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  The Perseus computational platform for comprehensive analysis of (prote)omics data

Tyanova, S., Temu, T., Sinitcyn, P., Carlson, A., Hein, M. Y., Geiger, T., et al. (2016). The Perseus computational platform for comprehensive analysis of (prote)omics data. Nature methods, 13(9), 731-740. doi:10.1038/NMETH.3901.

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 Creators:
Tyanova, Stefka1, Author              
Temu, Tikira1, Author              
Sinitcyn, Pavel1, Author              
Carlson, Arthur1, Author              
Hein, Marco Y.2, Author
Geiger, Tamar2, Author
Mann, Matthias3, Author              
Cox, Jürgen1, Author              
Affiliations:
1Cox, Jürgen / Computational Systems Biochemistry, Max Planck Institute of Biochemistry, Max Planck Society, ou_2063284              
2external, ou_persistent22              
3Mann, Matthias / Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Max Planck Society, ou_1565159              

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Free keywords: TANDEM MASS-SPECTROMETRY; QUANTITATIVE PROTEOMICS; PROTEIN IDENTIFICATION; GENE-EXPRESSION; RNA-SEQ; HUMAN INTERACTOME; CELL-LINE; IN-VIVO; QUANTIFICATION; ACCURATEBiochemistry & Molecular Biology;
 Abstract: A main bottleneck in proteomics is the downstream biological analysis of highly multivariate quantitative protein abundance data generated using mass spectrometry-based analysis. We developed the Perseus software platform (http://www.perseus-framework.org) to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data. Perseus contains a comprehensive portfolio of statistical toots for high-dimensional omics data analysis covering normalization, pattern recognition, time-series analysis, cross-omics comparisons and multiple hypothesis testing. A machine learning module supports the classification and validation of patient groups for diagnosis and prognosis, and it also detects predictive protein signatures. Central to Perseus is a user-friendly, interactive workflow environment that provides complete documentation of computational methods used in a publication. ALL activities in Perseus are realized as plugins, and users can extend the software by programming their own, which can be shared through a plugin store. We anticipate that Perseus's arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets.

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Language(s): eng - English
 Dates: 2016-01-282016-05-102016-06-272016
 Publication Status: Published in print
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 Rev. Type: -
 Identifiers: ISI: 000382896200015
DOI: 10.1038/NMETH.3901
 Degree: -

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Project name : FP7 grant agreement GA ERC-2012-SyG_318987–ToPAG (J.C.)
Grant ID : 686547
Funding program : Horizon 2020 (H2020)
Funding organization : European Commission (EC)

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Title: Nature methods
  Other : Nature methods
Source Genre: Journal
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Publ. Info: New York, NY : Nature Pub. Group
Pages: - Volume / Issue: 13 (9) Sequence Number: - Start / End Page: 731 - 740 Identifier: ISSN: 1548-7091
CoNE: https://pure.mpg.de/cone/journals/resource/111088195279556