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From Linnaean System to Machine Learning Based-SNP Barcoding: A Changing Epitome of Mosquito Species Identification

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Molecular Identification of Mosquito Vectors and Their Management

Abstract

Mosquitoes are one of the best known group of Diptera with 3556 valid species in 112 genera. Out of currently listed 112 genera, only three genera, viz. Anopheles, Culex, and Aedes mainly account for vectorial transmission. Accurate identification of mosquito vector species is paramount in several facets including the progress in mosquito vector control strategies. Conventional identification methods through morpho-taxonomy using differential morphology as Linnaeus have limitations to distinguish siblings and closely related indistinguishable mosquito species. Regardless of the general approach that traditional morpho-taxonomy is important, the downturn of the taxonomic approach and skill basis for identification for most of the mosquito vector is a prominent reality. Perhaps, this may be a reason for expanding the interest for shifting of identification method from conventional morpho-taxonomy to molecular taxonomy. Recently, molecular taxonomy based identification in the form of labeled DNA barcodes is considered as a landmark in the discrimination of mosquito vectors. Almost all the DNA barcodes contain detailed information of the barcoding gene in conjunction with some uninformative sequences of a particular species. Henceforth, a technique is highly essential to wipe out or to reduce the number of uninformative sequences and ought to create more accurate species-specific barcodes for discrimination. To overcome this issue, minor genetic sequence variants in the form of single nucleotide polymorphism (SNP) were analyzed and regarded as a novel approach for developing a fast, reliable, and high-throughput technique for the discrimination between known species. Conversely, machine learning approaches especially help in extracting information from a huge amount of continuously growing data and are particularly useful for applications where the data is complicated to analyze critically. To this end, the chapter explains the present status of knowledge regarding the implementation of a machine learning approach for the generation of SNP barcodes from the DNA barcoding gene sequences of various evolutionarily related mosquitoes species.

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Authors and Affiliations

  1. P. G. Department of Zoology, Berhampur University, Berhampur, Odisha, India

    Surya N. Swain & Tapan Kumar Barik

  2. P. G. Department of Biotechnology, Berhampur University, Berhampur, Odisha, India

    Surya N. Swain & Tapan Kumar Barik

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  1. Surya N. Swain
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  2. Tapan Kumar Barik
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Editors and Affiliations

  1. Post Graduate Department of Zoology, Berhampur University, Berhampur, Odisha, India

    Tapan Kumar Barik

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Swain, S.N., Barik, T.K. (2020). From Linnaean System to Machine Learning Based-SNP Barcoding: A Changing Epitome of Mosquito Species Identification. In: Barik, T.K. (eds) Molecular Identification of Mosquito Vectors and Their Management. Springer, Singapore. https://doi.org/10.1007/978-981-15-9456-4_2

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