Eric Deutsch

Senior Research Scientist

Dr. Deutsch’s research activities include software development for the analysis and integration of data for systems biology research. He is the lead designer for the Systems Biology Experiment Analysis Management System (SBEAMS). He contributes the development of minimum information standards, data formats, and databases for proteomics as chair of the HUPO Proteomics Standards Initiative (PSI). Dr. Deutsch  leads of the Trans-Proteomic Pipeline project, which is a free and open-source suite of tools for the processing and analysis of proteomics tandem mass spectrometry data. He also heads the PeptideAtlas Project, which aims to collect proteomics mass spectrometry data from labs around the world to synthesize a master list of observed peptides and proteins and disseminate the results back to the community.

Dr. Deutsch participates in these internal and external activities:

“A Blood Atlas of COVID-19 Defines Hallmarks of Disease Severity and Specificity | MedRxiv.” https://www.medrxiv.org/content/10.1101/2021.05.11.21256877v1.full (June 8, 2021). Cite
“COVID-19 Data Portal - Accelerating Scientific Research through Data.” https://www.covid19dataportal.org/ (June 13, 2021). Cite
“Integrated Plasma Proteomic and Single-Cell Immune Signaling Network Signatures Demarcate Mild, Moderate, and Severe COVID-19 | BioRxiv.” https://www.biorxiv.org/content/10.1101/2021.02.09.430269v1 (June 8, 2021). Cite
“A Blood Atlas of COVID-19 Defines Hallmarks of Disease Severity and Specificity | MedRxiv.” https://www.medrxiv.org/content/10.1101/2021.05.11.21256877v1.full (June 8, 2021). Cite
“A Proteome-Wide Genetic Investigation Identifies Several SARS-CoV-2-Exploited Host Targets of Clinical Relevance | MedRxiv.” https://www.medrxiv.org/content/10.1101/2021.03.15.21253625v1 (June 8, 2021). Cite
“WHO Coronavirus (COVID-19) Dashboard | WHO Coronavirus (COVID-19) Dashboard With Vaccination Data.” https://covid19.who.int/ (June 13, 2021). Cite
“Proteomics Uncovers Immunosuppression in COVID-19 Patients with Long Disease Course.” : 35. Cite Download
“WHO Coronavirus (COVID-19) Dashboard.” https://covid19.who.int (June 13, 2021). Cite
“Proteomics Uncovers Immunosuppression in COVID-19 Patients with Long Disease Course.” : 35. Cite Download
“A Proteome-Wide Genetic Investigation Identifies Several SARS-CoV-2-Exploited Host Targets of Clinical Relevance | MedRxiv.” https://www.medrxiv.org/content/10.1101/2021.03.15.21253625v1 (June 8, 2021). Cite
“A Blood Atlas of COVID-19 Defines Hallmarks of Disease Severity and Specificity | MedRxiv.” https://www.medrxiv.org/content/10.1101/2021.05.11.21256877v1.full (June 8, 2021). Cite
“A Blood Atlas of COVID-19 Defines Hallmarks of Disease Severity and Specificity | MedRxiv.” https://www.medrxiv.org/content/10.1101/2021.05.11.21256877v1.full (June 8, 2021). Cite
“Integrated Plasma Proteomic and Single-Cell Immune Signaling Network Signatures Demarcate Mild, Moderate, and Severe COVID-19 | BioRxiv.” https://www.biorxiv.org/content/10.1101/2021.02.09.430269v1 (June 8, 2021). Cite
“COVID-19 Data Portal - Accelerating Scientific Research through Data.” https://www.covid19dataportal.org/ (June 13, 2021). Cite
“WHO Coronavirus (COVID-19) Dashboard | WHO Coronavirus (COVID-19) Dashboard With Vaccination Data.” https://covid19.who.int/ (June 13, 2021). Cite
“WHO Coronavirus (COVID-19) Dashboard.” https://covid19.who.int (June 13, 2021). Cite
