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deepgrp-0.2.3
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مشاهده بیشترتوضیحات
DNA repeat annotations
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | deepgrp-0.2.3 |
| نام | deepgrp |
| نسخه کتابخانه | 0.2.3 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Fabian Hausmann |
| ایمیل نویسنده | fabian.hausmann@zmnh.uni-hamburg.de |
| آدرس صفحه اصلی | https://github.com/fhausmann/deepgrp |
| آدرس اینترنتی | https://pypi.org/project/deepgrp/ |
| مجوز | Apache-2.0 |
==================================================================
DeepGRP - Deep learning for Genomic Repetitive element Prediction
==================================================================
|PyPI version fury.io|
.. |PyPI version fury.io| image:: https://badge.fury.io/py/deepgrp.svg
:target: https://pypi.org/project/deepgrp/
DeepGRP is a python package used to predict genomic repetitive elements
with a deep learning model consisting of bidirectional gated recurrent units
with attention.
The idea of DeepGRP was initially based on `dna-nn`__, but was re-implemented
and extended using `TensorFlow`__ 2.1.
DeepGRP was tested for the prediction of HSAT2,3, alphoid, Alu
and LINE-1 elements.
.. __: https://github.com/lh3/dna-nn
.. __: https://www.tensorflow.org
Getting Started
===============
Installation
------------
For installation you can use the PyPI version with::
pip install deepgrp
or install from this repository with::
git clone https://github.com/fhausmann/deepgrp
cd deepgrp
pip install .
Additionally you can install the developmental version with `poetry`__::
git clone https://github.com/fhausmann/deepgrp
cd deepgrp
poetry install
.. __: https://python-poetry.org/
Data preprocessing
------------------
For training and hyperparameter optimization the data have to be preprocessed.
For inference / prediction the FASTA sequences can directly be used and you
can skip this process.
The provided script `parse_rm` can be used to extract repeat annotations from
`RepeatMasker`__ annotations to a TAB seperated format by::
parse_rm GENOME.fa.out > GENOME.bed
.. __: http://www.repeatmasker.org/
The FASTA sequences have to be converted to a one-hot-encoded representation,
which can be done with::
preprocess_sequence FASTAFILE.fa.gz
`preprocess_sequence` creates a one-hot-encoded representation in numpy
compressed format in the same directory.
Hyperparameter optimization
---------------------------
For Hyperparameter optimization the github repository provides
a jupyter `notebook`__ which can be used.
.. __: https://github.com/fhausmann/deepgrp/blob/master/notebooks/DeepGRP.ipynb
Hyperparameter optimization is based on the `hyperopt`__ package.
.. __: https://github.com/hyperopt/hyperopt
Training
--------
Training of a model can be performed with::
deepgrp train <parameter.toml> <TRAIN>.fa.gz.npz <VALIDATION>.fa.gz.npz <annotations.bed>
The prefix of `<TRAIN>` and `<VALIDATION>` should be as row identifier in the first column of `<annotations.bed>`.
For more fine-grained control of the training process you can also use the provided jupyter `notebook`__.
.. __: https://github.com/fhausmann/deepgrp/blob/master/notebooks/Training.ipynb
Prediction
----------
The prediction can be done with the deepgrp main function like::
deepgrp <modelfile> <fastafile> [<fastafile>, ...]
where `<modelfile>` contains the trained model in `HDF5`__
format and `<fastafile>` is a (multi-)FASTA file containing DNA sequences.
Several FASTA files can be given at once.
.. __: https://www.tensorflow.org/tutorials/keras/save_and_load
Requirements
============
Requirements are listed in `pyproject.toml`__.
.. __: https://github.com/fhausmann/deepgrp/blob/master/pyproject.toml
Additionally for compiling C/Cython code, a C compiler should be installed.
Contribution:
=============
First of all any contributing are very welcome.
If you want to contribute, please make a Pull request with your changes.
Your code should be formatted using `yapf`__ using the default settings,
they and they should pass all tests without issues.
For testing currently `mypy`__ and `pylint`__ static tests are used, while
`pytest`__ is used for functional tests.
.. __: https://github.com/google/yapf
.. __: https://mypy.readthedocs.io/en/latest/
.. __: https://pylint.pycqa.org/en/latest/
.. __: https://docs.pytest.org/en/6.2.x/
If you're adding new functionalities please provide corresponding tests
in the `tests`__ directory.
.. __: ./tests/
Feel free to ask in case of any questions.
Further information
===================
You can find material to reproduce
the results in the repository `deepgrp_reproducibility`__.
.. __: https://github.com/fhausmann/deepgrp_reproducibility
نیازمندی
| مقدار | نام |
|---|---|
| >=2.1.0,<2.6.0 | tensorflow |
| >=1.0.1,<2.0.0 | pandas |
| <1.20.0 | numpy |
| >=0.2.3,<0.3.0 | hyperopt |
| >=0.10.0,<0.11.0 | toml |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6.1,<3.9.0 | Python |
نحوه نصب
نصب پکیج whl deepgrp-0.2.3:
pip install deepgrp-0.2.3.whl
نصب پکیج tar.gz deepgrp-0.2.3:
pip install deepgrp-0.2.3.tar.gz