�ݺ�ߣshows by User: pandadebadatta

�ݺ�ߣshows by User: pandadebadatta / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: pandadebadatta / Mon, 09 Dec 2019 14:14:10 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: pandadebadatta Genome editing /slideshow/genome-editing-203577951/203577951 genomeediting-191209141410
Major hindrance in pest control Shorter life cycle High fecundity Faster rate of evolution Faster rate of evolution Insect data bases KAIKObase InsectBase Molecular Database On Indian Insects (MODII) Pathogen data bases Comprehensive Phytopathogen Genome Resource (CPGR), e-Fungi pathogenic fungal ESTs (COGEME) Other pest genome NCBI data bases PTGBase Ensembl Plants ]]>
Major hindrance in pest control Shorter life cycle High fecundity Faster rate of evolution Faster rate of evolution Insect data bases KAIKObase InsectBase Molecular Database On Indian Insects (MODII) Pathogen data bases Comprehensive Phytopathogen Genome Resource (CPGR), e-Fungi pathogenic fungal ESTs (COGEME) Other pest genome NCBI data bases PTGBase Ensembl Plants ]]> Mon, 09 Dec 2019 14:14:10 GMT /slideshow/genome-editing-203577951/203577951 pandadebadatta@slideshare.net(pandadebadatta) Genome editing pandadebadatta Major hindrance in pest control Shorter life cycle High fecundity Faster rate of evolution Faster rate of evolution Insect data bases KAIKObase InsectBase Molecular Database On Indian Insects (MODII) Pathogen data bases Comprehensive Phytopathogen Genome Resource (CPGR), e-Fungi pathogenic fungal ESTs (COGEME) Other pest genome NCBI data bases PTGBase Ensembl Plants <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/genomeediting-191209141410-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Major hindrance in pest control Shorter life cycle High fecundity Faster rate of evolution Faster rate of evolution Insect data bases KAIKObase InsectBase Molecular Database On Indian Insects (MODII) Pathogen data bases Comprehensive Phytopathogen Genome Resource (CPGR), e-Fungi pathogenic fungal ESTs (COGEME) Other pest genome NCBI data bases PTGBase Ensembl Plants

Genome editing from pandadebadatta

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0 Tilling and ecotilling /slideshow/tilling-and-ecotilling/203571920 tillingandecotilling-191209135807
Current Status of TILLING and EcoTILLING: TILLING and EcoTILLING technique have been adapted in diverse species including rice, maize, Lotus, poplar, Arabidopsis, wheat, barley, potato, tomato, sunflower, common bean, Field Mustard, clover, melon, pea, peanut, sorghum, rapeseed, soybean, melon, poplar, sugarcane, brassica and other for the purpose of gene detection, functional genomics, polymorphism assessment, plant breeding as described in case study part. Ecotilling: EcoTILLING is similar to TILLING, except that its objective is to identify natural genetic variation as opposed to induced mutations. Many species are not amenable to chemical mutagenesis; therefore, EcoTILLING can aid in the discovery of natural variants and their putative gene function This approach allows one to rapidly screen through many samples with a gene of interest to identify naturally occurring SNPs and / or small INs/DELS iTILLING: A new approach to the TILLING method that reduces costs and the time necessary to carry out mutation screening was developed for Arabidopsis and it is called iTILLING, individualized TILLING ]]>
Current Status of TILLING and EcoTILLING: TILLING and EcoTILLING technique have been adapted in diverse species including rice, maize, Lotus, poplar, Arabidopsis, wheat, barley, potato, tomato, sunflower, common bean, Field Mustard, clover, melon, pea, peanut, sorghum, rapeseed, soybean, melon, poplar, sugarcane, brassica and other for the purpose of gene detection, functional genomics, polymorphism assessment, plant breeding as described in case study part. Ecotilling: EcoTILLING is similar to TILLING, except that its objective is to identify natural genetic variation as opposed to induced mutations. Many species are not amenable to chemical mutagenesis; therefore, EcoTILLING can aid in the discovery of natural variants and their putative gene function This approach allows one to rapidly screen through many samples with a gene of interest to identify naturally occurring SNPs and / or small INs/DELS iTILLING: A new approach to the TILLING method that reduces costs and the time necessary to carry out mutation screening was developed for Arabidopsis and it is called iTILLING, individualized TILLING ]]> Mon, 09 Dec 2019 13:58:07 GMT /slideshow/tilling-and-ecotilling/203571920 pandadebadatta@slideshare.net(pandadebadatta) Tilling and ecotilling