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SNPpy--database management for SNP data from genome wide association studies.

Mitha F, Herodotou H, Borisov N, Jiang C, Yoder J, Owzar K - PLoS ONE (2011)

Bottom Line: To this end, SNPpy enables the user to filter the data and output the results as standardized GWAS file formats.It does low level and flexible data validation, including validation of patient data.SNPpy is a practical and extensible solution for investigators who seek to deploy central management of their GWAS data.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, United States of America. faheem@faheem.info

ABSTRACT

Background: We describe SNPpy, a hybrid script database system using the Python SQLAlchemy library coupled with the PostgreSQL database to manage genotype data from Genome-Wide Association Studies (GWAS). This system makes it possible to merge study data with HapMap data and merge across studies for meta-analyses, including data filtering based on the values of phenotype and Single-Nucleotide Polymorphism (SNP) data. SNPpy and its dependencies are open source software.

Results: The current version of SNPpy offers utility functions to import genotype and annotation data from two commercial platforms. We use these to import data from two GWAS studies and the HapMap Project. We then export these individual datasets to standard data format files that can be imported into statistical software for downstream analyses.

Conclusions: By leveraging the power of relational databases, SNPpy offers integrated management and manipulation of genotype and phenotype data from GWAS studies. The analysis of these studies requires merging across GWAS datasets as well as patient and marker selection. To this end, SNPpy enables the user to filter the data and output the results as standardized GWAS file formats. It does low level and flexible data validation, including validation of patient data. SNPpy is a practical and extensible solution for investigators who seek to deploy central management of their GWAS data.

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Database Layout.Datasets for different platforms are stored in separate databases, here represented by cylinders. Every dataset is stored in a separate database schema (namespace within a database). The same dataset can be stored in multiple schemas, differing in what options have been selected when loading the dataset. To illustrate this, the figure shows the schemas in red and the datasets in black. Each of the datasets HapMap 6 and CEU HapMap 610 is stored in two schemas. For further details see the manual.
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pone-0024982-g003: Database Layout.Datasets for different platforms are stored in separate databases, here represented by cylinders. Every dataset is stored in a separate database schema (namespace within a database). The same dataset can be stored in multiple schemas, differing in what options have been selected when loading the dataset. To illustrate this, the figure shows the schemas in red and the datasets in black. Each of the datasets HapMap 6 and CEU HapMap 610 is stored in two schemas. For further details see the manual.

Mentions: We currently support two genotyping platforms, Affymetrix and Illumina. We use both the database layouts described above for each platform. We use one database for each platform. Within each database, each schema corresponds to a dataset (PostgreSQL's namespace within a database). Figure 3 provides a graphical representation of the data architecture.


SNPpy--database management for SNP data from genome wide association studies.

Mitha F, Herodotou H, Borisov N, Jiang C, Yoder J, Owzar K - PLoS ONE (2011)

Database Layout.Datasets for different platforms are stored in separate databases, here represented by cylinders. Every dataset is stored in a separate database schema (namespace within a database). The same dataset can be stored in multiple schemas, differing in what options have been selected when loading the dataset. To illustrate this, the figure shows the schemas in red and the datasets in black. Each of the datasets HapMap 6 and CEU HapMap 610 is stored in two schemas. For further details see the manual.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3198468&req=5

pone-0024982-g003: Database Layout.Datasets for different platforms are stored in separate databases, here represented by cylinders. Every dataset is stored in a separate database schema (namespace within a database). The same dataset can be stored in multiple schemas, differing in what options have been selected when loading the dataset. To illustrate this, the figure shows the schemas in red and the datasets in black. Each of the datasets HapMap 6 and CEU HapMap 610 is stored in two schemas. For further details see the manual.
Mentions: We currently support two genotyping platforms, Affymetrix and Illumina. We use both the database layouts described above for each platform. We use one database for each platform. Within each database, each schema corresponds to a dataset (PostgreSQL's namespace within a database). Figure 3 provides a graphical representation of the data architecture.

Bottom Line: To this end, SNPpy enables the user to filter the data and output the results as standardized GWAS file formats.It does low level and flexible data validation, including validation of patient data.SNPpy is a practical and extensible solution for investigators who seek to deploy central management of their GWAS data.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, United States of America. faheem@faheem.info

ABSTRACT

Background: We describe SNPpy, a hybrid script database system using the Python SQLAlchemy library coupled with the PostgreSQL database to manage genotype data from Genome-Wide Association Studies (GWAS). This system makes it possible to merge study data with HapMap data and merge across studies for meta-analyses, including data filtering based on the values of phenotype and Single-Nucleotide Polymorphism (SNP) data. SNPpy and its dependencies are open source software.

Results: The current version of SNPpy offers utility functions to import genotype and annotation data from two commercial platforms. We use these to import data from two GWAS studies and the HapMap Project. We then export these individual datasets to standard data format files that can be imported into statistical software for downstream analyses.

Conclusions: By leveraging the power of relational databases, SNPpy offers integrated management and manipulation of genotype and phenotype data from GWAS studies. The analysis of these studies requires merging across GWAS datasets as well as patient and marker selection. To this end, SNPpy enables the user to filter the data and output the results as standardized GWAS file formats. It does low level and flexible data validation, including validation of patient data. SNPpy is a practical and extensible solution for investigators who seek to deploy central management of their GWAS data.

Show MeSH