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SNiPlay3: a web-based application for exploration and large scale analyses of genomic variations.

Dereeper A, Homa F, Andres G, Sempere G, Sarah G, Hueber Y, Dufayard JF, Ruiz M - Nucleic Acids Res. (2015)

Bottom Line: Version 3 now extends functionalities in order to easily manage and exploit SNPs derived from next generation sequencing technologies, such as GBS (genotyping by sequencing), WGRS (whole gre-sequencing) and RNA-Seq technologies.Based on the standard VCF (variant call format) format, the application offers an intuitive interface for filtering and comparing polymorphisms using user-defined sets of individuals and then establishing a reliable genotyping data matrix for further analyses.Additionally, we developed a suite of Galaxy wrappers for each step of the SNiPlay3 process, so that the complete pipeline can also be deployed on a Galaxy instance using the Galaxy ToolShed procedure and then be computed as a Galaxy workflow.

View Article: PubMed Central - PubMed

Affiliation: UMR Interaction Plante-Microorganismes et Environnement (IPME), Institut de Recherche pour le Développement (IRD), BP 64501, 34394 Montpellier Cedex 5, France alexis.dereeper@ird.fr.

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GWAS analyses in SNiPlay. (A) Data control: first step controls data concordance and outputs some statistics about genotypic (MAF distribution) and phenotypic (phenotypic values distribution) datasets. (B) QQ plot shows the expected distribution of association test statistics (X-axis) compared to the observed values (Y-axis). (C) Result interface displays an interactive Manhattan plot color-coded by chromosome that represents the association P-values between markers and the trait being measured. It supports zooming, which can be achieved by a ‘click, hold and drag mouse’ action on the region of interest.
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Figure 3: GWAS analyses in SNiPlay. (A) Data control: first step controls data concordance and outputs some statistics about genotypic (MAF distribution) and phenotypic (phenotypic values distribution) datasets. (B) QQ plot shows the expected distribution of association test statistics (X-axis) compared to the observed values (Y-axis). (C) Result interface displays an interactive Manhattan plot color-coded by chromosome that represents the association P-values between markers and the trait being measured. It supports zooming, which can be achieved by a ‘click, hold and drag mouse’ action on the region of interest.

Mentions: GWAS have proven a useful technique for identifying genetic loci involved in the control of agronomic traits. Using an efficient implementation of softwares devoted to GWAS, including TASSEL (20) (General Linear Model (GLM) or Mixed Linear Model (MLM) methods) or MLMM (Multi-Locus Mixed Model) R package (21), phenotypic values of traits can be uploaded and mapped with an accurate model in order to estimate causal variants. This module offers a user-friendly interface that includes interactive Manhattan plots of association P-values along the chromosomes, as well as the QQ-plots commonly used for GWAS to show the importance of correcting for population structure and kinship information (Figure 3).


SNiPlay3: a web-based application for exploration and large scale analyses of genomic variations.

Dereeper A, Homa F, Andres G, Sempere G, Sarah G, Hueber Y, Dufayard JF, Ruiz M - Nucleic Acids Res. (2015)

GWAS analyses in SNiPlay. (A) Data control: first step controls data concordance and outputs some statistics about genotypic (MAF distribution) and phenotypic (phenotypic values distribution) datasets. (B) QQ plot shows the expected distribution of association test statistics (X-axis) compared to the observed values (Y-axis). (C) Result interface displays an interactive Manhattan plot color-coded by chromosome that represents the association P-values between markers and the trait being measured. It supports zooming, which can be achieved by a ‘click, hold and drag mouse’ action on the region of interest.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4489301&req=5

Figure 3: GWAS analyses in SNiPlay. (A) Data control: first step controls data concordance and outputs some statistics about genotypic (MAF distribution) and phenotypic (phenotypic values distribution) datasets. (B) QQ plot shows the expected distribution of association test statistics (X-axis) compared to the observed values (Y-axis). (C) Result interface displays an interactive Manhattan plot color-coded by chromosome that represents the association P-values between markers and the trait being measured. It supports zooming, which can be achieved by a ‘click, hold and drag mouse’ action on the region of interest.
Mentions: GWAS have proven a useful technique for identifying genetic loci involved in the control of agronomic traits. Using an efficient implementation of softwares devoted to GWAS, including TASSEL (20) (General Linear Model (GLM) or Mixed Linear Model (MLM) methods) or MLMM (Multi-Locus Mixed Model) R package (21), phenotypic values of traits can be uploaded and mapped with an accurate model in order to estimate causal variants. This module offers a user-friendly interface that includes interactive Manhattan plots of association P-values along the chromosomes, as well as the QQ-plots commonly used for GWAS to show the importance of correcting for population structure and kinship information (Figure 3).

Bottom Line: Version 3 now extends functionalities in order to easily manage and exploit SNPs derived from next generation sequencing technologies, such as GBS (genotyping by sequencing), WGRS (whole gre-sequencing) and RNA-Seq technologies.Based on the standard VCF (variant call format) format, the application offers an intuitive interface for filtering and comparing polymorphisms using user-defined sets of individuals and then establishing a reliable genotyping data matrix for further analyses.Additionally, we developed a suite of Galaxy wrappers for each step of the SNiPlay3 process, so that the complete pipeline can also be deployed on a Galaxy instance using the Galaxy ToolShed procedure and then be computed as a Galaxy workflow.

View Article: PubMed Central - PubMed

Affiliation: UMR Interaction Plante-Microorganismes et Environnement (IPME), Institut de Recherche pour le Développement (IRD), BP 64501, 34394 Montpellier Cedex 5, France alexis.dereeper@ird.fr.

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