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  Computational Methods for Understanding Mass Spectrometry-Based Shotgun Proteomics Data

Sinitcyn, P., Rudolph, J. D., & Cox, J. (2018). Computational Methods for Understanding Mass Spectrometry-Based Shotgun Proteomics Data. Annual Review of Biomedical Data Science, 1(1), 207-234. doi:10.1146/annurev-biodatasci-080917-013516.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0003-CAA0-4 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-CAA1-3
Genre: Journal Article

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annurev-biodatasci-080917-013516.pdf (Publisher version), 4MB
 
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 Creators:
Sinitcyn, Pavel1, Author              
Rudolph, Jan Daniel1, Author              
Cox, Jürgen1, Author              
Affiliations:
1Cox, Jürgen / Computational Systems Biochemistry, Max Planck Institute of Biochemistry, Max Planck Society, ou_2063284              

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Free keywords: DATA-INDEPENDENT ACQUISITION; CHEMICAL CROSS-LINKING; PHOSPHORYLATION SITE LOCALIZATION; FALSE DISCOVERY RATE; PROTEIN IDENTIFICATION; PEPTIDE IDENTIFICATION; TARGETED PROTEOMICS; STATISTICAL-MODEL; ENABLES ACCURATE; SOFTWAREMathematical & Computational Biology; computational proteomics; mass spectrometry; posttranslational modifications; multiomics data analysis; multivariate analysis; network analysis;
 Abstract: Computational proteomics is the data science concerned with the identification and quantification of proteins from high-throughput data and the biological interpretation of their concentration changes, posttranslational modifications, interactions, and subcellular localizations. Today, these data most often originate from mass spectrometry-based shotgun proteomics experiments. In this review, we survey computational methods for the analysis of such proteomics data, focusing on the explanation of the key concepts. Starting with mass spectrometric feature detection, we then cover methods for the identification of peptides. Subsequently, protein inference and the control of false discovery rates are highly important topics covered. We then discuss methods for the quantification of peptides and proteins. A section on downstream data analysis covers exploratory statistics, network analysis, machine learning, and multiomics data integration. Finally, we discuss current developments and provide an outlook on what the near future of computational proteomics might bear.

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Language(s): eng - English
 Dates: 2018-07
 Publication Status: Published in print
 Pages: 28
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Degree: -

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Project name : -
Grant ID : 686547
Funding program : Horizon 2020 (H2020)
Funding organization : European Commission (EC)
Project name : GA ERC-2012-SyG_318987–ToPAG
Grant ID : 318987
Funding program : Funding Programme 7 (FP7)
Funding organization : European Commission (EC)

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Title: Annual Review of Biomedical Data Science
  Alternative Title : Annu. Rev. Biomed. Data Sci.
  Alternative Title : ANNU REV BIOMED DA S
Source Genre: Journal
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Publ. Info: PALO ALTO, CA 94303-0897 USA : ANNUAL REVIEWS
Pages: - Volume / Issue: 1 (1) Sequence Number: - Start / End Page: 207 - 234 Identifier: ISSN: 2574-3414