Fig. 7

Proteogenomics analysis. a Overview of the proteogenomics workflow and additional data levels used for validation. b Curated peptides from novel coding regions. Categories according to genome annotation in the respective loci. Inset shows Manhattan plot of novel peptide distribution across the human genome. c Orthogonal evidence of novel peptides by public domain data, indicated by the presence of black bars in corresponding rows for RNA-seq³³, and re-analysis of proteomics data on breast tumors⁵. See Supplementary Fig. 14 for details. d Prediction of MHC class I binding³⁶ and identification in normal tissues from draft proteome data³⁷ among novel peptides. e High levels of novel peptides from lncRNA lnc-AKAP14–1:3 in one Luminal A (top) tumor and in two tumors (Luminal A and B) for lnc-CXorf36–3:1 (bottom). f Unique and overlapping identifications of curated SAAV peptides from CanProVar and COSMIC databases. g Impact of SNPs (from iCOG array), with corresponding SAAV peptide identification, on protein levels. Impact score is plotted cumulatively for reference allele, hetero and homozygous SNPs. Percentage of impact scores below −2 and above 2 are shown in the inset. See Supplementary Fig. 15B for examples. h Allele-specific protein levels displaying SAAV peptide and matched reference allele peptide quantification cross the 45 tumors. Peptide quantification is categorized into reference allele (Ref), hetero- and homozygous SNPs, based on iCogs data. See Supplementary Fig. 15C for more examples