GOPHER: GenOmic Profile-model compreHensive EvaluatoR
Project description
GOPHER: GenOmic Profile-model compreHensive EvaluatoR
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.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file bio-gopher-1.0.3.tar.gz
.
File metadata
- Download URL: bio-gopher-1.0.3.tar.gz
- Upload date:
- Size: 42.1 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.8.10
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 133ccf0773d14cfddd0892ad68a059b8e1f8b7902c9f1d5ad8b8997a9d2e249e |
|
MD5 | af100d09290d7c571fab867eda3f599a |
|
BLAKE2b-256 | 23e92fc042ea4677e360f332007da88486fe89714151f3d1ac06cede83641d82 |
File details
Details for the file bio_gopher-1.0.3-py3-none-any.whl
.
File metadata
- Download URL: bio_gopher-1.0.3-py3-none-any.whl
- Upload date:
- Size: 47.2 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.8.10
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 7c2b73c10515fa3181b45381b6226a89e0b6a1372a742a7c28b73148ec571d4b |
|
MD5 | ae3de31de87d6461087e6991fdae3d0c |
|
BLAKE2b-256 | 9cd8730512d1427ea1a4fdecae31d47a5cabe456540ecc77c7e5eb575f033da3 |