the final set of parameter from the paper. Kernel size = [27, 14, 7], and maxpooling with kernel = 6

References

1. DeePromoter paper