miRNA及转录因子结合位点预测与调控功能分析
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摘要
随着包括人类在内的上百种生物的基因组核酸全序列测定的完成,生物学家们正在制定并实施后基因组计划。基因组学研究重心已开始从揭示生命的所有遗传信息转移到在分子整体水平对功能的研究上,其中,理解基因转录调控与转录后调控机制是后基因组时代的一个基本目标。
近年来,作为基因转录调控与转录后调控过程中的重要作用因子,转录因子与miRNA成为生物信息学的重要研究领域。其中,miRNA曾在2002和2003连续两年被Science杂志评选为十大科技新闻。越来越多的生物信息学研究人员致力于研究转录因子与miRNA的生物功能与调控机制。但目前的方法局限于单独研究转录因子或miRNA的调控功能,割离了转录因子与miRNA对基因表达的共同作用。因此,本文以基因表达作为切入点,研究转录因子与miRNA调控模型,预测它们的结合位点与调控功能,以及识别miRNA启动子区域。本文的主要内容包括：
(1)提出转录因子与miRNA共调控基因表达的结合位点预测算法。
本文对传统的利用芯片数据研究转录调控的计算方法进行了分析,并作了新的扩展,充分考虑转录因子与miRNA对基因表达的共同作用,设计并实现了基于基因表达芯片数据的转录因子及miRNA结合位点预测算法。算法通过测试5′端调控区与3′端非翻译区域中固定长度的所有调控序列以选择能够最切合基因表达水平的序列,预测转录因子及miRNA的结合位点。在小鼠胚胎酒精综合症细胞中使用该算法预测得到的转录因子与miRNA结合位点具有生物学意义,验证了算法的有效性。
(2)研究基于结合位点信息的转录因子与miRNA调控功能分析模型。
转录因子结合位点和miRNA靶基因预测一直是生物学研究的热点,已经有很多成熟的数据库和软件。本文讨论了利用相关生物学知识定位转录因子及miRNA结合位点的方法,结合已知转录因子模体及miRNA与靶基因相互作用的知识,设计了基于结合位点信息的转录因子及miRNA调控功能分析方法。该方法将结合位点的定位整合到相关的功能数据之中,允许从调控全局基因表达模式的角度上,对引起基因表达差异的转录因子和miRNA进行分析。使用该方法在前列腺癌细胞中预测出导致前列腺癌恶化的5个功能转录因子与7个miRNA,并通过各种生物知识验证了预测结果的正确性。
(3)提出基于CHIP-SEQ数据的miRNA启动子计算识别算法。
miRNA启动子识别是研究miRNA转录调控的一个难点问题。传统的方法使用基因组特征预测miRNA启动子。随着新一代测序技术的出现,CHIP-SEQ数据为启动子预测提供了新的数据支持,开辟了新的研究方向。本文利用RNA聚合酶Ⅱ的CHIP-SEQ数据,研究基于CHIP-SEQ数据的启动子区域表示模型,设计模式参数学习算法,并利用蛋白质编码基因的启动子上的数据优化模型参数,在miRNA上游区域预测启动子。算法被用在乳腺癌细胞的RNA聚合酶ⅡCHIP-SEQ数据中,预测出72个miRNA启动子,并分析了启动子区域的基因组特征。
(4)针对多种芯片数据,分析并讨论了多数据融合的转录因子与miRNA调控功能分析方法。
随着各种高通量数据的出现,采用多数据融合的方法分析基因转录调控机制变得越来越重要。本文从一个具体的生物实例出发,利用基因表达芯片、miRNA表达芯片、CHIP-CHIP实验数据分析了转录因子与miRNA的调控功能。多数据融合的分析方法为调控网络的构建与系统生物学的发展提供了有力的支持。
With the completion of the full length genome sequences determination of human and hundreds of other creatures, biologists are making plan of and carrying out the post genome project. The research focus is turned to working on the functions at molecular level from revealing creatures'genetic information. And understanding global transcriptional and post-transcriptional regulatory mechanisms is a fundamental goal of the post-genomic era.
In recent years, as an important factor of the global transcriptional and post-transcriptional regulation, research of transcription factors and miRNA is becoming an important part in bioinformatics. In 2002 and 2003, miRNA was selected as "Ten news of science" by Science Press. More and more researchers of bioinformatics are focusing on the functions and regulatory mechanisms of transcription factors and miRNA. Conventional methods are confined to work on the regulatory functions of transcription factors and miRNA separately, which ignore their interaction on gene expression. Therefore gene expression is taken as a cut-in-point of researches on developing regulatory model of transcription factors and miRNA, predicting the binding sites and regulatory functions, and identifying the promoter region of miRNA. The major research contents of this thesis include the following four parts:
(1)A binding sites predicting algorithm based on interactive regulating on gene expression of transcription factors and miRNA is proposed.
Conventional computational methods using microarray data to investigate transcriptional regulation is analyzed and extended. With adequate consideration of the combinatorial regulation of transcription factors and miRNA on gene expression, a novel algorithm for identifying potential transcription factor and miRNA binding sites from microarray-derived gene expression data and genomic DNA sequences is proposed. The algorithm identifys potential transcription factor and miRNA binding sites by testing random subsets of all possible motifs of a fixed size in the 5'-regulatory region and 3'untranslated regions, and selecting those motifs that best fit a combinatorial model of gene expression levels. The predicted transcription factor and miRNA binding sites in mouse infetal alcohol syndrome has a biological significance, and the effectivity of the algorithm is validated.
(2)Research on transcription factor and miRNA regulatory function analysis model based on binding sites information.
Prediction of transcription factor binding sites and targets of miRNAs are always the hot research topics, and many mature databases and softwares have been developed. Identification of transcription factors and miRNAs binding sites based on relevant biologic knowledge is discussed. With the knowledge about reported transcription factors motif and the interaction of miRNA and its targets, the locating of the binding sites is interacted into relevant functional data. From the point of regulating the global gene expression patterns, the transcription factors and the miRNAs which cause the difference of gene expression are analyzed. With this method,5 transcription factors and 7 miRNAs which cause the growth of prostate carcinoma are predicted in the prostate carcinoma cell line. The correctness of the predicted results was verified by various biological knowledges.
(3) A method using ChIP-seq derived data to identify promoter regions of miRNAs is presented.
Identification of miRNA promoter regions is one of the difficulties of research on miRNA transcription regulation. Conventional methods predict miRNA promoter regions with features of genomes. With the advent of next generation sequencing, a an new data support for promoter regions prediction is provided by ChIP-Seq data, and a new way is thus opened up. Using ChIP-seq derived RNA PolⅡbinding data, a model for identifying miRNA promoter regions is presented. A parameter learning algorithms is developed for parameter optimization with protein-coding genes' promoter regions data, and the promoter regions are prediction in the upstream regions of miRNAs. Through this method,72 miRNAs promoter regions were detected with RNA PolymeraseⅡChIP-Seq data of breast cancer cells, and the genomes features of promoter regions are also analyzed.
(4) Analysis and discussion on the regulatory function analyzing method of transcription factors and miRNAs based on multi-data fusion.
With the advent of high-throughput data, analyzing the regulation mechanisms of gene transcription with multi-data fusion method becomes more and more important. Based on a concrete example, the regulatory functions of transcription factors and miRNAs are analyzed with experiments data of gene expression chip-seq, miRNA chip-seq, and ChIP-chip data. Method of multi-data fusion provides forceful support to systems biology and the development of gene regulatory networks.
