1.
Keller, A., Nesvizhskii, A. I., Kolker, E. & Aebersold, R. Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal. Chem. 74, 5383–5392 (2002).
2.
Keller, A. et al. Experimental protein mixture for validating tandem mass spectral analysis. OMICS 6, 207–212 (2002).
3.
Kolker, E. et al. Initial proteome analysis of model microorganism Haemophilus influenzae strain Rd KW20. J. Bacteriol. 185, 4593–4602 (2003).
4.
Nesvizhskii, A. I., Keller, A., Kolker, E. & Aebersold, R. A statistical model for identifying proteins by tandem mass spectrometry. Anal. Chem. 75, 4646–4658 (2003).
5.
von Haller, P. D. et al. The application of new software tools to quantitative protein profiling via isotope-coded affinity tag (ICAT) and tandem mass spectrometry: I. Statistically annotated datasets for peptide sequences and proteins identified via the application of ICAT and tandem mass spectrometry to proteins copurifying with T cell lipid rafts. Mol. Cell Proteomics 2, 426–427 (2003).
6.
von Haller, P. D. et al. The application of new software tools to quantitative protein profiling via isotope-coded affinity tag (ICAT) and tandem mass spectrometry: II. Evaluation of tandem mass spectrometry methodologies for large-scale protein analysis, and the application of statistical tools for data analysis and interpretation. Mol. Cell Proteomics 2, 428–442 (2003).
7.
Carr, S. et al. The need for guidelines in publication of peptide and protein identification data: Working Group on Publication Guidelines for Peptide and Protein Identification Data. Mol. Cell Proteomics 3, 531–533 (2004).
8.
Marelli, M. et al. Quantitative mass spectrometry reveals a role for the GTPase Rho1p in actin organization on the peroxisome membrane. J. Cell Biol. 167, 1099–1112 (2004).
9.
Nesvizhskii, A. I. & Aebersold, R. Analysis, statistical validation and dissemination of large-scale proteomics datasets generated by tandem MS. Drug Discov. Today 9, 173–181 (2004).
10.
Wollscheid, B. et al. Lipid raft proteins and their identification in T lymphocytes. Subcell. Biochem. 37, 121–152 (2004).
11.
Desiere, F. et al. Integration with the human genome of peptide sequences obtained by high-throughput mass spectrometry. Genome Biol. 6, R9 (2005).
12.
Deutsch, E. W. et al. Human Plasma PeptideAtlas. Proteomics 5, 3497–3500 (2005).
13.
Martens, L. et al. Do we want our data raw? Including binary mass spectrometry data in public proteomics data repositories. Proteomics 5, 3501–3505 (2005).
14.
Martin, D. B., Eng, J. K., Nesvizhskii, A. I., Gemmill, A. & Aebersold, R. Investigation of neutral loss during collision-induced dissociation of peptide ions. Anal. Chem. 77, 4870–4882 (2005).
15.
Nesvizhskii, A. I. & Aebersold, R. Interpretation of shotgun proteomic data: the protein inference problem. Mol. Cell Proteomics 4, 1419–1440 (2005).
16.
Desiere, F. et al. The PeptideAtlas project. Nucleic Acids Res. 34, D655–658 (2006).
17.
King, N. L. et al. Analysis of the Saccharomyces cerevisiae proteome with PeptideAtlas. Genome Biol. 7, R106 (2006).
18.
Malmstrom, J. et al. Optimized peptide separation and identification for mass spectrometry based proteomics via free-flow electrophoresis. J. Proteome Res. 5, 2241–2249 (2006).
19.
Nesvizhskii, A. I. et al. Dynamic spectrum quality assessment and iterative computational analysis of shotgun proteomic data: toward more efficient identification of post-translational modifications, sequence polymorphisms, and novel peptides. Mol. Cell Proteomics 5, 652–670 (2006).
20.
Kim, B. et al. The transcription elongation factor TFIIS is a component of RNA polymerase II preinitiation complexes. Proc. Natl. Acad. Sci. U.S.A. 104, 16068–16073 (2007).
21.
Mueller, D. et al. A role for the MLL fusion partner ENL in transcriptional elongation and chromatin modification. Blood 110, 4445–4454 (2007).
22.
Nesvizhskii, A. I. Protein identification by tandem mass spectrometry and sequence database searching. Methods Mol. Biol. 367, 87–119 (2007).
23.
Nesvizhskii, A. I., Vitek, O. & Aebersold, R. Analysis and validation of proteomic data generated by tandem mass spectrometry. Nat. Methods 4, 787–797 (2007).
