A large-scale benchmark of gene prioritization methods.
A large-scale benchmark of gene prioritization methods.
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DOI：
10.1038/srep46598
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发表时间：
2017-04-21
影响因子：
4.6
通讯作者：
Sonnhammer ELL
中科院分区：
文献类型：
--
作者：
Guala D;Sonnhammer ELL
为了最大限度地利用高通量实验研究的结果，例如GWAS，对新的疾病相关基因进行识别和诊断，重要的是要有适当的分析和基准的基因优先排序工具。虽然前瞻性基准在试图区分基因优先排序工具的性能方面不能提供统计上的显著结果，但回溯性基准的策略一直缺失，新工具通常只提供内部验证。基因本体论(GO)包含聚集在注释术语周围的基因。GO的这一内在特性可用于构建针对问题领域的健壮基准。我们演示了如何利用FunCoup网络对基于网络的基因优先排序工具实现这一点。我们使用交叉验证和一组合适的性能指标来比较最新的基因优先排序算法：三种基于网络扩散的算法，NetRank和两种随机游走与重启的实现，以及利用网络邻域的MaxLink算法。我们的基准测试套件提供了一种系统和客观的方法来比较众多可用的和未来的基因优先排序工具，使研究人员能够为手头的任务选择最佳的基因优先排序工具，并帮助指导更准确的方法的开发。
In order to maximize the use of results from high-throughput experimental studies, e.g. GWAS, for identification and diagnostics of new disease-associated genes, it is important to have properly analyzed and benchmarked gene prioritization tools. While prospective benchmarks are underpowered to provide statistically significant results in their attempt to differentiate the performance of gene prioritization tools, a strategy for retrospective benchmarking has been missing, and new tools usually only provide internal validations. The Gene Ontology(GO) contains genes clustered around annotation terms. This intrinsic property of GO can be utilized in construction of robust benchmarks, objective to the problem domain. We demonstrate how this can be achieved for network-based gene prioritization tools, utilizing the FunCoup network. We use cross-validation and a set of appropriate performance measures to compare state-of-the-art gene prioritization algorithms: three based on network diffusion, NetRank and two implementations of Random Walk with Restart, and MaxLink that utilizes network neighborhood. Our benchmark suite provides a systematic and objective way to compare the multitude of available and future gene prioritization tools, enabling researchers to select the best gene prioritization tool for the task at hand, and helping to guide the development of more accurate methods.