多物种网络推理改进果蝇早期胚胎发育基因调控网络重建
Multi-species network inference improves gene regulatory network reconstruction for early embryonic development in Drosophila
摘要：
基因调控网络推断使用全基因组转录组测量响应于遗传、环境或动态扰动来预测基因之间的因果调控影响。我们假设进化也是一种合适的网络扰动，来自多个密切相关物种的数据整合可以改善基因调控网络的重建。为了验证这一假设，我们从6个果蝇物种早期胚胎发育期间测量的3，610个基因的时间基因表达数据预测网络，并将预测网络与5个物种发育转录因子的ChIP-chip和ChIP-seq相互作用的金标准网络进行比较。我们发现：（i）单物种网络的性能与测量金标准的物种无关;（ii）预测网络之间的差异反映了已知的遗传学和物种之间的生物学差异;（iii）一个整合的共识网络，它最大限度地减少了所有单物种网络的边缘增益和损失的总数，比任何单个网络都表现得更好。我们的研究结果表明，在一个进化保守的系统中，整合多个物种的可比实验数据，提高了基因调控网络的推理。他们提供了一个基础，为今后的研究，许多多物种的基因表达数据集的其他生物过程中的文献。
英文摘要：
Gene regulatory network inference uses genome-wide transcriptome measurements in response to genetic, environmental or dynamic perturbations to predict causal regulatory influences between genes. We hypothesized that evolution also acts as a suitable network perturbation and that integration of data from multiple closely related species can lead to improved reconstruction of gene regulatory networks. To test this hypothesis, we predicted networks from temporal gene expression data for 3,610 genes measured during early embryonic development in six Drosophila species and compared predicted networks to gold standard networks of ChIP-chip and ChIP-seq interactions for developmental transcription factors in five species. We found that (i) the performance of single-species networks was independent of the species where the gold standard was measured; (ii) differences between predicted networks reflected the known phylogeny and differences in biology between the species; (iii) an integrative consensus network which minimized the total number of edge gains and losses with respect to all single-species networks performed better than any individual network. Our results show that in an evolutionarily conserved system, integration of data from comparable experiments in multiple species improves the inference of gene regulatory networks. They provide a basis for future studies on the numerous multi-species gene expression datasets for other biological processes available in the literature.
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