Difference between revisions of "SSRPoly"

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(Created page with "== SSRPoly: an efficient tool for polymorphic Simple Sequence Repeat identification == The public availability of large quantities of gene sequence data enables mining for Simp...")
 
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The public availability of large quantities of gene sequence data enables mining for Simple Sequence Repeats (SSRs), molecular genetic markers which may be applied for genetic and genomic analysis. However, laboratory assessment of the SSRs is still required to assess their polymorphic status and consequently their applicability to genetic studies. We have developed the tool SSRPoly for the identification of polymorphic SSRs from EST (Expressed Sequence Tag) data, removing the requirement for this expensive and time consuming laboratory assessment. Polymorphic SSRs are distinguished from monomorphic SSRs by the representation of varying motif lengths within an alignment of sequence reads. Additional value is gained by integrating polymorphic SSR data with other relevant biological information such as predicted gene function and comparative map position. This tool can be applied for any species for which EST sequences are available. The SSRs identified may be used in applications such as genetic linkage analysis and trait mapping, diversity analysis, association studies, and marker assisted selection.  
 
The public availability of large quantities of gene sequence data enables mining for Simple Sequence Repeats (SSRs), molecular genetic markers which may be applied for genetic and genomic analysis. However, laboratory assessment of the SSRs is still required to assess their polymorphic status and consequently their applicability to genetic studies. We have developed the tool SSRPoly for the identification of polymorphic SSRs from EST (Expressed Sequence Tag) data, removing the requirement for this expensive and time consuming laboratory assessment. Polymorphic SSRs are distinguished from monomorphic SSRs by the representation of varying motif lengths within an alignment of sequence reads. Additional value is gained by integrating polymorphic SSR data with other relevant biological information such as predicted gene function and comparative map position. This tool can be applied for any species for which EST sequences are available. The SSRs identified may be used in applications such as genetic linkage analysis and trait mapping, diversity analysis, association studies, and marker assisted selection.  
  
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This software may be downloaded freely for academic use only. For commercial use, please contact David Edwards at Dave.Edwards@uq.edu.au to obtain a licence.
  
This software may be downloaded freely for academic use only. For commercial use, please contact David Edwards to obtain a licence.
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Revision as of 02:13, 3 July 2012

SSRPoly: an efficient tool for polymorphic Simple Sequence Repeat identification

The public availability of large quantities of gene sequence data enables mining for Simple Sequence Repeats (SSRs), molecular genetic markers which may be applied for genetic and genomic analysis. However, laboratory assessment of the SSRs is still required to assess their polymorphic status and consequently their applicability to genetic studies. We have developed the tool SSRPoly for the identification of polymorphic SSRs from EST (Expressed Sequence Tag) data, removing the requirement for this expensive and time consuming laboratory assessment. Polymorphic SSRs are distinguished from monomorphic SSRs by the representation of varying motif lengths within an alignment of sequence reads. Additional value is gained by integrating polymorphic SSR data with other relevant biological information such as predicted gene function and comparative map position. This tool can be applied for any species for which EST sequences are available. The SSRs identified may be used in applications such as genetic linkage analysis and trait mapping, diversity analysis, association studies, and marker assisted selection.

This software may be downloaded freely for academic use only. For commercial use, please contact David Edwards at Dave.Edwards@uq.edu.au to obtain a licence.

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