Metadata-Version: 2.1
Name: epitome
Version: 0.0.1a0
Summary: ML model for predicting ChIP-seq peaks in new cell types from ENCODE cell lines
Home-page: UNKNOWN
Author: Alyssa Kramer Morrow
Author-email: akmorrow@berkeley.edu
License: Apache License, Version 2.0
Project-URL: Documentation, https://readthedocs.org
Project-URL: Source, https://github.com/akmorrow13/epitome
Description: # Epitome
        
        Full pipeline for learning TFBS from epigenetic datasets.
        
        ![Epitome Diagram](docs/figures/epitome_diagram.png)
        
        Epitome leverages chromatin accessibility data to predict transcription factor binding sites on a novel cell type of interest. Epitome computes the chromatin similarity between 11 cell types in ENCODE and the novel cell types, and uses chromatin similarity to transfer binding information in known cell types to a novel cell type of interest. 
        
        
        ## Requirements:
        * [conda](https://docs.conda.io/en/latest/miniconda.html)
        * python > 3.6
        
        ## Setup:
        1. Create and activate a conda venv:
        ```
        conda create --name EpitomeEnv python=3.6
        source activate EpitomeEnv
        ```
        2. setup: 
        ```
        pip install -e .
        ```
        
        Note: Epitome is configured for tensorflow 1.12/Cuda 9. If you have a different
        version of cuda, update tensorflow-gpu version accordingly.
        
        To check your Cuda version:
        ```
        nvcc --version
        ```
        
        ## Configuring data
        To download and format training data, run the bin/get_deepsea_data.py script:
        
        
        ```
        python bin/get_deepsea_data.py 
        usage: get_deepsea_data.py [-h] --output_path OUTPUT_PATH
        ```
        
        ## Training a Model
        
        ```python
        
            from epitome.models import *
            model = VLP(data
                    test_celltypes,
                    matrix,
                    assaymap,
                    cellmap,
                    shuffle_size=2, 
                    batch_size=64)
            model.train(10000)
        ```
        
        
        
Keywords: ENCODE ChIP-seq_peaks prediction histone transcription_factor
Platform: UNKNOWN
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3
Description-Content-Type: text/markdown