近年来,作为基因转录调控与转录后调控过程中的重要作用因子,转录因子与miRNA成为生物信息学的重要研究领域。其中,miRNA曾在2002和2003连续两年被Science杂志评选为十大科技新闻。越来越多的生物信息学研究人员致力于研究转录因子与miRNA的生物功能与调控机制。但目前的方法局限于单独研究转录因子或miRNA的调控功能,割离了转录因子与miRNA对基因表达的共同作用。因此,本文以基因表达作为切入点,研究转录因子与miRNA调控模型,预测它们的结合位点与调控功能,以及识别miRNA启动子区域。本文的主要内容包括：
(1)提出转录因子与miRNA共调控基因表达的结合位点预测算法。
本文对传统的利用芯片数据研究转录调控的计算方法进行了分析,并作了新的扩展,充分考虑转录因子与miRNA对基因表达的共同作用,设计并实现了基于基因表达芯片数据的转录因子及miRNA结合位点预测算法。算法通过测试5′端调控区与3′端非翻译区域中固定长度的所有调控序列以选择能够最切合基因表达水平的序列,预测转录因子及miRNA的结合位点。在小鼠胚胎酒精综合症细胞中使用该算法预测得到的转录因子与miRNA结合位点具有生物学意义,验证了算法的有效性。
(2)研究基于结合位点信息的转录因子与miRNA调控功能分析模型。
转录因子结合位点和miRNA靶基因预测一直是生物学研究的热点,已经有很多成熟的数据库和软件。本文讨论了利用相关生物学知识定位转录因子及miRNA结合位点的方法,结合已知转录因子模体及miRNA与靶基因相互作用的知识,设计了基于结合位点信息的转录因子及miRNA调控功能分析方法。该方法将结合位点的定位整合到相关的功能数据之中,允许从调控全局基因表达模式的角度上,对引起基因表达差异的转录因子和miRNA进行分析。使用该方法在前列腺癌细胞中预测出导致前列腺癌恶化的5个功能转录因子与7个miRNA,并通过各种生物知识验证了预测结果的正确性。
(3)提出基于CHIP-SEQ数据的miRNA启动子计算识别算法。
miRNA启动子识别是研究miRNA转录调控的一个难点问题。传统的方法使用基因组特征预测miRNA启动子。随着新一代测序技术的出现,CHIP-SEQ数据为启动子预测提供了新的数据支持,开辟了新的研究方向。本文利用RNA聚合酶Ⅱ的CHIP-SEQ数据,研究基于CHIP-SEQ数据的启动子区域表示模型,设计模式参数学习算法,并利用蛋白质编码基因的启动子上的数据优化模型参数,在miRNA上游区域预测启动子。算法被用在乳腺癌细胞的RNA聚合酶ⅡCHIP-SEQ数据中,预测出72个miRNA启动子,并分析了启动子区域的基因组特征。
(4)针对多种芯片数据,分析并讨论了多数据融合的转录因子与miRNA调控功能分析方法。
随着各种高通量数据的出现,采用多数据融合的方法分析基因转录调控机制变得越来越重要。本文从一个具体的生物实例出发,利用基因表达芯片、miRNA表达芯片、CHIP-CHIP实验数据分析了转录因子与miRNA的调控功能。多数据融合的分析方法为调控网络的构建与系统生物学的发展提供了有力的支持。
With the completion of the full length genome sequences determination of human and hundreds of other creatures, biologists are making plan of and carrying out the post genome project. The research focus is turned to working on the functions at molecular level from revealing creatures'genetic information. And understanding global transcriptional and post-transcriptional regulatory mechanisms is a fundamental goal of the post-genomic era.
In recent years, as an important factor of the global transcriptional and post-transcriptional regulation, research of transcription factors and miRNA is becoming an important part in bioinformatics. In 2002 and 2003, miRNA was selected as "Ten news of science" by Science Press. More and more researchers of bioinformatics are focusing on the functions and regulatory mechanisms of transcription factors and miRNA. Conventional methods are confined to work on the regulatory functions of transcription factors and miRNA separately, which ignore their interaction on gene expression. Therefore gene expression is taken as a cut-in-point of researches on developing regulatory model of transcription factors and miRNA, predicting the binding sites and regulatory functions, and identifying the promoter region of miRNA. The major research contents of this thesis include the following four parts:
(1)A binding sites predicting algorithm based on interactive regulating on gene expression of transcription factors and miRNA is proposed.
Conventional computational methods using microarray data to investigate transcriptional regulation is analyzed and extended. With adequate consideration of the combinatorial regulation of transcription factors and miRNA on gene expression, a novel algorithm for identifying potential transcription factor and miRNA binding sites from microarray-derived gene expression data and genomic DNA sequences is proposed. The algorithm identifys potential transcription factor and miRNA binding sites by testing random subsets of all possible motifs of a fixed size in the 5'-regulatory region and 3'untranslated regions, and selecting those motifs that best fit a combinatorial model of gene expression levels. The predicted transcription factor and miRNA binding sites in mouse infetal alcohol syndrome has a biological significance, and the effectivity of the algorithm is validated.
(2)Research on transcription factor and miRNA regulatory function analysis model based on binding sites information.
Prediction of transcription factor binding sites and targets of miRNAs are always the hot research topics, and many mature databases and softwares have been developed. Identification of transcription factors and miRNAs binding sites based on relevant biologic knowledge is discussed. With the knowledge about reported transcription factors motif and the interaction of miRNA and its targets, the locating of the binding sites is interacted into relevant functional data. From the point of regulating the global gene expression patterns, the transcription factors and the miRNAs which cause the difference of gene expression are analyzed. With this method,5 transcription factors and 7 miRNAs which cause the growth of prostate carcinoma are predicted in the prostate carcinoma cell line. The correctness of the predicted results was verified by various biological knowledges.
(3) A method using ChIP-seq derived data to identify promoter regions of miRNAs is presented.
Identification of miRNA promoter regions is one of the difficulties of research on miRNA transcription regulation. Conventional methods predict miRNA promoter regions with features of genomes. With the advent of next generation sequencing, a an new data support for promoter regions prediction is provided by ChIP-Seq data, and a new way is thus opened up. Using ChIP-seq derived RNA PolⅡbinding data, a model for identifying miRNA promoter regions is presented. A parameter learning algorithms is developed for parameter optimization with protein-coding genes' promoter regions data, and the promoter regions are prediction in the upstream regions of miRNAs. Through this method,72 miRNAs promoter regions were detected with RNA PolymeraseⅡChIP-Seq data of breast cancer cells, and the genomes features of promoter regions are also analyzed.
(4) Analysis and discussion on the regulatory function analyzing method of transcription factors and miRNAs based on multi-data fusion.
With the advent of high-throughput data, analyzing the regulation mechanisms of gene transcription with multi-data fusion method becomes more and more important. Based on a concrete example, the regulatory functions of transcription factors and miRNAs are analyzed with experiments data of gene expression chip-seq, miRNA chip-seq, and ChIP-chip data. Method of multi-data fusion provides forceful support to systems biology and the development of gene regulatory networks.
引文
1. Collins FS, Morgan M, Patrinos A:The Human Genome Project:lessons from large-scale biology. Science 2003,300(5617):286-290.
2. Birney E, Stamatoyannopoulos JA, Dutta A, Guigo R, Gingeras TR, Margulies EH, Weng Z, Snyder M, Dermitzakis ET, Thurman RE et al:Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature 2007,447(7146):799-816.
3. Thomas DJ, Rosenbloom KR, Clawson H, Hinrichs AS, Trumbower H, Raney BJ, Karolchik D, Barber GP, Harte RA, Hillman-Jackson J et al:The ENCODE Project at UC Santa Cruz. Nucleic Acids Res 2007,35(Database issue):D663-667.
4. The ENCODE (ENCyclopedia Of DNA Elements) Project. Science 2004, 306(5696):636-640.
5. Nakao M, Bono H, Kawashima S, Kamiya T, Sato K, Goto S, Kanehisa M: Genome-scale Gene Expression Analysis and Pathway Reconstruction in KEGG. Genome Inform Ser Workshop Genome Inform 1999,10:94-103.
6. Latchman DS:Transcription factors:an overview. Int J Biochem Cell Biol 1997,29(12):1305-1312.
7. Karin M:Too many transcription factors:positive and negative interactions. New Biol 1990,2(2):126-131.
8. Lee RC, Feinbaum RL, Ambros V:The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell 1993, 75(5):843-854.
9. Ambros V:The functions of animal microRNAs. Nature 2004, 431(7006):350-355.
10. Bartel DP:MicroRNAs:genomics, biogenesis, mechanism, and function. Cell 2004,116(2):281-297.
11. Mitchell PJ, Tjian R:Transcriptional regulation in mammalian cells by sequence-specific DNA binding proteins. Science 1989,245(4916):371-378.
12. Ptashne M, Gann A:Transcriptional activation by recruitment. Nature 1997, 386(6625):569-577.
13. Xu L, Glass CK, Rosenfeld MG:Coactivator and corepressor complexes in nuclear receptor function. Curr Opin Genet Dev 1999,9(2):140-147.
14. Pawson T:Signal transduction--a conserved pathway from the membrane to the nucleus. Dev Genet 1993,14(5):333-338.
15. Wang JC:Finding primary targets of transcriptional regulators. Cell Cycle 2005,4(3):356-358.
16. Reinhart BJ, Slack FJ, Basson M, Pasquinelli AE, Bettinger JC, Rougvie AE, Horvitz HR, Ruvkun G:The 21-nucleotide let-7 RNA regulates developmental timing in Caenorhabditis elegans. Nature 2000,403(6772):901-906.
17. Lagos-Quintana M, Rauhut R, Yalcin A, Meyer J, Lendeckel W, Tuschl T: Identification of tissue-specific microRNAs from mouse. Curr Biol 2002, 12(9):735-739.
18. Lee RC, Ambros V:An extensive class of small RNAs in Caenorhabditis elegans. Science 2001,294(5543):862-864.
19. Lagos-Quintana M, Rauhut R, Meyer J, Borkhardt A, Tuschl T:New microRNAs from mouse and human. RNA 2003,9(2):175-179.
20. Lim LP, Glasner ME, Yekta S, Burge CB, Bartel DP:Vertebrate microRNA genes. Science 2003,299(5612):1540.
21. Lim LP, Lau NC, Weinstein EG, Abdelhakim A, Yekta S, Rhoades MW, Burge CB, Bartel DP:The microRNAs of Caenorhabditis elegans. Genes Dev 2003, 17(8):991-1008.
