Towards the identification of essential genes using targeted genome sequencing and comparative analysis.
Towards the identification of essential genes using targeted genome sequencing and comparative analysis.
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使用靶向基因组测序和比较分析来鉴定必需基因。
DOI：
10.1186/1471-2164-7-265
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发表时间：
2006-10-19
期刊：
影响因子：
4.4
通讯作者：
Kasif, Simon
中科院分区：
文献类型：
--
作者：
Gustafson, Adam M.;Snitkin, Evan S.;Parker, Stephen C. J.;DeLisi, Charles;Kasif, Simon
生存必需基因的鉴定对于理解细胞生命的最低需求具有理论上的重要性，并且对于鉴定新型病原体中的潜在药物靶标具有实际重要性。由于旨在构建给定生物体中必需基因目录的实验研究需要大量的时间和费用，因此能够高精度识别必需基因的计算方法将具有巨大的价值。我们收集了许多可以从基因组序列数据自动生成的特征，并评估了它们与必要性的关系，随后利用机器学习构建了酿酒酵母和大肠杆菌中必要基因的集成分类器。当观察单一特征时，系统保留（衡量直系同源生物数量的指标）是最能预测重要性的。此外，在构建我们的系统保留特征期间，我们首次探索了一组生物体之间的进化关系，其中基因的存在最能预测必要性。我们发现，在大肠杆菌和酿酒酵母中，最佳集始终包含具有与参考密切相关的小基因组的宿主相关生物。与使用所有可用的测序生物体相比，使用五种最佳选择的生物体，我们能够提高预测准确性。我们假设这些基因组的预测能力是还原进化过程的结果，许多寄生虫和共生体通过还原进化进化了它们的基因内容。此外，必需性是在丰富的培养基中测量的，这种条件类似于这些生物体在宿主中提供许多营养物质的环境。最后，我们证明，使用概率分类器集成我们最具预测性的特征，其准确性超过了任何单个特征。使用直接从序列数据获得的特征，我们能够构建一个可以高精度预测必需基因的分类器。此外，我们对一组基因组的分析（其中基因的存在最能预测必要性）可能会建议使用靶向测序来识别必要基因的方法。总之，这里提出的方法可以通过针对那些被预测为高概率必需的基因进行实验，有助于减少在必需基因鉴定上投入的时间和金钱。
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. 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. 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.
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