Vasilevski Aleksandar, Giorgi Federico M, Bertinetti Luca, Usadel Björn
Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany.
Mol Biosyst. 2012 Oct;8(10):2566-74. doi: 10.1039/c2mb25096a.
Whole genome transcript correlation-based approaches have been shown to be enormously useful for candidate gene detection. Consequently, simple Pearson correlation has been widely applied in several web based tools. That said, several more sophisticated methods based on e.g. mutual information or Bayesian network inference have been developed and have been shown to be theoretically superior but are not yet commonly applied. Here, we propose the application of a recently developed statistical regression technique, the LASSO, to detect novel candidates from high throughput transcriptomic datasets. We apply the LASSO to a tissue specific dataset in the model plant Arabidopsis thaliana to identify novel players in Arabidopsis thaliana seed coat mucilage synthesis. We built LASSO models based on a list of genes known to be involved in a sub-pathway of Arabidopsis mucilage synthesis. After identifying a putative transcription factor, we verified its involvement in mucilage synthesis by obtaining knock-out mutants for this gene. We show that a loss of function of this putative transcription factor leads to a significant decrease in mucilage pectin.
基于全基因组转录本相关性的方法已被证明在候选基因检测中非常有用。因此，简单的皮尔逊相关性已在多个基于网络的工具中广泛应用。话虽如此，已经开发出了几种更复杂的方法，例如基于互信息或贝叶斯网络推理的方法，并且已证明这些方法在理论上更具优势，但尚未得到普遍应用。在这里，我们提出应用一种最近开发的统计回归技术——套索（LASSO），从高通量转录组数据集中检测新的候选基因。我们将LASSO应用于模式植物拟南芥的组织特异性数据集，以鉴定拟南芥种皮黏液合成中的新参与者。我们基于已知参与拟南芥黏液合成子途径的基因列表构建了LASSO模型。在鉴定出一个假定的转录因子后，我们通过获得该基因的敲除突变体来验证其在黏液合成中的作用。我们表明，这个假定转录因子的功能丧失会导致黏液果胶显著减少。
