Analyzing causal relationships in proteomic profiles using CausalPath

Augustin Luna; Metin Can Siper; Anil Korkut; Funda Durupinar; Ugur Dogrusoz; Joseph E. Aslan; Chris Sander; Emek Demir; Ozgun Babur

doi:10.1016/j.xpro.2021.100955

Analyzing causal relationships in proteomic profiles using CausalPath

Augustin Luna, Metin Can Siper, Anil Korkut, Funda Durupinar, Ugur Dogrusoz, Joseph E. Aslan, Chris Sander, Emek Demir, Ozgun Babur

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

Abstract

CausalPath (causalpath.org) evaluates proteomic measurements against prior knowledge of biological pathways and infers causality between changes in measured features, such as global protein and phospho-protein levels. It uses pathway resources to determine potential causality between observable omic features, which are called prior relations. The subset of the prior relations that are supported by the proteomic profiles are reported and evaluated for statistical significance. The end result is a network model of signaling that explains the patterns observed in the experimental dataset. For complete details on the use and execution of this protocol, please refer to Babur et al. (2021).

Original language	English (US)
Article number	100955
Journal	STAR Protocols
Volume	2
Issue number	4
DOIs	https://doi.org/10.1016/j.xpro.2021.100955
State	Published - Dec 17 2021

Keywords

Bioinformatics
Proteomics
Systems biology

ASJC Scopus subject areas

General Immunology and Microbiology
General Biochemistry, Genetics and Molecular Biology
General Neuroscience

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10.1016/j.xpro.2021.100955

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abstract = "CausalPath (causalpath.org) evaluates proteomic measurements against prior knowledge of biological pathways and infers causality between changes in measured features, such as global protein and phospho-protein levels. It uses pathway resources to determine potential causality between observable omic features, which are called prior relations. The subset of the prior relations that are supported by the proteomic profiles are reported and evaluated for statistical significance. The end result is a network model of signaling that explains the patterns observed in the experimental dataset. For complete details on the use and execution of this protocol, please refer to Babur et al. (2021).",

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AU - Luna, Augustin

AU - Siper, Metin Can

AU - Korkut, Anil

AU - Durupinar, Funda

AU - Dogrusoz, Ugur

AU - Aslan, Joseph E.

AU - Sander, Chris

AU - Demir, Emek

AU - Babur, Ozgun

PY - 2021/12/17

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AB - CausalPath (causalpath.org) evaluates proteomic measurements against prior knowledge of biological pathways and infers causality between changes in measured features, such as global protein and phospho-protein levels. It uses pathway resources to determine potential causality between observable omic features, which are called prior relations. The subset of the prior relations that are supported by the proteomic profiles are reported and evaluated for statistical significance. The end result is a network model of signaling that explains the patterns observed in the experimental dataset. For complete details on the use and execution of this protocol, please refer to Babur et al. (2021).

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Analyzing causal relationships in proteomic profiles using CausalPath

Abstract

Keywords

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