目的:建立一套简便、对RNA样本起始量要求很低的RNA测序建库方法,在此基础上引入分子条形码技术,从而使RNA-seq的数据所反映的表达丰度更加客观真实。方法:将微量RNA用RNaseⅢ片段化后,利用反转录酶的反转录活性、模板转换活性以及DNA...目的:建立一套简便、对RNA样本起始量要求很低的RNA测序建库方法,在此基础上引入分子条形码技术,从而使RNA-seq的数据所反映的表达丰度更加客观真实。方法:将微量RNA用RNaseⅢ片段化后,利用反转录酶的反转录活性、模板转换活性以及DNA依赖的DNA聚合酶活性,一步实现从RNA到cDNA文库的反应,再通过PCR扩增获得适合测序仪的测序文库,通过测序分析验证建库效果,最后采用荧光定量PCR法测定部分基因的表达量以验证测序结果,同时比较分子条形码的引入对表达量分析的影响。结果:通过电泳观察和测序结果分析,选择Clontech公司的SMARTscribe,反转录和模板转换反应温度设为50℃,反转录完成后经AMPure Beads纯化再经PCR扩增得到测序文库,电泳检测文库DNA长度和浓度符合测序要求。比较发现,用分子条形码建库的测序结果能够更加真实地反应基因的实际表达量。结论:初步建立了针对10 ng RNA样品的RNA-seq建库技术,并在建库方案中加入分子条形码使其测序结果更加真实地反映基因表达量。该技术未来可适用于微量RNA样品的高通量测序。展开更多
Variable selection plays an important role in high-dimensional data analysis.But the high-dimensional data often induces the strongly correlated variables problem,which should be properly handled.In this paper,we prop...Variable selection plays an important role in high-dimensional data analysis.But the high-dimensional data often induces the strongly correlated variables problem,which should be properly handled.In this paper,we propose Elastic Net procedure for partially linear models and prove the group effect of its estimate.A simulation study shows that the Elastic Net procedure deals with the strongly correlated variables problem better than the Lasso,ALasso and the Ridge do.Based on the real world data study,we can get that the Elastic Net procedure is particularly useful when the number of predictors pffis much bigger than the sample size n.展开更多
文摘目的:建立一套简便、对RNA样本起始量要求很低的RNA测序建库方法,在此基础上引入分子条形码技术,从而使RNA-seq的数据所反映的表达丰度更加客观真实。方法:将微量RNA用RNaseⅢ片段化后,利用反转录酶的反转录活性、模板转换活性以及DNA依赖的DNA聚合酶活性,一步实现从RNA到cDNA文库的反应,再通过PCR扩增获得适合测序仪的测序文库,通过测序分析验证建库效果,最后采用荧光定量PCR法测定部分基因的表达量以验证测序结果,同时比较分子条形码的引入对表达量分析的影响。结果:通过电泳观察和测序结果分析,选择Clontech公司的SMARTscribe,反转录和模板转换反应温度设为50℃,反转录完成后经AMPure Beads纯化再经PCR扩增得到测序文库,电泳检测文库DNA长度和浓度符合测序要求。比较发现,用分子条形码建库的测序结果能够更加真实地反应基因的实际表达量。结论:初步建立了针对10 ng RNA样品的RNA-seq建库技术,并在建库方案中加入分子条形码使其测序结果更加真实地反映基因表达量。该技术未来可适用于微量RNA样品的高通量测序。
基金Supported by National Natural Science Foundation of China(No.71462002)the Project for Teaching Reform of Guangxi(GXZZJG2017B084)the Project for Fostering Distinguished Youth Scholars of Guangxi(2020KY50012)。
文摘Variable selection plays an important role in high-dimensional data analysis.But the high-dimensional data often induces the strongly correlated variables problem,which should be properly handled.In this paper,we propose Elastic Net procedure for partially linear models and prove the group effect of its estimate.A simulation study shows that the Elastic Net procedure deals with the strongly correlated variables problem better than the Lasso,ALasso and the Ridge do.Based on the real world data study,we can get that the Elastic Net procedure is particularly useful when the number of predictors pffis much bigger than the sample size n.
