TS-SNAD

Tumor-Specific Splicing Neo Antigen Detection (TS-SNAD), to discover potential novel splicing neoantigens, the dominant source of tumor-specific peptides. It integrates long-read sequencing with short-read sequencing sequencing technology. Long-read sequencing enables the discovery of unannotated transcripts, while short-read sequencing quantifies the expression of these novel transcripts with high throughput. The hybrid sequencing approach helps identify tumor-specific transcripts and their unique splice forms, leading to the generation of new antigens. Additionally, whole-genome sequencing (WGS) or Optitype is used to predict the individual's MHC-I molecules, with a focus on shared MHC-I molecules to ensure broader applicability across a larger patient population. Furthermore, netMHCpan is employed to predict the binding affinity of these new antigens to MHC-I molecules and assess their immunogenicity.

01_optitype.sh: OptiType is a novel HLA genotyping algorithm based on integer linear programming, capable of producing accurate 4-digit HLA genotyping predictions from NGS data by simultaneously selecting all major and minor HLA Class I alleles.

02_pvactools.sh: pVACtools is a cancer immunotherapy suite that includes various tools designed to identify and prioritize neoantigens derived from tumor-specific novel transcripts.

03_generate_neoantigen.sh & 05_neoantigen_filter.R: it enables the identification of tumor-specific exon-exon splicing junctions and the selection of novel neoantigens from all antigen candidates.

04_neoantigen_abundanc.sh: It further refines the selection of neoantigens by considering the abundance of the originating transcript and the universality of HLA-I.