Unsupervised segmentation of continuous genomic data
Unsupervised segmentation of continuous genomic data
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DOI：
10.1093/bioinformatics/btm096
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发表时间：
2007-06-01
期刊：
影响因子：
5.8
通讯作者：
Noble, William S.
中科院分区：
文献类型：
--
作者：
Day, Nathan;Hemmaplardh, Andrew;Noble, William S.
高密度，大量基因组数据的出现使工具需要在多个尺度上汇总大型数据集。 HMMSEG是使用隐藏的Markov模型（HMM）对连续基因组数据进行比例特定分割的命令行实用程序。比例特异性是通过基于可选小波的平滑操作实现的。 HMMSEG能够同时处理多个数据集，使其非常适合对表达，系统发育和功能基因组数据的整合分析。
The advent of high-density, high-volume genomic data has created the need for tools to summarize large datasets at multiple scales. HMMSeg is a command-line utility for the scale-specific segmentation of continuous genomic data using hidden Markov models (HMMs). Scale specificity is achieved by an optional wavelet-based smoothing operation. HMMSeg is capable of handling multiple datasets simultaneously, rendering it ideal for integrative analysis of expression, phylogenetic and functional genomic data.