Slonim Noam, Elemento Olivier, Tavazoie Saeed
Department of Physics, Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.
Mol Syst Biol. 2006;2:2006.0005. doi: 10.1038/msb4100047. Epub 2006 Jan 31.
Microbial species express an astonishing diversity of phenotypic traits, behaviors, and metabolic capacities. However, our molecular understanding of these phenotypes is based almost entirely on studies in a handful of model organisms that together represent only a small fraction of this phenotypic diversity. Furthermore, many microbial species are not amenable to traditional laboratory analysis because of their exotic lifestyles and/or lack of suitable molecular genetic techniques. As an adjunct to experimental analysis, we have developed a computational information-theoretic framework that produces high-confidence gene-phenotype predictions using cross-species distributions of genes and phenotypes across 202 fully sequenced archaea and eubacteria. In addition to identifying the genetic basis of complex traits, our approach reveals the organization of these genes into generic preferentially co-inherited modules, many of which correspond directly to known enzymatic pathways, molecular complexes, signaling pathways, and molecular machines.
微生物物种表现出惊人的表型特征、行为和代谢能力多样性。然而，我们对这些表型的分子理解几乎完全基于对少数模式生物的研究，而这些模式生物仅代表了这种表型多样性的一小部分。此外，许多微生物物种由于其奇特的生活方式和/或缺乏合适的分子遗传学技术，不适合进行传统的实验室分析。作为实验分析的辅助手段，我们开发了一种计算信息理论框架，该框架利用202种全基因组测序的古细菌和真细菌的基因和表型的跨物种分布，生成高可信度的基因-表型预测。除了确定复杂性状的遗传基础外，我们的方法还揭示了这些基因组织成一般优先共同遗传的模块，其中许多模块直接对应于已知的酶促途径、分子复合物、信号通路和分子机器。
