STRUCTURAL VISUALIZATION IN BIOINFORMATICS

生物信息学中的结构可视化

• 批准号: 7336123
• 负责人: KAY A ROBBINS
• 金额: $ 14.6万
• 依托单位: UNIVERSITY OF TEXAS SAN ANTONIO
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-08-01 至 2007-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7336123
• 关键词: STRUCTURAL; VISUALIZATION; BIOINFORMATICS

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. The specific aims of this grant are as stated in the original proposal. Since the grant was funded for 3 years rather than for 5 years, the investigator will focus more on development of specific analysis and visualization techniques and less on building general data-handling infrastructure than was originally proposed. Year 1 of the grant was to be devoted to acquisition of baseline knowledge and implementation of basic software infrastructure. Future work will continue as outlined in the proposal. Personnel: Funding for the grant did not officially arrive until November, 2004, delaying the initial hiring of staff until spring 2005. However, during the fall 2004 semester, the PI worked with two minority students on projects specifically addressing two aspects of the specific aims as described below. To compensate for lost time due to these delays, the investigator hired two half-time graduate research assistants (Egle Pilipaviciute and Dragana Veljkovic) and two undergraduate programmers (Jason Edwards and James Packer) in January of 2005. The full-time research software developer position was filled at the beginning of March, 2005 by Cory Burkhardt, a summa cum laude graduate of UTSA. The investigator also hired David Bigham, a recent UTSA graduate who is entering the CS Master?s program in the fall of 2005, to work for three months during the summer of 2005. David will be writing scripts to access microarray databases from Matlab. The investigator also will devote a significant portion of her time in June and July of 2005 to this project. Software development: During the course of this year, it was determined that software for this project will be developed on two platforms: Matlab and Davis. We evaluated several alternative platforms including GeneSpring and R and decided that neither of these platforms was flexible enough to support the types of visualizations needed for this work. Matlab, which has an enormous library of sophisticated algorithms, has undergone major improvements in its bioinformatics toolbox and data handling capabilities. Matlab supports user-developed GUIs (graphical user interfaces). After an application has been developed in Matlab, it can be compiled into a standalone application that does not require a Matlab license. Dragana Veljkovic worked on Matlab wavelet implementations and did some prototype development with Matlab GUIs. Davis (Data Viewing System) is a data visualization platform written in Java by the investigator and her students. Davis provides a data handling infrastructure that is not available in Matlab. In particular, it supports multiple simultaneous synchronized views and can be used to view data from the web. It is also a good platform for developing streaming algorithms for handling large data sets. Considerable personnel time during this grant year has been devoted to stabilizing the Davis platform to enable future development. The program was reorganized by the investigator during the period from November to April so that it would be easier to add new visualizations and new types of data. Cory Burkhardt rewrote the underlying synchronization and timing mechanisms for Davis and has begun documenting the program, implementing configuration profiles, and writing a user?s guide. Jason Edwards and James Packer reworked the preferences that allow the user to set visualization parameters such as color maps. Egle Pilipaviciute worked on the implementations of global KL decomposition techniques. The investigator implemented a wavelet capability in Davis for doing multi-resolution visualizations as well as techniques that combined KL decomposition with wavelets. Acquisition of baseline knowledge: The investigator continued to acquire background knowledge. She regularly attended the bioinformatics seminar, a weekly meeting in which graduate students present important papers in computational bioinformatics. She attended a tutorial in microarray technology at the RCMI meeting in December. She is planning to attend the IEEE Computational Systems Bioinformatics Conference, Aug. 8?11, 2005, along with tutorials associated with that meeting. She is also a member of doctoral dissertation committees of two students who are working in bioinformatics. She met with her RCMI mentor twice this year: at Coupled60, a conference that was held in Houston, TX in February and at the NSF Collaborative Research in Computational Neuroscience Meeting in April in Washington, DC. Summary of progress on specific aims: Specific aim 1: Develop new approaches for the visual analysis of microarray data sets. A. Apply