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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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