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National Academy of Agricultural Sciences (NAAS)
NAAS Score: *5.38 (2020)
[Effective from January 1, 2020]
For more details click here

ICV 2018: 95.39
Index Copernicus ICI Journals Master List 2018 - IJCMAS--ICV 2018: 95.39
For more details click here

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Index Copernicus ICV 2018: 95.39
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Int.J.Curr.Microbiol.App.Sci.2019.8(1): 2819-2829

Tools and Resources for SNP Mining in Crop Plants
Saurabh Pandey1*, Sunidhi Mishra2 and Kailash Chandra3
1National Institute of Plant Genome Research, Aruna Asaf Ali Marg, New Delhi-110067, India
2Department of Vegetable Sciences, Indira Gandhi Agricultural University Krishak Nagar, Raipur, Chhattisgarh 492012, India
3College of Agriculture (S.K.N.A.U. Jobner) Fatehpur Shekhawati, Sikar-332301 (Rajasthan), India
*Corresponding author

Molecular genetic markers correspond to highly potent source for the study of plant genomes and the association of inherited phenotypic traits with underneath genetic variation. Single Nucleotide Polymorphism (SNPs) are most abundant form of molecular genetic marker which represents a single nucleotide difference between two individuals at a defined location. Compare to others SNPs are direct sequence variation which offers the precise nature of the allelic variants among different genotypes. Further, it signify recurrent type of genetic polymorphism with high density genome coverage. Advent of Next generation sequencing technology drives the exploration of sequence diversity for various crops. These studies revealed abundance of SNPs in plant systems, with the frequency of 100-300bp per SNP.SNP detection based on EST(expressed sequence tags) sequence data has been performed for crops like maize, barley, tomato and trees like pine and in Arabidopsis which is a model plant. Similarly SNP identification based on array analyses has been published for Arabidopsis, rice, barley and maize. Amplicon resequencing approach has been utilized for the identification of SNPs in maize, soybean, Arabidopsis, rice, tomato, sugarbeet, barley and spruce. There are two sets of data to perform SNP mining one is reference sequence data and other is de novo sequence data. This mining for various datasets mainly comprise of subsequent steps: in first step we have to group sequence reads on the basis of their sequence resemblance and confirm identity of reads whether they are covering similar part of genome or they have the same transcript origin. Further we have to align confirm reads and finally identify and categorize sequence variants as probable polymorphic loci/marker. Thus SNP mining can provide better understanding of crops at the gene level, for the detailed analysis of germplasm and eventually for the resourceful management of genetic diversity on a whole genome level inside plant breeding.

Keywords: Tools, Resources, SNP Mining, Crop Plants
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How to cite this article:

Saurabh Pandey, Sunidhi Mishra and Kailash Chandra. 2019. Tools and Resources for SNP Mining in Crop Plants.Int.J.Curr.Microbiol.App.Sci. 8(1): 2819-2829. doi: