ENCODE计划和功能基因组研究

doi:10.3724/SP.J.1005.2014.0237

遗传 ›› 2014, Vol. 36 ›› Issue (3): 237-247.doi: 10.3724/SP.J.1005.2014.0237

ENCODE计划和功能基因组研究

丁楠^{1, 2}, 渠鸿竹¹, 方向东¹

1. 中国科学院北京基因组研究所, 中国科学院基因组科学及信息重点实验室, 北京 100101;
2. 中国科学院大学, 北京100049

收稿日期:2013-09-17 修回日期:2013-12-23 出版日期:2014-03-20 发布日期:2014-02-27
通讯作者: 方向东, 博士, 研究员, 研究方向：干细胞与重要疾病的组学与转化医学。E-mail: fangxd@big.ac.cn E-mail:fangxd@big.ac.cn
作者简介:丁楠, 在读博士研究生, 专业方向：基因组学数据挖掘。Tel: 010-84097538; E-mail: dingnan@big.ac.cn
基金资助:
中国科学院干细胞与再生医学研究战略性科技先导专项子课题(编号：XDA01040405)资助

The ENCODE project and functional genomics studies

Nan Ding^1,2, Hongzhu Qu¹, Xiangdong Fang¹

1. CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China

Received:2013-09-17 Revised:2013-12-23 Online:2014-03-20 Published:2014-02-27

摘要/Abstract

摘要：

人类基因组计划完成以来, 科学家们一直在努力阐释基因组信息所代表的生物学意义。自2003年开始, 美国国家人类基因组研究所(National Human Genome Research Institute, NHGRI)投资近3亿美元启动“DNA元件百科全书(Encyclopedia of DNA Elements, ENCODE)”计划, 集结了来自美国、中国、英国、日本、西班牙和新加坡等国家的32个实验室的440余名科学家, 共同鉴定并分析人类基因组中所有的功能调控元件。高通量测序技术等实验手段的发展和生物信息学技术的不断完善使得ENCODE计划取得了丰硕的成果：确定了甲基化和组蛋白修饰等表观修饰区域及其对染色质结构的作用, 进而确定染色质结构的改变影响基因表达; 确定了转录因子及其结合位点的信息, 并构建了转录因子调控网络; 进一步修订更新了假基因和非编码RNA数据库; 并确定了调控序列的单核苷酸多态性(Single nucleotide polymorphism, SNP)并与疾病相关联。这些发现一方面有助于系统解析基因和基因组信息、调控元件的调控作用以及非编码区转录调控等分子机制; 同时也将为转化医学等生命科学研究领域提供丰富的数据来源。文章综述了高通量测序技术等实验手段的发展和生物信息学技术的不断完善对ENCODE计划的贡献、表观遗传学研究与ENCODE计划的关联性、ENCODE计划的主要科学成果等, 同时展望了ENCODE计划对基础医学、临床医学和转化医学等生命科学研究领域的巨大推动作用。

关键词: ENCODE, 表观遗传学, 新一代测序技术, 转录调控

Abstract:

Upon the completion of the Human Genome Project, scientists have been trying to interpret the underlying genomic code for human biology. Since 2003, National Human Genome Research Institute (NHGRI) has invested nearly $0.3 billion and gathered over 440 scientists from more than 32 institutions in the United States, China, United Kingdom, Japan, Spain and Singapore to initiate the Encyclopedia of DNA Elements (ENCODE) project, aiming to identify and analyze all regulatory elements in the human genome. Taking advantage of the development of next-generation sequencing technologies and continuous improvement of experimental methods, ENCODE had made remarkable achievements: identified methylation and histone modification of DNA sequences and their regulatory effects on gene expression through altering chromatin structures, categorized binding sites of various transcription factors and constructed their regulatory networks, further revised and updated database for pseudogenes and non-coding RNA, and identified SNPs in regulatory sequences associated with diseases. These findings help to comprehensively understand information embedded in gene and genome sequences, the function of regulatory elements as well as the molecular mechanism underlying the transcriptional regulation by noncoding regions, and provide extensive data resource for life sciences, particularly for translational medicine. We re-viewed the contributions of high-throughput sequencing platform development and bioinformatical technology improve-ment to the ENCODE project, the association between epigenetics studies and the ENCODE project, and the major achievement of the ENCODE project. We also provided our prospective on the role of the ENCODE project in promoting the development of basic and clinical medicine.

