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Quality control of fighting fish nucleotide sequences in public repositories reveals a dark matter of systematic taxonomic implication

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Abstract

Background

The number of nucleotide sequences in public repositories has exploded recently. However, the data contain errors, leading to incorrect species identification. Several fighting fish (Betta spp.) are poorly described, with unresolved cryptic species complexes masking undescribed species. Here, DNA barcoding was used to detect erroneous sequences in public repositories.

Objective

This study reflects the current quantitative and qualitative status of DNA barcoding in fighting fish and provides a rapid and reliable identification tool.

Methods

A total of 1034 barcode sequences were analyzed from mitochondrial cytochrome c oxidase I (COI) and cytochrome b (Cytb) genes from 71 fighting fish species.

Results

The nearest neighbor test showed the highest percentage of intraspecific nearest neighbors at 93.41% for COI and 91.67% for Cytb, which can be used as reference barcodes for certain taxa. Intraspecific variation was usually less than 13%, while most species differed by more than 54%. The barcoding gap, calculated from the difference between inter- and intraspecific sequence divergences, was negative in the COI data set indicating overlapping intra- and interspecific sequence divergence. Sequence saturation was observed in the Cytb data set but not in the COI data set.

Conclusion

The COI gene should thus be used as the main barcoding marker for fighting fish.

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Data availability

The full dataset and metadata from this publication are available from the Dryad Digital Repository. Dataset, https://datadryad.org/stash/share/MhZqErReqixoxVCF_HEP5EFWzKQ44Dnn2NAoKVz05w (https://doi.org/10.5061/dryad.dz08kps14).

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Acknowledgements

We thank NCBI database for supplying betta fish accession number. We also thank the Faculty of Science for providing supporting research facilities (6501.0901.1) and the Center for Bio-Medical Engineering Core Facility at Dankook University.

Funding

This research was financially supported in part by the High-Quality Research Graduate Development Cooperation Project between Kasetsart University and the National Science and Technology Development Agency (NSTDA) (Grant no. 6417400247) awarded to TP and KS; the Thailand Science Research and Innovation through The Kasetsart University Reinventing University Program 2021 (Grant no. 3/2564) awarded to TP and KS; Fundamental Fund: Kasetsart University Research and Development Institute (KURDI) (FF(S-KU)17.66) awarded to SFA and KS; the e-ASIA Joint Research Program (Grant no. P1851131) awarded to WS and KS; a grant from the National Science and Technology Development Agency (NSTDA) (Grant no’s: NSTDA, P-19–52238 and JRA-CO-2564–14003-TH) awarded to KS and WS; a post-doctoral researcher award at Kasetsart University awarded to SFA and KS; the Higher Education for Industry Consortium (Hi-FI; no. 6414400777) awarded to NA and KS; the Office of the Ministry of Higher Education, Science, Research and Innovation. International SciKU Branding (ISB), Faculty of Science, Kasetsart University awarded funds to KS. No funding source was involved in the study design, collection, analysis, and interpretation of the data, writing the report, or the decision to submit the article for publication.

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Authors and Affiliations

  1. Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok, 10900, Thailand

    Thitipong Panthum, Nattakan Ariyaphong, Pish Wattanadilokchatkun, Worapong Singchat, Syed Farhan Ahmad, Ekaphan Kraichak, Sahabhop Dokkaew, Narongrit Muangmai, Prateep Duengkae & Kornsorn Srikulnath

  2. Faculty of Science, Interdisciplinary Graduate Program in Bioscience, Kasetsart University, Bangkok, 10900, Thailand

    Thitipong Panthum & Kornsorn Srikulnath

  3. Laboratory of Animal Cytogenetics and Comparative Genomics (ACCG), Department of Genetics, Faculty of Science, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok, 10900, Thailand

    Nattakan Ariyaphong & Kornsorn Srikulnath

  4. Special Research Unit for Wildlife Genomics (SRUWG), Department of Forest Biology, Faculty of Forestry, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok, 10900, Thailand

    Thitipong Panthum, Nattakan Ariyaphong, Worapong Singchat, Syed Farhan Ahmad, Prateep Duengkae & Kornsorn Srikulnath

