Molecular identification of sweet potato accessions using ARMS-PCR based on SNPs

Park, Hyung Jun; Kim, Su-Jung; Nie, Hualin; Kim, Jiseong; Lee, Jeongeun; Kim, Sun‐Hyung

doi:10.5010/jpb.2020.47.2.124

Cited by 3 publications

(4 citation statements)

References 30 publications

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“…As third-generation molecular markers, SNPs are highly stable and widely used for studies of crop molecular genetics (Brookes, 1999; Yu et al ., 2020). Tetra-primer ARMS PCR is a derivative technique based on standard PCR that can be specifically used to detect SNPs (Yang et al ., 2018), which is economical, rapid, simple and has been successfully applied for analysis and identification of genotypes in rice, wheat, sweet potato and other crops (Hou et al ., 2013; Zhang et al ., 2015; Park et al ., 2020). Yang et al .…”

Section: Discussionmentioning

confidence: 99%

Molecular cytology identification of 22 sugarcane germplasm clones from Sri Lanka

Mao

Chen

et al. 2022

Plant Genet. Resour.

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Germplasm innovation can provide materials for breeding sugarcane cultivars. Saccharum officinarum is the main source of high-sugar and high-yield genes in sugarcane breeding. ‘Nobilization’ is the theoretical basis for exploiting S. officinarum, and S. officinarum authenticity directly affects sugarcane nobility breeding efficiency. Herein, the authenticity of 22 SLC-series S. officinarum clones imported from Sri Lanka and preserved in the China National Germplasm Repository of Sugarcane (NGRS) was explored by four-primer amplification-arrested mutation PCR (ARMS PCR) and somatic chromosome number counting. The amplified bands from SLC 08 120 and SLC 08 131 were the same with those from S. officinarum clone Badila, i.e. a common band of 428 bp and a S. officinarum-specific band of 278 bp, hence they were tentatively assigned as S. officinarum clones. The other 20 SLC clones had both 278 bp (S. officinarum-specific) and 203 bp (S. spontaneum-specific) bands, which are hybrid characteristics. In addition, the chromosome numbers of SLC 08 120 and SLC 08 131 are both 80, belong to typical S. officinarum. While the chromosome numbers of the other 20 materials are ranging from 101 to 129, consistent with hybrids of S. officinarum and S. spontaneum. This molecular cytological characterization indicates that among the 22 introduced SLC-series clones, only two, SLC 08 120 and SLC 08 131, were S. officinarum. Future agronomic trait and resistance analyses could facilitate their use as crossing parents in sugarcane breeding.

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Section: Discussionmentioning

confidence: 99%

Molecular cytology identification of 22 sugarcane germplasm clones from Sri Lanka

Mao

Chen

et al. 2022

Plant Genet. Resour.

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“…Furthermore, these methods do not need fully expanded leaves but can be applied in any growth stage using any plant tissues (such as seeds, fruits, and young leaves), making plant growth redundant 51 . With these advantages, molecular marker-based methods are being used in many breeding institutes and companies for routine identification in a wide range of ornamental, vegetable, fruit, and cereal crops 52 – 57 . However, their disadvantages are also apparent.…”

Section: Discussionmentioning

confidence: 99%

MFCIS: an automatic leaf-based identification pipeline for plant cultivars using deep learning and persistent homology

et al. 2021

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Recognizing plant cultivars reliably and efficiently can benefit plant breeders in terms of property rights protection and innovation of germplasm resources. Although leaf image-based methods have been widely adopted in plant species identification, they seldom have been applied in cultivar identification due to the high similarity of leaves among cultivars. Here, we propose an automatic leaf image-based cultivar identification pipeline called MFCIS (Multi-feature Combined Cultivar Identification System), which combines multiple leaf morphological features collected by persistent homology and a convolutional neural network (CNN). Persistent homology, a multiscale and robust method, was employed to extract the topological signatures of leaf shape, texture, and venation details. A CNN-based algorithm, the Xception network, was fine-tuned for extracting high-level leaf image features. For fruit species, we benchmarked the MFCIS pipeline on a sweet cherry (Prunus avium L.) leaf dataset with >5000 leaf images from 88 varieties or unreleased selections and achieved a mean accuracy of 83.52%. For annual crop species, we applied the MFCIS pipeline to a soybean (Glycine max L. Merr.) leaf dataset with 5000 leaf images of 100 cultivars or elite breeding lines collected at five growth periods. The identification models for each growth period were trained independently, and their results were combined using a score-level fusion strategy. The classification accuracy after score-level fusion was 91.4%, which is much higher than the accuracy when utilizing each growth period independently or mixing all growth periods. To facilitate the adoption of the proposed pipelines, we constructed a user-friendly web service, which is freely available at http://www.mfcis.online.

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“…The inner forward or outer reverse and outer forward or inner reverse primer combination yield allele-specific amplicons depending on the genotype of the sample used. The placing of the inner primers is not the same as those of the corresponding outer primer to produce amplicons with different sizes and easily visualized on gel and distinction is made accordingly [42][43][44].…”

Section: Tetra Arms Allele Specific Pcrmentioning

confidence: 99%

“…SNP markers identified by genotyping-by-sequencing were used to distinguish four varieties of sweet potato through tetra-primer ARMS-PCR method. It was shown that three variety-specific fragments (164 bp and 241 bp of SNP 04-27457768 and 292 bp of SNP 03-16195623) were amplified in the Single Nucleotide Polymorphisms: A Modern Tool to Screen Plants for Desirable Traits DOI: http://dx.doi.org/10.5772/intechopen.94935 'Beniharuka', 'Pungwonmi', and 'Annobeni' sweet potato varieties [43]. Some SNP markers developed by Angiolillo et al [46] was used to resolve the issue of nomenclature for 65 olive samples as the markers were able to discriminate 77% of the olive cultivars.…”

Section: Tetra Arms Allele Specific Pcrmentioning

confidence: 99%

Single Nucleotide Polymorphisms: A Modern Tool to Screen Plants for Desirable Traits

Udoh¹,

Obaseojei²,

Uzoebo³

2021

Plant Breeding - Current and Future Views

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Single nucleotide polymorphism (SNP) represent a change in a single nucleotide within the genome. This can alter the phenotype of an individual within the same species if it occurs in a coding region of the gene. The change in nucleotide can produce desirable characteristic in plants and can become an object for selection. New SNPs have been discovered and subsequently converted to molecular markers using various non-gel based and next generation sequencing platforms. Considering that SNP markers are based on target genes, its abundance in the genome, high automation and multiplexability, has made it a marker of choice and an effective tool for screening plant germplasm for desirable traits. This chapter considers SNP as molecular marker, their discovery and different SNP genotyping methods was documented. A few case studies of SNP as allele specific markers and their association with traits of interest was considered. Thus, highlighting their efficacy as useful tool for marker assisted selection and plant germplasms screening.

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Molecular identification of sweet potato accessions using ARMS-PCR based on SNPs

Cited by 3 publications

References 30 publications

Molecular cytology identification of 22 sugarcane germplasm clones from Sri Lanka

Molecular cytology identification of 22 sugarcane germplasm clones from Sri Lanka

MFCIS: an automatic leaf-based identification pipeline for plant cultivars using deep learning and persistent homology

Single Nucleotide Polymorphisms: A Modern Tool to Screen Plants for Desirable Traits

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