| Literature DB >> 31147679 |
Rahul Singh Jasrotia1,2, Pramod Kumar Yadav1, Mir Asif Iquebal2, S B Bhatt3, Vasu Arora2, U B Angadi2, Rukam Singh Tomar3, Sarika Jaiswal2, Anil Rai2, Dinesh Kumar2.
Abstract
Genus Vigna represented by more than 100 species is a source of nutritious edible seeds and sprouts that are rich sources of protein and dietary supplements. It is further valuable because of therapeutic attributes due to its antioxidant and anti-diabetic properties. A highly diverse and an extremely ecological niche of different species can be valuable genomic resources for productivity enhancement. It is one of the most underutilized crops for food security and animal feeds. In spite of huge species diversity, only three species of Vigna have been sequenced; thus, there is a need for molecular markers for the remaining species. Computational approach of microsatellite marker discovery along with evaluation of polymorphism utilizing available genomic data of different genotypes can be a quick and an economical approach for genomic resource development. Cross-species transferability by e-PCR over available genomes can further prioritize the potential SSR markers, which could be used for genetic diversity and population differentiation of the remaining species saving cost and time. We present VigSatDB-the world's first comprehensive microsatellite database of genus Vigna, containing >875 K putative microsatellite markers with 772 354 simple and 103 865 compound markers mined from six genome assemblies of three Vigna species, namely, Vigna radiata (Mung bean), Vigna angularis (Adzuki bean) and Vigna unguiculata (Cowpea). It also contains 1976 validated published markers. Markers can be selected on the basis of chromosomes/location specificity, and primers can be generated using Primer3core tool integrated at backend. Efficacy of VigSatDB for microsatellite loci genotyping has been evaluated by 15 markers over a panel of 10 diverse genotype of V. radiata. Our web genomic resources can be used in diversity analysis, population and varietal differentiation, discovery of quantitative trait loci/genes, marker-assisted varietal improvement in endeavor of Vigna crop productivity and management.Entities:
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Year: 2019 PMID: 31147679 PMCID: PMC6542692 DOI: 10.1093/database/baz055
Source DB: PubMed Journal: Database (Oxford) ISSN: 1758-0463 Impact factor: 3.451
Genomic data of different varieties of Vigna species used in the study
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| VC1973A | GCA_000741045.2 | Chromosome | 463.638 | 34.40 |
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| RIL59 | GCA_001584445.1 | Scaffold | 454.907 | 32.00 |
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| Jingnong 6 | GCA_001190045.1 | Chromosome | 467.301 | 34.62 |
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| Kyungwonpat | GCA_001723775.1 | Chromosome | 444.439 | 31.77 |
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| IT213134 | GCA_000465365.1 | Scaffold | 291.824 | 33.10 |
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| IT97K-499-35 | GCA_001687525.1 | Scaffold | 695.046 | 13.6 |
Figure 1A schematic diagram of SSR marker discovery and primer generation.
Distribution of SSR types in Vigna species
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| 200 178 | 174 113 | 26 065 |
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| 200 642 | 173 405 | 27 237 |
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| 147 634 | 130 521 | 17 113 |
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| 143 109 | 126 601 | 16 508 |
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| 92 467 | 82 406 | 10 061 |
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| 92 189 | 85 308 | 6881 |
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| 876 219 | 772 354 | 103 865 |
Distribution of motif type found in each cultivar
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| 127 648 | 127 845 | 86 204 | 78 016 | 54 256 | 59 935 |
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| 51 742 | 50 825 | 42 703 | 48 685 | 25 646 | 21 813 |
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| 17 872 | 18 956 | 16 534 | 14 560 | 11 330 | 9260 |
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| 1827 | 1822 | 1212 | 1200 | 867 | 872 |
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| 912 | 1003 | 734 | 414 | 231 | 187 |
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| 177 | 191 | 247 | 234 | 137 | 122 |
Figure 2Chromosome-wise abundance of SSRs in V. radiata (cv. VC1973A), V. angularis (cv. Jingnong 6) and V. angularis (Kyungwonpat) varieties.
Chromosome-wise distribution of SSRs in Vigna cultivars VC1973A, Jingnong6 and Kyungwonpat
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| VC1973A | Density per | Jingnong 6 | Density per | Kyungwonpat | Density per | |
| Chr 1 | 15 207 | 416.6 | 14 964 | 354.3 | 10 229 | 339.1 |
| Chr 2 | 11 317 | 446.2 | 11 948 | 335.3 | 8989 | 317.9 |
| Chr 3 | 7424 | 573.2 | 13 679 | 331.9 | 8383 | 336.8 |
| Chr 4 | 9522 | 457.5 | 10 632 | 306.6 | 9097 | 332.3 |
| Chr 5 | 18 212 | 489.8 | 9411 | 306.6 | 4771 | 431.8 |
| Chr 6 | 15 399 | 411.3 | 7391 | 381.8 | 13 887 | 370.0 |
| Chr 7 | 26 280 | 472.6 | 12 016 | 304.9 | 3976 | 422.0 |
| Chr 8 | 20 521 | 448.7 | 8976 | 291.5 | 4156 | 321.7 |
| Chr 9 | 9144 | 435.2 | 10 605 | 334 | 5007 | 377.6 |
| Chr 10 | 9966 | 474.6 | 12 112 | 299.6 | 3102 | 401.8 |
| Chr 11 | 9768 | 495 | 7808 | 293.6 | 10 407 | 421.5 |
Figure 3Microsatellite polymorphism detection in 10 genotypes of V. radiata in QIAxcel system.
Figure 4Interface of VigSatDB for various searches.
Figure 5(A) In silico mining of SSRs and primer designing; (B)e-PCR evaluation of published primers using specific variety of Vigna species.
Comparison of PMDBase and VigSatDB databases
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| Number of |
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| Polymorphism prediction |
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| Chromosome-wise search option |
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| Motif-type search option |
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| Repeat motif |
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| Repeat kind |
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| Genic and non-genic marker search by users’ choice |
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| GC% content search option |
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| Chromosomal location search option |
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| Copy no. search option |
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| Non-nuclear (mitochondrion and chloroplast) |
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| Flexibility in primer designing |
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| ePCR option for evaluation of published primers |
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