Computational approaches to protein inference in shotgun proteomics
<p>Abstract</p> <p>Shotgun proteomics has recently emerged as a powerful approach to characterizing proteomes in biological samples. Its overall objective is to identify the form and quantity of each protein in a high-throughput manner by coupling liquid chromatography with tandem...
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| Format: | Article |
| Language: | English |
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BMC
2012-11-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/13/S16/S4 |
| _version_ | 1818578840340922368 |
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| author | Li Yong Radivojac Predrag |
| author_facet | Li Yong Radivojac Predrag |
| author_sort | Li Yong |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Shotgun proteomics has recently emerged as a powerful approach to characterizing proteomes in biological samples. Its overall objective is to identify the form and quantity of each protein in a high-throughput manner by coupling liquid chromatography with tandem mass spectrometry. As a consequence of its high throughput nature, shotgun proteomics faces challenges with respect to the analysis and interpretation of experimental data. Among such challenges, the identification of proteins present in a sample has been recognized as an important computational task. This task generally consists of (1) assigning experimental tandem mass spectra to peptides derived from a protein database, and (2) mapping assigned peptides to proteins and quantifying the confidence of identified proteins. Protein identification is fundamentally a statistical inference problem with a number of methods proposed to address its challenges. In this review we categorize current approaches into rule-based, combinatorial optimization and probabilistic inference techniques, and present them using integer programing and Bayesian inference frameworks. We also discuss the main challenges of protein identification and propose potential solutions with the goal of spurring innovative research in this area.</p> |
| first_indexed | 2024-12-16T06:52:12Z |
| format | Article |
| id | doaj.art-f6cfcd31198146e1a44e139df53d82b2 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-16T06:52:12Z |
| publishDate | 2012-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-f6cfcd31198146e1a44e139df53d82b22022-12-21T22:40:22ZengBMCBMC Bioinformatics1471-21052012-11-0113Suppl 16S410.1186/1471-2105-13-S16-S4Computational approaches to protein inference in shotgun proteomicsLi YongRadivojac Predrag<p>Abstract</p> <p>Shotgun proteomics has recently emerged as a powerful approach to characterizing proteomes in biological samples. Its overall objective is to identify the form and quantity of each protein in a high-throughput manner by coupling liquid chromatography with tandem mass spectrometry. As a consequence of its high throughput nature, shotgun proteomics faces challenges with respect to the analysis and interpretation of experimental data. Among such challenges, the identification of proteins present in a sample has been recognized as an important computational task. This task generally consists of (1) assigning experimental tandem mass spectra to peptides derived from a protein database, and (2) mapping assigned peptides to proteins and quantifying the confidence of identified proteins. Protein identification is fundamentally a statistical inference problem with a number of methods proposed to address its challenges. In this review we categorize current approaches into rule-based, combinatorial optimization and probabilistic inference techniques, and present them using integer programing and Bayesian inference frameworks. We also discuss the main challenges of protein identification and propose potential solutions with the goal of spurring innovative research in this area.</p>http://www.biomedcentral.com/1471-2105/13/S16/S4 |
| spellingShingle | Li Yong Radivojac Predrag Computational approaches to protein inference in shotgun proteomics BMC Bioinformatics |
| title | Computational approaches to protein inference in shotgun proteomics |
| title_full | Computational approaches to protein inference in shotgun proteomics |
| title_fullStr | Computational approaches to protein inference in shotgun proteomics |
| title_full_unstemmed | Computational approaches to protein inference in shotgun proteomics |
| title_short | Computational approaches to protein inference in shotgun proteomics |
| title_sort | computational approaches to protein inference in shotgun proteomics |
| url | http://www.biomedcentral.com/1471-2105/13/S16/S4 |
| work_keys_str_mv | AT liyong computationalapproachestoproteininferenceinshotgunproteomics AT radivojacpredrag computationalapproachestoproteininferenceinshotgunproteomics |