DiNuP: a systematic approach to identify regions of differential nucleosome positioning.
DiNuP: a systematic approach to identify regions of differential nucleosome positioning.
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DiNuP：识别差异核小体定位区域的系统方法
DOI：
10.1093/bioinformatics/bts329
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发表时间：
2012-08-01
期刊：
影响因子：
--
通讯作者：
Zhang Y
中科院分区：
文献类型：
--
作者：
Fu K;Tang Q;Feng J;Liu XS;Zhang Y
动机：随着高通量测序技术的快速发展，核小体定位的全基因组分析变得越来越经济实惠。许多未来的研究将调查核小体定位在细胞中的动态行为，具有不同的状态或暴露于不同的条件。然而，以前还没有一种有效识别差异核小体定位区域（RDNP）的稳健方法。结果如下：我们描述了一种新的计算方法，DiNuP，比较在各种条件下的高通量测序产生的核小体配置文件。DiNuP基于读段分布的差异为每个识别的RDNP提供统计P值。DiNuP还根据经验估计错误发现率作为两个样品具有不同测序深度时的截止值，并将可靠的RDNP与背景噪声区分开来。对DiNuP的评价表明，它对于检测核小体位置、占据和融合的变化既敏感又特异。使用公开可用的数据集鉴定的RDNP揭示，核小体定位动力学与转录的表观遗传调控密切相关。可用性和实现：DiNuP用Python实现，可在http://www.tongji.edu.cn/~zhanglab/DiNuP上免费获取。联系方式：yzhang@tongji.edu.cn补充信息：补充数据可在生物信息学在线获得。
Motivation: With the rapid development of high-throughput sequencing technologies, the genome-wide profiling of nucleosome positioning has become increasingly affordable. Many future studies will investigate the dynamic behaviour of nucleosome positioning in cells that have different states or that are exposed to different conditions. However, a robust method to effectively identify the regions of differential nucleosome positioning (RDNPs) has not been previously available. Results: We describe a novel computational approach, DiNuP, that compares nucleosome profiles generated by high-throughput sequencing under various conditions. DiNuP provides a statistical P-value for each identified RDNP based on the difference of read distributions. DiNuP also empirically estimates the false discovery rate as a cutoff when two samples have different sequencing depths and differentiate reliable RDNPs from the background noise. Evaluation of DiNuP showed it to be both sensitive and specific for the detection of changes in nucleosome location, occupancy and fuzziness. RDNPs that were identified using publicly available datasets revealed that nucleosome positioning dynamics are closely related to the epigenetic regulation of transcription. Availability and implementation: DiNuP is implemented in Python and is freely available at http://www.tongji.edu.cn/~zhanglab/DiNuP. Contact: yzhang@tongji.edu.cn Supplementary Information: Supplementary data are available at Bioinformatics online.
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