Exploring the cooccurrence patterns of multiple sets of genomic intervals.
Exploring the cooccurrence patterns of multiple sets of genomic intervals.
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DOI：
10.1155/2013/617545
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发表时间：
2013
影响因子：
--
通讯作者：
Qin ZS
中科院分区：
文献类型：
--
作者：
Wu H;Qin ZS
背景探索不同基因组特征的空间关系自基因组研究的早期以来一直受到极大的关注。这种关系有时为理解某些生物过程提供了有用的信息。ChIP-seq等高通量技术的最新进展以基因组间隔的形式产生了大量数据。大多数用于评估间隔之间的空间关系的现有方法是为成对比较而设计的，并且不能容易地按比例扩大。结果我们提出了一种统计方法和软件工具来表征多组基因组间隔的共现模式。基因组间隔的发生是由一个简单的有限混合模型，其中每个组件代表一个独特的共现模式。通过EM算法估计的模型参数，可以被看作是足够的统计同现模式。仿真和真实的数据结果表明，该模型可以准确地捕捉模式，并提供生物学意义的结果。该方法在免费提供的R包giClust中实现。结论.该方法和软件为生物学家探索相对大量的基因组间隔集之间的共现模式提供了一种方便的途径。
Background. Exploring the spatial relationship of different genomic features has been of great interest since the early days of genomic research. The relationship sometimes provides useful information for understanding certain biological processes. Recent advances in high-throughput technologies such as ChIP-seq produce large amount of data in the form of genomic intervals. Most of the existing methods for assessing spatial relationships among the intervals are designed for pairwise comparison and cannot be easily scaled up. Results. We present a statistical method and software tool to characterize the cooccurrence patterns of multiple sets of genomic intervals. The occurrences of genomic intervals are described by a simple finite mixture model, where each component represents a distinct cooccurrence pattern. The model parameters are estimated via an EM algorithm and can be viewed as sufficient statistics of the cooccurrence patterns. Simulation and real data results show that the model can accurately capture the patterns and provide biologically meaningful results. The method is implemented in a freely available R package giClust. Conclusions. The method and the software provide a convenient way for biologists to explore the cooccurrence patterns among a relatively large number of sets of genomic intervals.
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影响因子：
12.3
作者：
Zeng X;Sanalkumar R;Bresnick EH;Li H;Chang Q;Keleş S
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Keleş S
影响因子：
14.9
作者：
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影响因子：
5.8
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Qin, Zhaohui S.
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Qin, Zhaohui S.
DOI：
10.1111/j.2517-6161.1977.tb01600.x
发表时间：
1977-01-01
期刊：
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-METHODOLOGICAL
影响因子：
--
作者：
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RUBIN, DB
影响因子：
30.8
作者：
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Feinberg, Andrew P.