Inferring orthologous gene regulatory networks using interspecies data fusion.
Inferring orthologous gene regulatory networks using interspecies data fusion.
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DOI：
10.1093/bioinformatics/btv267
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发表时间：
2015-06-15
期刊：
影响因子：
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通讯作者：
Wild DL
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文献类型：
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作者：
Penfold CA;Millar JB;Wild DL
动机：联合学习相关物种中的基因调控网络（GRNs）或利用相关物种之间的GRNs的能力将允许在模型生物中获得的大量遗留数据为更复杂的或经济或医学相关的对应物的GRNs提供信息。例子包括将信息从拟南芥转移到相关的作物物种用于粮食安全目的，或从小鼠转移到人类用于医疗应用。在这里，我们开发了两个相关的贝叶斯网络推理方法，允许GRNs联合推断，或利用之间，几个相关的物种：在一个框架中，网络信息直接传播物种之间;在第二层次的方法，网络信息传播通过一个未观察到的“超网络”。在这两个框架中，关于网络相似性的信息是通过图形内核捕获的，网络还通过物种特定的时间序列基因表达数据来通知，当可用时，使用高斯过程来模拟基因表达的动态。结果如下：计算机基准测试的结果表明，联合推理和利用物种之间的已知网络，比独立推理提供了更好的准确性。当物种相对较少时，通过非层次框架直接传播网络信息更合适，而当物种较多时，层次方法更适合。这两种方法对少量的直系同源物的错误标记都是稳健的。最后，使用酿酒酵母的数据和网络来告知芽殖酵母裂殖酵母中网络的推断，预测Gas 1（SPAC19B12.02c）（一种1，3-β-葡聚糖基转移酶）在细胞周期调控中的新作用。可用性和实施：MATLAB代码可从http://go.warwick.ac.uk/systemsbiology/software/获得。联系方式：d.l. warwick.ac.uk补充信息：补充数据可在生物信息学在线获得。
Motivation: The ability to jointly learn gene regulatory networks (GRNs) in, or leverage GRNs between related species would allow the vast amount of legacy data obtained in model organisms to inform the GRNs of more complex, or economically or medically relevant counterparts. Examples include transferring information from Arabidopsis thaliana into related crop species for food security purposes, or from mice into humans for medical applications. Here we develop two related Bayesian approaches to network inference that allow GRNs to be jointly inferred in, or leveraged between, several related species: in one framework, network information is directly propagated between species; in the second hierarchical approach, network information is propagated via an unobserved ‘hypernetwork’. In both frameworks, information about network similarity is captured via graph kernels, with the networks additionally informed by species-specific time series gene expression data, when available, using Gaussian processes to model the dynamics of gene expression. Results: Results on in silico benchmarks demonstrate that joint inference, and leveraging of known networks between species, offers better accuracy than standalone inference. The direct propagation of network information via the non-hierarchical framework is more appropriate when there are relatively few species, while the hierarchical approach is better suited when there are many species. Both methods are robust to small amounts of mislabelling of orthologues. Finally, the use of Saccharomyces cerevisiae data and networks to inform inference of networks in the budding yeast Schizosaccharomyces pombe predicts a novel role in cell cycle regulation for Gas1 (SPAC19B12.02c), a 1,3-beta-glucanosyltransferase. Availability and implementation: MATLAB code is available from http://go.warwick.ac.uk/systemsbiology/software/. Contact: d.l.wild@warwick.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.