Loading…

The VRNetzer platform enables interactive network analysis in Virtual Reality

Networks provide a powerful representation of interacting components within complex systems, making them ideal for visually and analytically exploring big data. However, the size and complexity of many networks render static visualizations on typically-sized paper or screens impractical, resulting i...

Full description

Saved in:
Bibliographic Details
Published in:Nature communications 2021-04, Vol.12 (1), p.2432-2432, Article 2432
Main Authors: Pirch, Sebastian, Müller, Felix, Iofinova, Eugenia, Pazmandi, Julia, Hütter, Christiane V. R., Chiettini, Martin, Sin, Celine, Boztug, Kaan, Podkosova, Iana, Kaufmann, Hannes, Menche, Jörg
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c540t-34aabd58f0798548933237a396d9dd0b7433bd26f6189f236af7dadf345230283
cites cdi_FETCH-LOGICAL-c540t-34aabd58f0798548933237a396d9dd0b7433bd26f6189f236af7dadf345230283
container_end_page 2432
container_issue 1
container_start_page 2432
container_title Nature communications
container_volume 12
creator Pirch, Sebastian
Müller, Felix
Iofinova, Eugenia
Pazmandi, Julia
Hütter, Christiane V. R.
Chiettini, Martin
Sin, Celine
Boztug, Kaan
Podkosova, Iana
Kaufmann, Hannes
Menche, Jörg
description Networks provide a powerful representation of interacting components within complex systems, making them ideal for visually and analytically exploring big data. However, the size and complexity of many networks render static visualizations on typically-sized paper or screens impractical, resulting in proverbial ‘hairballs’. Here, we introduce a Virtual Reality (VR) platform that overcomes these limitations by facilitating the thorough visual, and interactive, exploration of large networks. Our platform allows maximal customization and extendibility, through the import of custom code for data analysis, integration of external databases, and design of arbitrary user interface elements, among other features. As a proof of concept, we show how our platform can be used to interactively explore genome-scale molecular networks to identify genes associated with rare diseases and understand how they might contribute to disease development. Our platform represents a general purpose, VR-based data exploration platform for large and diverse data types by providing an interface that facilitates the interaction between human intuition and state-of-the-art analysis methods. Data-rich networks can be difficult to interpret beyond a certain size. Here, the authors introduce a platform that uses virtual reality to allow the visual exploration of large networks, while interfacing with data repositories and other analytical methods to improve the interpretation of big data.
doi_str_mv 10.1038/s41467-021-22570-w
format article
fullrecord <record><control><sourceid>proquest_doaj_</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_a9c0ec8e93de4f90bf7ae10005d40be9</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><doaj_id>oai_doaj_org_article_a9c0ec8e93de4f90bf7ae10005d40be9</doaj_id><sourcerecordid>2517102818</sourcerecordid><originalsourceid>FETCH-LOGICAL-c540t-34aabd58f0798548933237a396d9dd0b7433bd26f6189f236af7dadf345230283</originalsourceid><addsrcrecordid>eNp9UU1v1DAQtRCIVkv_AAcUiQuXgL8S2xckVBWoVECqSq_WJJ5ss2TjxXa6Wn493k0pLQd8sTXvzfO8eYS8ZPQto0K_i5LJWpWUs5LzStFy-4QccypZyRQXTx-8j8hJjCuajzBMS_mcHAmhjeBaHJMvVzdYXF9-xfQLQ7EZIHU-rAscoRkwFv2YMECb-lssRkxbH34UMMKwi_0eLK77kCYYikuEoU-7F-RZB0PEk7t7Qb5_PLs6_VxefPt0fvrhomwrSVMpJEDjKt1RZXQl8yyCCwXC1M44RxslhWgcr7uaadNxUUOnHLhOyIoLmudekPNZ13lY2U3o1xB21kNvDwUflhZC6tsBLZiWYqvRCIeyM7TpFCDLy6icpE0uL8j7WWszNWt0LY4pwPBI9DEy9jd26W-tpnXFapkF3twJBP9zwpjsuo8tDgOM6KdoecU0Z0bRPfX1P9SVn0Le54GlWPbG9u74zGqDjzFgdz8Mo3Yfvp3Dtzl8ewjfbnPTq4c27lv-RJ0JYibEDI1LDH___o_sb-e3urI</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2517102818</pqid></control><display><type>article</type><title>The VRNetzer platform enables interactive network analysis in Virtual Reality</title><source>Open Access: PubMed Central</source><source>Nature</source><source>Publicly Available Content (ProQuest)</source><source>Springer Nature - nature.com Journals - Fully Open Access</source><creator>Pirch, Sebastian ; Müller, Felix ; Iofinova, Eugenia ; Pazmandi, Julia ; Hütter, Christiane V. R. ; Chiettini, Martin ; Sin, Celine ; Boztug, Kaan ; Podkosova, Iana ; Kaufmann, Hannes ; Menche, Jörg</creator><creatorcontrib>Pirch, Sebastian ; Müller, Felix ; Iofinova, Eugenia ; Pazmandi, Julia ; Hütter, Christiane V. R. ; Chiettini, Martin ; Sin, Celine ; Boztug, Kaan ; Podkosova, Iana ; Kaufmann, Hannes ; Menche, Jörg</creatorcontrib><description>Networks provide a powerful representation of interacting components within complex systems, making