STRUCTURAL VISUALIZATION IN BIOINFORMATICS

生物信息学中的结构可视化

• 批准号: 7715341
• 负责人: KAY A ROBBINS
• 金额: $ 15.35万
• 依托单位: UNIVERSITY OF TEXAS SAN ANTONIO
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2009-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7715341
• 关键词: Accession Number; Algorithms; Applications Grants; Architecture; Area; Award; Back; Behavior; Bioinformatics; Biological; Biology; Chicago; Collaborations; Collection; Computer Retrieval of Information on Scientific Projects Database; Data; Data Files; Data Set; Databases; Development; Doctor of Philosophy; Educational process of instructing; Electrodes; Extensible Markup Language; Faculty; Funding; Genbank; Gene Expression; Genes; Goals; Grant; Healthcare; Image; Imagery; Individual; Institution; Java; Laboratories; Learning; Left; Lifting; Mediating; Memory; Meta-Analysis; Methods; Metric; Microarray Analysis; Minority; Modeling; Motor Cortex; Nature; Neurosciences; Paper; Phenotype; Preparation; Procedures; Process; Publishing; Research; Research Infrastructure; Research Personnel; Resources; Series; Services; Source; Standards of Weights and Measures; Structure; Students; System; Techniques; Technology; Testing; Texas; Time; Translating; Translations; United States National Institutes of Health; Universities; Update; Visit; Work; Writing; abstracting; base; falls; insight; member; programs; prototype; research study; software development; spatiotemporal; technique development; tool; two-dimensional

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. A. Specific Aims The specific aims of this grant are as stated in the original proposal. B. Studies and Results Year 3 of the grant was focused on tools to handle large data sets and multiple scales. We have made progress in three areas: 1) assembling and handling of microarray datasets, 2) analysis with workflows and hypothesis testing and 3) navigation and multiple scale analysis by lifting to abstract feature spaces. Progress on these efforts is described below. I also wrote a grant proposal for and was awarded a UTSA faculty development leave. This leave will allow me to work on research without teaching or service responsibilities during the fall semester of 2007. 1) Assembling and handling of microarray datasets. An essential step in performing structural visualization across large groups of microarray data sets is that the data be translated into a common format for comparison. The NCBI GEO (Gene Expression Omnibus) has gathered a large collection of microarray experiments in a single database. They provide the data in two formats: XML (MINiML) and SOFT. We have developed programs to download and parse the data into individual data files and to automatically assemble this data into comma separated spreadsheets for visualization and analysis. A major logistical problem with comparing datasets across platforms is that different microarray platforms use different, and sometimes inconsistent, methods for identifying genes on the microarrays. We have decided to use the NCBI Gene ID as the standard identifier in our work. Unfortunately, some common microarray platforms use the GenBank Accession Number instead of the GeneID. These platforms require a circuitous translation to Gene ID, which Cory Burkhardt has semi-automated. He has downloaded, reformatted and identified most of the microarray experiments from the top 20 platforms