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通过期望最大化进行遗传相互作用基序发现——一种从合成致死性推断基因模块的新型统计模型。

Genetic Interaction Motif Finding by expectation maximization--a novel statistical model for inferring gene modules from synthetic lethality.

作者信息

Qi Yan, Ye Ping, Bader Joel S

机构信息

Biomedical Engineering Department, Johns Hopkins University, Baltimore, MD 21218, USA.

出版信息

BMC Bioinformatics. 2005 Dec 6;6:288. doi: 10.1186/1471-2105-6-288.

Abstract

BACKGROUND

Synthetic lethality experiments identify pairs of genes with complementary function. More direct functional associations (for example greater probability of membership in a single protein complex) may be inferred between genes that share synthetic lethal interaction partners than genes that are directly synthetic lethal. Probabilistic algorithms that identify gene modules based on motif discovery are highly appropriate for the analysis of synthetic lethal genetic interaction data and have great potential in integrative analysis of heterogeneous datasets.

RESULTS

We have developed Genetic Interaction Motif Finding (GIMF), an algorithm for unsupervised motif discovery from synthetic lethal interaction data. Interaction motifs are characterized by position weight matrices and optimized through expectation maximization. Given a seed gene, GIMF performs a nonlinear transform on the input genetic interaction data and automatically assigns genes to the motif or non-motif category. We demonstrate the capacity to extract known and novel pathways for Saccharomyces cerevisiae (budding yeast). Annotations suggested for several uncharacterized genes are supported by recent experimental evidence. GIMF is efficient in computation, requires no training and automatically down-weights promiscuous genes with high degrees.

CONCLUSION

GIMF effectively identifies pathways from synthetic lethality data with several unique features. It is mostly suitable for building gene modules around seed genes. Optimal choice of one single model parameter allows construction of gene networks with different levels of confidence. The impact of hub genes the generic probabilistic framework of GIMF may be used to group other types of biological entities such as proteins based on stochastic motifs. Analysis of the strongest motifs discovered by the algorithm indicates that synthetic lethal interactions are depleted between genes within a motif, suggesting that synthetic lethality occurs between-pathway rather than within-pathway.

摘要

背景

合成致死实验可识别具有互补功能的基因对。相较于直接具有合成致死关系的基因,共享合成致死相互作用伙伴的基因之间可能存在更直接的功能关联(例如更有可能属于单个蛋白质复合物)。基于基序发现来识别基因模块的概率算法非常适合分析合成致死遗传相互作用数据,并且在异质数据集的整合分析中具有巨大潜力。

结果

我们开发了遗传相互作用基序发现算法(GIMF),这是一种用于从合成致死相互作用数据中进行无监督基序发现的算法。相互作用基序通过位置权重矩阵进行表征,并通过期望最大化进行优化。给定一个种子基因,GIMF对输入的遗传相互作用数据进行非线性变换,并自动将基因分配到基序或非基序类别。我们展示了为酿酒酵母(芽殖酵母)提取已知和新途径的能力。最近的实验证据支持了对几个未表征基因的注释建议。GIMF计算效率高,无需训练,并能自动降低高度混杂基因的权重。

结论

GIMF有效地从合成致死数据中识别出具有几个独特特征的途径。它最适合围绕种子基因构建基因模块。单个模型参数的最佳选择允许构建具有不同置信水平的基因网络。GIMF的通用概率框架中枢纽基因的影响可用于基于随机基序对其他类型的生物实体(如蛋白质)进行分组。对该算法发现的最强基序的分析表明,基序内的基因之间合成致死相互作用减少,这表明合成致死发生在途径之间而非途径之内。

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