alexa paSNPg: A GBS-Based Pipeline for Protein-Associated SNP Discovery and Genotyping in Non-Model Species
ISSN: 0974-276X

Journal of Proteomics & Bioinformatics
Open Access

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Research Article

paSNPg: A GBS-Based Pipeline for Protein-Associated SNP Discovery and Genotyping in Non-Model Species

Yong-Bi Fu* and Yibo Dong

Plant Gene Resources of Canada, Saskatoon Research Centre, Agriculture and Agri-Food Canada, Saskatoon, Canada

*Corresponding Author:
Yong-Bi Fu
Plant Gene Resources of Canada
Saskatoon Research Centre
Agriculture and Agri-Food Canada
107 Science Place, Saskatoon
SK S7N 0X2, Canada
Tel: 1-306-385-9298
Fax: 1-306-385-9489
E-mail: [email protected]

Received date: July 02, 2015 Accepted date: July 27, 2015 Published date: August 01, 2015

Citation: Fu YB, Dong Y (2015) paSNPg: A GBS-Based Pipeline for Protein-Associated SNP Discovery and Genotyping in Non-Model Species. J Proteomics Bioinform 8: 190-194. doi: 10.4172/jpb.1000368

Copyright: © 2015 Fu YB, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 

Abstract

Genotyping-by-sequencing (GBS) has recently developed as a feasible genomic approach for exploring genome-wide genetic variation for population and evolutionary genomic analyses of non-model species. To facilitate the acquisition of function-associated genetic variation data in natural populations, we present a GBS-based pipeline called paSNPg for protein-associated SNP (paSNP) discovery and genotyping in non-model organisms. The pipeline was developed through the expansion of the published npGeno utility to assemble nuclear contigs from raw GBS sequence data, separate protein-associated contigs from all assembled contigs based on published PEP (or Predictions on Entire Proteomes) sequence data sets, and call paSNPs across assayed samples based on protein-associated contigs. Testing the pipeline with two GBS sequence data sets, Arabidopsis thaliana and Oryza sativa, revealed its potential use in exploring protein-associated genetic variation from genomic DNAs of non-model species.

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