Metadata-Version: 2.1
Name: SNAF
Version: 0.6.0
Summary: A Python package to predict, prioritize and visualize splicing derived neoantigens, including MHC-bound peptides (T cell antigen) and altered surface protein (B cell antigen)
Home-page: https://github.com/frankligy/SNAF
Author: Guangyuan(Frank) Li
Author-email: li2g2@mail.uc.edu
Maintainer: Guangyuan(Frank) Li
Maintainer-email: li2g2@mail.uc.edu
License: UNKNOWN
Project-URL: Documentation, https://snaf.readthedocs.io
Description: [![Documentation Status](https://readthedocs.org/projects/snaf/badge/?version=latest)](https://snaf.readthedocs.io/en/latest/?badge=latest)  [![Pypi](https://img.shields.io/pypi/v/snaf?logo=PyPI)](https://pypi.org/project/snaf/)  [![Downloads](https://pepy.tech/badge/snaf)](https://pypi.org/project/snaf/)  [![Stars](https://img.shields.io/github/stars/frankligy/SNAF)](https://github.com/frankligy/SNAF/stargazers)
        
        # SNAF
        Splicing Neo Antigen Finder (SNAF) is an easy-to-use Python package to identify splicing-derived tumor neoantigens from RNA sequencing data, it can 
        predict, prioritize and visualize MHC-bound neoantigen for T cell (T antigen) and altered surface protein for B cell (B antigen).
        
        ![workflow](./images/fig1.png)
        
        
        # Tutorial and documentation
        
        [Full Documentation](https://snaf.readthedocs.io)
        
        # Input and Output
        
        Simply put, user needs to supply ``a folder with bam files``, and the ``HLA type`` assciated with each patient (using your favorite HLA typing tool). And it will generate predicted immunogenic MHC-bound peptides and altered surface protein. Moreover, there's a myriad of convenient function that enables users to conduct survival analysis, association analysis and publication-quality visualiztion. Check our tutorials for more detail.
        
        # Interactive Viewers
        
        <p float="left">
          <img src="images/T_viewer.gif" width="45%" />
          <img src="images/B_viewer.gif" width="45%" /> 
        </p>
        
        # Citation
        
        [Guangyuan Li, Nathan Salomonis. SNAF: Accurate and compatible computational framework for identifying splicing derived neoantigens [abstract]. Cancer Res 2022;82(12_Suppl)](https://aacrjournals.org/cancerres/article/82/12_Supplement/1898/701846/Abstract-1898-SNAF-Accurate-and-compatible)
        
        A preprint will be released soon.
        
        # Contact
        
        Guangyuan(Frank) Li
        
        Email: li2g2@mail.uc.edu
        
        PhD student, Biomedical Informatics
        
        Cincinnati Children’s Hospital Medical Center(CCHMC)
        
        University of Cincinnati, College of Medicine
Platform: UNKNOWN
Requires-Python: >=3.7
Description-Content-Type: text/markdown
