A statistical framework for improving genomic annotations of prokaryotic essential genes.

Large-scale systematic analysis of gene essentiality is an important step closer toward unraveling the complex relationship between genotypes and phenotypes. Such analysis cannot be accomplished without unbiased and accurate annotations of essential genes. In current genomic databases, most of the e...

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Autores principales:	Jingyuan Deng, Shengchang Su, Xiaodong Lin, Daniel J Hassett, Long Jason Lu
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1834ca55abcf499cb2f2813ef82160ff
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spelling	oai:doaj.org-article:1834ca55abcf499cb2f2813ef82160ff2021-11-18T07:54:11ZA statistical framework for improving genomic annotations of prokaryotic essential genes.1932-620310.1371/journal.pone.0058178https://doaj.org/article/1834ca55abcf499cb2f2813ef82160ff2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23520492/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Large-scale systematic analysis of gene essentiality is an important step closer toward unraveling the complex relationship between genotypes and phenotypes. Such analysis cannot be accomplished without unbiased and accurate annotations of essential genes. In current genomic databases, most of the essential gene annotations are derived from whole-genome transposon mutagenesis (TM), the most frequently used experimental approach for determining essential genes in microorganisms under defined conditions. However, there are substantial systematic biases associated with TM experiments. In this study, we developed a novel Poisson model-based statistical framework to simulate the TM insertion process and subsequently correct the experimental biases. We first quantitatively assessed the effects of major factors that potentially influence the accuracy of TM and subsequently incorporated relevant factors into the framework. Through iteratively optimizing parameters, we inferred the actual insertion events occurred and described each gene's essentiality on probability measure. Evaluated by the definite mapping of essential gene profile in Escherichia coli, our model significantly improved the accuracy of original TM datasets, resulting in more accurate annotations of essential genes. Our method also showed encouraging results in improving subsaturation level TM datasets. To test our model's broad applicability to other bacteria, we applied it to Pseudomonas aeruginosa PAO1 and Francisella tularensis novicida TM datasets. We validated our predictions by literature as well as allelic exchange experiments in PAO1. Our model was correct on six of the seven tested genes. Remarkably, among all three cases that our predictions contradicted the TM assignments, experimental validations supported our predictions. In summary, our method will be a promising tool in improving genomic annotations of essential genes and enabling large-scale explorations of gene essentiality. Our contribution is timely considering the rapidly increasing essential gene sets. A Webserver has been set up to provide convenient access to this tool. All results and source codes are available for download upon publication at http://research.cchmc.org/essentialgene/.Jingyuan DengShengchang SuXiaodong LinDaniel J HassettLong Jason LuPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 3, p e58178 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jingyuan Deng Shengchang Su Xiaodong Lin Daniel J Hassett Long Jason Lu A statistical framework for improving genomic annotations of prokaryotic essential genes.
description	Large-scale systematic analysis of gene essentiality is an important step closer toward unraveling the complex relationship between genotypes and phenotypes. Such analysis cannot be accomplished without unbiased and accurate annotations of essential genes. In current genomic databases, most of the essential gene annotations are derived from whole-genome transposon mutagenesis (TM), the most frequently used experimental approach for determining essential genes in microorganisms under defined conditions. However, there are substantial systematic biases associated with TM experiments. In this study, we developed a novel Poisson model-based statistical framework to simulate the TM insertion process and subsequently correct the experimental biases. We first quantitatively assessed the effects of major factors that potentially influence the accuracy of TM and subsequently incorporated relevant factors into the framework. Through iteratively optimizing parameters, we inferred the actual insertion events occurred and described each gene's essentiality on probability measure. Evaluated by the definite mapping of essential gene profile in Escherichia coli, our model significantly improved the accuracy of original TM datasets, resulting in more accurate annotations of essential genes. Our method also showed encouraging results in improving subsaturation level TM datasets. To test our model's broad applicability to other bacteria, we applied it to Pseudomonas aeruginosa PAO1 and Francisella tularensis novicida TM datasets. We validated our predictions by literature as well as allelic exchange experiments in PAO1. Our model was correct on six of the seven tested genes. Remarkably, among all three cases that our predictions contradicted the TM assignments, experimental validations supported our predictions. In summary, our method will be a promising tool in improving genomic annotations of essential genes and enabling large-scale explorations of gene essentiality. Our contribution is timely considering the rapidly increasing essential gene sets. A Webserver has been set up to provide convenient access to this tool. All results and source codes are available for download upon publication at http://research.cchmc.org/essentialgene/.
format	article
author	Jingyuan Deng Shengchang Su Xiaodong Lin Daniel J Hassett Long Jason Lu
author_facet	Jingyuan Deng Shengchang Su Xiaodong Lin Daniel J Hassett Long Jason Lu
author_sort	Jingyuan Deng
title	A statistical framework for improving genomic annotations of prokaryotic essential genes.
title_short	A statistical framework for improving genomic annotations of prokaryotic essential genes.
title_full	A statistical framework for improving genomic annotations of prokaryotic essential genes.
title_fullStr	A statistical framework for improving genomic annotations of prokaryotic essential genes.
title_full_unstemmed	A statistical framework for improving genomic annotations of prokaryotic essential genes.
title_sort	statistical framework for improving genomic annotations of prokaryotic essential genes.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/1834ca55abcf499cb2f2813ef82160ff
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A statistical framework for improving genomic annotations of prokaryotic essential genes.

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