GeSICA: genome segmentation from intra-chromosomal associations.
GeSICA: genome segmentation from intra-chromosomal associations.
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GeSICA：染色体内关联的基因组分割
DOI：
10.1186/1471-2164-13-164
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发表时间：
2012-05-04
期刊：
影响因子：
4.4
通讯作者：
Zhang Y
中科院分区：
文献类型：
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作者：
Liu L;Zhang Y;Feng J;Zheng N;Yin J;Zhang Y
背景基因组组织的各个方面已经基于来自不同技术的数据进行了探索，包括组蛋白修饰ChIP-Seq，3C及其衍生物。最近开发的Hi-C技术，使全基因组范围内的映射的DNA interactomes，从而提供了机会，研究基因组组织的细节，但这些方法也带来了挑战，在methodology development.ResultsWe开发的基因组分割从染色体内协会，或GeSICA，探索基因组组织和Hi-C数据在人类GM 06990和K562细胞的方法。GeSICA计算一个简单的对数比率，以有效地将人类基因组分为两个不同的区域，分别对应于丰富和贫穷的功能元件状态。在丰富的区域内，马尔可夫聚类随后被应用于将区域分离成更详细的聚类。的绝缘体，凝聚力和转录复合物的结合位点富集在相邻的集群之间的边界，表明推断的集群可能具有优良的组织features.ConclusionsOur研究提出了一种新的分析方法，被称为GeSICA，它提供了深入了解基因组组织的基础上Hi-C数据。GeSICA是开源的，可在以下网站免费获得： http://web.tongji.edu.cn/~zhanglab/GeSICA/
BackgroundVarious aspects of genome organization have been explored based on data from distinct technologies, including histone modification ChIP-Seq, 3C, and its derivatives. Recently developed Hi-C techniques enable the genome wide mapping of DNA interactomes, thereby providing the opportunity to study genome organization in detail, but these methods also pose challenges in methodology development.ResultsWe developed Genome Segmentation from Intra Chromosomal Associations, or GeSICA, to explore genome organization and applied the method to Hi-C data in human GM06990 and K562 cells. GeSICA calculates a simple logged ratio to efficiently segment the human genome into regions with two distinct states that correspond to rich and poor functional element states. Inside the rich regions, Markov Clustering was subsequently applied to segregate the regions into more detailed clusters. The binding sites of the insulator, cohesion, and transcription complexes are enriched in the boundaries between neighboring clusters, indicating that inferred clusters may have fine organizational features.ConclusionsOur study presents a novel analysis method, known as GeSICA, which gives insight into genome organization based on Hi-C data. GeSICA is open source and freely available at: http://web.tongji.edu.cn/~zhanglab/GeSICA/
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影响因子：
64.8
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通讯作者：
Ren, Bing
影响因子：
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作者：
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通讯作者：
Fakan, Stanislav
影响因子：
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作者：
Cuddapah, Suresh;Jothi, Raja;Zhao, Keji
通讯作者：
Zhao, Keji
影响因子：
64.8
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