通过基于网络的转录组学、表观基因组学和蛋白质组学肿瘤数据整合发现改变的调控和信号传导
Discovering Altered Regulation and Signaling Through Network-based Integration of Transcriptomic, Epigenomic, and Proteomic Tumor Data.
作者信息
Kedaigle Amanda J, Fraenkel Ernest
机构信息
Computational and Systems Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
出版信息
Methods Mol Biol. 2018;1711:13-26. doi: 10.1007/978-1-4939-7493-1_2.
Abstract
With the extraordinary rise in available biological data, biologists and clinicians need unbiased tools for data integration in order to reach accurate, succinct conclusions. Network biology provides one such method for high-throughput data integration, but comes with its own set of algorithmic problems and needed expertise. We provide a step-by-step guide for using Omics Integrator, a software package designed for the integration of transcriptomic, epigenomic, and proteomic data. Omics Integrator can be found at http://fraenkel.mit.edu/omicsintegrator .
摘要
随着可用生物数据的激增，生物学家和临床医生需要公正无偏的工具来进行数据整合，以便得出准确、简洁的结论。网络生物学提供了一种用于高通量数据整合的方法，但它也伴随着自身一系列的算法问题和所需的专业知识。我们提供了一份使用Omics Integrator的分步指南，Omics Integrator是一个专门设计用于整合转录组学、表观基因组学和蛋白质组学数据的软件包。可在http://fraenkel.mit.edu/omicsintegrator找到Omics Integrator。
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