Key words: ENCODE, epigenetics, next-generation sequencing, transcriptional regulation

丁楠, 渠鸿竹, 方向东. ENCODE计划和功能基因组研究[J]. 遗传, 2014, 36(3): 237-247.

Nan Ding, Hongzhu Qu, Xiangdong Fang. The ENCODE project and functional genomics studies[J]. HEREDITAS, 2014, 36(3): 237-247.

参考文献

[1] Qu HZ, Fang XD. A brief review on the Human Encyclopedia of DNA Elements (ENCODE) project. Genomics Proteomics Bioinformatics, 2013, 11(3): 135–141. <\p>

[2] Weinstock GM. ENCODE: more genomic empowerment. Genome Res, 2007, 17(6): 667–668. <\p>

[3] ENCODE Project Consortium, Birney E, Stamatoyannopoulos JA, Dutta A。 Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature, 2007, 447(7146): 799–816. <\p>

[4] American Association for Cancer Research Human Epigenome Task Force E U, Network of Excellence, Scientific Advisory Board. Moving ahead with an international human epigenome project. Nature, 2008, 454(7205): 711– 715. <\p>

[5] Shendure J, Ji H. Next-generation DNA sequencing. Nat Biotechnol, 2008, 26(10): 1135–1145. <\p>

[6] Metzker ML. Sequencing technologies-the next generation. Nat Rev Genet, 2010, 11(1): 31–46. <\p>

[7] Thurman RE, Rynes E, Humbert R, Vierstra J, Maurano MT, Haugen E, Sheffield NC, Stergachis AB, Wang H, Vernot B, Garg K, John S, Sandstrom R, Bates D, Boatman L, Canfield TK, Diegel M, Dunn D, Ebersol AK, Frum T, Giste E, Johnson AK, Johnson EM, Kutyavin T, Lajoie B, Lee BK, Lee K, London D, Lotakis D, Neph S, Neri F, Nguyen ED, Qu H, Reynolds AP, Roach V, Safi A, Sanchez ME, Sanyal A, Shafer A, Simon JM, Song L, Vong S, Weaver M, Yan Y, Zhang Z, Zhang Z, Lenhard B, Tewari M, Dorschner MO, Hansen RS, Navas PA, Stamatoyannopoulos G, Iyer VR, Lieb JD, Sunyaev SR, Akey JM, Sabo PJ, Kaul R, Furey TS, Dekker J, Crawford GE, Stamatoyannopoulos JA. The accessible chromatin landscape of the human genome. Nature, 2012, 489(7414): 75– 82. <\p>

[8] Satterlee JS, Schubeler D, Ng HH. Tackling the epigenome: challenges and opportunities for collaboration. Nat Biotechnol, 2010, 28(10): 1039–1044. <\p>

[9] Harrow J, Frankish A, Gonzalez JM, Tapanari E, Diekhans M, Kokocinski F, Aken BL, Barrell D, Zadissa A, Searle S, Barnes I, Bignell A, Boychenko V, Hunt T, Kay M, Mukherjee G, Rajan J, Despacio-Reyes G, Saunders G, Steward C, Harte R, Lin M, Howald C, Tanzer A, Derrien T, Chrast J, Walters N, Balasubramanian S, Pei B, Tress M, Rodriguez JM, Ezkurdia I, van Baren J, Brent M, Haussler D, Kellis M, Valencia A, Reymond A, Gerstein M, Guigo R, Hubbard TJ. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res, 2012, 22(9): 1760–1774. <\p>

