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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: Dec 16, 2025

Low-input Nucleus Isolation and Multiplexing with Barcoded Antibodies of Mouse Sympathetic Ganglia for Single-nucleus RNA Sequencing

Low-input Nucleus Isolation and Multiplexing with Barcoded Antibodies of Mouse Sympathetic Ganglia for Single-nucleus RNA Sequencing

Published on: March 23, 2022

Quantile normalization of single-cell RNA-seq read counts without unique molecular identifiers.

F William Townes¹, Rafael A Irizarry^2,3

¹Department of Computer Science, Princeton University, Princeton, NJ, USA. ftownes@princeton.edu.

|July 5, 2020

Summary

We introduce quasi-UMIs, a novel normalization method for single-cell RNA sequencing (scRNA-seq) data without unique molecular identifiers (UMIs). This technique improves gene expression accuracy and allows UMI-based analysis on non-UMI datasets.

Keywords:

Gene expression Normalization Quasi-UMI RNA-seq Single cell

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Last Updated: Dec 16, 2025

Low-input Nucleus Isolation and Multiplexing with Barcoded Antibodies of Mouse Sympathetic Ganglia for Single-nucleus RNA Sequencing

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Published on: March 23, 2022

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Published on: January 10, 2019

G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome

G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome

Published on: March 22, 2018

Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Single-cell RNA sequencing (scRNA-seq) is crucial for understanding cellular heterogeneity.
Polymerase chain reaction (PCR) amplification introduces noise in scRNA-seq read counts.
Unique molecular identifiers (UMIs) mitigate PCR duplication bias but are not always available.

Purpose of the Study:

To develop a method for accurate gene expression profiling in scRNA-seq data lacking UMIs.
To enable the application of UMI-specific analysis tools to non-UMI datasets.
To improve the reliability of scRNA-seq data analysis.

Main Methods:

Proposed quasi-UMIs: a normalization technique for scRNA-seq data.
Quantile normalization of read counts to a compound Poisson distribution.
Empirical derivation of the distribution from UMI-containing datasets.

Main Results:

Quasi-UMI normalization demonstrated higher accuracy compared to existing methods on ground-truth datasets.
The method effectively simulates UMIs for non-UMI scRNA-seq data.
Enables downstream analysis pipelines designed for UMI data.

Conclusions:

Quasi-UMIs provide a robust solution for analyzing scRNA-seq data without UMIs.
This method enhances the utility and accessibility of scRNA-seq data.
Facilitates broader application of advanced computational tools in single-cell genomics.