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Leveraging the 3000 Rice Genome Data for Computational Design of Polymorphic Markers in a Local Rice Variety Lacking Sequence Data

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@article{IJASEIT12535,
   author = {Dani Satyawan and Ahmad Warsun and Ahmad Dadang and Muhamad Yunus},
   title = {Leveraging the 3000 Rice Genome Data for Computational Design of Polymorphic Markers in a Local Rice Variety Lacking Sequence Data},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {11},
   number = {2},
   year = {2021},
   pages = {820--827},
   keywords = {Rice; SNP marker; computational marker design; genome sequence utilization.},
   abstract = {DNA markers can detect DNA sequence variations in the genome, and they are useful for genetic studies, DNA fingerprinting, and genotype-based selection in breeding programs. Rice, as one of the model plants for genetic and genomic studies, has abundant DNA markers stored in various online databases. Selecting markers in rice is not limited by marker availability but rather by their polymorphism in the target population. We developed a computational method to screen millions of single nucleotide polymorphism (SNP) markers listed in IRRI 3000 rice genome database in order to find a subset of markers that are polymorphic in an F2 mapping population created from a cross between a parental line with a known genome sequence and a local Indonesian variety with no genome sequence data. The parental lines were genotyped using an affordable medium-density SNP array. The genotype data was cross-referenced with the rice genome database to perform phylogenetic analysis and identify accessions clusters with the highest genetic similarities to each parental line. The cluster data was then used to identify monomorphic SNP candidates within the cluster but exhibit consistent polymorphism between the two clusters. Using this method, we obtained a SNP marker set for a segment in rice chromosome 8 with 76.19% polymorphism rate, which is much higher than the expected 1.06% polymorphism rate if the SNP markers were chosen randomly. The improved polymorphism rate was also observed when the method was applied to other random chromosome segments and randomly chosen parental candidates.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12535},
   doi = {10.18517/ijaseit.11.2.12535}
}

EndNote

%A Satyawan, Dani
%A Warsun, Ahmad
%A Dadang, Ahmad
%A Yunus, Muhamad
%D 2021
%T Leveraging the 3000 Rice Genome Data for Computational Design of Polymorphic Markers in a Local Rice Variety Lacking Sequence Data
%B 2021
%9 Rice; SNP marker; computational marker design; genome sequence utilization.
%! Leveraging the 3000 Rice Genome Data for Computational Design of Polymorphic Markers in a Local Rice Variety Lacking Sequence Data
%K Rice; SNP marker; computational marker design; genome sequence utilization.
%X DNA markers can detect DNA sequence variations in the genome, and they are useful for genetic studies, DNA fingerprinting, and genotype-based selection in breeding programs. Rice, as one of the model plants for genetic and genomic studies, has abundant DNA markers stored in various online databases. Selecting markers in rice is not limited by marker availability but rather by their polymorphism in the target population. We developed a computational method to screen millions of single nucleotide polymorphism (SNP) markers listed in IRRI 3000 rice genome database in order to find a subset of markers that are polymorphic in an F2 mapping population created from a cross between a parental line with a known genome sequence and a local Indonesian variety with no genome sequence data. The parental lines were genotyped using an affordable medium-density SNP array. The genotype data was cross-referenced with the rice genome database to perform phylogenetic analysis and identify accessions clusters with the highest genetic similarities to each parental line. The cluster data was then used to identify monomorphic SNP candidates within the cluster but exhibit consistent polymorphism between the two clusters. Using this method, we obtained a SNP marker set for a segment in rice chromosome 8 with 76.19% polymorphism rate, which is much higher than the expected 1.06% polymorphism rate if the SNP markers were chosen randomly. The improved polymorphism rate was also observed when the method was applied to other random chromosome segments and randomly chosen parental candidates.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12535
%R doi:10.18517/ijaseit.11.2.12535
%J International Journal on Advanced Science, Engineering and Information Technology
%V 11
%N 2
%@ 2088-5334

