論文

査読有り
2017年10月

Next-generation sequencing-based analysis of reverse transcriptase fidelity

BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS
  • Kiyoshi Yasukawa
  • ,
  • Kei Iida
  • ,
  • Hiroyuki Okano
  • ,
  • Ryota Hidese
  • ,
  • Misato Baba
  • ,
  • Itaru Yanagihara
  • ,
  • Kenji Kojima
  • ,
  • Teisuke Takita
  • ,
  • Shinsuke Fujiwara

492
2
開始ページ
147
終了ページ
153
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1016/j.bbrc.2017.07.169
出版者・発行元
ACADEMIC PRESS INC ELSEVIER SCIENCE

In this study, we devised a simple and rapid method to analyze fidelity of reverse transcriptase (RT) using next-generation sequencing (NGS). The method comprises a cDNA synthesis reaction from standard RNA with a primer containing a tag of 14 randomized bases and the RT to be tested, PCR using high-fidelity DNA polymerase, and NGS. By comparing the sequence of each read with the reference sequence, mutations were identified. The mutation can be identified to be due to an error introduced by either cDNA synthesis, PCR, or NGS based on whether the sequence reads with the same tag contain the same mutation or not. The error rates in cDNA synthesis with Moloney murine leukemia virus (MMLV) RT thermostable variant MM4 or the recently developed 16-tuple variant of family B DNA polymerase with RT activity, RTX, from Thermococcus kodakarensis, were 0.75-1.0 x 10(-4) errors/base, while that in the reaction with the wild-type human immunodeficiency virus type I (HIV-1) RT was 2.6 x 10(-4) errors/base. Overall, our method could precisely evaluate the fidelity of various RTs with different reaction conditions in a high-throughput manner without the use of expensive optics and troublesome adaptor ligation. (C) 2017 Elsevier Inc. All rights reserved.

リンク情報
DOI
https://doi.org/10.1016/j.bbrc.2017.07.169
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000411423000001&DestApp=WOS_CPL
ID情報
  • DOI : 10.1016/j.bbrc.2017.07.169
  • ISSN : 0006-291X
  • eISSN : 1090-2104
  • Web of Science ID : WOS:000411423000001

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