Using ProteomeScout: A Resource of Post‐Translational Modifications, Their Experiments, and the Proteins That They Annotate

Arshag D. Mooradian1, Jason M. Held2, Kristen M. Naegle3

1 Department of Medicine, Division of Hematology and Oncology, Washington University School of Medicine in St. Louis, St. Louis, Missouri, 2 Department of Anesthesiology, Washington University School of Medicine in St. Louis, St. Louis, Missouri, 3 Department of Biomedical Engineering and Center for Biological Systems Engineering, Washington University in St. Louis, St. Louis, Missouri
Publication Name:  Current Protocols in Bioinformatics
Unit Number:  Unit 13.32
DOI:  10.1002/cpbi.31
Online Posting Date:  September, 2017
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Abstract

Post‐translational modifications (PTMs) of protein amino acids are ubiquitous and important to protein function, localization, degradation, and more. In recent years, there has been an explosion in the discovery of PTMs as a result of improvements in PTM measurement techniques, including quantitative measurements of PTMs across multiple conditions. ProteomeScout is a repository for such discovery and quantitative experiments and provides tools for visualizing PTMs within proteins, including where they are relative to other PTMS, domains, mutations, and structure. ProteomeScout additionally provides analysis tools for identifying statistically significant relationships in experimental datasets. This unit describes four basic protocols for working with the ProteomeScout Web interface or programmatically with the database download. © 2017 by John Wiley & Sons, Inc.

Keywords: compendia; dataset analysis; post‐translational modifications; protein annotations; protein visualization

     
 
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Table of Contents

  • Introduction
  • Basic Protocol 1: Upload a Dataset to ProteomeScout
  • Alternate Protocol 1: Upload a Site Annotation Dataset to ProteomeScout
  • Support Protocol 1: Use Dataset Tools
  • Basic Protocol 2: Dataset Evaluation: Identify Significant Relationships Between Elements in a Dataset
  • Alternate Protocol 2: Dataset Evaluation: Flexible Subgrouping Offline
  • Basic Protocol 3: Protein Viewer and Protein Pages: Explore the Relationship Between Protein Elements
  • Alternate Protocol 3: Protein Annotations: Using Batch Search to Get Protein Annotations
  • Basic Protocol 4: Work with ProteomeScout Data Offline
  • Support Protocol 2: Installing a ProteomeScoutAPI Session and Downloading the ProteomeScout Database
  • Guidelines for Understanding Results
  • Commentary
  • Literature Cited
  • Figures
  • Tables
     
 
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Materials

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Figures

Videos

Literature Cited

  Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological), 57(1), 289–300.
  Crooks, G. E., Hon, G., Chandonia, J., & Brenner, S. E. (2004). WebLogo: A sequence logo generator. Genome Research, 1188–1190. Retrieved from http://doi.org/10.1101/gr.849004.
  Finn, R. D., Bateman, A., Clements, J., Coggill, P., Eberhardt, R. Y., Eddy, S. R., … Punta, M. (2014). Pfam: The protein families database. Nucleic Acids Research, 42(Database issue), D222–30. Retrieved from http://doi.org/10.1093/nar/gkt1223.
  Goel, R., Harsha, H. C., Pandey, A., & Prasad, T. S. K. (2012). Human protein reference database and human proteinpedia as resources for phosphoproteome analysis. Molecular bioSystems, 8(2), 453–463. Retrieved from http://doi.org/10.1039/c1mb05340j.
  Holehouse, A. S., & Naegle, K. M. (2015). Reproducible Analysis of post‐translational modifications in proteomes: Application to human mutations. PLoS One, 10(12), 1–19. doi: 10.1371/journal.pone.0144692.
  Joughin, B. A., Naegle, K. M., Huang, P. H., Yaffe, M. B., Lauffenburger, D. A., & White, F. M. (2009). An integrated comparative phosphoproteomic and bioinformatic approach reveals a novel class of MPM‐2 motifs upregulated in EGFRvIII‐expressing glioblastoma cells. Molecular BioSystems, 5(1), 59–67. Retrieved from http://doi.org/10.1039/b815075c.
  Maglott, D., Ostell, J., Pruitt, K. D., & Tatusova, T. (2011). Entrez Gene: Gene‐centered information at NCBI. Nucleic Acids Research, 39(Database issue), D52–7. Retrieved from http://doi.org/10.1093/nar/gkq1237.
  Matlock, M. K., Holehouse, A. S., & Naegle, K. M. (2015). ProteomeScout: A repository and analysis resource for post‐translational modifications and proteins. Nucleic Acids Research, 43(D1), D521–D530. Retrieved from http://doi.org/10.1093/nar/gku1154.
  Naegle, K. M., Gymrek, M., Joughin, B. A., Wagner, J. P., Welsch, R. E., Yaffe, M. B., … White, F. M. (2010). PTMScout, a Web resource for analysis of high throughput post‐translational proteomics studies. Molecular & Cellular Proteomics: MCP, 9, 2558–2570. Retrieved from http://doi.org/10.1074/mcp.M110.001206.
  Naegle, K. M., Welsch, R. E., Yaffe, M. B., White, F. M., & Lauffenburger, D. A. (2011). MCAM: Multiple clustering analysis methodology for deriving hypotheses and insights from high‐throughput proteomic datasets. PLoS Computational Biology, 7(7), e1002119. Retrieved from http://doi.org/10.1371/journal.pcbi.1002119.
  Obenauer, J. C., Cantley, L. C., & Yaffe, M. B. (2003). Scansite 2.0: Proteome‐wide prediction of cell signaling interactions using short sequence motifs. Nucleic Acids Research, 31(13), 3635–3641. Retrieved from http://doi.org/10.1093/nar/gkg584.
  Sherry, S. T., Ward, M. H., Kholodov, M., Baker, J., Phan, L., Smigielski, E. M., & Sirotkin, K. (2001). dbSNP: The NCBI database of genetic variation. Nucleic Acids Research, 29(1), 308–311. Retrieved from http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=29783&tool=pmcentrez&rendertype=abstract.
  Su, A. I., Wiltshire, T., Batalov, S., Lapp, H., Ching, K. A., Block, D., … Hogenesch, J. B. (2004). A gene atlas of the mouse and human protein‐encoding transcriptomes. PNAS, 101(16), 6062–6067. doi: 10.1073/pnas.0400782101.
  The Gene Ontology Consortium. (2013). Gene Ontology annotations and resources. Nucleic Acids Research, 41(Database issue), D530–535. Retrieved from http://doi.org/10.1093/nar/gks1050.
  The UniProt Consortium. (2014). Activities at the Universal Protein Resource (UniProt). Nucleic Acids Research, 42(1), D191–198. Retrieved from http://doi.org/10.1093/nar/gkt1140.
  Wolf‐Yadlin, A., Kumar, N., Zhang, Y., Hautaniemi, S., Zaman, M., Kim, H.‐D., … White, F. M. (2006). Effects of HER2 overexpression on cell signaling networks governing proliferation and migration. Molecular Systems Biology, 2(1), 1–15. Retrieved from http://doi.org/10.1038/msb4100094.
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