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

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

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.

Hyperparameter optimization is based on the hyperopt package.

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.

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.