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Journal Article

A Network Module for the Perseus Software for Computational Proteomics Facilitates Proteome Interaction Graph Analysis

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Rudolph,  Jan Daniel
Cox, Jürgen / Computational Systems Biochemistry, Max Planck Institute of Biochemistry, Max Planck Society;

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Cox,  Jürgen
Cox, Jürgen / Computational Systems Biochemistry, Max Planck Institute of Biochemistry, Max Planck Society;

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Fulltext (public)

acs.jproteome.8b00927.pdf
(Publisher version), 4MB

Supplementary Material (public)

pr8b00927_si_001.pdf
(Supplementary material), 519KB

pr8b00927_si_002.txt
(Supplementary material), 2MB

pr8b00927_si_003.txt
(Supplementary material), 6MB

pr8b00927_si_004.txt
(Supplementary material), 4MB

pr8b00927_si_005.zip
(Supplementary material), 3KB

Citation

Rudolph, J. D., & Cox, J. (2019). A Network Module for the Perseus Software for Computational Proteomics Facilitates Proteome Interaction Graph Analysis. Journal of Proteome Research, 18(5), 2052-2064. doi:10.1021/acs.jproteome.8b00927.


Cite as: https://hdl.handle.net/21.11116/0000-0003-DD30-E
Abstract
Proteomics data analysis strongly benefits from not studying single proteins in isolation but taking their multivariate interdependence into account. We introduce PerseusNet, the new Perseus network module for the biological analysis of proteomics data. Proteomics is commonly used to generate networks, e.g., with affinity purification experiments, but networks are also used to explore proteomics data. PerseusNet supports the biomedical researcher for both modes of data analysis with a multitude of activities. For affinity purification, a volcano-plot-based statistical analysis method for network generation is featured which is scalable to large numbers of baits. For posttranslational modifications of proteins, such as phosphorylation, a collection of dedicated network analysis tools helps in elucidating cellular signaling events. Co-expression network analysis of proteomics data adopts established tools from transcriptome co-expression analysis. PerseusNet is extensible through a plugin architecture in a multi-lingual way, integrating analyses in C#, Python, and R, and is freely available at http://www.perseus-framework.org.