Fast and accurate construction of ultra-dense consensus genetic maps using evolution strategy optimization
Our aim was to develop a fast and accurate algorithm for constructing consensus genetic maps for chip-based SNP genotyping data with a high proportion of shared markers between mapping populations. Chip-based genotyping of SNP markers allows producing high-density genetic maps with a relatively stan...
Gespeichert in:
| Veröffentlicht in: | PloS one 2015-04, Vol.10 (4), p.e0122485-e0122485 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | e0122485 |
|---|---|
| container_issue | 4 |
| container_start_page | e0122485 |
| container_title | PloS one |
| container_volume | 10 |
| creator | Mester, David Ronin, Yefim Schnable, Patrick Aluru, Srinivas Korol, Abraham |
| description | Our aim was to develop a fast and accurate algorithm for constructing consensus genetic maps for chip-based SNP genotyping data with a high proportion of shared markers between mapping populations. Chip-based genotyping of SNP markers allows producing high-density genetic maps with a relatively standardized set of marker loci for different mapping populations. The availability of a standard high-throughput mapping platform simplifies consensus analysis by ignoring unique markers at the stage of consensus mapping thereby reducing mathematical complicity of the problem and in turn analyzing bigger size mapping data using global optimization criteria instead of local ones. Our three-phase analytical scheme includes automatic selection of ~100-300 of the most informative (resolvable by recombination) markers per linkage group, building a stable skeletal marker order for each data set and its verification using jackknife re-sampling, and consensus mapping analysis based on global optimization criterion. A novel Evolution Strategy optimization algorithm with a global optimization criterion presented in this paper is able to generate high quality, ultra-dense consensus maps, with many thousands of markers per genome. This algorithm utilizes "potentially good orders" in the initial solution and in the new mutation procedures that generate trial solutions, enabling to obtain a consensus order in reasonable time. The developed algorithm, tested on a wide range of simulated data and real world data (Arabidopsis), outperformed two tested state-of-the-art algorithms by mapping accuracy and computation time. |
| doi_str_mv | 10.1371/journal.pone.0122485 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_plos_</sourceid><recordid>TN_cdi_plos_journals_1674474957</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><doaj_id>oai_doaj_org_article_d9aaf15c375d4066a4f066c5f7ae7083</doaj_id><sourcerecordid>1673374277</sourcerecordid><originalsourceid>FETCH-LOGICAL-c526t-c4d86a8caa4a8fbb987f7d1cf4d51ba1b21cb17562c99f8438206ce2d99745bf3</originalsourceid><addsrcrecordid>eNptUstu1DAUjRCIlsIfIIjEhk0Gvx8bpKqiD6kSG1hbN36EjBJ7sJNK5evJzKRVi9jYV_eec3yPdarqPUYbTCX-sk1zjjBsdin6DcKEMMVfVKdYU9IIgujLJ_VJ9aaULUKcKiFeVyeEKyE1o6fV9hLKVEN0NVg7Z5h8bVMsU57t1KdYp1DPw5ShcT6W42wp5lJ3Pvqpt_UIu1LPpY9d7e_SMB9YC39R6u7rtJv6sf8D--7b6lWAofh3631W_bz89uPiurn9fnVzcX7bWE7E1FjmlABlARio0LZaySAdtoE5jlvALcG2xZILYrUOilFFkLCeOK0l422gZ9XHo-5uSMWs31QMFpIxyTSXC-LmiHAJtmaX-xHyvUnQm0Mj5c5AXswN3jgNEDC3VHLHkBDAwnJaHiR4iRRdtL6ur83t6J31cfE-PBN9Pon9L9OlO8Oo5kjpReDzKpDT79mXyYx9sX4YIPo0H_amVDIi93t_-gf6f3fsiLI5lZJ9eFwGI7OPzgPL7KNj1ugstA9PjTySHrJC_wKRecVi</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1674474957</pqid></control><display><type>article</type><title>Fast and accurate construction of ultra-dense consensus genetic maps using evolution strategy optimization</title><source>MEDLINE</source><source>DOAJ Directory of Open Access Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>Public Library of Science (PLoS)</source><source>PubMed Central</source><source>Free Full-Text Journals in Chemistry</source><creator>Mester, David ; Ronin, Yefim ; Schnable, Patrick ; Aluru, Srinivas ; Korol, Abraham</creator><contributor>Barash, Danny</contributor><creatorcontrib>Mester, David ; Ronin, Yefim ; Schnable, Patrick ; Aluru, Srinivas ; Korol, Abraham ; Barash, Danny</creatorcontrib><description>Our aim was to develop a fast and accurate algorithm for constructing consensus genetic maps for chip-based SNP genotyping data with a high proportion of shared markers between mapping populations. Chip-based genotyping of SNP markers allows producing high-density genetic maps with a relatively standardized set of marker loci for different mapping populations. The availability of a standard high-throughput mapping platform simplifies consensus analysis by ignoring unique markers at the stage of consensus mapping thereby reducing mathematical complicity of the problem and in turn analyzing bigger size mapping data using global optimization criteria instead of local ones. Our three-phase analytical scheme includes automatic selection of ~100-300 of the most informative (resolvable by recombination) markers per linkage group, building a stable skeletal marker order for each data set and its verification using jackknife re-sampling, and consensus mapping analysis based on global optimization criterion. A novel Evolution Strategy optimization algorithm with a global optimization criterion presented in this paper is able to generate high quality, ultra-dense consensus maps, with many thousands of markers per genome. This algorithm utilizes "potentially good orders" in the initial solution and in the new mutation procedures that generate trial solutions, enabling to obtain a consensus order in reasonable time. The developed algorithm, tested on a wide range of simulated data and real world data (Arabidopsis), outperformed two tested state-of-the-art algorithms by mapping accuracy and computation time.