24.
Yoo, C. et al. Comprehensive analysis of proteins of pH fractionated samples using monolithic LC/MS/MS, intact MW measurement and MALDI-QIT-TOF MS. J Mass Spectrom 42, 312–334 (2007).
25.
Cao, X. & Nesvizhskii, A. I. Improved sequence tag generation method for peptide identification in tandem mass spectrometry. J. Proteome Res. 7, 4422–4434 (2008).
26.
Choi, H., Fermin, D. & Nesvizhskii, A. I. Significance analysis of spectral count data in label-free shotgun proteomics. Mol. Cell Proteomics 7, 2373–2385 (2008).
27.
Choi, H., Ghosh, D. & Nesvizhskii, A. I. Statistical validation of peptide identifications in large-scale proteomics using the target-decoy database search strategy and flexible mixture modeling. J. Proteome Res. 7, 286–292 (2008).
28.
Choi, H. & Nesvizhskii, A. I. False discovery rates and related statistical concepts in mass spectrometry-based proteomics. J. Proteome Res. 7, 47–50 (2008).
29.
Choi, H. & Nesvizhskii, A. I. Semisupervised model-based validation of peptide identifications in mass spectrometry-based proteomics. J. Proteome Res. 7, 254–265 (2008).
30.
Ding, Y., Choi, H. & Nesvizhskii, A. I. Adaptive discriminant function analysis and reranking of MS/MS database search results for improved peptide identification in shotgun proteomics. J. Proteome Res. 7, 4878–4889 (2008).
31.
Marelli, M., Nesvizhskii, A. I. & Aitchison, J. D. Identifying bona fide components of an organelle by isotope-coded labeling of subcellular fractions : an example in peroxisomes. Methods Mol. Biol. 432, 357–371 (2008).
32.
Searle, B. C., Turner, M. & Nesvizhskii, A. I. Improving sensitivity by probabilistically combining results from multiple MS/MS search methodologies. J. Proteome Res. 7, 245–253 (2008).
33.
Taylor, B. S. et al. Humoral response profiling reveals pathways to prostate cancer progression. Mol. Cell Proteomics 7, 600–611 (2008).
34.
Ulintz, P. J., Bodenmiller, B., Andrews, P. C., Aebersold, R. & Nesvizhskii, A. I. Investigating MS2/MS3 matching statistics: a model for coupling consecutive stage mass spectrometry data for increased peptide identification confidence. Mol. Cell Proteomics 7, 71–87 (2008).
35.
Choi, H., Nesvizhskii, A. I., Ghosh, D. & Qin, Z. S. Hierarchical hidden Markov model with application to joint analysis of ChIP-chip and ChIP-seq data. Bioinformatics 25, 1715–1721 (2009).
36.
Goudreault, M. et al. A PP2A phosphatase high density interaction network identifies a novel striatin-interacting phosphatase and kinase complex linked to the cerebral cavernous malformation 3 (CCM3) protein. Mol. Cell Proteomics 8, 157–171 (2009).
37.
Ho, L. et al. An embryonic stem cell chromatin remodeling complex, esBAF, is essential for embryonic stem cell self-renewal and pluripotency. Proc. Natl. Acad. Sci. U.S.A. 106, 5181–5186 (2009).
38.
Rodriguez, H. et al. Recommendations from the 2008 International Summit on Proteomics Data Release and Sharing Policy: the Amsterdam principles. J. Proteome Res. 8, 3689–3692 (2009).
39.
Ulintz, P. J. et al. Comparison of MS(2)-only, MSA, and MS(2)/MS(3) methodologies for phosphopeptide identification. J. Proteome Res. 8, 887–899 (2009).
40.
Vellaichamy, A. et al. Proteomic interrogation of androgen action in prostate cancer cells reveals roles of aminoacyl tRNA synthetases. PLoS ONE 4, e7075 (2009).
41.
Bodenmiller, B. et al. Phosphoproteomic analysis reveals interconnected system-wide responses to perturbations of kinases and phosphatases in yeast. Sci Signal 3, rs4 (2010).
42.
Breitkreutz, A. et al. A global protein kinase and phosphatase interaction network in yeast. Science 328, 1043–1046 (2010).
43.
Choi, H., Kim, S., Gingras, A. C. & Nesvizhskii, A. I. Analysis of protein complexes through model-based biclustering of label-free quantitative AP-MS data. Mol. Syst. Biol. 6, 385 (2010).
