Browse > Article
http://dx.doi.org/10.5657/KFAS.2021.0489

Machine Learning SNP for Classification of Korean Abalone Species (Genus Haliotis)  

Noh, Eun Soo (Biotechnology Research Division, National Institute of Fisheries Science)
Kim, Ju-Won (Biotechnology Research Division, National Institute of Fisheries Science)
Kim, Dong-Gyun (Biotechnology Research Division, National Institute of Fisheries Science)
Publication Information
Korean Journal of Fisheries and Aquatic Sciences / v.54, no.4, 2021 , pp. 489-497 More about this Journal
Abstract
Climate change is affecting the evolutionary trajectories of individual species and ecological communities, partly through the creation of new species groups. As population shift geographically and temporally as a result of climate change, reproductive interactions between previously isolated species are inevitable and it could potentially lead to invasion, speciation, or even extinction. Four species of abalone, genus Haliotis are present along the Korean coastline and these species are important for commercial and fisheries resources management. In this study, genetic markers for fisheries resources management were discovered based on genomic information, as part of the management of endemic species in response to climate change. Two thousand one hundred and sixty one single nucleotide polymorphisms (SNPs) were discovered using genotyping-by-sequencing (GBS) method. Forty-one SNPs were selected based on their features for species classification. Machine learning analysis using these SNPs makes it possible to differentiate four Haliotis species and hybrids. In conclusion, the proposed machine learning method has potentials for species classification of the genus Haliotis. Our results will provide valuable data for biodiversity conservation and management of abalone population in Korea.
Keywords
Machine learning; Single nucleotide polymorphism; Abalone; Fisheries resource management;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Ang JC, Mirzal A, Haron H and Hamed HNA. 2015. Supervised, unsupervised, and semi-supervised feature selection: A review on gene selection. IEEE-ACM Trans. Comput Biol Bioinform 13, 971-989. https://doi.org/10.1109/TCBB.2015.2478454.   DOI
2 Narum SR, Buerkel CA, Davey JW, Miller MR and Hohenlohe PA. 2013. Genotyping-by-sequencing in ecological and conservation genomics. Mol Ecol 22, 2841-2847. https://doi.org/10.1111/mec.12350.   DOI
3 Jordan MI and Mitchell TM. 2015. Machine learning: Trends, perspectives, and prospects. Science 349, 255-260. https://doi.org/10.1126/science.aaa8415.   DOI
4 Mitchell TM. 1999. Machine learning and data mining. Commun ACM 42, 30-36. https://doi.org/10.1145/319382.319388.   DOI
5 Nam BH, Kwak WR, Kim YO, Kim DG, Kong HJ, Kim WJ, Kang JH, Park JY, An CM, Moon JY, Park CJ, Yu JW, Yoon J, Seo MS, Kim KD, Kim DK, Lee SB, Sung SS, Lee C, Shin YH, Jung MH, Kang BC, Shin GH, Ka SJ, Anolles KS, Cho SA and Kim HB. 2017. Genome sequence of Pacific abalone Haliotis discus hannai: The first draft genome in family Haliotidae. Gigascience 6, 1-8. https://doi.org/10.1093/gigascience/gix014.   DOI
6 Swan AL, Mobasheri A, Allaway D, Liddell S and Bacardit J. 2013. Application of machine learning to proteomics data: Classification and biomarker identification in postgenomics biology. OMICS 17, 595-610. https://doi.org/10.1089/omi.2013.0017.   DOI
7 Park CJ, Nam WS, Lee JH, Noh JK, Kim HC, Park JW, Hwang IJ and Kim SY. 2013. Analysis of genetic divergence according to each mitochondrial DNA region of Haliotis discus hannai. Korean J Malacol 29, 335-341. https://doi.org/10.9710/kjm.2013.29.4.335.   DOI
8 Park MW, Kim HJ, Kim BH, Som M, Choi JS and Lee J. 2014. Reproductive cycle of the abalone Haliotis discus hannai collected from Jindo of Korea. Korean J Malacol 30, 243-248. https://doi.org/10.9710/kjm.2014.30.3.243.   DOI
9 Rhymer JM and Simberloff D. 1996. Extinction by hybridization and introgression. Annu Rev Ecol Syst 27, 83-109. https://doi.org/10.1146/annurev.ecolsys.27.1.83.   DOI
