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IDEAL-Q, an Automated Tool for Label-free Quantitation Analysis Using an Efficient Peptide Alignment Approach and Spectral Data Validation
In this study, we present a fully automated tool, called IDEAL-Q, for label-free quantitation analysis. It accepts raw data in the standard mzXML format as well as search results from major search engines, including Mascot, SEQUEST, and X!Tandem, as input data. To quantify as many identified peptide...
Autores principales: | , , , , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
The American Society for Biochemistry and Molecular Biology
2010
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2808259/ https://www.ncbi.nlm.nih.gov/pubmed/19752006 http://dx.doi.org/10.1074/mcp.M900177-MCP200 |
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author | Tsou, Chih-Chiang Tsai, Chia-Feng Tsui, Ying-Hao Sudhir, Putty-Reddy Wang, Yi-Ting Chen, Yu-Ju Chen, Jeou-Yuan Sung, Ting-Yi Hsu, Wen-Lian |
author_facet | Tsou, Chih-Chiang Tsai, Chia-Feng Tsui, Ying-Hao Sudhir, Putty-Reddy Wang, Yi-Ting Chen, Yu-Ju Chen, Jeou-Yuan Sung, Ting-Yi Hsu, Wen-Lian |
author_sort | Tsou, Chih-Chiang |
collection | PubMed |
description | In this study, we present a fully automated tool, called IDEAL-Q, for label-free quantitation analysis. It accepts raw data in the standard mzXML format as well as search results from major search engines, including Mascot, SEQUEST, and X!Tandem, as input data. To quantify as many identified peptides as possible, IDEAL-Q uses an efficient algorithm to predict the elution time of a peptide unidentified in a specific LC-MS/MS run but identified in other runs. Then, the predicted elution time is used to detect peak clusters of the assigned peptide. Detected peptide peaks are processed by statistical and computational methods and further validated by signal-to-noise ratio, charge state, and isotopic distribution criteria (SCI validation) to filter out noisy data. The performance of IDEAL-Q has been evaluated by several experiments. First, a serially diluted protein mixed with Escherichia coli lysate showed a high correlation with expected ratios and demonstrated good linearity (R(2) = 0.996). Second, in a biological replicate experiment on the THP-1 cell lysate, IDEAL-Q quantified 87% (1,672 peptides) of all identified peptides, surpassing the 45.7% (909 peptides) achieved by the conventional identity-based approach, which only quantifies peptides identified in all LC-MS/MS runs. Manual validation on all 11,940 peptide ions in six replicate LC-MS/MS runs revealed that 97.8% of the peptide ions were correctly aligned, and 93.3% were correctly validated by SCI. Thus, the mean of the protein ratio, 1.00 ± 0.05, demonstrates the high accuracy of IDEAL-Q without human intervention. Finally, IDEAL-Q was applied again to the biological replicate experiment but with an additional SDS-PAGE step to show its compatibility for label-free experiments with fractionation. For flexible workflow design, IDEAL-Q supports different fractionation strategies and various normalization schemes, including multiple spiked internal standards. User-friendly interfaces are provided to facilitate convenient inspection, validation, and modification of quantitation results. In summary, IDEAL-Q is an efficient, user-friendly, and robust quantitation tool. It is available for download. |
format | Text |
id | pubmed-2808259 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | The American Society for Biochemistry and Molecular Biology |
record_format | MEDLINE/PubMed |
spelling | pubmed-28082592010-01-21 IDEAL-Q, an Automated Tool for Label-free Quantitation Analysis Using an Efficient Peptide Alignment Approach and Spectral Data Validation Tsou, Chih-Chiang Tsai, Chia-Feng Tsui, Ying-Hao Sudhir, Putty-Reddy Wang, Yi-Ting Chen, Yu-Ju Chen, Jeou-Yuan Sung, Ting-Yi Hsu, Wen-Lian Mol Cell Proteomics Research In this study, we present a fully automated tool, called IDEAL-Q, for label-free quantitation analysis. It accepts raw data in the standard mzXML format as well as search results from major search engines, including Mascot, SEQUEST, and X!Tandem, as input data. To quantify as many identified peptides as possible, IDEAL-Q uses an efficient algorithm to predict the elution time of a peptide unidentified in a specific LC-MS/MS run but identified in other runs. Then, the predicted elution time is used to detect peak clusters of the assigned peptide. Detected peptide peaks are processed by statistical and computational methods and further validated by signal-to-noise ratio, charge state, and isotopic distribution criteria (SCI validation) to filter out noisy data. The performance of IDEAL-Q has been evaluated by several experiments. First, a serially diluted protein mixed with Escherichia coli lysate showed a high correlation with expected ratios and demonstrated good linearity (R(2) = 0.996). Second, in a biological replicate experiment on the THP-1 cell lysate, IDEAL-Q quantified 87% (1,672 peptides) of all identified peptides, surpassing the 45.7% (909 peptides) achieved by the conventional identity-based approach, which only quantifies peptides identified in all LC-MS/MS runs. Manual validation on all 11,940 peptide ions in six replicate LC-MS/MS runs revealed that 97.8% of the peptide ions were correctly aligned, and 93.3% were correctly validated by SCI. Thus, the mean of the protein ratio, 1.00 ± 0.05, demonstrates the high accuracy of IDEAL-Q without human intervention. Finally, IDEAL-Q was applied again to the biological replicate experiment but with an additional SDS-PAGE step to show its compatibility for label-free experiments with fractionation. For flexible workflow design, IDEAL-Q supports different fractionation strategies and various normalization schemes, including multiple spiked internal standards. User-friendly interfaces are provided to facilitate convenient inspection, validation, and modification of quantitation results. In summary, IDEAL-Q is an efficient, user-friendly, and robust quantitation tool. It is available for download. The American Society for Biochemistry and Molecular Biology 2010-01 2009-09-13 /pmc/articles/PMC2808259/ /pubmed/19752006 http://dx.doi.org/10.1074/mcp.M900177-MCP200 Text en © 2010 by The American Society for Biochemistry and Molecular Biology, Inc. Author's Choice—Final version full access. Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) applies to Author Choice Articles |
spellingShingle | Research Tsou, Chih-Chiang Tsai, Chia-Feng Tsui, Ying-Hao Sudhir, Putty-Reddy Wang, Yi-Ting Chen, Yu-Ju Chen, Jeou-Yuan Sung, Ting-Yi Hsu, Wen-Lian IDEAL-Q, an Automated Tool for Label-free Quantitation Analysis Using an Efficient Peptide Alignment Approach and Spectral Data Validation |
title | IDEAL-Q, an Automated Tool for Label-free Quantitation Analysis Using an Efficient Peptide Alignment Approach and Spectral Data Validation |
title_full | IDEAL-Q, an Automated Tool for Label-free Quantitation Analysis Using an Efficient Peptide Alignment Approach and Spectral Data Validation |
title_fullStr | IDEAL-Q, an Automated Tool for Label-free Quantitation Analysis Using an Efficient Peptide Alignment Approach and Spectral Data Validation |
title_full_unstemmed | IDEAL-Q, an Automated Tool for Label-free Quantitation Analysis Using an Efficient Peptide Alignment Approach and Spectral Data Validation |
title_short | IDEAL-Q, an Automated Tool for Label-free Quantitation Analysis Using an Efficient Peptide Alignment Approach and Spectral Data Validation |
title_sort | ideal-q, an automated tool for label-free quantitation analysis using an efficient peptide alignment approach and spectral data validation |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2808259/ https://www.ncbi.nlm.nih.gov/pubmed/19752006 http://dx.doi.org/10.1074/mcp.M900177-MCP200 |
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