dimension-reducing techniques such as KL decomposition. The investigator jointly supervised (with Yufeng Wang of Biology) MBRS-RISE student Maribel Sanchez in an independent study. Using Matlab and GeneSpring, Maribel applied KL decomposition to analyze cell cycles in the Malaria data challenge data set produced by the DeRisi lab at UC San Francisco for the CAMDA 2004 (Critical Assessment of Microarray Data) contest. She found that KL decomposition captured the cell cycle and was able to predict the phase of the data. B. Develop and apply general techniques for the analysis of waves. No work was done on this specific aim beyond the cell cycle analysis of part A and the development of general wave techniques as part of Davis. C. Develop visual techniques for understanding gene cluster relationships. CS PhD student Robert Baltimore did an independent study on visualization of clustering for microarray data. In particular, he looked at techniques for clustering microarray data in restricted directions. D. Develop techniques for structural analysis of microarray data. MBRS/RISE student Magdaliz Gorritz did an independent study in which she gathered a large number of microarray data sets as well as links to other information. She worked using the program R to run simultaneous analysis on a large number of these data sets. This data will be used as test data for the techniques being developed. Specific aim 2: Develop new visualization techniques for multi-scale analysis and exploration of microarray data sets. A. Create a web-based data browser for navigating microarray data sets at multiple levels. No specific progress was made on navigating microarray data. However, wavelet analysis for multi-resolution analysis was implemented in Davis. B. Integrate online databases with the data browser. In the spring of 2005, the investigator supervised an independent study with Li Zhao, a CS master?s student interested in bioinformatics. Li worked with the COG databases and the supplemental data provided in the paper ?Use of Logic Relationships to Decipher Protein Network Organization? by Bowers et al. (Science 306:2246-2249, 2004). She implemented their algorithm to use phylogenetic profiles of triplets of proteins to infer network relationships. We plan to use these logic relationships to annotate clusters in microarray data. This is work in progress. C. Use 3D technology and navigation to explore microarray data. Master?s thesis student Mark Robinson continued to work on the development of techniques for overlaying two surfaces in 3D in order to compare scalar data sets. Master?s thesis student Rachel Smith is developing algorithms and an implementation to use a data glove to navigate through data in 3D. Both Mark and Rachel are conducting user studies and have approved human subjects? forms. Undergraduate student Jason Johnson is investigating the feasibility of using VTK (Visualization toolkit) to do 3D visualization in Davis. We have made progress in using these technologies but are not at the stage of applying them to microarray data. New collaborations: Another aspect of this development grant is the formation of new collaborations in bioinformatics. The investiagor has started three new research collaborations this year as a direct result of her involvement with the RCMI program: 1) Nicholas Hatsopoulos ? University of Chicago, performs multi-electrode recordings in monkey motor cortex. These records produce large amounts of spatial-temporal data with wave-like activity. His data will be useful for looking at data handling and multi-resolution issues. The investigator has formatted this data for Davis visualization. One of his undergraduate honors thesis students, Doug Rubio, visited and worked with the investigator for two days in December to learn the wave techniques and to discuss what analysis will be applicable to this data. Nicholas came as an RCMI seminar speaker in February and the collaboration will continue this summer through Doug on an analysis of the spatial dependence of directionality in the data. 2) Colleen Witt ? a postdoctoral fellow from Berkeley, works on cell motility during T-cell development. She is a former student of Richard LaBaron, and Richard suggested the collaboration. The investigator has written a suite of analysis tools in Matlab to look at cell motility characteristics in two-photon microscopy data. This experience will allow the investigator to assist other researchers who will be using the two-photon microscopy RCMI core facility that should come on line next year. 3) Matthew Gdovin ? UTSA RCMI project director, works on central respiratory chemoreception. After a discussion of his data at the April RCMI meeting in Houston, the two investigators realized that the wavelet signal analysis techniques would be applicable to the respiration data. The two investigators will collaborate directly and through their graduate students, Vonnie Veit and Dragana Veljkovic.

