| Chapter title |
Target-Decoy Search Strategy for Mass Spectrometry-Based Proteomics
|
|---|---|
| Chapter number | 5 |
| Book title |
Proteome Bioinformatics
|
| Published in |
Methods in molecular biology, December 2009
|
| DOI | 10.1007/978-1-60761-444-9_5 |
| Pubmed ID | |
| Book ISBNs |
978-1-60761-443-2, 978-1-60761-444-9
|
| Authors |
Joshua E. Elias, Steven P. Gygi, Elias, Joshua E., Gygi, Steven P. |
| Editors |
Simon J. Hubbard, Andrew R. Jones |
| Abstract |
Accurate and precise methods for estimating incorrect peptide and protein identifications are crucial for effective large-scale proteome analyses by tandem mass spectrometry. The target-decoy search strategy has emerged as a simple, effective tool for generating such estimations. This strategy is based on the premise that obvious, necessarily incorrect "decoy" sequences added to the search space will correspond with incorrect search results that might otherwise be deemed to be correct. With this knowledge, it is possible not only to estimate how many incorrect results are in a final data set but also to use decoy hits to guide the design of filtering criteria that sensitively partition a data set into correct and incorrect identifications. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 2 | 50% |
| United States | 1 | 25% |
| United Kingdom | 1 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 75% |
| Scientists | 1 | 25% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | <1% |
| United Kingdom | 2 | <1% |
| Russia | 2 | <1% |
| Belgium | 2 | <1% |
| Germany | 1 | <1% |
| Spain | 1 | <1% |
| South Africa | 1 | <1% |
| Unknown | 348 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 100 | 28% |
| Researcher | 61 | 17% |
| Student > Bachelor | 44 | 12% |
| Student > Master | 38 | 11% |
| Student > Doctoral Student | 18 | 5% |
| Other | 41 | 11% |
| Unknown | 58 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 109 | 30% |
| Agricultural and Biological Sciences | 86 | 24% |
| Chemistry | 31 | 9% |
| Computer Science | 18 | 5% |
| Engineering | 12 | 3% |
| Other | 41 | 11% |
| Unknown | 63 | 18% |