“A Time-Resolved Proteomic and Prognostic Map of COVID-19: Cell Systems.” https://www.cell.com/cell-systems/fulltext/S2405-4712(21)00160-5 (June 20, 2021). Cite
“Plasma Proteome Profiling to Detect and Avoid Sample‐related Biases in Biomarker Studies | EMBO Molecular Medicine.” https://www.embopress.org/doi/full/10.15252/emmm.201910427 (June 20, 2021). Cite
“Mass Spectrometry-Based Plasma Proteomics: Considerations from Sample Collection to Achieving Translational Data | Journal of Proteome Research.” https://pubs.acs.org/doi/10.1021/acs.jproteome.9b00503 (June 20, 2021). Cite
“High‐resolution Serum Proteome Trajectories in COVID‐19 Reveal Patient‐specific Seroconversion.” https://www.embopress.org/doi/epdf/10.15252/emmm.202114167 (July 10, 2021). Cite
“High‐resolution Serum Proteome Trajectories in COVID‐19 Reveal Patient‐specific Seroconversion | EMBO Molecular Medicine.” https://www.embopress.org/doi/full/10.15252/emmm.202114167 (July 10, 2021). Cite
“High‐resolution Serum Proteome Trajectories in COVID‐19 Reveal Patient‐specific Seroconversion.” https://www.embopress.org/doi/epdf/10.15252/emmm.202114167 (July 10, 2021). Cite
“High‐resolution Serum Proteome Trajectories in COVID‐19 Reveal Patient‐specific Seroconversion.” https://www.embopress.org/doi/epdf/10.15252/emmm.202114167 (July 10, 2021). Cite
“[No Title Found].” Cite
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Eng, Jimmy K., Ashley L. McCormack, and John R. Yates. 1994. “An Approach to Correlate Tandem Mass Spectral Data of Peptides with Amino Acid Sequences in a Protein Database.” Journal of the American Society for Mass Spectrometry 5(11): 976–89. http://www.sciencedirect.com/science/article/pii/1044030594800162 (July 19, 2019). Cite Download
Brody, E. N. et al. 1999. “The Use of Aptamers in Large Arrays for Molecular Diagnostics.” Molecular Diagnosis: A Journal Devoted to the Understanding of Human Disease Through the Clinical Application of Molecular Biology 4(4): 381–88. Cite
Perkins, D. N., D. J. Pappin, D. M. Creasy, and J. S. Cottrell. 1999. “Probability-Based Protein Identification by Searching Sequence Databases Using Mass Spectrometry Data.” Electrophoresis 20(18): 3551–67. Cite
Fredriksson, Simon et al. 2002. “Protein Detection Using Proximity-Dependent DNA Ligation Assays.” Nature Biotechnology 20(5): 473–77. Cite
Hanash, Sam, and Julio E. Celis. 2002. “The Human Proteome Organization: A Mission to Advance Proteome Knowledge.” Molecular & cellular proteomics: MCP 1(6): 413–14. Cite Download
Hanash, Sam, and Julio E. Celis. 2002. “The Human Proteome Organization: A Mission to Advance Proteome Knowledge.” Molecular & cellular proteomics: MCP 1(6): 413–14. Cite
Keller, Andrew, Alexey I. Nesvizhskii, Eugene Kolker, and Ruedi Aebersold. 2002. “Empirical Statistical Model to Estimate the Accuracy of Peptide Identifications Made by MS/MS and Database Search.” Analytical Chemistry 74(20): 5383–92. Cite
Anderson, N. Leigh, and Norman G. Anderson. 2002. “The Human Plasma Proteome: History, Character, and Diagnostic Prospects.” Molecular & cellular proteomics: MCP 1(11): 845–67. Cite
Adkins, Joshua N. et al. 2002. “Toward a Human Blood Serum Proteome: Analysis by Multidimensional Separation Coupled with Mass Spectrometry.” Molecular & cellular proteomics: MCP 1(12): 947–55. Cite
Lundblad, R. 2003. “Considerations for the Use of Blood Plasma and Serum for Proteomic Analysis.” The Internet Journal of Genomics and Proteomics 1(2): 8. Cite Download
Orchard, Sandra, Paul Kersey, Henning Hermjakob, and Rolf Apweiler. 2003. “The HUPO Proteomics Standards Initiative Meeting: Towards Common Standards for Exchanging Proteomics Data.” Comparative and Functional Genomics 4(1): 16–19. Cite Download
