Slamon, D. J. et al. Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2. N. Engl. J. Med. 344, 783–792 (2001).
Google Scholar
Druker, B. J. et al. Five-year follow-up of patients receiving imatinib for chronic myeloid leukemia. N. Engl. J. Med. 355, 2408–2417 (2006).
Google Scholar
Awad, M. M. & Shaw, A. T. ALK inhibitors in non-small cell lung cancer: crizotinib and beyond. Clin. Adv. Hematol. Oncol. 12, 429–439 (2014).
Google Scholar
Shaw, A. T. et al. Resensitization to crizotinib by the lorlatinib ALK resistance mutation L1198F. N. Engl. J. Med. 374, 54–61 (2016).
Google Scholar
Robert, C. et al. Improved overall survival in melanoma with combined dabrafenib and trametinib. N. Engl. J. Med. 372, 30–39 (2015).
Google Scholar
Brown, A.-L., Li, M., Goncearenco, A. & Panchenko, A. R. Finding driver mutations in cancer: elucidating the role of background mutational processes. PLoS Comput. Biol. 15, e1006981 (2019).
Google Scholar
Krug, K. et al. Proteogenomic landscape of breast cancer tumorigenesis and targeted therapy. Cell 183, 1436–1456.e31 (2020).
Google Scholar
Satpathy, S. et al. Microscaled proteogenomic methods for precision oncology. Nat. Commun. 11, 532 (2020). This proteogenomic study of core needle biopsy samples establishes proof of concept for genomic and proteomic profiling starting from small sample quantities.
Google Scholar
Krug, K., Nahnsen, S. & Macek, B. Mass spectrometry at the interface of proteomics and genomics. Mol. Biosyst. 7, 284–291 (2011).
Google Scholar
Menschaert, G. & Fenyö, D. Proteogenomics from a bioinformatics angle: a growing field. Mass. Spectrom. Rev. 36, 584–599 (2017).
Google Scholar
Nesvizhskii, A. I. Proteogenomics: concepts, applications and computational strategies. Nat. Methods 11, 1114–1125 (2014).
Google Scholar
Ruggles, K. V. et al. Methods, tools and current perspectives in proteogenomics. Mol. Cell. Proteom. 16, 959–981 (2017). This work reviews the tools and techniques used to analyse proteogenomics data.
Google Scholar
Zhang, B. et al. Clinical potential of mass spectrometry-based proteogenomics. Nat. Rev. Clin. Oncol. 16, 256–268 (2019).
Google Scholar
Archer, T. C. et al. Proteomics, post-translational modifications, and integrative analyses reveal molecular heterogeneity within medulloblastoma subgroups. Cancer Cell 34, 396–410.e8 (2018).
Google Scholar
Chen, Y.-J. et al. Proteogenomics of non-smoking lung cancer in East Asia delineates molecular signatures of pathogenesis and progression. Cell 182, e17 (2020). This comprehensive proteogenomic study includes a large number of samples and multiple omics data types, focusing on the biology of LUAD in non-smokers.
Clark, D. J. et al. Integrated proteogenomic characterization of clear cell renal cell carcinoma. Cell 179, 964–983.e31 (2019).
Google Scholar
Dou, Y. et al. Proteogenomic characterization of endometrial carcinoma. Cell 180, 729–748.e26 (2020).
Google Scholar
Gao, Q. et al. Integrated proteogenomic characterization of HBV-related hepatocellular carcinoma. Cell 179, 561–577.e22 (2019).
Google Scholar
Gillette, M. A. et al. Proteogenomic characterization reveals therapeutic vulnerabilities in lung adenocarcinoma. Cell 182, 200–225.e35 (2020). This typical CPTAC proteogenomic study with extensive data and expansive analyses characterizes LUAD biology and therapeutic possibilities.
Google Scholar
McDermott, J. E. et al. Proteogenomic characterization of ovarian HGSC implicates mitotic kinases, replication stress in observed chromosomal instability. Cell Rep. Med. 1, 100004 (2020).
