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Panthum T, Ariyaphong N, Wattanadilokchatkun P, Singchat W, Ahmad SF, Kraichak E, Dokkaew S, Muangmai N, Han K, Duengkae P, Srikulnath K. Quality control of fighting fish nucleotide sequences in public repositories reveals a dark matter of systematic taxonomic implication. Genes Genomics 2023; 45:169-181. [PMID: 36512198 DOI: 10.1007/s13258-022-01353-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 12/02/2022] [Indexed: 12/15/2022]
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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Affiliation(s)
- Thitipong Panthum
- Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok, 10900, Thailand
- Faculty of Science, Interdisciplinary Graduate Program in Bioscience, Kasetsart University, Bangkok, 10900, Thailand
- Special Research Unit for Wildlife Genomics (SRUWG), Department of Forest Biology, Faculty of Forestry, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok, 10900, Thailand
| | - Nattakan Ariyaphong
- Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok, 10900, Thailand
- Laboratory of Animal Cytogenetics and Comparative Genomics (ACCG), Department of Genetics, Faculty of Science, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok, 10900, Thailand
- Special Research Unit for Wildlife Genomics (SRUWG), Department of Forest Biology, Faculty of Forestry, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok, 10900, Thailand
| | - Pish Wattanadilokchatkun
- Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok, 10900, Thailand
- Master of Science Program in Fishery Science and Technology (International Program), Faculty of Fisheries, Kasetsart University, Bangkok, 10900, Thailand
| | - Worapong Singchat
- Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok, 10900, Thailand
- Special Research Unit for Wildlife Genomics (SRUWG), Department of Forest Biology, Faculty of Forestry, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok, 10900, Thailand
| | - Syed Farhan Ahmad
- Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok, 10900, Thailand
- Special Research Unit for Wildlife Genomics (SRUWG), Department of Forest Biology, Faculty of Forestry, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok, 10900, Thailand
| | - Ekaphan Kraichak
- Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok, 10900, Thailand
- Department of Botany, Faculty of Science, Kasetsart University, Bangkok, 10900, Thailand
| | - Sahabhop Dokkaew
- Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok, 10900, Thailand
- Department of Aquaculture, Faculty of Fisheries, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok, 10900, Thailand
| | - Narongrit Muangmai
- Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok, 10900, Thailand
- Department of Fishery Biology, Faculty of Fisheries, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok, 10900, Thailand
| | - Kyudong Han
- Department of Microbiology, Dankook University, Cheonan, 31116, Korea
- Center for Bio-Medical Engineering Core Facility, Dankook University, Cheonan, 31116, Korea
| | - Prateep Duengkae
- Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok, 10900, Thailand
- Special Research Unit for Wildlife Genomics (SRUWG), Department of Forest Biology, Faculty of Forestry, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok, 10900, Thailand
| | - Kornsorn Srikulnath
- Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok, 10900, Thailand.
- Faculty of Science, Interdisciplinary Graduate Program in Bioscience, Kasetsart University, Bangkok, 10900, Thailand.
- Laboratory of Animal Cytogenetics and Comparative Genomics (ACCG), Department of Genetics, Faculty of Science, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok, 10900, Thailand.
- Special Research Unit for Wildlife Genomics (SRUWG), Department of Forest Biology, Faculty of Forestry, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok, 10900, Thailand.
- Master of Science Program in Fishery Science and Technology (International Program), Faculty of Fisheries, Kasetsart University, Bangkok, 10900, Thailand.
- Amphibian Research Center, Hiroshima University, 1-3-1, Kagamiyama, Higashihiroshima, 739-8526, Japan.
