| Chapter title |
Mass Spectrometry-Based Protein Quantification
|
|---|---|
| Chapter number | 15 |
| Book title |
Modern Proteomics – Sample Preparation, Analysis and Practical Applications
|
| Published in |
Advances in experimental medicine and biology, December 2016
|
| DOI | 10.1007/978-3-319-41448-5_15 |
| Pubmed ID | |
| Book ISBNs |
978-3-31-941446-1, 978-3-31-941448-5
|
| Authors |
Yun Chen, Fuqiang Wang, Feifei Xu, Ting Yang |
| Editors |
Hamid Mirzaei, Martin Carrasco |
| Abstract |
Quantification of individual proteins and even entire proteomes is an important theme in proteomics research. Quantitative proteomics is an approach to obtain quantitative information about proteins in a sample. Compared to qualitative or semi-quantitative proteomics, this approach can provide more insight into the effects of a specific stimulus, such as a change in the expression level of a protein and its posttranslational modifications, or to a panel of proposed biomarkers in a given disease state. Proteomics methodologies, along with a variety of bioinformatics approaches, are a major tool in quantitative proteomics. As the theory and technological aspects underlying the proteomics methodologies will be extensively described in Chap. 20 , and protein identification as a prerequisite of quantification has been discussed in Chap. 17 , we will focus on the quantitative proteomics bioinformatics algorithms and software tools in this chapter. Our goal is to provide researchers and newcomers a rational framework to select suitable bioinformatics tools for data analysis, interpretation, and integration in protein quantification. Before doing so, a brief overview of quantitative proteomics is provided. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 61 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 14 | 23% |
| Student > Master | 10 | 16% |
| Researcher | 7 | 11% |
| Student > Bachelor | 4 | 7% |
| Student > Doctoral Student | 2 | 3% |
| Other | 6 | 10% |
| Unknown | 18 | 30% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 20 | 33% |
| Chemistry | 6 | 10% |
| Pharmacology, Toxicology and Pharmaceutical Science | 3 | 5% |
| Agricultural and Biological Sciences | 3 | 5% |
| Medicine and Dentistry | 2 | 3% |
| Other | 3 | 5% |
| Unknown | 24 | 39% |