Department of Computer Science.
Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI, USA.
Bioinformatics. 2017 Jul 1;33(13):1987-1994. doi: 10.1093/bioinformatics/btx097.
The ultimate goal of any experiment is to understand the biological phenomena underlying the condition investigated. This process often results in genes network through which a certain biological mechanism is explained. Such networks have been proven to be extremely useful, for the prediction of mechanisms of action of drugs or the responses of an organism to a specific impact (e.g. a disease, a treatment, etc.). Here, we introduce an approach able to build a network that captures the putative mechanisms at play in the given condition, by using datasets from multiple experiments studying the same phenotype. This method takes advantage of known interactions extracted from multiple sources such as protein-protein interactions and curated biological pathways. Based on such prior knowledge, we overcome the drawbacks of snap-shot data by considering the possible effects of each gene on its neighbors.
We show the effectiveness of this approach in three different case studies and validate the results in two ways considering the identified genes and interactions between them. We compare our findings with the results of two widely-used methods in the same category as well as the classical approach of selecting differentially expressed (DE) genes in an investigated condition. The results show that 'neighbor-net' analysis is able to report biological mechanisms that are significantly relevant to the given diseases in all the three case studies, and performs better compared to all reference methods using both validation approaches.
The proposed method is implemented as in R and will be available an a Bioconductor package.
Supplementary data are available at Bioinformatics online.
任何实验的最终目标都是为了理解所研究条件下的生物学现象。这一过程通常会产生基因网络，通过该网络可以解释某种生物学机制。事实证明，这些网络对于预测药物的作用机制或生物体对特定影响（例如疾病、治疗等）的反应非常有用。在这里，我们介绍了一种方法，该方法能够通过使用研究同一表型的多个实验的数据集，构建一个能够捕获给定条件下潜在作用机制的网络。该方法利用了从多个来源（如蛋白质相互作用和精心策划的生物途径）中提取的已知相互作用。基于这些先验知识，我们通过考虑每个基因对其邻居的可能影响来克服快照数据的缺点。
我们在三个不同的案例研究中展示了该方法的有效性，并通过考虑所识别的基因及其之间的相互作用，从两种方式验证了结果。我们将我们的发现与两种广泛用于同一类别的方法以及在研究条件下选择差异表达（DE）基因的经典方法的结果进行了比较。结果表明，在所有三个案例研究中，“邻居网络”分析都能够报告与所给疾病显著相关的生物学机制，并且在使用两种验证方法时，与所有参考方法相比，性能都更好。
所提出的方法在 R 中实现，并将作为 Bioconductor 包提供。
补充数据可在 Bioinformatics 在线获得。
