ABI Innovation: Gini-based methodologies to enhance network-scale transcriptome analysis in plants

在生物学中，网络技术已经被应用于解释基因之间的相互作用，包括蛋白质的物理相互作用以及转录因子和靶标之间的调控关系。虽然已经开发了许多方法来从表达数据中推断网络，但一些计算挑战仍然没有解决，例如如何推导转录因子和靶之间的非线性关系，如何将网络适当地分解为单独的子网络模块，如何通过网络规模的比较来预测具有生物学意义的基因，如何整合和使用不同形式的生物相互作用数据来促进网络分析，以及如何无缝地可视化大规模网络以进行交互式数据挖掘。为了解决这些问题，本项目的主要目标是开发一个软件包-GINI网络分析工具包(GNAT)，它利用基于GINI的方法：一系列数学解决方案，已广泛用于经济学、物理学、信息网络和社会网络中分析非正态分布的数据。GNAT中的核心功能模块和算法包括使用有监督的机器学习方法来推断转录网络，使用基尼相关系数来推导非线性调控关系，使用基尼回归分析来分解时间序列网络，使用基尼指数来测量和比较不同生物条件下模块和基因的网络属性的分布，最后通过系统扰动和决策树分析来发现生物重要基因。PI还将开发一个网络资源管理器BioNetscape，以使用k-core分解算法、AJAX技术和GPU(图形处理单元)计算技术有效地组织和可视化从GNAT产生的大量网络数据。GNAT将在R中实施，并以精简的工作流程组织起来，以弥补传统基因尺度转录组分析方法的缺点。GNAT软件将极大地促进正在进行的植物研究网络开发项目。GNAT将被整合到iPlants发现环境、拟南芥信息资源(TIR)、植物表达数据库(PLEXdb)和其他联盟数据库中，以增强植物网络分析和基因发现的功能。GNAT还将集成到Galaxy和GenePattern平台中，以提供用户友好的图形界面。源代码和R包将被发布到公共领域，以便在植物、动物和微生物生物学中更广泛地使用。为了将研究融入教育，皮？S实验室将开发一个基于网络的虚拟下一代测序研讨会，培训非生物信息学专家的生物学家分析基因组、表观基因组、转录和小核糖核酸数据。研讨会的课程包括在PI的课堂上准备的教材、自我练习数据集和一个虚拟的UNIXWeb控制台，用于培训生物学家分析不同类型的下一代测序数据，并对编程技能的要求最低。该项目明确涉及多个层次的跨学科研究培训，鼓励亚利桑那大学计算机科学和数学专业代表不足的群体参与，他们将致力于回答生物学问题。来自亚利桑那大学ASEMS(亚利桑那科学、工程和数学学者)和IGERT项目的学生将参加PI的团队，开发GNAT、BioNetscape和VNW，并在他们的研究中使用这些工具。

In biology, network techniques have been applied to interpret the interactions between genes, including the physical interactions of proteins and regulatory relationships between transcription factors and targets. Although numerous methods have been developed to infer a network from expression data, several computational challenges remain unsolved, such as, how to derive non-linear relationships between transcription factors and targets, how to properly decompose a network into individual sub-network modules, how to predict biologically significant genes via network-scale comparisons, how to integrate and use the heterogeneous forms of biological interaction data to facilitate network analysis, and how to seamlessly visualize a large-scale network for interactive data mining. To solve these problems, the primary goal of this project is to develop a software package - the Gini Network Analysis Toolkit (GNAT) that utilizes the Gini-based methodologies: a family of mathematical solutions that have been widely used in economics, physics, informatic networks, and social networks in analyzing non-normally distributed data. The core functional modules and algorithms in the GNAT include the use of supervised machine learning methods to infer transcriptional networks, the Gini correlation coefficient to derive non-linear regulatory relationships, the Gini regression analysis to decompose a time-series network, the Gini index to measure and compare the distributions of the network properties of modules and genes under different biological conditions, and eventually the discovery of biologically important genes with system perturbation and decision tree analysis. The PI will also develop a network explorer, BioNetscape, to efficiently organize and visualize the tremendous amount of network data generated from the GNAT using the k-core decomposition algorithm, Ajax technology and GPU (graphical processing unit) computing techniques. The GNAT will be implemented in R and organized as a streamlined workflow to compensate the shortcomings of the traditional gene-scale transcriptome analysis methods.The GNAT software will greatly facilitate the ongoing network development projects in plant research. The GNAT will be made available to be integrated into the iPlant Discovery Environment, The Arabidopsis Information Resource (TAIR), Plant Expression Database (PLEXdb) and other consortium databases to enhance the function of network analysis and gene discovery in plants. The GNAT will also be integrated into the Galaxy and GenePattern platforms to provide a user-friendly graphical interface. The source-code and R packages will be released into the public domain for broader use in plant, animal and microbial biology. To integrate research into education, the PI?s laboratory will develop a web-based Virtual Next Generation Sequencing Workshop for training biologists who are not specialists in bioinformatics to analyze genomic, epigenomic, transcriptomic and small RNA data. The workshop courseware is composed of teaching materials prepared in the PI's class, self-practice datasets and a virtual UNIX web-console for training biologists to analyze different types of next generation sequencing data with minimal requirements for programming skills. This project explicitly addresses cross-disciplinary research training at multiple levels that will encourage the participation of underrepresented groups in computer sciences and mathematics at the University of Arizona, who will work to answering biological questions. The students from the ASEMS (Arizona Science, Engineering, and Math Scholars) and IGERT programs at the University of Arizona will participate in the PI's team to develop the GNAT, BioNetscape and VNW, and use these tools in their research.