NORMAL: accurate nucleosome positioning using a modified Gaussian mixture model.
NORMAL: accurate nucleosome positioning using a modified Gaussian mixture model.
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DOI：
10.1093/bioinformatics/bts206
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发表时间：
2012-06-15
期刊：
影响因子：
--
通讯作者：
Lonardi S
中科院分区：
文献类型：
--
作者：
Polishko A;Ponts N;Le Roch KG;Lonardi S
动机：核小体是染色质结构的基本元素。它们控制 DNA 的包装，并通过允许物理接触转录因子在基因调控中发挥关键作用。第二代测序的出现使得对几种模式生物的核小体位置进行具有里程碑意义的全基因组研究成为可能。目前确定核小体定位的方法首先通过将核小体富集的测序读数映射到参考基因组来计算占据覆盖度概况；然后，根据覆盖率分布的峰值放置核小体。这些方法在放置分离的核小体方面相当准确，但它们不能正确处理更复杂的配置。此外，它们只能提供核小体的位置及其占据水平，而为分子生物学家提供有关核小体的额外信息非常有益，例如放置的概率、核小体富集的 DNA 片段的大小和/或核小体在测序的细胞样本中是否定位良好或“模糊”。结果：我们通过提供一种基于参数概率模型的新颖方法来解决这些问题。期望最大化算法用于推断混合分布的参数。我们将我们的方法在两个真实数据集上的性能与模板过滤进行比较，模板过滤被认为是当前最先进的。在合成数据上，我们表明我们的方法可以更准确地解析核小体的复杂配置，并且对用户定义的参数更稳健。根据真实数据，我们表明我们的方法检测到明显更高数量的核小体。可用性：访问 http://www.cs.ucr.edu/~polishka 联系方式：stelo@cs.ucr.edu 或 Polishka@cs.ucr.edu
Motivation: Nucleosomes are the basic elements of chromatin structure. They control the packaging of DNA and play a critical role in gene regulation by allowing physical access to transcription factors. The advent of second-generation sequencing has enabled landmark genome-wide studies of nucleosome positions for several model organisms. Current methods to determine nucleosome positioning first compute an occupancy coverage profile by mapping nucleosome-enriched sequenced reads to a reference genome; then, nucleosomes are placed according to the peaks of the coverage profile. These methods are quite accurate on placing isolated nucleosomes, but they do not properly handle more complex configurations. Also, they can only provide the positions of nucleosomes and their occupancy level, whereas it is very beneficial to supply molecular biologists additional information about nucleosomes like the probability of placement, the size of DNA fragments enriched for nucleosomes and/or whether nucleosomes are well positioned or ‘fuzzy’ in the sequenced cell sample. Results: We address these issues by providing a novel method based on a parametric probabilistic model. An expectation maximization algorithm is used to infer the parameters of the mixture of distributions. We compare the performance of our method on two real datasets against Template Filtering, which is considered the current state-of-the-art. On synthetic data, we show that our method can resolve more accurately complex configurations of nucleosomes, and it is more robust to user-defined parameters. On real data, we show that our method detects a significantly higher number of nucleosomes. Availability: Visit http://www.cs.ucr.edu/~polishka Contact: stelo@cs.ucr.edu or polishka@cs.ucr.edu
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