44.
Deutsch, E. W. et al. A guided tour of the Trans-Proteomic Pipeline. Proteomics 10, 1150–1159 (2010).
45.
Khan, A. P. et al. Quantitative proteomic profiling of prostate cancer reveals a role for miR-128 in prostate cancer. Mol. Cell Proteomics 9, 298–312 (2010).
46.
Liu, G. et al. ProHits: integrated software for mass spectrometry-based interaction proteomics. Nat. Biotechnol. 28, 1015–1017 (2010).
47.
Nesvizhskii, A. I. A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics. J Proteomics 73, 2092–2123 (2010).
48.
Ning, K., Fermin, D. & Nesvizhskii, A. I. Computational analysis of unassigned high-quality MS/MS spectra in proteomic data sets. Proteomics 10, 2712–2718 (2010).
49.
Ning, K. & Nesvizhskii, A. I. The utility of mass spectrometry-based proteomic data for validation of novel alternative splice forms reconstructed from RNA-Seq data: a preliminary assessment. BMC Bioinformatics 11 Suppl 11, S14 (2010).
50.
Ning, K., Ng, H. K., Srihari, S., Leong, H. W. & Nesvizhskii, A. I. Examination of the relationship between essential genes in PPI network and hub proteins in reverse nearest neighbor topology. BMC Bioinformatics 11, 505 (2010).
51.
Saleem, R. A. et al. Integrated phosphoproteomics analysis of a signaling network governing nutrient response and peroxisome induction. Mol. Cell Proteomics 9, 2076–2088 (2010).
52.
Vellaichamy, A. et al. ‘Topological significance’ analysis of gene expression and proteomic profiles from prostate cancer cells reveals key mechanisms of androgen response. PLoS ONE 5, e10936 (2010).
53.
Bandeira, N., Nesvizhskii, A. & McIntosh, M. Advancing next-generation proteomics through computational research. J. Proteome Res. 10, 2895 (2011).
54.
Bayona, J. C. et al. SUMOylation pathway in Trypanosoma cruzi: functional characterization and proteomic analysis of target proteins. Mol. Cell Proteomics 10, M110.007369 (2011).
55.
Bergerat, A. et al. Prestroke proteomic changes in cerebral microvessels in stroke-prone, transgenic[hCETP]-Hyperlipidemic, Dahl salt-sensitive hypertensive rats. Mol. Med. 17, 588–598 (2011).
56.
Choi, H. et al. SAINT: probabilistic scoring of affinity purification-mass spectrometry data. Nat. Methods 8, 70–73 (2011).
57.
Emmer, B. T. et al. Global analysis of protein palmitoylation in African trypanosomes. Eukaryotic Cell 10, 455–463 (2011).
58.
Fermin, D., Basrur, V., Yocum, A. K. & Nesvizhskii, A. I. Abacus: a computational tool for extracting and pre-processing spectral count data for label-free quantitative proteomic analysis. Proteomics 11, 1340–1345 (2011).
59.
Glatter, T. et al. Modularity and hormone sensitivity of the Drosophila melanogaster insulin receptor/target of rapamycin interaction proteome. Mol. Syst. Biol. 7, 547 (2011).
60.
Kwon, T., Choi, H., Vogel, C., Nesvizhskii, A. I. & Marcotte, E. M. MSblender: A probabilistic approach for integrating peptide identifications from multiple database search engines. J. Proteome Res. 10, 2949–2958 (2011).
61.
Shteynberg, D. et al. iProphet: multi-level integrative analysis of shotgun proteomic data improves peptide and protein identification rates and error estimates. Mol. Cell Proteomics 10, M111.007690 (2011).
62.
Skarra, D. V. et al. Label-free quantitative proteomics and SAINT analysis enable interactome mapping for the human Ser/Thr protein phosphatase 5. Proteomics 11, 1508–1516 (2011).
63.
Vareed, S. K. et al. Metabolites of purine nucleoside phosphorylase (NP) in serum have the potential to delineate pancreatic adenocarcinoma. PLoS ONE 6, e17177 (2011).
64.
Choi, H., Glatter, T., Gstaiger, M. & Nesvizhskii, A. I. SAINT-MS1: protein-protein interaction scoring using label-free intensity data in affinity purification-mass spectrometry experiments. J. Proteome Res. 11, 2619–2624 (2012).
65.
Choi, H. et al. Analyzing protein-protein interactions from affinity purification-mass spectrometry data with SAINT. Curr Protoc Bioinformatics Chapter 8, Unit8.15 (2012).
