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@article{36006,
author = {Horský, Vladimír and Bendová, Veronika and Toušek, Dominik and Koča, Jaroslav and Svobodová Vařeková, Radka},
article_location = {Oxford (UK)},
article_number = {24},
keywords = {PDB; PDBe; Protein Data Bank; three-dimensional macromolecular structure; validation; wwPDB validation pipeline; ligands; ValTrendsDB; X-ray crystallography; NMR spectroscopy; 3DEM; database; trends in quality; visualization; statistical analysis},
language = {eng},
issn = {1367-4803},
journal = {Bioinformatics},
title = {ValTrendsDB: bringing Protein Data Bank validation information closer to the user},
url = {https://dx.doi.org/10.1093/bioinformatics/btz532},
volume = {35},
year = {2019}
}
TY - JOUR
ID - 36006
AU - Horský, Vladimír - Bendová, Veronika - Toušek, Dominik - Koča, Jaroslav - Svobodová Vařeková, Radka
PY - 2019
TI - ValTrendsDB: bringing Protein Data Bank validation information closer to the user
JF - Bioinformatics
VL - 35
IS - 24
SP - 5389-5390
EP - 5389-5390
PB - Oxford University Press
SN - 1367-4803
KW - PDB
KW - PDBe
KW - Protein Data Bank
KW - three-dimensional macromolecular structure
KW - validation
KW - wwPDB validation pipeline
KW - ligands
KW - ValTrendsDB
KW - X-ray crystallography
KW - NMR spectroscopy
KW - 3DEM
KW - database
KW - trends in quality
KW - visualization
KW - statistical analysis
UR - https://dx.doi.org/10.1093/bioinformatics/btz532
N2 - Structures in PDB tend to contain errors. This is a very serious issue for authors that rely on such potentially problematic data. The community of structural biologists develops validation methods as countermeasures, which are also included in the PDB deposition system. But how are these validation efforts influencing the structure quality of subsequently published data? Which quality aspects are improving, and which remain problematic? We developed ValTrendsDB, a database that provides the results of an extensive exploratory analysis of relationships between quality criteria, size and metadata of biomacromolecules. Key input data are sourced from PDB. The discovered trends are presented via precomputed information-rich plots. ValTrendsDB also supports the visualization of a set of user-defined structures on top of general quality trends. Therefore, ValTrendsDB enables users to see the quality of structures published by selected author, laboratory or journal, discover quality outliers, etc. ValTrendsDB is updated weekly. ValTrendsDB is freely accessible at http://ncbr.muni.cz/ValTrendsDB. The web interface was implemented in JavaScript. The database was implemented in C++.
ER -
HORSKÝ, Vladimír, Veronika BENDOVÁ, Dominik TOUŠEK, Jaroslav KOČA and Radka SVOBODOVÁ VAŘEKOVÁ. ValTrendsDB: bringing Protein Data Bank validation information closer to the user. \textit{Bioinformatics}. Oxford (UK): Oxford University Press, 2019, vol.~35, No~24, p.~5389-5390. ISSN~1367-4803.
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