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最先进的数据管理:提高结构生物学和体外生化实验的可重复性、一致性和可追溯性。

State-of-the-Art Data Management: Improving the Reproducibility, Consistency, and Traceability of Structural Biology and in Vitro Biochemical Experiments.

机构信息

Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA, USA.

Center for Structural Genomics of Infectious Diseases, University of Virginia, Charlottesville, VA, USA.

出版信息

Methods Mol Biol. 2021;2199:209-236. doi: 10.1007/978-1-0716-0892-0_13.

DOI:10.1007/978-1-0716-0892-0_13
PMID:33125653
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8019398/
Abstract

Efficient and comprehensive data management is an indispensable component of modern scientific research and requires effective tools for all but the most trivial experiments. The LabDB system developed and used in our laboratory was originally designed to track the progress of a structure determination pipeline in several large National Institutes of Health (NIH) projects. While initially designed for structural biology experiments, its modular nature makes it easily applied in laboratories of various sizes in many experimental fields. Over many years, LabDB has transformed into a sophisticated system integrating a range of biochemical, biophysical, and crystallographic experimental data, which harvests data both directly from laboratory instruments and through human input via a web interface. The core module of the system handles many types of universal laboratory management data, such as laboratory personnel, chemical inventories, storage locations, and custom stock solutions. LabDB also tracks various biochemical experiments, including spectrophotometric and fluorescent assays, thermal shift assays, isothermal titration calorimetry experiments, and more. LabDB has been used to manage data for experiments that resulted in over 1200 deposits to the Protein Data Bank (PDB); the system is currently used by the Center for Structural Genomics of Infectious Diseases (CSGID) and several large laboratories. This chapter also provides examples of data mining analyses and warnings about incomplete and inconsistent experimental data. These features, together with its capabilities for detailed tracking, analysis, and auditing of experimental data, make the described system uniquely suited to inspect potential sources of irreproducibility in life sciences research.

摘要

高效、全面的数据管理是现代科学研究不可或缺的组成部分,几乎所有实验都需要有效的工具。我们实验室开发和使用的 LabDB 系统最初是为了跟踪几个大型美国国立卫生研究院(NIH)项目中结构测定管道的进展而设计的。虽然最初是为结构生物学实验设计的,但它的模块化性质使其很容易应用于各种规模的实验室和许多实验领域。多年来,LabDB 已经发展成为一个复杂的系统,集成了一系列生化、生物物理和晶体学实验数据,通过实验室仪器直接采集数据,并通过网络界面进行人工输入。该系统的核心模块处理许多类型的通用实验室管理数据,如实验室人员、化学库存、存储位置和自定义储备溶液。LabDB 还跟踪各种生化实验,包括分光光度法和荧光测定法、热转移测定法、等温滴定量热法实验等。LabDB 已被用于管理超过 1200 个数据存入蛋白质数据库(PDB)的实验数据;该系统目前由传染病结构基因组学中心(CSGID)和几个大型实验室使用。本章还提供了数据挖掘分析的示例以及关于实验数据不完整和不一致的警告。这些功能,以及其对实验数据进行详细跟踪、分析和审核的能力,使得所描述的系统非常适合检查生命科学研究中潜在的不可重复性来源。