Orchard, Sandra, Paul Kersey, Henning Hermjakob, and Rolf Apweiler. 2003. “The HUPO Proteomics Standards Initiative Meeting: Towards Common Standards for Exchanging Proteomics Data.” Comparative and Functional Genomics 4(1): 16–19. Cite Download
Orchard, Sandra, Paul Kersey, Henning Hermjakob, and Rolf Apweiler. 2003. “The HUPO Proteomics Standards Initiative Meeting: Towards Common Standards for Exchanging Proteomics Data.” Comparative and Functional Genomics 4(1): 16–19. Cite Download
Pieper, Rembert et al. 2003. “Multi-Component Immunoaffinity Subtraction Chromatography: An Innovative Step towards a Comprehensive Survey of the Human Plasma Proteome.” Proteomics 3(4): 422–32. Cite
Lathrop, Julia Tait, N. Leigh Anderson, Norman G. Anderson, and David J. Hammond. 2003. “Therapeutic Potential of the Plasma Proteome.” Current Opinion in Molecular Therapeutics 5(3): 250–57. Cite
Pieper, Rembert et al. 2003. “The Human Serum Proteome: Display of Nearly 3700 Chromatographically Separated Protein Spots on Two-Dimensional Electrophoresis Gels and Identification of 325 Distinct Proteins.” Proteomics 3(7): 1345–64. Cite
Tirumalai, Radhakrishna S. et al. 2003. “Characterization of the Low Molecular Weight Human Serum Proteome.” Molecular & cellular proteomics: MCP 2(10): 1096–1103. Cite Download
Rosenblatt, Kevin P. et al. 2004. “Serum Proteomics in Cancer Diagnosis and Management.” Annual Review of Medicine 55: 97–112. Cite
Anderson, N. Leigh et al. 2004. “Mass Spectrometric Quantitation of Peptides and Proteins Using Stable Isotope Standards and Capture by Anti-Peptide Antibodies (SISCAPA).” Journal of Proteome Research 3(2): 235–44. Cite
Irizarry, Michael C. 2004. “Biomarkers of Alzheimer Disease in Plasma.” NeuroRx: The Journal of the American Society for Experimental NeuroTherapeutics 1(2): 226–34. Cite Download
Clemente, Luis Felipe et al. 2018. “Identification of the Missing Protein Hyaluronan Synthase 1 in Human Mesenchymal Stem Cells Derived from Adipose Tissue or Umbilical Cord.” Journal of Proteome Research. Cite
Macron, Charlotte, Lydie Lane, Antonio Núñez Galindo, and Loïc Dayon. 2018. “Identification of Missing Proteins in Normal Human Cerebrospinal Fluid.” Journal of Proteome Research. Cite
Hwang, Heeyoun et al. 2018. “Identification of Missing Proteins in Human Olfactory Epithelial Tissue by Liquid Chromatography-Tandem Mass Spectrometry.” Journal of Proteome Research. Cite
Yu, Kun-Hsing et al. 2018. “A Cloud-Based Metabolite and Chemical Prioritization System for the Biology/Disease-Driven Human Proteome Project.” Journal of Proteome Research. Cite
Omenn, Gilbert S. et al. 2018. “Progress on Identifying and Characterizing the Human Proteome: 2018 Metrics from the HUPO Human Proteome Project.” Journal of Proteome Research. Cite
Wang, Jie et al. 2018. “Integrated Dissection of Cysteine Oxidative Post-Translational Modification Proteome During Cardiac Hypertrophy.” Journal of Proteome Research. Cite
Macron, Charlotte, Lydie Lane, Antonio Núñez Galindo, and Loïc Dayon. 2018. “Deep Dive on the Proteome of Human Cerebrospinal Fluid: A Valuable Data Resource for Biomarker Discovery and Missing Protein Identification.” Journal of Proteome Research. Cite
Melaine, Nathalie et al. 2018. “Deciphering the Dark Proteome: Use of the Testis and Characterization of Two Dark Proteins.” Journal of Proteome Research. Cite
