Department of Mathematical Modelling, Statistics and Bioinformatics, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium.
Environ Mol Mutagen. 2014 Apr;55(3):155-70. doi: 10.1002/em.21841. Epub 2013 Dec 10.
Epigenetics refers to the collection of heritable features that modulate the genome-environment interaction without being encoded in the actual DNA sequence. While being mitotically and sometimes even meiotically transmitted, epigenetic traits often demonstrate extensive flexibility. This allows cells to acquire diverse gene expression patterns during differentiation, but also to adapt to a changing environment. However, epigenetic alterations are not always beneficial to the organism, as they are, for example, frequently identified in human diseases such as cancer. Accurate and cost-efficient genome-scale profiling of epigenetic features is thus of major importance to pinpoint these "epimutations," for example, to monitor the epigenetic impact of environmental exposure. Over the last decade, the field of epigenetics has been revolutionized by several innovative "epigenomics" technologies exactly addressing this need. In this review, we discuss and compare widely used next-generation methods to assess DNA methylation and hydroxymethylation, noncoding RNA expression, histone modifications, and nucleosome positioning. Although recent methods are typically based on "second-generation" sequencing, we also pay attention to still commonly used array- and PCR-based methods, and look forward to the additional advantages of single-molecule sequencing. As the current bottleneck in epigenomics research is the analysis rather than generation of data, the basic difficulties and problem-solving strategies regarding data preprocessing and statistical analysis are introduced for the different technologies. Finally, we also consider the complications associated with epigenomic studies of species with yet unsequenced genomes and possible solutions.
表观遗传学是指一组遗传特征，这些特征可以调节基因组-环境相互作用，而不在实际的 DNA 序列中编码。虽然表观遗传特征可以在有丝分裂中传递，甚至在减数分裂中传递，但它们通常表现出广泛的灵活性。这使得细胞在分化过程中可以获得不同的基因表达模式，但也可以适应不断变化的环境。然而，表观遗传改变并不总是对生物体有益的，例如，它们经常在癌症等人类疾病中被发现。因此，准确且具有成本效益的大规模表观遗传特征基因组分析对于确定这些“表观突变”非常重要，例如，用于监测环境暴露对表观遗传的影响。在过去的十年中，表观遗传学领域已经被几种创新的“表观基因组学”技术所颠覆，这些技术正是为了满足这一需求而发展起来的。在这篇综述中，我们讨论并比较了广泛使用的下一代方法，以评估 DNA 甲基化和羟甲基化、非编码 RNA 表达、组蛋白修饰和核小体定位。尽管最近的方法通常基于“第二代”测序，但我们也关注仍然常用的基于阵列和 PCR 的方法，并期待单分子测序的额外优势。由于当前表观基因组学研究的瓶颈是数据分析而不是数据生成，因此我们为不同的技术引入了数据预处理和统计分析的基本困难和解决策略。最后，我们还考虑了与尚未测序基因组物种的表观基因组研究相关的复杂性以及可能的解决方案。
