A Bayesian framework for combining heterogeneous data sources for gene function prediction (in Saccharomyces cerevisiae)
A Bayesian framework for combining heterogeneous data sources for gene function prediction (in Saccharomyces cerevisiae)
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DOI：
10.1073/pnas.0832373100
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发表时间：
2003-07-08
影响因子：
11.1
通讯作者：
Botstein, D
中科院分区：
文献类型：
--
作者：
Troyanskaya, OG;Dolinski, K;Botstein, D
基因组测序不再是一个新鲜事物，但基因功能注释仍然是现代生物学中的一个关键挑战。有多种功能基因组学实验技术可用，从亲和沉淀等经典方法到基因表达微阵列等先进的高通量技术。在未来，将开发更多不同的方法，进一步增加对这些研究产生的数据进行综合计算分析的需要。我们用MAGIC(多源基因集成集群关联)来解决这个问题，这是一个通用的框架，它使用形式贝叶斯推理来集成不同类型的高通量生物数据(如大规模双杂交筛选和多个微阵列分析)，以实现准确的基因功能预测。该系统正式纳入了关于数据源相对准确性的专家知识，以便将它们结合在一个规范框架内。Magic通过其输出提供了一个信任级别，允许用户改变预测的严格性。我们将MAGIC应用于酿酒酵母的遗传和物理相互作用、微阵列和转录因子结合位点数据，并使用酿酒酵母基因组数据库产生的基因本体论注释来评估基因分组的生物学相关性。我们发现，与单独的微阵列分析相比，通过基于不同数据类型创建功能分组，MAGIC提高了分组的准确性。我们描述了几个已识别的生物基因群。
Genomic sequencing is no longer a novelty, but gene function annotation remains a key challenge in modern biology. A variety of functional genomics experimental techniques are available, from classic methods such as affinity precipitation to advanced high-throughput techniques such as gene expression microarrays. In the future, more disparate methods will be developed, further increasing the need for integrated computational analysis of data generated by these studies. We address this problem with MAGIC (Multisource Association of Genes by Integration of Clusters), a general framework that uses formal Bayesian reasoning to integrate heterogeneous types of high-throughput biological data (such as large-scale two-hybrid screens and multiple microarray analyses) for accurate gene function prediction. The system formally incorporates expert knowledge about relative accuracies of data sources to combine them within a normative framework. MAGIC provides a belief level with its output that allows the user to vary the stringency of predictions. We applied MAGIC to Saccharomyces cerevisiae genetic and physical interactions, microarray, and transcription factor binding sites data and assessed the biological relevance of gene groupings using Gene Ontology annotations produced by the Saccaromyces Genome Database. We found that by creating functional groupings based on heterogeneous data types, MAGIC improved accuracy of the groupings compared with microarray analysis alone. We describe several of the biological gene groupings identified.