scholarly journals Development of Citrus Cultivar Identification by CAPS Markers and Parentage Analysis

2015 ◽  
Vol 14 (2) ◽  
pp. 127-133 ◽  
Author(s):  
Taizo Ninomiya ◽  
Takehiko Shimada ◽  
Tomoko Endo ◽  
Keisuke Nonaka ◽  
Mitsuo Omura ◽  
...  
Keyword(s):  
Cultivar Identification ◽  
Parentage Analysis ◽  
Citrus Cultivar ◽  
Caps Markers

scholarly journals Development of Acid Citrus Cultivar Identification by CAPS Markers

2021 ◽  
Vol 20 (1) ◽  
pp. 17-27
Author(s):  
Eri Niimi ◽  
Hiroshi Fujii ◽  
Satoshi Ohta ◽  
Takuya Iwakura ◽  
Tomoko Endo ◽  
...  
Keyword(s):  
Cultivar Identification ◽  
Citrus Cultivar ◽  
Caps Markers

scholarly journals Identification and Parentage Analysis of Citrus Cultivars Developed in Japan by CAPS Markers

2017 ◽  
Vol 86 (2) ◽  
pp. 208-221 ◽  
Author(s):  
Keisuke Nonaka ◽  
Hiroshi Fujii ◽  
Masayuki Kita ◽  
Takehiko Shimada ◽  
Tomoko Endo ◽  
...  
Keyword(s):  
Parentage Analysis ◽  
Caps Markers

Mining and Identification of SNP from EST Sequences and Convertion of CAPS Markers in Soybean

2010 ◽  
Vol 36 (4) ◽  
pp. 574-579
Author(s):  
Yong-Jun SHU ◽  
Yong LI ◽  
La-Hu WU Na ◽  
Xi BAI ◽  
Hua CAI ◽  
...  
Keyword(s):  
Caps Markers

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

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yanping Zhang ◽  
Jing Peng ◽  
Xiaohui Yuan ◽  
Lisi Zhang ◽  
Dongzi Zhu ◽  
...  
Keyword(s):  
Prunus Avium ◽  
Cultivar Identification ◽  
Leaf Shape ◽  
Persistent Homology ◽  
Growth Period ◽  
Image Features ◽  
Crop Species ◽  
Score Level Fusion ◽  
Level Fusion ◽  
Leaf Image

AbstractRecognizing 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.

scholarly journals Parentage Analysis in Giant Grouper (Epinephelus lanceolatus) Using Microsatellite and SNP Markers from Genotyping-by-Sequencing Data

Genes ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 1042
Author(s):  
Zhuoying Weng ◽  
Yang Yang ◽  
Xi Wang ◽  
Lina Wu ◽  
Sijie Hua ◽  
...  
Keyword(s):  
Fishery Management ◽  
Genotyping By Sequencing ◽  
Parentage Analysis ◽  
Snp Markers ◽  
Individual Identification ◽  
Pedigree Information ◽  
Nucleotide Polymorphisms ◽  
Sequencing Data ◽  
Polymorphic Snps ◽  
Mixed Family

Pedigree information is necessary for the maintenance of diversity for wild and captive populations. Accurate pedigree is determined by molecular marker-based parentage analysis, which may be influenced by the polymorphism and number of markers, integrity of samples, relatedness of parents, or different analysis programs. Here, we described the first development of 208 single nucleotide polymorphisms (SNPs) and 11 microsatellites for giant grouper (Epinephelus lanceolatus) taking advantage of Genotyping-by-sequencing (GBS), and compared the power of SNPs and microsatellites for parentage and relatedness analysis, based on a mixed family composed of 4 candidate females, 4 candidate males and 289 offspring. CERVUS, PAPA and COLONY were used for mutually verification. We found that SNPs had a better potential for relatedness estimation, exclusion of non-parentage and individual identification than microsatellites, and > 98% accuracy of parentage assignment could be achieved by 100 polymorphic SNPs (MAF cut-off < 0.4) or 10 polymorphic microsatellites (mean Ho = 0.821, mean PIC = 0.651). This study provides a reference for the development of molecular markers for parentage analysis taking advantage of next-generation sequencing, and contributes to the molecular breeding, fishery management and population conservation.

scholarly journals Metabolomics for Biomarker Discovery: Key Signatory Metabolic Profiles for the Identification and Discrimination of Oat Cultivars

Metabolites ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 165
Author(s):  
Chanel J. Pretorius ◽  
Fidele Tugizimana ◽  
Paul A. Steenkamp ◽  
Lizelle A. Piater ◽  
Ian A. Dubery
Keyword(s):  
High Performance ◽  
Biomarker Discovery ◽  
Cultivar Identification ◽  
Principal Component ◽  
Rapid Identification ◽  
Maturity Stage ◽  
Metabolic Profiles ◽  
High Definition ◽  
Metabolic Markers ◽  
Leaves And Roots

The first step in crop introduction—or breeding programmes—requires cultivar identification and characterisation. Rapid identification methods would therefore greatly improve registration, breeding, seed, trade and inspection processes. Metabolomics has proven to be indispensable in interrogating cellular biochemistry and phenotyping. Furthermore, metabolic fingerprints are chemical maps that can provide detailed insights into the molecular composition of a biological system under consideration. Here, metabolomics was applied to unravel differential metabolic profiles of various oat (Avena sativa) cultivars (Magnifico, Dunnart, Pallinup, Overberg and SWK001) and to identify signatory biomarkers for cultivar identification. The respective cultivars were grown under controlled conditions up to the 3-week maturity stage, and leaves and roots were harvested for each cultivar. Metabolites were extracted using 80% methanol, and extracts were analysed on an ultra-high performance liquid chromatography (UHPLC) system coupled to a quadrupole time-of-flight (qTOF) high-definition mass spectrometer analytical platform. The generated data were processed and analysed using multivariate statistical methods. Principal component analysis (PCA) models were computed for both leaf and root data, with PCA score plots indicating cultivar-related clustering of the samples and pointing to underlying differential metabolic profiles of these cultivars. Further multivariate analyses were performed to profile differential signatory markers, which included carboxylic acids, amino acids, fatty acids, phenolic compounds (hydroxycinnamic and hydroxybenzoic acids, and associated derivatives) and flavonoids, among the respective cultivars. Based on the key signatory metabolic markers, the cultivars were successfully distinguished from one another in profiles derived from both leaves and roots. The study demonstrates that metabolomics can be used as a rapid phenotyping tool for cultivar differentiation.

Application of ISSRs for cultivar identification and assessment of genetic relationships in rose

2009 ◽  
Vol 128 (5) ◽  
pp. 501-506 ◽  
Author(s):  
L. Crespel ◽  
A. Pernet ◽  
M. Le Bris ◽  
S. Gudin ◽  
L. Hibrand Saint Oyant
Keyword(s):  
Cultivar Identification ◽  
Genetic Relationships
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