Date of Original Version

1-1-2012

Type

Article

PubMed ID

22471660

Rights Management

© 2012 Curtis et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract or Description

BACKGROUND: Structured association mapping is proving to be a powerful strategy to find genetic polymorphisms associated with disease. However, these algorithms are often distributed as command line implementations that require expertise and effort to customize and put into practice. Because of the difficulty required to use these cutting-edge techniques, geneticists often revert to simpler, less powerful methods.

RESULTS: To make structured association mapping more accessible to geneticists, we have developed an automatic processing system called Auto-SAM. Auto-SAM enables geneticists to run structured association mapping algorithms automatically, using parallelization. Auto-SAM includes algorithms to discover gene-networks and find population structure. Auto-SAM can also run popular association mapping algorithms, in addition to five structured association mapping algorithms.

CONCLUSIONS: Auto-SAM is available through GenAMap, a front-end desktop visualization tool. GenAMap and Auto-SAM are implemented in JAVA; binaries for GenAMap can be downloaded from http://sailing.cs.cmu.edu/genamap.

DOI

10.1186/1471-2156-13-24

Creative Commons


This work is licensed under a Creative Commons Attribution 3.0 License.

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Published In

BMC genetics, 13, 24-24.