Chapter title |
Introduction to Proteomics Technologies.
|
---|---|
Chapter number | 1 |
Book title |
Statistical Analysis in Proteomics
|
Published in |
Methods in molecular biology, January 2016
|
DOI | 10.1007/978-1-4939-3106-4_1 |
Pubmed ID | |
Book ISBNs |
978-1-4939-3105-7, 978-1-4939-3106-4
|
Authors |
Lenz, Christof, Dihazi, Hassan, Christof Lenz, Hassan Dihazi |
Abstract |
Compared to genomics or transcriptomics, proteomics is often regarded as an "emerging technology," i.e., as not having reached the same level of maturity. While the successful implementation of proteomics workflows and technology still requires significant levels of expertise and specialization, great strides have been made to make the technology more powerful, streamlined and accessible. In 2014, two landmark studies published the first draft versions of the human proteome.We aim to provide an introduction specifically into the background of mass spectrometry (MS)-based proteomics. Within the field, mass spectrometry has emerged as a core technology. Coupled to increasingly powerful separations and data processing and bioinformatics solution, it allows the quantitative analysis of whole proteomes within a matter of days, a timescale that has made global comparative proteome studies feasible at last. We present and discuss the basic concepts behind proteomics mass spectrometry and the accompanying topic of protein and peptide separations, with a focus on the properties of datasets emerging from such studies. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Canada | 2 | 50% |
United Kingdom | 1 | 25% |
Unknown | 1 | 25% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 3 | 75% |
Scientists | 1 | 25% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 69 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Bachelor | 13 | 19% |
Student > Ph. D. Student | 12 | 17% |
Student > Master | 10 | 14% |
Student > Doctoral Student | 5 | 7% |
Researcher | 3 | 4% |
Other | 8 | 12% |
Unknown | 18 | 26% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 17 | 25% |
Agricultural and Biological Sciences | 11 | 16% |
Pharmacology, Toxicology and Pharmaceutical Science | 4 | 6% |
Medicine and Dentistry | 4 | 6% |
Computer Science | 2 | 3% |
Other | 9 | 13% |
Unknown | 22 | 32% |