Cantwell-Dorris, E. R., O’Leary, J. J. & Sheils, O. M. BRAFV600E: implications for carcinogenesis and molecular therapy. Mol. Cancer Ther. 10, 385–394 (2011).
Hart, J. R. et al. The butterfly effect in cancer: a single base mutation can remodel the cell. Proc. Natl Acad. Sci. USA 112, 1131–1136 (2015).
Wright, A. & Vissel, B. The essential role of AMPA receptor GluR2 subunit RNA editing in the normal and diseased brain. Front. Mol. Neurosci. 5, 34 (2012).
Parker, J. & Friesen, J. D. “Two out of three” codon reading leading to mistranslation in vivo. Mol. Gen. Genet. 177, 439–445 (1980).
Savitski, M. M., Nielsen, M. L. & Zubarev, R. A. ModifiComb, a new proteomic tool for mapping substoichiometric post-translational modifications, finding novel types of modifications, and fingerprinting complex protein mixtures. Mol. Cell. Proteomics 5, 935–948 (2006).
Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).
Wilhelm, M. et al. Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics. Nat. Commun. 12, 3346 (2021).
Picciani, M. et al. Oktoberfest: open-source spectral library generation and rescoring pipeline based on Prosit. Proteomics 24, e2300112 (2024).
Yang, K. L. et al. MSBooster: improving peptide identification rates using deep learning-based features. Nat. Commun. 14, 4539 (2023).
Leduc, A. & Slavov, N. Impact of protein degradation and cell growth on mammalian proteomes. Preprint at bioRxiv https://doi.org/10.1101/2025.02.10.637566 (2025).
Clark, D. J. et al. Integrated proteogenomic characterization of clear cell renal cell carcinoma. Cell 179, 964–983 (2019).
Krug, K. et al. Proteogenomic landscape of breast cancer tumorigenesis and targeted therapy. Cell 183, 1436–1456 (2020).
Gillette, M. A. et al. Proteogenomic characterization reveals therapeutic vulnerabilities in lung adenocarcinoma. Cell 182, 200–225 (2020).
Dou, Y. et al. Proteogenomic characterization of endometrial carcinoma. Cell 180, 729–748 (2020).
Cao, L. et al. Proteogenomic characterization of pancreatic ductal adenocarcinoma. Cell 184, 5031–5052 (2021).
Satpathy, S. et al. A proteogenomic portrait of lung squamous cell carcinoma. Cell 184, 4348–4371 (2021).
Wang, D. et al. A deep proteome and transcriptome abundance atlas of 29 healthy human tissues. Mol. Syst. Biol. 15, e8503 (2019).
Batut, B. et al. Community-driven data analysis training for biology. Cell Syst. 6, 752–758 (2018).
Mordret, E. et al. Systematic detection of amino acid substitutions in proteomes reveals mechanistic basis of ribosome errors and selection for translation fidelity. Mol. Cell 75, 427–441 (2019).
Ma, C. et al. Improved peptide retention time prediction in liquid chromatography through deep learning. Anal. Chem. 90, 10881–10888 (2018).
Wen, B., Wang, X. & Zhang, B. PepQuery enables fast, accurate, and convenient proteomic validation of novel genomic alterations. Genome Res. 29, 485–493 (2019).
Mohler, K. & Ibba, M. Translational fidelity and mistranslation in the cellular response to stress. Nat. Microbiol. 2, 17117 (2017).
Liigand, P., Kaupmees, K. & Kruve, A. Influence of the amino acid composition on the ionization efficiencies of small peptides. J. Mass Spectrom. 54, 481–487 (2019).
Serrano, G., Guruceaga, E. & Segura, V. DeepMSPeptide: peptide detectability prediction using deep learning. Bioinformatics 36, 1279–1280 (2020).
Gessulat, S. et al. Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning. Nat. Methods 16, 509–518 (2019).
Gabriel, W. et al. Prosit-TMT: deep learning boosts identification of TMT-labeled peptides. Anal. Chem. 94, 7181–7190 (2022).
Wiśniewski, J. R., Hein, M. Y., Cox, J. & Mann, M. A “proteomic ruler” for protein copy number and concentration estimation without spike-in standards. Mol. Cell. Proteomics 13, 1535–9484 (2014).
Wu, Q. et al. Translation affects mRNA stability in a codon-dependent manner in human cells. eLife 8, e45396 (2019).
Drummond, D. A. & Wilke, C. O. Mistranslation-induced protein misfolding as a dominant constraint on coding-sequence evolution. Cell 134, 341–352 (2008).
Quax, T. E., Claassens, N. J., Söll, D. & van der Oost, J. Codon bias as a means to fine-tune gene expression. Mol. Cell 59, 149–161 (2015).
McCormick, C. A. et al. mRNA psi profiling using nanopore DRS reveals cell type-specific pseudouridylation. Preprint at bioRxiv https://doi.org/10.1101/2024.05.08.593203 (2024).
Mathieson, T. et al. Systematic analysis of protein turnover in primary cells. Nat. Commun. 9, 689 (2018).
Kong, A. T., Leprevost, F. V., Avtonomov, D. M., Mellacheruvu, D. & Nesvizhskii, A. I. MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry-based proteomics. Nat. Methods 14, 513–520 (2017).