them ideal for visually and analytically exploring big data. However, the size and complexity of many networks render static visualizations on typically-sized paper or screens impractical, resulting in proverbial ‘hairballs’. Here, we introduce a Virtual Reality (VR) platform that overcomes these limitations by facilitating the thorough visual, and interactive, exploration of large networks. Our platform allows maximal customization and extendibility, through the import of custom code for data analysis, integration of external databases, and design of arbitrary user interface elements, among other features. As a proof of concept, we show how our platform can be used to interactively explore genome-scale molecular networks to identify genes associated with rare diseases and understand how they might contribute to disease development. Our platform represents a general purpose, VR-based data exploration platform for large and diverse data types by providing an interface that facilitates the interaction between human intuition and state-of-the-art analysis methods. Data-rich networks can be difficult to interpret beyond a certain size. Here, the authors introduce a platform that uses virtual reality to allow the visual exploration of large networks, while interfacing with data repositories and other analytical methods to improve the interpretation of big data.</description><identifier>ISSN: 2041-1723</identifier><identifier>EISSN: 2041-1723</identifier><identifier>DOI: 10.1038/s41467-021-22570-w</identifier><identifier>PMID: 33893283</identifier><language>eng</language><publisher>London: Nature Publishing Group UK</publisher><subject>631/114/2398 ; 631/114/2408 ; 631/553/794 ; 639/166/985 ; 692/4017 ; Analytical methods ; Big Data ; Complex systems ; Complexity ; Computer applications ; Data analysis ; Exploration ; Genomes ; Humanities and Social Sciences ; multidisciplinary ; Network analysis ; Rare diseases ; Science ; Science (multidisciplinary) ; Virtual networks ; Virtual reality</subject><ispartof>Nature communications, 2021-04, Vol.12 (1), p.2432-2432, Article 2432</ispartof><rights>The Author(s) 2021</rights><rights>The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c540t-34aabd58f0798548933237a396d9dd0b7433bd26f6189f236af7dadf345230283</citedby><cites>FETCH-LOGICAL-c540t-34aabd58f0798548933237a396d9dd0b7433bd26f6189f236af7dadf345230283</cites><orcidid>0000-0002-1583-6404 ; 0000-0002-0322-9869 ; 0000-0002-4975-4618</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2517102818/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2517102818?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,724,777,781,882,25734,27905,27906,36993,36994,44571,53772,53774,74875</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33893283$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Pirch, Sebastian</creatorcontrib><creatorcontrib>Müller, Felix</creatorcontrib><creatorcontrib>Iofinova, Eugenia</creatorcontrib><creatorcontrib>Pazmandi, Julia</creatorcontrib><creatorcontrib>Hütter, Christiane V. R.</creatorcontrib><creatorcontrib>Chiettini, Martin</creatorcontrib><creatorcontrib>Sin, Celine</creatorcontrib><creatorcontrib>Boztug, Kaan</creatorcontrib><creatorcontrib>Podkosova, Iana</creatorcontrib><creatorcontrib>Kaufmann, Hannes</creatorcontrib><creatorcontrib>Menche, Jörg</creatorcontrib><title>The VRNetzer platform enables interactive network analysis in Virtual Reality</title><title>Nature communications</title><addtitle>Nat Commun</addtitle><addtitle>Nat Commun</addtitle><description>Networks provide a powerful representation of interacting components within complex systems, making them ideal for visually and analytically exploring big data. However, the size and complexity of many networks render static visualizations on typically-sized paper or screens impractical, resulting in proverbial ‘hairballs’. Here, we introduce a Virtual Reality (VR) platform that overcomes these limitations by facilitating the thorough visual, and interactive, exploration of large networks. Our platform allows maximal customization and extendibility, through the import of custom code for data analysis, integration of external databases, and design of arbitrary user interface elements, among other features. As a proof of concept, we show how our platform can be used to interactively explore genome-scale molecular networks to identify genes associated with rare diseases and understand how they might contribute to disease development. Our platform represents a general purpose, VR-based data exploration platform for large and diverse data types by providing an interface that facilitates the interaction between human intuition and state-of-the-art analysis methods. Data-rich networks can be difficult to interpret beyond a certain size. Here, the authors introduce a platform that uses virtual reality to allow the visual exploration of large networks, while interfacing with data repositories and other analytical methods to improve the interpretation of big data.