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@article{1726121,
author = {Sehnal, David and Bittrich, Sebastian and Velankar, Sameer and Koča, Jaroslav and Svobodová, Radka and Burley, Stephen K. and Rose, Alexander S.},
article_location = {San Francisco},
article_number = {10},
doi = {http://dx.doi.org/10.1371/journal.pcbi.1008247},
keywords = {Structural Biology; Molecular Graphics; Data Curation},
language = {eng},
issn = {1553-734X},
journal = {PLoS Computational Biology},
title = {BinaryCIF and CIFTools-Lightweight, efficient and extensible macromolecular data management},
url = {https://doi.org/10.1371/journal.pcbi.1008247},
volume = {16},
year = {2020}
}
TY - JOUR
ID - 1726121
AU - Sehnal, David - Bittrich, Sebastian - Velankar, Sameer - Koča, Jaroslav - Svobodová, Radka - Burley, Stephen K. - Rose, Alexander S.
PY - 2020
TI - BinaryCIF and CIFTools-Lightweight, efficient and extensible macromolecular data management
JF - PLoS Computational Biology
VL - 16
IS - 10
SP - 1-13
EP - 1-13
PB - Public Library of Science
SN - 1553734X
KW - Structural Biology
KW - Molecular Graphics
KW - Data Curation
UR - https://doi.org/10.1371/journal.pcbi.1008247
L2 - https://doi.org/10.1371/journal.pcbi.1008247
N2 - 3D macromolecular structural data is growing ever more complex and plentiful in the wake of substantive advances in experimental and computational structure determination methods including macromolecular crystallography, cryo-electron microscopy, and integrative methods. Efficient means of working with 3D macromolecular structural data for archiving, analyses, and visualization are central to facilitating interoperability and reusability in compliance with the FAIR Principles. We address two challenges posed by growth in data size and complexity. First, data size is reduced by bespoke compression techniques. Second, complexity is managed through improved software tooling and fully leveraging available data dictionary schemas. To this end, we introduce BinaryCIF, a serialization of Crystallographic Information File (CIF) format files that maintains full compatibility to related data schemas, such as PDBx/mmCIF, while reducing file sizes by more than a factor of two versus gzip compressed CIF files. Moreover, for the largest structures, BinaryCIF provides even better compression-factor ten and four versus CIF files and gzipped CIF files, respectively. Herein, we describe CIFTools, a set of libraries in Java and TypeScript for generic and typed handling of CIF and BinaryCIF files. Together, BinaryCIF and CIFTools enable lightweight, efficient, and extensible handling of 3D macromolecular structural data.
ER -
SEHNAL, David, Sebastian BITTRICH, Sameer VELANKAR, Jaroslav KOČA, Radka SVOBODOVÁ, Stephen K. BURLEY and Alexander S. ROSE. BinaryCIF and CIFTools-Lightweight, efficient and extensible macromolecular data management. \textit{PLoS Computational Biology}. San Francisco: Public Library of Science, 2020, vol.~16, No~10, p.~1-13. ISSN~1553-734X. Available from: https://dx.doi.org/10.1371/journal.pcbi.1008247.
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