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Pinho C, Cardoso V, Hey J. A population genetic assessment of taxonomic species: The case of Lake Malawi cichlid fishes. Mol Ecol Resour 2019; 19:1164-1180. [PMID: 31012255 PMCID: PMC6764894 DOI: 10.1111/1755-0998.13027] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Revised: 03/20/2019] [Accepted: 04/10/2019] [Indexed: 02/05/2023]
Abstract
Organisms sampled for population‐level research are typically assigned to species by morphological criteria. However, if those criteria are limited to one sex or life stage, or the organisms come from a complex of closely related forms, the species assignments may misdirect analyses. The impact of such sampling can be assessed from the correspondence of genetic clusters, identified only from patterns of genetic variation, to the species identified using only phenotypic criteria. We undertook this protocol with the rock‐dwelling mbuna cichlids of Lake Malawi, for which species within genera are usually identified using adult male coloration patterns. Given high local endemism of male colour patterns, and considerable allele sharing among species, there persists considerable taxonomic uncertainty in these fishes. Over 700 individuals from a single transect were photographed, genotyped and separately assigned: (a) to morphospecies using photographs; and (b) to genetic clusters using five widely used methods. Overall, the correspondence between clustering methods was strong for larger clusters, but methods varied widely in estimated number of clusters. The correspondence between morphospecies and genetic clusters was also strong for larger clusters, as well as some smaller clusters for some methods. These analyses generally affirm (a) adult male‐limited sampling and (b) the taxonomic status of Lake Malawi mbuna, as the species in our study largely appear to be well‐demarcated genetic entities. More generally, our analyses highlight the challenges for clustering methods when the number of populations is unknown, especially in cases of highly uneven sample sizes.
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Affiliation(s)
- Catarina Pinho
- CIBIO/InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto, Vairão, Portugal
| | - Vera Cardoso
- CIBIO/InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto, Vairão, Portugal
| | - Jody Hey
- Rutgers, the State University of New Jersey, Piscataway, New Jersey.,CCGG, Center for Computational Genetics and Genomics, Department of Biology, Temple University, Philadelphia, Pennsylvania
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Ma L, Sun K, Tu K, Pan L, Zhang W. Identification of double-yolked duck egg using computer vision. PLoS One 2017; 12:e0190054. [PMID: 29267387 PMCID: PMC5739493 DOI: 10.1371/journal.pone.0190054] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2016] [Accepted: 12/07/2017] [Indexed: 11/19/2022] Open
Abstract
The double-yolked (DY) egg is quite popular in some Asian countries because it is considered as a sign of good luck, however, the double yolk is one of the reasons why these eggs fail to hatch. The usage of automatic methods for identifying DY eggs can increase the efficiency in the poultry industry by decreasing egg loss during incubation or improving sale proceeds. In this study, two methods for DY duck egg identification were developed by using computer vision technology. Transmittance images of DY and single-yolked (SY) duck eggs were acquired by a CCD camera to identify them according to their shape features. The Fisher’s linear discriminant (FLD) model equipped with a set of normalized Fourier descriptors (NFDs) extracted from the acquired images and the convolutional neural network (CNN) model using primary preprocessed images were built to recognize duck egg yolk types. The classification accuracies of the FLD model for SY and DY eggs were 100% and 93.2% respectively, while the classification accuracies of the CNN model for SY and DY eggs were 98% and 98.8% respectively. The CNN-based algorithm took about 0.12 s to recognize one sample image, which was slightly faster than the FLD-based (about 0.20 s). Finally, this work compared two classification methods and provided the better method for DY egg identification.