Chen, S. et al. A genomic mutational constraint map using variation in 76,156 human genomes. Nature 625, 92–100 (2024).
Giansanti, P. et al. Mass spectrometry-based draft of the mouse proteome. Nat. Methods 19, 803–811 (2022).
Lawrence, M. S. et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214–218 (2013).
Specht, H. et al. PSMtags improve peptide sequencing and throughput in sensitive proteomics. Preprint at bioRxiv https://doi.org/10.1101/2025.05.22.655509 (2025).
Slavov, N. Single-cell proteomic technologies: tools in the quest for principles. Annu. Rev. Biophys. 55, 253–275 (2026).
Leduc, A., Khoury, L., Cantlon, J., Khan, S. & Slavov, N. Massively parallel sample preparation for multiplexed single-cell proteomics using nPOP. Nat. Protoc. 19, 3750–3776 (2024).
Huffman, R. G. et al. Prioritized mass spectrometry increases the depth, sensitivity and data completeness of single-cell proteomics. Nat. Methods 20, 714–722 (2023).
Sun, L. et al. Evolutionary gain of alanine mischarging to noncognate tRNAs with a G4: U69 base pair. J. Am. Chem. Soc. 138, 12948–12955 (2016).
Netzer, N. et al. Innate immune and chemically triggered oxidative stress modifies translational fidelity. Nature 462, 522–526 (2009).
Danecek, P. et al. Twelve years of SAMtools and BCFtools. GigaScience https://doi.org/10.1093/gigascience/giab008 (2021).
Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018).
Kim, D., Paggi, J. M., Park, C., Bennett, C. & Salzberg, S. L. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 37, 907–915 (2019).
Pertea, M. et al. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 33, 290–295 (2015).
Pertea, G. & Pertea, M. GFF Utilities: GffRead and GffCompare. F10000Research 9, 304 (2020).
Garrison, E. & Marth, G. Haplotype-based variant detection from short-read sequencing. Preprint at https://doi.org/10.48550/arXiv.1207.3907 (2012).
Wang, X. & Zhang, B. customProDB: an R package to generate customized protein databases from RNA-seq data for proteomics search. Bioinformatics 29, 3235–3237 (2013).
Lautenbacher, L. et al. Koina: Democratizing machine learning for proteomics research. Nat. Commun. 16, 9933 (2025).
Huber, F. et al. matchms—processing and similarity evaluation of mass spectrometry data. J. Open Source Softw. 5, 2411 (2020).
Wan, K. X., Vidavsky, I. & Gross, M. L. Comparing similar spectra: From similarity index to spectral contrast angle. J. Am. Soc. Mass Spectrom. 13, 85–88 (2002).
Halloran, J. T. & Rocke, D. M. Matter of time: faster percolator analysis via efficient SVM learning for large-scale proteomics. J. Proteome Res. 17, 1978–1982 (2018).
Huttlin, E. L. et al. The BioPlex network: a systematic exploration of the human interactome. Cell 162, 425–440 (2015).
Marino, A. et al. Aging and diet alter the protein ubiquitylation landscape in the mouse brain. Nat. Commun. 16, 5266 (2025).
Li, J. et al. Proteome-wide mapping of short-lived proteins in human cells. Mol. Cell 81, 4722–4735 (2021).
Nettling, M. et al. DiffLogo: a comparative visualization of sequence motifs. BMC Bioinformatics 16, 387 (2015).
Behle, A. et al. Manipulation of topoisomerase expression inhibits cell division but not growth and reveals a distinctive promoter structure in Synechocystis. Nucleic Acids Res. 50, 12790–12808 (2022).
Erdős, G., Pajkos, M. & Dosztányi, Z. IUPred3: prediction of protein disorder enhanced with unambiguous experimental annotation and visualization of evolutionary conservation. Nucleic Acids Res. 49, W297–W303 (2021).
Eddy, S. Accelerated profile HMM searches. PLoS Comput. Biol. 7, e1002195 (2011).
Moffat, L. & Jones, D. Increasing the accuracy of single sequence prediction methods using a deep semi-supervised learning framework. Bioinformatics 37, 3744–3751 (2021).
Hu, G. et al. flDPnn: accurate intrinsic disorder prediction with putative propensities of disorder functions. Nat. Commun. 12, 4438 (2021).
Peng, Z. & Kurgan, L. High-throughput prediction of RNA, DNA and protein binding regions mediated by intrinsic disorder. Nucleic Acids Res. 43, e121 (2015).
Steinegger, M. & Soding, J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat. Biotechnol. 35, 1026–1028 (2017).
Zhao, B. et al. DescribePROT: database of amino acid-level protein structure and function predictions. Nucleic Acids Res. 49, D298–D308 (2021).
Meng, E. et al. UCSF ChimeraX: Tools for structure building and analysis. Protein Sci. 32, e4792 (2023).
Chen, T. & Guestrin, C. XGBoost: A scalable tree boosting system. In Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’16)785–794 (ACM, 2016).
Seplyarskiy, V. et al. A mutation rate model at the basepair resolution identifies the mutagenic effect of polymerase III transcription. Nat. Genet. 55, 2235–2242 (2023).
Cheng, J. et al. Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science 381, eadg7492 (2023).
