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首页> 外文期刊>Briefings in functional genomics & proteomics >Network-guided genetic screening: building, testing and using gene networks to predict gene function
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Network-guided genetic screening: building, testing and using gene networks to predict gene function

机译：网络指导的遗传筛选：建立，测试和使用基因网络来预测基因功能

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摘要

A challenge facing nearly all biologists is to identify the complete set of genes that are important for a process or disease. This applies to scientists investigating fundamental pathways in model organisms, but also to clinicians trying to understand human disease. There are many different types of experimental data that can be used to predict the genes that are important for a process, but these data are normally dispersed across numerous publications and databases, and are of varying and unknown quality. Integrated functional gene networks aim to gather functional information from all of these data into a single intuitive graph model that can be used to predict gene functions. In this approach, the ability of each data set to predict functional associations between genes is first measured using a standard benchmark, and then the scored predictions by each data set are combined. The resulting integrated probabilistic gene network can be used by all researchers to predict gene function, with much greater coverage and accuracy than any individual data set. In this review, we discuss how such integrated gene networks are constructed, how their predictive power for gene function can be tested, and how experimental biologists can use these networks to guide their research. We pay particular attention to such networks constructed for Caenorhabditis elegans, because in this complex multicellular model system functional predictions for genes can be rapidly tested in vivo using RNAi. The approach is, however, widely applicable to any system, and might soon be a common method used to dissect the genetics of human complex diseases.
机译：几乎所有生物学家都面临的挑战是，确定对过程或疾病重要的完整基因。这适用于研究模型生物的基本途径的科学家，也适用于试图了解人类疾病的临床医生。有许多不同类型的实验数据可用于预测对过程至关重要的基因，但这些数据通常散布在众多出版物和数据库中，并且质量不同且未知。集成的功能基因网络旨在将所有这些数据中的功能信息收集到一个可用于预测基因功能的直观图表模型中。在这种方法中，首先使用标准基准测量每个数据集预测基因之间功能关联的能力，然后将每个数据集的计分预测值进行组合。由此产生的综合概率基因网络可被所有研究人员用来预测基因功能，其覆盖范围和准确性远高于任何单个数据集。在这篇综述中，我们讨论了如何构建这种整合的基因网络，如何测试其对基因功能的预测能力，以及实验生物学家如何使用这些网络来指导其研究。我们特别注意为秀丽隐杆线虫构建的此类网络，因为在这种复杂的多细胞模型系统中，可以使用RNAi在体内快速测试基因的功能预测。然而，该方法广泛适用于任何系统，并且可能很快将成为剖析人类复杂疾病遗传学的常用方法。

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