Development of network analysis tool BioLayout Express3D

网络分析工具BioLayout Express3D开发

• 批准号: BB/F003722/1
• 负责人: Tom Freeman
• 金额: $ 12.91万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2008
• 资助国家: 英国
• 起止时间: 2008 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FF003722%2F1
• 关键词: Development; network; analysis; tool; BioLayout

Enormous amounts of data pertaining to the functions of genes and proteins and their interactions in the cell, have now been generated by a range of techniques including but not limited to: expression profiling, mass spectrometry, RNAi and Y2H assays. Such functional genomics and proteomics approaches, when combined with computational biology and the emerging discipline of systems biology, finally allow us to begin comprehensive mapping of cellular and molecular networks and pathways. One of the main difficulties we currently face is how best to integrate these disparate data sources and use them to better understand biological systems. Visualisation and analysis of biological data as networks is becoming an increasingly important approach to explore a variety of biological relationships. Such approaches have already been used successfully in the study of sequence similarity, protein structure, protein interactions and evolution. Shifting biological data into a graph/network paradigm allows one to utilise algorithms, techniques, ideas and statistics previously developed in graph theory, engineering, computer science and computational systems biology. In networks derived from biological data, nodes are usually genes, transcripts or proteins, while edges tend to represent experimentally determined similarities or functional linkages between them. While network analysis of biological data has shown great promise, little attention has been paid to microarray data. These data are now abundant, generally of high quality and consist of the type of high-dimensional data for which such approaches are well suited. We have developed a new program called BioLayout Express3D that constructs networks out of microarray expression data. This is achieved by measuring the similarity between individual gene expression profiles and where similar i.e. above a defined threshold, a line is used to connect them. In circumstances where there are groups of co-expressed genes within a given dataset, these nodes form a clique of interconnected nodes. Given the complexity of the data from modern array platforms tools that provide a means of visualising and analysing large amounts of data are very much needed. The current version of BioLayout Express3D can construct graphs composing of over 10K nodes and 1M edges. Visual representation of the graphs is enhanced by a unique layout algorithm combined with an OpenGL graphics engine that renders the network graphs in 3-D space. The layout data in this manner has a number of distinct advantages. The position of each node (gene) within the network can be determined relative to its immediate neighbours i.e. genes that are closest in expression (share edges) to that selected. This visualisation also allows the user to quickly identify structures and features in the graph by eye that would not have been obvious previously. Definition of these structures has also been enhanced by a graph-based clustering algorithm (MCL). Using this approach, large graphs can be divided in groups of highly connected nodes or expression data clusters of co-expressed genes. Having now looked at numerous 1- and 2-colour microarray expression datasets varying in size from less than 20 chips to over 200, we are very happy with the basic performance of the tool. However, we urgently need to add features that will extend its analytical capabilities. The other area in which this Biolayout Express3D is likely to play an important role is in modelling other types of biological relationships. In particular we have begun to use this tool construct graphs based on relationships in protein similarities and in particular networks based on large-scale interaction and pathway datasets. In this respect the tool is showing great promise over other available software packages, but again the tool is in need of further development to enhance its capabilities in this area.