66.
Gingras, A. C. & Nesvizhskii, A. Protein complexes and interaction networks. Proteomics 12, 1475–1477 (2012).
67.
Homer, C. R. et al. A dual role for receptor-interacting protein kinase 2 (RIP2) kinase activity in nucleotide-binding oligomerization domain 2 (NOD2)-dependent autophagy. J. Biol. Chem. 287, 25565–25576 (2012).
68.
Liu, G. et al. Using ProHits to store, annotate, and analyze affinity purification-mass spectrometry (AP-MS) data. Curr Protoc Bioinformatics Chapter 8, Unit8.16 (2012).
69.
Ma, K., Vitek, O. & Nesvizhskii, A. I. A statistical model-building perspective to identification of MS/MS spectra with PeptideProphet. BMC Bioinformatics 13 Suppl 16, S1 (2012).
70.
Nesvizhskii, A. I. Computational and informatics strategies for identification of specific protein interaction partners in affinity purification mass spectrometry experiments. Proteomics 12, 1639–1655 (2012).
71.
Ning, K., Fermin, D. & Nesvizhskii, A. I. Comparative analysis of different label-free mass spectrometry based protein abundance estimates and their correlation with RNA-Seq gene expression data. J. Proteome Res. 11, 2261–2271 (2012).
72.
Richmond, A. L. et al. The nucleotide synthesis enzyme CAD inhibits NOD2 antibacterial function in human intestinal epithelial cells. Gastroenterology 142, 1483–1492 (2012).
73.
Rodriguez-Pineiro, A. M. et al. Proteomic study of the mucin granulae in an intestinal goblet cell model. J. Proteome Res. 11, 1879–1890 (2012).
74.
Balbin, O. A. et al. Reconstructing targetable pathways in lung cancer by integrating diverse omics data. Nat Commun 4, 2617 (2013).
75.
Choi, H., Fermin, D., Nesvizhskii, A. I., Ghosh, D. & Qin, Z. S. Sparsely correlated hidden Markov models with application to genome-wide location studies. Bioinformatics 29, 533–541 (2013).
76.
Conlon, K. P. et al. Fusion peptides from oncogenic chimeric proteins as putative specific biomarkers of cancer. Mol. Cell Proteomics 12, 2714–2723 (2013).
77.
Fermin, D., Walmsley, S. J., Gingras, A. C., Choi, H. & Nesvizhskii, A. I. LuciPHOr: algorithm for phosphorylation site localization with false localization rate estimation using modified target-decoy approach. Mol. Cell Proteomics 12, 3409–3419 (2013).
78.
Joshi, P. et al. The functional interactome landscape of the human histone deacetylase family. Mol. Syst. Biol. 9, 672 (2013).
79.
Kolker, E. et al. Toward More Transparent and Reproducible Omics Studies Through a Common Metadata Checklist and Data Publications. Big Data 1, 196–201 (2013).
80.
Mellacheruvu, D. et al. The CRAPome: a contaminant repository for affinity purification-mass spectrometry data. Nat. Methods 10, 730–736 (2013).
81.
Reisdorph, N. et al. Hands-on workshops as an effective means of learning advanced technologies including genomics, proteomics and bioinformatics. Genomics Proteomics Bioinformatics 11, 368–377 (2013).
82.
Shively, C. A. et al. Genetic networks inducing invasive growth in Saccharomyces cerevisiae identified through systematic genome-wide overexpression. Genetics 193, 1297–1310 (2013).
83.
Shteynberg, D., Nesvizhskii, A. I., Moritz, R. L. & Deutsch, E. W. Combining results of multiple search engines in proteomics. Mol. Cell Proteomics 12, 2383–2393 (2013).
84.
Walmsley, S. J. et al. Comprehensive analysis of protein digestion using six trypsins reveals the origin of trypsin as a significant source of variability in proteomics. J. Proteome Res. 12, 5666–5680 (2013).
85.
Johnson, C. et al. The yeast Sks1p kinase signaling network regulates pseudohyphal growth and glucose response. PLoS Genet. 10, e1004183 (2014).
86.
Kolker, E. et al. Toward more transparent and reproducible omics studies through a common metadata checklist and data publications. OMICS 18, 10–14 (2014).
87.
Nesvizhskii, A. I. Proteogenomics: concepts, applications and computational strategies. Nat. Methods 11, 1114–1125 (2014).
88.
