Evidence map›Paper›PMID 33075050›Full record

ArticlePLoS computational biology2020

BinaryCIF and CIFTools-Lightweight, efficient and extensible macromolecular data management.

David Sehnal, Sebastian Bittrich, Sameer Velankar, Jaroslav Koča, Radka Svobodová, Stephen K Burley, Alexander S Rose

Abstract read
In one paragraph

Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

28 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Mesoscale explorer: Visual exploration of large-scale molecular models.Protein science : a publication of the Protein Society · 2024
    Article
  9. Article
  10. Mesoscale Explorer - Visual Exploration of Large-Scale Molecular Models.bioRxiv : the preprint server for biology · 2024
    Article
  11. Article
  12. Article
  13. Efficient protein structure archiving using ProteStAr.Bioinformatics (Oxford, England) · 2024
    Article
  14. Article
  15. Article
  16. Review
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

David SehnalCEITEC, Central European Institute of Technology, Masaryk University, Brno, Czech Republic.ORCID 0000-0002-0682-3089
Sebastian BittrichRCSB Protein Data Bank, San Diego Supercomputer Center University of California, San Diego, La Jolla, CA 92093, USA.ORCID 0000-0003-3576-0387
Sameer VelankarProtein Data Bank in Europe (PDBe), European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK.ORCID 0000-0002-8439-5964
Jaroslav KočaCEITEC, Central European Institute of Technology, Masaryk University, Brno, Czech Republic.ORCID 0000-0002-2780-4901
Radka SvobodováCEITEC, Central European Institute of Technology, Masaryk University, Brno, Czech Republic.
Stephen K BurleyRCSB Protein Data Bank, San Diego Supercomputer Center University of California, San Diego, La Jolla, CA 92093, USA.
Alexander S RoseRCSB Protein Data Bank, San Diego Supercomputer Center University of California, San Diego, La Jolla, CA 92093, USA.ORCID 0000-0002-0893-5551

Funding

PDB MANAGEMENT BY THE RESEARCH COLLABORATORY FOR STRUCTURAL BIOINFORMATICSR01GM133198 · NIGMS · RUTGERS, THE STATE UNIV OF N.J. · PI BURLEY, STEPHEN K · 2019 to 2023
$19.3M
NIGMS NIH HHS R01 GM133198Wellcome TrustWellcome Trust 104948
6 · The paper itself

Abstract

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.

Indexed as

Models, MolecularSoftwareCrystallographyDatabases, ChemicalData CompressionMacromolecular SubstancesMacromolecular Substances

Identifiers

PMID33075050
PMCPMC7595629

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.