iQuantitator: A tool for protein expression inference using iTRAQ
<p>Abstract</p> <p>Background</p> <p>Isobaric Tags for Relative and Absolute Quantitation (iTRAQ™) [Applied Biosystems] have seen increased application in differential protein expression analysis. To facilitate the growing need to analyze iTRAQ data, especially for case...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2009-10-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/10/342 |
| _version_ | 1811319929604931584 |
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| author | Comte-Walters Susana Krug Edward L Hill Elizabeth G Schwacke John H Schey Kevin L |
| author_facet | Comte-Walters Susana Krug Edward L Hill Elizabeth G Schwacke John H Schey Kevin L |
| author_sort | Comte-Walters Susana |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Isobaric Tags for Relative and Absolute Quantitation (iTRAQ™) [Applied Biosystems] have seen increased application in differential protein expression analysis. To facilitate the growing need to analyze iTRAQ data, especially for cases involving multiple iTRAQ experiments, we have developed a modeling approach, statistical methods, and tools for estimating the relative changes in protein expression under various treatments and experimental conditions.</p> <p>Results</p> <p>This modeling approach provides a unified analysis of data from multiple iTRAQ experiments and links the observed quantity (reporter ion peak area) to the experiment design and the calculated quantity of interest (treatment-dependent protein and peptide fold change) through an additive model under log transformation. Others have demonstrated, through a case study, this modeling approach and noted the computational challenges of parameter inference in the unbalanced data set typical of multiple iTRAQ experiments. Here we present the development of an inference approach, based on hierarchical regression with batching of regression coefficients and Markov Chain Monte Carlo (MCMC) methods that overcomes some of these challenges. In addition to our discussion of the underlying method, we also present our implementation of the software, simulation results, experimental results, and sample output from the resulting analysis report.</p> <p>Conclusion</p> <p>iQuantitator's process-based modeling approach overcomes limitations in current methods and allows for application in a variety of experimental designs. Additionally, hypertext-linked documents produced by the tool aid in the interpretation and exploration of results.</p> |
| first_indexed | 2024-04-13T12:50:53Z |
| format | Article |
| id | doaj.art-19ebd7e6da8a49d791190f6f0061f4ab |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-13T12:50:53Z |
| publishDate | 2009-10-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-19ebd7e6da8a49d791190f6f0061f4ab2022-12-22T02:46:12ZengBMCBMC Bioinformatics1471-21052009-10-0110134210.1186/1471-2105-10-342iQuantitator: A tool for protein expression inference using iTRAQComte-Walters SusanaKrug Edward LHill Elizabeth GSchwacke John HSchey Kevin L<p>Abstract</p> <p>Background</p> <p>Isobaric Tags for Relative and Absolute Quantitation (iTRAQ™) [Applied Biosystems] have seen increased application in differential protein expression analysis. To facilitate the growing need to analyze iTRAQ data, especially for cases involving multiple iTRAQ experiments, we have developed a modeling approach, statistical methods, and tools for estimating the relative changes in protein expression under various treatments and experimental conditions.</p> <p>Results</p> <p>This modeling approach provides a unified analysis of data from multiple iTRAQ experiments and links the observed quantity (reporter ion peak area) to the experiment design and the calculated quantity of interest (treatment-dependent protein and peptide fold change) through an additive model under log transformation. Others have demonstrated, through a case study, this modeling approach and noted the computational challenges of parameter inference in the unbalanced data set typical of multiple iTRAQ experiments. Here we present the development of an inference approach, based on hierarchical regression with batching of regression coefficients and Markov Chain Monte Carlo (MCMC) methods that overcomes some of these challenges. In addition to our discussion of the underlying method, we also present our implementation of the software, simulation results, experimental results, and sample output from the resulting analysis report.</p> <p>Conclusion</p> <p>iQuantitator's process-based modeling approach overcomes limitations in current methods and allows for application in a variety of experimental designs. Additionally, hypertext-linked documents produced by the tool aid in the interpretation and exploration of results.</p>http://www.biomedcentral.com/1471-2105/10/342 |
| spellingShingle | Comte-Walters Susana Krug Edward L Hill Elizabeth G Schwacke John H Schey Kevin L iQuantitator: A tool for protein expression inference using iTRAQ BMC Bioinformatics |
| title | iQuantitator: A tool for protein expression inference using iTRAQ |
| title_full | iQuantitator: A tool for protein expression inference using iTRAQ |
| title_fullStr | iQuantitator: A tool for protein expression inference using iTRAQ |
| title_full_unstemmed | iQuantitator: A tool for protein expression inference using iTRAQ |
| title_short | iQuantitator: A tool for protein expression inference using iTRAQ |
| title_sort | iquantitator a tool for protein expression inference using itraq |
| url | http://www.biomedcentral.com/1471-2105/10/342 |
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