Machine learning, transcriptome, and genotyping chip analyses provide insights into SNP markers identifying flower color in Platycodon grandiflorus

Abstract Bellflower is an edible ornamental gardening plant in Asia. For predicting the flower color in bellflower plants, a transcriptome-wide approach based on machine learning, transcriptome, and genotyping chip analyses was used to identify SNP markers. Six machine learning methods were deployed...

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Autores principales:	Go-Eun Yu, Younhee Shin, Sathiyamoorthy Subramaniyam, Sang-Ho Kang, Si-Myung Lee, Chuloh Cho, Seung-Sik Lee, Chang-Kug Kim
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/315537c8b84243b4b178f8d17ea3d7d7
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spelling	oai:doaj.org-article:315537c8b84243b4b178f8d17ea3d7d72021-12-02T18:03:26ZMachine learning, transcriptome, and genotyping chip analyses provide insights into SNP markers identifying flower color in Platycodon grandiflorus10.1038/s41598-021-87281-02045-2322https://doaj.org/article/315537c8b84243b4b178f8d17ea3d7d72021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87281-0https://doaj.org/toc/2045-2322Abstract Bellflower is an edible ornamental gardening plant in Asia. For predicting the flower color in bellflower plants, a transcriptome-wide approach based on machine learning, transcriptome, and genotyping chip analyses was used to identify SNP markers. Six machine learning methods were deployed to explore the classification potential of the selected SNPs as features in two datasets, namely training (60 RNA-Seq samples) and validation (480 Fluidigm chip samples). SNP selection was performed in sequential order. Firstly, 96 SNPs were selected from the transcriptome-wide SNPs using the principal compound analysis (PCA). Then, 9 among 96 SNPs were later identified using the Random forest based feature selection method from the Fluidigm chip dataset. Among six machines, the random forest (RF) model produced higher classification performance than the other models. The 9 SNP marker candidates selected for classifying the flower color classification were verified using the genomic DNA PCR with Sanger sequencing. Our results suggest that this methodology could be used for future selection of breeding traits even though the plant accessions are highly heterogeneous.Go-Eun YuYounhee ShinSathiyamoorthy SubramaniyamSang-Ho KangSi-Myung LeeChuloh ChoSeung-Sik LeeChang-Kug KimNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Go-Eun Yu Younhee Shin Sathiyamoorthy Subramaniyam Sang-Ho Kang Si-Myung Lee Chuloh Cho Seung-Sik Lee Chang-Kug Kim Machine learning, transcriptome, and genotyping chip analyses provide insights into SNP markers identifying flower color in Platycodon grandiflorus
description	Abstract Bellflower is an edible ornamental gardening plant in Asia. For predicting the flower color in bellflower plants, a transcriptome-wide approach based on machine learning, transcriptome, and genotyping chip analyses was used to identify SNP markers. Six machine learning methods were deployed to explore the classification potential of the selected SNPs as features in two datasets, namely training (60 RNA-Seq samples) and validation (480 Fluidigm chip samples). SNP selection was performed in sequential order. Firstly, 96 SNPs were selected from the transcriptome-wide SNPs using the principal compound analysis (PCA). Then, 9 among 96 SNPs were later identified using the Random forest based feature selection method from the Fluidigm chip dataset. Among six machines, the random forest (RF) model produced higher classification performance than the other models. The 9 SNP marker candidates selected for classifying the flower color classification were verified using the genomic DNA PCR with Sanger sequencing. Our results suggest that this methodology could be used for future selection of breeding traits even though the plant accessions are highly heterogeneous.
format	article
author	Go-Eun Yu Younhee Shin Sathiyamoorthy Subramaniyam Sang-Ho Kang Si-Myung Lee Chuloh Cho Seung-Sik Lee Chang-Kug Kim
author_facet	Go-Eun Yu Younhee Shin Sathiyamoorthy Subramaniyam Sang-Ho Kang Si-Myung Lee Chuloh Cho Seung-Sik Lee Chang-Kug Kim
author_sort	Go-Eun Yu
title	Machine learning, transcriptome, and genotyping chip analyses provide insights into SNP markers identifying flower color in Platycodon grandiflorus
title_short	Machine learning, transcriptome, and genotyping chip analyses provide insights into SNP markers identifying flower color in Platycodon grandiflorus
title_full	Machine learning, transcriptome, and genotyping chip analyses provide insights into SNP markers identifying flower color in Platycodon grandiflorus
title_fullStr	Machine learning, transcriptome, and genotyping chip analyses provide insights into SNP markers identifying flower color in Platycodon grandiflorus
title_full_unstemmed	Machine learning, transcriptome, and genotyping chip analyses provide insights into SNP markers identifying flower color in Platycodon grandiflorus
title_sort	machine learning, transcriptome, and genotyping chip analyses provide insights into snp markers identifying flower color in platycodon grandiflorus
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/315537c8b84243b4b178f8d17ea3d7d7
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Machine learning, transcriptome, and genotyping chip analyses provide insights into SNP markers identifying flower color in Platycodon grandiflorus

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