  5. Master of Science Program in Fishery Science and Technology (International Program), Faculty of Fisheries, Kasetsart University, Bangkok, 10900, Thailand

    Pish Wattanadilokchatkun & Kornsorn Srikulnath

  6. Department of Botany, Faculty of Science, Kasetsart University, Bangkok, 10900, Thailand

    Ekaphan Kraichak

  7. Department of Aquaculture, Faculty of Fisheries, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok, 10900, Thailand

    Sahabhop Dokkaew

  8. Department of Fishery Biology, Faculty of Fisheries, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok, 10900, Thailand

    Narongrit Muangmai

  9. Department of Microbiology, Dankook University, Cheonan, 31116, Korea

    Kyudong Han

  10. Center for Bio-Medical Engineering Core Facility, Dankook University, Cheonan, 31116, Korea

    Kyudong Han

  11. Amphibian Research Center, Hiroshima University, 1-3-1, Kagamiyama, Higashihiroshima, 739-8526, Japan

    Kornsorn Srikulnath

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  1. Thitipong Panthum
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Contributions

Conceptualization, TP and KS; formal analysis, TP, NA, PW, WS, SFA, EK, and KS; funding acquisition, KS; investigation, TP; methodology, TP and KS; project administration, KS; visualization, NM and KS; writing—original draft, TP and KS; writing—review and editing, TP, NA, PW, WS, SFA, EK, SD, NM, PD and KS. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Kornsorn Srikulnath.

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Conflict of interest

The authors declare no competing financial interests or personal relationships that have influenced this study.

Ethical approval

All animal care and experimental procedures were approved (approval no. ACKU63-SCI-007) by the Animal Experiment Committee of Kasetsart University and conducted in accordance with the Regulations on Animal Experiments at Kasetsart University.

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Supplementary Information

Below is the link to the electronic supplementary material.

13258_2022_1353_MOESM1_ESM.jpg

Supplementary file1 A phylogram clarifying the phylogenetic relationships among the 746 GenBank accessions, constructed from a Bayesian inference analysis using mitochondrial cytochrome c oxidase I (COI) sequences. (JPG 15800 KB)

13258_2022_1353_MOESM2_ESM.jpg

Supplementary file2 A phylogram clarifying the phylogenetic relationships among the 288 GenBank accessions, constructed from a Bayesian inference analysis using mitochondrial cytochrome b (Cytb) sequences. (JPG 6350 KB)

13258_2022_1353_MOESM3_ESM.jpg

Supplementary file3 Phylogram clarifying the phylogenetic relationships among 288 GenBank accessions, constructed from Bayesian inference analysis using mitochondrial cytochrome c oxidase I (COI) sequences. Group 1: higher-level similarity with the same species. Group 2: higher-level similarity with multiple species. Group 3: unique sequences with no similarity within most sequences. Class 1: sequences with the same species name exhibiting intraspecific cohesive clustering and interspecific distinct clustering with high posterior probability (0.90–1.00). Class 2: sequences with the same species name that do not exhibit intraspecific cohesive clustering. Class 3: sequences with a different species name exhibiting cohesive clustering. There is only 1 accession number (asterisk symbol: *). (JPG 16033 KB)

13258_2022_1353_MOESM4_ESM.jpg

Supplementary file4 Phylogram clarifying the phylogenetic relationships among 746 GenBank accessions, constructed from Bayesian inference analysis using mitochondrial cytochrome b (Cytb) sequences. Group 1: higher-level similarity with the same species. Group 2: higher-level similarity with multiple species. Group 3: unique sequences with no similarity within most sequences. Class 1: sequences with the same species name exhibiting intraspecific cohesive clustering and interspecific distinct clustering with high posterior probability (0.90–1.00). Class 2: sequences with the same species name that do not exhibit intraspecific cohesive clustering. Class 3: sequences with a different species name exhibiting cohesive clustering. There is only 1 accession number (asterisk symbol: *). (JPG 7083 KB)

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Panthum, T., Ariyaphong, N., Wattanadilokchatkun, P. et al. Quality control of fighting fish nucleotide sequences in public repositories reveals a dark matter of systematic taxonomic implication. Genes Genom 45, 169–181 (2023). https://doi.org/10.1007/s13258-022-01353-7

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