National Center for Biotechnology (CNB-CSIC), c/ Darwin, 3, 28049, Madrid, Spain.
BMC Genomics. 2018 Nov 28;19(1):847. doi: 10.1186/s12864-018-5286-5.
Epigenetic phenomena are crucial for explaining the phenotypic plasticity seen in the cells of different tissues, developmental stages and diseases, all holding the same DNA sequence. As technology is allowing to retrieve epigenetic information in a genome-wide fashion, massive epigenomic datasets are being accumulated in public repositories. New approaches are required to mine those data to extract useful knowledge. We present here an automatic approach for detecting genomic regions with epigenetic variation patterns across samples related to a grouping of these samples, as a way of detecting regions functionally associated to the phenomenon behind the classification.
We show that the regions automatically detected by the method in the whole human genome associated to three different classifications of a set of epigenomes (cancer vs. healthy, brain vs. other organs, and fetal vs. adult tissues) are enriched in genes associated to these processes.
The method is fully automatic and can exhaustively scan the whole human genome at any resolution using large collections of epigenomes as input, although it also produces good results with small datasets. Consequently, it will be valuable for obtaining functional information from the incoming epigenomic information as it continues to accumulate.
表观遗传现象对于解释不同组织、发育阶段和疾病的细胞中出现的表型可塑性至关重要，所有这些细胞都具有相同的 DNA 序列。随着技术能够以全基因组的方式获取表观遗传信息，大量的表观基因组数据集正在公共存储库中积累。需要新的方法来挖掘这些数据以提取有用的知识。我们在这里提出了一种自动方法，用于检测与样本分组相关的样本中具有表观遗传变异模式的基因组区域，作为检测与分类背后现象功能相关的区域的一种方法。
我们表明，该方法在整个人类基因组中自动检测到的与一组表观基因组的三个不同分类（癌症与健康、大脑与其他器官、胎儿与成人组织）相关的区域，富集了与这些过程相关的基因。
该方法是全自动的，可以使用大型表观基因组数据集作为输入，以任何分辨率全面扫描整个人类基因组，尽管它也可以用小数据集产生良好的结果。因此，随着不断积累的表观基因组信息的到来，它将为获取功能信息提供有价值的帮助。