22. Griffiths-Jones S, Saini HK, van Dongen S, Enright AJ:miRBase:tools for microRNA genomics. Nucleic Acids Res 2008,36(Database issue):D154-158.
23. Griffiths-Jones S:The microRNA Registry. Nucleic Acids Res 2004, 32(Database issue):D109-111.
24. Lee R, Feinbaum R, Ambros V:A short history of a short RNA. Cell 2004, 116(2 Suppl):S89-92,81 p following S96.
25. Chen CZ, Li L, Lodish HF, Bartel DP:MicroRNAs modulate hematopoietic lineage differentiation. Science 2004,303(5654):83-86.
26. Calin GA, Dumitru CD, Shimizu M, Bichi R, Zupo S, Noch E, Aldler H, Rattan S, Keating M, Rai K et al:Frequent deletions and down-regulation of micro-RNA genes miR15 and miR16 at 13q14 in chronic lymphocytic leukemia. Proc Natl Acad Sci U S A 2002,99(24):15524-15529.
27. Yang M, Li Y, Padgett RW:MicroRNAs:Small regulators with a big impact. Cytokine Growth Factor Rev 2005,16(4-5):387-393.
28. Kasashima K, Nakamura Y, Kozu T:Altered expression profiles of microRNAs during TPA-induced differentiation of HL-60 cells. Biochem Biophys Res Commun 2004,322(2):403-410.
29. Inoue K:[MicroRNA function in animal development]. Tanpakushitsu Kakusan Koso 2007,52(3):197-204.
30. Xu P, Guo M, Hay BA:MicroRNAs and the regulation of cell death. Trends Genet 2004,20(12):617-624.
31. Volinia S, Calin GA, Liu CG, Ambs S, Cimmino A, Petrocca F, Visone R, Iorio M, Roldo C, Ferracin M et al:A microRNA expression signature of human solid tumors defines cancer gene targets. Proc Natl Acad Sci U S A 2006, 103(7):2257-2261.
32. Calin GA, Croce CM:MicroRNA signatures in human cancers. Nat Rev Cancer 2006,6(11):857-866.
33. Yang B, Lin H, Xiao J, Lu Y, Luo X, Li B, Zhang Y, Xu C, Bai Y, Wang H et al: The muscle-specific microRNA miR-1 regulates cardiac arrhythmogenic potential by targeting GJA1 and KCNJ2. Nat Med 2007,13(4):486-491.
34. Huang Q, Gumireddy K, Schrier M, le Sage C, Nagel R, Nair S, Egan DA, Li A, Huang G, Klein-Szanto AJ et al:The microRNAs miR-373 and miR-520c promote tumour invasion and metastasis. Nat Cell Biol 2008,10(2):202-210.
35.腾晓坤,肖华胜：基因芯片与高通量DNA测序技术前景分析.中国科学C辑：生命科学2008,38(10)：891-899.
36. Barrett T, Edgar R:Gene expression omnibus:microarray data storage, submission, retrieval, and analysis. Methods Enzymol 2006,411:352-369.
37. Edgar R, Domrachev M, Lash AE:Gene Expression Omnibus:NCBI gene expression and hybridization array data repository. Nucleic Acids Res 2002, 30(1):207-210.
38. Mardis ER:The impact of next-generation sequencing technology on genetics. Trends Genet 2008,24(3):133-141.
39. Wheeler DA, Srinivasan M, Egholm M, Shen Y, Chen L, McGuire A, He W, Chen YJ, Makhijani V, Roth GT et al:The complete genome of an individual by massively parallel DNA sequencing. Nature 2008,452(7189):872-876.
40. Wang J, Wang W, Li R, Li Y, Tian G, Goodman L, Fan W, Zhang J, Li J, Guo Y et al:The diploid genome sequence of an Asian individual. Nature 2008, 456(7218):60-65.
41. Ley TJ, Mardis ER, Ding L, Fulton B, McLellan MD, Chen K, Dooling D, Dunford-Shore BH, McGrath S, Hickenbotham M et al:DNA sequencing of a cytogenetically normal acute myeloid leukaemia genome. Nature 2008, 456(7218):66-72.
42. Bentley DR, Balasubramanian S, Swerdlow HP, Smith GP, Milton J, Brown CG, Hall KP, Evers DJ, Barnes CL, Bignell HR et al:Accurate whole human genome sequencing using reversible terminator chemistry. Nature 2008, 456(7218):53-59.
43. Robertson G, Hirst M, Bainbridge M, Bilenky M, Zhao Y, Zeng T, Euskirchen G, Bernier B, Varhol R, Delaney A et al:Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing. Nat Methods 2007,4(8):651-657.
44. Jothi R, Cuddapah S, Barski A, Cui K, Zhao K:Genome-wide identification of in vivo protein-DNA binding sites from ChIP-Seq data. Nucleic Acids Res 2008,36(16):5221-5231.
45. Schmid CD, Bucher P:ChIP-Seq data reveal nucleosome architecture of human promoters. Cell 2007,131(5):831-832; author reply 832-833.
46. Barski A, Cuddapah S, Cui K, Roh TY, Schones DE, Wang Z, Wei G, Chepelev I, Zhao K:High-resolution profiling of histone methylations in the human genome. Cell 2007,129(4):823-837.
47.孙啸,路祖宏,谢建明：生物信息学基础.清华大学出版社2005.
48.李婷婷,蒋博,汪小我,张学工：转录因子结合位点的计算分析方法.生物物理学报2008,24(5)：334-346.
49. Schneider TD, Stephens RM:Sequence logos:a new way to display consensus sequences. Nucleic Acids Res 1990,18(20):6097-6100.
50. Bussemaker HJ, Li H, Siggia ED:Building a dictionary for genomes: identification of presumptive regulatory sites by statistical analysis. Proc Natl Acad Sci U S A 2000,97(18):10096-10100.
51. Sinha S, Tompa M:YMF:A program for discovery of novel transcription factor binding sites by statistical overrepresentation. Nucleic Acids Res 2003, 31(13):3586-3588.
52. Sinha S, Tompa M:Discovery of novel transcription factor binding sites by statistical overrepresentation. Nucleic Acids Res 2002,30(24):5549-5560.
53. Leung MY, Marsh GM, Speed TP:Over-and underrepresentation of short DNA words in herpesvirus genomes. J Comput Biol 1996,3(3):345-360.
54.吴昕,罗静初,李伍举：基因调控元件的计算机识别和基因调控网络构建.http://www.cbi.pku.edu.cn/chinese/documents/papers/WuX. pdf 2004
55. Hertz GZ, Stormo GD:Identifying DNA and protein patterns with statistically significant alignments of multiple sequences. Bioinformatics 1999, 15(7-8):563-577.
56. Hertz GZ, Hartzell GW,3rd, Stormo GD:Identification of consensus patterns in unaligned DNA sequences known to be functionally related. Comput Appl Biosci 1990,6(2):81-92.
57. Lawrence CE, Reilly AA:An expectation maximization (EM) algorithm for the identification and characterization of common sites in unaligned biopolymer sequences. Proteins 1990,7(1):41-51.
58. Lawrence CE, Altschul SF, Boguski MS, Liu JS, Neuwald AF, Wootton JC: Detecting subtle sequence signals:a Gibbs sampling strategy for multiple alignment. Science 1993,262(5131):208-214.
59. Thijs G, Marchal K, Lescot M, Rombauts S, De Moor B, Rouze P, Moreau Y:A Gibbs sampling method to detect overrepresented motifs in the upstream regions of coexpressed genes. J Comput Biol 2002,9(2):447-464.
60. Thijs G, Lescot M, Marchal K, Rombauts S, De Moor B, Rouze P, Moreau Y:A higher-order background model improves the detection of promoter regulatory elements by Gibbs sampling. Bioinformatics 2001,17(12):1113-1122.
61. Hughes JD, Estep PW, Tavazoie S, Church GM:Computational identification of cis-regulatory elements associated with groups of functionally related genes in Saccharomyces cerevisiae. J Mol Biol 2000,296(5):1205-1214.
62. Liu X, Brutlag DL, Liu JS:BioProspector:discovering conserved DNA motifs in upstream regulatory regions of co-expressed genes. Pac Symp Biocomput 2001:127-138.
63. Thompson WA, Newberg LA, Conlan S, McCue LA, Lawrence CE:The Gibbs Centroid Sampler. Nucleic Acids Res 2007,35(Web Server issue):W232-237.
64. Bailey TL, Williams N, Misleh C, Li WW:MEME:discovering and analyzing DNA and protein sequence motifs. Nucleic Acids Res 2006,34(Web Server issue):W369-373.
65. Ren B, Robert F, Wyrick JJ, Aparicio O, Jennings EG, Simon I, Zeitlinger J, Schreiber J, Hannett N, Kanin E et al:Genome-wide location and function of DNA binding proteins. Science 2000,290(5500):2306-2309.
