Shi Yanxin, Klutstein Michael, Simon Itamar, Mitchell Tom, Bar-Joseph Ziv
Machine Learning Department, School of Computer Science, Carnegie Mellon University , Pittsburgh, PA 15213, USA.
J Comput Biol. 2009 Aug;16(8):1035-49. doi: 10.1089/cmb.2009.0024.
Methods suggested for reconstructing regulatory networks can be divided into two sets based on how the activity level of transcription factors (TFs) is inferred. The first group of methods relies on the expression levels of TFs, assuming that the activity of a TF is highly correlated with its mRNA abundance. The second treats the activity level as unobserved and infers it from the expression of the genes that the TF regulates. While both types of methods were successfully applied, each suffers from drawbacks that limit their accuracy. For the first set, the assumption that mRNA levels are correlated with activity is violated for many TFs due to post-transcriptional modifications. For the second, the expression level of a TF which might be informative is completely ignored. Here we present the post-transcriptional modification model (PTMM) that, unlike previous methods, utilizes both sources of data concurrently. Our method uses a switching model to determine whether a TF is transcriptionally or post-transcriptionally regulated. This model is combined with a factorial HMM to reconstruct the interactions in a dynamic regulatory network. Using simulated and real data, we show that PTMM outperforms the other two approaches discussed above. Using real data, we also show that PTMM can recover meaningful TF activity levels and identify post-transcriptionally modified TFs, many of which are supported by other sources. Supporting website: www.sb.cs.cmu.edu/PTMM/PTMM.html.
根据转录因子（TFs）活性水平的推断方式，用于重建调控网络的方法可分为两类。第一类方法依赖于转录因子的表达水平，假设转录因子的活性与其mRNA丰度高度相关。第二类方法将活性水平视为不可观测的，并从转录因子所调控基因的表达中推断其活性。虽然这两类方法都得到了成功应用，但它们各自都有局限性，影响了其准确性。对于第一类方法，由于转录后修饰的存在，许多转录因子违反了mRNA水平与活性相关的假设。对于第二类方法，可能具有信息价值的转录因子表达水平被完全忽略了。在此，我们提出了转录后修饰模型（PTMM），与以往方法不同，它同时利用了这两种数据来源。我们的方法使用一个切换模型来确定转录因子是受转录调控还是转录后调控。该模型与一个因子隐马尔可夫模型相结合，以重建动态调控网络中的相互作用。通过模拟数据和真实数据，我们表明PTMM优于上述其他两种方法。利用真实数据，我们还表明PTMM能够恢复有意义的转录因子活性水平，并识别出转录后修饰的转录因子，其中许多都得到了其他来源的支持。支持网站：www.sb.cs.cmu.edu/PTMM/PTMM.html。
