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

David R. Cooper, Marek Grabowski, Matthew D. Zimmerman, Przemyslaw J. Porebski, Ivan G. Shabalin, Magdalena Woinska, Marcin J. Domagalski, Heping Zheng, Piotr Sroka, Marcin Cymborowski, Mateusz P. Czub, Ewa Niedzialkowska, Barat S. Venkataramany, Tomasz Osinski, Zbigniew Fratczak, Jacek Bajor, Juliusz Gonera, Elizabeth MacLean, Kamila Wojciechowska, Krzysztof KoninaWojciech Wajerowicz, Maksymilian Chruszcz, Wladek Minor

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

4 Scopus citations

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.

Original languageEnglish
Title of host publicationMethods in Molecular Biology
PublisherHumana Press Inc.
Pages209-236
Number of pages28
DOIs
StatePublished - 2021
Externally publishedYes

Publication series

NameMethods in Molecular Biology
Volume2199
ISSN (Print)1064-3745
ISSN (Electronic)1940-6029

Keywords

  • Databases
  • LIMS
  • Reproducibility
  • Structural biology

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