pandadebadatta Current Status of TILLING and EcoTILLING: TILLING and EcoTILLING technique have been adapted in diverse species including rice, maize, Lotus, poplar, Arabidopsis, wheat, barley, potato, tomato, sunflower, common bean, Field Mustard, clover, melon, pea, peanut, sorghum, rapeseed, soybean, melon, poplar, sugarcane, brassica and other for the purpose of gene detection, functional genomics, polymorphism assessment, plant breeding as described in case study part. Ecotilling: EcoTILLING is similar to TILLING, except that its objective is to identify natural genetic variation as opposed to induced mutations. Many species are not amenable to chemical mutagenesis; therefore, EcoTILLING can aid in the discovery of natural variants and their putative gene function This approach allows one to rapidly screen through many samples with a gene of interest to identify naturally occurring SNPs and / or small INs/DELS iTILLING: A new approach to the TILLING method that reduces costs and the time necessary to carry out mutation screening was developed for Arabidopsis and it is called iTILLING, individualized TILLING <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/tillingandecotilling-191209135807-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Current Status of TILLING and EcoTILLING: TILLING and EcoTILLING technique have been adapted in diverse species including rice, maize, Lotus, poplar, Arabidopsis, wheat, barley, potato, tomato, sunflower, common bean, Field Mustard, clover, melon, pea, peanut, sorghum, rapeseed, soybean, melon, poplar, sugarcane, brassica and other for the purpose of gene detection, functional genomics, polymorphism assessment, plant breeding as described in case study part. Ecotilling: EcoTILLING is similar to TILLING, except that its objective is to identify natural genetic variation as opposed to induced mutations. Many species are not amenable to chemical mutagenesis; therefore, EcoTILLING can aid in the discovery of natural variants and their putative gene function This approach allows one to rapidly screen through many samples with a gene of interest to identify naturally occurring SNPs and / or small INs/DELS iTILLING: A new approach to the TILLING method that reduces costs and the time necessary to carry out mutation screening was developed for Arabidopsis and it is called iTILLING, individualized TILLING

Tilling and ecotilling from pandadebadatta

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0 Genomic selection /slideshow/genomic-selection/203562141 genomicselection-191209133543
Introduction: Proposed by Meuwissen et al. (2001) GS is a specialized form of MAS, in which information from genotype data on marker alleles covering the entire genome forms the basis of selection. The effects associated with all the marker loci, irrespective of whether the effects are significant or not, covering the entire genome are estimated. The marker effect estimates are used to calculate the genomic estimated breeding values (GEBVs) of different individuals/lines, which form the basis of selection. Why to go for genomic selection: Marker-assisted selection (MAS) is well-suited for handling oligogenes and quantitative trait loci (QTLs) with large effects but not for minor QTLs. MARS attempts to take into account small effect QTLs by combining trait phenotype data with marker genotype data into a combined selection index. Based on markers showing significant association with the trait(s) and for this reason has been criticized as inefficient The genomic selection (GS) scheme was to rectify the deficiency of MAS and MARS schemes. The GS scheme utilizes information from genome-wide marker data whether or not their associations with the concerned trait(s) are significant. GEBV: GenomicEstimated Breeding Values- The sum total of effects associated with all the marker alleles present in the individual and included in the GS model applied to the population under selection Calculated on a single individual basis Gene-assisted genomic selection: A GS model that uses information about prior known QTLs, the targeted QTLs were accumulated in much higher frequencies than when the standard ridge regression was used The sum total of effects associated with all the marker alleles present in the individual and included in the GS model applied to the population under selection Calculated on a single individual basis Population used: Training population: used for training of the GS model and for obtaining estimates of the marker-associated effects needed for estimation of GEBVs of individuals/lines in the breeding population. Breeding population: the population subjected to GS for achieving the desired improvement and isolation of superior lines for use as new varieties/parents of new improved hybrids. Training population- large enough: must be representative of the breeding population: max. trait variance with marker : by cluster analysis should have either equal or comparable LD, LD decay rates with breeding populations Updated by including individuals/lines from the breeding population Training more than one generation Low colinearity between markers is needed since high colinearity tends to reduce prediction accuracy of certain GS models. (colinearity disturbed by recombination)]]>