Google Scholar
Mertins, P. et al. Proteogenomics connects somatic mutations to signalling in breast cancer. Nature 534, 55–62 (2016).
Google Scholar
Mun, D.-G. et al. Proteogenomic characterization of human early-onset gastric cancer. Cancer Cell 35, 111–124.e10 (2019).
Google Scholar
Petralia, F. et al. Integrated proteogenomic characterization across major histological types of pediatric brain cancer. Cell 183, 1962–1985.e31 (2020).
Google Scholar
Satpathy, S. et al. A proteogenomic portrait of lung squamous cell carcinoma. Cell 184, 4348–4371.e40 (2021).
Google Scholar
Stewart, P. A. et al. Proteogenomic landscape of squamous cell lung cancer. Nat. Commun. 10, 3578 (2019).
Google Scholar
Vasaikar, S. et al. Proteogenomic analysis of human colon cancer reveals new therapeutic opportunities. Cell 177, 1035–1049.e19 (2019).
Google Scholar
Zhang, B. et al. Proteogenomic characterization of human colon and rectal cancer. Nature 513, 382–387 (2014).
Google Scholar
Huang, C. et al. Proteogenomic insights into the biology and treatment of HPV-negative head and neck squamous cell carcinoma. Cancer Cell 39, 361–379.e16 (2021).
Google Scholar
Zhang, H. et al. Integrated proteogenomic characterization of human high-grade serous ovarian cancer. Cell 166, 755–765 (2016).
Google Scholar
Wang, L.-B. et al. Proteogenomic and metabolomic characterization of human glioblastoma. Cancer Cell 39, 509–528.e20 (2021).
Google Scholar
Johansson, H. J. et al. Breast cancer quantitative proteome and proteogenomic landscape. Nat. Commun. 10, 1600 (2019).
Google Scholar
Sinha, A. et al. The proteogenomic landscape of curable prostate cancer. Cancer Cell 35, 414–427.e6 (2019).
Google Scholar
Yang, M. et al. Proteogenomics and Hi-C reveal transcriptional dysregulation in high hyperdiploid childhood acute lymphoblastic leukemia. Nat. Commun. 10, 1519 (2019).
Google Scholar
Li, C. et al. Integrated omics of metastatic colorectal cancer. Cancer Cell 38, 734–747.e9 (2020).
Google Scholar
Aure, M. R. et al. Integrative clustering reveals a novel split in the luminal A subtype of breast cancer with impact on outcome. Breast Cancer Res. 19, 44 (2017).
Google Scholar
Hu, Y. et al. Integrated proteomic and glycoproteomic characterization of human high-grade serous ovarian carcinoma. Cell Rep. 33, 108276 (2020).
Google Scholar
Pan, J. et al. Glycoproteomics-based signatures for tumor subtyping and clinical outcome prediction of high-grade serous ovarian cancer. Nat. Commun. 11, 6139 (2020).
Google Scholar
Austen, M., Cerni, C., Lüscher-Firzlaff, J. M. & Lüscher, B. YY1 can inhibit c-Myc function through a mechanism requiring DNA binding of YY1 but neither its transactivation domain nor direct interaction with c-Myc. Oncogene 17, 511–520 (1998).
Google Scholar
Cancer Genome Atlas Research Network. Comprehensive molecular profiling of lung adenocarcinoma. Nature 511, 543–550 (2014).
Xu, J.-Y. et al. Integrative proteomic characterization of human lung adenocarcinoma. Cell 182, 245–261.e17 (2020).
Google Scholar
Roper, N. et al. APOBEC mutagenesis and copy-number alterations are drivers of proteogenomic tumor evolution and heterogeneity in metastatic thoracic tumors. Cell Rep. 26, 2651–2666.e6 (2019).
Google Scholar
Cancer Genome Atlas Research Network. Comprehensive molecular characterization of gastric adenocarcinoma. Nature 513, 202–209 (2014).
