Batra Richa, Alcaraz Nicolas, Gitzhofer Kevin, Pauling Josch, Ditzel Henrik J, Hellmuth Marc, Baumbach Jan, List Markus
Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark.
Institute of Computational Biology, Helmholtz Zentrum München, Munich, Germany.
NPJ Syst Biol Appl. 2017 Mar 3;3:6. doi: 10.1038/s41540-017-0007-2. eCollection 2017.
De novo pathway enrichment is a powerful approach to discover previously uncharacterized molecular mechanisms in addition to already known pathways. To achieve this, condition-specific functional modules are extracted from large interaction networks. Here, we give an overview of the state of the art and present the first framework for assessing the performance of existing methods. We identified 19 tools and selected seven representative candidates for a comparative analysis with more than 12,000 runs, spanning different biological networks, molecular profiles, and parameters. Our results show that none of the methods consistently outperforms the others. To mitigate this issue for biomedical researchers, we provide guidelines to choose the appropriate tool for a given dataset. Moreover, our framework is the first attempt for a quantitative evaluation of de novo methods, which will allow the bioinformatics community to objectively compare future tools against the state of the art.
从头通路富集是一种强大的方法，除了已知通路外，还能发现以前未被表征的分子机制。为实现这一目标，从大型相互作用网络中提取特定条件下的功能模块。在此，我们概述了当前的技术水平，并提出了第一个评估现有方法性能的框架。我们识别出19种工具，并选择了7个具有代表性的候选工具进行超过12000次运行的比较分析，涵盖不同的生物网络、分子图谱和参数。我们的结果表明，没有一种方法始终优于其他方法。为了给生物医学研究人员缓解这个问题，我们提供了针对给定数据集选择合适工具的指导原则。此外，我们的框架是对从头方法进行定量评估的首次尝试，这将使生物信息学社区能够客观地将未来的工具与当前的技术水平进行比较。