该子项目是利用NIH/NCRR资助的中心赠款提供的资源的许多研究子项目之一。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。列出的机构适用于该中心，这不一定是调查员的机构。这笔赠款的具体目的是原始提案中所述。由于该赠款是资助了3年而不是5年，因此研究人员将更多地专注于特定分析和可视化技术的开发，而不是最初提出的一般数据处理基础架构。赠款的第一年将专门用于获取基准知识和基本软件基础架构的实施。未来的工作将继续如提案中的概述。 人员：赠款的资金直到2004年11月才正式到达，将最初的员工雇用推迟到2005年春季。但是，在2004年秋季学期期间，PI与两名少数族裔学生合作，专门针对特定目标的两个方面，如下所述。 To compensate for lost time due to these delays, the investigator hired two half-time graduate research assistants (Egle Pilipaviciute and Dragana Veljkovic) and two undergraduate programmers (Jason Edwards and James Packer) in January of 2005. The full-time research software developer position was filled at the beginning of March, 2005 by Cory Burkhardt, a summa cum laude graduate of UTSA.调查人员还聘请了最近在2005年秋季进入CS Master计划的UTSA毕业生David Bigham在2005年夏季工作了三个月。David将编写脚本以访问Matlab的微阵列数据库。调查人员还将在2005年6月和7月将她的大部分时间用于该项目。 软件开发：在今年的过程中，确定该项目的软件将在两个平台上开发：Matlab和Davis。我们评估了包括Genespring和R在内的几个替代平台，并决定这些平台都不足够灵活，无法支持这项工作所需的可视化类型。 MATLAB具有巨大的复杂算法库，它在其生物信息学工具箱和数据处理能力方面都有重大改进。 MATLAB支持用户开发的GUI（图形用户界面）。在MATLAB开发了应用程序后，可以将其编译到不需要MATLAB许可证的独立应用程序中。 Dragana Veljkovic从事MATLAB小波的实现，并使用Matlab Guis进行了一些原型开发。 戴维斯（数据查看系统）是研究人员及其学生用Java编写的数据可视化平台。戴维斯提供了MATLAB中无法使用的数据处理基础架构。特别是，它支持多个同时同步视图，可用于从Web查看数据。它也是开发用于处理大型数据集的流算法的好平台。在这个赠款年度，大量的人事时间致力于稳定戴维斯平台，以实现未来的发展。该计划在11月至4月的期间由研究人员重组，以便更容易添加新的可视化和新类型的数据。 Cory Burkhardt重写了戴维斯的基本同步和定时机制，并已开始记录程序，实施配置配置文件以及编写用户指南。 Jason Edwards和James Packer重新设计了允许用户设置可视化参数（例如颜色地图）的首选项。 Egle Pilipaviciute致力于全球KL分解技术的实现。研究人员在戴维斯（Davis）实现了小波能力，以进行多分辨率可视化以及将KL分解与小波相结合的技术。 获得基线知识：研究人员继续获取背景知识。她定期参加生物信息学研讨会，这是每周一次的会议，研究生在该研讨会上介绍了计算生物信息学的重要论文。她在12月的RCMI会议上参加了微阵列技术教程。她计划参加2005年8月8日11日的IEEE计算系统生物信息学会议，以及与该会议相关的教程。她还是两个从事生物信息学工作的学生的博士学位论文委员会成员。她今年两次与她的RCMI导师会面：在2月在德克萨斯州休斯敦举行的Coupled60，在4月在华盛顿特区举行的计算神经科学会议上的NSF合作研究。 特定目的的进度摘要：特定目标1：开发新方法，以进行微阵列数据集的视觉分析。