66. Liu XS, Brutlag DL, Liu JS:An algorithm for finding protein-DNA binding sites with applications to chromatin-immunoprecipitation microarray experiments. Nat Biotechnol 2002,20(8):835-839.
67. Foat BC, Morozov AV, Bussemaker HJ:Statistical mechanical modeling of genome-wide transcription factor occupancy data by MatrixREDUCE. Bioinformatics 2006,22(14):e141-149.
68. Chen X, Hughes TR, Morris Q:RankMotif++:a motif-search algorithm that accounts for relative ranks of K-mers in binding transcription factors. Bioinformatics 2007,23(13):i72-79.
69. Reiss DJ, Facciotti MT, Baliga NS:Model-based deconvolution of genome-wide DNA binding. Bioinformatics 2008,24(3):396-403.
70. Johnson DS, Mortazavi A, Myers RM, Wold B:Genome-wide mapping of in vivo protein-DNA interactions. Science 2007,316(5830):1497-1502.
71. Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, Nussbaum C, Myers RM, Brown M, Li W et al:Model-based analysis of ChIP-Seq (MACS). Genome Biol 2008,9(9):R137.
72. Rozowsky J, Euskirchen G, Auerbach RK, Zhang ZD, Gibson T, Bjornson R, Carriero N, Snyder M, Gerstein MB:PeakSeq enables systematic scoring of ChIP-seq experiments relative to controls. Nat Biotechnol 2009,27(1):66-75.
73. Frith MC, Li MC, Weng Z:Cluster-Buster:Finding dense clusters of motifs in DNA sequences. Nucleic Acids Res 2003,31(13):3666-3668.
74. Moses AM, Chiang DY, Pollard DA, Iyer VN, Eisen MB:MONKEY: identifying conserved transcription-factor binding sites in multiple alignments using a binding site-specific evolutionary model. Genome Biol 2004, 5(12):R98.
75. Cliften P, Sudarsanam P, Desikan A, Fulton L, Fulton B, Majors J, Waterston R, Cohen BA, Johnston M:Finding functional features in Saccharomyces genomes by phylogenetic footprinting. Science 2003,301(5629):71-76.
76. Siddharthan R:PhyloGibbs-MP:module prediction and discriminative motif-finding by Gibbs sampling. PLoS Comput Biol 2008,4(8):e1000156.
77. Wang XJ, Reyes JL, Chua NH, Gaasterland T:Prediction and identification of Arabidopsis thaliana microRNAs and their mRNA targets. Genome Biol 2004, 5(9):R65.
78. Lai EC, Tomancak P, Williams RW, Rubin GM:Computational identification of Drosophila microRNA genes. Genome Biol 2003,4(7):R42.
79. Ghosh Z, Chakrabarti J, Mallick B:miRNomics-The bioinformatics of microRNA genes. Biochem Biophys Res Commun 2007,363(1):6-11.
80. Sullivan CS, Grundhoff AT, Tevethia S, Pipas JM, Ganem D:SV40-encoded microRNAs regulate viral gene expression and reduce susceptibility to cytotoxic T cells. Nature 2005,435(7042):682-686.
81. Adai A, Johnson C, Mlotshwa S, Archer-Evans S, Manocha V, Vance V, Sundaresan V:Computational prediction of miRNAs in Arabidopsis thaliana. Genome Res 2005,15(1):78-91.
82. Zhang B, Pan X, Cannon CH, Cobb GP, Anderson TA:Conservation and divergence of plant microRNA genes. Plant J 2006,46(2):243-259.
83. Nam JW, Kim J, Kim SK, Zhang BT:ProMiR Ⅱ:a web server for the probabilistic prediction of clustered, nonclustered, conserved and nonconserved microRNAs. Nucleic Acids Res 2006,34(Web Server issue):W455-458.
84. Hertel J, Stadler PF:Hairpins in a Haystack:recognizing microRNA precursors in comparative genomics data. Bioinformatics 2006,22(14):e197-202.
85. Jiang P, Wu H, Wang W, Ma W, Sun X, Lu Z:MiPred:classification of real and pseudo microRNA precursors using random forest prediction model with combined features. Nucleic Acids Res 2007,35(Web Server issue):W339-344.
86. Wang X, Zhang J, Li F, Gu J, He T, Zhang X, Li Y:MicroRNA identification based on sequence and structure alignment. Bioinformatics 2005, 21(18):3610-3614.
87. Xue C, Li F, He T, Liu GP, Li Y, Zhang X:Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine. BMC Bioinformatics 2005,6:310.
88. Wang X, Gu J, Zhang MQ, Li Y:Identification of phylogenetically conserved microRNA cis-regulatory elements across 12 Drosophila species. Bioinformatics 2008,24(2):165-171.
89.汪小我,张学工,李衍达：脊椎动物中微小RNA进化模式研究.中国科学C辑：生命科学2008,38(4)：348-355.
90. Boross G, Orosz K, Farkas IJ:Human microRNAs co-silence in well-separated groups and have different predicted essentialities. Bioinformatics 2009, 25(8):1063-1069.
91. Lewis BP, Burge CB, Bartel DP:Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 2005,120(1):15-20.
92. Brennecke J, Stark A, Russell RB, Cohen SM:Principles of microRNA-target recognition. PLoS Biol 2005,3(3):e85.
93. Grun D, Wang YL, Langenberger D, Gunsalus KC, Rajewsky N:microRNA target predictions across seven Drosophila species and comparison to mammalian targets. PLoS Comput Biol 2005, 1(1):e13.
94. Maragkakis M, Reczko M, Simossis VA, Alexiou P, Papadopoulos GL, Dalamagas T, Giannopoulos G, Goumas G, Koukis E, Kourtis K et al: DIANA-microT web server:elucidating microRNA functions through target prediction. Nucleic Acids Res 2009,37(Web Server issue):W273-276.
95. http://www.mirz.unibas.ch/E1MMo2/.
96. John B, Sander C, Marks DS:Prediction of human microRNA targets. Methods Mol Biol 2006,342:101-113.
97. Kertesz M, Iovino N, Unnerstall U, Gaul U, Segal E:The role of site accessibility in microRNA target recognition. Nat Genet 2007, 39(10):1278-1284.
98. Houbaviy HB, Dennis L, Jaenisch R, Sharp PA:Characterization of a highly variable eutherian microRNA gene. RNA 2005,11(8):1245-1257.
99. Ohler U, Yekta S, Lim LP, Bartel DP, Burge CB:Patterns of flanking sequence conservation and a characteristic upstream motif for microRNA gene identification. RNA 2004,10(9):1309-1322.
100. Zhou X, Ruan J, Wang G, Zhang W:Characterization and identification of microRNA core promoters in four model species. PLoS Comput Biol 2007, 3(3):e37.
101.Fujita S, Iba H:Putative promoter regions of miRNA genes involved in evolutionarily conserved regulatory systems among vertebrates. Bioinformatics 2008,24(3):303-308.
102. Gu J, He T, Pei Y, Li F, Wang X, Zhang J, Zhang X, Li Y:Primary transcripts and expressions of mammal intergenic microRNAs detected by mapping ESTs to their flanking sequences. Mamm Genome 2006,17(10):1033-1041.
103. Saini HK, Griffiths-Jones S, Enright AJ:Genomic analysis of human microRNA transcripts. Proc Natl Acad Sci U S A 2007,104(45):17719-17724.
104. Bussemaker HJ, Li H, Siggia ED:Regulatory element detection using correlation with expression. Nat Genet 2001,27(2):167-171.
105. Conlon EM, Liu XS, Lieb JD, Liu JS:Integrating regulatory motif discovery and genome-wide expression analysis. Proc Natl Acad Sci U S A 2003, 100(6):3339-3344.
106. Liu Y, Taylor MW, Edenberg HJ:Model-based identification of cis-acting elements from micro array data. Genomics 2006,88(4):452-461.
107. Ogawa T, Kuwagata M, Ruiz J, Zhou FC:Differential teratogenic effect of alcohol on embryonic development between C57BL/6 and DBA/2 mice:a new view. Alcohol Clin Exp Res 2005,29(5):855-863.
108. Matys V, Fricke E, Geffers R, Gossling E, Haubrock M, Hehl R, Hornischer K, Karas D, Kel AE, Kel-Margoulis OV et al:TRANSFAC:transcriptional regulation, from patterns to profiles. Nucleic Acids Res 2003,31(1):374-378.
109. Matys V, Kel-Margoulis OV, Fricke E, Liebich I, Land S, Barre-Dirrie A, Reuter I, Chekmenev D, Krull M, Hornischer K et al:TRANSFAC and its module TRANSCompel:transcriptional gene regulation in eukaryotes. Nucleic Acids Res 2006,34(Database issue):D108-110.
110. McClintick JN, Edenberg HJ:Effects of filtering by Present call on analysis of microarray experiments. BMC Bioinformatics 2006,7:49.