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0122485</identifier><identifier>PMID: 25867943</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Computer simulation ; Consensus Sequence ; Construction ; Criteria ; Evolution ; Evolution, Molecular ; Evolutionary algorithms ; Gene mapping ; Genotyping ; Global optimization ; Mapping ; Markers ; Mutation ; Optimization ; Polymorphism, Single Nucleotide ; Populations ; Recombination ; Single-nucleotide polymorphism ; Triticeae</subject><ispartof>PloS one, 2015-04, Vol.10 (4), p.e0122485-e0122485</ispartof><rights>This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication: https://creativecommons.org/publicdomain/zero/1.0/ (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-c526t-c4d86a8caa4a8fbb987f7d1cf4d51ba1b21cb17562c99f8438206ce2d99745bf3</citedby><cites>FETCH-LOGICAL-c526t-c4d86a8caa4a8fbb987f7d1cf4d51ba1b21cb17562c99f8438206ce2d99745bf3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4395089/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4395089/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2100,2926,23865,27923,27924,53790,53792,79371,79372</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25867943$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Barash, Danny</contributor><creatorcontrib>Mester, David</creatorcontrib><creatorcontrib>Ronin, Yefim</creatorcontrib><creatorcontrib>Schnable, Patrick</creatorcontrib><creatorcontrib>Aluru, Srinivas</creatorcontrib><creatorcontrib>Korol, Abraham</creatorcontrib><title>Fast and accurate construction of ultra-dense consensus genetic maps using evolution strategy optimization</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Our aim was to develop a fast and accurate algorithm for constructing consensus genetic maps for chip-based SNP genotyping data with a high proportion of shared markers between mapping populations. Chip-based genotyping of SNP markers allows producing high-density genetic maps with a relatively standardized set of marker loci for different mapping populations. The availability of a standard high-throughput mapping platform simplifies consensus analysis by ignoring unique markers at the stage of consensus mapping thereby reducing mathematical complicity of the problem and in turn analyzing bigger size mapping data using global optimization criteria instead of local ones. Our three-phase analytical scheme includes automatic selection of ~100-300 of the most informative (resolvable by recombination) markers per linkage group, building a stable skeletal marker order for each data set and its verification using jackknife re-sampling, and consensus mapping analysis based on global optimization criterion. A novel Evolution Strategy optimization algorithm with a global optimization criterion presented in this paper is able to generate high quality, ultra-dense consensus maps, with many thousands of markers per genome. This algorithm utilizes "potentially good orders" in the initial solution and in the new mutation procedures that generate trial solutions, enabling to obtain a consensus order in reasonable time. The developed algorithm, tested on a wide range of simulated data and real world data (Arabidopsis), outperformed two tested state-of-the-art algorithms by mapping accuracy and computation time.</description><subject>Algorithms</subject><subject>Computer simulation</subject><subject>Consensus Sequence</subject><subject>Construction</subject><subject>Criteria</subject><subject>Evolution</subject><subject>Evolution, Molecular</subject><subject>Evolutionary algorithms</subject><subject>Gene mapping</subject><subject>Genotyping</subject><subject>Global optimization</subject><subject>Mapping</subject><subject>Markers</subject><subject>Mutation</subject><subject>Optimization</subject><subject>Polymorphism, Single Nucleotide</subject><subject>Populations</subject><subject>Recombination</subject><subject>Single-nucleotide polymorphism</subject><subject>Triticeae</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNptUstu1DAUjRCIlsIfIIjEhk0Gvx8bpKqiD6kSG1hbN36EjBJ7sJNK5evJzKRVi9jYV_eec3yPdarqPUYbTCX-sk1zjjBsdin6DcKEMMVfVKdYU9IIgujLJ_VJ9aaULUKcKiFeVyeEKyE1o6fV9hLKVEN0NVg7Z5h8bVMsU57t1KdYp1DPw5ShcT6W42wp5lJ3Pvqpt_UIu1LPpY9d7e_SMB9YC39R6u7rtJv6sf8D--7b6lWAofh3631W_bz89uPiurn9fnVzcX7bWE7E1FjmlABlARio0LZaySAdtoE5jlvALcG2xZILYrUOilFFkLCeOK0l422gZ9XHo-5uSMWs31QMFpIxyTSXC-LmiHAJtmaX-xHyvUnQm0Mj5c5AXswN3jgNEDC3VHLHkBDAwnJaHiR4iRRdtL6ur83t6J31cfE-PBN9Pon9L9OlO8Oo5kjpReDzKpDT79mXyYx9sX4YIPo0H_amVDIi93t_-gf6f3fsiLI5lZJ9eFwGI7OPzgPL7KNj1ugstA9PjTySHrJC_wKRecVi</recordid><startdate>20150413</startdate><enddate>20150413</enddate><creator>Mester, David</creator><creator>Ronin, Yefim</creator><creator>Schnable, Patrick</creator><creator>Aluru, Srinivas</creator><creator>Korol, Abraham</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</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>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</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>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20150413</creationdate><title>Fast