Ravikiran Donthu, Jose A P Marcelino, Rosanna Giordano, Yudong Tao, Everett Weber, Arian Avalos, Mark Band, Tatsiana Akraiko, Shu-Ching Chen, Maria P Reyes, Haiping Hao, Yarira Ortiz-Alvarado, Charles A Cuff, Eddie Pérez Claudio, Felipe Soto-Adames, Allan H Smith-Pardo, William G Meikle, Jay D Evans, Tugrul Giray, Faten B Abdelkader, Mike Allsopp, Daniel Ball, Susana B Morgado, Shalva Barjadze, Adriana Correa-Benitez, Amina Chakir, David R Báez, Nabor H M Chavez, Anne Dalmon, Adrian B Douglas, Carmen Fraccica, Hermógenes Fernández-Marín, Alberto Galindo-Cardona, Ernesto Guzman-Novoa, Robert Horsburgh, Meral Kence, Joseph Kilonzo, Mert Kükrer, Yves Le Conte, Gaetana Mazzeo, Fernando Mota, Elliud Muli, Devrim Oskay, José A Ruiz-Martínez, Eugenia Oliveri, Igor Pichkhaia, Abderrahmane Romane, Cesar Guillen Sanchez, Evans Sikombwa, Alberto Satta, Alejandra A Scannapieco, Brandi Stanford, Victoria Soroker, Rodrigo A Velarde, Monica Vercelli, Zachary Huang
{"title":"HBeeID: a molecular tool that identifies honey bee subspecies from different geographic populations.","authors":"Ravikiran Donthu, Jose A P Marcelino, Rosanna Giordano, Yudong Tao, Everett Weber, Arian Avalos, Mark Band, Tatsiana Akraiko, Shu-Ching Chen, Maria P Reyes, Haiping Hao, Yarira Ortiz-Alvarado, Charles A Cuff, Eddie Pérez Claudio, Felipe Soto-Adames, Allan H Smith-Pardo, William G Meikle, Jay D Evans, Tugrul Giray, Faten B Abdelkader, Mike Allsopp, Daniel Ball, Susana B Morgado, Shalva Barjadze, Adriana Correa-Benitez, Amina Chakir, David R Báez, Nabor H M Chavez, Anne Dalmon, Adrian B Douglas, Carmen Fraccica, Hermógenes Fernández-Marín, Alberto Galindo-Cardona, Ernesto Guzman-Novoa, Robert Horsburgh, Meral Kence, Joseph Kilonzo, Mert Kükrer, Yves Le Conte, Gaetana Mazzeo, Fernando Mota, Elliud Muli, Devrim Oskay, José A Ruiz-Martínez, Eugenia Oliveri, Igor Pichkhaia, Abderrahmane Romane, Cesar Guillen Sanchez, Evans Sikombwa, Alberto Satta, Alejandra A Scannapieco, Brandi Stanford, Victoria Soroker, Rodrigo A Velarde, Monica Vercelli, Zachary Huang","doi":"10.1186/s12859-024-05776-9","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Honey bees are the principal commercial pollinators. Along with other arthropods, they are increasingly under threat from anthropogenic factors such as the incursion of invasive honey bee subspecies, pathogens and parasites. Better tools are needed to identify bee subspecies. Genomic data for economic and ecologically important organisms is increasing, but in its basic form its practical application to address ecological problems is limited.</p><p><strong>Results: </strong>We introduce HBeeID a means to identify honey bees. The tool utilizes a knowledge-based network and diagnostic SNPs identified by discriminant analysis of principle components and hierarchical agglomerative clustering. Tests of HBeeID showed that it identifies African, Americas-Africanized, Asian, and European honey bees with a high degree of certainty even when samples lack the full 272 SNPs of HBeeID. Its prediction capacity decreases with highly admixed samples.</p><p><strong>Conclusion: </strong>HBeeID is a high-resolution genomic, SNP based tool, that can be used to identify honey bees and screen species that are invasive. Its flexible design allows for future improvements via sample data additions from other localities.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":null,"pages":null},"PeriodicalIF":2.9000,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11348773/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Bioinformatics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1186/s12859-024-05776-9","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
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Abstract
Background: Honey bees are the principal commercial pollinators. Along with other arthropods, they are increasingly under threat from anthropogenic factors such as the incursion of invasive honey bee subspecies, pathogens and parasites. Better tools are needed to identify bee subspecies. Genomic data for economic and ecologically important organisms is increasing, but in its basic form its practical application to address ecological problems is limited.
Results: We introduce HBeeID a means to identify honey bees. The tool utilizes a knowledge-based network and diagnostic SNPs identified by discriminant analysis of principle components and hierarchical agglomerative clustering. Tests of HBeeID showed that it identifies African, Americas-Africanized, Asian, and European honey bees with a high degree of certainty even when samples lack the full 272 SNPs of HBeeID. Its prediction capacity decreases with highly admixed samples.
Conclusion: HBeeID is a high-resolution genomic, SNP based tool, that can be used to identify honey bees and screen species that are invasive. Its flexible design allows for future improvements via sample data additions from other localities.
期刊介绍：
BMC Bioinformatics is an open access, peer-reviewed journal that considers articles on all aspects of the development, testing and novel application of computational and statistical methods for the modeling and analysis of all kinds of biological data, as well as other areas of computational biology.
BMC Bioinformatics is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We offer an efficient, fair and friendly peer review service, and are committed to publishing all sound science, provided that there is some advance in knowledge presented by the work.
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