Ten recommendations for supporting open pathogen genomic analysis in public health
Increasingly, public-health agencies are using pathogen genomic sequence data to support surveillance and epidemiological investigations. As access to whole-genome sequencing has grown, greater amounts of molecular data have helped improve the ability to detect and track outbreaks of diseases such a...
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| Veröffentlicht in: | Nature medicine 2020-06, Vol.26 (6), p.832-841 |
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| creator | Black, Allison MacCannell, Duncan R. Sibley, Thomas R. Bedford, Trevor |
| description | Increasingly, public-health agencies are using pathogen genomic sequence data to support surveillance and epidemiological investigations. As access to whole-genome sequencing has grown, greater amounts of molecular data have helped improve the ability to detect and track outbreaks of diseases such as COVID-19, investigate transmission chains and explore large-scale population dynamics, such as the spread of antibiotic resistance. However, the wide adoption of whole-genome sequencing also poses new challenges for public-health agencies that must adapt to support a new set of expertise, which means that the capacity to perform genomic data assembly and analysis has not expanded as widely as the adoption of sequencing itself. In this Perspective, we make recommendations for developing an accessible, unified informatic ecosystem to support pathogen genomic analysis in public-health agencies across income settings. We hope that the creation of this ecosystem will allow agencies to effectively and efficiently share data, workflows and analyses and thereby increase the reproducibility, accessibility and auditability of pathogen genomic analysis while also supporting agency autonomy.
To support progress in genomic epidemiology, a transparent, user-friendly approach is required. |
| doi_str_mv | 10.1038/s41591-020-0935-z |
| format | Article |
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To support progress in genomic epidemiology, a transparent, user-friendly approach is required.</description><identifier>ISSN: 1078-8956</identifier><identifier>EISSN: 1546-170X</identifier><identifier>DOI: 10.1038/s41591-020-0935-z</identifier><identifier>PMID: 32528156</identifier><language>eng</language><publisher>New York: Nature Publishing Group US</publisher><subject>631/114/2398 ; 631/208/212 ; 706/648/496 ; Accessibility ; Analysis ; Antibiotic resistance ; Antibiotics ; Autonomy ; Betacoronavirus - genetics ; Betacoronavirus - pathogenicity ; Biomedical and Life Sciences ; Biomedicine ; Cancer Research ; Coronavirus Infections - epidemiology ; Coronavirus Infections - genetics ; Coronavirus Infections - transmission ; Coronavirus Infections - virology ; COVID-19 ; Data analysis ; Disease Outbreaks ; Drug Resistance, Microbial - genetics ; Epidemiology ; Gene sequencing ; Genetic aspects ; Genomes ; Genomic analysis ; Genomics ; Health aspects ; Humans ; Infectious Diseases ; Metabolic Diseases ; Molecular Medicine ; Neurosciences ; Pandemics ; Pathogens ; Perspective ; Pneumonia, Viral - epidemiology ; Pneumonia, Viral - genetics ; Pneumonia, Viral - transmission ; Pneumonia, Viral - virology ; Population Dynamics ; Public health ; SARS-CoV-2 ; Whole Genome Sequencing</subject><ispartof>Nature medicine, 2020-06, Vol.26 (6), p.832-841</ispartof><rights>Springer Nature America, Inc. 2020</rights><rights>COPYRIGHT 2020 Nature Publishing Group</rights><rights>Springer Nature America, Inc. 2020.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c674t-4cf3ccee1b61f180f52d0a851a76b7f4685947058f0d9db5a85be94395b110913</citedby><cites>FETCH-LOGICAL-c674t-4cf3ccee1b61f180f52d0a851a76b7f4685947058f0d9db5a85be94395b110913</cites><orcidid>0000-0002-6618-4127 ; 0000-0001-5269-2297</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,885,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32528156$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Black, Allison</creatorcontrib><creatorcontrib>MacCannell, Duncan R.</creatorcontrib><creatorcontrib>Sibley, Thomas R.</creatorcontrib><creatorcontrib>Bedford, Trevor</creatorcontrib><title>Ten recommendations for supporting open pathogen genomic analysis in public health</title><title>Nature medicine</title><addtitle>Nat Med</addtitle><addtitle>Nat Med</addtitle><description>Increasingly, public-health agencies are using pathogen genomic sequence data to support surveillance and epidemiological investigations. As access to whole-genome sequencing has grown, greater amounts of molecular data have helped improve the ability to detect and track outbreaks of diseases such as COVID-19, investigate transmission chains and explore large-scale population dynamics, such as the spread of antibiotic resistance. However, the wide adoption of whole-genome sequencing also poses new challenges for public-health agencies that must adapt to support a new set of expertise, which means that the capacity to perform genomic data assembly and analysis has not expanded as widely as the adoption of sequencing itself. In this Perspective, we make recommendations for developing an accessible, unified informatic ecosystem to support pathogen genomic analysis in public-health agencies across income settings. We hope that the creation of this ecosystem will allow agencies to effectively and efficiently share data, workflows and analyses and thereby increase the reproducibility, accessibility and auditability of pathogen genomic analysis while also supporting agency autonomy.