Introduction: Proposed by Meuwissen et al. (2001) GS is a specialized form of MAS, in which information from genotype data on marker alleles covering the entire genome forms the basis of selection. The effects associated with all the marker loci, irrespective of whether the effects are significant or not, covering the entire genome are estimated. The marker effect estimates are used to calculate the genomic estimated breeding values (GEBVs) of different individuals/lines, which form the basis of selection. Why to go for genomic selection: Marker-assisted selection (MAS) is well-suited for handling oligogenes and quantitative trait loci (QTLs) with large effects but not for minor QTLs. MARS attempts to take into account small effect QTLs by combining trait phenotype data with marker genotype data into a combined selection index. Based on markers showing significant association with the trait(s) and for this reason has been criticized as inefficient The genomic selection (GS) scheme was to rectify the deficiency of MAS and MARS schemes. The GS scheme utilizes information from genome-wide marker data whether or not their associations with the concerned trait(s) are significant. GEBV: GenomicEstimated Breeding Values- The sum total of effects associated with all the marker alleles present in the individual and included in the GS model applied to the population under selection Calculated on a single individual basis Gene-assisted genomic selection: A GS model that uses information about prior known QTLs, the targeted QTLs were accumulated in much higher frequencies than when the standard ridge regression was used The sum total of effects associated with all the marker alleles present in the individual and included in the GS model applied to the population under selection Calculated on a single individual basis Population used: Training population: used for training of the GS model and for obtaining estimates of the marker-associated effects needed for estimation of GEBVs of individuals/lines in the breeding population. Breeding population: the population subjected to GS for achieving the desired improvement and isolation of superior lines for use as new varieties/parents of new improved hybrids. Training population- large enough: must be representative of the breeding population: max. trait variance with marker : by cluster analysis should have either equal or comparable LD, LD decay rates with breeding populations Updated by including individuals/lines from the breeding population Training more than one generation Low colinearity between markers is needed since high colinearity tends to reduce prediction accuracy of certain GS models. (colinearity disturbed by recombination)]]> Mon, 09 Dec 2019 13:35:43 GMT /slideshow/genomic-selection/203562141 pandadebadatta@slideshare.net(pandadebadatta) Genomic selection pandadebadatta Introduction: Proposed by Meuwissen et al. (2001) GS is a specialized form of MAS, in which information from genotype data on marker alleles covering the entire genome forms the basis of selection. The effects associated with all the marker loci, irrespective of whether the effects are significant or not, covering the entire genome are estimated. The marker effect estimates are used to calculate the genomic estimated breeding values (GEBVs) of different individuals/lines, which form the basis of selection. Why to go for genomic selection: Marker-assisted selection (MAS) is well-suited for handling oligogenes and quantitative trait loci (QTLs) with large effects but not for minor QTLs. MARS attempts to take into account small effect QTLs by combining trait phenotype data with marker genotype data into a combined selection index. Based on markers showing significant association with the trait(s) and for this reason has been criticized as inefficient The genomic selection (GS) scheme was to rectify the deficiency of MAS and MARS schemes. The GS scheme utilizes information from genome-wide marker data whether or not their associations with the concerned