111. Xie X, Lu J, Kulbokas EJ, Golub TR, Mootha V, Lindblad-Toh K, Lander ES, Kellis M:Systematic discovery of regulatory motifs in human promoters and 3' UTRs by comparison of several mammals. Nature 2005,434(7031):338-345.
112. Chan CS, Elemento O, Tavazoie S:Revealing posttranscriptional regulatory elements through network-level conservation. PLoS Comput Biol 2005, 1(7):e69.
113. Farh KK, Grimson A, Jan C, Lewis BP, Johnston WK, Lim LP, Burge CB, Bartel DP:The widespread impact of mammalian MicroRNAs on mRNA repression and evolution. Science 2005,310(5755):1817-1821.
114. Zhang W, Wagner BJ, Ehrenman K, Schaefer AW, DeMaria CT, Crater D, DeHaven K, Long L, Brewer G:Purification, characterization, and cDNA cloning of an AU-rich element RNA-binding protein, AUF1. Mol Cell Biol 1993,13(12):7652-7665.
115. Karin M, Liu Z, Zandi E:AP-1 function and regulation. Curr Opin Cell Biol 1997,9(2):240-246.
116. Glover JN, Harrison SC:Crystal structure of the heterodimeric bZIP transcription factor c-Fos-c-Jun bound to DNA. Nature 1995, 373(6511):257-261.
117. Pienta KJ, Bradley D:Mechanisms underlying the development of androgen-independent prostate cancer. Clin Cancer Res 2006, 12(6):1665-1671.
118. Ripple MO, Henry WF, Schwarze SR, Wilding G, Weindruch R:Effect of antioxidants on androgen-induced AP-1 and NF-kappaB DNA-binding activity in prostate carcinoma cells. J Natl Cancer Inst 1999,91(14):1227-1232.
119. Sato N, Sadar MD, Bruchovsky N, Saatcioglu F, Rennie PS, Sato S, Lange PH, Gleave ME:Androgenic induction of prostate-specific antigen gene is repressed by protein-protein interaction between the androgen receptor and AP-1/c-Jun in the human prostate cancer cell line LNCaP. J Biol Chem 1997, 272(28):17485-17494.
120. Tzukerman M, Zhang XK, Pfahl M:Inhibition of estrogen receptor activity by the tumor promoter 12-O-tetradeconylphorbol-13-acetate:a molecular analysis. Mol Endocrinol 1991,5(12):1983-1992.
121.Yang-Yen HF, Chambard JC, Sun YL, Smeal T, Schmidt TJ, Drouin J, Karin M: Transcriptional interference between c-Jun and the glucocorticoid receptor: mutual inhibition of DNA binding due to direct protein-protein interaction. Cell 1990,62(6):1205-1215.
122. Nicholson RC, Mader S, Nagpal S, Leid M, Rochette-Egly C, Chambon P: Negative regulation of the rat stromelysin gene promoter by retinoic acid is mediated by an API binding site. EMBO J 1990,9(13):4443-4454.
123. Owen TA, Bortell R, Yocum SA, Smock SL, Zhang M, Abate C, Shalhoub V, Aronin N, Wright KL, van Wijnen AJ et al:Coordinate occupancy of AP-1 sites in the vitamin D-responsive and CCAAT box elements by Fos-Jun in the osteocalcin gene:model for phenotype suppression of transcription. Proc Natl Acad Sci U S A 1990,87(24):9990-9994.
124. Zhang XK, Wills KN, Husmann M, Hermann T, Pfahl M:Novel pathway for thyroid hormone receptor action through interaction with jun and fos oncogene activities. Mol Cell Biol 1991, 11(12):6016-6025.
125. Yang SZ, Abdulkadir SA:Early growth response gene 1 modulates androgen receptor signaling in prostate carcinoma cells. J Biol Chem 2003, 278(41):39906-39911.
126. Cantor SB, Bell DW, Ganesan S, Kass EM, Drapkin R, Grossman S, Wahrer DC, Sgroi DC, Lane WS, Haber DA et al:BACH1, a novel helicase-like protein, interacts directly with BRCA1 and contributes to its DNA repair function. Cell 2001,105(1):149-160.
127. Yeh S, Hu YC, Rahman M, Lin HK, Hsu CL, Ting HJ, Kang HY, Chang C: Increase of androgen-induced cell death and androgen receptor transactivation by BRCA1 in prostate cancer cells. Proc Natl Acad Sci U S A 2000, 97(21):11256-11261.
128. Gao B, Shen X, Kunos G, Meng Q, Goldberg ID, Rosen EM, Fan S: Constitutive activation of JAK-STAT3 signaling by BRCA1 in human prostate cancer cells. FEBS Lett 2001,488(3):179-184.
129. Grossmann ME, Huang H, Tindall DJ:Androgen receptor signaling in androgen-refractory prostate cancer. J Natl Cancer Inst 2001, 93(22):1687-1697.
130.Heinlein CA, Chang C:Androgen receptor in prostate cancer. Endocr Rev 2004, 25(2):276-308.
131.Begbie M, Mueller C, Lillicrap D:Enhanced binding of HLF/DBP heterodimers represents one mechanism of PAR protein transactivation of the factor Ⅷ and factor Ⅸ genes. DNA Cell Biol 1999,18(2):165-173.
132. Zhao Y, Srivastava D:A developmental view of microRNA function. Trends Biochem Sci 2007,32(4):189-197.
133. Marson A, Levine SS, Cole MF, Frampton GM, Brambrink T, Johnstone S, Guenther MG, Johnston WK, Wernig M, Newman J et al:Connecting microRNA genes to the core transcriptional regulatory circuitry of embryonic stem cells. Cell 2008,134(3):521-533.
134. Corcoran DL, Pandit KV, Gordon B, Bhattacharjee A, Kaminski N, Benos PV: Features of mammalian microRNA promoters emerge from polymerase Ⅱ chromatin immunoprecipitation data. PLoS One 2009,4(4):e5279.
135. Lasiuk GC, Hegadoren KM:The effects of estradiol on central serotonergic systems and its relationship to mood in women. Biol Res Nurs 2007, 9(2):147-160.
136. Li X, Huang J, Yi P, Bambara RA, Hilf R, Muyan M:Single-chain estrogen receptors (ERs) reveal that the ERalpha/beta heterodimer emulates functions of the ERalpha dimer in genomic estrogen signaling pathways. Mol Cell Biol 2004,24(17):7681-7694.
137. Campbell RA, Bhat-Nakshatri P, Patel NM, Constantinidou D, Ali S, Nakshatri H:Phosphatidylinositol 3-kinase/AKT-mediated activation of estrogen receptor alpha:a new model for anti-estrogen resistance. J Biol Chem 2001, 276(13):9817-9824.
138. Graf S, Nielsen FG, Kurtz S, Huynen MA, Birney E, Stunnenberg H, Flicek P: Optimized design and assessment of whole genome tiling arrays. Bioinformatics 2007,23(13):i195-204.
139. Thomson JM, Parker J, Perou CM, Hammond SM:A custom microarray platform for analysis of microRNA gene expression. Nat Methods 2004, 1(1):47-53.
140. Johnson WE, Li W, Meyer CA, Gottardo R, Carroll JS, Brown M, Liu XS: Model-based analysis of tiling-arrays for ChIP-chip. Proc Natl Acad Sci U S A 2006,103(33):12457-12462.
2. Birney E, Stamatoyannopoulos JA, Dutta A, Guigo R, Gingeras TR, Margulies EH, Weng Z, Snyder M, Dermitzakis ET, Thurman RE et al:Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature 2007,447(7146):799-816.
3. Thomas DJ, Rosenbloom KR, Clawson H, Hinrichs AS, Trumbower H, Raney BJ, Karolchik D, Barber GP, Harte RA, Hillman-Jackson J et al:The ENCODE Project at UC Santa Cruz. Nucleic Acids Res 2007,35(Database issue):D663-667.
4. The ENCODE (ENCyclopedia Of DNA Elements) Project. Science 2004, 306(5696):636-640.
5. Nakao M, Bono H, Kawashima S, Kamiya T, Sato K, Goto S, Kanehisa M: Genome-scale Gene Expression Analysis and Pathway Reconstruction in KEGG. Genome Inform Ser Workshop Genome Inform 1999,10:94-103.
6. Latchman DS:Transcription factors:an overview. Int J Biochem Cell Biol 1997,29(12):1305-1312.
7. Karin M:Too many transcription factors:positive and negative interactions. New Biol 1990,2(2):126-131.
8. Lee RC, Feinbaum RL, Ambros V:The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell 1993, 75(5):843-854.
9. Ambros V:The functions of animal microRNAs. Nature 2004, 431(7006):350-355.
10. Bartel DP:MicroRNAs:genomics, biogenesis, mechanism, and function. Cell 2004,116(2):281-297.
11. Mitchell PJ, Tjian R:Transcriptional regulation in mammalian cells by sequence-specific DNA binding proteins. Science 1989,245(4916):371-378.
