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SNiPlay: a web-based tool for detection, management and analysis of SNPs. Application to grapevine diversity projects.

Dereeper A, Nicolas S, Le Cunff L, Bacilieri R, Doligez A, Peros JP, Ruiz M, This P - BMC Bioinformatics (2011)

Bottom Line: It allows the user to retrieve SNPs using various filters (such as genomic position, missing data, polymorphism type, allele frequency), to compare SNP patterns between populations, and to export genotyping data or sequences in various formats.Our experiments on grapevine genetic projects showed that SNiPlay allows geneticists to rapidly obtain advanced results in several key research areas of plant genetic diversity.Both the management and treatment of large amounts of SNP data are rendered considerably easier for end-users through automation and integration.

View Article: PubMed Central - HTML - PubMed

Affiliation: Diversity, Genetics and Genomics of grapevine, UMR DIAPC, INRA, Montpellier, France. alexis.dereeper@ird.fr

ABSTRACT

Background: High-throughput re-sequencing, new genotyping technologies and the availability of reference genomes allow the extensive characterization of Single Nucleotide Polymorphisms (SNPs) and insertion/deletion events (indels) in many plant species. The rapidly increasing amount of re-sequencing and genotyping data generated by large-scale genetic diversity projects requires the development of integrated bioinformatics tools able to efficiently manage, analyze, and combine these genetic data with genome structure and external data.

Results: In this context, we developed SNiPlay, a flexible, user-friendly and integrative web-based tool dedicated to polymorphism discovery and analysis. It integrates:1) a pipeline, freely accessible through the internet, combining existing softwares with new tools to detect SNPs and to compute different types of statistical indices and graphical layouts for SNP data. From standard sequence alignments, genotyping data or Sanger sequencing traces given as input, SNiPlay detects SNPs and indels events and outputs submission files for the design of Illumina's SNP chips. Subsequently, it sends sequences and genotyping data into a series of modules in charge of various processes: physical mapping to a reference genome, annotation (genomic position, intron/exon location, synonymous/non-synonymous substitutions), SNP frequency determination in user-defined groups, haplotype reconstruction and network, linkage disequilibrium evaluation, and diversity analysis (Pi, Watterson's Theta, Tajima's D).Furthermore, the pipeline allows the use of external data (such as phenotype, geographic origin, taxa, stratification) to define groups and compare statistical indices.2) a database storing polymorphisms, genotyping data and grapevine sequences released by public and private projects. It allows the user to retrieve SNPs using various filters (such as genomic position, missing data, polymorphism type, allele frequency), to compare SNP patterns between populations, and to export genotyping data or sequences in various formats.

Conclusions: Our experiments on grapevine genetic projects showed that SNiPlay allows geneticists to rapidly obtain advanced results in several key research areas of plant genetic diversity. Both the management and treatment of large amounts of SNP data are rendered considerably easier for end-users through automation and integration. Current developments are taking into account new advances in high-throughput technologies.SNiPlay is available at: http://sniplay.cirad.fr/.

Show MeSH
Web interface for SNP queries. The system is able to identify SNPs in a user-defined subset of genotypes (A) considering a set of selected genes (B). It can merge allelic data from different origins and different experiments and report polymorphic positions in different colors (C): green if no difference appeared when merging data, yellow if some differences were detected and white if no merging is needed.
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Figure 3: Web interface for SNP queries. The system is able to identify SNPs in a user-defined subset of genotypes (A) considering a set of selected genes (B). It can merge allelic data from different origins and different experiments and report polymorphic positions in different colors (C): green if no difference appeared when merging data, yellow if some differences were detected and white if no merging is needed.

Mentions: After having selected a project, the user has to opt for genotypes on the left side and genomic regions or genes on the right side and adjust the filters; the system then dynamically extracts SNPs for this selection and calculates corresponding frequencies and consensus sequences (Figure 3A). If the subset of genotypes corresponds to the sample initially submitted to the database, pre-calculated and stored SNPs are promptly reported, otherwise they are re-calculated for the selected set of genotypes.


