Kravchenko-Balasha Nataly, Simon Simcha, Levine R D, Remacle F, Exman Iaakov
NanoSystems Biology Cancer Center, Division of Chemistry, Caltech, Pasadena, California, United States of America.
Software Engineering Department, The Jerusalem College of Engineering, Azrieli, Jerusalem, Israel.
PLoS One. 2014 Nov 18;9(11):e108549. doi: 10.1371/journal.pone.0108549. eCollection 2014.
Surprisal analysis is increasingly being applied for the examination of transcription levels in cellular processes, towards revealing inner network structures and predicting response. But to achieve its full potential, surprisal analysis should be integrated into a wider range computational tool. The purposes of this paper are to combine surprisal analysis with other important computation procedures, such as easy manipulation of the analysis results--e.g. to choose desirable result sub-sets for further inspection--, retrieval and comparison with relevant datasets from public databases, and flexible graphical displays for heuristic thinking. The whole set of computation procedures integrated into a single practical tool is what we call Computational Surprisal Analysis. This combined kind of analysis should facilitate significantly quantitative understanding of different cellular processes for researchers, including applications in proteomics and metabolomics. Beyond that, our vision is that Computational Surprisal Analysis has the potential to reach the status of a routine method of analysis for practitioners. The resolving power of Computational Surprisal Analysis is here demonstrated by its application to a variety of cellular cancer process transcription datasets, ours and from the literature. The results provide a compact biological picture of the thermodynamic significance of the leading gene expression phenotypes in every stage of the disease. For each transcript we characterize both its inherent steady state weight, its correlation with the other transcripts and its variation due to the disease. We present a dedicated website to facilitate the analysis for researchers and practitioners.
意外性分析越来越多地应用于细胞过程中转录水平的检测，以揭示内部网络结构并预测反应。但为了充分发挥其潜力，意外性分析应集成到更广泛的计算工具中。本文的目的是将意外性分析与其他重要的计算程序相结合，例如便于对分析结果进行操作——例如选择理想的结果子集进行进一步检查——、从公共数据库中检索相关数据集并与之比较，以及进行灵活的图形显示以启发思维。将整套计算程序集成到一个实用工具中就是我们所说的计算意外性分析。这种组合式分析应能极大地促进研究人员对不同细胞过程的定量理解，包括在蛋白质组学和代谢组学中的应用。除此之外，我们的愿景是计算意外性分析有潜力成为从业者的常规分析方法。本文通过将计算意外性分析应用于我们自己以及文献中的各种细胞癌过程转录数据集，展示了其解析能力。结果提供了该疾病各个阶段主要基因表达表型的热力学意义的简洁生物学图景。对于每个转录本，我们既描述了其固有的稳态权重、与其他转录本的相关性，也描述了其因疾病导致的变化。我们提供了一个专门的网站，以方便研究人员和从业者进行分析。
