Wikidata as a knowledge graph for the life sciences

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Abstract

Wikidata is a community-maintained knowledge base that has been assembled from repositories in the fields of genomics, proteomics, genetic variants, pathways, chemical compounds, and diseases, and that adheres to the FAIR principles of findability, accessibility, interoperability and reusability. Here we describe the breadth and depth of the biomedical knowledge contained within Wikidata, and discuss the open-source tools we have built to add information to Wikidata and to synchronize it with source databases. We also demonstrate several use cases for Wikidata, including the crowdsourced curation of biomedical ontologies, phenotype-based diagnosis of disease, and drug repurposing.

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Most cited references 60

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UniProt: a worldwide hub of protein knowledge

(2018)

Abstract The UniProt Knowledgebase is a collection of sequences and annotations for over 120 million proteins across all branches of life. Detailed annotations extracted from the literature by expert curators have been collected for over half a million of these proteins. These annotations are supplemented by annotations provided by rule based automated systems, and those imported from other resources. In this article we describe significant updates that we have made over the last 2 years to the resource. We have greatly expanded the number of Reference Proteomes that we provide and in particular we have focussed on improving the number of viral Reference Proteomes. The UniProt website has been augmented with new data visualizations for the subcellular localization of proteins as well as their structure and interactions. UniProt resources are available under a CC-BY (4.0) license via the web at https://www.uniprot.org/.

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The Pfam protein families database in 2019

Sara El-Gebali, Jaina Mistry, Alex Bateman … (2018)

Abstract The last few years have witnessed significant changes in Pfam (https://pfam.xfam.org). The number of families has grown substantially to a total of 17,929 in release 32.0. New additions have been coupled with efforts to improve existing families, including refinement of domain boundaries, their classification into Pfam clans, as well as their functional annotation. We recently began to collaborate with the RepeatsDB resource to improve the definition of tandem repeat families within Pfam. We carried out a significant comparison to the structural classification database, namely the Evolutionary Classification of Protein Domains (ECOD) that led to the creation of 825 new families based on their set of uncharacterized families (EUFs). Furthermore, we also connected Pfam entries to the Sequence Ontology (SO) through mapping of the Pfam type definitions to SO terms. Since Pfam has many community contributors, we recently enabled the linking between authorship of all Pfam entries with the corresponding authors’ ORCID identifiers. This effectively permits authors to claim credit for their Pfam curation and link them to their ORCID record.

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Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking.

Mingxun Wang, Jeremy J. Carver, Vanessa V Phelan … (2016)

The potential of the diverse chemistries present in natural products (NP) for biotechnology and medicine remains untapped because NP databases are not searchable with raw data and the NP community has no way to share data other than in published papers. Although mass spectrometry (MS) techniques are well-suited to high-throughput characterization of NP, there is a pressing need for an infrastructure to enable sharing and curation of data. We present Global Natural Products Social Molecular Networking (GNPS; http://gnps.ucsd.edu), an open-access knowledge base for community-wide organization and sharing of raw, processed or identified tandem mass (MS/MS) spectrometry data. In GNPS, crowdsourced curation of freely available community-wide reference MS libraries will underpin improved annotations. Data-driven social-networking should facilitate identification of spectra and foster collaborations. We also introduce the concept of 'living data' through continuous reanalysis of deposited data.

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Author and article information

Contributors

Andrew I Su:

ORCID: https://orcid.org/0000-0002-9859-4104

Peter Rodgers: Role: Reviewing Editor

Peter Rodgers: Role: Senior Editor

Journal

Journal ID (nlm-ta): eLife

Journal ID (iso-abbrev): Elife

Journal ID (publisher-id): eLife

Title: eLife

Publisher: eLife Sciences Publications, Ltd

ISSN (Electronic): 2050-084X

Publication date (Electronic, pub): 17 March 2020

Publication date Collection: 2020

Volume: 9

Electronic Location Identifier: e52614

Affiliations

[1 ]Micelio AntwerpenBelgium

[2 ]Department of Integrative Structural and Computational Biology, The Scripps Research Institute La JollaUnited States

[3 ]Center for Integrative Bioinformatics Vienna, Max Perutz Laboratories, University of Vienna and Medical University of Vienna ViennaAustria

[4 ]McDonnell Genome Institute, Washington University School of Medicine St. LouisUnited States

[5 ]Institute of Data Science and Biotechnology, Gladstone Institutes San FranciscoUnited States

[6 ]European Bioinformatics Institute (EMBL-EBI) HinxtonUnited Kingdom

[7 ]School of Chemistry, The University of Sydney SydneyAustralia

[8 ]Division of Allergy and Infectious Diseases, Department of Medicine, University of Washington SeattleUnited States

[9 ]Wellcome Trust Sanger Institute CambridgeUnited Kingdom

[10 ]School of Data Science, University of Virginia CharlottesvilleUnited States

[11 ]University of Maryland School of Medicine BaltimoreUnited States

[12 ]Department of Animal Plant and Soil Sciences, La Trobe University MelbourneAustralia

[13 ]Department of Bioinformatics-BiGCaT, NUTRIM, Maastricht University MaastrichtNetherlands

[14 ]Retired researcher BerlinGermany

[15 ]Yale University Library, Yale University New HavenUnited States

eLife United Kingdom

United States

Author notes

[†]

These authors contributed equally to this work.

