目录
摘要II
关键词II
AbstractIII
引言
引言（或绪论）1
1 材料与方法2
1.1 miR122靶基因的软件预测2
1.1.1 使用TargetScan预测2
1.1.2 使用miRanda预测2
1.1.3 使用PicTar预测3
1.1.4 对预测结果的处理3
1.2 miR122相关表达谱数据的分析3
1.2.1 miR122相关表达谱数据的获取3
1.2.2 miR122相关表达谱数据的处理4
1.3 表达谱数据和软件预测结果的分析4
1.4 双荧光素酶报告基因法4
1.4.1 实验材料及试剂4
1.4.2 常用试剂的配置5
1.4.3 细胞复苏5
1.4.4 细胞传代6
1.4.5 细胞转染6
1.4.6 荧光素酶活性检测6
1.4.7 抑制程度的计算6
2 结果与分析7
2.1 生物信息学软件预测结果的对比7
2.2 表达谱数据的对比8
2.3 表达谱数据和软件预测结果的对比分析9
2.3.1 表达谱数据可靠性的分析10
2.3.2 软件预测结果的分析10
2.4 双荧光素酶报告基因法对预测结果的验证12
3 讨论13
致谢15
参考文献16
microRNA122靶基因的预测及鉴定
摘 要
microRNAs是一种非编码RNA，它们的种子序列可以与靶基因mRNA的3’非翻译区特异性结合，进而可以调控靶基因的表达水平。microRNA122是一种保守的肝脏特异性microRNA，在肝脏中起重要作用，如抑制肝细胞癌、促进丙型肝炎病毒的复制、降低血清胆固醇等。microRNA122靶向众多靶基因，这些基因参与肝脏中的许多重要的生理活动，如细胞的分裂与分化、糖代谢和脂代谢等，目前对microRNA122靶基因的研究尚不全面。为了准确的 *51今日免费论文网|www.jxszl.com +Q: ￥351916072$ 
鉴定出microRNA122的靶基因，明确各因素对判断microRNA122靶基因的影响程度，并得出microRNA122对其抑制程度的大小，本研究通过分析生物信息学软件预测结果和数据库中相关的表达谱数据，鉴定出了11个可信度较高的microRNA122的靶基因，明确了各软件预测结果的特点；接着通过双荧光素酶报告基因法验证了其中的几个靶基因，并得出了microRNA122对这些靶基因表达的抑制程度。本研究为今后对microRNA122的研究提供了理论基础，为改进生物信息学软件预测算法提供了参考。
PREDICTION AND IDENTIFICATION OF MICRORNA122 TARGET GENES
ABSTRACT
microRNAs are noncoding RNAs. Their seed region can complementary pair with the 3 untranslated region of the target gene mRNA, and can regulate the expression level of the target gene. microRNA122 is a conservative liverspecific microRNA that plays a crucial role in the liver, such as inhibiting hepatocellular carcinoma, promoting the replication of hepatitis C virus, and lowering serum cholesterol. microRNA122 targets many target genes, these genes are involved in many important physiological activities in the liver, such as cell division and differentiation, carbohydrate metabolism and lipid metabolism. At present, research on microRNA122 target genes is not comprehensive. In order to accurately identify the target gene of microRNA122, determine the effect of various factors on judging the microRNA122 target gene, and to figure out the inhibition level of microRNA122 to its target genes, I analyzed the prediction results of bioinformatics software and related transcriptome data in the database, found out 11 target genes of microRNA122 with high credibility, clarified the characteristics of results predicted by each software. Then I used the dualluciferase assay to determine the inhibition rate of these target genes by microRNA122. This study provides a theoretical basis for future research on microRNA122 and provides a reference for improving bioinformatics software prediction algorithms.

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