[10] Natarajan A, Yardimci GG, Sheffield NC, Crawford GE, Ohler U. Predicting cell-type-specific gene expression from regions of open chromatin. Genome Res, 2012, 22(9): 1711–1722. <\p>

[11] Cheng C, Yan KK, Yip KY, Rozowsky J, Alexander R, Shou C, Gerstein M. A statistical framework for modeling gene expression using chromatin features and application to modENCODE datasets. Genome Biol, 2011, 12(2): R15. <\p>

[12] Dong X, Greven MC, Kundaje A, Djebali S, Brown JB, Cheng C, Gingeras TR, Gerstein M, Guigo R, Birney E, Weng Z. Modeling gene expression using chromatin features in various cellular contexts. Genome Biol, 2012, 13(9): R53. <\p>

[13] Li G, Ruan X, Auerbach RK, Sandhu K S, Zheng M, Wang P, Poh HM, Goh Y, Lim J, Zhang J, Sim HS, Peh SQ, Mulawadi FH, Ong CT, Orlov YL, Hong S, Zhang Z, Landt S, Raha D, Euskirchen G, Wei CL, Ge W, Wang H, Davis C, Fisher-Aylor K I, Mortazavi A, Gerstein M, Gingeras T, Wold B, Sun Y, Fullwood M J, Cheung E, Liu E, Sung WK, Snyder M, Ruan Y. Extensive promoter- centered chromatin interactions provide a topological basis for transcription regulation. Cell, 2012, 148(1–2): 84–98. <\p>

[14] Tilgner H, Knowles DG, Johnson R, Davis CA, Chakrabortty S, Djebali S, Curado J, Snyder M, Gingeras TR, Guigo R. Deep sequencing of subcellular RNA fractions shows splicing to be predominantly co-transcriptional in the human genome but inefficient for lncRNAs. Genome Res, 2012, 22(9): 1616–1625. <\p>

[15] Sanyal A, Lajoie BR, Jain G, Dekker J. The long-range interaction landscape of gene promoters. Nature, 2012, 489(7414): 109–113. <\p>

[16] Hannenhalli S. Eukaryotic transcription factor binding sites--modeling and integrative search methods. Bioinformatics, 2008, 24(11): 1325–1331. <\p>

[17] ENCODE Project Consortium, Bernstein BE, Birney E, Dunham I, Green ED, Gunter C, Snyder M. An integrated encyclopedia of DNA elements in the human genome. Nature, 2012, 489(7414): 57–74. <\p>

[18] Frietze S, Wang R, Yao L, Tak YG, Ye Z, Gaddis M, Witt H, Farnham PJ, Jin VX. Cell type-specific binding patterns reveal that TCF7L2 can be tethered to the genome by association with GATA3. Genome Biol, 2012, 13(9): R52. <\p>

[19] Yip KY, Cheng C, Bhardwaj N, Brown JB, Leng J, Kundaje A, Rozowsky J, Birney E, Bickel P, Snyder M, Gerstein M. Classification of human genomic regions based on experimentally determined binding sites of more than 100 transcription-related factors. Genome Biol, 2012, 13(9): R48. <\p>

[20] Whitfield TW, Wang J, Collins PJ, Partridge EC, Aldred SF, Trinklein ND, Myers RM, Weng Z. Functional analysis of transcription factor binding sites in human promoters. Genome Biol, 2012, 13(9): R50. <\p>

[21] Neph S, Vierstra J, Stergachis AB, Reynolds AP, Haugen E, Vernot B, Thurman RE, John S, Sandstrom R, Johnson AK, Maurano MT, Humbert R, Rynes E, Wang H, Vong S, Lee K, Bates D, Diegel M, Roach V, Dunn D, Neri J, Schafer A, Hansen R S, Kutyavin T, Giste E, Weaver M, Canfield T, Sabo P, Zhang M, Balasundaram G, Byron R, MacCoss MJ, Akey JM, Bender MA, Groudine M, Kaul R, Stamatoyannopoulos JA. An expansive human regulatory lexicon encoded in transcription factor footprints. Nature, 2012, 489(7414): 83–90. <\p>