Wang, K. et al. Whole-genome sequencing and comprehensive molecular profiling identify new driver mutations in gastric cancer. Nat. Genet. 46, 573–582 (2014).
Google Scholar
Hoang, M. L. et al. Mutational signature of aristolochic acid exposure as revealed by whole-exome sequencing. Sci. Transl. Med. 5, 197ra102 (2013).
Google Scholar
Yuan, G. et al. Elevated NSD3 histone methylation activity drives squamous cell lung cancer. Nature 590, 504–508 (2021).
Google Scholar
Yanovich-Arad, G. et al. Proteogenomics of glioblastoma associates molecular patterns with survival. Cell Rep. 34, 108787 (2021).
Google Scholar
Harding, J. & Burtness, B. Cetuximab: an epidermal growth factor receptor chemeric human–murine monoclonal antibody. Drugs Today 41, 107–127 (2005).
Google Scholar
Seiwert, T. Y. et al. Safety and clinical activity of pembrolizumab for treatment of recurrent or metastatic squamous cell carcinoma of the head and neck (KEYNOTE-012): an open-label, multicentre, phase 1b trial. Lancet Oncol. 17, 956–965 (2016).
Google Scholar
Northcott, P. A. et al. The whole-genome landscape of medulloblastoma subtypes. Nature 547, 311–317 (2017).
Google Scholar
Brennan, C. W. et al. The somatic genomic landscape of glioblastoma. Cell 155, 462–477 (2013).
Google Scholar
Rivero-Hinojosa, S. et al. Proteomic analysis of medulloblastoma reveals functional biology with translational potential. Acta Neuropathol. Commun. 6, 48 (2018).
Google Scholar
Forget, A. et al. Aberrant ERBB4–SRC signaling as a hallmark of Group 4 medulloblastoma revealed by integrative phosphoproteomic profiling. Cancer Cell 34, 379–395.e7 (2018).
Google Scholar
Latonen, L. et al. Integrative proteomics in prostate cancer uncovers robustness against genomic and transcriptomic aberrations during disease progression. Nat. Commun. 9, 1176 (2018).
Google Scholar
Bateman, N. W. et al. Proteogenomic landscape of uterine leiomyomas from hereditary leiomyomatosis and renal cell cancer patients. Sci. Rep. 11, 9371 (2021).
Google Scholar
Buccitelli, C. & Selbach, M. mRNAs, proteins and the emerging principles of gene expression control. Nat. Rev. Genet. 21, 630–644 (2020).
Google Scholar
Flores-Morales, A. et al. Proteogenomic characterization of patient-derived xenografts highlights the role of REST in neuroendocrine differentiation of castration-resistant prostate cancer. Clin. Cancer Res. 25, 595–608 (2019).
Google Scholar
Huang, K.-L. et al. Proteogenomic integration reveals therapeutic targets in breast cancer xenografts. Nat. Commun. 8, 14864 (2017).
Google Scholar
Mundt, F. et al. Mass spectrometry-based proteomics reveals potential roles of NEK9 and MAP2K4 in resistance to PI3K inhibition in triple-negative breast cancers. Cancer Res. 78, 2732–2746 (2018).
Google Scholar
Sheth, M., Zhang, J. & Zenklusen, J. C. Collaborative Genomics Projects: A Comprehensive Guide Ch. 4 (Academic, 2016).
Mertins, P. et al. Ischemia in tumors induces early and sustained phosphorylation changes in stress kinase pathways but does not affect global protein levels. Mol. Cell. Proteom. 13, 1690–1704 (2014).
Google Scholar
Mertins, P. et al. Reproducible workflow for multiplexed deep-scale proteome and phosphoproteome analysis of tumor tissues by liquid chromatography-mass spectrometry. Nat. Protoc. 13, 1632–1661 (2018).
Google Scholar
Tian, C. et al. Proteomic analyses of ECM during pancreatic ductal adenocarcinoma progression reveal different contributions by tumor and stromal cells. Proc. Natl Acad. Sci. USA 116, 19609–19618 (2019).
