1. KAUTIR (Kerala Agricultural University Theses Information and Retrieval)
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Item Prediction of SSR and SNP markers for anthracnose resiistance in YAM using bioinformatics tools and their validation(Department of Plant Biotechnology, College of Agriculture, Vellayani, 2018) Sahla, K; Sreekumar, JThe study entitled “Prediction of SSR and SNP markers for anthracnose resistance in yam using bioinformatics tools and their validation” was conducted at ICAR-Central Tuber Crop Research Institute, Sreekariyam, Thiruvananthapuram during October 2107 to August 2018. The objectives of the study is to computationally identify SNPs and SSRs for anthracnose resistance in Greater Yam and the verification of identified markers using resistant and susceptible varieties. The preliminary data set for the identification of SSR and SNP markers was obtained from the EST section of NCBI. A total of 44134 sequences was obtained. The dataset was reduced to 44114 sequences after several pre-processing and screening steps. The resulting sequences were assembled and aligned using CAP3 and 5940 contigs were obtained. SNPs and SSRs were predicted from these datasets using respective prediction tools. The SNP prediction tools such as QualitySNP and AutoSNP were compared for their performance. Analysis was performed to identify the tool with the ability to annotate and identify more viable nonsynonymous and synonymous SNPs. For SSRs the SSR prediction tools such as MISA and SSRIT was compared and analysis was performed to identify the tool having the ability to predict more viable SSRs and the ability to classify them as mono, di, tri, tetra, penta, hexa and poly SSRs. Using QualitySNP, 1789 nonsynonymous SNPs and 73 synonymous SNPs were identified. Using MISA, 359 mono SSRs, 268 di SSRs, 342 tri SSRs, 17 tetra SSRs, 7 penta SSRs, and 9 hexa SSRs were identified. Five sequences from identified SNPs and SSRs which having high hit percentage and low E value were selected for validation and primer designing for anthracnose resistant genes. These primers were validated using 3 resistant and 3 susceptible yam varieties. Among the primers after validation in wet lab, three SNPs (DaSNP1, DaSNP2, DaSNP3) and two SSRs (DaSSR1 and DaSSR2) primer was able to clearly differentiate between the resistant and susceptible varieties which can be used as potential markers in the breeding program for screening anthracnose resistance in yam.Item Identification and evaluation of endophytes from tropical tuber crops against colletotrichum gloeosporioides (penz.) sacc. causing anthracnose in greater yam (dioscorea alata L.)(Department of Plant Biotechnology, College of Agriculture, Vellayani, 2018) Shahana, N; Jeeva, M LItem Molecular characterization of pathogens associated with post harvest diseases in elephant foot yam(Department of Plant Biotechnology, College of Agriculture, Vellayani, 2018) Adithya, V; Veena, S SItem Characterisation of phytopathogenic fungi in nursery seedlings of Tectona grandis L.F, Swietenia macrophylla King and Cassia fistula L. In Central Kerala(Department of Forest Management and Utilisation, College of Forestry, Vellanikkara, 2017) Kiran Mohan; Gopakumar, SItem Cataloguing, documentation and management of fungal diseases of strawberry (Fragaria x ananassa Duch.)(Department of Plant Pathology, College of Horticulture, Vellanikkara, 2017) Amrutha, P; Reshmy VijayaraghavanItem Characterization and management of fungal pathogens of cabbage (Brassica oleracea var. capitata L.) and cauliflower (Brassica oleracea var. botrytis L)(Department of Plant Pathology, College of Horticulture, Vellanikkara, 2017) Nusrath Beegum, C H; Yamini Varma, C K