BMC Bioinformatics (Aug 2024)

HBeeID: a molecular tool that identifies honey bee subspecies from different geographic populations

  • 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
https://doi.org/10.1186/s12859-024-05776-9
Journal volume & issue
Vol. 25, no. 1
pp. 1 – 33

Abstract

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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.

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