Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA.
BMC Bioinformatics. 2011 Oct 21;12:408. doi: 10.1186/1471-2105-12-408.
Genome-wide mapping of protein-DNA interactions has been widely used to investigate biological functions of the genome. An important question is to what extent such interactions are regulated at the DNA sequence level. However, current investigation is hampered by the lack of computational methods for systematic evaluating sequence specificity.
We present a simple, unbiased quantitative measure for DNA sequence specificity called the Motif Independent Measure (MIM). By analyzing both simulated and real experimental data, we found that the MIM measure can be used to detect sequence specificity independent of presence of transcription factor (TF) binding motifs. We also found that the level of specificity associated with H3K4me1 target sequences is highly cell-type specific and highest in embryonic stem (ES) cells. We predicted H3K4me1 target sequences by using the N- score model and found that the prediction accuracy is indeed high in ES cells.The software to compute the MIM is freely available at: https://github.com/lucapinello/mim.
Our method provides a unified framework for quantifying DNA sequence specificity and serves as a guide for development of sequence-based prediction models.
全基因组范围内的蛋白质-DNA 相互作用图谱已被广泛用于研究基因组的生物学功能。一个重要的问题是，这种相互作用在多大程度上受到 DNA 序列水平的调控。然而，目前的研究受到缺乏系统评估序列特异性的计算方法的阻碍。
我们提出了一种简单、无偏的称为 motif 独立度量（MIM）的 DNA 序列特异性定量度量方法。通过分析模拟和真实实验数据，我们发现 MIM 度量可用于检测与转录因子（TF）结合基序无关的序列特异性。我们还发现，与 H3K4me1 靶序列相关的特异性水平在细胞类型特异性中非常高，在胚胎干细胞（ES）中最高。我们使用 N-得分模型预测了 H3K4me1 靶序列，并发现该模型在 ES 细胞中的预测准确性确实很高。计算 MIM 的软件可在以下网址免费获取：https://github.com/lucapinello/mim。
我们的方法为量化 DNA 序列特异性提供了一个统一的框架，并为基于序列的预测模型的开发提供了指导。