Rolland, D. et al. Global phosphoproteomic profiling reveals distinct signatures in B-cell non-Hodgkin lymphomas. Am. J. Pathol. 184, 1331–1342 (2014).
89.
Shanmugam, A. K., Yocum, A. K. & Nesvizhskii, A. I. Utility of RNA-seq and GPMDB protein observation frequency for improving the sensitivity of protein identification by tandem MS. J. Proteome Res. 13, 4113–4119 (2014).
90.
Teo, G. et al. SAINTexpress: improvements and additional features in Significance Analysis of INTeractome software. J Proteomics 100, 37–43 (2014).
91.
Balbin, O. A. et al. The landscape of antisense gene expression in human cancers. Genome Res. 25, 1068–1079 (2015).
92.
Choi, H., Kim, S., Fermin, D., Tsou, C. C. & Nesvizhskii, A. I. QPROT: Statistical method for testing differential expression using protein-level intensity data in label-free quantitative proteomics. J Proteomics 129, 121–126 (2015).
93.
Dytfeld, D. et al. Proteomic profiling of naïve multiple myeloma patient plasma cells identifies pathways associated with favourable response to bortezomib-based treatment regimens. Br. J. Haematol. 170, 66–79 (2015).
94.
Fermin, D., Avtonomov, D., Choi, H. & Nesvizhskii, A. I. LuciPHOr2: site localization of generic post-translational modifications from tandem mass spectrometry data. Bioinformatics 31, 1141–1143 (2015).
95.
Hsiao, J. J. et al. Research Resource: Androgen Receptor Activity Is Regulated Through the Mobilization of Cell Surface Receptor Networks. Mol. Endocrinol. 29, 1195–1218 (2015).
96.
Kao, S. H. et al. Analysis of Protein Stability by the Cycloheximide Chase Assay. Bio Protoc 5, (2015).
97.
Kitata, R. B. et al. Mining Missing Membrane Proteins by High-pH Reverse-Phase StageTip Fractionation and Multiple Reaction Monitoring Mass Spectrometry. J. Proteome Res. 14, 3658–3669 (2015).
98.
Malik, R. et al. Targeting the MLL complex in castration-resistant prostate cancer. Nat. Med. 21, 344–352 (2015).
99.
Omenn, G. S. et al. Metrics for the Human Proteome Project 2015: Progress on the Human Proteome and Guidelines for High-Confidence Protein Identification. J. Proteome Res. 14, 3452–3460 (2015).
100.
Shanmugam, A. K. & Nesvizhskii, A. I. Effective Leveraging of Targeted Search Spaces for Improving Peptide Identification in Tandem Mass Spectrometry Based Proteomics. J. Proteome Res. 14, 5169–5178 (2015).
101.
Shively, C. A. et al. Large-Scale Analysis of Kinase Signaling in Yeast Pseudohyphal Development Identifies Regulation of Ribonucleoprotein Granules. PLoS Genet. 11, e1005564 (2015).
102.
Teo, G. et al. mapDIA: Preprocessing and statistical analysis of quantitative proteomics data from data independent acquisition mass spectrometry. J Proteomics 129, 108–120 (2015).
103.
Tsai, C. F. et al. Large-scale determination of absolute phosphorylation stoichiometries in human cells by motif-targeting quantitative proteomics. Nat Commun 6, 6622 (2015).
104.
Tsou, C. C. et al. DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics. Nat. Methods 12, 258–264 (2015).
105.
Avtonomov, D. M., Raskind, A. & Nesvizhskii, A. I. BatMass: a Java Software Platform for LC-MS Data Visualization in Proteomics and Metabolomics. J. Proteome Res. 15, 2500–2509 (2016).
106.
Dimitrakopoulos, L. et al. Proteogenomics: Opportunities and Caveats. Clin. Chem. 62, 551–557 (2016).
107.
Kochen, M. A. et al. Greazy: Open-Source Software for Automated Phospholipid Tandem Mass Spectrometry Identification. Anal. Chem. 88, 5733–5741 (2016).
108.
Liu, G. et al. Data Independent Acquisition analysis in ProHits 4.0. J Proteomics 149, 64–68 (2016).
109.
Navarro, P. et al. A multicenter study benchmarks software tools for label-free proteome quantification. Nat. Biotechnol. 34, 1130–1136 (2016).
110.
Tsou, C. C., Tsai, C. F., Teo, G. C., Chen, Y. J. & Nesvizhskii, A. I. Untargeted, spectral library-free analysis of data-independent acquisition proteomics data generated using Orbitrap mass spectrometers. Proteomics 16, 2257–2271 (2016).
