McLean Colin, Sorokin Anatoly, Simpson Thomas Ian, Armstrong James Douglas, Sorokina Oksana
Edinburgh Cancer Research Centre, Institute for Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, United Kingdom.
Biological Systems Unit, Okinawa Institute of Science and Technology, Onna, Okinawa 904-0495, Japan.
Bioinform Adv. 2023 Sep 29;3(1):vbad137. doi: 10.1093/bioadv/vbad137. eCollection 2023.
Biological function in protein complexes emerges from more than just the sum of their parts: molecules interact in a range of different sub-complexes and transfer signals/information around internal pathways. Modern proteomic techniques are excellent at producing a parts-list for such complexes, but more detailed analysis demands a network approach linking the molecules together and analysing the emergent architectural properties. Methods developed for the analysis of networks in social sciences have proven very useful for splitting biological networks into communities leading to the discovery of sub-complexes enriched with molecules associated with specific diseases or molecular functions that are not apparent from the constituent components alone.
Here, we present the Bioconductor package BioNAR, which supports step-by-step analysis of biological/biomedical networks with the aim of quantifying and ranking each of the network's vertices based on network topology and clustering. Examples demonstrate that while BioNAR is not restricted to proteomic networks, it can predict a protein's impact within multiple complexes, and enables estimation of the co-occurrence of metadata, i.e. diseases and functions across the network, identifying the clusters whose components are likely to share common function and mechanisms.
The package is available from Bioconductor release 3.17: https://bioconductor.org/packages/release/bioc/html/BioNAR.html.
蛋白质复合物中的生物学功能不仅仅源于其各部分的简单相加：分子在一系列不同的亚复合物中相互作用，并在内部途径中传递信号/信息。现代蛋白质组学技术在生成此类复合物的成分列表方面表现出色，但更详细的分析需要一种将分子连接在一起并分析其涌现的结构特性的网络方法。为社会科学中的网络分析开发的方法已被证明非常有助于将生物网络划分为不同群落，从而发现富含与特定疾病或分子功能相关分子的亚复合物，而这些从单独的组成成分中并不明显。
在此，我们展示了Bioconductor软件包BioNAR，它支持对生物/生物医学网络进行逐步分析，目的是基于网络拓扑结构和聚类对网络的每个顶点进行量化和排名。示例表明，虽然BioNAR不限于蛋白质组学网络，但它可以预测蛋白质在多个复合物中的影响，并能够估计元数据（即整个网络中的疾病和功能）的共现情况，识别其成分可能共享共同功能和机制的聚类。
该软件包可从Bioconductor 3.17版本获取：https://bioconductor.org/packages/release/bioc/html/BioNAR.html。