trait(s) are significant. GEBV: Genomic�Estimated Breeding Values- The sum total of effects associated with all the marker alleles present in the individual and included in the GS model applied to the population under selection Calculated on a single individual basis Gene-assisted genomic selection: A GS model that uses information about prior known QTLs, the targeted QTLs were accumulated in much higher frequencies than when the standard ridge regression was used The sum total of effects associated with all the marker alleles present in the individual and included in the GS model applied to the population under selection Calculated on a single individual basis Population used: Training population: used for training of the GS model and for obtaining estimates of the marker-associated effects needed for estimation of GEBVs of individuals/lines in the breeding population. Breeding population: the population subjected to GS for achieving the desired improvement and isolation of superior lines for use as new varieties/parents of new improved hybrids. Training population- large enough: must be representative of the breeding population: max. trait variance with marker : by cluster analysis should have either equal or comparable LD, LD decay rates with breeding populations Updated by including individuals/lines from the breeding population Training more than one generation Low colinearity between markers is needed since high colinearity tends to reduce prediction accuracy of certain GS models. (colinearity disturbed by recombination) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/genomicselection-191209133543-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Introduction: Proposed by Meuwissen et al. (2001) GS is a specialized form of MAS, in which information from genotype data on marker alleles covering the entire genome forms the basis of selection. The effects associated with all the marker loci, irrespective of whether the effects are significant or not, covering the entire genome are estimated. The marker effect estimates are used to calculate the genomic estimated breeding values (GEBVs) of different individuals/lines, which form the basis of selection. Why to go for genomic selection: Marker-assisted selection (MAS) is well-suited for handling oligogenes and quantitative trait loci (QTLs) with large effects but not for minor QTLs. MARS attempts to take into account small effect QTLs by combining trait phenotype data with marker genotype data into a combined selection index. Based on markers showing significant association with the trait(s) and for this reason has been criticized as inefficient The genomic selection (GS) scheme was to rectify the deficiency of MAS and MARS schemes. The GS scheme utilizes information from genome-wide marker data whether or not their associations with the concerned trait(s) are significant. GEBV: Genomic�Estimated Breeding Values- The sum total of effects associated with all the marker alleles present in the individual and included in the GS model applied to the population under selection Calculated on a single individual basis Gene-assisted genomic selection: A GS model that uses information about prior known QTLs, the targeted QTLs were accumulated in much higher frequencies than when the standard ridge regression was used The sum total of effects associated with all the marker alleles present in the individual and included in the GS model applied to the population under selection Calculated on a single individual basis Population used: Training population: used for training of the GS model and for obtaining estimates of the marker-associated effects needed for estimation of GEBVs of individuals/lines in the breeding population. Breeding population: the population subjected to GS for achieving the desired improvement and isolation of superior lines for use as new varieties/parents of new improved hybrids. Training population- large enough: must be representative of the breeding population: max. trait variance with marker : by cluster analysis should have either equal or comparable LD, LD decay rates with breeding populations Updated by including individuals/lines from the breeding population Training more than one generation Low colinearity between markers is needed since high colinearity tends to reduce prediction accuracy of certain GS models. (colinearity disturbed by recombination)

Genomic selection from pandadebadatta

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0 Groundnut final /slideshow/groundnut-final/203558718 groundnutfinal-191209132409
