Integration of biological data by kernels on graph nodes allows prediction of new genes involved in mitotic chromosome condensation.
Integration of biological data by kernels on graph nodes allows prediction of new genes involved in mitotic chromosome condensation.
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DOI：
10.1091/mbc.e13-04-0221
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发表时间：
2014-08-15
影响因子：
3.3
通讯作者：
Ellenberg J
中科院分区：
文献类型：
--
作者：
Hériché JK;Lees JG;Morilla I;Walter T;Petrova B;Roberti MJ;Hossain MJ;Adler P;Fernández JM;Krallinger M;Haering CH;Vilo J;Valencia A;Ranea JA;Orengo C;Ellenberg J
描述了一种适用于靶向RNA干扰库设计的基因功能预测方法，并用于预测染色体浓缩基因。通过自动显微镜和定量图像分析在聚焦RNAi筛选中对候选基因的系统实验验证揭示了许多新的染色体浓缩因子。基于全基因组RNA干扰（RNAi）的屏幕的出现使我们能够识别人类细胞执行的所有功能的基因。然而，对于许多功能，测定的复杂性和成本使得基因组规模的敲除实验是不可能的。因此，需要预测细胞功能所需基因的方法，以将来自全基因组的RNAi筛选集中在最可能的候选者上。虽然存在不同的用于基因功能预测的生物信息学工具，但它们缺乏实验验证，因此很少被实验学家使用。为了解决这个问题，我们开发了一种有效的计算基因选择策略，该策略将关于基因的公共数据表示为图，然后使用图节点上的内核来分析这些图以预测函数关系。为了证明其性能，我们预测了一个知之甚少的细胞功能-有丝分裂染色体浓缩所需的人类基因，并通过自动显微镜用聚焦RNAi筛选实验验证了前100名候选人。对图像的定量分析表明，候选人确实强烈富集了缩合基因，包括发现了几个新的因子。通过将生物信息学预测与实验验证相结合，我们的研究表明，图节点上的内核是整合公共生物数据和预测涉及感兴趣的细胞功能的基因的强大工具。
A gene function prediction method suitable for the design of targeted RNAi libraries is described and used to predict chromosome condensation genes. Systematic experimental validation of candidate genes in a focused RNAi screen by automated microscopy and quantitative image analysis reveals many new chromosome condensation factors. The advent of genome-wide RNA interference (RNAi)–based screens puts us in the position to identify genes for all functions human cells carry out. However, for many functions, assay complexity and cost make genome-scale knockdown experiments impossible. Methods to predict genes required for cell functions are therefore needed to focus RNAi screens from the whole genome on the most likely candidates. Although different bioinformatics tools for gene function prediction exist, they lack experimental validation and are therefore rarely used by experimentalists. To address this, we developed an effective computational gene selection strategy that represents public data about genes as graphs and then analyzes these graphs using kernels on graph nodes to predict functional relationships. To demonstrate its performance, we predicted human genes required for a poorly understood cellular function—mitotic chromosome condensation—and experimentally validated the top 100 candidates with a focused RNAi screen by automated microscopy. Quantitative analysis of the images demonstrated that the candidates were indeed strongly enriched in condensation genes, including the discovery of several new factors. By combining bioinformatics prediction with experimental validation, our study shows that kernels on graph nodes are powerful tools to integrate public biological data and predict genes involved in cellular functions of interest.