Development of network analysis tool BioLayout Express3D

关于基因和蛋白质的功能以及它们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、质谱学、RNAi和Y2H分析。这样的功能基因组学和蛋白质组学方法，当与计算生物学和新兴的系统生物学学科相结合时，最终允许我们开始全面绘制细胞和分子网络和路径的图谱。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，并利用它们更好地了解生物系统。将生物数据作为网络进行可视化和分析正成为探索各种生物关系的一种日益重要的方法。这些方法已经成功地用于序列相似性、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/网络范例使人们能够利用先前在图论、工程学、计算机科学和计算系统生物学中开发的算法、技术、想法和统计学。在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了巨大的希望，但对微阵列数据的关注却很少。这些数据现在很丰富，通常质量很高，并且由这种方法非常适合的高维数据类型组成。我们开发了一个名为BioLayout Express3D的新程序，它利用微阵列表达数据构建网络。这是通过测量单个基因表达谱之间的相似性来实现的，如果相似，即在定义的阈值以上，则使用一条线将它们连接起来。在给定数据集中有多组共表达基因的情况下，这些节点形成一个相互连接的节点集团。鉴于来自现代阵列平台的数据的复杂性，非常需要提供可视化和分析大量数据的工具。目前版本的BioLayout Express3D可以构建由超过10K个节点和1M条边组成的图形。通过独特的布局算法和OpenGL图形引擎相结合，增强了图形的可视化表示，该引擎可以在3-D空间中渲染网络图形。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近邻，即在表达(共享边缘)上最接近所选择的基因来确定。这种可视化还允许用户用眼睛快速识别图形中的结构和特征，这些结构和特征在以前是不明显的。基于图的聚类算法(MCL)也增强了这些结构的清晰度。使用这种方法，可以将大图划分为高度连接的节点组或共表达基因的表达数据簇。现在我们已经研究了许多大小不等的单色和双色微阵列表达数据集，从不到20个芯片到超过200个，我们对该工具的基本性能非常满意。然而，我们迫切需要添加功能，以扩展其分析能力。Biolayout Express3D可能在其他类型的生物关系建模方面发挥重要作用的另一个领域。特别是，我们已经开始使用这个工具来构建基于蛋白质相似性中的关系的图表，特别是基于大规模相互作用和路径数据集的网络。在这方面，该工具比其他可用的软件包显示出巨大的前景，但该工具还需要进一步开发，以增强其在这一领域的能力。

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.