Predicting polymorphic EST-SSRs in silico

Chris Duran, Richa Singhania, Harsh Raman, Jacqueline Batley, David Edwards

Research output: Contribution to journalArticle

19 Citations (Scopus)

Abstract

The public availability of large quantities of gene sequence data provides a valuable resource of the mining of Simple Sequence Repeat (SSR) molecular genetic markers for genetic analysis. These markers are inexpensive, require minimal labour to produce and can frequently be associated with functionally annotated genes. This study presents the characterization of barley EST-SSRs and the identification of putative polymorphic SSRs from EST data. Polymorphic SSRs are distinguished from monomorphic SSRs by the representation of varying motif lengths within an alignment of sequence reads. Two measures of confidence are calculated, redundancy of a polymorphism and co-segregation with accessions. The utility of this method is demonstrated through the discovery of 597 candidate polymorphic SSRs, from a total of 452 642 consensus expressed sequences. PCR amplification primers were designed for the identified SSRs. Ten primer pairs were validated for polymorphism in barley and for transferability across species. Analysis of the polymorphisms in relation to SSR motif, length, position and annotation is discussed.
Original languageEnglish
Pages (from-to)538-545
Number of pages8
JournalMolecular Ecology Resources
Volume13
Issue number3
DOIs
Publication statusPublished - May 2013

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Expressed Sequence Tags
Hordeum
Computer Simulation
Microsatellite Repeats
polymorphism
genetic polymorphism
barley
Sequence Alignment
Consensus Sequence
Genetic Markers
microsatellite repeats
Genes
Molecular Biology
gene
genetic marker
genetic analysis
sequence alignment
molecular genetics
Polymerase Chain Reaction
genetic techniques and protocols

Cite this

Duran, C., Singhania, R., Raman, H., Batley, J., & Edwards, D. (2013). Predicting polymorphic EST-SSRs in silico. Molecular Ecology Resources, 13(3), 538-545. https://doi.org/10.1111/1755-0998.12078
Duran, Chris ; Singhania, Richa ; Raman, Harsh ; Batley, Jacqueline ; Edwards, David. / Predicting polymorphic EST-SSRs in silico. In: Molecular Ecology Resources. 2013 ; Vol. 13, No. 3. pp. 538-545.
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Duran, C, Singhania, R, Raman, H, Batley, J & Edwards, D 2013, 'Predicting polymorphic EST-SSRs in silico', Molecular Ecology Resources, vol. 13, no. 3, pp. 538-545. https://doi.org/10.1111/1755-0998.12078

Predicting polymorphic EST-SSRs in silico. / Duran, Chris; Singhania, Richa; Raman, Harsh; Batley, Jacqueline; Edwards, David.

In: Molecular Ecology Resources, Vol. 13, No. 3, 05.2013, p. 538-545.

Research output: Contribution to journalArticle

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AU - Duran, Chris

AU - Singhania, Richa

AU - Raman, Harsh

AU - Batley, Jacqueline

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AB - The public availability of large quantities of gene sequence data provides a valuable resource of the mining of Simple Sequence Repeat (SSR) molecular genetic markers for genetic analysis. These markers are inexpensive, require minimal labour to produce and can frequently be associated with functionally annotated genes. This study presents the characterization of barley EST-SSRs and the identification of putative polymorphic SSRs from EST data. Polymorphic SSRs are distinguished from monomorphic SSRs by the representation of varying motif lengths within an alignment of sequence reads. Two measures of confidence are calculated, redundancy of a polymorphism and co-segregation with accessions. The utility of this method is demonstrated through the discovery of 597 candidate polymorphic SSRs, from a total of 452 642 consensus expressed sequences. PCR amplification primers were designed for the identified SSRs. Ten primer pairs were validated for polymorphism in barley and for transferability across species. Analysis of the polymorphisms in relation to SSR motif, length, position and annotation is discussed.

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Duran C, Singhania R, Raman H, Batley J, Edwards D. Predicting polymorphic EST-SSRs in silico. Molecular Ecology Resources. 2013 May;13(3):538-545. https://doi.org/10.1111/1755-0998.12078