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本文引用的文献

1
Epigenetic biomarkers in colorectal cancer: premises and prospects.结直肠癌中的表观遗传生物标志物:前提与前景
Biomarkers. 2018 Mar;23(2):105-114. doi: 10.1080/1354750X.2016.1252961. Epub 2016 Nov 10.
2
Integrating DNA methylation and microRNA biomarkers in sputum for lung cancer detection.整合痰液中的DNA甲基化和微小RNA生物标志物用于肺癌检测。
Clin Epigenetics. 2016 Oct 19;8:109. doi: 10.1186/s13148-016-0275-5. eCollection 2016.
3
Urinary DNA Methylation Biomarkers for Noninvasive Prediction of Aggressive Disease in Patients with Prostate Cancer on Active Surveillance.用于主动监测前列腺癌患者侵袭性疾病非侵入性预测的尿液 DNA 甲基化生物标志物。
J Urol. 2017 Feb;197(2):335-341. doi: 10.1016/j.juro.2016.08.081. Epub 2016 Aug 18.
4
Transcription factors as readers and effectors of DNA methylation.作为DNA甲基化的读取器和效应器的转录因子。
Nat Rev Genet. 2016 Aug 1;17(9):551-65. doi: 10.1038/nrg.2016.83.
5
tRNA-Derived Small Non-Coding RNAs in Response to Ischemia Inhibit Angiogenesis.响应缺血的tRNA衍生小非编码RNA抑制血管生成。
Sci Rep. 2016 Feb 11;6:20850. doi: 10.1038/srep20850.
6
HiCUP: pipeline for mapping and processing Hi-C data.HiCUP:用于映射和处理Hi-C数据的流程
F1000Res. 2015 Nov 20;4:1310. doi: 10.12688/f1000research.7334.1. eCollection 2015.
7
A peptide encoded by a transcript annotated as long noncoding RNA enhances SERCA activity in muscle.一种由注释为长链非编码RNA的转录本编码的肽可增强肌肉中的肌浆网钙ATP酶活性。
Science. 2016 Jan 15;351(6270):271-5. doi: 10.1126/science.aad4076.
8
A comprehensive comparison of tools for differential ChIP-seq analysis.用于差异染色质免疫沉淀测序(ChIP-seq)分析的工具的全面比较。
Brief Bioinform. 2016 Nov;17(6):953-966. doi: 10.1093/bib/bbv110. Epub 2016 Jan 13.
9
DNA Methylation Analysis: Choosing the Right Method.DNA甲基化分析:选择正确的方法。
Biology (Basel). 2016 Jan 6;5(1):3. doi: 10.3390/biology5010003.
10
Addressing Bias in Small RNA Library Preparation for Sequencing: A New Protocol Recovers MicroRNAs that Evade Capture by Current Methods.解决用于测序的小RNA文库制备中的偏差问题:一种新方案可回收目前方法无法捕获的微小RNA。
Front Genet. 2015 Dec 22;6:352. doi: 10.3389/fgene.2015.00352. eCollection 2015.

将表观基因组学融入对生物医学见解的理解中。

Integrating Epigenomics into the Understanding of Biomedical Insight.

作者信息

Han Yixing, He Ximiao

机构信息

Mouse Cancer Genetics Program, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, USA.; Present address: Genetics and Biochemistry Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA.

Laboratory of Metabolism, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.; Present address: Department of Medical Genetics, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.

出版信息

Bioinform Biol Insights. 2016 Dec 4;10:267-289. doi: 10.4137/BBI.S38427. eCollection 2016.

DOI:10.4137/BBI.S38427
PMID:27980397
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5138066/
Abstract

Epigenetics is one of the most rapidly expanding fields in biomedical research, and the popularity of the high-throughput next-generation sequencing (NGS) highlights the accelerating speed of epigenomics discovery over the past decade. Epigenetics studies the heritable phenotypes resulting from chromatin changes but without alteration on DNA sequence. Epigenetic factors and their interactive network regulate almost all of the fundamental biological procedures, and incorrect epigenetic information may lead to complex diseases. A comprehensive understanding of epigenetic mechanisms, their interactions, and alterations in health and diseases genome widely has become a priority in biological research. Bioinformatics is expected to make a remarkable contribution for this purpose, especially in processing and interpreting the large-scale NGS datasets. In this review, we introduce the epigenetics pioneering achievements in health status and complex diseases; next, we give a systematic review of the epigenomics data generation, summarize public resources and integrative analysis approaches, and finally outline the challenges and future directions in computational epigenomics.

摘要

表观遗传学是生物医学研究中发展最为迅速的领域之一,高通量下一代测序(NGS)的普及凸显了过去十年间表观基因组学发现的加速进程。表观遗传学研究染色质变化导致的可遗传表型,而DNA序列不发生改变。表观遗传因子及其相互作用网络调控几乎所有基本生物学过程,错误的表观遗传信息可能导致复杂疾病。全面了解表观遗传机制、它们的相互作用以及在健康和疾病基因组中的改变已成为生物学研究的当务之急。预计生物信息学将为此做出显著贡献,尤其是在处理和解释大规模NGS数据集方面。在本综述中,我们介绍表观遗传学在健康状况和复杂疾病方面的开创性成就;接下来,我们对表观基因组学数据生成进行系统综述,总结公共资源和整合分析方法,最后概述计算表观基因组学面临的挑战和未来方向。