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BMC Bioinformatics. 2013 Feb 8;14:45. doi: 10.1186/1471-2105-14-45.

Snpdat: easy and rapid annotation of results from de novo snp discovery projects for model and non-model organisms.

Author information

  • 1Teagasc Animal and Bioscience Research Department, Animal & Grassland Research and Innovation Centre, Teagasc, Grange, Dunsany, Co, Meath, Ireland.

Abstract

BACKGROUND:

Single nucleotide polymorphisms (SNPs) are the most abundant genetic variant found in vertebrates and invertebrates. SNP discovery has become a highly automated, robust and relatively inexpensive process allowing the identification of many thousands of mutations for model and non-model organisms. Annotating large numbers of SNPs can be a difficult and complex process. Many tools available are optimised for use with organisms densely sampled for SNPs, such as humans. There are currently few tools available that are species non-specific or support non-model organism data.

RESULTS:

Here we present SNPdat, a high throughput analysis tool that can provide a comprehensive annotation of both novel and known SNPs for any organism with a draft sequence and annotation. Using a dataset of 4,566 SNPs identified in cattle using high-throughput DNA sequencing we demonstrate the annotations performed and the statistics that can be generated by SNPdat.

CONCLUSIONS:

SNPdat provides users with a simple tool for annotation of genomes that are either not supported by other tools or have a small number of annotated SNPs available. SNPdat can also be used to analyse datasets from organisms which are densely sampled for SNPs. As a command line tool it can easily be incorporated into existing SNP discovery pipelines and fills a niche for analyses involving non-model organisms that are not supported by many available SNP annotation tools. SNPdat will be of great interest to scientists involved in SNP discovery and analysis projects, particularly those with limited bioinformatics experience.

PMID:
23390980
[PubMed - indexed for MEDLINE]
PMCID:
PMC3574845
Free PMC Article
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