John A. Kronenberger, Taylor M. Wilcox, Michael K. Young, Daniel H. Mason, Thomas W. Franklin, Michael K. Schwartz
{"title":"利用增强型硅学框架大规模验证 46 种入侵物种检测方法","authors":"John A. Kronenberger, Taylor M. Wilcox, Michael K. Young, Daniel H. Mason, Thomas W. Franklin, Michael K. Schwartz","doi":"10.1002/edn3.548","DOIUrl":null,"url":null,"abstract":"<p>The need for widespread occurrence data to inform species conservation has prompted interest in large, national-scale environmental DNA (eDNA) monitoring strategies. However, targeted eDNA assays are seldom validated for use across broad geographic areas. Here, we validated 46 new and previously published probe-based qPCR assays targeting invasive species throughout the continental United States. We drew upon current taxonomies, range maps, publicly available sequences, and tissue archives to evaluate all potentially sympatric confamilial species and genetically similar extrafamilial taxa. Out of 5276 unique assay-nontarget taxon combinations, we were able to test 4206 (80%). We characterized levels of validation and specificity for each of eight federal geographic regions and provided an online tool with state-level information, as well as detailed assay descriptions in an appendix. Specificity testing benefited from extensive use of <i>eDNAssay</i>—a machine learning classifier trained to predict qPCR cross-amplification—which we found to be 96% accurate in 649 unique tests that underwent paired in silico and in vitro testing. Predictions of assay specificity (the true negative rate) were 98–100% accurate, depending on the classification threshold used. This work provides both an immediate resource for invasive species surveillance and demonstrates an enhanced in silico, geographically subdivided validation framework to aid in future large-scale validation efforts.</p>","PeriodicalId":52828,"journal":{"name":"Environmental DNA","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/edn3.548","citationCount":"0","resultStr":"{\"title\":\"Large-scale validation of 46 invasive species assays using an enhanced in silico framework\",\"authors\":\"John A. Kronenberger, Taylor M. Wilcox, Michael K. Young, Daniel H. Mason, Thomas W. Franklin, Michael K. Schwartz\",\"doi\":\"10.1002/edn3.548\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The need for widespread occurrence data to inform species conservation has prompted interest in large, national-scale environmental DNA (eDNA) monitoring strategies. However, targeted eDNA assays are seldom validated for use across broad geographic areas. Here, we validated 46 new and previously published probe-based qPCR assays targeting invasive species throughout the continental United States. We drew upon current taxonomies, range maps, publicly available sequences, and tissue archives to evaluate all potentially sympatric confamilial species and genetically similar extrafamilial taxa. Out of 5276 unique assay-nontarget taxon combinations, we were able to test 4206 (80%). We characterized levels of validation and specificity for each of eight federal geographic regions and provided an online tool with state-level information, as well as detailed assay descriptions in an appendix. Specificity testing benefited from extensive use of <i>eDNAssay</i>—a machine learning classifier trained to predict qPCR cross-amplification—which we found to be 96% accurate in 649 unique tests that underwent paired in silico and in vitro testing. Predictions of assay specificity (the true negative rate) were 98–100% accurate, depending on the classification threshold used. This work provides both an immediate resource for invasive species surveillance and demonstrates an enhanced in silico, geographically subdivided validation framework to aid in future large-scale validation efforts.</p>\",\"PeriodicalId\":52828,\"journal\":{\"name\":\"Environmental DNA\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-04-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/edn3.548\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Environmental DNA\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/edn3.548\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Agricultural and Biological Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental DNA","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/edn3.548","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Agricultural and Biological Sciences","Score":null,"Total":0}
Large-scale validation of 46 invasive species assays using an enhanced in silico framework
The need for widespread occurrence data to inform species conservation has prompted interest in large, national-scale environmental DNA (eDNA) monitoring strategies. However, targeted eDNA assays are seldom validated for use across broad geographic areas. Here, we validated 46 new and previously published probe-based qPCR assays targeting invasive species throughout the continental United States. We drew upon current taxonomies, range maps, publicly available sequences, and tissue archives to evaluate all potentially sympatric confamilial species and genetically similar extrafamilial taxa. Out of 5276 unique assay-nontarget taxon combinations, we were able to test 4206 (80%). We characterized levels of validation and specificity for each of eight federal geographic regions and provided an online tool with state-level information, as well as detailed assay descriptions in an appendix. Specificity testing benefited from extensive use of eDNAssay—a machine learning classifier trained to predict qPCR cross-amplification—which we found to be 96% accurate in 649 unique tests that underwent paired in silico and in vitro testing. Predictions of assay specificity (the true negative rate) were 98–100% accurate, depending on the classification threshold used. This work provides both an immediate resource for invasive species surveillance and demonstrates an enhanced in silico, geographically subdivided validation framework to aid in future large-scale validation efforts.