12. Ptashne M, Gann A:Transcriptional activation by recruitment. Nature 1997, 386(6625):569-577.
13. Xu L, Glass CK, Rosenfeld MG:Coactivator and corepressor complexes in nuclear receptor function. Curr Opin Genet Dev 1999,9(2):140-147.
14. Pawson T:Signal transduction--a conserved pathway from the membrane to the nucleus. Dev Genet 1993,14(5):333-338.
15. Wang JC:Finding primary targets of transcriptional regulators. Cell Cycle 2005,4(3):356-358.
16. Reinhart BJ, Slack FJ, Basson M, Pasquinelli AE, Bettinger JC, Rougvie AE, Horvitz HR, Ruvkun G:The 21-nucleotide let-7 RNA regulates developmental timing in Caenorhabditis elegans. Nature 2000,403(6772):901-906.
17. Lagos-Quintana M, Rauhut R, Yalcin A, Meyer J, Lendeckel W, Tuschl T: Identification of tissue-specific microRNAs from mouse. Curr Biol 2002, 12(9):735-739.
18. Lee RC, Ambros V:An extensive class of small RNAs in Caenorhabditis elegans. Science 2001,294(5543):862-864.
19. Lagos-Quintana M, Rauhut R, Meyer J, Borkhardt A, Tuschl T:New microRNAs from mouse and human. RNA 2003,9(2):175-179.
20. Lim LP, Glasner ME, Yekta S, Burge CB, Bartel DP:Vertebrate microRNA genes. Science 2003,299(5612):1540.
21. Lim LP, Lau NC, Weinstein EG, Abdelhakim A, Yekta S, Rhoades MW, Burge CB, Bartel DP:The microRNAs of Caenorhabditis elegans. Genes Dev 2003, 17(8):991-1008.
22. Griffiths-Jones S, Saini HK, van Dongen S, Enright AJ:miRBase:tools for microRNA genomics. Nucleic Acids Res 2008,36(Database issue):D154-158.
23. Griffiths-Jones S:The microRNA Registry. Nucleic Acids Res 2004, 32(Database issue):D109-111.
24. Lee R, Feinbaum R, Ambros V:A short history of a short RNA. Cell 2004, 116(2 Suppl):S89-92,81 p following S96.
25. Chen CZ, Li L, Lodish HF, Bartel DP:MicroRNAs modulate hematopoietic lineage differentiation. Science 2004,303(5654):83-86.
26. Calin GA, Dumitru CD, Shimizu M, Bichi R, Zupo S, Noch E, Aldler H, Rattan S, Keating M, Rai K et al:Frequent deletions and down-regulation of micro-RNA genes miR15 and miR16 at 13q14 in chronic lymphocytic leukemia. Proc Natl Acad Sci U S A 2002,99(24):15524-15529.
27. Yang M, Li Y, Padgett RW:MicroRNAs:Small regulators with a big impact. Cytokine Growth Factor Rev 2005,16(4-5):387-393.
28. Kasashima K, Nakamura Y, Kozu T:Altered expression profiles of microRNAs during TPA-induced differentiation of HL-60 cells. Biochem Biophys Res Commun 2004,322(2):403-410.
29. Inoue K:[MicroRNA function in animal development]. Tanpakushitsu Kakusan Koso 2007,52(3):197-204.
30. Xu P, Guo M, Hay BA:MicroRNAs and the regulation of cell death. Trends Genet 2004,20(12):617-624.
31. Volinia S, Calin GA, Liu CG, Ambs S, Cimmino A, Petrocca F, Visone R, Iorio M, Roldo C, Ferracin M et al:A microRNA expression signature of human solid tumors defines cancer gene targets. Proc Natl Acad Sci U S A 2006, 103(7):2257-2261.
32. Calin GA, Croce CM:MicroRNA signatures in human cancers. Nat Rev Cancer 2006,6(11):857-866.
33. Yang B, Lin H, Xiao J, Lu Y, Luo X, Li B, Zhang Y, Xu C, Bai Y, Wang H et al: The muscle-specific microRNA miR-1 regulates cardiac arrhythmogenic potential by targeting GJA1 and KCNJ2. Nat Med 2007,13(4):486-491.
34. Huang Q, Gumireddy K, Schrier M, le Sage C, Nagel R, Nair S, Egan DA, Li A, Huang G, Klein-Szanto AJ et al:The microRNAs miR-373 and miR-520c promote tumour invasion and metastasis. Nat Cell Biol 2008,10(2):202-210.
35.腾晓坤,肖华胜：基因芯片与高通量DNA测序技术前景分析.中国科学C辑：生命科学2008,38(10)：891-899.
36. Barrett T, Edgar R:Gene expression omnibus:microarray data storage, submission, retrieval, and analysis. Methods Enzymol 2006,411:352-369.
37. Edgar R, Domrachev M, Lash AE:Gene Expression Omnibus:NCBI gene expression and hybridization array data repository. Nucleic Acids Res 2002, 30(1):207-210.
38. Mardis ER:The impact of next-generation sequencing technology on genetics. Trends Genet 2008,24(3):133-141.
39. Wheeler DA, Srinivasan M, Egholm M, Shen Y, Chen L, McGuire A, He W, Chen YJ, Makhijani V, Roth GT et al:The complete genome of an individual by massively parallel DNA sequencing. Nature 2008,452(7189):872-876.
40. Wang J, Wang W, Li R, Li Y, Tian G, Goodman L, Fan W, Zhang J, Li J, Guo Y et al:The diploid genome sequence of an Asian individual. Nature 2008, 456(7218):60-65.
41. Ley TJ, Mardis ER, Ding L, Fulton B, McLellan MD, Chen K, Dooling D, Dunford-Shore BH, McGrath S, Hickenbotham M et al:DNA sequencing of a cytogenetically normal acute myeloid leukaemia genome. Nature 2008, 456(7218):66-72.
42. Bentley DR, Balasubramanian S, Swerdlow HP, Smith GP, Milton J, Brown CG, Hall KP, Evers DJ, Barnes CL, Bignell HR et al:Accurate whole human genome sequencing using reversible terminator chemistry. Nature 2008, 456(7218):53-59.
43. Robertson G, Hirst M, Bainbridge M, Bilenky M, Zhao Y, Zeng T, Euskirchen G, Bernier B, Varhol R, Delaney A et al:Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing. Nat Methods 2007,4(8):651-657.
44. Jothi R, Cuddapah S, Barski A, Cui K, Zhao K:Genome-wide identification of in vivo protein-DNA binding sites from ChIP-Seq data. Nucleic Acids Res 2008,36(16):5221-5231.
45. Schmid CD, Bucher P:ChIP-Seq data reveal nucleosome architecture of human promoters. Cell 2007,131(5):831-832; author reply 832-833.
46. Barski A, Cuddapah S, Cui K, Roh TY, Schones DE, Wang Z, Wei G, Chepelev I, Zhao K:High-resolution profiling of histone methylations in the human genome. Cell 2007,129(4):823-837.
47.孙啸,路祖宏,谢建明：生物信息学基础.清华大学出版社2005.
48.李婷婷,蒋博,汪小我,张学工：转录因子结合位点的计算分析方法.生物物理学报2008,24(5)：334-346.
49. Schneider TD, Stephens RM:Sequence logos:a new way to display consensus sequences. Nucleic Acids Res 1990,18(20):6097-6100.
50. Bussemaker HJ, Li H, Siggia ED:Building a dictionary for genomes: identification of presumptive regulatory sites by statistical analysis. Proc Natl Acad Sci U S A 2000,97(18):10096-10100.
51. Sinha S, Tompa M:YMF:A program for discovery of novel transcription factor binding sites by statistical overrepresentation. Nucleic Acids Res 2003, 31(13):3586-3588.
52. Sinha S, Tompa M:Discovery of novel transcription factor binding sites by statistical overrepresentation. Nucleic Acids Res 2002,30(24):5549-5560.
53. Leung MY, Marsh GM, Speed TP:Over-and underrepresentation of short DNA words in herpesvirus genomes. J Comput Biol 1996,3(3):345-360.
54.吴昕,罗静初,李伍举：基因调控元件的计算机识别和基因调控网络构建.http://www.cbi.pku.edu.cn/chinese/documents/papers/WuX. pdf 2004
55. Hertz GZ, Stormo GD:Identifying DNA and protein patterns with statistically significant alignments of multiple sequences. Bioinformatics 1999, 15(7-8):563-577.
56. Hertz GZ, Hartzell GW,3rd, Stormo GD:Identification of consensus patterns in unaligned DNA sequences known to be functionally related. Comput Appl Biosci 1990,6(2):81-92.
57. Lawrence CE, Reilly AA:An expectation maximization (EM) algorithm for the identification and characterization of common sites in unaligned biopolymer sequences. Proteins 1990,7(1):41-51.
58. Lawrence CE, Altschul SF, Boguski MS, Liu JS, Neuwald AF, Wootton JC: Detecting subtle sequence signals:a Gibbs sampling strategy for multiple alignment. Science 1993,262(5131):208-214.