Google Scholar
Petralia, F. et al. BayesDeBulk: a flexible bayesian algorithm for the deconvolution of bulk tumor data. bioRxiv https://doi.org/10.1101/2021.06.25.449763 (2021).
Google Scholar
Buczak, K. et al. Spatially resolved analysis of FFPE tissue proteomes by quantitative mass spectrometry. Nat. Protoc. 15, 2956–2979 (2020). This work uses laser-capture microdissection followed by MS to profile FFPE tissues to quantify intratumour heterogeneity.
Google Scholar
Ezzoukhry, Z. et al. Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation. Nat. Commun. 9, 2031 (2018).
Google Scholar
Fan, Y. et al. Proteomic profiling of gastric signet ring cell carcinoma tissues reveals characteristic changes of the complement cascade pathway. Mol. Cell. Proteom. 20, 100068 (2021).
Google Scholar
Großerueschkamp, F. et al. Spatial and molecular resolution of diffuse malignant mesothelioma heterogeneity by integrating label-free FTIR imaging, laser capture microdissection and proteomics. Sci. Rep. 7, 44829 (2017).
Google Scholar
Hiroshima, Y. et al. Novel targets identified by integrated cancer–stromal interactome analysis of pancreatic adenocarcinoma. Cancer Lett. 469, 217–227 (2020).
Google Scholar
Staunton, L. et al. Pathology-driven comprehensive proteomic profiling of the prostate cancer tumor microenvironment. Mol. Cancer Res. 15, 281–293 (2017).
Google Scholar
Zupa, A. et al. A pilot characterization of human lung NSCLC by protein pathway activation mapping. J. Thorac. Oncol. 7, 1755–1766 (2012).
Google Scholar
Corchete, L. A. et al. Systematic comparison and assessment of RNA-seq procedures for gene expression quantitative analysis. Sci. Rep. 10, 19737 (2020).
Google Scholar
Pabinger, S. et al. A survey of tools for variant analysis of next-generation genome sequencing data. Brief. Bioinform. 15, 256–278 (2014).
Google Scholar
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Google Scholar
Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).
Google Scholar
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
Google Scholar
Schroeder, C. M. et al. A comprehensive quality control workflow for paired tumor–normal NGS experiments. Bioinformatics 33, 1721–1722 (2017).
Google Scholar
Wang, L., Wang, S. & Li, W. RSeQC: quality control of RNA-seq experiments. Bioinformatics 28, 2184–2185 (2012).
Google Scholar
Bian, X. et al. Comparing the performance of selected variant callers using synthetic data and genome segmentation. BMC Bioinforma. 19, 429 (2018).
Google Scholar
Kim, S. et al. Strelka2: fast and accurate calling of germline and somatic variants. Nat. Methods 15, 591–594 (2018).
Google Scholar
Koboldt, D. C. et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 22, 568–576 (2012).
Google Scholar
Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinforma. 12, 323 (2011).
Google Scholar
Trapnell, C. et al. Transcript assembly and quantification by RNA-seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511–515 (2010).
Google Scholar
Conesa, A. et al. A survey of best practices for RNA-seq data analysis. Genome Biol. 17, 13 (2016).
Google Scholar
DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).
Google Scholar
McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).
Google Scholar
Van der Auwera, G. A. et al. From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr. Protoc. Bioinforma. 43, 11.10.1–11.10.33 (2013).
Grossman, R. L. et al. Toward a shared vision for cancer genomic data. N. Engl. J. Med. 375, 1109–1112 (2016).
Google Scholar
Kim, S. & Pevzner, P. A. MS-GF+ makes progress towards a universal database search tool for proteomics. Nat. Commun. 5, 5277 (2014).
Google Scholar
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).
Google Scholar
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).
Google Scholar
da Veiga Leprevost, F. et al. Philosopher: a versatile toolkit for shotgun proteomics data analysis. Nat. Methods 17, 869–870 (2020). This work presents a pipeline for processing MS data.
