Chapter title |
Proteomics as a Functional Genomics Tool
|
---|---|
Chapter number | -1592593736 |
Book title |
Plant Functional Genomics
|
Published in |
Methods in molecular biology, January 2003
|
DOI | 10.1385/1-59259-413-1:395 |
Pubmed ID | |
Book ISBNs |
978-1-59259-413-9, 978-1-58829-145-5
|
Authors |
Mathesius, Ulrike, Imin, Nijat, Natera, Siria H. A., Rolfe, Barry G., Ulrike Mathesius, Nijat Imin, Siria H. A. Natera, Barry G. Rolfe |
Editors |
Erich Grotewold |
Abstract |
To understand the function of all the genes in an organism, one needs to know not only which genes are expressed, when, and where, but also what the protein end products are and under which conditions they accumulate in certain tissues. Proteomics aims at describing the whole protein output of the genome and complements transcriptomic and metabolomic studies. Proteomics depends on extracting, separating, visualizing, identifying, and quantifying the proteins and their interactions present in an organism or tissue at any one time. All of these stages have limitations. Therefore, it is, at present, impossible to describe the whole proteome of any organism. Plants might synthesize many thousands of proteins at one time, and the whole potentially synthesized proteome certainly exceeds the number of estimated genes for that genome. This occurs because the gene products of one gene can differ due to alternative splicing and a variety of possible posttranslational modifications. It is, therefore, essential to optimize every step towards detecting the whole proteome while realizing the limitations. We concentrate here on the most commonly used steps in high-throughput plant proteomics with the techniques we have found most reproducible and with the highest resolution and quality. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Mexico | 1 | 3% |
Uruguay | 1 | 3% |
South Africa | 1 | 3% |
Unknown | 26 | 90% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 10 | 34% |
Researcher | 6 | 21% |
Professor | 2 | 7% |
Student > Postgraduate | 2 | 7% |
Student > Master | 2 | 7% |
Other | 4 | 14% |
Unknown | 3 | 10% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 17 | 59% |
Biochemistry, Genetics and Molecular Biology | 4 | 14% |
Chemistry | 2 | 7% |
Linguistics | 1 | 3% |
Medicine and Dentistry | 1 | 3% |
Other | 1 | 3% |
Unknown | 3 | 10% |