111.
Veeneman, B. A., Shukla, S., Dhanasekaran, S. M., Chinnaiyan, A. M. & Nesvizhskii, A. I. Two-pass alignment improves novel splice junction quantification. Bioinformatics 32, 43–49 (2016).
112.
Zhang, H. et al. MLL1 Inhibition Reprograms Epiblast Stem Cells to Naive Pluripotency. Cell Stem Cell 18, 481–494 (2016).
113.
Bruderer, R. et al. New targeted approaches for the quantification of data-independent acquisition mass spectrometry. Proteomics 17, (2017).
114.
da Veiga Leprevost, F. et al. BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics 33, 2580–2582 (2017).
115.
Knight, J. D. R. et al. ProHits-viz: a suite of web tools for visualizing interaction proteomics data. Nat. Methods 14, 645–646 (2017).
116.
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).
117.
Meyer, J. G. et al. PIQED: automated identification and quantification of protein modifications from DIA-MS data. Nat. Methods 14, 646–647 (2017).
118.
Perez-Riverol, Y. et al. Discovering and linking public omics data sets using the Omics Discovery Index. Nat. Biotechnol. 35, 406–409 (2017).
119.
Rolland, D. C. M. et al. Functional proteogenomics reveals biomarkers and therapeutic targets in lymphomas. Proc. Natl. Acad. Sci. U.S.A. 114, 6581–6586 (2017).
120.
Rosenberger, G. et al. Statistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses. Nat. Methods 14, 921–927 (2017).
121.
Xu, T. et al. RBPJ/CBF1 interacts with L3MBTL3/MBT1 to promote repression of Notch signaling via histone demethylase KDM1A/LSD1. EMBO J. 36, 3232–3249 (2017).
122.
Anwar, T. et al. p38-mediated phosphorylation at T367 induces EZH2 cytoplasmic localization to promote breast cancer metastasis. Nat Commun 9, 2801 (2018).
123.
Avtonomov, D. M., Kong, A. & Nesvizhskii, A. I. DeltaMass: Automated detection and visualization of mass shifts in proteomic open search results. J. Proteome Res. (2018).
124.
Avtonomov, D. M., Polasky, D. A., Ruotolo, B. T. & Nesvizhskii, A. I. IMTBX and Grppr: Software for Top-Down Proteomics Utilizing Ion Mobility-Mass Spectrometry. Anal. Chem. 90, 2369–2375 (2018).
125.
Brady, G. F. et al. Nuclear lamina genetic variants, including a truncated LAP2, in twins and siblings with nonalcoholic fatty liver disease. Hepatology 67, 1710–1725 (2018).
126.
Feltham, R. et al. Mind Bomb Regulates Cell Death during TNF Signaling by Suppressing RIPK1’s Cytotoxic Potential. Cell Rep 23, 470–484 (2018).
127.
Glazier, A. A. et al. HSC70 is a chaperone for wild-type and mutant cardiac myosin binding protein C. JCI Insight 3, (2018).
128.
Hawkins, A. G. et al. The Ewing Sarcoma Secretome and Its Response to Activation of Wnt/beta-catenin Signaling. Mol. Cell Proteomics 17, 901–912 (2018).
129.
Khoriaty, R. et al. Functions of the COPII gene paralogs SEC23A and SEC23B are interchangeable in vivo. Proc. Natl. Acad. Sci. U.S.A. 115, E7748–E7757 (2018).
130.
Liccardi, G. et al. RIPK1 and Caspase-8 Ensure Chromosome Stability Independently of Their Role in Cell Death and Inflammation. Mol. Cell (2018).
131.
Ropa, J. et al. PAF1 complex interactions with SETDB1 mediate promoter H3K9 methylation and transcriptional repression of Hoxa9 and Meis1 in acute myeloid leukemia. Oncotarget 9, 22123–22136 (2018).
132.
Shao, W. et al. The SysteMHC Atlas project. Nucleic Acids Res. 46, D1237–D1247 (2018).
133.
Shi, J. et al. Determining Allele-Specific Protein Expression (ASPE) Using a Novel Quantitative Concatamer Based Proteomics Method. J. Proteome Res. 17, 3606–3612 (2018).
134.
Shi, J. et al. Response to the comments on ‘Determining Allele-Specific Protein Expression (ASPE) Using a Novel QconCAT-Based Proteomics Method’. J. Proteome Res. (2019).