Simple molecular based method for selected Oligochaeta (Annelida: Clitellata) genera identification
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Aquatic Oligochaeta are one of most dominant taxa in freshwater sediments. Additionally, they have low dispersal capacity, and are highly sensitive to environmental changes. These characteristics make them important bioindicators to assess aquatic environment quality. Although many groups require experienced taxonomists for identification, the cytochrome C oxidase subunit I gene (COI) has been successfully used to identify Oligochaeta groups. Therefore, molecular barcoding strategy and evaluation, along with already deposited sequences, may be used to simplify Oligochaeta identification and environmental quality monitoring. A total of 1267 COI sequences, with 615–660 length, of fifteen genera of Oligochaeta and three genera of Polychaeta, as outgroups, were retrieved from NCBI GenBank. The sequences were aligned with MAFFT, curated with BMGE and Maximum Likelihood (ML) tree was inferred with GTR as evolutionary model, empirical equilibrium frequencies, SPR tree topology search and approximate Bayes branch support as statistical test. All analyses were performed with NGPhylogeny.fr server and ML tree editing was performed with MEGA X software. The inferred ML tree was able to robustly group different orders, and to satisfactorily differentiate the studied genera. Herein a method using free and intuitive bioinformatics tools is presented to assist non-specialists with a method to identify Oligochaeta genus, using molecular data. To improve the reliability of the method, including other genera, more efforts should be taken to increase the number of available COI sequences along with high quality morphological identification, especially for Neotropical environments.
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Barcoding, Bioinformatics, COI, Database, Oligochaetes, Worms
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Inglês
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Biologia.
