Data availabilityThe analyses described in this work use sequencing and proteomic data from previously published datasets deposited in public data repositories. CPTAC raw MS proteomic data were downloaded from the CPTAC Data Portal with the following PDC study IDs: PDC000120 (BRCA), PDC000127 (CCRCC), PDC000234 (LSCC), PDC000153 (LUAD), PDC000270 (PDAC) and PDC000125 (UCEC). CPTAC genomic and transcriptomic sequencing reads were accessed through the GDC Data Portal with the following dbGaP study accessions: phs000892 (CPTAC-2) and phs001287 (CPTAC-3). Whole exome sequencing data from the following PDC studies were analysed: PDC000127, PDC000446, PDC000204, PDC000221, PDC000234, PDC000153, PDC000489, PDC000270, PDC000393, PDC000125, PDC000439, and PDC000464. Access to controlled data was granted after application to NCBI (project no. 24007: Investigation of Mistranslation Rates in Cancer). RNA-seq data for healthy human tissues17 were downloaded from ArrayExpress with the identifier E-MTAB-2836, and corresponding raw MS data were downloaded from PRIDE with project accession PXD010154. Mouse transcriptome sequence data were downloaded from ArrayExpress under the identifier E-MTAB-10276. The mouse MS proteomic data were downloaded from PRIDE with the dataset identifier PXD030983. Primary cell MS data were downloaded from PRIDE with the following accession numbers: PXD008511 (B cells), PXD008512 (hepatocytes), PXD008513 (monocytes) and PXD008515 (natural killer cells). Immunoprecipitation–MS proteomic data were downloaded from MassIVE with accession MSV000088555. Supporting information, data and documentation are available at decode.slavovlab.net.Code availabilitySoftware, data-analysis pipelines and other supporting documentation are available at decode.slavovlab.net. 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Allen Frontiers Group to N.S., an NIGMS award (R01GM144967) to N.S., NCI awards UG3CA268117 and UH3CA268117 to N.S., an NIGMS award (R35GM148218) to N.S., an NIA award (R01AG092460) to N.S., and a Bits to Bytes award from MLSC to N.S.Author informationAuthors and AffiliationsDepartments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, Northeastern University, Boston, MA, USAShira Tsour, Rainer Machné, Andrew Leduc, Simon Widmer, Eunice Koo & Nikolai SlavovProgram in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USAJeremy Guez & Konrad J. KarczewskiAnalytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USAJeremy Guez & Konrad J. KarczewskiParallel Squared Technology Institute, Watertown, MA, USANikolai SlavovAuthorsShira TsourView author publicationsSearch author on:PubMed Google ScholarRainer MachnéView author publicationsSearch author on:PubMed Google ScholarAndrew LeducView author publicationsSearch author on:PubMed Google ScholarSimon WidmerView author publicationsSearch author on:PubMed Google ScholarEunice KooView author publicationsSearch author on:PubMed Google ScholarJeremy GuezView author publicationsSearch author on:PubMed Google ScholarKonrad J. KarczewskiView author publicationsSearch author on:PubMed Google ScholarNikolai SlavovView author publicationsSearch author on:PubMed Google ScholarContributionsStudy design, supervision and raising funding: N.S. Data analyses: S.T., R.M., A.L., S.W., E.K. and N.S. gnomAD analyses: J.G., S.T. and K.J.K. Initial draft: S.T. and N.S. Writing: all authors approved the final manuscript.Corresponding authorCorrespondence to Nikolai Slavov.Ethics declarationsCompeting interestsN.S. is a founding director and CEO of Parallel Squared Technology Institute, which is a non-profit research institute. S.T. is an employee of Alnylam Pharmaceuticals. All other authors declare no competing interests.Peer reviewPeer review informationNature thanks Yitzhak Pilpel, Mikhail Savitski and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.Additional informationPublisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Extended data figures and tablesExtended Data Fig. 1 Systematic identification and validation of amino acid substitutions.(a) Number of tumor and normal samples analyzed from each CPTAC dataset. (b) Number of samples analyzed for each healthy tissue from the label-free dataset. (c) Distribution of the percentage of each transcript with a read that is included in the patient-specific databases. (d) Distribution of the number of transcripts with 100% sequence coverage included in each patient-specific protein database. (e) Non-substitution modifications identified in the dependent peptide search are majorly comprised of post-translational modifications, and include artifacts and chemical derivatives from MS analysis. (f)–(k) (Continued on the next page) (f) The number of modified peptides identified as having an amino acid substitution or other type of post-translational or chemical modification. (g) Mass error distributions for SAAP and all peptides identified in the database search show no significant differences. The lower, middle, and upper lines of the boxplots correspond to the first quartile, median, and third quartiles, respectively. The upper whisker extends from the third quartile to the largest value and the lower whisker extends from the first quartile to the smallest value, each at most 1.5XIQR of the hinge. Data beyond the whiskers are outliers that are plotted as individual data points. (h) Butterfly plots showing a systematic mass shift in MS2 spectra between SAAP and BP for a representative SAAP with median RAAS = 1.2 in ribosome-binding protein 1 isoform 1 (RRBP1). The fragmentation spectra were predicted by the Prosit TMT model26. (i) Cumulative density distributions of p-values (MaxQuant) and FDR-controlled q-values computed using only SAAP. Red dashed line indicates confidence threshold for SAAP inclusion in further analysis. (j) Over 80% of substitutions identified from lysine (K) or arginine (R) are at sites of missed cleavage or are substitutions between K and R. (k) Observed and predicted (DeepRT+,20) retention times show strong agreement for all main peptides identified in standard database search and for SAAP. (l) TMT and label-free spectra for the same SAAP provide complementary evidence fragments and are in strong agreement with Prosit predictions25,26. (m) Observed spectra for SAAP quantified in both TMT and label-free datasets are in stronger agreement with the correctly matched prosit model, i.e. TMT spectra with Prosit TMT26 and label-free spectra with Prosit HCD25 than with the mismatched Prosit model.Extended Data Fig. 2 Establishing confidence in AAS abundance.(a) SAAP with high RAAS ≥ 1 are identified with the same FDR-controlled confidence as SAAP with low RAAS