Molecular marker development for cassava mosaic disease resistance using bioinformatics tools
By: Ambu Vijayan.
Contributor(s): Sreekumar J(Guide).
Material type:
BookPublisher: Vellayani Department of Plant Biotechnology 2015Description: 83 Pages.Subject(s): BiotechnologyDDC classification: 660.6 Online resources: Click here to access online Dissertation note: MSc Abstract: The study entitled “Molecular marker development for cassava mosaic disease
resistance using bioinformatics tools” was conducted at ICAR-CTCRI,
Sreekariyam, Thiruvananthapuram during October 2104 to October 2015. The
objectives of the study included development and evaluation of various SNP and
SSR prediction pipelines, computational prediction and characterization of SNP
and SSR in cassava, verification of SNP and SSR markers for cassava mosaic
disease (CMD) resistant and susceptible breeding lines. The preliminary data set
for the identification of SSR and SNP markers was obtained from the EST section
of NCBI and the cassava transcript sequences from the Phytozome. A total of
120461 sequences was classified into 20 cultivars. The dataset was reduced to
14336 sequences after several pre-processing and screening steps. The resulting
sequences were assembled and aligned using CAP3 and 2088 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.
The SSR prediction tools such as MISA and SSRIT was compared for their
performance. 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, thirty nonsynonymous SNPs and twenty-six synonymous SNPs
were identified. Using MISA, n 217 mono SSRs, 132 di SSRs, 139 tri SSRs, 3 tetra
SSRs, 1 penta SSRs, 3 hexa SSRs and 42 complex SSRs were identified. Five
sequences from identified SNPs and SSRs which have high hit percentage were
selected for validation and primer designing for CMD resistant genes. These
primers were validated using 5 resistant and 5 susceptible cassava varieties. Among
the 10 primers after validation in wet lab, one SNP (SNP896) and one SSR (SSR
2063) 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 CMD resistance in cassava.
| Item type | Current location | Collection | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|---|
Theses
|
KAU Central Library, Thrissur Theses | Reference Book | 660.6 AMB/MO (Browse shelf) | Not For Loan | 173722 |
Browsing KAU Central Library, Thrissur Shelves , Shelving location: Theses , Collection code: Reference Book Close shelf browser
MSc
The study entitled “Molecular marker development for cassava mosaic disease
resistance using bioinformatics tools” was conducted at ICAR-CTCRI,
Sreekariyam, Thiruvananthapuram during October 2104 to October 2015. The
objectives of the study included development and evaluation of various SNP and
SSR prediction pipelines, computational prediction and characterization of SNP
and SSR in cassava, verification of SNP and SSR markers for cassava mosaic
disease (CMD) resistant and susceptible breeding lines. The preliminary data set
for the identification of SSR and SNP markers was obtained from the EST section
of NCBI and the cassava transcript sequences from the Phytozome. A total of
120461 sequences was classified into 20 cultivars. The dataset was reduced to
14336 sequences after several pre-processing and screening steps. The resulting
sequences were assembled and aligned using CAP3 and 2088 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.
The SSR prediction tools such as MISA and SSRIT was compared for their
performance. 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, thirty nonsynonymous SNPs and twenty-six synonymous SNPs
were identified. Using MISA, n 217 mono SSRs, 132 di SSRs, 139 tri SSRs, 3 tetra
SSRs, 1 penta SSRs, 3 hexa SSRs and 42 complex SSRs were identified. Five
sequences from identified SNPs and SSRs which have high hit percentage were
selected for validation and primer designing for CMD resistant genes. These
primers were validated using 5 resistant and 5 susceptible cassava varieties. Among
the 10 primers after validation in wet lab, one SNP (SNP896) and one SSR (SSR
2063) 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 CMD resistance in cassava.
There are no comments for this item.