Google Scholar
Rudnick, P. A. et al. A description of the Clinical Proteomic Tumor Analysis Consortium (CPTAC) common data analysis pipeline. J. Proteome Res. 15, 1023–1032 (2016).
Google Scholar
Chen, C., Hou, J., Tanner, J. J. & Cheng, J. Bioinformatics methods for mass spectrometry-based proteomics data analysis. Int. J. Mol. Sci. 21, 2873 (2020).
Google Scholar
Ma, W. et al. DreamAI: algorithm for the imputation of proteomics data. bioRxiv https://doi.org/10.1101/2020.07.21.214205 (2021).
Google Scholar
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).
Google Scholar
Ruggles, K. V. et al. An analysis of the sensitivity of proteogenomic mapping of somatic mutations and novel splicing events in cancer. Mol. Cell. Proteom. 15, 1060–1071 (2016). This work uses mutations identified in DNA and RNA to detect mutated peptides in corresponding proteins.
Google Scholar
Johnson, W. E., Evan Johnson, W., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).
Google Scholar
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).
Google Scholar
Song, X. et al. Insights into impact of DNA copy number alteration and methylation on the proteogenomic landscape of human ovarian cancer via a multi-omics integrative analysis. Mol. Cell. Proteom. 18, S52–S65 (2019).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
Google Scholar
Wang, J., Vasaikar, S., Shi, Z., Greer, M. & Zhang, B. WebGestalt 2017: a more comprehensive, powerful, flexible and interactive gene set enrichment analysis toolkit. Nucleic Acids Res. 45, W130–W137 (2017).
Google Scholar
Krug, K. et al. A curated resource for phosphosite-specific signature analysis. Mol. Cell. Proteom. 18, 576–593 (2019). This work introduces a pathway database resource based on phosphosites, along with determination of site-specific enrichment.
Google Scholar
Coudray, N. et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat. Med. 24, 1559–1567 (2018).
Google Scholar
Wen, B. et al. Deep learning in proteomics. Proteomics 20, e1900335 (2020).
Google Scholar
Coscia, F. et al. Multi-level proteomics identifies CT45 as a chemosensitivity mediator and immunotherapy target in ovarian cancer. Cell 175, 159–170.e16 (2018).
Google Scholar
Rozenblatt-Rosen, O. et al. The Human Tumor Atlas Network: charting tumor transitions across space and time at single-cell resolution. Cell 181, 236–249 (2020).
Google Scholar
Kelly, R. T. Single-cell proteomics: progress and prospects. Mol. Cell. Proteom. 19, 1739–1748 (2020). This work reviews strategies for proteomic profiling of single cells and samples with very low input amounts.
Google Scholar
Slavov, N. Single-cell protein analysis by mass spectrometry. Curr. Opin. Chem. Biol. 60, 1–9 (2021).
Google Scholar
Tsai, C.-F. et al. An improved boosting to amplify signal with isobaric labeling (iBASIL) strategy for precise quantitative single-cell proteomics. Mol. Cell. Proteom. 19, 828–838 (2020).
Google Scholar
Rodriguez, H., Zenklusen, J. C., Staudt, L. M., Doroshow, J. H. & Lowy, D. R. The next horizon in precision oncology: proteogenomics to inform cancer diagnosis and treatment. Cell 184, 1661–1670 (2021). This work is a perspective on the role of proteogenomics in the diagnosis and treatment of patients with cancer, and its promise for precision oncology.
Google Scholar
Aebersold, R. & Mann, M. Mass-spectrometric exploration of proteome structure and function. Nature 537, 347–355 (2016).
Google Scholar
Lössl, P., van de Waterbeemd, M. & Heck, A. Jr The diverse and expanding role of mass spectrometry in structural and molecular biology. EMBO J. 35, 2634–2657 (2016).
Google Scholar
Zhang, G., Annan, R. S., Carr, S. A. & Neubert, T. A. Overview of peptide and protein analysis by mass spectrometry. Curr. Protoc. Protein Sci. 62, 16.1.1–16.1.30 (2010).
