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https://github.com/shtoneyan/gopher
Toolset for training quantitative sequence to function models.
https://github.com/shtoneyan/gopher
Last synced: 3 months ago
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Toolset for training quantitative sequence to function models.
- Host: GitHub
- URL: https://github.com/shtoneyan/gopher
- Owner: shtoneyan
- License: mit
- Created: 2022-02-10T21:17:14.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-03-15T17:03:12.000Z (8 months ago)
- Last Synced: 2024-07-24T13:40:51.459Z (4 months ago)
- Language: Jupyter Notebook
- Size: 86.5 MB
- Stars: 20
- Watchers: 4
- Forks: 9
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-dl4g - GOPHER - [TensorFlow] - scripts for data preprocessing, training deep learning models for DNA sequence to epigenetic function prediction and evaluation of models. (2022) (Software packages / DL4G Packages)
README
**GOPHER**: **G**en**O**mic **P**rofile-model compre**H**ensive **E**valuato**R**
## Installation
```
$ pip install bio-gopher
```
Note that for proper installation, numpy needs to be installed before pyBigWig.
This repository contains scripts for data preprocessing, training deep learning models for DNA sequence to epigenetic function prediction and evaluation of models.
The repo contains a set of tutorial jupyter notebooks that illustrate these steps on a toy dataset. The two notebooks below are required prerequisites for the rest of tutorials:
- preprocessing/preprocessing/quant_dataset_tutorial.ipynb
- tutorials/train_model.ipynb
To replicate the results of the manuscript run the scripts in the analyzis directory. As a prerequisite download and unzip dataset.zip, trained_models.zip from zenodo https://doi.org/10.5281/zenodo.6464031 within the git repo. These contain test sets and pre-trained models. The analysis scripts can be ran in any order as long as paper_run_evaluate.py is ran first, in order to produce model evaluations which is required for further steps.