COMPUTER AIDED TWO-DIMENSIONAL ELECTROPHORETIC GEL ANALYSIS

GELLAB-II软件系统是一个探索性的数据分析系统 二维凝胶电泳图分析。它 集成复杂的子系统，用于图像采集、处理、 数据库操作、图表和统计分析。一直以来 适用于各种实验系统，其中定量和 在成百上千种蛋白质中的一种或多种蛋白质的质变 没有改变的蛋白质，是基本的分析问题。跟踪 使用这些方法检测到的更改也是 系统。复合凝胶数据库可以在不同的 探索性数据分析条件，以及统计差异和 从一个有效的3D数据库的“切片”中阐明的微妙图案。 结果可以在各种表格、曲线图或派生图像中显示 以及在广域网上的工作站上。GELLAB-II的研究已经 与CSPI/Scanalytics Inc.达成技术转让CRADA， 商用系统GELLAB-II，使这项技术 癌症研究人员可以在廉价的Microsoft Windows上轻松访问 PC(1996年发布)。这种商业化导致了更广泛的使用和 对GELLAB-II技术的支持比我们所能提供的更好。GELLAB- 这一时期的应用包括：正在进行的核基质研究 前列腺癌中的蛋白质变化有助于男性的筛查和分期 患有前列腺癌(J.Hopkins)；以及延长早期的Rett综合征 首页--期刊主要分类--期刊细介绍--期刊题录与文摘内容电泳法16：1176-83， 1995年。在前列腺癌研究中，我们正在寻找这两个失踪的 蛋白质和微妙的数量变化 与实验条件和分期相关的蛋白质和 前列腺癌男性筛查。我们也在做类似的 肾细胞核基质蛋白图谱的研究 癌和其他泌尿生殖系统组织。霍普金斯研究小组的前列腺 分子工作已经通过测序鉴定了几个蛋白质点 分析，并正在完成确认这些序列的研究。 GRABT=Z01BC08382 要完全了解RNA分子的功能，需要 关于高阶结构及其特性的知识 它们的主要序列。的二维和三维结构 RNA对许多功能都很重要，包括调节 转录和翻译、催化和蛋白质的运输 穿过膜。对这些功能的理解对于 基础生物学以及生物分析和药物开发 可以干预的情况下，这些病理功能 分子就会出现。我们一直参与开发计算软件 改进RNA折叠预测的方法及对预测结果的分析 结果。 我们一直在开发和改进一种新的RNA折叠技术 使用遗传算法中的概念。我们是第一个申请的团体 这种解决这个问题的方法。我们的遗传算法运行在大规模的 拥有16384个处理器的并行超级计算机，最近已经 适用于其他大规模并行超级计算机，Cray/SGI T3E和 起源于2000年。我们已经看到了与更多的 传统的折叠算法，并采用了新的方法 它们加快了收敛，指定了停止条件，并允许 三级相互作用(H型假结)的预测。除了GA之外 我们还扩展了我们的计算RNA分析工作台， STRUCTURELAB，以包括允许 核糖核酸结构大型数据库的系统发育比较。我们也 有能力生成各种不同类型的三维原子模型 折叠的RNA结构。我们已经将这些算法用于研究各种 艾滋病毒和肠道病毒的毒株。这包括3维的 对HIV的一部分进行建模。

The GELLAB-II software system is an exploratory data analysis system for the analysis of sets of 2D electrophoretic protein gel images. It incorporates sophisticated subsystems for image acquisition, processing, database manipulation, graphics and statistical analysis. It has been applied to a variety of experimental systems in which quantitative and qualitative changes, in one or more proteins among hundreds or thousands of unaltered proteins, is the basic analytic problem. Keeping track of changes detected using these methods is also a major attribute of the system. A composite gel database may be "viewed" under different exploratory data analysis conditions, and statistical differences and subtle patterns elucidated from "slices" of an effective 3D database. Results can be presented in a variety of tables, plots or derived images and on workstations over wide area networks. The GELLAB-II research has resulted in a technology transfer CRADA with CSPI/Scanalytics Inc., for a commercially available system GELLAB-II+, making this technology easily available to cancer researchers on inexpensive Microsoft Windows PCs (released in 1996). Such commercialization results in wider use and better support of the GELLAB-II technology than we can provide. GELLAB- II applications this period include: ongoing studies of nuclear matrix protein changes in prostate cancer to aid screening and staging of men with prostate cancer (J.Hopkins); and extending an earlier Rett syndrome study (CDC) Robinson MK, Myrick J, etal. Electrophoresis 16: 1176-83, 1995. In the prostate cancer studies,we are looking for both missing proteins as well as subtle quantitative changes in patterns on sets of proteins which correlates with experimental conditions and staging and screening of men with prostate cancer. We are also doing a similar investigation of nuclear matrix protein patterns from renal cell carcinoma and other genitourinary tissues. The Hopkins group's prostate molecular work has identified several of the protein spots by sequence analysis and are completing studies to confirm these sequences. GRABT=Z01BC08382 A complete understanding of the function of RNA molecules requires a knowledge of the higher order structures as well as the characteristics of their primary sequence. The two and three-dimensional structure of RNA are important for many functions, including regulation of transcription and translation, catalysis, and the transport of proteins across membranes. The understanding of these functions is important for basic biology as well as for bioassays and the development of drugs that can intervene in cases where pathological functionality of these molecules occur. We have been involved in developing computational approaches for improving RNA folding prediction and the analysis of the results. We have been developing and improving a novel RNA folding technique that uses concepts from genetic algorithms. We were the first group to apply this approach to this problem. Our genetic algorithm runs on a massively parallel supercomputer with 16384 processors, and has recently been adapted to other massively parallel supercomputers, a CRAY/SGI T3E and ORIGIN 2000. We have seen improvements in results compared with more conventional folding algorithms and have incorporated new approaches which speed up convergence, specify stopping criteria, and allow the prediction of tertiary interactions (H-type pseudknots). Besides the GA we have also expanded our computational RNA analysis workbench, STRUCTURELAB, to include new analysis algorithms that permit the phylogenetic comparision of large databases of RNA structures. We also have the ability to generate 3-dimensional atomic models of various folded RNA structures. We have used these algorithms in studying various strains of HIV and the enteroviruses. This includes the 3-dimensional modeling of a portion of HIV.