| 000 -LEADER |
|---|
| fixed length control field |
02714nam a22001697a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
660.6 |
| Item number |
SAH/PR |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Sahla, K |
| 245 ## - TITLE STATEMENT |
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| Title |
Prediction of SSR and SNP markers for anthracnose resiistance in YAM using bioinformatics tools and their validation |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
Vellayani |
| Name of publisher, distributor, etc |
Department of Plant Biotechnology |
| -- |
College of Agriculture |
| Date of publication, distribution, etc |
2018 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
79p. |
| 502 ## - DISSERTATION NOTE |
|---|
| Dissertation note |
BSc-MSc (Integrated) |
| 520 3# - SUMMARY, ETC. |
|---|
| Abstract |
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. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Biotechnology |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Sreekumar, J (Guide) |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
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| Uniform Resource Identifier |
https://krishikosh.egranth.ac.in/handle/1/5810149335 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Item type |
Theses |