</description><subject>631/114/2398</subject><subject>631/114/2408</subject><subject>631/553/794</subject><subject>639/166/985</subject><subject>692/4017</subject><subject>Analytical methods</subject><subject>Big Data</subject><subject>Complex systems</subject><subject>Complexity</subject><subject>Computer applications</subject><subject>Data analysis</subject><subject>Exploration</subject><subject>Genomes</subject><subject>Humanities and Social Sciences</subject><subject>multidisciplinary</subject><subject>Network analysis</subject><subject>Rare diseases</subject><subject>Science</subject><subject>Science (multidisciplinary)</subject><subject>Virtual networks</subject><subject>Virtual reality</subject><issn>2041-1723</issn><issn>2041-1723</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNp9UU1v1DAQtRCIVkv_AAcUiQuXgL8S2xckVBWoVECqSq_WJJ5ss2TjxXa6Wn493k0pLQd8sTXvzfO8eYS8ZPQto0K_i5LJWpWUs5LzStFy-4QccypZyRQXTx-8j8hJjCuajzBMS_mcHAmhjeBaHJMvVzdYXF9-xfQLQ7EZIHU-rAscoRkwFv2YMECb-lssRkxbH34UMMKwi_0eLK77kCYYikuEoU-7F-RZB0PEk7t7Qb5_PLs6_VxefPt0fvrhomwrSVMpJEDjKt1RZXQl8yyCCwXC1M44RxslhWgcr7uaadNxUUOnHLhOyIoLmudekPNZ13lY2U3o1xB21kNvDwUflhZC6tsBLZiWYqvRCIeyM7TpFCDLy6icpE0uL8j7WWszNWt0LY4pwPBI9DEy9jd26W-tpnXFapkF3twJBP9zwpjsuo8tDgOM6KdoecU0Z0bRPfX1P9SVn0Le54GlWPbG9u74zGqDjzFgdz8Mo3Yfvp3Dtzl8ewjfbnPTq4c27lv-RJ0JYibEDI1LDH___o_sb-e3urI</recordid><startdate>20210423</startdate><enddate>20210423</enddate><creator>Pirch, Sebastian</creator><creator>Müller, Felix</creator><creator>Iofinova, Eugenia</creator><creator>Pazmandi, Julia</creator><creator>Hütter, Christiane V. R.</creator><creator>Chiettini, Martin</creator><creator>Sin, Celine</creator><creator>Boztug, Kaan</creator><creator>Podkosova, Iana</creator><creator>Kaufmann, Hannes</creator><creator>Menche, Jörg</creator><general>Nature Publishing Group UK</general><general>Nature Publishing Group</general><general>Nature Portfolio</general><scope>C6C</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QL</scope><scope>7QP</scope><scope>7QR</scope><scope>7SN</scope><scope>7SS</scope><scope>7ST</scope><scope>7T5</scope><scope>7T7</scope><scope>7TM</scope><scope>7TO</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>RC3</scope><scope>SOI</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-1583-6404</orcidid><orcidid>https://orcid.org/0000-0002-0322-9869</orcidid><orcidid>https://orcid.org/0000-0002-4975-4618</orcidid></search><sort><creationdate>20210423</creationdate><title>The VRNetzer platform enables interactive network analysis in Virtual Reality</title><author>Pirch, Sebastian ; Müller, Felix ; Iofinova, Eugenia ; Pazmandi, Julia ; Hütter, Christiane V. R. ; Chiettini, Martin ; Sin, Celine ; Boztug, Kaan ; Podkosova, Iana ; Kaufmann, Hannes ; Menche, Jörg</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c540t-34aabd58f0798548933237a396d9dd0b7433bd26f6189f236af7dadf345230283</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>631/114/2398</topic><topic>631/114/2408</topic><topic>631/553/794</topic><topic>639/166/985</topic><topic>692/4017</topic><topic>Analytical methods</topic><topic>Big Data</topic><topic>Complex systems</topic><topic>Complexity</topic><topic>Computer applications</topic><topic>Data analysis</topic><topic>Exploration</topic><topic>Genomes</topic><topic>Humanities and Social Sciences</topic><topic>multidisciplinary</topic><topic>Network analysis</topic><topic>Rare diseases</topic><topic>Science</topic><topic>Science (multidisciplinary)</topic><topic>Virtual networks</topic><topic>Virtual reality</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pirch, Sebastian</creatorcontrib><creatorcontrib>Müller, Felix</creatorcontrib><creatorcontrib>Iofinova, Eugenia</creatorcontrib><creatorcontrib>Pazmandi, Julia</creatorcontrib><creatorcontrib>Hütter, Christiane V. R.