Li, J. et al. TMTpro reagents: a set of isobaric labeling mass tags enables simultaneous proteome-wide measurements across 16 samples. Nat. Methods 17, 399–404 (2020).
Google Scholar
Thompson, A. et al. TMTpro: design, synthesis, and initial evaluation of a proline-based isobaric 16-plex Tandem Mass Tag reagent set. Anal. Chem. 91, 15941–15950 (2019).
Google Scholar
Hughes, C. S. et al. Single-pot, solid-phase-enhanced sample preparation for proteomics experiments. Nat. Protoc. 14, 68–85 (2019).
Google Scholar
Marchione, D. M. et al. HYPERsol: high-quality data from archival FFPE tissue for clinical proteomics. J. Proteome Res. 19, 973–983 (2020).
Google Scholar
Piehowski, P. D. et al. Residual tissue repositories as a resource for population-based cancer proteomic studies. Clin. Proteom. 15, 26 (2018).
Hebert, A. S. et al. Comprehensive single-shot proteomics with FAIMS on a hybrid orbitrap mass spectrometer. Anal. Chem. 90, 9529–9537 (2018).
Google Scholar
Schweppe, D. K. et al. Characterization and optimization of multiplexed quantitative analyses using high-field asymmetric-waveform ion mobility mass spectrometry. Anal. Chem. 91, 4010–4016 (2019).
Google Scholar
Udeshi, N. D. et al. Rapid and deep-scale ubiquitylation profiling for biology and translational research. Nat. Commun. 11, 359 (2020).
Google Scholar
Erickson, B. K. et al. Active instrument engagement combined with a real-time database search for improved performance of sample multiplexing workflows. J. Proteome Res. 18, 1299–1306 (2019).
Google Scholar
Chapman, J. D., Goodlett, D. R. & Masselon, C. D. Multiplexed and data-independent tandem mass spectrometry for global proteome profiling. Mass. Spectrom. Rev. 33, 452–470 (2014).
Google Scholar
Betancourt, L. H. et al. The human melanoma proteome atlas—defining the molecular pathology. Clin. Transl. Med. 11, e473 (2021).
Google Scholar
Meier-Abt, F. et al. The protein landscape of chronic lymphocytic leukemia (CLL). Blood 138, 2514–2525 (2021).
Google Scholar
Mell, P. M. & Grance, T. The NIST definition of cloud computing. https://doi.org/10.6028/nist.sp.800-145 (National Institute of Standards and Technology, 2011).
Birger, C. et al. FireCloud, a scalable cloud-based platform for collaborative genome analysis: strategies for reducing and controlling costs. bioRxiv https://doi.org/10.1101/209494 (2017).
Google Scholar
Van der Auwera, G. A. & O’Connor, B. D. Genomics in the Cloud: Using Docker, GATK, and WDL in Terra (‘O’Reilly Media, 2020).
Mani, D. R. et al. PANOPLY: a cloud-based platform for automated and reproducible proteogenomic data analysis. Nat. Methods 18, 580–582 (2021). This work presents an open-source pipeline for comprehensive and integrated analysis of proteogenomics data, encapsulating common methods from published flagship studies.
Google Scholar
Bantscheff, M., Lemeer, S., Savitski, M. M. & Kuster, B. Quantitative mass spectrometry in proteomics: critical review update from 2007 to the present. Anal. Bioanal. Chem. 404, 939–965 (2012).
Google Scholar
Bantscheff, M. et al. Robust and sensitive iTRAQ quantification on an LTQ Orbitrap mass spectrometer. Mol. Cell. Proteom. 7, 1702–1713 (2008).
Google Scholar
Gan, C. S., Chong, P. K., Pham, T. K. & Wright, P. C. Technical, experimental, and biological variations in isobaric tags for relative and absolute quantitation (iTRAQ). J. Proteome Res. 6, 821–827 (2007).