in the NCBI Geo database. We have also developed various programs for parsing the data from the XML specifications and have developed software for serializing the data that isn't being used during program execution using Java SoftReference technology. This allows us to deal with more data than will fit into memory in a clean, object-oriented way. Jason Edwards and James Packer developed a CacheManager Architecture for handling large microarray datasets. This architecture will be deployed in the microarray analysis tool that we have under development. We will also investigate converting Davis (our Java-based data visualization platform) to use this caching technique in the coming year. 2) Analysis with workflows and hypothesis testing. This year we focused on developing a workflow-based tool infrastructure based on wizards. Wizards are a familiar application interface in which the user navigates through a procedure in a step-by-step process using Next and Back buttons. In their undergraduate honors theses, Jason Edwards and James Packer developed the infrastructure to support a simple prototype workflow for comparing the behavior of two genes across many microarray platforms. Their prototype application is written in Java and is called MicroMetal (Microarray Meta Analysis). This is the starting point for more general workflow-hypothesis testing approach. 3) Navigation and multiple scale analysis by lifting to abstract feature spaces. Doctoral student Dragana Veljkovic and I continue to work on the development of techniques for abstracting features for comprehensive multi-scale navigation of datasets. The idea is that a time window of a spatiotemporal data set (e.g., a multi-electrode recording or a series of microarray experiments) is represented by a low-dimensional subspace (for example, the two-dimensional space spanned by its largest two principal components). The data in each time window then becomes a single point in feature space. The distance between two feature points is computed using a distance metric on the subspaces. We can then project this feature space into a plane using a manifold learning algorithm such as ISOMAP. We are developing navigation and summary techniques that describe the distribution of very large datasets by summarizing their abstract features. A paper describing a MATLAB tool that implements these navigation techniques is under preparation. Other activities: We continue to develop and test Davis (DAta VIewing System). Our collaborators, particularly David Senseman and his students, are using Davis extensively for their research. We have documented Davis and created video tutorials to make learning to use Davis easier. Update on collaborations formed because of this grant: Another aspect of this development grant is the formation of collaborations in biosciences. The following collaborations that were formed last year have proceeded: 1) Nicholas Hatsopoulos, University of Chicago, with Doug Rubino from his laboratory: Our paper entitled "Propagating waves mediate information transfer in the motor cortex" was published in Nature Neuroscience in December. Doug, who was an undergraduate when this collaboration started, has visited several times. He entered a PhD program in neuroscience at the University of San Diego in fall semester of 2006. 