To support progress in genomic epidemiology, a transparent, user-friendly approach is required.</description><subject>631/114/2398</subject><subject>631/208/212</subject><subject>706/648/496</subject><subject>Accessibility</subject><subject>Analysis</subject><subject>Antibiotic resistance</subject><subject>Antibiotics</subject><subject>Autonomy</subject><subject>Betacoronavirus - genetics</subject><subject>Betacoronavirus - pathogenicity</subject><subject>Biomedical and Life Sciences</subject><subject>Biomedicine</subject><subject>Cancer Research</subject><subject>Coronavirus Infections - epidemiology</subject><subject>Coronavirus Infections - genetics</subject><subject>Coronavirus Infections - transmission</subject><subject>Coronavirus Infections - virology</subject><subject>COVID-19</subject><subject>Data analysis</subject><subject>Disease Outbreaks</subject><subject>Drug Resistance, Microbial - genetics</subject><subject>Epidemiology</subject><subject>Gene sequencing</subject><subject>Genetic aspects</subject><subject>Genomes</subject><subject>Genomic analysis</subject><subject>Genomics</subject><subject>Health aspects</subject><subject>Humans</subject><subject>Infectious Diseases</subject><subject>Metabolic Diseases</subject><subject>Molecular Medicine</subject><subject>Neurosciences</subject><subject>Pandemics</subject><subject>Pathogens</subject><subject>Perspective</subject><subject>Pneumonia, Viral - epidemiology</subject><subject>Pneumonia, Viral - genetics</subject><subject>Pneumonia, Viral - transmission</subject><subject>Pneumonia, Viral - virology</subject><subject>Population Dynamics</subject><subject>Public health</subject><subject>SARS-CoV-2</subject><subject>Whole Genome Sequencing</subject><issn>1078-8956</issn><issn>1546-170X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNqNkm1r1TAUx4sobl79AL6RgiD6ojNpHtq8EcbwYTAYzCm-C2ma9Ga0yV3Situn99x1bqtcQULIIed3TsL5_7PsJUYHGJH6faKYCVygEhVIEFZcP8r2MaO8wBX68RhiVNVFLRjfy56ldIEQIoiJp9keKVlZY8b3s7Nz4_NodBgG41s1uuBTbkPM07TZhDg63-VhA8xGjevQQQA7DE7nyqv-KrmUO0hOTQ9Xa6P6cf08e2JVn8yL23OVffv08fzoS3Fy-vn46PCk0LyiY0G1JVobgxuOLa6RZWWLVM2wqnhTWcprJmiFWG1RK9qGQaoxghLBGoyRwGSVfZj7wuuDabXxY1S93EQ3qHglg3JymfFuLbvwU1aEEwazWGVvbxvEcDmZNMrBJW36XnkTpiRLikshGGYloK__Qi_CFGECNxRlFYex31Od6o103gZ4V2-bykNeVohQUgugih0UjNXAJ4M31sH1gj_YwcNqDeiws-DdogCY0fwaOzWlJI-_nv0_e_p9yb55wM5ip9BPN6ZZgngGdQwpRWPvRMFIbp0rZ-dKcK7cOldeQ82rh2reVfyxKgDlDCRI-c7Eewn-3fU3WTD2Ew</recordid><startdate>20200601</startdate><enddate>20200601</enddate><creator>Black, Allison</creator><creator>MacCannell, Duncan R.</creator><creator>Sibley, Thomas R.</creator><creator>Bedford, Trevor</creator><general>Nature Publishing Group US</general><general>Nature Publishing Group</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QP</scope><scope>7QR</scope><scope>7T5</scope><scope>7TK</scope><scope>7TM</scope><scope>7TO</scope><scope>7U7</scope><scope>7U9</scope><scope>7X7</scope><scope>7XB</scope><scope>88A</scope><scope>88E</scope><scope>88I</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</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>GUQSH</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>M2P</scope><scope>M7N</scope><scope>M7P</scope><scope>MBDVC</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-6618-4127</orcidid><orcidid>https://orcid.org/0000-0001-5269-2297</orcidid></search><sort><creationdate>20200601</creationdate><title>Ten recommendations for supporting open pathogen genomic analysis in public health</title><author>Black, Allison ; MacCannell, Duncan R. ; Sibley, Thomas