Sajulga, Ray et al. 2018. “Bridging the Chromosome-Centric and Biology/Disease-Driven Human Proteome Projects: Accessible and Automated Tools for Interpreting the Biological and Pathological Impact of Protein Sequence Variants Detected via Proteogenomics.” Journal of Proteome Research. Cite
Robin, Thibault et al. 2018. “Large-Scale Reanalysis of Publicly Available HeLa Cell Proteomics Data in the Context of the Human Proteome Project.” Journal of Proteome Research. Cite
Duek, Paula, Alain Gateau, Amos Bairoch, and Lydie Lane. 2018. “Exploring the Uncharacterized Human Proteome Using NeXtProt.” Journal of Proteome Research. Cite
Weldemariam, Mehari Muuz et al. 2018. “Subcellular Proteome Landscape of Human Embryonic Stem Cells Revealed Missing Membrane Proteins.” Journal of Proteome Research. Cite
Zhang, Chengxin, Xiaoqiong Wei, Gilbert S. Omenn, and Yang Zhang. 2018. “Structure and Protein Interaction-Based Gene Ontology Annotations Reveal Likely Functions of Uncharacterized Proteins on Human Chromosome 17.” Journal of Proteome Research. Cite
Boersema, Paul J. et al. 2018. “Biology/Disease-Driven Initiative on Protein-Aggregation Diseases of the Human Proteome Project: Goals and Progress to Date.” Journal of Proteome Research. Cite
Mendoza, Luis et al. 2018. “Flexible and Fast Mapping of Peptides to a Proteome with ProteoMapper.” Journal of Proteome Research. Cite
Sjöstedt, Evelina et al. 2018. “Integration of Transcriptomics and Antibody-Based Proteomics for Exploration of Proteins Expressed in Specialized Tissues.” Journal of Proteome Research. Cite
Deutsch, Eric W. et al. 2018. “Expanding the Use of Spectral Libraries in Proteomics.” Journal of Proteome Research. Cite
Paik, Young-Ki et al. 2018. “Launching the C-HPP Pilot Project for Functional Characterization of Identified Proteins with No Known Function.” Journal of Proteome Research. Cite
He, Cuitong et al. 2018. “Digging for Missing Proteins Using Low-Molecular-Weight Protein Enrichment and a ‘Mirror Protease’ Strategy.” Journal of Proteome Research. Cite
Naryzhny, Stanislav N. et al. 2018. “Next Steps on in Silico 2DE Analyses of Chromosome 18 Proteoforms.” Journal of Proteome Research. Cite
Sun, Jinshuai et al. 2018. “Multi-Proteases Combined with High-PH Reverse-Phase Separation Strategy Verified Fourteen Missing Proteins in Human Testis Tissue.” Journal of Proteome Research. Cite
Siddiqui, Omer, Hongjiu Zhang, Yuanfang Guan, and Gilbert S. Omenn. 2018. “Chromosome 17 Missing Proteins: Recent Progress and Future Directions as Part of the Next-50MP Challenge.” Journal of Proteome Research. Cite
Ronci, Maurizio et al. 2018. “Sequential Fractionation Strategy Identifies Three Missing Proteins in the Mitochondrial Proteome of Commonly Used Cell Lines.” Journal of Proteome Research. Cite
Jeong, Seul-Ki, Chae-Yeon Kim, and Young-Ki Paik. 2018. “ASV-ID, a Proteogenomic Workflow to Predict Candidate Protein Isoforms Based on Transcript Evidence.” Journal of Proteome Research. Cite
Monti, Chiara, Lydie Lane, Mauro Fasano, and Tiziana Alberio. 2018. “Update of the Functional Mitochondrial Human Proteome Network.” Journal of Proteome Research. Cite
Lau, Edward et al. 2018. “Identifying High-Priority Proteins Across the Human Diseasome Using Semantic Similarity.” Journal of Proteome Research. Cite
Kopylov, Arthur T. et al. 2019. “200+ Protein Concentrations in Healthy Human Blood Plasma: Targeted Quantitative SRM SIS Screening of Chromosomes 18, 13, Y, and the Mitochondrial Chromosome Encoded Proteome.” Journal of Proteome Research 18(1): 120–29. Cite