Google Scholar
Karp, N. A. et al. Addressing accuracy and precision issues in iTRAQ quantitation. Mol. Cell. Proteom. 9, 1885–1897 (2010).
Google Scholar
Mertins, P. et al. iTRAQ labeling is superior to mTRAQ for quantitative global proteomics and phosphoproteomics. Mol. Cell. Proteom. 11, M111.014423 (2012).
Ow, S. Y. et al. iTRAQ underestimation in simple and complex mixtures: ‘the good, the bad and the ugly’. J. Proteome Res. 8, 5347–5355 (2009).
Google Scholar
Savitski, M. M. et al. Measuring and managing ratio compression for accurate iTRAQ/TMT quantification. J. Proteome Res. 12, 3586–3598 (2013).
Google Scholar
Svinkina, T. et al. Deep, quantitative coverage of the lysine acetylome using novel anti-acetyl-lysine antibodies and an optimized proteomic workflow. Mol. Cell. Proteom. 14, 2429–2440 (2015).
Google Scholar
Chen, R., Im, H. & Snyder, M. Whole-exome enrichment with the illumina truseq exome enrichment platform. Cold Spring Harb. Protoc. 2015, 642–648 (2015).
Google Scholar
Slatko, B. E., Gardner, A. F. & Ausubel, F. M. Overview of next-generation sequencing technologies. Curr. Protoc. Mol. Biol. 122, e59 (2018).
Google Scholar
van Dijk, E. L., Jaszczyszyn, Y., Naquin, D. & Thermes, C. The third revolution in sequencing technology. Trends Genet. 34, 666–681 (2018).
Google Scholar
Goodwin, S., McPherson, J. D. & McCombie, W. R. Coming of age: ten years of next-generation sequencing technologies. Nat. Rev. Genet. 17, 333–351 (2016).
Google Scholar
[No authors listed]. Method of the year 2013. Nat. Methods 11, 1 (2014).
Choi, J. R., Yong, K. W., Choi, J. Y. & Cowie, A. C. Single-cell RNA requencing and its combination with protein and DNA analyses. Cells 9, 1130 (2020).
Google Scholar
Ramsköld, D. et al. Full-length mRNA-seq from single-cell levels of RNA and individual circulating tumor cells. Nat. Biotechnol. 30, 777–782 (2012).
Google Scholar
Hashimshony, T., Wagner, F., Sher, N. & Yanai, I. CEL-seq: single-cell RNA-seq by multiplexed linear amplification. Cell Rep. 2, 666–673 (2012).
Google Scholar
Sasagawa, Y. et al. Quartz-seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals non-genetic gene-expression heterogeneity. Genome Biol. 14, R31 (2013).
Google Scholar
Hu, P. et al. Dissecting cell-type composition and activity-dependent transcriptional state in mammalian brains by massively parallel single-nucleus RNA-seq. Mol. Cell 68, 1006–1015.e7 (2017).
Google Scholar
Chen, S., Lake, B. B. & Zhang, K. High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell. Nat. Biotechnol. 37, 1452–1457 (2019).
Google Scholar
Ji, Z., Zhou, W., Hou, W. & Ji, H. Single-cell ATAC-seq signal extraction and enhancement with SCATE. Genome Biol. 21, 161 (2020).
Google Scholar
Yu, W., Uzun, Y., Zhu, Q., Chen, C. & Tan, K. scATAC-pro: a comprehensive workbench for single-cell chromatin accessibility sequencing data. Genome Biol. 21, 94 (2020).
Google Scholar
Kim, W. et al. Systematic and quantitative assessment of the ubiquitin-modified proteome. Mol. Cell 44, 325–340 (2011).
Google Scholar
Udeshi, N. D. et al. Refined preparation and use of anti-diglycine remnant (K-ε-GG) antibody enables routine quantification of 10,000s of ubiquitination sites in single proteomics experiments. Mol. Cell. Proteom. 12, 825–831 (2013).