Author information

Andra Waagmeester https://orcid.org/0000-0001-9773-4008

Gregory Stupp https://orcid.org/0000-0002-0644-7212

Sebastian Burgstaller-Muehlbacher https://orcid.org/0000-0003-4640-3510

Benjamin M Good https://orcid.org/0000-0002-7334-7852

Malachi Griffith https://orcid.org/0000-0002-6388-446X

Obi L Griffith https://orcid.org/0000-0002-0843-4271

Kristina Hanspers https://orcid.org/0000-0001-5410-599X

Henning Hermjakob https://orcid.org/0000-0001-8479-0262

Toby S Hudson https://orcid.org/0000-0002-3348-3622

Kevin Hybiske https://orcid.org/0000-0002-2967-3079

Sarah M Keating https://orcid.org/0000-0002-3356-3542

Magnus Manske https://orcid.org/0000-0001-5916-0947

Michael Mayers https://orcid.org/0000-0002-7792-0150

Daniel Mietchen https://orcid.org/0000-0001-9488-1870

Elvira Mitraka https://orcid.org/0000-0003-0719-3485

Alexander R Pico https://orcid.org/0000-0001-5706-2163

Timothy Putman https://orcid.org/0000-0002-4291-0737

Anders Riutta https://orcid.org/0000-0002-4693-0591

Nuria Queralt-Rosinach https://orcid.org/0000-0003-0169-8159

Lynn M Schriml https://orcid.org/0000-0001-8910-9851

Thomas Shafee https://orcid.org/0000-0002-2298-7593

Denise Slenter https://orcid.org/0000-0001-8449-1318

Ralf Stephan https://orcid.org/0000-0002-4650-631X

Katherine Thornton https://orcid.org/0000-0002-4499-0451

Ginger Tsueng https://orcid.org/0000-0001-9536-9115

Roger Tu https://orcid.org/0000-0002-7899-1604

Sabah Ul-Hasan https://orcid.org/0000-0001-6334-452X

Egon Willighagen https://orcid.org/0000-0001-7542-0286

Chunlei Wu https://orcid.org/0000-0002-2629-6124

Andrew I Su https://orcid.org/0000-0002-9859-4104

Article

Publisher ID: 52614

DOI: 10.7554/eLife.52614

PMC ID: 7077981

PubMed ID: 32180547

SO-VID: 8985a17e-4625-40d3-b5f0-c6874586b873

License:

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

History

Date received : 09 October 2019

Date accepted : 28 February 2020

Funding

Funded by: FundRef http://dx.doi.org/10.13039/100000057, National Institute of General Medical Sciences;

Award ID: R01 GM089820

Award Recipient : Andrew I Su

Funded by: FundRef http://dx.doi.org/10.13039/100000057, National Institute of General Medical Sciences;

Award ID: U54 GM114833

Award Recipient : Henning Hermjakob Andrew I Su

Funded by: FundRef http://dx.doi.org/10.13039/100000057, National Institute of General Medical Sciences;

Award ID: R01 GM100039

Award Recipient : Alexander R Pico

Funded by: FundRef http://dx.doi.org/10.13039/100000051, National Human Genome Research Institute;

Award ID: R00HG007940

Award Recipient : Malachi Griffith

Funded by: FundRef http://dx.doi.org/10.13039/100000054, National Cancer Institute;

Award ID: U24CA237719

Award Recipient : Malachi Griffith

Funded by: FundRef http://dx.doi.org/10.13039/100001368, V Foundation for Cancer Research;

Award ID: V2018-007

Award Recipient : Malachi Griffith

Funded by: FundRef http://dx.doi.org/10.13039/100000060, National Institute of Allergy and Infectious Diseases;

Award ID: R01 AI126785

Award Recipient : Kevin Hybiske

Funded by: FundRef http://dx.doi.org/10.13039/100006108, National Center for Advancing Translational Sciences;

Award ID: UL1 TR002550

Award Recipient : Andrew I Su

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Custom metadata

Author impact statement Wikidata is continuously-updated resource that could improve the efficiency and accuracy of research in many areas of the life and biomedical sciences.

Template 5

ScienceOpen disciplines: Life sciences

Keywords: science forum,knowledge graphs,data mining,drug repurposing,wikidata,none

Data availability:

ScienceOpen disciplines: Life sciences

Keywords: science forum, knowledge graphs, data mining, drug repurposing, wikidata, none

Wikidata as a knowledge graph for the life sciences

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Abstract

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Drug Repurposing Research Collection

Most cited references 60

UniProt: a worldwide hub of protein knowledge

The Pfam protein families database in 2019

Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking.

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