five

cgaTOH: Extended Approach for Identifying Tracts of Homozygosity

收藏
Figshare2016-01-18 更新2026-04-29 收录
下载链接:
https://figshare.com/articles/dataset/cgaTOH_Extended_Approach_for_Identifying_Tracts_of_Homozygosity__/643749
下载链接
链接失效反馈
官方服务:
资源简介:
Identification of disease variants via homozygosity mapping and investigation of the effects of genome-wide homozygosity regions on traits of biomedical importance have been widely applied recently. Nonetheless, the existing methods and algorithms to identify long tracts of homozygosity (TOH) are not able to provide efficient and rigorous regions for further downstream association investigation. We expanded current methods to identify TOHs by defining “surrogate-TOH”, a region covering a cluster of TOHs with specific characteristics. Our defined surrogate-TOH includes cTOH, viz a common TOH region where at least ten TOHs present; gTOH, whereby a group of highly overlapping TOHs share proximal boundaries; and aTOH, which are allelically-matched TOHs. Searching for gTOH and aTOH was based on a repeated binary spectral clustering algorithm, where a hierarchy of clusters is created and represented by a TOH cluster tree. Based on the proposed method of identifying different species of surrogate-TOH, our cgaTOH software was developed. The software provides an intuitive and interactive visualization tool for better investigation of the high-throughput output with special interactive navigation rings, which will find its applicability in both conventional association studies and more sophisticated downstream analyses. NCBI genome map viewer is incorporated into the system. Moreover, we discuss the choice of implementing appropriate empirical ranges of critical parameters by applying to disease models. This method identifies various patterned clusters of SNPs demonstrating extended homozygosity, thus one can observe different aspects of the multi-faceted characteristics of TOHs.
创建时间:
2016-01-18
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作