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Modeling SAGE tag formation and its effects on data interpretation within a Bayesian framework
BACKGROUND: Serial Analysis of Gene Expression (SAGE) is a high-throughput method for inferring mRNA expression levels from the experimentally generated sequence based tags. Standard analyses of SAGE data, however, ignore the fact that the probability of generating an observable tag varies across ge...
Autores principales: | , , |
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2007
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2217564/ https://www.ncbi.nlm.nih.gov/pubmed/17945026 http://dx.doi.org/10.1186/1471-2105-8-403 |
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author | Gilchrist, Michael A Qin, Hong Zaretzki, Russell |
author_facet | Gilchrist, Michael A Qin, Hong Zaretzki, Russell |
author_sort | Gilchrist, Michael A |
collection | PubMed |
description | BACKGROUND: Serial Analysis of Gene Expression (SAGE) is a high-throughput method for inferring mRNA expression levels from the experimentally generated sequence based tags. Standard analyses of SAGE data, however, ignore the fact that the probability of generating an observable tag varies across genes and between experiments. As a consequence, these analyses result in biased estimators and posterior probability intervals for gene expression levels in the transcriptome. RESULTS: Using the yeast Saccharomyces cerevisiae as an example, we introduce a new Bayesian method of data analysis which is based on a model of SAGE tag formation. Our approach incorporates the variation in the probability of tag formation into the interpretation of SAGE data and allows us to derive exact joint and approximate marginal posterior distributions for the mRNA frequency of genes detectable using SAGE. Our analysis of these distributions indicates that the frequency of a gene in the tag pool is influenced by its mRNA frequency, the cleavage efficiency of the anchoring enzyme (AE), and the number of informative and uninformative AE cleavage sites within its mRNA. CONCLUSION: With a mechanistic, model based approach for SAGE data analysis, we find that inter-genic variation in SAGE tag formation is large. However, this variation can be estimated and, importantly, accounted for using the methods we develop here. As a result, SAGE based estimates of mRNA frequencies can be adjusted to remove the bias introduced by the SAGE tag formation process. |
format | Text |
id | pubmed-2217564 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2007 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-22175642008-01-30 Modeling SAGE tag formation and its effects on data interpretation within a Bayesian framework Gilchrist, Michael A Qin, Hong Zaretzki, Russell BMC Bioinformatics Methodology Article BACKGROUND: Serial Analysis of Gene Expression (SAGE) is a high-throughput method for inferring mRNA expression levels from the experimentally generated sequence based tags. Standard analyses of SAGE data, however, ignore the fact that the probability of generating an observable tag varies across genes and between experiments. As a consequence, these analyses result in biased estimators and posterior probability intervals for gene expression levels in the transcriptome. RESULTS: Using the yeast Saccharomyces cerevisiae as an example, we introduce a new Bayesian method of data analysis which is based on a model of SAGE tag formation. Our approach incorporates the variation in the probability of tag formation into the interpretation of SAGE data and allows us to derive exact joint and approximate marginal posterior distributions for the mRNA frequency of genes detectable using SAGE. Our analysis of these distributions indicates that the frequency of a gene in the tag pool is influenced by its mRNA frequency, the cleavage efficiency of the anchoring enzyme (AE), and the number of informative and uninformative AE cleavage sites within its mRNA. CONCLUSION: With a mechanistic, model based approach for SAGE data analysis, we find that inter-genic variation in SAGE tag formation is large. However, this variation can be estimated and, importantly, accounted for using the methods we develop here. As a result, SAGE based estimates of mRNA frequencies can be adjusted to remove the bias introduced by the SAGE tag formation process. BioMed Central 2007-10-18 /pmc/articles/PMC2217564/ /pubmed/17945026 http://dx.doi.org/10.1186/1471-2105-8-403 Text en Copyright © 2007 Gilchrist et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Methodology Article Gilchrist, Michael A Qin, Hong Zaretzki, Russell Modeling SAGE tag formation and its effects on data interpretation within a Bayesian framework |
title | Modeling SAGE tag formation and its effects on data interpretation within a Bayesian framework |
title_full | Modeling SAGE tag formation and its effects on data interpretation within a Bayesian framework |
title_fullStr | Modeling SAGE tag formation and its effects on data interpretation within a Bayesian framework |
title_full_unstemmed | Modeling SAGE tag formation and its effects on data interpretation within a Bayesian framework |
title_short | Modeling SAGE tag formation and its effects on data interpretation within a Bayesian framework |
title_sort | modeling sage tag formation and its effects on data interpretation within a bayesian framework |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2217564/ https://www.ncbi.nlm.nih.gov/pubmed/17945026 http://dx.doi.org/10.1186/1471-2105-8-403 |
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