/SNAF

Splicing Neo Antigen Finder (SNAF) is an easy-to-use Python package to identify splicing-derived tumor neoantigens from RNA sequencing data, it further leverages both deep learning and hierarchical Bayesian models to prioritize certain candidates for experimental validation

Primary LanguagePythonMIT LicenseMIT

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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

Tutorial and documentation

Full Documentation

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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.

Citation

Guangyuan Li, Nathan Salomonis. SNAF: Accurate and compatible computational framework for identifying splicing derived neoantigens [abstract]. Cancer Res 2022;82(12_Suppl)

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