A random walk-based method to identify driver genes by integrating the subcellular localization and variation frequency into bipartite graph
A random walk-based method to identify driver genes by integrating the subcellular localization and variation frequency into bipartite graph
复制标题
一种基于随机游走的方法，通过将亚细胞定位和变异频率整合到二部图中来识别驱动基因
DOI：
10.1186/s12859-019-2847-9
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发表时间：
2019-05-14
影响因子：
3
通讯作者：
Wang, Feng
中科院分区：
文献类型：
--
作者：
Song, Junrong;Peng, Wei;Wang, Feng
背景癌症是一个全球性的问题，是由基因组改变驱动的。随着高通量测序技术的出现，每秒都会产生大量的基因组数据，这些数据提供了许多有价值的癌症信息，同时也给研究人员带来了巨大的挑战。由于癌症的主要特征是异质性，大多数改变被认为是无用的乘客突变，对癌症的进展没有贡献。因此，如何从这些海量的数据中挖掘出影响肿瘤细胞选择性生长优势的驱动基因，仍然是一个迫切的任务。结果针对以往基于网络的方法忽略了驱动基因的一些重要生物学特性以及基因交互网络的可靠性不高的问题，提出了一种融合亚细胞定位信息的随机游走方法Subdyquency，变异频率及其与其他失调基因的相互作用，以提高驱动基因的预测准确性。我们将我们的模型应用于三种不同的癌症：肺癌，前列腺癌和乳腺癌。结果表明，该模型不仅可以识别出已知的重要驱动基因，而且可以优先考虑罕见的未知驱动基因。此外，与现有的方法相比，我们的方法可以提高准确率，召回率和fscore到更高的水平，为大多数癌症类型。结论最终的结果表明，驱动基因是那些倾向于有更高的变异频率和影响更多的失调基因在共同的显着区间。 https://github.com/weiba/Subdyquency .
BackgroundCancer as a worldwide problem is driven by genomic alterations. With the advent of high-throughput sequencing technology, a huge amount of genomic data generates at every second which offer many valuable cancer information and meanwhile throw a big challenge to those investigators. As the major characteristic of cancer is heterogeneity and most of alterations are supposed to be useless passenger mutations that make no contribution to the cancer progress. Hence, how to dig out driver genes that have effect on a selective growth advantage in tumor cells from those tremendously and noisily data is still an urgent task.ResultsConsidering previous network-based method ignoring some important biological properties of driver genes and the low reliability of gene interactive network, we proposed a random walk method named as Subdyquency that integrates the information of subcellular localization, variation frequency and its interaction with other dysregulated genes to improve the prediction accuracy of driver genes. We applied our model to three different cancers: lung, prostate and breast cancer. The results show our model can not only identify the well-known important driver genes but also prioritize the rare unknown driver genes. Besides, compared with other existing methods, our method can improve the precision, recall and fscore to a higher level for most of cancer types.ConclusionsThe final results imply that driver genes are those prone to have higher variation frequency and impact more dysregulated genes in the common significant compartment.AvailabilityThe source code can be obtained at https://github.com/weiba/Subdyquency .