Integrated resource for reproducibility in macromolecular crystallography

大分子晶体学重现性的综合资源

• 批准号: 8875830
• 负责人: WLADEK MINOR
• 金额: $ 46万
• 依托单位: UNIVERSITY OF VIRGINIA
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-06-01 至 2018-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8875830
• 关键词: 3-Dimensional; Address; Algorithmic Software; Algorithms; Archives; Biological; Calibration; Collection; Communities; Computer software; Crystallography; Data; Data Analyses; Data Collection; Data Set; Databases; Deposition; Detection; Development; Diffuse; Discipline; Disease; Educational workshop; Ensure; Fraud; Goals; Ice; Image; Image Analysis; Infection; Lead; Learning; Libraries; Life; Ligands; Link; Location; Metadata; Methods; Microscopic; Modeling; Molecular; Morphologic artifacts; Network-based; Online Systems; Procedures; Process; Proteins; Protocols documentation; Reproducibility; Research; Research Personnel; Resolution; Resources; Roentgen Rays; Semantics; Site; Software Tools; Structural Biologist; Structure; Synchrotrons; System; Technology; Technology Transfer; Testing; Training; Twin Multiple Birth; Validation; Vendor; Work; X-Ray Crystallography; beamline; beneficiary; cell dimension; data mining; data reduction; density; design; detector; electron density; geometric methodologies; heuristics; improved; member; prevent; programs; public health relevance; repository; research study; statistics; structural biology; structural genomics; syntax; tool; tool development; working group

 DESCRIPTION (provided by applicant): We propose the development of a collection of data wrangling tools to store, parse, manipulate, validate, curate, analyze, and disseminate macromolecular diffraction images together with all associated relevant metadata. The proposed system will have several benefits, by (1) creating a means to improve existing structures as technology for processing diffraction image advances, (2) detecting errors (and potentially, fraud) in existing structures to ensure structure quality and reproducibility, (3) preventing the loss of data collected by structural genomics and other programs that have closed or will close, (4) providing data for analysis of diffuse diffraction effects, and (5) buildng a "training set" for new diffraction analysis algorithms and hardware. Biologists, bioinformaticians, and software and hardware developers will all be beneficiaries of these tools. The proposed research is designed for semantic rather than syntactic analysis of diffraction images, and has several specific goals. First, we will develop tools for automatically extracting and curating diffraction images and associated metadata, as well as producing descriptions of all data needed for reprocessing when methods for structure determination improve. Second, we will create a web-based system for organizing, searching, analyzing, and data mining of appropriate subsets of diffraction images and associated metadata in machine- readable formats. This will include a comprehensive API for programmatic access, the ability to link multiple instances into a distributed federation, and state-of-the-art compression and transfer technologies. Third, we will develop tools to automatically validate, preprocess, and score diffraction images, and to detect potential issues and errors. These tools will make use of new and existing programs for image and data analysis, contain heuristics to identify possible errors, and provide statistics to correlate errors with specific metadata. Fourth, we will create a mechanism to discover diffraction data that have not yielded X-ray structures with currently available methods. Fifth, we will set up a pilot resource incorporating all the developed tools, and collect a test data set for the development of new tools for validation and error detection. We will work closely with multiple collaborators. Most important is the RCSB Protein Data Bank (PDB), who will help us ensure the accuracy and completeness of the diffraction metadata. Other partners will include the diffuse X-ray scattering community, detector vendors, synchrotron beamline managers, members of the IUCr Diffraction Data Deposition Working Group (DDDWG) and the crystallographic community in general. Together with the RCSB PDB, we will organize workshop(s) with these communities in order to (a) improve metadata extraction and (b) better define subsets of diffraction images. By addressing the currently common, irreversible and unnecessary loss of raw diffraction data during the data reduction process, our project helps ensure that the discipline of macromolecular crystallography is capable of continuous self-improvement.

 描述（由申请人提供）：我们建议开发一系列数据整理工具来存储、解析、操作、验证、策划、分析和传播大分子衍射图像以及所有相关的相关元数据。拟议的系统将有几个好处，通过（1）随着处理衍射图像技术的进步，创建一种改进现有结构的方法，（2）检测现有结构中的错误（以及潜在的欺诈）以确保结构质量和可重复性，（3）防止结构基因组学和其他已关闭或将关闭的程序收集的数据丢失，（4）提供用于分析漫射衍射效应的数据，（5）建立“培训” 设置”用于新的衍射分析算法和硬件。生物学家、生物信息学家、 而软件和硬件开发商都将是这些工具的受益者。所提出的研究旨在对衍射图像进行语义分析而不是句法分析，并且有几个具体目标。首先，我们将开发用于自动提取和整理衍射图像和相关元数据的工具，以及在结构确定方法改进时生成重新处理所需的所有数据的描述。其次，我们将创建一个基于网络的系统，用于组织、搜索、分析和数据挖掘机器可读格式的衍射图像和相关元数据的适当子集。这将包括用于编程访问的全面 API、将多个实例链接到分布式联合的能力以及最先进的压缩和传输技术。第三，我们将开发工具来自动验证、预处理和评分衍射图像，并检测潜在的问题和错误。这些工具将利用新的和现有的图像和数据分析程序，包含启发式方法来识别可能的错误，并提供统计数据以将错误与特定元数据关联起来。第四，我们将创建一种机制来发现尚未用现有方法产生 X 射线结构的衍射数据。第五，我们 将建立一个包含所有开发工具的试点资源，并收集测试数据集 开发用于验证和错误检测的新工具。我们将与多个合作者密切合作。最重要的是RCSB蛋白质数据库（PDB），它将帮助我们确保衍射元数据的准确性和完整性。其他合作伙伴将包括漫 X 射线散射社区、探测器供应商、同步加速器束线管理器、IUCr 衍射数据沉积工作组 (DDDWG) 的成员以及整个晶体学社区。我们将与 RCSB PDB 一起与这些社区组织研讨会，以便 (a) 改进元数据提取和 (b) 更好地定义衍射图像的子集。通过解决目前在数据缩减过程中常见的、不可逆的和不必要的原始衍射数据丢失问题，我们的项目有助于确保高分子晶体学学科能够不断自我完善。