2) Colleen Witt, director of the RCMI imaging facility at UTSA and I are continuing to talk about potential integration of visualization and modeling with imaging. We are planning to revise and resubmit our Texas ARP research program grant proposal if the program is offered this year. Dr. Witt supervised minority student Alejandro Montelongo's undergraduate honors thesis on this work (completed spring 2007). I am a member of this student's thesis committee. C. Significance Accessible tools for analysis of microarray data are needed to fully realize the potential of high-throughput data-driven biology. In addition, other types of data (such as phenotype information) must be integrated in order to apply the results to health-care. Current tools tend to be simplistic in their data handling and present results that are difficult to relate to actual biological questions. The development of general data-handling infrastructure, meaningful visualizations, and fusion of diverse types of information is critical. The goal of this project is to build tools that allow hypothesis-driven inquiry of structure across multiple data sets at multiple scales in order to derive higher-level insight into fundamental mechanism. We have made some initial progress towards this type of deployment.

该副本是利用众多研究子项目之一 由NIH/NCRR资助的中心赠款提供的资源。子弹和 调查员（PI）可能已经从其他NIH来源获得了主要资金， 因此可以在其他清晰的条目中代表。列出的机构是 对于中心，这不一定是调查员的机构。 A.具体目标 这笔赠款的具体目的是原始提案中所述。 B.研究和结果 赠款的第3年集中在处理大型数据集和多个量表的工具上。我们在三个领域取得了进展：1）组装和处理微阵列数据集，2）分析工作流程和假设测试以及3）通过提升到抽象特征空间来进行导航和多尺度分析。这些努力的进展如下。我还写了一份赠款提案，并被授予UTSA教师发展假。在2007年秋季学期期间，这次休假将使我能够在不教书或服务职责的情况下进行研究。 1）组装和处理微阵列数据集。在大量的微阵列数据集中执行结构可视化的一个重要步骤是，将数据转换为常用格式以进行比较。 NCBI GEO（基因表达综合）在单个数据库中收集了大量的微阵列实验。他们以两种格式提供数据：XML（最小值）和软。我们已经开发了将数据下载和解析到单个数据文件中的程序，并将这些数据自动组装到逗号分隔的电子表格中以进行可视化和分析。 跨平台比较数据集的一个主要后勤问题是，不同的微阵列平台使用不同的，有时不一致的方法来识别微阵列上的基因。我们已决定将NCBI基因ID用作我们工作的标准标识符。不幸的是，一些常见的微阵列平台使用GenBank登录号而不是GeneID。这些平台需要向基因ID进行循环翻译，Cory Burkhardt已半自动。他已经从NCBI GEO数据库的前20个平台下载，重新格式化和确定了大多数微阵列实验。 我们还开发了各种程序来解析XML规范的数据，并开发了用于使用Java Softreference技术在程序执行过程中未使用的数据的软件。这使我们能够以干净，面向对象的方式处理更多的数据。杰森·爱德华兹（Jason Edwards）和詹姆斯·帕克（James Packer）开发了一种用于处理大型微阵列数据集的CacheManager架构。该体系结构将部署在我们正在开发的微阵列分析工具中。我们还将研究Converting Davis（基于Java的数据可视化平台）在来年使用此缓存技术。 2）分析工作流程和假设检验。今年，我们专注于开发基于巫师的基于工作流的工具基础架构。向导是一个熟悉的应用程序接口，在该接口中，用户在下一个和后面的按钮中逐步过程中通过过程导航。杰森·爱德华兹（Jason Edwards）和詹姆斯·帕克（James Packer）在他们的本科荣誉论文中开发了基础架构，以支持一个简单的原型工作流程，以比较许多微阵列平台上的两个基因的行为。它们的原型应用程序用Java编写，称为微米（微阵列元分析）。这是更通用的工作流 - 假设测试方法的起点。 3）通过提起抽象特征空间来导航和多个比例分析。博士生Dragana veljkovic和我继续致力于开发用于抽象功能的技术，以全面的数据集多尺度导航。这个想法是，时空数据集的时间窗口（例如，多电极记录或一系列微阵列实验）由低维子空间表示（例如，由其最大两个主要成分跨越二维空间）。然后，每个时间窗口中的数据成为特征空间中的一个点。使用子空间上的距离度量计算两个特征点之间的距离。然后，我们可以使用多种学习算法（例如ISOMAP）将此特征空间投射到平面上。我们正在开发导航和摘要技术，这些技术通过总结其抽象特征来描述非常大数据集的分布。描述实现这些导航技术的MATLAB工具的论文正在准备中。 其他活动：我们继续开发和测试戴维斯（数据查看系统）。 我们的合作者，尤其是David Senseman和他的学生，正在广泛使用戴维斯进行研究。我们记录了戴维斯（Davis）并创建了视频教程，以使学习使用戴维斯（Davis）更容易。 由于这笔赠款而形成的协作更新：该开发赠款的另一个方面是生物科学合作的形成。去年成立的以下合作进行了进行： 1）芝加哥大学的尼古拉斯·哈索普洛斯（Nicholas Hatsopoulos）与他的实验室的道格·鲁比诺（Doug Rubino）：我们的论文题为“传播波介导了汽车皮层中的信息传输”，于12月在自然神经科学上发表。道格（Doug）曾在这次合作开始时曾是本科生，他曾多次访问过。他于2006年秋季学期在圣地亚哥大学参加神经科学博士学位课程。 2）UTSA的RCMI成像设施总监Colleen Witt和我继续谈论可视化和建模与成像的潜在整合。如果今年提供该计划，我们计划修改和重新提交我们的德克萨斯州ARP研究计划赠款提案。 Witt博士监督少数民族学生亚历杭德罗·蒙特隆戈（Alejandro Montelongo）的本科荣誉论文（2007年春季完成）。我是这个学生论文委员会的成员。 C.意义 需要进行微阵列数据分析的可访问工具，以充分实现高通量数据驱动生物学的潜力。此外，必须集成其他类型的数据（例如表型信息），以将结果应用于医疗保健。当前的工具在其数据处理方面往往很简单，并且提出了与实际生物学问题相关的结果。一般数据处理基础架构，有意义的可视化和各种信息的融合的开发至关重要。该项目的目的是构建工具，允许在多个尺度的多个数据集中对假设驱动的结构进行查询，以便获得对基本机制的更高水平的洞察力。我们已经在这种部署方面取得了一些最初的进步。