Zhifeng Li, Wenyan Zhang, Yang Liu, Shaofeng Qu, Yan Wang, Liping Zhu, Yuezhong Li
{"title":"[基于基因片段分析系统的转录起始位点分析:从预测到数据评价]。","authors":"Zhifeng Li, Wenyan Zhang, Yang Liu, Shaofeng Qu, Yan Wang, Liping Zhu, Yuezhong Li","doi":"","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>To establish a pipeline for unknown transcriptional start site (TSS) identification without radioactivity, we used genetic fragment analysis system and replenished two steps regarding prediction and evaluation.</p><p><strong>Methods: </strong>We used unknown TSSs of GroEL genes from M. xanthus as a case. Firstly, we predicted the potential TSSs through bioinformatics databases. According to the prediction, we designed and synthesized fluorescence labeled primers to carry out the reverse transcription reactions. Further, we took advantage of the genetic fragment analysis system to identify TSSs with internal standards. Finally, we applied the normal distribution theory to evaluate the data.</p><p><strong>Results: </strong>We determined the numbers, abundances and accurate sites of the TSSs:GroEL1 has one promoter and the site is TSS(286), whereas GroEL2 has two promoters, and the sites are TSS548 and TSS(502). TSS(286) is 14.3 times more abundant than TSS(548) and TSS(548) is 13.8 times more than TSS(502).</p><p><strong>Conclusion: </strong>The bioinformatics analyzing indicates the range for the experimental design. TSS determination through genetic fragment analysis system is safer, more automatic and accurate. Normal distribution theory further refines the reliability of results. Combination of the three techniques establishes a more complete pipeline of primer extension for unknown TSS determination.</p>","PeriodicalId":7120,"journal":{"name":"微生物学报","volume":"57 2","pages":"254-63"},"PeriodicalIF":0.0000,"publicationDate":"2017-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"[Transcriptional start site analysis based on genetic fragment analysis system: from prediction to data evaluation].\",\"authors\":\"Zhifeng Li, Wenyan Zhang, Yang Liu, Shaofeng Qu, Yan Wang, Liping Zhu, Yuezhong Li\",\"doi\":\"\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>To establish a pipeline for unknown transcriptional start site (TSS) identification without radioactivity, we used genetic fragment analysis system and replenished two steps regarding prediction and evaluation.</p><p><strong>Methods: </strong>We used unknown TSSs of GroEL genes from M. xanthus as a case. Firstly, we predicted the potential TSSs through bioinformatics databases. According to the prediction, we designed and synthesized fluorescence labeled primers to carry out the reverse transcription reactions. Further, we took advantage of the genetic fragment analysis system to identify TSSs with internal standards. Finally, we applied the normal distribution theory to evaluate the data.</p><p><strong>Results: </strong>We determined the numbers, abundances and accurate sites of the TSSs:GroEL1 has one promoter and the site is TSS(286), whereas GroEL2 has two promoters, and the sites are TSS548 and TSS(502). TSS(286) is 14.3 times more abundant than TSS(548) and TSS(548) is 13.8 times more than TSS(502).</p><p><strong>Conclusion: </strong>The bioinformatics analyzing indicates the range for the experimental design. TSS determination through genetic fragment analysis system is safer, more automatic and accurate. Normal distribution theory further refines the reliability of results. Combination of the three techniques establishes a more complete pipeline of primer extension for unknown TSS determination.</p>\",\"PeriodicalId\":7120,\"journal\":{\"name\":\"微生物学报\",\"volume\":\"57 2\",\"pages\":\"254-63\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-02-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"微生物学报\",\"FirstCategoryId\":\"1089\",\"ListUrlMain\":\"\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"微生物学报","FirstCategoryId":"1089","ListUrlMain":"","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
[Transcriptional start site analysis based on genetic fragment analysis system: from prediction to data evaluation].
Objective: To establish a pipeline for unknown transcriptional start site (TSS) identification without radioactivity, we used genetic fragment analysis system and replenished two steps regarding prediction and evaluation.
Methods: We used unknown TSSs of GroEL genes from M. xanthus as a case. Firstly, we predicted the potential TSSs through bioinformatics databases. According to the prediction, we designed and synthesized fluorescence labeled primers to carry out the reverse transcription reactions. Further, we took advantage of the genetic fragment analysis system to identify TSSs with internal standards. Finally, we applied the normal distribution theory to evaluate the data.
Results: We determined the numbers, abundances and accurate sites of the TSSs:GroEL1 has one promoter and the site is TSS(286), whereas GroEL2 has two promoters, and the sites are TSS548 and TSS(502). TSS(286) is 14.3 times more abundant than TSS(548) and TSS(548) is 13.8 times more than TSS(502).
Conclusion: The bioinformatics analyzing indicates the range for the experimental design. TSS determination through genetic fragment analysis system is safer, more automatic and accurate. Normal distribution theory further refines the reliability of results. Combination of the three techniques establishes a more complete pipeline of primer extension for unknown TSS determination.
期刊介绍:
Acta Microbiologica Sinica(AMS) is a peer-reviewed monthly (one volume per year)international journal,founded in 1953.It covers a wide range of topics in the areas of general and applied microbiology.The journal
publishes original papers,reviews in microbiological science,and short communications describing unusual observations.
Acta Microbiologica Sinica has been indexed in Index Copernicus (IC),Chemical Abstract (CA),Excerpt Medica Database (EMBASE),AJ of Viniti (Russia),Biological Abstracts (BA),Chinese Science Citation Database
(CSCD),China National Knowledge Infrastructure(CNKI),Institute of Scientific and Technical Information of China(ISTIC),Chinese Journal Citation Report(CJCR),Chinese Biological Abstracts,Chinese Pharmaceutical
Abstracts,Chinese Medical Abstracts and Chinese Science Abstracts.