[Transcriptional start site analysis based on genetic fragment analysis system: from prediction to data evaluation].

微生物学报 Pub Date : 2017-02-04
Zhifeng Li, Wenyan Zhang, Yang Liu, Shaofeng Qu, Yan Wang, Liping Zhu, Yuezhong Li
{"title":"[Transcriptional start site analysis based on genetic fragment analysis system: from prediction to data evaluation].","authors":"Zhifeng Li,&nbsp;Wenyan Zhang,&nbsp;Yang Liu,&nbsp;Shaofeng Qu,&nbsp;Yan Wang,&nbsp;Liping Zhu,&nbsp;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":null,"pages":null},"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}
引用次数: 0

Abstract

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.

[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]。
目的:建立无放射性未知转录起始位点(TSS)鉴定管道,采用基因片段分析系统,补充预测和评价两个步骤。方法:以黄豆属植物GroEL基因的未知tss为例。首先,通过生物信息学数据库对潜在的tss进行预测。根据预测,我们设计并合成了荧光标记引物进行逆转录反应。此外,我们利用遗传片段分析系统鉴定具有内部标准的tss。最后,我们应用正态分布理论对数据进行评估。结果:我们确定了TSS的数量、丰度和准确位点:GroEL1有1个启动子,位点为TSS(286); GroEL2有2个启动子,位点为TSS548和TSS(502)。TSS(286)比TSS(548)多14.3倍,TSS(548)比TSS(502)多13.8倍。结论:生物信息学分析为实验设计指明了范围。通过基因片段分析系统测定TSS更安全、自动化和准确。正态分布理论进一步完善了结果的可靠性。三种技术的结合为未知TSS的测定建立了更完整的引物延伸管道。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
7960
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.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信