59. Thijs G, Marchal K, Lescot M, Rombauts S, De Moor B, Rouze P, Moreau Y:A Gibbs sampling method to detect overrepresented motifs in the upstream regions of coexpressed genes. J Comput Biol 2002,9(2):447-464.
60. Thijs G, Lescot M, Marchal K, Rombauts S, De Moor B, Rouze P, Moreau Y:A higher-order background model improves the detection of promoter regulatory elements by Gibbs sampling. Bioinformatics 2001,17(12):1113-1122.
61. Hughes JD, Estep PW, Tavazoie S, Church GM:Computational identification of cis-regulatory elements associated with groups of functionally related genes in Saccharomyces cerevisiae. J Mol Biol 2000,296(5):1205-1214.
62. Liu X, Brutlag DL, Liu JS:BioProspector:discovering conserved DNA motifs in upstream regulatory regions of co-expressed genes. Pac Symp Biocomput 2001:127-138.
63. Thompson WA, Newberg LA, Conlan S, McCue LA, Lawrence CE:The Gibbs Centroid Sampler. Nucleic Acids Res 2007,35(Web Server issue):W232-237.
64. Bailey TL, Williams N, Misleh C, Li WW:MEME:discovering and analyzing DNA and protein sequence motifs. Nucleic Acids Res 2006,34(Web Server issue):W369-373.
65. Ren B, Robert F, Wyrick JJ, Aparicio O, Jennings EG, Simon I, Zeitlinger J, Schreiber J, Hannett N, Kanin E et al:Genome-wide location and function of DNA binding proteins. Science 2000,290(5500):2306-2309.
66. Liu XS, Brutlag DL, Liu JS:An algorithm for finding protein-DNA binding sites with applications to chromatin-immunoprecipitation microarray experiments. Nat Biotechnol 2002,20(8):835-839.
67. Foat BC, Morozov AV, Bussemaker HJ:Statistical mechanical modeling of genome-wide transcription factor occupancy data by MatrixREDUCE. Bioinformatics 2006,22(14):e141-149.
68. Chen X, Hughes TR, Morris Q:RankMotif++:a motif-search algorithm that accounts for relative ranks of K-mers in binding transcription factors. Bioinformatics 2007,23(13):i72-79.
69. Reiss DJ, Facciotti MT, Baliga NS:Model-based deconvolution of genome-wide DNA binding. Bioinformatics 2008,24(3):396-403.
70. Johnson DS, Mortazavi A, Myers RM, Wold B:Genome-wide mapping of in vivo protein-DNA interactions. Science 2007,316(5830):1497-1502.
71. Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, Nussbaum C, Myers RM, Brown M, Li W et al:Model-based analysis of ChIP-Seq (MACS). Genome Biol 2008,9(9):R137.
72. Rozowsky J, Euskirchen G, Auerbach RK, Zhang ZD, Gibson T, Bjornson R, Carriero N, Snyder M, Gerstein MB:PeakSeq enables systematic scoring of ChIP-seq experiments relative to controls. Nat Biotechnol 2009,27(1):66-75.
73. Frith MC, Li MC, Weng Z:Cluster-Buster:Finding dense clusters of motifs in DNA sequences. Nucleic Acids Res 2003,31(13):3666-3668.
74. Moses AM, Chiang DY, Pollard DA, Iyer VN, Eisen MB:MONKEY: identifying conserved transcription-factor binding sites in multiple alignments using a binding site-specific evolutionary model. Genome Biol 2004, 5(12):R98.
75. Cliften P, Sudarsanam P, Desikan A, Fulton L, Fulton B, Majors J, Waterston R, Cohen BA, Johnston M:Finding functional features in Saccharomyces genomes by phylogenetic footprinting. Science 2003,301(5629):71-76.
76. Siddharthan R:PhyloGibbs-MP:module prediction and discriminative motif-finding by Gibbs sampling. PLoS Comput Biol 2008,4(8):e1000156.
77. Wang XJ, Reyes JL, Chua NH, Gaasterland T:Prediction and identification of Arabidopsis thaliana microRNAs and their mRNA targets. Genome Biol 2004, 5(9):R65.
78. Lai EC, Tomancak P, Williams RW, Rubin GM:Computational identification of Drosophila microRNA genes. Genome Biol 2003,4(7):R42.
79. Ghosh Z, Chakrabarti J, Mallick B:miRNomics-The bioinformatics of microRNA genes. Biochem Biophys Res Commun 2007,363(1):6-11.
80. Sullivan CS, Grundhoff AT, Tevethia S, Pipas JM, Ganem D:SV40-encoded microRNAs regulate viral gene expression and reduce susceptibility to cytotoxic T cells. Nature 2005,435(7042):682-686.
81. Adai A, Johnson C, Mlotshwa S, Archer-Evans S, Manocha V, Vance V, Sundaresan V:Computational prediction of miRNAs in Arabidopsis thaliana. Genome Res 2005,15(1):78-91.
82. Zhang B, Pan X, Cannon CH, Cobb GP, Anderson TA:Conservation and divergence of plant microRNA genes. Plant J 2006,46(2):243-259.
83. Nam JW, Kim J, Kim SK, Zhang BT:ProMiR Ⅱ:a web server for the probabilistic prediction of clustered, nonclustered, conserved and nonconserved microRNAs. Nucleic Acids Res 2006,34(Web Server issue):W455-458.
84. Hertel J, Stadler PF:Hairpins in a Haystack:recognizing microRNA precursors in comparative genomics data. Bioinformatics 2006,22(14):e197-202.
85. Jiang P, Wu H, Wang W, Ma W, Sun X, Lu Z:MiPred:classification of real and pseudo microRNA precursors using random forest prediction model with combined features. Nucleic Acids Res 2007,35(Web Server issue):W339-344.
86. Wang X, Zhang J, Li F, Gu J, He T, Zhang X, Li Y:MicroRNA identification based on sequence and structure alignment. Bioinformatics 2005, 21(18):3610-3614.
87. Xue C, Li F, He T, Liu GP, Li Y, Zhang X:Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine. BMC Bioinformatics 2005,6:310.
88. Wang X, Gu J, Zhang MQ, Li Y:Identification of phylogenetically conserved microRNA cis-regulatory elements across 12 Drosophila species. Bioinformatics 2008,24(2):165-171.
89.汪小我,张学工,李衍达：脊椎动物中微小RNA进化模式研究.中国科学C辑：生命科学2008,38(4)：348-355.
90. Boross G, Orosz K, Farkas IJ:Human microRNAs co-silence in well-separated groups and have different predicted essentialities. Bioinformatics 2009, 25(8):1063-1069.
91. Lewis BP, Burge CB, Bartel DP:Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 2005,120(1):15-20.
92. Brennecke J, Stark A, Russell RB, Cohen SM:Principles of microRNA-target recognition. PLoS Biol 2005,3(3):e85.
93. Grun D, Wang YL, Langenberger D, Gunsalus KC, Rajewsky N:microRNA target predictions across seven Drosophila species and comparison to mammalian targets. PLoS Comput Biol 2005, 1(1):e13.
94. Maragkakis M, Reczko M, Simossis VA, Alexiou P, Papadopoulos GL, Dalamagas T, Giannopoulos G, Goumas G, Koukis E, Kourtis K et al: DIANA-microT web server:elucidating microRNA functions through target prediction. Nucleic Acids Res 2009,37(Web Server issue):W273-276.
95. http://www.mirz.unibas.ch/E1MMo2/.
96. John B, Sander C, Marks DS:Prediction of human microRNA targets. Methods Mol Biol 2006,342:101-113.
97. Kertesz M, Iovino N, Unnerstall U, Gaul U, Segal E:The role of site accessibility in microRNA target recognition. Nat Genet 2007, 39(10):1278-1284.
98. Houbaviy HB, Dennis L, Jaenisch R, Sharp PA:Characterization of a highly variable eutherian microRNA gene. RNA 2005,11(8):1245-1257.
99. Ohler U, Yekta S, Lim LP, Bartel DP, Burge CB:Patterns of flanking sequence conservation and a characteristic upstream motif for microRNA gene identification. RNA 2004,10(9):1309-1322.
100. Zhou X, Ruan J, Wang G, Zhang W:Characterization and identification of microRNA core promoters in four model species. PLoS Comput Biol 2007, 3(3):e37.
101.Fujita S, Iba H:Putative promoter regions of miRNA genes involved in evolutionarily conserved regulatory systems among vertebrates. Bioinformatics 2008,24(3):303-308.
102. Gu J, He T, Pei Y, Li F, Wang X, Zhang J, Zhang X, Li Y:Primary transcripts and expressions of mammal intergenic microRNAs detected by mapping ESTs to their flanking sequences. Mamm Genome 2006,17(10):1033-1041.
103. Saini HK, Griffiths-Jones S, Enright AJ:Genomic analysis of human microRNA transcripts. Proc Natl Acad Sci U S A 2007,104(45):17719-17724.
