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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 microarray-based...

Sample Size Calculation

Sample Size Calculation

Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...

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Related Experiment Video

Updated: May 8, 2026

AQRNA-seq for Quantifying Small RNAs

AQRNA-seq for Quantifying Small RNAs

Published on: February 2, 2024

Calculating sample size estimates for RNA sequencing data.

Steven N Hart¹, Terry M Therneau, Yuji Zhang

¹1 Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic , Rochester, Minnesota.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology

|August 22, 2013

Summary

This summary is machine-generated.

Next-generation sequencing of mRNA (RNA-Seq) is a powerful tool for gene expression studies. This research provides a model to determine optimal sequencing depth and sample size for accurate gene expression analysis.

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Identification of Circular RNAs using RNA Sequencing

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Next-generation sequencing of mRNA (RNA-Seq) offers superior resolution and reproducibility compared to microarrays for gene expression measurement.
Key experimental design considerations for RNA-Seq include determining appropriate sequencing depth and the number of biological replicates required.

Purpose of the Study:

To develop a model for estimating statistical power in RNA-Seq experiments.
To provide guidance on optimal sequencing depth and sample size for gene expression studies.

Main Methods:

Analysis of gene expression distributions from 127 RNA-Seq experiments.
Empirical estimation of biological and technical variation from large datasets.
Development of a statistical model to estimate power for identifying differentially expressed genes.

Main Results:

Approximately 91% ± 4% of annotated genes are sequenced at a frequency of 0.1 per million bases mapped across diverse samples.
A model was developed to estimate the statistical power for detecting differential gene expression based on sequencing data and variation parameters.

Conclusions:

The study provides essential references for optimizing sample size, statistical power, and sequencing depth in RNA-Seq gene expression studies.
Accessible R code and an Excel worksheet are provided to aid researchers in experimental design calculations.