[22] Wang J, Zhuang J, Iyer S, Lin X, Whitfield T W, Greven MC, Pierce BG, Dong X, Kundaje A, Cheng Y, Rando OJ, Birney E, Myers RM, Noble WS, Snyder M, Weng Z. Sequence features and chromatin structure around the genomic regions bound by 119 human transcription factors. Genome Res, 2012, 22(9): 1798–1812. <\p>

[23] Gerstein MB, Kundaje A. Architecture of the human regulatory network derived from ENCODE data. Nature, 2012, 489(7414): 91–100. <\p>

[24] Pei B, Sisu C, Frankish A, Howald C, Habegger L, Mu XJ, Harte R, Balasubramanian S, Tanzer A, Diekhans M, Reymond A, Hubbard TJ, Harrow J, Gerstein MB. The GENCODE pseudogene resource. Genome Biol, 2012, 13(9): R51. <\p>

[25] Banfai B, Jia H, Khatun J, Wood E, Risk B, Gundling WE, Jr., Kundaje A, Gunawardena HP, Yu Y, Xie L, Krajewski K, Strahl BD, Chen X, Bickel P, Giddings MC, Brown JB, Lipovich L. Long noncoding RNAs are rarely translated in two human cell lines. Genome Res, 2012, 22(9): 1646– 1657. <\p>

[26] Derrien T, Johnson R, Bussotti G, Tanzer A, Djebali S, Tilgner H, Guernec G, Martin D, Merkel A, Knowles DG, Lagarde J, Veeravalli L, Ruan X, Ruan Y, Lassmann T, Carninci P, Brown JB, Lipovich L, Gonzalez JM, Thomas M, Davis CA, Shiekhattar R, Gingeras TR, Hubbard TJ, Notredame C, Harrow J, Guigó R. The GENCODE v7 catalog of human long noncoding RNAs: analysis of their gene structure, evolution, and expression. Genome Res, 2012, 22(9): 1775–1789. <\p>

[27] Ishii N, Ozaki K, Sato H, Mizuno H, Saito S, Takahashi A, Miyamoto Y, Ikegawa S, Kamatani N, Hori M, Saito S, Nakamura Y, Tanaka T. Identification of a novel non-coding RNA, MIAT, that confers risk of myocardial infarction. J Hum Genet, 2006, 51(12): 1087–1099. <\p>

[28] Wang L, Park HJ, Dasari S, Wang S, Kocher JP, Li W. CPAT: Coding-Potential Assessment Tool using an alignment-free logistic regression model. Nucleic Acids Res, 2013, 41(6): e74. <\p>

[29] Guo X, Gao L, Liao Q, Xiao H, Ma X, Yang X, Luo H, Zhao G, Bu D, Jiao F, Shao Q, Chen R, Zhao Y. Long non-coding RNAs function annotation: a global prediction method based on bi-colored networks. Nucleic Acids Res, 2013, 41(2): e35. <\p>

[30] Ladewig E, Okamura K, Flynt AS, Westholm JO, Lai EC. Discovery of hundreds of mirtrons in mouse and human small RNA data. Genome Res, 2012, 22(9): 1634–1645. <\p>

[31] Meissner A, Mikkelsen TS, Gu H, Wernig M, Hanna J, Sivachenko A, Zhang X, Bernstein BE, Nusbaum C, Jaffe DB, Gnirke A, Jaenisch R, Lander ES. Genome-scale DNA methylation maps of pluripotent and differentiated cells. Nature, 2008, 454(7205): 766–770. <\p>

[32] Wang H, Maurano MT, Qu H, Varley KE, Gertz J, Pauli F, Lee K, Canfield T, Weaver M, Sandstrom R, Thurman R E, Kaul R, Myers RM, Stamatoyannopoulos JA. Widespread plasticity in CTCF occupancy linked to DNA methylation. Genome Res, 2012, 22(9): 1680–1688. <\p>

