Metadata-Version: 2.1
Name: deepshm
Version: 0.0.1
Summary: A package for training and applying neural networks aimed to deal with genomic data
Home-page: https://gitlab.com/maccarthyslab/deepshm
Author: akrantsevich
Author-email: akrantsevich@gmail.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE

# DeepSHM
This repository implements the model from "Deep learning model of somatic hypermutation reveals importance of sequence context beyond targeting of AID and Polη hotspots" by Tang, Krantsevich & MacCarthy. Using DeepSHM you can simply use one of the provided models to make SHM-related predictions for your data, or you can train your own CNN model for any task that uses DNA/RNA one-hot encoded data as input.


## Instalation
### If you want to play with the model 
DeepSHM is on pypi, so it can be installed using pip:

    pip install deepshm

### If you are not familiar with python
You can also just download the deepshm_predict.zip archive and 0nce you extract it you can call the file deepshm_predict.py from the command line. Note that you still will need python installed as wells as several packages: tensorflow, numpy, scipy. deepshm_predict.py can be called in the following way:

    python deepshm_predict.py input.fasta -o output.csv -k 15

where the input fasta file is the required input and the path to the output file and the length of k-mer (5, 9, 15 and 21) are optional. Default value for the output file is output.csv and default k is 15.


