Towards the Identification of Essential Genes Using Targeted Genome Sequencing and Comparative Analysis


Show simple item record Gustafson, Adam M en_US Snitkin, Evan S en_US Parker, Stephen CJ en_US DeLisi, Charles en_US Kasif, Simon en_US 2011-12-29T22:56:30Z 2011-12-29T22:56:30Z 2006 en_US 2006-10-19 en_US
dc.identifier.citation Gustafson, Adam M, Evan S Snitkin, Stephen CJ Parker, Charles DeLisi, Simon Kasif. "Towards the identification of essential genes using targeted genome sequencing and comparative analysis." BMC Genomics 7:265. (2006) en_US
dc.identifier.issn 1471-2164 en_US
dc.description.abstract BACKGROUND: The identification of genes essential for survival is of theoretical importance in the understanding of the minimal requirements for cellular life, and of practical importance in the identification of potential drug targets in novel pathogens. With the great time and expense required for experimental studies aimed at constructing a catalog of essential genes in a given organism, a computational approach which could identify essential genes with high accuracy would be of great value. RESULTS: We gathered numerous features which could be generated automatically from genome sequence data and assessed their relationship to essentiality, and subsequently utilized machine learning to construct an integrated classifier of essential genes in both S. cerevisiae and E. coli. When looking at single features, phyletic retention, a measure of the number of organisms an ortholog is present in, was the most predictive of essentiality. Furthermore, during construction of our phyletic retention feature we for the first time explored the evolutionary relationship among the set of organisms in which the presence of a gene is most predictive of essentiality. We found that in both E. coli and S. cerevisiae the optimal sets always contain host-associated organisms with small genomes which are closely related to the reference. Using five optimally selected organisms, we were able to improve predictive accuracy as compared to using all available sequenced organisms. We hypothesize the predictive power of these genomes is a consequence of the process of reductive evolution, by which many parasites and symbionts evolved their gene content. In addition, essentiality is measured in rich media, a condition which resembles the environments of these organisms in their hosts where many nutrients are provided. Finally, we demonstrate that integration of our most highly predictive features using a probabilistic classifier resulted in accuracies surpassing any individual feature. CONCLUSION: Using features obtainable directly from sequence data, we were able to construct a classifier which can predict essential genes with high accuracy. Furthermore, our analysis of the set of genomes in which the presence of a gene is most predictive of essentiality may suggest ways in which targeted sequencing can be used in the identification of essential genes. In summary, the methods presented here can aid in the reduction of time and money invested in essential gene identification by targeting those genes for experimentation which are predicted as being essential with a high probability. en_US
dc.description.sponsorship National Institute of Health (IP20GM066401; IT32GM070409; ROL HG003367-01A1); National Science Foundation (ITR-048715) en_US
dc.language.iso en en_US
dc.publisher BioMed Central en_US
dc.rights Copyright 2006 Gustafson et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution 2.0 License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. en_US
dc.rights.uri en_US
dc.title Towards the Identification of Essential Genes Using Targeted Genome Sequencing and Comparative Analysis en_US
dc.type article en_US
dc.identifier.doi 10.1186/1471-2164-7-265 en_US
dc.identifier.pubmedid 17052348 en_US
dc.identifier.pmcid 1624830 en_US

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