Carnegie Mellon University
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Enhancing the usability and performance of structured association mapping algorithms using automation, parallelization, and visualization in the GenAMap software system.

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journal contribution
posted on 2012-01-01, 00:00 authored by Ross E. Curtis, Anuj Goyal, Eric P Xing

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


Publisher Statement

© 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.



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