Genome Biology Program, Department of Energy Joint Genome Institute, Walnut Creek, California, United States of America.
PLoS One. 2009 Nov 24;4(11):e7979. doi: 10.1371/journal.pone.0007979.
Computational methods for determining the function of genes in newly sequenced genomes have been traditionally based on sequence similarity to genes whose function has been identified experimentally. Function prediction methods can be extended using gene context analysis approaches such as examining the conservation of chromosomal gene clusters, gene fusion events and co-occurrence profiles across genomes. Context analysis is based on the observation that functionally related genes are often having similar gene context and relies on the identification of such events across phylogenetically diverse collection of genomes. We have used the data management system of the Integrated Microbial Genomes (IMG) as the framework to implement and explore the power of gene context analysis methods because it provides one of the largest available genome integrations. Visualization and search tools to facilitate gene context analysis have been developed and applied across all publicly available archaeal and bacterial genomes in IMG. These computations are now maintained as part of IMG's regular genome content update cycle. IMG is available at: http://img.jgi.doe.gov.
传统上，用于确定新测序基因组中基因功能的计算方法是基于与已通过实验确定功能的基因的序列相似性。可以使用基因上下文分析方法（例如检查染色体基因簇、基因融合事件和跨基因组的共现谱的保守性）来扩展功能预测方法。上下文分析基于这样的观察，即功能相关的基因通常具有相似的基因背景，并依赖于在系统发育多样化的基因组集合中识别此类事件。我们使用集成微生物基因组 (IMG) 的数据管理系统作为框架来实现和探索基因上下文分析方法的功能，因为它提供了最大的可用基因组集成之一。已经开发并应用了可视化和搜索工具来促进基因上下文分析，这些工具适用于 IMG 中所有公开可用的古细菌和细菌基因组。这些计算现在作为 IMG 定期基因组内容更新周期的一部分进行维护。IMG 可在以下网址获得：http://img.jgi.doe.gov。
