Lysenko Artem, Boroevich Keith Anthony, Tsunoda Tatsuhiko
Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi, Yokohama, 230-0045 Japan.
Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8510 Japan.
BioData Min. 2017 Jul 6;10:22. doi: 10.1186/s13040-017-0141-9. eCollection 2017.
Refinement of candidate gene lists to select the most promising candidates for further experimental verification remains an essential step between high-throughput exploratory analysis and the discovery of specific causal genes. Given the qualitative and semantic complexity of biological data, successfully addressing this challenge requires development of flexible and interoperable solutions for making the best possible use of the largest possible fraction of all available data.
We have developed an easily accessible framework that links two established network-based gene prioritization approaches with a supporting isolation forest-based integrative ranking method. The defining feature of the method is that both topological information of the biological networks and additional sources of evidence can be considered at the same time. The implementation was realized as an app extension for the Cytoscape graph analysis suite, and therefore can further benefit from the synergy with other analysis methods available as part of this system.
We provide efficient reference implementations of two popular gene prioritization algorithms - DIAMOnD and random walk with restart for the Cytoscape system. An extension of those methods was also developed that allows outputs of these algorithms to be combined with additional data. To demonstrate the utility of our software, we present two example disease gene prioritization application cases and show how our tool can be used to evaluate these different approaches.
优化候选基因列表以挑选出最有前景的候选基因用于进一步实验验证，仍然是高通量探索性分析与发现特定致病基因之间的关键步骤。鉴于生物数据的定性和语义复杂性，要成功应对这一挑战，需要开发灵活且可互操作的解决方案，以便尽可能充分地利用所有可用数据的最大部分。
我们开发了一个易于访问的框架，该框架将两种既定的基于网络的基因优先级排序方法与一种支持的基于孤立森林的综合排名方法相联系。该方法的显著特点是可以同时考虑生物网络的拓扑信息和其他证据来源。该实现被作为Cytoscape图形分析套件的应用扩展来实现，因此可以进一步受益于与作为该系统一部分的其他分析方法的协同作用。
我们为Cytoscape系统提供了两种流行的基因优先级排序算法——DIAMOnD和带重启的随机游走的高效参考实现。还开发了这些方法的扩展，允许将这些算法的输出与其他数据相结合。为了展示我们软件的实用性，我们展示了两个疾病基因优先级排序应用案例，并展示了我们的工具如何用于评估这些不同的方法。