Google Scholar
Wagner, S. A. et al. A proteome-wide, quantitative survey of in vivo ubiquitylation sites reveals widespread regulatory roles. Mol. Cell. Proteom. 10, M111.013284 (2011).
Mertins, P. et al. Integrated proteomic analysis of post-translational modifications by serial enrichment. Nat. Methods 10, 634–637 (2013).
Google Scholar
Vasaikar, S. V., Straub, P., Wang, J. & Zhang, B. LinkedOmics: analyzing multi-omics data within and across 32 cancer types. Nucleic Acids Res. 46, D956–D963 (2018).
Google Scholar
Wu, P. et al. Integration and analysis of CPTAC proteomics data in the context of cancer genomics in the cBioPortal. Mol. Cell. Proteom. 18, 1893–1898 (2019).
Google Scholar
Lindgren, C. M. et al. Simplified and unified access to cancer proteogenomic data. J. Proteome Res. 20, 1902–1910 (2021).
Google Scholar
Sharma, V. et al. Panorama: a targeted proteomics knowledge base. J. Proteome Res. 13, 4205–4210 (2014).
Google Scholar
MacLean, B. et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26, 966–968 (2010).
Google Scholar
Tyanova, S., Temu, T. & Cox, J. The MaxQuant computational platform for mass spectrometry-based shotgun proteomics. Nat. Protoc. 11, 2301–2319 (2016).
Google Scholar
Geiszler, D. J. et al. PTM-shepherd: analysis and summarization of post-translational and chemical modifications from open search results. Mol. Cell. Proteom. 20, 100018 (2020).
Yu, F. et al. Fast quantitative analysis of timstof PASEF data with MSFragger and IonQuant. Mol. Cell. Proteom. 19, 1575–1585 (2020).
Google Scholar
Yu, F., Haynes, S. E. & Nesvizhskii, A. I. IonQuant enables accurate and sensitive label-free quantification with FDR-controlled match-between-runs. Mol. Cell. Proteom. 20, 100077 (2021).
Google Scholar
Shi, Z., Wang, J. & Zhang, B. NetGestalt: integrating multidimensional omics data over biological networks. Nat. Methods 10, 597–598 (2013). This work presents a network analysis and visualization tool supporting analysis of multi-omics data in a web-based, easy to use, interface.
Google Scholar
Petralia, F. et al. A new method for constructing tumor specific gene co-expression networks based on samples with tumor purity heterogeneity. Bioinformatics 34, i528–i536 (2018).
Google Scholar
Tyanova, S. et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat. Methods 13, 731–740 (2016).
Google Scholar
Tyanova, S. & Cox, J. Perseus: a bioinformatics platform for integrative analysis of proteomics data in cancer research. Methods Mol. Biol. 1711, 133–148 (2018).
Google Scholar
Wen, B., Wang, X. & Zhang, B. PepQuery enables fast, accurate, and convenient proteomic validation of novel genomic alterations. Genome Res. 29, 485–493 (2019).
Google Scholar
Wen, B., Li, K., Zhang, Y. & Zhang, B. Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis. Nat. Commun. 11, 1759 (2020).
Google Scholar
Liao, Y., Wang, J., Jaehnig, E. J., Shi, Z. & Zhang, B. WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs. Nucleic Acids Res. 47, W199–W205 (2019).
Google Scholar
Rudolph, J. D., de Graauw, M., van de Water, B., Geiger, T. & Sharan, R. Elucidation of signaling pathways from large-scale phosphoproteomic data using protein interaction networks. Cell Syst. 3, 585–593.e3 (2016).
Google Scholar
Huang, K.-L. et al. Spatially interacting phosphorylation sites and mutations in cancer. Nat. Commun. 12, 2313 (2021).
Google Scholar
Blumenberg, L. et al. BlackSheep: a Bioconductor and Bioconda package for differential extreme value analysis. J. Proteome Res. 20, 3767–3773 (2021).
Google Scholar
Martens, M. et al. WikiPathways: connecting communities. Nucleic Acids Res. 49, D613–D621 (2021).
Google Scholar

