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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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PED file merged write timings.Timing results for writing the PED file corresponding to the merger of the 2000 patient Illumina simulated dataset with the corresponding HapMap datasets compared to timings for writing the PED file for each of the 2,000 patient simulated dataset and the Hapmap dataset. All these timings are for the Geno Shard layout. For all these datasets, the number of SNPs is 620,901. All timings correspond to warm cache.
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pone-0024982-g006: PED file merged write timings.Timing results for writing the PED file corresponding to the merger of the 2000 patient Illumina simulated dataset with the corresponding HapMap datasets compared to timings for writing the PED file for each of the 2,000 patient simulated dataset and the Hapmap dataset. All these timings are for the Geno Shard layout. For all these datasets, the number of SNPs is 620,901. All timings correspond to warm cache.

Mentions: Import and export (into PED format) timings for simulated Illumina datasets are shown in Figures 4 and 5. All timings correspond to warm cache (the query was run before timings were taken, so the data is already in memory). Timing results for merging simulated Illumina datasets with the corresponding HapMap datasets are shown in Figure 6.


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)

PED file merged write timings.Timing results for writing the PED file corresponding to the merger of the 2000 patient Illumina simulated dataset with the corresponding HapMap datasets compared to timings for writing the PED file for each of the 2,000 patient simulated dataset and the Hapmap dataset. All these timings are for the Geno Shard layout. For all these datasets, the number of SNPs is 620,901. All timings correspond to warm cache.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0024982-g006: PED file merged write timings.Timing results for writing the PED file corresponding to the merger of the 2000 patient Illumina simulated dataset with the corresponding HapMap datasets compared to timings for writing the PED file for each of the 2,000 patient simulated dataset and the Hapmap dataset. All these timings are for the Geno Shard layout. For all these datasets, the number of SNPs is 620,901. All timings correspond to warm cache.
Mentions: Import and export (into PED format) timings for simulated Illumina datasets are shown in Figures 4 and 5. All timings correspond to warm cache (the query was run before timings were taken, so the data is already in memory). Timing results for merging simulated Illumina datasets with the corresponding HapMap datasets are shown in Figure 6.

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