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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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 Urheber:
Sinitcyn, Pavel1, Autor           
Rudolph, Jan Daniel1, Autor           
Cox, Jürgen1, Autor           
Affiliations:
1Cox, Jürgen / Computational Systems Biochemistry, Max Planck Institute of Biochemistry, Max Planck Society, ou_2063284              

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Schlagwörter: 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;
 Zusammenfassung: 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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Sprache(n): eng - English
 Datum: 2018-07
 Publikationsstatus: Erschienen
 Seiten: 28
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Projektname : -
Grant ID : 686547
Förderprogramm : Horizon 2020 (H2020)
Förderorganisation : European Commission (EC)
Projektname : GA ERC-2012-SyG_318987–ToPAG
Grant ID : 318987
Förderprogramm : Funding Programme 7 (FP7)
Förderorganisation : European Commission (EC)

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Titel: Annual Review of Biomedical Data Science
  Alternativer Titel : Annu. Rev. Biomed. Data Sci.
  Alternativer Titel : ANNU REV BIOMED DA S
Genre der Quelle: Zeitschrift
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Ort, Verlag, Ausgabe: PALO ALTO, CA 94303-0897 USA : ANNUAL REVIEWS
Seiten: - Band / Heft: 1 (1) Artikelnummer: - Start- / Endseite: 207 - 234 Identifikator: ISSN: 2574-3414