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通过 cDNA 多重靶向重测序定量差异基因表达。

Quantification of differential gene expression by multiplexed targeted resequencing of cDNA.

机构信息

Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, PO Box 9101, 6500 HB, Nijmegen, The Netherlands.

Department of Molecular Developmental Biology, Radboud Institute for Molecular Life Sciences, Radboud University, PO Box 9101, 6500 HB, Nijmegen, The Netherlands.

出版信息

Nat Commun. 2017 May 5;8:15190. doi: 10.1038/ncomms15190.

Abstract

Whole-transcriptome or RNA sequencing (RNA-Seq) is a powerful and versatile tool for functional analysis of different types of RNA molecules, but sample reagent and sequencing cost can be prohibitive for hypothesis-driven studies where the aim is to quantify differential expression of a limited number of genes. Here we present an approach for quantification of differential mRNA expression by targeted resequencing of complementary DNA using single-molecule molecular inversion probes (cDNA-smMIPs) that enable highly multiplexed resequencing of cDNA target regions of ∼100 nucleotides and counting of individual molecules. We show that accurate estimates of differential expression can be obtained from molecule counts for hundreds of smMIPs per reaction and that smMIPs are also suitable for quantification of relative gene expression and allele-specific expression. Compared with low-coverage RNA-Seq and a hybridization-based targeted RNA-Seq method, cDNA-smMIPs are a cost-effective high-throughput tool for hypothesis-driven expression analysis in large numbers of genes (10 to 500) and samples (hundreds to thousands).

摘要

全转录组或 RNA 测序(RNA-Seq)是一种强大且通用的工具,可用于分析不同类型的 RNA 分子的功能,但对于以定量少数特定基因的差异表达为目的的假设驱动研究,样本试剂和测序成本可能会受到限制。在这里,我们提出了一种通过使用单分子分子反转探针(cDNA-smMIPs)对 cDNA 进行靶向重测序来定量差异 mRNA 表达的方法,该方法能够对约 100 个核苷酸的 cDNA 靶区域进行高度多重重测序并对单个分子进行计数。我们表明,对于每个反应的数百个 smMIP,可以从分子计数中获得差异表达的准确估计,并且 smMIP 也适用于相对基因表达和等位基因特异性表达的定量。与低覆盖度的 RNA-Seq 和基于杂交的靶向 RNA-Seq 方法相比,cDNA-smMIPs 是一种经济高效的高通量工具,适用于对大量基因(10 到 500 个)和样本(数百到数千个)进行假设驱动的表达分析。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0754/5424154/e9c79abf3284/ncomms15190-f1.jpg

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