SNiPlay: a web-based tool for detection, management and analysis of SNPs. Application to grapevine diversity projects.

Dereeper A, Nicolas S, Le Cunff L, Bacilieri R, Doligez A, Peros JP, Ruiz M, This P - BMC Bioinformatics (2011)

Web interface for SNP queries. The system is able to identify SNPs in a user-defined subset of genotypes (A) considering a set of selected genes (B). It can merge allelic data from different origins and different experiments and report polymorphic positions in different colors (C): green if no difference appeared when merging data, yellow if some differences were detected and white if no merging is needed.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3102043&req=5

Figure 3: Web interface for SNP queries. The system is able to identify SNPs in a user-defined subset of genotypes (A) considering a set of selected genes (B). It can merge allelic data from different origins and different experiments and report polymorphic positions in different colors (C): green if no difference appeared when merging data, yellow if some differences were detected and white if no merging is needed.
Mentions: After having selected a project, the user has to opt for genotypes on the left side and genomic regions or genes on the right side and adjust the filters; the system then dynamically extracts SNPs for this selection and calculates corresponding frequencies and consensus sequences (Figure 3A). If the subset of genotypes corresponds to the sample initially submitted to the database, pre-calculated and stored SNPs are promptly reported, otherwise they are re-calculated for the selected set of genotypes.

Bottom Line: It allows the user to retrieve SNPs using various filters (such as genomic position, missing data, polymorphism type, allele frequency), to compare SNP patterns between populations, and to export genotyping data or sequences in various formats.Our experiments on grapevine genetic projects showed that SNiPlay allows geneticists to rapidly obtain advanced results in several key research areas of plant genetic diversity.Both the management and treatment of large amounts of SNP data are rendered considerably easier for end-users through automation and integration.

View Article: PubMed Central - HTML - PubMed

Affiliation: Diversity, Genetics and Genomics of grapevine, UMR DIAPC, INRA, Montpellier, France. alexis.dereeper@ird.fr

ABSTRACT

Background: High-throughput re-sequencing, new genotyping technologies and the availability of reference genomes allow the extensive characterization of Single Nucleotide Polymorphisms (SNPs) and insertion/deletion events (indels) in many plant species. The rapidly increasing amount of re-sequencing and genotyping data generated by large-scale genetic diversity projects requires the development of integrated bioinformatics tools able to efficiently manage, analyze, and combine these genetic data with genome structure and external data.

Results: In this context, we developed SNiPlay, a flexible, user-friendly and integrative web-based tool dedicated to polymorphism discovery and analysis. It integrates:1) a pipeline, freely accessible through the internet, combining existing softwares with new tools to detect SNPs and to compute different types of statistical indices and graphical layouts for SNP data. From standard sequence alignments, genotyping data or Sanger sequencing traces given as input, SNiPlay detects SNPs and indels events and outputs submission files for the design of Illumina's SNP chips. Subsequently, it sends sequences and genotyping data into a series of modules in charge of various processes: physical mapping to a reference genome, annotation (genomic position, intron/exon location, synonymous/non-synonymous substitutions), SNP frequency determination in user-defined groups, haplotype reconstruction and network, linkage disequilibrium evaluation, and diversity analysis (Pi, Watterson's Theta, Tajima's D).Furthermore, the pipeline allows the use of external data (such as phenotype, geographic origin, taxa, stratification) to define groups and compare statistical indices.2) a database storing polymorphisms, genotyping data and grapevine sequences released by public and private projects. It allows the user to retrieve SNPs using various filters (such as genomic position, missing data, polymorphism type, allele frequency), to compare SNP patterns between populations, and to export genotyping data or sequences in various formats.

Conclusions: Our experiments on grapevine genetic projects showed that SNiPlay allows geneticists to rapidly obtain advanced results in several key research areas of plant genetic diversity. Both the management and treatment of large amounts of SNP data are rendered considerably easier for end-users through automation and integration. Current developments are taking into account new advances in high-throughput technologies.SNiPlay is available at: http://sniplay.cirad.fr/.

Show MeSH