IEEE

Dani Satyawan,Ahmad Warsun,Ahmad Dadang and Muhamad Yunus,"Leveraging the 3000 Rice Genome Data for Computational Design of Polymorphic Markers in a Local Rice Variety Lacking Sequence Data," International Journal on Advanced Science, Engineering and Information Technology, vol. 11, no. 2, pp. 820-827, 2021. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.11.2.12535.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Satyawan, Dani
AU  - Warsun, Ahmad
AU  - Dadang, Ahmad
AU  - Yunus, Muhamad
PY  - 2021
TI  - Leveraging the 3000 Rice Genome Data for Computational Design of Polymorphic Markers in a Local Rice Variety Lacking Sequence Data
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 11 (2021) No. 2
Y2  - 2021
SP  - 820
EP  - 827
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Rice; SNP marker; computational marker design; genome sequence utilization.
N2  - DNA markers can detect DNA sequence variations in the genome, and they are useful for genetic studies, DNA fingerprinting, and genotype-based selection in breeding programs. Rice, as one of the model plants for genetic and genomic studies, has abundant DNA markers stored in various online databases. Selecting markers in rice is not limited by marker availability but rather by their polymorphism in the target population. We developed a computational method to screen millions of single nucleotide polymorphism (SNP) markers listed in IRRI 3000 rice genome database in order to find a subset of markers that are polymorphic in an F2 mapping population created from a cross between a parental line with a known genome sequence and a local Indonesian variety with no genome sequence data. The parental lines were genotyped using an affordable medium-density SNP array. The genotype data was cross-referenced with the rice genome database to perform phylogenetic analysis and identify accessions clusters with the highest genetic similarities to each parental line. The cluster data was then used to identify monomorphic SNP candidates within the cluster but exhibit consistent polymorphism between the two clusters. Using this method, we obtained a SNP marker set for a segment in rice chromosome 8 with 76.19% polymorphism rate, which is much higher than the expected 1.06% polymorphism rate if the SNP markers were chosen randomly. The improved polymorphism rate was also observed when the method was applied to other random chromosome segments and randomly chosen parental candidates.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12535
DO  - 10.18517/ijaseit.11.2.12535

RefWorks

RT Journal Article
ID 12535
A1 Satyawan, Dani
A1 Warsun, Ahmad
A1 Dadang, Ahmad
A1 Yunus, Muhamad
T1 Leveraging the 3000 Rice Genome Data for Computational Design of Polymorphic Markers in a Local Rice Variety Lacking Sequence Data
JF International Journal on Advanced Science, Engineering and Information Technology
VO 11
IS 2
YR 2021
SP 820
OP 827
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Rice; SNP marker; computational marker design; genome sequence utilization.
AB DNA markers can detect DNA sequence variations in the genome, and they are useful for genetic studies, DNA fingerprinting, and genotype-based selection in breeding programs. Rice, as one of the model plants for genetic and genomic studies, has abundant DNA markers stored in various online databases. Selecting markers in rice is not limited by marker availability but rather by their polymorphism in the target population. We developed a computational method to screen millions of single nucleotide polymorphism (SNP) markers listed in IRRI 3000 rice genome database in order to find a subset of markers that are polymorphic in an F2 mapping population created from a cross between a parental line with a known genome sequence and a local Indonesian variety with no genome sequence data. The parental lines were genotyped using an affordable medium-density SNP array. The genotype data was cross-referenced with the rice genome database to perform phylogenetic analysis and identify accessions clusters with the highest genetic similarities to each parental line. The cluster data was then used to identify monomorphic SNP candidates within the cluster but exhibit consistent polymorphism between the two clusters. Using this method, we obtained a SNP marker set for a segment in rice chromosome 8 with 76.19% polymorphism rate, which is much higher than the expected 1.06% polymorphism rate if the SNP markers were chosen randomly. The improved polymorphism rate was also observed when the method was applied to other random chromosome segments and randomly chosen parental candidates.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12535
DO  - 10.18517/ijaseit.11.2.12535