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网络方法绘制健康与疾病的发育起源的转录组学和表观遗传学图谱。

Network Approaches for Charting the Transcriptomic and Epigenetic Landscape of the Developmental Origins of Health and Disease.

机构信息

Max Perutz Labs, Department of Structural and Computational Biology, University of Vienna, 1030 Vienna, Austria.

CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1030 Vienna, Austria.

出版信息

Genes (Basel). 2022 Apr 26;13(5):764. doi: 10.3390/genes13050764.

Abstract

The early developmental phase is of critical importance for human health and disease later in life. To decipher the molecular mechanisms at play, current biomedical research is increasingly relying on large quantities of diverse omics data. The integration and interpretation of the different datasets pose a critical challenge towards the holistic understanding of the complex biological processes that are involved in early development. In this review, we outline the major transcriptomic and epigenetic processes and the respective datasets that are most relevant for studying the periconceptional period. We cover both basic data processing and analysis steps, as well as more advanced data integration methods. A particular focus is given to network-based methods. Finally, we review the medical applications of such integrative analyses.

摘要

早期发育阶段对人类健康和晚年疾病至关重要。为了解密发挥作用的分子机制,当前的生物医学研究越来越依赖大量不同的组学数据。不同数据集的整合和解释对全面理解涉及早期发育的复杂生物过程构成了重大挑战。在这篇综述中,我们概述了与研究围孕期最相关的主要转录组和表观遗传过程及其相应的数据集。我们涵盖了基本的数据处理和分析步骤,以及更高级的数据整合方法。特别关注基于网络的方法。最后,我们回顾了这种整合分析的医学应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc42/9141211/f0e04d818c76/genes-13-00764-g001.jpg

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