Introduction- Popularly known as peanut, monkey nut, goober nut, manila nut, earth nut, wonder legume and mung phali pea :a leguminous plant nut :because of its high nutritional value It is crop of the world. An seasonal annual herbaceous legume, self pollinated, autotetraploid with amphidiploid condition (2n = 4x = 40) the13th most important food crop and 4th most important oilseed nutritional qualities- Oil content -44-55% Protein content- 22-32% Soluble sugars- 8-14% Rich in Ca, Fe, Vit. B & E Cake : 45-50% protein- rich in all amino acids except Leucine & Methionine Antinutritional factors- Trypsin inhibitor & Phytic acid (inactivated by boiling & roasting) Aflatoxin (mycotoxin): Produced by Aspergillus flavus & A. parasiticus (Facultative saprophytes) Invades G.nut before or after harvest, during storage & transit. Cause liver cirrosis, cancer in animals(also to human) Upper limit of aflatoxin for human use- 30μg/kg Origin and disribution- The groundnut or peanut was probably first domesticated and cultivated in the valleys of Paraguay. Cultivated groundnut originates from South America (Wiess 2000). Grown in nearly 100 countries. Major producers are China, India, Nigeria, USA, Indonesia and Sudan. Its cultivation is mostly confined to the tropical countries ranging from 40º N to 40º S. Seasonal requirements: Mainly grown mainly in rainy season (Kharif; June-September: about 80% of the total production) In the Southern and Southeastern regions: grown in rice fallows during post-rainy season (Rabi; October to March) If irrigation facilities are available, it can be grown during January to May as a spring or summer crop. Monsoon variations cause major fluctuations in groundnut production. Cropping systems : sequential, multiple and intercropping (Basu and Ghosh 1995). Wild Proginators - Probable ancestors of A.hypogaea are A. duranensis (A genome) A. ipaensis (B genome) (Smalt Itle,1978) According to centromeric bands & RFLP data A. villosa & A.ipaensis are diploid proginators of A. hypogaea & A. monticola Arachis genus has more than 70 wild species existing in nature. ]]>
Introduction- Popularly known as peanut, monkey nut, goober nut, manila nut, earth nut, wonder legume and mung phali pea :a leguminous plant nut :because of its high nutritional value It is crop of the world. An seasonal annual herbaceous legume, self pollinated, autotetraploid with amphidiploid condition (2n = 4x = 40) the13th most important food crop and 4th most important oilseed nutritional qualities- Oil content -44-55% Protein content- 22-32% Soluble sugars- 8-14% Rich in Ca, Fe, Vit. B & E Cake : 45-50% protein- rich in all amino acids except Leucine & Methionine Antinutritional factors- Trypsin inhibitor & Phytic acid (inactivated by boiling & roasting) Aflatoxin (mycotoxin): Produced by Aspergillus flavus & A. parasiticus (Facultative saprophytes) Invades G.nut before or after harvest, during storage & transit. Cause liver cirrosis, cancer in animals(also to human) Upper limit of aflatoxin for human use- 30μg/kg Origin and disribution- The groundnut or peanut was probably first domesticated and cultivated in the valleys of Paraguay. Cultivated groundnut originates from South America (Wiess 2000). Grown in nearly 100 countries. Major producers are China, India, Nigeria, USA, Indonesia and Sudan. Its cultivation is mostly confined to the tropical countries ranging from 40º N to 40º S. Seasonal requirements: Mainly grown mainly in rainy season (Kharif; June-September: about 80% of the total production) In the Southern and Southeastern regions: grown in rice fallows during post-rainy season (Rabi; October to March) If irrigation facilities are available, it can be grown during January to May as a spring or summer crop. Monsoon variations cause major fluctuations in groundnut production. Cropping systems : sequential, multiple and intercropping (Basu and Ghosh 1995). Wild Proginators - Probable ancestors of A.hypogaea are A. duranensis (A genome) A. ipaensis (B genome) (Smalt Itle,1978) According to centromeric bands & RFLP data A. villosa & A.ipaensis are diploid proginators of A. hypogaea & A. monticola Arachis genus has more than 70 wild species existing in nature. ]]> Mon, 09 Dec 2019 13:24:09 GMT /slideshow/groundnut-final/203558718 pandadebadatta@slideshare.net(pandadebadatta) Groundnut final pandadebadatta Introduction- Popularly known as peanut, monkey nut, goober nut, manila nut, earth nut, wonder legume and mung phali pea :a leguminous plant nut :because of its high nutritional value It is crop of the world. An seasonal annual herbaceous legume, self pollinated, autotetraploid with amphidiploid condition (2n = 4x = 40) the13th most important food crop and 4th most important