期刊文献+

禽类转录因子与DNA互作的研究方法

Methods of Transcription Factors and DNA Interactions in Poultry
下载PDF
导出
摘要 在禽类生长发育中,基因的适时适量表达起到至关重要的作用。研究转录因子与DNA的互作,对认识禽类生长发育过程具有重要意义。文章总结了禽类常用的转录因子与DNA互作研究方法,其中:生物信息学预测结合位点有助于针对性的开展后续研究;电泳迁移率变动分析(Electrophoretic mobility shift assay,EMSA)和染色质免疫共沉淀(Chromatin Immunoprecipitation,ChIP)能够分别从体外和体内验证转录因子与DNA结合;双荧光素酶报告基因可以鉴定对转录活性的影响。综合运用上述方法可有效开展转录因子-DNA互作研究,有助于揭示禽类生长发育调控机制。 Timely and proper gene expressions play a great part in poultry growth and development. Study the binding of transcription factors and DNA is contributed to recognize the poultry growth process. Our article summarized the current commonly methods of transcription factors and DNA interactions. Bioinformatics prediction of transcription factor binding sites help target to the follow-up experiments. Electrophoretic mobility shift assay (EMSA) and chromatin immunoprecipitation (CHIP)can respectively detect the binding of transcription factors and DNA in vitro and in vivo. Dual luciferase reporter gene assay could reveal the transcription activities of the binding sites. Together, combining the above methods could carry out the transcription factors and DNA interaction analysis effectively, and help to re- veal the mechanism of poultry development and growth step by step.
出处 《中国家禽》 北大核心 2017年第21期51-55,共5页 China Poultry
基金 国家自然科学基金(31402061) 2016年国家级大学生创新创业训练计划资助项目(201610221002)
关键词 转录因子 DNA 生物信息学预测 电泳迁移率变动分析 染色质免疫共沉淀 transcription factor DNA bioinformatics analysis electrophoretic mobility shift assay (EMSA) chromatin immunopreeipitation (CHIP)
  • 相关文献

参考文献7

二级参考文献258

  • 1Biemar F, Zinzen R, Ronshaugen M, Sementchenko V, Manak JR, Levine MS. Spatial regulation of microRNA gene expression in the Drosophila embryo. Proc Natl Acad Sci USA, 2005,102(44):15907-15911
  • 2Bulyk ML, Johnson PL, Church GM. Nucleotides of transcription factor binding sites exert interdependent effects on the binding affinities of transcription factors. Nucleic Acids Res, 2002,30(5):1255-1261
  • 3Man TK, Stormo GD. Non-independence of Mnt repressor-operator interaction determined by a new quantitative multiple fluorescence relative affinity (QuMFRA) assay. Nucleic Acids Res, 2001,29(12):2471-2478
  • 4Zhou Q, Liu JS. Modeling within-motif dependence for transcription factor binding site predictions. Bioinformatics, 2004,20(6) :909-916
  • 5Xing EP, Wu W, Jordan MI, Karp RM. Logos: a modular bayesian model for de novo motif detection. J Bioinform Comput Biol, 2004,2(1):127-154
  • 6Hong P, Liu XS, Zhou Q, Lu X, Liu JS, Wong WH. A boosting approach for motif modeling using ChiP-chip data. Bioinformatics, 2005,21(11):2636-2643
  • 7Schneider TD, Stephens RM. Sequence logos: a new way to display consensus sequences. Nucleic Acids Res, 1990,18(20): 6097-6100
  • 8Bussemaker H J, Li H, Siggia ED. Building a dictionary for genomes: identification of presumptive regulatory sites by statistical analysis. Proc Natl Acad Sci USA, 2000,97(18): 10096~10100
  • 9Sinha S, Tompa M. Discovery of novel transcription factor binding sites by statistical overrepresentation. Nucleic Acids Res, 2002,30(24):5549-5560
  • 10Sinha S, Tompa M. YMF: A program for discovery of novel transcription factor binding sites by statistical overrepresentation. Nucleic Acids R es, 2003,31(13):3586-3588

共引文献78

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部