Thumbnail Image

Theses

Browse

20182
Reset filters
Subject: Biotechnology × End date: 2018 × Start date: 2016 × Type: Thesis ×
Reset filters

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    ThesisItemOpen Access
    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; KAU; Sreekumar, J
    The 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.
  • Thumbnail Image
    ThesisItemOpen Access
    Development of molecular markers for blight disease resistance in taro using bioinformatics tools
    (Department of Plant Biotechnology College of Agriculture, Vellayani, 2018) Athul, V S; Sreekumar, J
    Development of molecular markers using sequential information publicly available in the biological databases has enhanced their credibility over the years. The study entitled “Development of Molecular markers for blight disease resistance in taro using bioinformatics tools” was conducted at the Central Tuber Crop Research Institute (CTCRI) during 2017-2018. The objectives of the study included the development and evaluation of various Single Nucleotide Polymorphism (SNP) and Simple Sequence Repeats (SSR) prediction pipelines, computational prediction and validation of the molecular markers for blight disease resistance in taro. The preliminary data set for the study was obtained from the Sequence Read Archive (SRA) section of NCBI. A total of 6,479,882 sequences obtained initially were reduced to 6,319,834 after pre-processing. The processed sequences were reduced to 79,608 sequences after de novo assembly and were finally assembled to 8547 contigs and 59,242 singlets. The contigs were then processed with various prediction pipelines to predict SSRs and SNPs. The tools, QualitySNP and AutoSNP were employed to detect the SNPs present within the contig sequences. The efficiency of these tools in determining the number of synonymous and non-synonymous SNPs was also analyzed. The tools, MISA and SSRIT were used to detect the SSRs within the sequences. The efficiency in predicting more number and types of reliable repeats were considered. The analysis was done with a wide range of repeats such as mono-, di-, tri-, tetra-, penta-, hexa-, and poly repeats and their numbers. QualitySNP identified 518 synonymous and 44 non-synonymous SNPs from the 8547 contigs. MISA identified 967 mono-, 1484 di-, 558 tri-, 14 tetra-, 2 penta-, 9 hexa-, and 393 compound SSRs. Five SNP and SSR primers were designed and synthesized from the contigs containing SSRs and SNPs. The synthesized SNP and SSR primers were then validated against tolerant and susceptible varieties of taro leaf blight. Among the primers synthesized the SSR primer CeSSR4 and SNP primer CeSNP3 were capable of differentiating leaf blight resistant and susceptible varieties. The markers need to be analyzed further with a large number of samples to develop them as a marker for taro leaf blight. Once analyzed, they could be used in marker-assisted selection and breeding programmes of taro.