R. ; Bedford, Trevor</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c674t-4cf3ccee1b61f180f52d0a851a76b7f4685947058f0d9db5a85be94395b110913</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>631/114/2398</topic><topic>631/208/212</topic><topic>706/648/496</topic><topic>Accessibility</topic><topic>Analysis</topic><topic>Antibiotic resistance</topic><topic>Antibiotics</topic><topic>Autonomy</topic><topic>Betacoronavirus - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Nature medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Black, Allison</au><au>MacCannell, Duncan R.</au><au>Sibley, Thomas R.</au><au>Bedford, Trevor</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Ten recommendations for supporting open pathogen genomic analysis in public health</atitle><jtitle>Nature medicine</jtitle><stitle>Nat Med</stitle><addtitle>Nat Med</addtitle><date>2020-06-01</date><risdate>2020</risdate><volume>26</volume><issue>6</issue><spage>832</spage><epage>841</epage><pages>832-841</pages><issn>1078-8956</issn><eissn>1546-170X</eissn><abstract>Increasingly, public-health agencies are using pathogen genomic sequence data to support surveillance and epidemiological investigations. As access to whole-genome sequencing has grown, greater amounts of molecular data have helped improve the ability to detect and track outbreaks of diseases such as COVID-19, investigate transmission chains and explore large-scale population dynamics, such as the spread of antibiotic resistance. However, the wide adoption of whole-genome sequencing also poses new challenges for public-health agencies that must adapt to support a new set of expertise, which means that the capacity to perform genomic data assembly and analysis has not expanded as widely as the adoption of sequencing itself. In this Perspective, we make recommendations for developing an accessible, unified informatic ecosystem to support pathogen genomic analysis in public-health agencies across income settings. We hope that the creation of this ecosystem will allow agencies to effectively and efficiently share data, workflows and analyses and thereby increase the reproducibility, accessibility and auditability of pathogen genomic analysis while also supporting agency autonomy.
To support progress in genomic epidemiology, a transparent, user-friendly approach is required.</abstract><cop>New York</cop><pub>Nature Publishing Group US</pub><pmid>32528156</pmid><doi>10.1038/s41591-020-0935-z</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0002-6618-4127</orcidid><orcidid>https://orcid.org/0000-0001-5269-2297</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | 631/114/2398 631/208/212 706/648/496 Accessibility Analysis Antibiotic resistance Antibiotics Autonomy Betacoronavirus - genetics Betacoronavirus - pathogenicity Biomedical and Life Sciences Biomedicine Cancer Research Coronavirus Infections - epidemiology Coronavirus Infections - genetics Coronavirus Infections - transmission Coronavirus Infections - virology COVID-19 Data analysis Disease Outbreaks Drug Resistance, Microbial - genetics Epidemiology Gene sequencing Genetic aspects Genomes Genomic analysis Genomics Health aspects Humans Infectious Diseases Metabolic Diseases Molecular Medicine Neurosciences Pandemics Pathogens Perspective Pneumonia, Viral - epidemiology Pneumonia, Viral - genetics Pneumonia, Viral - transmission Pneumonia, Viral - virology Population Dynamics Public health SARS-CoV-2 Whole Genome Sequencing |
| title | Ten recommendations for supporting open pathogen genomic analysis in public health |
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