Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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...

Ribosome Profiling

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Compressing the collective knowledge of ESM into a single protein language model.

Nature methods·2026

Same author

Fezf2 regulates differentiation of Aire-expressing and post-Aire mimetic epithelial populations maintaining thymic homeostasis.

bioRxiv : the preprint server for biology·2026

Same author

Leveraging protein language models to identify complex trait associations with previously inaccessible classes of functional rare variants.

Cell genomics·2025

Same author

Left Tracheal Sleeve Intrapericardial Pneumonectomy: A Single-Stage Approach.

Annals of thoracic surgery short reports·2025

Same author

Phenotypic pleiotropy of missense variants in human B cell confinement receptor P2RY8.

Cell genomics·2025

Same author

Uncovering differential tolerance to deletions versus substitutions with a protein language model.

Cell systems·2025

Same journal

Chlorinated VSLSs Surpass HCFCs in CFC-11-Equivalent Emissions for Ozone Layer Depletion in China.

Nature communications·2026

Same journal

Author Correction: Charge transfer in triphenylamine-tetrazine covalent organic frameworks for solar-driven hydrogen peroxide production.

Nature communications·2026

Same journal

Vegetation browning patterns under compound soil and atmospheric dryness in northern permafrost ecosystems.

Nature communications·2026

Same journal

Voltage imaging of CA1 pyramidal cells and SST+ interneurons reveals stability and plasticity mechanisms of spatial firing.

Nature communications·2026

Same journal

Radical-omics reveals the hydrogen-abstraction pathway of isoprene oxidation.

Nature communications·2026

Same journal

Toughening elastomer via sequentially activated multi-pathway energy dissipation.

Nature communications·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Dec 29, 2025

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

Determining sequencing depth in a single-cell RNA-seq experiment.

Martin Jinye Zhang¹, Vasilis Ntranos^1,2, David Tse³

¹Department of Electrical Engineering, Stanford University, Stanford, CA, USA.

Nature Communications

|February 9, 2020

Summary

This summary is machine-generated.

For single-cell RNA sequencing, sequencing many cells shallowly is optimal. The best strategy involves sequencing at approximately one read per cell per gene for accurate gene property estimation.

More Related Videos

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

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Published on: November 7, 2025

Related Experiment Videos

Last Updated: Dec 29, 2025

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

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

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Published on: November 7, 2025

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) enables gene expression analysis at the individual cell level.
Limited sequencing resources necessitate strategic allocation between sequencing depth and cell number.
Determining the optimal sequencing strategy is crucial for maximizing data utility.

Purpose of the Study:

To develop a mathematical framework for optimizing sequencing budget allocation in scRNA-seq experiments.
To identify the ideal sequencing depth for accurate estimation of gene properties.
To compare the performance of different statistical estimators for scRNA-seq data analysis.

Main Methods:

Development of a theoretical framework based on mathematical modeling.
Analysis of gene property estimation under varying sequencing depths.
Comparison of empirical Bayes estimators with traditional plug-in estimators.

Main Results:

The optimal sequencing strategy involves shallow sequencing of a large number of cells.
An optimal sequencing depth of approximately one read per cell per gene was identified for estimating gene properties.
An empirical Bayes estimator demonstrated superior performance compared to the standard plug-in estimator.

Conclusions:

Shallow sequencing of many cells is a more effective strategy for scRNA-seq experiments with limited budgets.
The findings provide guidance for experimental design in scRNA-seq to enhance data quality and biological insights.
Empirical Bayes methods offer a more robust approach for analyzing scRNA-seq data compared to conventional methods.