10 Scheben A, Batley J and Edwards D. 2017. Genotyping-by-sequencing approaches to characterize crop genomes: choosing the right tool for the right application. Plant Biotechnol J 15, 149-161. https://doi.org/10.1111/pbi.12645.   DOI
11 Dong CH, Lee MN, Kang JH, Park JY, Nam BH, Noh JK, Kim PY, Cho YA and Kim EM. 2018. Development of a rapid and simple method for identification of Haliotis gigantean using species-specific PCR. Korean J Malacol 34, 51-58. https://doi.org/10.9710/kjm.2018.34.1.51.   DOI
12 Bolger AM, Lohse M and Usadel B. 2014. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114-2120. https://doi.org/10.1093/bioinformatics/btu170.   DOI
13 Allendorf FW, Leary RF, Spruell P and Wenberg JK. 2001. The problems with hybrids: setting conservation guidelines. Trend Ecol Evol 16, 613-622. https://doi.org/10.1016/S0169-5347(01)02290-X.   DOI
14 Amanda JC. 2014. Hybridization in a warmer world. Ecol Evol 4, 2019-2031. https://doi.org/10.1002/ece3.1052.   DOI
15 Anderson E. 1948. Hybridization of the habitat. Evolution 2, 1-9. https://doi.org/10.2307/2405610.   DOI
16 Asaad I, Lundquist CJ, Erdmann MV and Costello MJ. 2017. Ecological criteria to identify areas for biodiversity conservation. Biol Conserv 213, 309-316. https://doi.org/10.1016/j.biocon.2016.10.007.   DOI
17 Barton NH. 2001. The role of hybridization in evolution. Mol Ecol 10, 551-568. https://doi.org/10.1046/j.1365-294x.2001.01216.x.   DOI
18 Breed MF, Harrison PA, Blyth C, Byrne M, Gaget V, Gellie NJC, Groom SVC, Hodgson R, Mills JG, Prowse TAA, Steane DA and Mohr JJ. 2019. The potential of genomics for restoring ecosystems and biodiversity. Nat Rev Genet 20, 615-628. https://doi.org/10.1038/s41576-019-0152-0.   DOI
19 Kim HJ, Kim HJ, Kim YS and Lee JS. 2020. Microstructural differentiation of the oocyte in the abalone Haliotis discus hannai. Korean J Fish Aquat Sci 53, 90-97. https://doi.org/10.5657/KFAS.2020.0090.   DOI
20 Appeltans W, Ahyong ST, Anderson G, Angel MV, Artois T, Bailly N, Bamber R, Barber A, Bartsch I, Berta A, Blazewicz-Paszkowycz M, Bock P, Boxshall G, Boyko CB, Brandao SN, Bray RA, Bruce NL, Cairns SD and Costello MJ. 2012. The magnitude of global marine species diversity. Curr Biol 22, 2189-2202. https://doi.org/10.1016/j.cub.2012.09.036.   DOI
21 Kim JW, Lee BW, Kang JC, Min EY, Won SH, Lim HG, Kang SW, Jeon MA and Lee JS. 2015. Reproductive cycle of the abalone, Haliotis discus discus collected from Jeju island of Korea. Korean J Malacol 31, 21-26. https://doi.org/10.9710/kjm.2015.31.1.21.   DOI
22 Candek K and Kuntner M. 2014. DNA barcoding gap: reliable species identification over morphological and geographical scales. Mol Ecol Resour 15, 268-277. https://doi.org/10.1111/1755-0998.12304.   DOI
23 Canturk KM, Emre R, Kinoglu K, Baspinar B, Sahin F and Ozen M. 2014. Current status of the use of single-nucleotide polymorphisms in forensic practices. Genet Test Mol Biomark 18, 455-460. https://doi.org/10.1089/gtmb.2013.0466.   DOI
24 Schwartz MK, Luikart G and Waples RS. 2007. Genetic monitoring as a promising tool for conservation and management. Trends Ecol Evol 22, 25-33. https://doi.org/10.1016/j.tree.2006.08.009.   DOI
25 Herten K, Hestand MS, Vermeesch JR and Van Houdt JK. 2015. GBSX: a toolkit for experimental design and demultiplexing genotyping by sequencing expreriments. BMC Bioinformatics 16, 73. https://doi.org/10.1186/s12859-015-0514-3.   DOI
26 Davey JW, Hohenlohe PA, Etter PD, Boone JQ, Catchen JM and Blaxter ML. 2011 Genome-wide genetic marker discovery and genotyping using next-generation sequencing. Nat Rev Genet 12, 499-510. https://doi.org/10.1038/nrg3012.   DOI
27 Han S, Liang Y, Li Y and Du W. 2016. Long noncoding RNA identification: Comparing machine learning based tools for long noncoding transcripts discrimination. Biomed Res Int 2016, 8496165. https://doi.org/10.1155/2016/8496165.   DOI
28 Ino T. 1953. Biological studies on the propagation of Japanese abalone (genus Haliotis). Bull Tokai reg Fish Res Lab 5, 1-102.
