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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/.

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Example of comparative diversity maps. (A) A comparison of Tajima's D values over chromosomes 17 and 18 between cultivated and wild grapevine. An asterisk indicates genes for which the difference in Tajima's D value between groups is significant. This map comparison is possible only between the first 2 groups.(B) A genomic region near a "berry size" QTL displays differential D values, which are being further investigated to test for potential association with the wild-cultivated berry size differential.
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Figure 8: Example of comparative diversity maps. (A) A comparison of Tajima's D values over chromosomes 17 and 18 between cultivated and wild grapevine. An asterisk indicates genes for which the difference in Tajima's D value between groups is significant. This map comparison is possible only between the first 2 groups.(B) A genomic region near a "berry size" QTL displays differential D values, which are being further investigated to test for potential association with the wild-cultivated berry size differential.

Mentions: • A comparative diversity map can be generated to show a colour distribution of Tajima's D values along chromosomes for each group (Figure 8). SNiPlay makes it possible to compare different taxa to search for discriminant markers that could facilitate genotype classification into taxa (species, cultivated/wild) and reveal evolutionary patterns.


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)

Example of comparative diversity maps. (A) A comparison of Tajima's D values over chromosomes 17 and 18 between cultivated and wild grapevine. An asterisk indicates genes for which the difference in Tajima's D value between groups is significant. This map comparison is possible only between the first 2 groups.(B) A genomic region near a "berry size" QTL displays differential D values, which are being further investigated to test for potential association with the wild-cultivated berry size differential.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 8: Example of comparative diversity maps. (A) A comparison of Tajima's D values over chromosomes 17 and 18 between cultivated and wild grapevine. An asterisk indicates genes for which the difference in Tajima's D value between groups is significant. This map comparison is possible only between the first 2 groups.(B) A genomic region near a "berry size" QTL displays differential D values, which are being further investigated to test for potential association with the wild-cultivated berry size differential.
Mentions: • A comparative diversity map can be generated to show a colour distribution of Tajima's D values along chromosomes for each group (Figure 8). SNiPlay makes it possible to compare different taxa to search for discriminant markers that could facilitate genotype classification into taxa (species, cultivated/wild) and reveal evolutionary patterns.

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