答：应用减少尺寸的技术，例如KL分解。 研究人员在一项独立的研究中共同监督了MBRS-Rise学生Maribel Sanchez的MBRS-Rise学生Maribel Sanchez。使用MATLAB和Genespring，Maribel应用KL分解来分析由Derisi Lab在UC San Francisco在CAMDA 2004（微阵列数据的批判性评估）竞赛的疟疾数据挑战数据集中的细胞周期。她发现KL分解捕获了细胞周期，并能够预测数据的相位。 B.开发并应用一般技术进行波的分析。除了A部分的细胞周期分析以及作为戴维斯的一部分的一般波技术的发展之外，没有对这一特定目标进行的工作。 C.开发视觉技术以理解基因簇关系。 CS博士学位学生罗伯特·巴尔的摩（Robert Baltimore）对微阵列数据的聚类可视化进行了独立研究。特别是，他研究了以限制的方向将微阵列数据聚类的技术。 D.开发用于微阵列数据结构分析的技术。 MBRS/RISE学生Magdaliz Gorritz进行了一项独立的研究，其中她收集了大量的微阵列数据集以及指向其他信息的链接。她使用程序R进行了对大量数据集的同时分析。这些数据将用作开发技术的测试数据。 特定目标2：开发新的可视化技术，用于多尺度分析和微阵列数据集的探索。答：创建一个基于Web的数据浏览器，用于在多个级别上导航微阵列数据集。在导航微阵列数据方面没有取得特定的进展。但是，戴维斯实施了用于多分辨率分析的小波分析。 B.将在线数据库与数据浏览器集成在一起。 在2005年春季，研究人员与CS大师的学生对生物信息学有兴趣的学生李赵（Li Zhao）进行了一项独立研究。 Li与COG数据库和论文中提供的补充数据合作？使用逻辑关系来解密蛋白质网络组织吗？ Bowers等人。 （科学306：2246-2249，2004）。她实施了他们的算法来使用蛋白质三胞胎的系统发育特征来推断网络关系。我们计划使用这些逻辑关系来注释微阵列数据中的集群。这是正在进行的工作。 C.使用3D技术和导航探索微阵列数据。主题论文学生马克·鲁滨逊（Mark Robinson）继续致力于开发覆盖3D表面的技术，以比较标量数据集。硕士论文学生瑞秋·史密斯（Rachel Smith）正在开发算法和使用数据手套来浏览3D中数据的实现。马克和雷切尔都在进行用户研究并批准了人类受试者？表格。本科生杰森·约翰逊（Jason Johnson）正在研究使用VTK（可视化工具包）在戴维斯进行3D可视化的可行性。我们在使用这些技术方面取得了进展，但并不是将它们应用于微阵列数据的阶段。 新的合作：这项开发资助的另一个方面是形成了生物信息学领域的新合作。由于她参与RCMI计划的直接结果，Investiagor今年已经开始了三项新的研究合作：1）Nicholas Hatsopoulos？芝加哥大学，在Monkey Motor Cortex中进行多电极录音。这些记录产生了具有波动活性的大量时空数据。他的数据对于查看数据处理和多分辨率问题将很有用。研究者已将这些数据格式化用于戴维斯可视化。他的一位本科荣誉论文学生道格·卢比奥（Doug Rubio）在12月与研究人员一起访问并与研究人员合作了两天，以学习波浪技术，并讨论将适用于该数据的分析。尼古拉斯（Nicholas）是2月份的RCMI研讨会演讲者，今年夏天将通过道格（Doug）继续进行合作，以分析数据中方向性的空间依赖性。 2）Colleen Witt？来自伯克利的博士后研究员在T细胞开发过程中从事细胞运动。她是理查德·拉巴伦（Richard Labaron）的前学生，理查德（Richard）提出了合作。研究者已经在MATLAB中写了一套分析工具，以查看两光子显微镜数据中的细胞运动特性。这种经验将使研究人员能够协助其他将使用两光子显微镜RCMI核心设施的研究人员，该设施应在明年上线。 3）Matthew Gdovin？ UTSA RCMI项目总监，从事中央呼吸系统化学感受。在休斯顿举行的4月RCMI会议上讨论了他的数据后，两名调查人员意识到小波信号分析技术将适用于呼吸数据。两位调查人员将直接合作，并通过其研究生Vonnie Veit和Dragana Veljkovic进行合作。