Please use this identifier to cite or link to this item:
Title: SNPpy - Database management for SNP data from genome wide association studies
Authors: Owzar, Kouros 
Borisov, Nedyalko
Jiang, Chen 
Mitha, Faheem 
Herodotou, Herodotos 
Yoder, Josh 
Major Field of Science: Engineering and Technology
Field Category: Electrical Engineering - Electronic Engineering - Information Engineering
Keywords: Databases, Genetic;Genome-Wide Association Study;Humans;Polymorphism, Single Nucleotide;Software
Issue Date: 25-Oct-2011
Source: PLoS ONE, 2011, vol. 6, no. 10
Volume: 6
Issue: 10
Journal: PLoS ONE
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. © 2011 Mitha et al.
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0024982
Rights: © PLOS
Type: Article
Affiliation : Duke University 
Appears in Collections:Άρθρα/Articles

CORE Recommender
Show full item record


checked on Jun 7, 2021


Last Week
Last month
checked on Apr 22, 2021

Page view(s)

Last Week
Last month
checked on Jun 13, 2021

Google ScholarTM



Items in KTISIS are protected by copyright, with all rights reserved, unless otherwise indicated.