关于基因和蛋白质的功能及其在细胞中的相互作用的大量数据现在已经通过一系列技术产生，包括但不限于：表达谱分析、质谱、RNAi和Y2 H测定。这种功能基因组学和蛋白质组学方法，当与计算生物学和系统生物学的新兴学科相结合时，最终使我们能够开始全面绘制细胞和分子网络和途径。我们目前面临的主要困难之一是如何最好地整合这些不同的数据源，并利用它们来更好地了解生物系统。生物数据的可视化和分析网络正在成为探索各种生物关系的一种越来越重要的方法。这种方法已经成功地应用于序列相似性、蛋白质结构、蛋白质相互作用和进化的研究。将生物数据转换为图形/网络范式允许人们利用以前在图论，工程，计算机科学和计算系统生物学中开发的算法，技术，思想和统计数据。在从生物数据衍生的网络中，节点通常是基因、转录本或蛋白质，而边往往代表实验确定的相似性或它们之间的功能联系。虽然生物数据的网络分析显示出巨大的希望，但很少关注微阵列数据。这些数据现在是丰富的，通常是高质量的，并包括这种方法非常适合的高维数据类型。我们开发了一个名为BioLayout 3D的新程序，它可以从微阵列表达数据中构建网络。这是通过测量个体基因表达谱之间的相似性来实现的，并且在相似的情况下，即高于限定的阈值，使用线来连接它们。在给定数据集中存在共表达基因组的情况下，这些节点形成互连节点的集团。鉴于现代阵列平台数据的复杂性，非常需要提供可视化和分析大量数据的工具。当前版本的BioLayout 3D可以构建由超过10 K个节点和1 M条边组成的图形。图形的视觉表示是由一个独特的布局算法与OpenGL图形引擎相结合，使网络图在3-D空间增强。以这种方式的布局数据具有许多明显的优点。每个节点（基因）在网络中的位置可以相对于它的直接邻居（即在表达上最接近（共享边缘）的基因）来确定。这种可视化还允许用户通过眼睛快速识别图形中以前不明显的结构和特征。这些结构的定义也得到了增强的基于图的聚类算法（MCL）。使用这种方法，可以将大型图划分为高度连接的节点组或共表达基因的表达数据簇。现在查看了许多尺寸从不到20个芯片到超过200个芯片不等的单色和双色微阵列表达数据集，我们对该工具的基本性能非常满意。然而，我们迫切需要添加一些功能，以扩展其分析能力。Biolayout 3D可能发挥重要作用的另一个领域是模拟其他类型的生物关系。特别是，我们已经开始使用这个工具构建基于蛋白质相似性关系的图表，特别是基于大规模相互作用和途径数据集的网络。在这方面，该工具比其他现有软件包显示出更大的前景，但该工具仍需进一步开发，以增强其在这一领域的能力。

项目成果

Meta-analysis reveals conserved cell cycle transcriptional network across multiple human cell types.

• DOI: 10.1186/s12864-016-3435-2
• 发表时间: 2017-01-05
• 期刊: BMC genomics
• 影响因子: 4.4
• 作者: Giotti B;Joshi A;Freeman TC
• 通讯作者: Freeman TC

The genome of a pathogenic rhodococcus: cooptive virulence underpinned by key gene acquisitions.

• DOI: 10.1371/journal.pgen.1001145
• 发表时间: 2010-09-30
• 期刊: PLoS genetics
• 影响因子: 4.5
• 作者: Letek M;González P;Macarthur I;Rodríguez H;Freeman TC;Valero-Rello A;Blanco M;Buckley T;Cherevach I;Fahey R;Hapeshi A;Holdstock J;Leadon D;Navas J;Ocampo A;Quail MA;Sanders M;Scortti MM;Prescott JF;Fogarty U;Meijer WG;Parkhill J;Bentley SD;Vázquez-Boland JA
• 通讯作者: Vázquez-Boland JA

Coexpression analysis of large cancer datasets provides insight into the cellular phenotypes of the tumour microenvironment.

• DOI: 10.1186/1471-2164-14-469
• 发表时间: 2013-07-11
• 期刊: BMC genomics
• 影响因子: 4.4
• 作者: Doig TN;Hume DA;Theocharidis T;Goodlad JR;Gregory CD;Freeman TC
• 通讯作者: Freeman TC

Distribution of Misfolded Prion Protein Seeding Activity Alone Does Not Predict Regions of Neurodegeneration.

• DOI: 10.1371/journal.pbio.1002579
• 发表时间: 2016-11
• 期刊: PLoS biology
• 影响因子: 9.8
• 作者: Alibhai J;Blanco RA;Barria MA;Piccardo P;Caughey B;Perry VH;Freeman TC;Manson JC
• 通讯作者: Manson JC

Replicable and Coupled Changes in Innate and Adaptive Immune Gene Expression in Two Case-Control Studies of Blood Microarrays in Major Depressive Disorder.

• DOI: 10.1016/j.biopsych.2017.01.021
• 发表时间: 2018-01-01
• 期刊: Biological psychiatry
• 影响因子: 10.6
• 作者: Leday GGR;Vértes PE;Richardson S;Greene JR;Regan T;Khan S;Henderson R;Freeman TC;Pariante CM;Harrison NA;MRC Immunopsychiatry Consortium;Perry VH;Drevets WC;Wittenberg GM;Bullmore ET
• 通讯作者: Bullmore ET