A comparison of microRNA sequencing reproducibility and noise reduction using mirVana and TRIzol isolation methods.

Q4 Pharmacology, Toxicology and Pharmaceutics International Journal of Computational Biology and Drug Design Pub Date : 2014-01-01 Epub Date: 2014-05-28 DOI:10.1504/IJCBDD.2014.061642
Yan Guo, Amma Bosompem, Xu Zhong, Travis Clark, Yu Shyr, Annette S Kim
{"title":"A comparison of microRNA sequencing reproducibility and noise reduction using mirVana and TRIzol isolation methods.","authors":"Yan Guo,&nbsp;Amma Bosompem,&nbsp;Xu Zhong,&nbsp;Travis Clark,&nbsp;Yu Shyr,&nbsp;Annette S Kim","doi":"10.1504/IJCBDD.2014.061642","DOIUrl":null,"url":null,"abstract":"<p><p>MicroRNAseq (miRNAseq) is a form of RNAseq technology that has become an increasingly popular alternative to miRNA expression profiling. Unlike messenger RNA (mRNA), miRNA extraction can be difficult, and sequencing such small RNA can also be problematic. We designed a study to test the reproducibility of miRNAseq technology and the performance of the two popular miRNA isolation methods, mirVana and TRIzol, by sequencing replicated samples using microRNA isolated with each kit. Through careful analysis of our data, we found excellent repeatability of miRNAseq technology. The mirVana method performed better than TRIzol in terms of useful reads sequenced, number of miRNA identified, and reproducibility. Finally, we identified a baseline noise level for miRNAseq technology; this baseline noise level can be used as a filter in future miRNAseq studies. </p>","PeriodicalId":39227,"journal":{"name":"International Journal of Computational Biology and Drug Design","volume":"7 2-3","pages":"102-12"},"PeriodicalIF":0.0000,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/IJCBDD.2014.061642","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Computational Biology and Drug Design","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/IJCBDD.2014.061642","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2014/5/28 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"Pharmacology, Toxicology and Pharmaceutics","Score":null,"Total":0}
引用次数: 12

Abstract

MicroRNAseq (miRNAseq) is a form of RNAseq technology that has become an increasingly popular alternative to miRNA expression profiling. Unlike messenger RNA (mRNA), miRNA extraction can be difficult, and sequencing such small RNA can also be problematic. We designed a study to test the reproducibility of miRNAseq technology and the performance of the two popular miRNA isolation methods, mirVana and TRIzol, by sequencing replicated samples using microRNA isolated with each kit. Through careful analysis of our data, we found excellent repeatability of miRNAseq technology. The mirVana method performed better than TRIzol in terms of useful reads sequenced, number of miRNA identified, and reproducibility. Finally, we identified a baseline noise level for miRNAseq technology; this baseline noise level can be used as a filter in future miRNAseq studies.

Abstract Image

Abstract Image

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
mirVana和TRIzol分离方法对microRNA测序可重复性和降噪效果的比较。
MicroRNAseq (miRNAseq)是RNAseq技术的一种形式,已成为miRNA表达谱的一种日益流行的替代方法。与信使RNA (mRNA)不同,miRNA的提取可能很困难,而且对如此小的RNA进行测序也可能存在问题。我们设计了一项研究,通过使用每种试剂盒分离的microRNA对复制样本进行测序,来测试miRNAseq技术的可重复性以及两种流行的miRNA分离方法mirVana和TRIzol的性能。通过对数据的仔细分析,我们发现miRNAseq技术具有良好的可重复性。mirVana方法在有用reads序列、鉴定的miRNA数量和可重复性方面优于TRIzol。最后,我们确定了miRNAseq技术的基线噪声水平;该基线噪声水平可在未来的miRNAseq研究中用作滤波器。
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 去求助
来源期刊
International Journal of Computational Biology and Drug Design
International Journal of Computational Biology and Drug Design Pharmacology, Toxicology and Pharmaceutics-Drug Discovery
CiteScore
1.00
自引率
0.00%
发文量
8
期刊最新文献
Assessment and Validation of Emulgel Based Salicylic acid Formulation Development to Drug release and Optimization by Statistical Design EyeRIS: Image-Based Identification of Goats using Iris Advanced DEEPCNN Breast Cancer Mammogram Image Detection and Classification with Butterfly Optimization Algorithm A Unique Noise Detector Developed for the Filtering of X-Ray Images of Bone Fractures Residue Interaction Network analysis and Molecular dynamics simulation of 6K Viroporin: Chikungunya Virus Channel Proteins
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1