Data-driven normalization strategies for high-throughput quantitative RT-PCR
<p>Abstract</p> <p>Background</p> <p>High-throughput real-time quantitative reverse transcriptase polymerase chain reaction (qPCR) is a widely used technique in experiments where expression patterns of genes are to be profiled. Current stage technology allows the acquis...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2009-04-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/10/110 |
| _version_ | 1811318014182686720 |
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| author | Suzuki Harukazu Hayashizaki Yoshihide Irvine Katharine M Schroder Kate Kimura Yasumasa Mar Jessica C Hume David Quackenbush John |
| author_facet | Suzuki Harukazu Hayashizaki Yoshihide Irvine Katharine M Schroder Kate Kimura Yasumasa Mar Jessica C Hume David Quackenbush John |
| author_sort | Suzuki Harukazu |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>High-throughput real-time quantitative reverse transcriptase polymerase chain reaction (qPCR) is a widely used technique in experiments where expression patterns of genes are to be profiled. Current stage technology allows the acquisition of profiles for a moderate number of genes (50 to a few thousand), and this number continues to grow. The use of appropriate normalization algorithms for qPCR-based data is therefore a highly important aspect of the data preprocessing pipeline.</p> <p>Results</p> <p>We present and evaluate two data-driven normalization methods that directly correct for technical variation and represent robust alternatives to standard housekeeping gene-based approaches. We evaluated the performance of these methods against a single gene housekeeping gene method and our results suggest that quantile normalization performs best. These methods are implemented in freely-available software as an R package qpcrNorm distributed through the Bioconductor project.</p> <p>Conclusion</p> <p>The utility of the approaches that we describe can be demonstrated most clearly in situations where standard housekeeping genes are regulated by some experimental condition. For large qPCR-based data sets, our approaches represent robust, data-driven strategies for normalization.</p> |
| first_indexed | 2024-04-13T12:18:34Z |
| format | Article |
| id | doaj.art-875cd281420047b08af597bc4cd3f15f |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-13T12:18:34Z |
| publishDate | 2009-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-875cd281420047b08af597bc4cd3f15f2022-12-22T02:47:17ZengBMCBMC Bioinformatics1471-21052009-04-0110111010.1186/1471-2105-10-110Data-driven normalization strategies for high-throughput quantitative RT-PCRSuzuki HarukazuHayashizaki YoshihideIrvine Katharine MSchroder KateKimura YasumasaMar Jessica CHume DavidQuackenbush John<p>Abstract</p> <p>Background</p> <p>High-throughput real-time quantitative reverse transcriptase polymerase chain reaction (qPCR) is a widely used technique in experiments where expression patterns of genes are to be profiled. Current stage technology allows the acquisition of profiles for a moderate number of genes (50 to a few thousand), and this number continues to grow. The use of appropriate normalization algorithms for qPCR-based data is therefore a highly important aspect of the data preprocessing pipeline.</p> <p>Results</p> <p>We present and evaluate two data-driven normalization methods that directly correct for technical variation and represent robust alternatives to standard housekeeping gene-based approaches. We evaluated the performance of these methods against a single gene housekeeping gene method and our results suggest that quantile normalization performs best. These methods are implemented in freely-available software as an R package qpcrNorm distributed through the Bioconductor project.</p> <p>Conclusion</p> <p>The utility of the approaches that we describe can be demonstrated most clearly in situations where standard housekeeping genes are regulated by some experimental condition. For large qPCR-based data sets, our approaches represent robust, data-driven strategies for normalization.</p>http://www.biomedcentral.com/1471-2105/10/110 |
| spellingShingle | Suzuki Harukazu Hayashizaki Yoshihide Irvine Katharine M Schroder Kate Kimura Yasumasa Mar Jessica C Hume David Quackenbush John Data-driven normalization strategies for high-throughput quantitative RT-PCR BMC Bioinformatics |
| title | Data-driven normalization strategies for high-throughput quantitative RT-PCR |
| title_full | Data-driven normalization strategies for high-throughput quantitative RT-PCR |
| title_fullStr | Data-driven normalization strategies for high-throughput quantitative RT-PCR |
| title_full_unstemmed | Data-driven normalization strategies for high-throughput quantitative RT-PCR |
| title_short | Data-driven normalization strategies for high-throughput quantitative RT-PCR |
| title_sort | data driven normalization strategies for high throughput quantitative rt pcr |
| url | http://www.biomedcentral.com/1471-2105/10/110 |
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