GeneVetter: a web tool for quantitative monogenic assessment of rare diseases
When performing DNA sequencing to diagnose affected individuals with monogenic forms of rare diseases, accurate attribution of causality to detected variants is imperative but imperfect. Even if a gene has variants already known to cause a disease, rare disruptive variants predicted to be causal are...
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Veröffentlicht in: | Bioinformatics (Oxford, England) England), 2015-11, Vol.31 (22), p.3682-3684 |
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creator | Gillies, Christopher E Robertson, Catherine C Sampson, Matthew G Kang, Hyun Min |
description | When performing DNA sequencing to diagnose affected individuals with monogenic forms of rare diseases, accurate attribution of causality to detected variants is imperative but imperfect. Even if a gene has variants already known to cause a disease, rare disruptive variants predicted to be causal are not always so, mainly due to imperfect ability to predict the pathogenicity of variants. Existing population-scale sequence resources such as 1000 Genomes are useful to quantify the 'background prevalence' of an unaffected individual being falsely predicted to carry causal variants. We developed GeneVetter to allow users to quantify the 'background prevalence' of subjects with predicted causal variants within specific genes under user-specified filtering parameters. GeneVetter helps quantify uncertainty in monogenic diagnosis and design genetic studies with support for power and sample size calculations for specific genes with specific filtering criteria. GeneVetter also allows users to analyze their own sequence data without sending genotype information over the Internet. Overall, GeneVetter is an interactive web tool that facilitates quantifying and accounting for the background prevalence of predicted pathogenic variants in a population.
GeneVetter is available at http://genevetter.org/
mgsamps@med.umich.edu or hmkang@umich.edu
Supplementary data are available at Bioinformatics online. |
doi_str_mv | 10.1093/bioinformatics/btv432 |
format | Article |
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GeneVetter is available at http://genevetter.org/
mgsamps@med.umich.edu or hmkang@umich.edu
Supplementary data are available at Bioinformatics online.</description><identifier>ISSN: 1367-4803</identifier><identifier>EISSN: 1367-4811</identifier><identifier>DOI: 10.1093/bioinformatics/btv432</identifier><identifier>PMID: 26209433</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>Applications Notes ; Diabetes Mellitus, Type 2 - genetics ; Humans ; Internet ; Nephrotic Syndrome - genetics ; Rare Diseases - genetics ; Sequence Analysis, DNA ; Software</subject><ispartof>Bioinformatics (Oxford, England), 2015-11, Vol.31 (22), p.3682-3684</ispartof><rights>The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.</rights><rights>The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com 2015</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c411t-6ee712eb61d8e2848d49afa3cc62a3f0cf4f87101071c65e4240fc4329b6f15f3</citedby><cites>FETCH-LOGICAL-c411t-6ee712eb61d8e2848d49afa3cc62a3f0cf4f87101071c65e4240fc4329b6f15f3</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/PMC4643620/pdf/$$EPDF$$P50$$Gpubmedcentral$$H</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4643620/$$EHTML$$P50$$Gpubmedcentral$$H</linktohtml><link.rule.ids>230,314,727,780,784,885,27922,27923,53789,53791</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26209433$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Gillies, Christopher E</creatorcontrib><creatorcontrib>Robertson, Catherine C</creatorcontrib><creatorcontrib>Sampson, Matthew G</creatorcontrib><creatorcontrib>Kang, Hyun Min</creatorcontrib><title>GeneVetter: a web tool for quantitative monogenic assessment of rare diseases</title><title>Bioinformatics (Oxford, England)</title><addtitle>Bioinformatics</addtitle><description>When performing DNA sequencing to diagnose affected individuals with monogenic forms of rare diseases, accurate attribution of causality to detected variants is imperative but imperfect. Even if a gene has variants already known to cause a disease, rare disruptive variants predicted to be causal are not always so, mainly due to imperfect ability to predict the pathogenicity of variants. Existing population-scale sequence resources such as 1000 Genomes are useful to quantify the 'background prevalence' of an unaffected individual being falsely predicted to carry causal variants. We developed GeneVetter to allow users to quantify the 'background prevalence' of subjects with predicted causal variants within specific genes under user-specified filtering parameters. GeneVetter helps quantify uncertainty in monogenic diagnosis and design genetic studies with support for power and sample size calculations for specific genes with specific filtering criteria. GeneVetter also allows users to analyze their own sequence data without sending genotype information over the Internet. Overall, GeneVetter is an interactive web tool that facilitates quantifying and accounting for the background prevalence of predicted pathogenic variants in a population.
