State-of-the-Art Data Management: Improving the Reproducibility, Consistency, and Traceability of Structural Biology and in Vitro Biochemical Experiments.
Databases
LIMS
Reproducibility
Structural biology
Journal
Methods in molecular biology (Clifton, N.J.)
ISSN: 1940-6029
Titre abrégé: Methods Mol Biol
Pays: United States
ID NLM: 9214969
Informations de publication
Date de publication:
2021
2021
Historique:
entrez:
30
10
2020
pubmed:
31
10
2020
medline:
25
3
2021
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 33125653
doi: 10.1007/978-1-0716-0892-0_13
pmc: PMC8019398
mid: NIHMS1681441
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
209-236Subventions
Organisme : NIGMS NIH HHS
ID : R01 GM132595
Pays : United States
Organisme : NHGRI NIH HHS
ID : U01 HG008424
Pays : United States
Organisme : NIGMS NIH HHS
ID : R01 GM117080
Pays : United States
Organisme : NIAID NIH HHS
ID : HHSN272201700060C
Pays : United States
Organisme : NIGMS NIH HHS
ID : R01 GM117325
Pays : United States
Organisme : NIAID NIH HHS
ID : HHSN272201200026C
Pays : United States
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