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Methods Mol Biol. 2017;1549:177-197.

Network Tools for the Analysis of Proteomic Data.

Author information

1
Department of Computer Science and Information Technology, School of Engineering and Mathematical Sciencesy, La Trobe University, Melbourne, VIC, 3086, Australia.
2
Department of Biochemistry and Genetics, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, VIC, 3086, Australia.
3
Department of Computer Science and Information Technology, School of Engineering and Mathematical Sciencesy, La Trobe University, Melbourne, VIC, 3086, Australia. n.chilamkurti@latrobe.edu.au.

Abstract

Recent advancements in high-throughput technologies such as mass spectrometry have led to an increase in the rate at which data is generated and accumulated. As a result, standard statistical methods no longer suffice as a way of analyzing such gigantic amounts of data. Network analysis, the evaluation of how nodes relate to one another, has over the years become an integral tool for analyzing high throughput proteomic data as they provide a structure that helps reduce the complexity of the underlying data.Computational tools, including pathway databases and network building tools, have therefore been developed to store, analyze, interpret, and learn from proteomics data. These tools enable the visualization of proteins as networks of signaling, regulatory, and biochemical interactions. In this chapter, we provide an overview of networks and network theory fundamentals for the analysis of proteomics data. We further provide an overview of interaction databases and network tools which are frequently used for analyzing proteomics data.

KEYWORDS:

Bioinformatics; Network analysis; Network theory; Network tools; Protein–protein interactions; Proteomics

PMID:
27975292
DOI:
10.1007/978-1-4939-6740-7_14
[Indexed for MEDLINE]

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