Literature DB >> 15766705

Adaptor-tagged competitive polymerase chain reaction: amplification bias and quantified gene expression levels.

Hiroko Kita-Matsuo1, Naoto Yukinawa, Ryo Matoba, Sakae Saito, Shigeyuki Oba, Shin Ishii, Kikuya Kato.   

Abstract

Adaptor-tagged competitive polymerase chain reaction (ATAC-PCR) is an advanced version of quantitative competitive PCR characterized by the addition of unique adaptors to different cDNA samples. It is currently the only quantitative PCR technique that enables large-scale gene expression analysis. Multiplex application of ATAC-PCR employs seven adaptors, two or three of which are used as controls to generate a calibration curve. The characteristics of the ATAC-PCR method for large-scale data production, including any adaptor- and gene-dependent amplification biases, were evaluated by using this method to analyze the expression of 384 mouse brain genes. Short adaptors tended to amplify at higher efficiency than did long adaptors. The population of genes with a high amplification bias increased with the use of short adaptors. Subtracting the median value of all adaptor-dependent biases could reduce this bias; the majority of genes displayed a small gene-dependent bias, which facilitated reliable quantification. We modified ATAC-PCR to estimate molecular numbers of transcripts by introducing synthetic standards. This modification demonstrated that gene expression levels in mammalian cells are varied over seven orders of magnitude.

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Year:  2005        PMID: 15766705     DOI: 10.1016/j.ab.2004.11.014

Source DB:  PubMed          Journal:  Anal Biochem        ISSN: 0003-2697            Impact factor:   3.365


  3 in total

1.  Conversion of a molecular classifier obtained by gene expression profiling into a classifier based on real-time PCR: a prognosis predictor for gliomas.

Authors:  Satoru Kawarazaki; Kazuya Taniguchi; Mitsuaki Shirahata; Yoji Kukita; Manabu Kanemoto; Nobuhiro Mikuni; Nobuo Hashimoto; Susumu Miyamoto; Jun A Takahashi; Kikuya Kato
Journal:  BMC Med Genomics       Date:  2010-11-10       Impact factor: 3.063

2.  A multi-class predictor based on a probabilistic model: application to gene expression profiling-based diagnosis of thyroid tumors.

Authors:  Naoto Yukinawa; Shigeyuki Oba; Kikuya Kato; Kazuya Taniguchi; Kyoko Iwao-Koizumi; Yasuhiro Tamaki; Shinzaburo Noguchi; Shin Ishii
Journal:  BMC Genomics       Date:  2006-07-27       Impact factor: 3.969

3.  A novel technique for measuring variations in DNA copy-number: competitive genomic polymerase chain reaction.

Authors:  Kyoko Iwao-Koizumi; Kazunori Maekawa; Yohko Nakamura; Sakae Saito; Shoko Kawamoto; Akira Nakagawara; Kikuya Kato
Journal:  BMC Genomics       Date:  2007-07-02       Impact factor: 3.969

  3 in total

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