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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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相关实验视频

Updated: Jan 10, 2026

Preparation of Small RNA Libraries for Sequencing from Early Mouse Embryos

Preparation of Small RNA Libraries for Sequencing from Early Mouse Embryos

Published on: October 9, 2020

针对RNA测序研究的优化小鼠样本大小从大规模比较分析中揭示出来.

Gabor Halasz¹, Jennifer Schmahl², Nicole Negron¹

¹Molecular Profiling & Data Science, Regeneron Pharmaceuticals, New York, NY, USA.

Nature communications

|November 19, 2025

概括

选择正确的样本大小 (N) 对于大量RNA测序至关重要. 研究表明,需要N=6-7小鼠来平衡假阳性和真实发现,而N=8-12显著改善了结果.

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A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools

A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools

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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

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Last Updated: Jan 10, 2026

Preparation of Small RNA Libraries for Sequencing from Early Mouse Embryos

Preparation of Small RNA Libraries for Sequencing from Early Mouse Embryos

Published on: October 9, 2020

A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools

A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools

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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Published on: January 10, 2019

科学领域:

基因组学就是基因组学.
生物信息学是一种生物信息学.
实验设计实验设计

背景情况:

准确的样本大小确定对于可靠的大量RNA测序 (RNA-Seq) 结果至关重要.
低功率的实验 (小N) 可以导致误导性结论和错过的发现.
当前的实践可能无法充分满足对稳健RNA-Seq分析的统计要求.

研究的目的:

确定批量RNA-Seq实验的最佳样本大小 (N),以尽量减少假阳性结果并最大限度地增加真实发现.
评估不同样本大小对基因表达分析可靠性和敏感性的影响.
在小鼠模型RNA-Seq研究中为样本大小选择提供数据驱动的建议.

主要方法:

分析了两项大量的RNA-Seq分析研究,比较了野生型小鼠和基因被删除的小鼠 (共N=30).
系统地评估各种样本大小 (N=4或更小,N=6-7,N=8-12) 的结果,使用2倍表达式差异切断.
对不同样本大小的虚假阳性率和检测灵敏度的评估.

主要成果:

用N=4或更少的实验产生了高度误导的结果,具有高的错误阳性率.
需要6-7只小鼠的N来持续降低假阳性率到50%以下,并提高检测灵敏度到50%以上.
将样本大小增加到N=8-12的小鼠显著改善了完整实验结果的总结.
在功率不足的实验中提高折叠变换切断值是无效的,导致效果大小膨胀和灵敏度降低.

结论:

对于可靠的大批量RNA-Seq研究,建议每组至少采用6-7只小鼠的样本大小,截止时间为2倍.
较大的样本大小 (N=8-12) 在结果可重现性和发现能力方面提供了实质性的好处.
增加折叠变换截止值并不能替代适当的样本大小,并且可以掩盖真正的生物信号.