104. Bussemaker HJ, Li H, Siggia ED:Regulatory element detection using correlation with expression. Nat Genet 2001,27(2):167-171.
105. Conlon EM, Liu XS, Lieb JD, Liu JS:Integrating regulatory motif discovery and genome-wide expression analysis. Proc Natl Acad Sci U S A 2003, 100(6):3339-3344.
106. Liu Y, Taylor MW, Edenberg HJ:Model-based identification of cis-acting elements from micro array data. Genomics 2006,88(4):452-461.
107. Ogawa T, Kuwagata M, Ruiz J, Zhou FC:Differential teratogenic effect of alcohol on embryonic development between C57BL/6 and DBA/2 mice:a new view. Alcohol Clin Exp Res 2005,29(5):855-863.
108. Matys V, Fricke E, Geffers R, Gossling E, Haubrock M, Hehl R, Hornischer K, Karas D, Kel AE, Kel-Margoulis OV et al:TRANSFAC:transcriptional regulation, from patterns to profiles. Nucleic Acids Res 2003,31(1):374-378.
109. Matys V, Kel-Margoulis OV, Fricke E, Liebich I, Land S, Barre-Dirrie A, Reuter I, Chekmenev D, Krull M, Hornischer K et al:TRANSFAC and its module TRANSCompel:transcriptional gene regulation in eukaryotes. Nucleic Acids Res 2006,34(Database issue):D108-110.
110. McClintick JN, Edenberg HJ:Effects of filtering by Present call on analysis of microarray experiments. BMC Bioinformatics 2006,7:49.
111. Xie X, Lu J, Kulbokas EJ, Golub TR, Mootha V, Lindblad-Toh K, Lander ES, Kellis M:Systematic discovery of regulatory motifs in human promoters and 3' UTRs by comparison of several mammals. Nature 2005,434(7031):338-345.
112. Chan CS, Elemento O, Tavazoie S:Revealing posttranscriptional regulatory elements through network-level conservation. PLoS Comput Biol 2005, 1(7):e69.
113. Farh KK, Grimson A, Jan C, Lewis BP, Johnston WK, Lim LP, Burge CB, Bartel DP:The widespread impact of mammalian MicroRNAs on mRNA repression and evolution. Science 2005,310(5755):1817-1821.
114. Zhang W, Wagner BJ, Ehrenman K, Schaefer AW, DeMaria CT, Crater D, DeHaven K, Long L, Brewer G:Purification, characterization, and cDNA cloning of an AU-rich element RNA-binding protein, AUF1. Mol Cell Biol 1993,13(12):7652-7665.
115. Karin M, Liu Z, Zandi E:AP-1 function and regulation. Curr Opin Cell Biol 1997,9(2):240-246.
116. Glover JN, Harrison SC:Crystal structure of the heterodimeric bZIP transcription factor c-Fos-c-Jun bound to DNA. Nature 1995, 373(6511):257-261.
117. Pienta KJ, Bradley D:Mechanisms underlying the development of androgen-independent prostate cancer. Clin Cancer Res 2006, 12(6):1665-1671.
118. Ripple MO, Henry WF, Schwarze SR, Wilding G, Weindruch R:Effect of antioxidants on androgen-induced AP-1 and NF-kappaB DNA-binding activity in prostate carcinoma cells. J Natl Cancer Inst 1999,91(14):1227-1232.
119. Sato N, Sadar MD, Bruchovsky N, Saatcioglu F, Rennie PS, Sato S, Lange PH, Gleave ME:Androgenic induction of prostate-specific antigen gene is repressed by protein-protein interaction between the androgen receptor and AP-1/c-Jun in the human prostate cancer cell line LNCaP. J Biol Chem 1997, 272(28):17485-17494.
120. Tzukerman M, Zhang XK, Pfahl M:Inhibition of estrogen receptor activity by the tumor promoter 12-O-tetradeconylphorbol-13-acetate:a molecular analysis. Mol Endocrinol 1991,5(12):1983-1992.
121.Yang-Yen HF, Chambard JC, Sun YL, Smeal T, Schmidt TJ, Drouin J, Karin M: Transcriptional interference between c-Jun and the glucocorticoid receptor: mutual inhibition of DNA binding due to direct protein-protein interaction. Cell 1990,62(6):1205-1215.
122. Nicholson RC, Mader S, Nagpal S, Leid M, Rochette-Egly C, Chambon P: Negative regulation of the rat stromelysin gene promoter by retinoic acid is mediated by an API binding site. EMBO J 1990,9(13):4443-4454.
123. Owen TA, Bortell R, Yocum SA, Smock SL, Zhang M, Abate C, Shalhoub V, Aronin N, Wright KL, van Wijnen AJ et al:Coordinate occupancy of AP-1 sites in the vitamin D-responsive and CCAAT box elements by Fos-Jun in the osteocalcin gene:model for phenotype suppression of transcription. Proc Natl Acad Sci U S A 1990,87(24):9990-9994.
124. Zhang XK, Wills KN, Husmann M, Hermann T, Pfahl M:Novel pathway for thyroid hormone receptor action through interaction with jun and fos oncogene activities. Mol Cell Biol 1991, 11(12):6016-6025.
125. Yang SZ, Abdulkadir SA:Early growth response gene 1 modulates androgen receptor signaling in prostate carcinoma cells. J Biol Chem 2003, 278(41):39906-39911.
126. Cantor SB, Bell DW, Ganesan S, Kass EM, Drapkin R, Grossman S, Wahrer DC, Sgroi DC, Lane WS, Haber DA et al:BACH1, a novel helicase-like protein, interacts directly with BRCA1 and contributes to its DNA repair function. Cell 2001,105(1):149-160.
127. Yeh S, Hu YC, Rahman M, Lin HK, Hsu CL, Ting HJ, Kang HY, Chang C: Increase of androgen-induced cell death and androgen receptor transactivation by BRCA1 in prostate cancer cells. Proc Natl Acad Sci U S A 2000, 97(21):11256-11261.
128. Gao B, Shen X, Kunos G, Meng Q, Goldberg ID, Rosen EM, Fan S: Constitutive activation of JAK-STAT3 signaling by BRCA1 in human prostate cancer cells. FEBS Lett 2001,488(3):179-184.
129. Grossmann ME, Huang H, Tindall DJ:Androgen receptor signaling in androgen-refractory prostate cancer. J Natl Cancer Inst 2001, 93(22):1687-1697.
130.Heinlein CA, Chang C:Androgen receptor in prostate cancer. Endocr Rev 2004, 25(2):276-308.
131.Begbie M, Mueller C, Lillicrap D:Enhanced binding of HLF/DBP heterodimers represents one mechanism of PAR protein transactivation of the factor Ⅷ and factor Ⅸ genes. DNA Cell Biol 1999,18(2):165-173.
132. Zhao Y, Srivastava D:A developmental view of microRNA function. Trends Biochem Sci 2007,32(4):189-197.
133. Marson A, Levine SS, Cole MF, Frampton GM, Brambrink T, Johnstone S, Guenther MG, Johnston WK, Wernig M, Newman J et al:Connecting microRNA genes to the core transcriptional regulatory circuitry of embryonic stem cells. Cell 2008,134(3):521-533.
134. Corcoran DL, Pandit KV, Gordon B, Bhattacharjee A, Kaminski N, Benos PV: Features of mammalian microRNA promoters emerge from polymerase Ⅱ chromatin immunoprecipitation data. PLoS One 2009,4(4):e5279.
135. Lasiuk GC, Hegadoren KM:The effects of estradiol on central serotonergic systems and its relationship to mood in women. Biol Res Nurs 2007, 9(2):147-160.
136. Li X, Huang J, Yi P, Bambara RA, Hilf R, Muyan M:Single-chain estrogen receptors (ERs) reveal that the ERalpha/beta heterodimer emulates functions of the ERalpha dimer in genomic estrogen signaling pathways. Mol Cell Biol 2004,24(17):7681-7694.
137. Campbell RA, Bhat-Nakshatri P, Patel NM, Constantinidou D, Ali S, Nakshatri H:Phosphatidylinositol 3-kinase/AKT-mediated activation of estrogen receptor alpha:a new model for anti-estrogen resistance. J Biol Chem 2001, 276(13):9817-9824.
138. Graf S, Nielsen FG, Kurtz S, Huynen MA, Birney E, Stunnenberg H, Flicek P: Optimized design and assessment of whole genome tiling arrays. Bioinformatics 2007,23(13):i195-204.
139. Thomson JM, Parker J, Perou CM, Hammond SM:A custom microarray platform for analysis of microRNA gene expression. Nat Methods 2004, 1(1):47-53.
140. Johnson WE, Li W, Meyer CA, Gottardo R, Carroll JS, Brown M, Liu XS: Model-based analysis of tiling-arrays for ChIP-chip. Proc Natl Acad Sci U S A 2006,103(33):12457-12462.