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

STAR Protoc. 2021 Nov 23;2(4):100955. doi: 10.1016/j.xpro.2021.100955. eCollection 2021 Dec 17.

Authors

Augustin Luna^{1

2

3}, Metin Can Siper⁴, Anil Korkut⁵, Funda Durupinar⁶, Ugur Dogrusoz⁷, Joseph E Aslan⁸, Chris Sander¹, Emek Demir^{4

9

10}, Ozgun Babur⁶

Affiliations

¹ Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA.
² Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
³ Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.
⁴ Computational Biology Program, Oregon Health and Science University, 3181 SW Sam Jackson Park Rd, Portland, OR 97239, USA.
⁵ Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
⁶ Computer Science Department, University of Massachusetts Boston, 100 William T. Morrissey Blvd, Boston, MA 02125, USA.
⁷ Computer Engineering Department, Bilkent University, Ankara 06800, Turkey.
⁸ Knight Cardiovascular Institute, Oregon Health and Science University, 3181 SW Sam Jackson Park Rd, Portland, OR 97239, USA.
⁹ Department of Molecular and Medical Genetics, Oregon Health and Science University, 3181 SW Sam Jackson Park Rd, Portland, OR 97239, USA.
¹⁰ Pacific Northwest National Laboratories, 902 Battelle Blvd, Richland, WA 99354, USA.

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).

Keywords: Bioinformatics; Proteomics; Systems biology.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Causality
Databases, Protein
Humans
Protein Interaction Mapping / methods*
Proteins* / metabolism
Proteins* / physiology
Proteomics / methods*
Signal Transduction / physiology*
Software

Substances

Proteins

Abstract

Publication types

MeSH terms

Substances

Grants and funding