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Affiliation(s)
- Long Ma
- College of Food Science and Technology, Nanjing Agricultural University, Nanjing, Jiangsu, People's Republic of China
- College of Food and Biological Engineering, Bengbu University, Bengbu, Anhui, People's Republic of China
| | - Ke Sun
- College of Food Science and Technology, Nanjing Agricultural University, Nanjing, Jiangsu, People's Republic of China
| | - Kang Tu
- College of Food Science and Technology, Nanjing Agricultural University, Nanjing, Jiangsu, People's Republic of China
- * E-mail:
| | - Leiqing Pan
- College of Food Science and Technology, Nanjing Agricultural University, Nanjing, Jiangsu, People's Republic of China
| | - Wei Zhang
- College of Food Science, Nanjing Xiaozhuang University, Nanjing, Jiangsu, People's Republic of China
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Unger J, Merhof D, Renner S. Computer vision applied to herbarium specimens of German trees: testing the future utility of the millions of herbarium specimen images for automated identification. BMC Evol Biol 2016; 16:248. [PMID: 27852219 PMCID: PMC5112707 DOI: 10.1186/s12862-016-0827-5] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2016] [Accepted: 11/10/2016] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Global Plants, a collaborative between JSTOR and some 300 herbaria, now contains about 2.48 million high-resolution images of plant specimens, a number that continues to grow, and collections that are digitizing their specimens at high resolution are allocating considerable recourses to the maintenance of computer hardware (e.g., servers) and to acquiring digital storage space. We here apply machine learning, specifically the training of a Support-Vector-Machine, to classify specimen images into categories, ideally at the species level, using the 26 most common tree species in Germany as a test case. RESULTS We designed an analysis pipeline and classification system consisting of segmentation, normalization, feature extraction, and classification steps and evaluated the system in two test sets, one with 26 species, the other with 17, in each case using 10 images per species of plants collected between 1820 and 1995, which simulates the empirical situation that most named species are represented in herbaria and databases, such as JSTOR, by few specimens. We achieved 73.21% accuracy of species assignments in the larger test set, and 84.88% in the smaller test set. CONCLUSIONS The results of this first application of a computer vision algorithm trained on images of herbarium specimens shows that despite the problem of overlapping leaves, leaf-architectural features can be used to categorize specimens to species with good accuracy. Computer vision is poised to play a significant role in future rapid identification at least for frequently collected genera or species in the European flora.
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Affiliation(s)
- Jakob Unger
- Institute of Imaging and Computer Vision, RWTH Aachen University, Kopernikusstr. 16, 52074, Aachen, Germany
| | - Dorit Merhof
- Institute of Imaging and Computer Vision, RWTH Aachen University, Kopernikusstr. 16, 52074, Aachen, Germany.
| | - Susanne Renner
- Systematic Botany and Mycology, University of Munich (LMU), Menzinger-Str. 67, 80638, Munich, Germany.
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Soleymani A, Pennekamp F, Petchey OL, Weibel R. Developing and Integrating Advanced Movement Features Improves Automated Classification of Ciliate Species. PLoS One 2015; 10:e0145345. [PMID: 26680591 PMCID: PMC4682988 DOI: 10.1371/journal.pone.0145345] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2015] [Accepted: 12/02/2015] [Indexed: 11/25/2022] Open
Abstract
Recent advances in tracking technologies such as GPS or video tracking systems describe the movement paths of individuals in unprecedented details and are increasingly used in different fields, including ecology. However, extracting information from raw movement data requires advanced analysis techniques, for instance to infer behaviors expressed during a certain period of the recorded trajectory, or gender or species identity in case data is obtained from remote tracking. In this paper, we address how different movement features affect the ability to automatically classify the species identity, using a dataset of unicellular microbes (i.e., ciliates). Previously, morphological attributes and simple movement metrics, such as speed, were used for classifying ciliate species. Here, we demonstrate that adding advanced movement features, in particular such based on discrete wavelet transform, to morphological features can improve classification. These results may have practical applications in automated monitoring of waste water facilities as well as environmental monitoring of aquatic systems.
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Affiliation(s)
- Ali Soleymani
- Department of Geography, University of Zurich, Zurich, Switzerland
- * E-mail:
| | - Frank Pennekamp
- Institute of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland
| | - Owen L. Petchey
- Institute of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland
- Eawag: Swiss Federal Institute of Aquatic Science and Technology, Department of Aquatic Ecology, Dübendorf, Switzerland
| | - Robert Weibel
- Department of Geography, University of Zurich, Zurich, Switzerland
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