Metadata-Version: 2.1
Name: epitome
Version: 0.0.1a2
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
        
        Pipeline for predicting ChIP-seq peaks in novel cell types using chromatin accessibility.
        
        ![Epitome Diagram](https://github.com/YosefLab/epitome/raw/master/docs/figures/epitome_diagram_celllines.png)
        
        Epitome leverages chromatin accessibility (either DNase-seq or ATAC-seq) to predict epigenetic events in a novel cell type of interest. Such epigenetic events include transcription factor binding sites and histone modifications. Epitome computes chromatin accessibility similarity between ENCODE cell types and the novel cell type, and uses this information to transfer known epigentic signal to the novel cell type of interest.
        
        
        ## Requirements
        * [conda](https://docs.conda.io/en/latest/miniconda.html)
        * python >= 3.6
        
        ## Setup and Installation
        1. Create and activate a conda environment:
        ```
        conda create --name EpitomeEnv python=3.6 pip
        source activate EpitomeEnv
        ```
        2. Install Epitome:
        ```
        pip install epitome
        ```
        
        
        ## Training a Model
        
        TODO: link to documentation
        
        First, create an Epitome dataset that defines the cell types and ChIP-seq
        targets you want to train on,
        
        
        ```python
        
            from epitome.dataset import *
        
            targets = ['CTCF','RAD21','SMC3']
            celltypes = ['K562', 'A549', 'GM12878']
        
            dataset = EpitomeDataset(targets=targets, cells=celltypes)
        
        ```
        
        Now, you can create and train your model:
        
        ```python
        
            from epitome.models import *
        
            model = EpitomeModel(dataset, test_celltypes = ["K562"])
            model.train(5000) # train for 5000 batches
        ```
        
        ## Evaluate a Model:
        
        ```python
        
           model.test(1000) # evaluate how well the model performs on a validation chromosome
        
        ```
        
        ## Using Epitome on your own dataset:
        
        Epitome can perform genome wide predictions or region specific predictions on
        a sample that has either DNase-seq or ATAC-seq.
        
        To score specific regions:
        
        ```python
        
           chromatin_peak_file = ... # path to peak called ATAC-seq or DNase-seq in bed format
           regions_file = ...        # path to bed file of regions to score
           results = model.score_peak_file([chromatin_peak_file], regions_file)
        
        ```
        
        To score on the whole genome:
        
        ```python
        
           chromatin_peak_file = ... # path to peak called ATAC-seq or DNase-seq in bed format
           file_prefix = ...        # file to save compressed numpy predictions to.
           model.score_whole_genome([chromatin_peak_file], file_prefix)
        
        ```
        
        
        # Install Epitome for development
        
        To build Epitome for development, run:
        
        ```
        make develop
        ```
        
        ## Running unit tests
        
        ```
        make test
        ```
        
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
