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PhenoGeneRanker: Gene and Phenotype Prioritization Using Multiplex Heterogeneous Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics ( IF 4.5 ) Pub Date : 2021-07-20 , DOI: 10.1109/tcbb.2021.3098278
Cagatay Dursun 1 , Anne E. Kwitek 2 , Serdar Bozdag 3
Affiliation  

Uncovering genotype-phenotype relationships is a fundamental challenge in genomics. Gene prioritization is an important step for this endeavor to make a short manageable list from a list of thousands of genes coming from high-throughput studies. Network propagation methods are promising and state of the art methods for gene prioritization based on the premise that functionally related genes tend to be close to each other in the biological networks. Recently, we introduced PhenoGeneRanker, a network-propagation algorithm for multiplex heterogeneous networks. PhenoGeneRanker allows multi-layer gene and phenotype networks. It also calculates empirical p values of gene and phenotype ranks using random stratified sampling of seeds of genes and phenotypes based on their connectivity degree in the network. In this study, we introduce the PhenoGeneRanker Bioconductor package and its application to multi-omics rat genome datasets to rank hypertension disease-related genes and strains. We showed that PhenoGeneRanker performed better to rank hypertension disease-related genes using multiplex gene networks than aggregated gene networks. We also showed that PhenoGeneRanker performed better to rank hypertension disease-related strains using multiplex phenotype network than single or aggregated phenotype networks. We performed a rigorous hyperparameter analysis and, finally showed that Gene Ontology (GO) enrichment of statistically significant top-ranked genes resulted in hypertension disease-related GO terms.

中文翻译:

PhenoGeneRanker:使用多重异质网络进行基因和表型优先排序

揭示基因型-表型关系是基因组学的一项基本挑战。基因优先排序是这一努力的重要一步,目的是从来自高通量研究的数千个基因列表中制作一个简短的可管理列表。网络传播方法是有前途的,并且是基于功能相关基因在生物网络中往往彼此靠近的前提下进行基因优先排序的最先进方法。最近,我们介绍了 PhenoGeneRanker,一种用于多重异构网络的网络传播算法。PhenoGeneRanker 允许多层基因和表型网络。它还计算经验值基因和表型的 p 值根据它们在网络中的连接程度,使用基因和表型种子的随机分层抽样进行排序。在这项研究中,我们介绍了 PhenoGeneRanker Bioconductor 包及其在多组学大鼠基因组数据集中对高血压疾病相关基因和菌株进行排序的应用。我们表明,PhenoGeneRanker 使用多重基因网络比聚合基因网络对高血压疾病相关基因的排名表现更好。我们还表明,与单一或聚合表型网络相比,PhenoGeneRanker 使用多重表型网络对高血压疾病相关菌株进行排名表现更好。我们进行了严格的超参数分析,并且,
更新日期:2021-07-20
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