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利用 GenAMap 软件系统中的自动化、并行化和可视化技术，增强结构化关联映射算法的可用性和性能。

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

机构信息

Joint Carnegie Mellon - University of Pittsburgh PhD Program in Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

出版信息

BMC Genet. 2012 Apr 3;13:24. doi: 10.1186/1471-2156-13-24.

DOI:10.1186/1471-2156-13-24
PMID:22471660
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3342145/
Abstract

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.

摘要

背景

结构关联映射被证明是一种强大的策略，可以找到与疾病相关的遗传多态性。然而，这些算法通常作为命令行实现分发，需要专业知识和精力来定制和实施。由于使用这些尖端技术所需的难度，遗传学家经常退而使用更简单、功能较弱的方法。

结果

为了使结构关联映射更容易被遗传学家使用，我们开发了一个名为 Auto-SAM 的自动处理系统。Auto-SAM 使遗传学家能够自动运行结构关联映射算法，同时进行并行化处理。Auto-SAM 包括发现基因网络和寻找群体结构的算法。Auto-SAM 不仅可以运行流行的关联映射算法，还可以运行五个结构关联映射算法。

结论

Auto-SAM 可通过 GenAMap 获得，这是一个前端桌面可视化工具。GenAMap 和 Auto-SAM 是用 JAVA 实现的；GenAMap 的二进制文件可从 http://sailing.cs.cmu.edu/genamap 下载。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c7e/3342145/bf63bfcc2568/1471-2156-13-24-3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c7e/3342145/a51e89ae6eaf/1471-2156-13-24-1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c7e/3342145/27be47f9228c/1471-2156-13-24-2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c7e/3342145/bf63bfcc2568/1471-2156-13-24-3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c7e/3342145/a51e89ae6eaf/1471-2156-13-24-1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c7e/3342145/27be47f9228c/1471-2156-13-24-2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c7e/3342145/bf63bfcc2568/1471-2156-13-24-3.jpg

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