Machine learning, transcriptome, and genotyping chip analyses provide insights into SNP markers identifying flower color in Platycodon grandiflorus
| العنوان: | Machine learning, transcriptome, and genotyping chip analyses provide insights into SNP markers identifying flower color in Platycodon grandiflorus |
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| المؤلفون: | Chuloh Cho, Younhee Shin, Go-Eun Yu, Sang-Ho Kang, Si-Myung Lee, Chang-Kug Kim, Sathiyamoorthy Subramaniyam, Seung-Sik Lee |
| المصدر: | Scientific Reports, Vol 11, Iss 1, Pp 1-9 (2021) Scientific Reports |
| بيانات النشر: | Nature Portfolio, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | 0106 biological sciences, 0301 basic medicine, Platycodon, Genotype, Science, Feature selection, Single-nucleotide polymorphism, Platycodon grandiflorus, Biology, Machine learning, computer.software_genre, 01 natural sciences, Polymorphism, Single Nucleotide, Article, Machine Learning, 03 medical and health sciences, symbols.namesake, SNP, Genotyping, Selection (genetic algorithm), Sanger sequencing, Multidisciplinary, business.industry, biology.organism_classification, Random forest, Computational biology and bioinformatics, 030104 developmental biology, symbols, Medicine, Artificial intelligence, business, Transcriptome, Plant sciences, computer, 010606 plant biology & botany |
| الوصف: | 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. |
| اللغة: | English |
| تدمد: | 2045-2322 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::86286b501f62b153927929e50de26720 https://doaj.org/article/315537c8b84243b4b178f8d17ea3d7d7 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....86286b501f62b153927929e50de26720 |
| قاعدة البيانات: | OpenAIRE |
Find this article in full text from Springer Verlag. | تدمد: | 20452322 |
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