and accurate construction of ultra-dense consensus genetic maps using evolution strategy optimization</title><author>Mester, David ; Ronin, Yefim ; Schnable, Patrick ; Aluru, Srinivas ; Korol, Abraham</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c526t-c4d86a8caa4a8fbb987f7d1cf4d51ba1b21cb17562c99f8438206ce2d99745bf3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Algorithms</topic><topic>Computer simulation</topic><topic>Consensus Sequence</topic><topic>Construction</topic><topic>Criteria</topic><topic>Evolution</topic><topic>Evolution, Molecular</topic><topic>Evolutionary algorithms</topic><topic>Gene mapping</topic><topic>Genotyping</topic><topic>Global optimization</topic><topic>Mapping</topic><topic>Markers</topic><topic>Mutation</topic><topic>Optimization</topic><topic>Polymorphism, Single Nucleotide</topic><topic>Populations</topic><topic>Recombination</topic><topic>Single-nucleotide polymorphism</topic><topic>Triticeae</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mester, David</creatorcontrib><creatorcontrib>Ronin, Yefim</creatorcontrib><creatorcontrib>Schnable, Patrick</creatorcontrib><creatorcontrib>Aluru, Srinivas</creatorcontrib><creatorcontrib>Korol, Abraham</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</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>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</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 & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Agricultural Science Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Materials Science Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Mester, David</au><au>Ronin, Yefim</au><au>Schnable, Patrick</au><au>Aluru, Srinivas</au><au>Korol, Abraham</au><au>Barash, Danny</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Fast and accurate construction of ultra-dense consensus genetic maps using evolution strategy optimization</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2015-04-13</date><risdate>2015</risdate><volume>10</volume><issue>4</issue><spage>e0122485</spage><epage>e0122485</epage><pages>e0122485-e0122485</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Our aim was to develop a fast and accurate algorithm for constructing consensus genetic maps for chip-based SNP genotyping data with a high proportion of shared markers between mapping populations. Chip-based genotyping of SNP markers allows producing high-density genetic maps with a relatively standardized set of marker loci for different mapping populations. The availability of a standard high-throughput mapping platform simplifies consensus analysis by ignoring unique markers at the stage of consensus mapping thereby reducing mathematical complicity of the problem and in turn analyzing bigger size mapping data using global optimization criteria instead of local ones. Our three-phase analytical scheme includes automatic selection of ~100-300 of the most informative (resolvable by recombination) markers per linkage group, building a stable skeletal marker order for each data set and its verification using jackknife re-sampling, and consensus mapping analysis based on global optimization criterion. A novel Evolution Strategy optimization algorithm with a global optimization criterion presented in this paper is able to generate high quality, ultra-dense consensus maps, with many thousands of markers per genome. This algorithm utilizes "potentially good orders" in the initial solution and in the new mutation procedures that generate trial solutions, enabling to obtain a consensus order in reasonable time. The developed algorithm, tested on a wide range of simulated data and real world data (Arabidopsis), outperformed two tested state-of-the-art algorithms by mapping accuracy and computation time.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>25867943</pmid><doi>10.1371/journal.pone.0122485</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2015-04, Vol.10 (4), p.e0122485-e0122485 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_1674474957 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Public Library of Science (PLoS); PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Algorithms Computer simulation Consensus Sequence Construction Criteria Evolution Evolution, Molecular Evolutionary algorithms Gene mapping Genotyping Global optimization Mapping Markers Mutation Optimization Polymorphism, Single Nucleotide Populations Recombination Single-nucleotide polymorphism Triticeae |
| title | Fast and accurate construction of ultra-dense consensus genetic maps using evolution strategy optimization |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-11T17%3A13%3A50IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Fast%20and%20accurate%20construction%20of%20ultra-dense%20consensus%20genetic%20maps%20using%20evolution%20strategy%20optimization&rft.jtitle=PloS%20one&rft.au=Mester,%20David&rft.date=2015-04-13&rft.volume=10&rft.issue=4&rft.spage=e0122485&rft.epage=e0122485&rft.pages=e0122485-e0122485&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0122485&rft_dat=%3Cproquest_plos_%3E1673374277%3C/proquest_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1674474957&rft_id=info:pmid/25867943&rft_doaj_id=oai_doaj_org_article_d9aaf15c375d4066a4f066c5f7ae7083&rfr_iscdi=true |