</creatorcontrib><creatorcontrib>Chiettini, Martin</creatorcontrib><creatorcontrib>Sin, Celine</creatorcontrib><creatorcontrib>Boztug, Kaan</creatorcontrib><creatorcontrib>Podkosova, Iana</creatorcontrib><creatorcontrib>Kaufmann, Hannes</creatorcontrib><creatorcontrib>Menche, Jörg</creatorcontrib><collection>Springer_OA刊</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Calcium &amp; Calcified Tissue Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Environment Abstracts</collection><collection>Immunology Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Nucleic Acids Abstracts</collection><collection>Oncogenes and Growth Factors Abstracts</collection><collection>ProQuest Health and Medical</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central</collection><collection>Advanced Technologies &amp; Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health &amp; Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>ProQuest Biological Science Journals</collection><collection>Advanced Technologies &amp; Aerospace Database</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Publicly Available Content (ProQuest)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Genetics Abstracts</collection><collection>Environment Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Nature communications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Pirch, Sebastian</au><au>Müller, Felix</au><au>Iofinova, Eugenia</au><au>Pazmandi, Julia</au><au>Hütter, Christiane V. R.</au><au>Chiettini, Martin</au><au>Sin, Celine</au><au>Boztug, Kaan</au><au>Podkosova, Iana</au><au>Kaufmann, Hannes</au><au>Menche, Jörg</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The VRNetzer platform enables interactive network analysis in Virtual Reality</atitle><jtitle>Nature communications</jtitle><stitle>Nat Commun</stitle><addtitle>Nat Commun</addtitle><date>2021-04-23</date><risdate>2021</risdate><volume>12</volume><issue>1</issue><spage>2432</spage><epage>2432</epage><pages>2432-2432</pages><artnum>2432</artnum><issn>2041-1723</issn><eissn>2041-1723</eissn><abstract>Networks provide a powerful representation of interacting components within complex systems, making them ideal for visually and analytically exploring big data. However, the size and complexity of many networks render static visualizations on typically-sized paper or screens impractical, resulting in proverbial ‘hairballs’. Here, we introduce a Virtual Reality (VR) platform that overcomes these limitations by facilitating the thorough visual, and interactive, exploration of large networks. Our platform allows maximal customization and extendibility, through the import of custom code for data analysis, integration of external databases, and design of arbitrary user interface elements, among other features. As a proof of concept, we show how our platform can be used to interactively explore genome-scale molecular networks to identify genes associated with rare diseases and understand how they might contribute to disease development. Our platform represents a general purpose, VR-based data exploration platform for large and diverse data types by providing an interface that facilitates the interaction between human intuition and state-of-the-art analysis methods. Data-rich networks can be difficult to interpret beyond a certain size. Here, the authors introduce a platform that uses virtual reality to allow the visual exploration of large networks, while interfacing with data repositories and other analytical methods to improve the interpretation of big data.</abstract><cop>London</cop><pub>Nature Publishing Group UK</pub><pmid>33893283</pmid><doi>10.1038/s41467-021-22570-w</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0002-1583-6404</orcidid><orcidid>https://orcid.org/0000-0002-0322-9869</orcidid><orcidid>https://orcid.org/0000-0002-4975-4618</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 2041-1723
ispartof Nature communications, 2021-04, Vol.12 (1), p.2432-2432, Article 2432
issn 2041-1723
2041-1723
language eng
recordid cdi_doaj_primary_oai_doaj_org_article_a9c0ec8e93de4f90bf7ae10005d40be9
source Open Access: PubMed Central; Nature; Publicly Available Content (ProQuest); Springer Nature - nature.com Journals - Fully Open Access
subjects 631/114/2398
631/114/2408
631/553/794
639/166/985
692/4017
Analytical methods
Big Data
Complex systems
Complexity
Computer applications
Data analysis
Exploration
Genomes
Humanities and Social Sciences
multidisciplinary
Network analysis
Rare diseases
Science
Science (multidisciplinary)
Virtual networks
Virtual reality
title The VRNetzer platform enables interactive network analysis in Virtual Reality
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-21T07%3A14%3A44IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=The%20VRNetzer%20platform%20enables%20interactive%20network%20analysis%20in%20Virtual%20Reality&rft.jtitle=Nature%20communications&rft.au=Pirch,%20Sebastian&rft.date=2021-04-23&rft.volume=12&rft.issue=1&rft.spage=2432&rft.epage=2432&rft.pages=2432-2432&rft.artnum=2432&rft.issn=2041-1723&rft.eissn=2041-1723&rft_id=info:doi/10.1038/s41467-021-22570-w&rft_dat=%3Cproquest_doaj_%3E2517102818%3C/proquest_doaj_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c540t-34aabd58f0798548933237a396d9dd0b7433bd26f6189f236af7dadf345230283%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2517102818&rft_id=info:pmid/33893283&rfr_iscdi=true