[33] Strawbridge RJ, Dupuis J, Prokopenko I. Genome-Wide Association Identi?es Nine Common Variants Associated With Fasting Proinsulin Levels and Provides New Insights Into the Pathophysiology of Type 2 Diabetes. Diabetes, 2011, 60(10): 2624–2634. <\p>

[34] Schaub MA, Boyle AP, Kundaje A, Batzoglou S, Snyder M. Linking disease associations with regulatory information in the human genome. Genome Res, 2012, 22(9): 1748–1759. <\p>

[35] Birney E. Lessons for big-data projects. Nature, 2012, 489(7414): 49–51. <\p>

[36] Ecker JR, Bickmore WA, Barroso I, Pritchard JK, Gilad Y, Segal E. Genomics: ENCODE explained. Nature, 2012, 489(7414): 52–55.<\p>

编辑推荐

Metrics

www.chinagene.cn
备案号：京ICP备09063187号-4
总访问:,今日访问:,当前在线:

ENCODE计划和功能基因组研究

The ENCODE project and functional genomics studies

PDF (PC)

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	张競文,续倩,李国亮. 癌症发生发展中的表观遗传学研究[J]. 遗传, 2019, 41(7): 567-581.
[2]	郑晓飞,黄海燕,吴强. 染色质架构蛋白CTCF调控UGT1基因簇的表达[J]. 遗传, 2019, 41(6): 509-523.
[3]	刘振宁, 袁黎, VenkatesanSundaresan, 余小林. 拟南芥CKI1基因上游转录调控因子筛选及鉴定[J]. 遗传, 2019, 41(5): 430-438.
[4]	马志鹏, 陈军. 无义突变与“遗传补偿效应”[J]. 遗传, 2019, 41(5): 359-364.
[5]	岳敏, 杨禹, 郭改丽, 秦曦明. 哺乳动物生物钟的遗传和表观遗传研究进展[J]. 遗传, 2017, 39(12): 1122-1137.
[6]	程敏, 张文建, 邢天宇, 闫晓红, 李玉茂, 李辉, 王宁. 鸡miR-17-92基因簇上游调控区功能分析[J]. 遗传, 2016, 38(8): 724-735.
[7]	李元丰, 韩玉波, 曹鹏博, 孟金凤, 李海北, 秦庚, 张锋, 靳光付, 杨勇, 邬玲仟, 平杰, 周钢桥. 2015年中国医学遗传学研究领域若干重要进展[J]. 遗传, 2016, 38(5): 363-390.
[8]	张笑, 贾桂芳. RNA表观遗传修饰：N⁶-甲基腺嘌呤[J]. 遗传, 2016, 38(4): 275-288.
[9]	方科, 张凯翔, 王建, 付志猛, 赵湘辉. 表观遗传学新标记--5-羟甲基胞嘧啶检测方法的研究进展[J]. 遗传, 2016, 38(3): 206-216.
[10]	孙凌云, 李星逾, 孙志为. 原发性肝癌的表观遗传学及其治疗[J]. 遗传, 2015, 37(6): 517-527.
[11]	任才芳，孙红艳，王立中，张国敏，樊懿萱，颜光耀，王丹，王锋. iPSCs遗传稳定性与重编程机制的研究进展[J]. 遗传, 2014, 36(9): 879-887.
[12]	邓大君. DNA甲基化和去甲基化的研究现状及思考[J]. 遗传, 2014, 36(5): 403-410.
[13]	李美婷, 曹林林, 杨洋. 表观遗传修饰在糖脂代谢中的作用[J]. 遗传, 2014, 36(3): 200-207.
[14]	沈圣, 屈彦纯, 张军. 下一代测序技术在表观遗传学研究中的重要应用及进展[J]. 遗传, 2014, 36(3): 256-275.
[15]	王庭璋单杲徐建红薛庆中. 基因组规模DNA甲基化测序数据预处理及表观遗传分析[J]. 遗传, 2013, 35(6): 685-684.