oilseed nutritional qualities- Oil content -44-55% Protein content- 22-32% Soluble sugars- 8-14% Rich in Ca, Fe, Vit. B & E Cake : 45-50% protein- rich in all amino acids except Leucine & Methionine Antinutritional factors- Trypsin inhibitor & Phytic acid (inactivated by boiling & roasting) Aflatoxin (mycotoxin): Produced by Aspergillus flavus & A. parasiticus (Facultative saprophytes) Invades G.nut before or after harvest, during storage & transit. Cause liver cirrosis, cancer in animals(also to human) Upper limit of aflatoxin for human use- 30μg/kg Origin and disribution- The groundnut or peanut was probably first domesticated and cultivated in the valleys of Paraguay. Cultivated groundnut originates from South America (Wiess 2000). Grown in nearly 100 countries. Major producers are China, India, Nigeria, USA, Indonesia and Sudan. Its cultivation is mostly confined to the tropical countries ranging from 40º N to 40º S. Seasonal requirements: Mainly grown mainly in rainy season (Kharif; June-September: about 80% of the total production) In the Southern and Southeastern regions: grown in rice fallows during post-rainy season (Rabi; October to March) If irrigation facilities are available, it can be grown during January to May as a spring or summer crop. Monsoon variations cause major fluctuations in groundnut production. Cropping systems : sequential, multiple and intercropping (Basu and Ghosh 1995). Wild Proginators - Probable ancestors of A.hypogaea are A. duranensis (A genome) A. ipaensis (B genome) (Smalt Itle,1978) According to centromeric bands & RFLP data A. villosa & A.ipaensis are diploid proginators of A. hypogaea & A. monticola Arachis genus has more than 70 wild species existing in nature. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/groundnutfinal-191209132409-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Introduction- Popularly known as peanut, monkey nut, goober nut, manila nut, earth nut, wonder legume and mung phali pea :a leguminous plant nut :because of its high nutritional value It is crop of the world. An seasonal annual herbaceous legume, self pollinated, autotetraploid with amphidiploid condition (2n = 4x = 40) the13th most important food crop and 4th most important oilseed nutritional qualities- Oil content -44-55% Protein content- 22-32% Soluble sugars- 8-14% Rich in Ca, Fe, Vit. B & E Cake : 45-50% protein- rich in all amino acids except Leucine & Methionine Antinutritional factors- Trypsin inhibitor & Phytic acid (inactivated by boiling & roasting) Aflatoxin (mycotoxin): Produced by Aspergillus flavus & A. parasiticus (Facultative saprophytes) Invades G.nut before or after harvest, during storage & transit. Cause liver cirrosis, cancer in animals(also to human) Upper limit of aflatoxin for human use- 30μg/kg Origin and disribution- The groundnut or peanut was probably first domesticated and cultivated in the valleys of Paraguay. Cultivated groundnut originates from South America (Wiess 2000). Grown in nearly 100 countries. Major producers are China, India, Nigeria, USA, Indonesia and Sudan. Its cultivation is mostly confined to the tropical countries ranging from 40º N to 40º S. Seasonal requirements: Mainly grown mainly in rainy season (Kharif; June-September: about 80% of the total production) In the Southern and Southeastern regions: grown in rice fallows during post-rainy season (Rabi; October to March) If irrigation facilities are available, it can be grown during January to May as a spring or summer crop. Monsoon variations cause major fluctuations in groundnut production. Cropping systems : sequential, multiple and intercropping (Basu and Ghosh 1995). Wild Proginators - Probable ancestors of A.hypogaea are A. duranensis (A genome) A. ipaensis (B genome) (Smalt Itle,1978) According to centromeric bands & RFLP data A. villosa & A.ipaensis are diploid proginators of A. hypogaea & A. monticola Arachis genus has more than 70 wild species existing in nature.

Groundnut final from pandadebadatta

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0 https://public.slidesharecdn.com/v2/images/profile-picture.png https://cdn.slidesharecdn.com/ss_thumbnails/genomeediting-191209141410-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/genome-editing-203577951/203577951 Genome editing https://cdn.slidesharecdn.com/ss_thumbnails/tillingandecotilling-191209135807-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/tilling-and-ecotilling/203571920 Tilling and ecotilling https://cdn.slidesharecdn.com/ss_thumbnails/genomicselection-191209133543-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/genomic-selection/203562141 Genomic selection