Fig. 1

Workflow for HLA typing and neoepitope prediction using WGS and RNA-seq. a Overview of analytical process. Somatic missense SNVs for each tumor are identified and annotated based on variants in the aligned WGS data. Gene fusions and expression status of the identified somatic SNVs are analyzed using RNAseq data. All the information is incorporated into a data matrix containing the HLA type, mutation class, amino acid change, protein gi number, mRNA accession number, mutant read count in the tumor, total read count in the tumor, mutant read count in the normal sample, total read count in the normal sample, and reference allele and mutant allele for variants in each sample. The peptide sequences flanking the variations are subsequently extracted and used as input for epitope prediction. b Identification of fusion junction peptides at the fusion breakpoints for epitope prediction. An example of ETV6-RUNX1 fusion in SJETV002_D is shown to illustrate this process. Expressed junction reads are assembled from RNAseq. Peptide sequences along the junction position are generated for in-frame coding regions. The tiling nonameric peptides overlapping the fusion breakpoints are subsequently used for epitope prediction