GC-Content Normalization for RNA-Seq Data
<p>Abstract</p> <p>Background</p> <p>Transcriptome sequencing (RNA-Seq) has become the assay of choice for high-throughput studies of gene expression. However, as is the case with microarrays, major technology-related artifacts and biases affect the resulting expression...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2011-12-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/12/480 |
| _version_ | 1818485719627202560 |
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| author | Risso Davide Schwartz Katja Sherlock Gavin Dudoit Sandrine |
| author_facet | Risso Davide Schwartz Katja Sherlock Gavin Dudoit Sandrine |
| author_sort | Risso Davide |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Transcriptome sequencing (RNA-Seq) has become the assay of choice for high-throughput studies of gene expression. However, as is the case with microarrays, major technology-related artifacts and biases affect the resulting expression measures. Normalization is therefore essential to ensure accurate inference of expression levels and subsequent analyses thereof.</p> <p>Results</p> <p>We focus on biases related to GC-content and demonstrate the existence of strong sample-specific GC-content effects on RNA-Seq read counts, which can substantially bias differential expression analysis. We propose three simple within-lane gene-level GC-content normalization approaches and assess their performance on two different RNA-Seq datasets, involving different species and experimental designs. Our methods are compared to state-of-the-art normalization procedures in terms of bias and mean squared error for expression fold-change estimation and in terms of Type I error and <it>p</it>-value distributions for tests of differential expression. The exploratory data analysis and normalization methods proposed in this article are implemented in the open-source Bioconductor R package EDASeq.</p> <p>Conclusions</p> <p>Our within-lane normalization procedures, followed by between-lane normalization, reduce GC-content bias and lead to more accurate estimates of expression fold-changes and tests of differential expression. Such results are crucial for the biological interpretation of RNA-Seq experiments, where downstream analyses can be sensitive to the supplied lists of genes.</p> |
| first_indexed | 2024-12-10T16:12:41Z |
| format | Article |
| id | doaj.art-839a2a6afd7b4a8e9b11c6a85ec695cf |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-10T16:12:41Z |
| publishDate | 2011-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-839a2a6afd7b4a8e9b11c6a85ec695cf2022-12-22T01:42:03ZengBMCBMC Bioinformatics1471-21052011-12-0112148010.1186/1471-2105-12-480GC-Content Normalization for RNA-Seq DataRisso DavideSchwartz KatjaSherlock GavinDudoit Sandrine<p>Abstract</p> <p>Background</p> <p>Transcriptome sequencing (RNA-Seq) has become the assay of choice for high-throughput studies of gene expression. However, as is the case with microarrays, major technology-related artifacts and biases affect the resulting expression measures. Normalization is therefore essential to ensure accurate inference of expression levels and subsequent analyses thereof.</p> <p>Results</p> <p>We focus on biases related to GC-content and demonstrate the existence of strong sample-specific GC-content effects on RNA-Seq read counts, which can substantially bias differential expression analysis. We propose three simple within-lane gene-level GC-content normalization approaches and assess their performance on two different RNA-Seq datasets, involving different species and experimental designs. Our methods are compared to state-of-the-art normalization procedures in terms of bias and mean squared error for expression fold-change estimation and in terms of Type I error and <it>p</it>-value distributions for tests of differential expression. The exploratory data analysis and normalization methods proposed in this article are implemented in the open-source Bioconductor R package EDASeq.</p> <p>Conclusions</p> <p>Our within-lane normalization procedures, followed by between-lane normalization, reduce GC-content bias and lead to more accurate estimates of expression fold-changes and tests of differential expression. Such results are crucial for the biological interpretation of RNA-Seq experiments, where downstream analyses can be sensitive to the supplied lists of genes.</p>http://www.biomedcentral.com/1471-2105/12/480 |
| spellingShingle | Risso Davide Schwartz Katja Sherlock Gavin Dudoit Sandrine GC-Content Normalization for RNA-Seq Data BMC Bioinformatics |
| title | GC-Content Normalization for RNA-Seq Data |
| title_full | GC-Content Normalization for RNA-Seq Data |
| title_fullStr | GC-Content Normalization for RNA-Seq Data |
| title_full_unstemmed | GC-Content Normalization for RNA-Seq Data |
| title_short | GC-Content Normalization for RNA-Seq Data |
| title_sort | gc content normalization for rna seq data |
| url | http://www.biomedcentral.com/1471-2105/12/480 |
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