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).

Tutorial and documentation

Full Documentation

Interactive Viewers (below, take a few seconds to load)

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.

Auxiliary Programs developed within SNAF

• RNA-SPRINT: Infering splicing factor activity from RNA-Seq.

• BayesTS: BayesTS can infer tumor specificity for any features (gene, protein, splicing junction, etc).

• DeepImmuno: DeepImmuno can infer immunogenicity for peptide-MHC complex.

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: [email protected]

PhD student, Biomedical Informatics

Cincinnati Children’s Hospital Medical Center(CCHMC)

University of Cincinnati, College of Medicine