Chapter title |
Statistical Analysis of ATM-Dependent Signaling in Quantitative Mass Spectrometry Phosphoproteomics
|
---|---|
Chapter number | 17 |
Book title |
ATM Kinase
|
Published in |
Methods in molecular biology, January 2017
|
DOI | 10.1007/978-1-4939-6955-5_17 |
Pubmed ID | |
Book ISBNs |
978-1-4939-6953-1, 978-1-4939-6955-5, 978-1-4939-6953-1, 978-1-4939-6955-5
|
Authors |
Ashley J. Waardenberg |
Editors |
Sergei V. Kozlov |
Abstract |
Ataxia-telangiectasia mutated (ATM) is a serine/threonine protein kinase, which when perturbed is associated with modified protein signaling that ultimately leads to a range of neurological and DNA repair defects. Recent advances in phospho-proteomics coupled with high-resolution mass-spectrometry provide new opportunities to dissect signaling pathways that ATM utilize under a number of conditions. This chapter begins by providing a brief overview of ATM function, its various regulatory roles and then leads into a workflow focused on the use of the statistical programming language R, together with code, for the identification of ATM-dependent substrates in the cytoplasm. This chapter cannot cover statistical properties in depth nor the range of possible methods in great detail, but instead aims to equip researchers with a set of tools to perform analysis between two conditions through examples with R functions. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 50% |
Unknown | 1 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 1 | 50% |
Members of the public | 1 | 50% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 10 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 4 | 40% |
Student > Master | 2 | 20% |
Researcher | 2 | 20% |
Professor > Associate Professor | 1 | 10% |
Unknown | 1 | 10% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 2 | 20% |
Medicine and Dentistry | 2 | 20% |
Computer Science | 2 | 20% |
Biochemistry, Genetics and Molecular Biology | 1 | 10% |
Neuroscience | 1 | 10% |
Other | 1 | 10% |
Unknown | 1 | 10% |