29 Sonah H. Bastien M. Iquira E, Tardivel A, Legare G, Boyle B, Normandeau E, Laroche J, Larose S, Jean M and Belzile F. 2013. An improved genotyping by sequencing (GBS) approach offering increased versatility and efficiency of SNP discovery and genotyping. PLoS One 8, e54603. https://doi.org/10.1371/journal.pone.0054603.   DOI
30 Riley HP. 1938. A character analysis of colonies of Iris fulva, Iris hexagona var. giganticaerulea and natural hybrids. Am J Bot 25, 727-738. https://doi.org/10.2307/2436599.   DOI
31 Seo YB, Kang SC, Choi SS, Lee JK, Jeong TH, Lim HK and Kim GD. 2016. Phylogenetic study of genus Haliotis in Korea by cytochrome c oxidase subunit 1 and RAPD analysis. J Life Sci 26, 406-413. https://doi.org/10.5352/JLS.2016.26.4.406.   DOI
32 McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M and DePristo MA. 2010. The genome analysis toolkit: a map reduce framework for analyzing next-generation DNA sequencing data. Genome Res 20, 1297-1303. https://doi.org/10.1101/gr.107524.110.   DOI
33 Leache AD and Oaks JR. 2017. The utility of single nucleotide polymorphism (SNP) data in phylogenetics. Ann Ecol Evol Syst 48, 69-84. https://doi.org/10.1146/annurev-ecolsys-110316-022645.   DOI
34 Lee JK, Seo YB, Kim GD and Lim HK. 2016. Molecular and physiological aspects of breeding program for development of hybrids between abalones distributed in the coast of Korea. J Life Sci 26, 1218-1223. https://doi.org/10.5352/JLS.2016.26.10.1218.   DOI
35 Lee JS, Won SH, Kim SK, Lim HK and Lee JS. 2014. Classification and description of genus Hordotis (Gastropoda: Vestigastropoda) from Korea. Korean J Malacol 30, 79-86. https://doi.org/10.9710/KJM.2014.30.1.79.   DOI
36 Muhlfeld CC, Kovach RP, Jones LA, Chokhachy RA, Boyer MC, Leary RF, Lowe WH, Luikart G and Allendorf FW. 2014. Invasive hybridization in a threatened species is accelerated by climate change. Nat Clim Chang 4, 620-624. https://doi.org/10.1038/nclimate2252.   DOI
37 Langmead B and Salzberg SL. 2012. Fast gapped-read alignment with bowtie 2. Nat Methods 9, 357-359. https://doi.org/10.1038/nmeth.1923.   DOI
38 Kang JH, Noh ES, Lim JH, Han HK, Kim BS and Lim SK. 2014. Genetic differentiation of Carassius auratus and C. cuvieri by the cytochrome c oxidase I gene analysis. J Aquac Res Development 5, 1-4. https://doi.org/10.4172/2155-9546.1000231.   DOI
39 Sylvester EVA, Bentzen P, Bradbury IR, Clement M, Pearce J, Horne J and Beiko RG. 2017. Applications of random forest feature selection for fine-scale genetic population assignment. Evol Appl 11, 153-165. https://doi.org/10.1111/eva.12524.   DOI
40 Wiegand KM. 1935. A taxonomist's experience with hybrids in the wild. Science 81, 161-166. https://doi.org/10.1126/science.81.2094.161.   DOI
41 Kang JH, Yang SG, Kim EM, Noh ES, Kim DG, Kim BS and Choi TJ. 2015. Possibility of natural hybridization between red seabream Pagrus major and blackhead seabream Acanthopagrus schlegeli. J Life Sci 25, 16-20. http://doi.org/10.5352/JLS.2015.25.1.16.   DOI
42 Zheng X, Levine D, Shen J, Gogarten SM, Laurie C and Weir BS. 2012. A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics 28, 3326-3328. https://doi.org/10.1093/bioinformatics/bts606.   DOI