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Mol Cell Proteomics. 2017 Jun;16(6):959-981. doi: 10.1074/mcp.MR117.000024. Epub 2017 Apr 29.

Methods, Tools and Current Perspectives in Proteogenomics.

Author information

1
From the ‡Department of Medicine, New York University School of Medicine, New York, New York 10016.
2
§The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142.
3
¶Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, Texas 77030.
4
‖Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030.
5
**Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354.
6
‡‡Department of Biochemistry and Molecular Pharmacology, New York University School of Medicine, New York, New York 10016; manidr@broadinstitute.org bing.zhang@bcm.edu David.Fenyo@nyumc.org.
7
§§Institute for Systems Genetics, New York University School of Medicine, New York, New York 10016.
8
¶Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, Texas 77030; manidr@broadinstitute.org bing.zhang@bcm.edu David.Fenyo@nyumc.org.
9
§The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142; manidr@broadinstitute.org bing.zhang@bcm.edu David.Fenyo@nyumc.org.

Abstract

With combined technological advancements in high-throughput next-generation sequencing and deep mass spectrometry-based proteomics, proteogenomics, i.e. the integrative analysis of proteomic and genomic data, has emerged as a new research field. Early efforts in the field were focused on improving protein identification using sample-specific genomic and transcriptomic sequencing data. More recently, integrative analysis of quantitative measurements from genomic and proteomic studies have identified novel insights into gene expression regulation, cell signaling, and disease. Many methods and tools have been developed or adapted to enable an array of integrative proteogenomic approaches and in this article, we systematically classify published methods and tools into four major categories, (1) Sequence-centric proteogenomics; (2) Analysis of proteogenomic relationships; (3) Integrative modeling of proteogenomic data; and (4) Data sharing and visualization. We provide a comprehensive review of methods and available tools in each category and highlight their typical applications.

PMID:
28456751
PMCID:
PMC5461547
DOI:
10.1074/mcp.MR117.000024
[Indexed for MEDLINE]
Free PMC Article

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