Sequencing technologies have changed not only our approaches to classical genetics, but also the field of epigenetics. Specific methods allow scientists to identify novel genome-wide epigenetic patterns of DNA methylation down to single-nucleotide resolution. DNA methylation is the most researched epigenetic mark involved in various processes in the human cell, including gene regulation and development of diseases, such as cancer. Increasing numbers of DNA methylation sequencing datasets from human genome are produced using various platforms-from methylated DNA precipitation to the whole genome bisulfite sequencing. Many of those datasets are fully accessible for repeated analyses. Sequencing experiments have become routine in laboratories around the world, while analysis of outcoming data is still a challenge among the majority of scientists, since in many cases it requires advanced computational skills. Even though various tools are being created and published, guidelines for their selection are often not clear, especially to non-bioinformaticians with limited experience in computational analyses. Separate tools are often used for individual steps in the analysis, and these can be challenging to manage and integrate. However, in some instances, tools are combined into pipelines that are capable to complete all the essential steps to achieve the result. In the case of DNA methylation sequencing analysis, the goal of such pipeline is to map sequencing reads, calculate methylation levels, and distinguish differentially methylated positions and/or regions. The objective of this review is to describe basic principles and steps in the analysis of DNA methylation sequencing data that in particular have been used for mammalian genomes, and more importantly to present and discuss the most pronounced computational pipelines that can be used to analyze such data. We aim to provide a good starting point for scientists with limited experience in computational analyses of DNA methylation and hydroxymethylation data, and recommend a few tools that are powerful, but still easy enough to use for their own data analysis.

中文翻译：

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通过测序的DNA甲基化数据：用于数据分析的工具和管道的实验方法和建议。

测序技术不仅改变了我们经典遗传学的方法，而且改变了表观遗传学领域。特定的方法使科学家能够鉴定出直至单核苷酸分辨率的DNA甲基化的新型全基因组表观遗传模式。DNA甲基化是涉及人类细胞各种过程（包括基因调节和疾病，例如癌症）的研究最多的表观遗传标记。从甲基化DNA沉淀到整个基因组亚硫酸氢盐测序，人们使用各种平台生成了越来越多的来自人类基因组的DNA甲基化测序数据集。这些数据集中的许多数据集都可以完全访问以进行重复分析。测序实验已成为世界各地实验室的日常工作，而对结果数据的分析仍然是大多数科学家所面临的挑战，因为在许多情况下，它需要高级的计算技能。即使正在创建和发布各种工具，其选择指南通常也不清楚，尤其是对于在计算分析方面经验有限的非生物信息学家而言。单独的工具通常用于分析中的各个步骤，而这些工具在管理和集成方面可能具有挑战性。但是，在某些情况下，工具会组合到流水线中，这些流水线能够完成所有必要步骤以实现结果。在DNA甲基化测序分析的情况下，此类流程的目标是绘制测序读图，计算甲基化水平并区分差异甲基化的位置和/或区域。这篇综述的目的是描述DNA甲基化测序数据分析的基本原理和步骤，特别是用于哺乳动物基因组的DNA甲基化测序数据，更重要的是介绍和讨论可用于分析此类数据的最明显的计算流程。 。我们旨在为在DNA甲基化和羟甲基化数据的计算分析方面经验有限的科学家提供一个良好的起点，并推荐一些功能强大但仍然足够易于用于自己的数据分析的工具。