Wu Guanming, Dawson Eric, Duong Adrian, Haw Robin, Stein Lincoln
Ontario Institute for Cancer Research, Toronto, Ontario M5G 0A3, Canada ; DMICE, Oregon Health & Science University, Portland, Oregon 97239, USA.
Section of Integrative Biology, Institute for Cellular and Molecular Biology, and Center for Computational Biology and Bioinformatics, The University of Texas at Austin, Austin, TX 78712, USA.
F1000Res. 2014 Jul 1;3:146. doi: 10.12688/f1000research.4431.2. eCollection 2014.
High-throughput experiments are routinely performed in modern biological studies. However, extracting meaningful results from massive experimental data sets is a challenging task for biologists. Projecting data onto pathway and network contexts is a powerful way to unravel patterns embedded in seemingly scattered large data sets and assist knowledge discovery related to cancer and other complex diseases. We have developed a Cytoscape app called "ReactomeFIViz", which utilizes a highly reliable gene functional interaction network combined with human curated pathways derived from Reactome and other pathway databases. This app provides a suite of features to assist biologists in performing pathway- and network-based data analysis in a biologically intuitive and user-friendly way. Biologists can use this app to uncover network and pathway patterns related to their studies, search for gene signatures from gene expression data sets, reveal pathways significantly enriched by genes in a list, and integrate multiple genomic data types into a pathway context using probabilistic graphical models. We believe our app will give researchers substantial power to analyze intrinsically noisy high-throughput experimental data to find biologically relevant information.
高通量实验在现代生物学研究中经常进行。然而,从海量实验数据集中提取有意义的结果对生物学家来说是一项具有挑战性的任务。将数据投影到通路和网络背景中是一种强大的方法,可用于揭示看似分散的大数据集中所蕴含的模式,并有助于发现与癌症和其他复杂疾病相关的知识。我们开发了一个名为“ReactomeFIViz”的Cytoscape应用程序,它利用高度可靠的基因功能相互作用网络,结合来自Reactome和其他通路数据库的人工策划的人类通路。该应用程序提供了一套功能,以帮助生物学家以生物学直观且用户友好的方式进行基于通路和网络的数据分析。生物学家可以使用此应用程序来发现与其研究相关的网络和通路模式,从基因表达数据集中搜索基因特征,揭示列表中基因显著富集的通路,并使用概率图形模型将多种基因组数据类型整合到通路背景中。我们相信我们的应用程序将赋予研究人员强大的能力,以分析本质上有噪声的高通量实验数据,从而找到生物学相关信息。