GeneVetter is available at http://genevetter.org/
mgsamps@med.umich.edu or hmkang@umich.edu
Supplementary data are available at Bioinformatics online.</description><subject>Applications Notes</subject><subject>Diabetes Mellitus, Type 2 - genetics</subject><subject>Humans</subject><subject>Internet</subject><subject>Nephrotic Syndrome - genetics</subject><subject>Rare Diseases - genetics</subject><subject>Sequence Analysis, DNA</subject><subject>Software</subject><issn>1367-4803</issn><issn>1367-4811</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpVUctOwzAQtBCIlsIngHzkUmrHjpNwQEIVFKQiLsDVcpx1MUri1naL-HuMChU97WofM7M7CJ1TckVJxSa1dbY3zncqWh0mddxwlh2gIWWiGPOS0sNdTtgAnYTwQQjJSS6O0SATGak4Y0P0NIMe3iBG8NdY4U-ocXSuxQkZr9aqjzYmgg3gzvVuAb3VWIUAIXTQR-wM9soDbmwAlaqn6MioNsDZbxyh1_u7l-nDeP48e5zezseaUxrHAqCgGdSCNiVkJS8bXimjmNYiU8wQbbgpC0ooKagWOfCME6PTfVUtDM0NG6GbLe5yXXfQ6KTFq1Yuve2U_5JOWbnf6e27XLiN5IKzdHsCuPwF8G61hhBlZ4OGtlU9uHWQtGC0SBJEmUbz7aj2LgQPZkdDifyxQu5bIbdWpL2L_xp3W3-_Z999o4zU</recordid><startdate>20151115</startdate><enddate>20151115</enddate><creator>Gillies, Christopher E</creator><creator>Robertson, Catherine C</creator><creator>Sampson, Matthew G</creator><creator>Kang, Hyun Min</creator><general>Oxford University Press</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>7X8</scope><scope>5PM</scope></search><sort><creationdate>20151115</creationdate><title>GeneVetter: a web tool for quantitative monogenic assessment of rare diseases</title><author>Gillies, Christopher E ; Robertson, Catherine C ; Sampson, Matthew G ; Kang, Hyun Min</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c411t-6ee712eb61d8e2848d49afa3cc62a3f0cf4f87101071c65e4240fc4329b6f15f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Applications Notes</topic><topic>Diabetes Mellitus, Type 2 - genetics</topic><topic>Humans</topic><topic>Internet</topic><topic>Nephrotic Syndrome - genetics</topic><topic>Rare Diseases - genetics</topic><topic>Sequence Analysis, DNA</topic><topic>Software</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gillies, Christopher E</creatorcontrib><creatorcontrib>Robertson, Catherine C</creatorcontrib><creatorcontrib>Sampson, Matthew G</creatorcontrib><creatorcontrib>Kang, Hyun Min</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Bioinformatics (Oxford, England)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gillies, Christopher E</au><au>Robertson, Catherine C</au><au>Sampson, Matthew G</au><au>Kang, Hyun Min</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>GeneVetter: a web tool for quantitative monogenic assessment of rare diseases</atitle><jtitle>Bioinformatics (Oxford, England)</jtitle><addtitle>Bioinformatics</addtitle><date>2015-11-15</date><risdate>2015</risdate><volume>31</volume><issue>22</issue><spage>3682</spage><epage>3684</epage><pages>3682-3684</pages><issn>1367-4803</issn><eissn>1367-4811</eissn><abstract>When performing DNA sequencing to diagnose affected individuals with monogenic forms of rare diseases, accurate attribution of causality to detected variants is imperative but imperfect. Even if a gene has variants already known to cause a disease, rare disruptive variants predicted to be causal are not always so, mainly due to imperfect ability to predict the pathogenicity of variants. Existing population-scale sequence resources such as 1000 Genomes are useful to quantify the 'background prevalence' of an unaffected individual being falsely predicted to carry causal variants. We developed GeneVetter to allow users to quantify the 'background prevalence' of subjects with predicted causal variants within specific genes under user-specified filtering parameters. GeneVetter helps quantify uncertainty in monogenic diagnosis and design genetic studies with support for power and sample size calculations for specific genes with specific filtering criteria. GeneVetter also allows users to analyze their own sequence data without sending genotype information over the Internet. Overall, GeneVetter is an interactive web tool that facilitates quantifying and accounting for the background prevalence of predicted pathogenic variants in a population.
GeneVetter is available at http://genevetter.org/
mgsamps@med.umich.edu or hmkang@umich.edu
Supplementary data are available at Bioinformatics online.</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>26209433</pmid><doi>10.1093/bioinformatics/btv432</doi><tpages>3</tpages><oa>free_for_read</oa></addata></record> |
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source | MEDLINE; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Oxford Journals Open Access Collection; PubMed Central; Alma/SFX Local Collection |
subjects | Applications Notes Diabetes Mellitus, Type 2 - genetics Humans Internet Nephrotic Syndrome - genetics Rare Diseases - genetics Sequence Analysis, DNA Software |
title | GeneVetter: a web tool for quantitative monogenic assessment of rare diseases |
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