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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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Related Experiment Video

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

Optimized murine sample sizes for RNA sequencing studies revealed from large scale comparative analysis.

Gabor Halasz¹, Jennifer Schmahl², Nicole Negron¹

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

Nature Communications

|November 19, 2025

Summary

Choosing the right sample size (N) for bulk RNA sequencing is crucial. Studies show N=6-7 mice are needed to balance false positives and true discoveries, while N=8-12 significantly improves results.

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Last Updated: Jan 10, 2026

Preparation of Small RNA Libraries for Sequencing from Early Mouse Embryos

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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

Area of Science:

Genomics
Bioinformatics
Experimental Design

Background:

Accurate sample size determination is essential for reliable bulk RNA sequencing (RNA-Seq) results.
Underpowered experiments (small N) can lead to misleading conclusions and missed discoveries.
Current practices may not adequately address the statistical requirements for robust RNA-Seq analysis.

Purpose of the Study:

To determine the optimal sample size (N) for bulk RNA-Seq experiments to minimize false positives and maximize true discoveries.
To evaluate the impact of different sample sizes on the reliability and sensitivity of gene expression analysis.
To provide data-driven recommendations for sample size selection in mouse model RNA-Seq studies.

Main Methods:

Analysis of two bulk RNA-Seq profiling studies comparing wild-type mice and gene-deleted mice (N=30 total).
Systematic evaluation of results across various sample sizes (N=4 or less, N=6-7, N=8-12) using a 2-fold expression difference cutoff.
Assessment of false positive rates and detection sensitivity at different sample sizes.

Main Results:

Experiments with N=4 or less yielded highly misleading results with high false positive rates.
An N of 6-7 mice was required to consistently reduce the false positive rate below 50% and increase detection sensitivity above 50%.
Increasing sample size to N=8-12 mice significantly improved the recapitulation of full experimental findings.
Raising the fold-change cutoff in underpowered experiments is ineffective, leading to inflated effect sizes and reduced sensitivity.

Conclusions:

A minimum sample size of 6-7 mice per group is recommended for reliable bulk RNA-Seq studies with a 2-fold cutoff.
Larger sample sizes (N=8-12) offer substantial benefits in terms of result reproducibility and discovery power.
Increasing fold-change cutoffs is not a substitute for adequate sample size and can mask true biological signals.