全基因组功能关联网络:背景、数据和最新资源。
Genome-wide functional association networks: background, data & state-of-the-art resources.
机构信息
Science for Life Laboratory, Stockholm Bioinformatics Center, Department of Biochemistry and Biophysics, Stockholm University, Box 1031, 17121 Solna, Sweden.
Computational Cell Maps, Institute of Computational Biology, Helmholtz Center Munich, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany.
出版信息
Brief Bioinform. 2020 Jul 15;21(4):1224-1237. doi: 10.1093/bib/bbz064.
The vast amount of experimental data from recent advances in the field of high-throughput biology begs for integration into more complex data structures such as genome-wide functional association networks. Such networks have been used for elucidation of the interplay of intra-cellular molecules to make advances ranging from the basic science understanding of evolutionary processes to the more translational field of precision medicine. The allure of the field has resulted in rapid growth of the number of available network resources, each with unique attributes exploitable to answer different biological questions. Unfortunately, the high volume of network resources makes it impossible for the intended user to select an appropriate tool for their particular research question. The aim of this paper is to provide an overview of the underlying data and representative network resources as well as to mention methods of integration, allowing a customized approach to resource selection. Additionally, this report will provide a primer for researchers venturing into the field of network integration.
近年来,高通量生物学领域的大量实验数据迫切需要整合到更复杂的数据结构中,如全基因组功能关联网络。这些网络已被用于阐明细胞内分子的相互作用,从而在从进化过程的基础科学理解到更具转化意义的精准医学领域取得了进展。该领域的吸引力导致了可用网络资源数量的快速增长,每个资源都具有独特的可利用属性,可以回答不同的生物学问题。不幸的是,网络资源的大量增加使得目标用户不可能为他们特定的研究问题选择合适的工具。本文的目的是概述底层数据和有代表性的网络资源,并提到整合方法,从而允许对资源进行定制化选择。此外,本报告还将为涉足网络整合领域的研究人员提供入门指南。