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A Chromosome-centric Human Proteome Project (C-HPP) to Characterize the Sets of Proteins Encoded in Chromosome 17

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    A Chromosome-centric Human Proteome Project (C-HPP) to Characterize the Sets of Proteins Encoded in Chromosome 17
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    † Barnett Institute and Department of Chemistry and Chemical Biology, Northeastern University, Boston, Massachusetts 02115, United States
    ‡ Stanford University, Palo Alto, California, United States
    § Swiss Institute of Bioinformatics (SIB) and University of Geneva, Geneva, Switzerland
    ∥ Fox Chase Cancer Center, Philadelphia, Pennsylvania, United States
    ⊥ Institute for System Biology, Seattle, Washington, United States
    School of Medicine, New York University, New York, United States
    $ Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, NSW, Australia
    ○ MD Anderson Cancer Center, Houston, Texas, United States
    ■ Yonsei University College of Medicine, Yonsei University, Seoul, Korea
    △ Yonsei Proteome Research Center, Yonsei University, Seoul, Korea
    ⬢ Departments of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics, and School of Public Health, University of Michigan, Ann Arbor, Michigan, United States
    □ Monash Antibody Technologies Facility, Monash University, Clayton, VIC 3800, Australia
    # Department of Integrated Omics for Biomedical Science, Yonsei University, Seoul, Korea
    ● Compendia Biosciences Inc., Ann Arbor, Michigan, United States
    ⬡ Science for Life Laboratory and Albanova University Center, Royal Institute of Technology (KTH), Stockholm, Sweden
    ▲ Complex Carbohydrate Research Center, University of Georgia, 315 Riverbend Road, Athens, Georgia, United States
    *E-mail: [email protected]. Telephone: 617-373-4881. Fax: 617-373-8795.
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    Journal of Proteome Research

    Cite this: J. Proteome Res. 2013, 12, 1, 45–57
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    https://doi.org/10.1021/pr300985j
    Published December 21, 2012
    Copyright © 2012 American Chemical Society
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    Abstract

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    We report progress assembling the parts list for chromosome 17 and illustrate the various processes that we have developed to integrate available data from diverse genomic and proteomic knowledge bases. As primary resources, we have used GPMDB, neXtProt, PeptideAtlas, Human Protein Atlas (HPA), and GeneCards. All sites share the common resource of Ensembl for the genome modeling information. We have defined the chromosome 17 parts list with the following information: 1169 protein-coding genes, the numbers of proteins confidently identified by various experimental approaches as documented in GPMDB, neXtProt, PeptideAtlas, and HPA, examples of typical data sets obtained by RNASeq and proteomic studies of epithelial derived tumor cell lines (disease proteome) and a normal proteome (peripheral mononuclear cells), reported evidence of post-translational modifications, and examples of alternative splice variants (ASVs). We have constructed a list of the 59 “missing” proteins as well as 201 proteins that have inconclusive mass spectrometric (MS) identifications. In this report we have defined a process to establish a baseline for the incorporation of new evidence on protein identification and characterization as well as related information from transcriptome analyses. This initial list of “missing” proteins that will guide the selection of appropriate samples for discovery studies as well as antibody reagents. Also we have illustrated the significant diversity of protein variants (including post-translational modifications, PTMs) using regions on chromosome 17 that contain important oncogenes. We emphasize the need for mandated deposition of proteomics data in public databases, the further development of improved PTM, ASV, and single nucleotide variant (SNV) databases, and the construction of Web sites that can integrate and regularly update such information. In addition, we describe the distribution of both clustered and scattered sets of protein families on the chromosome. Since chromosome 17 is rich in cancer-associated genes, we have focused the clustering of cancer-associated genes in such genomic regions and have used the ERBB2 amplicon as an example of the value of a proteogenomic approach in which one integrates transcriptomic with proteomic information and captures evidence of coexpression through coordinated regulation.

    Copyright © 2012 American Chemical Society

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    Cited By

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    This article is cited by 31 publications.

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    Journal of Proteome Research

    Cite this: J. Proteome Res. 2013, 12, 1, 45–57
    Click to copy citationCitation copied!
    https://doi.org/10.1021/pr300985j
    Published December 21, 2012
    Copyright © 2012 American Chemical Society
    Request reuse permissions

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