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

Combinatorial transcription factor interactions drive modular gene regulatory networks.

bioRxiv : the preprint server for biology·2026

Same author

Parallel Activation and Interference CRISPR (PAIR) with Sequencing Uncovers DNA Repair Networks Guiding Precision Cell Engineering.

bioRxiv : the preprint server for biology·2026

Same author

PEPR-GNN: Perturbation-Enhancer-Promoter-RNA Graph Neural Networks for Multiome Perturb-Seq modeling of regulomes.

bioRxiv : the preprint server for biology·2026

Same author

Olfactomedin4 marks luminal progenitor cells that give rise to secretory cell lineage in the mouse cervix.

bioRxiv : the preprint server for biology·2026

Same author

Comprehensive perturbation of transcription factors in human cardiomyocytes reveals the regulatory architecture of congenital heart disease.

bioRxiv : the preprint server for biology·2025

Same author

Benchmarking and optimizing Perturb-seq in differentiating human pluripotent stem cells.

bioRxiv : the preprint server for biology·2025

Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026

Same journal

KASSPer: Kinase Active Site Structure Prediction using Protein and Ligand Language Models and Its Application to Virtual Screening.

Bioinformatics (Oxford, England)·2026

Same journal

IDR searcher: a search engine solution for public image resources.

Bioinformatics (Oxford, England)·2026

Same journal

KCFtools: Rapid alignment-free method for introgression screening and GWAS using k-mer profiles.

Bioinformatics (Oxford, England)·2026

Same journal

Meta2DB: Curated shotgun metagenomic feature sets and metadata for health state prediction.

Bioinformatics (Oxford, England)·2026

Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Nov 5, 2025

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Published on: January 7, 2020

FBA: feature barcoding analysis for single cell RNA-Seq.

Jialei Duan¹, Gary C Hon^1,2

¹The Green Center, Tecil H. and Ida Green Center for Reproductive Biology Sciences, Dallas, TX, 75390, USA.

Bioinformatics (Oxford, England)

|May 17, 2021

Summary

This summary is machine-generated.

Feature barcoding assays enhance single-cell RNA sequencing (scRNA-Seq) by adding molecular and cellular feature data. The FBA package streamlines analysis for these advanced scRNA-Seq experiments.

More Related Videos

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

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

Published on: May 23, 2018

Related Experiment Videos

Last Updated: Nov 5, 2025

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Published on: January 7, 2020

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

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

Published on: May 23, 2018

Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Single cell RNA sequencing (scRNA-Seq) reveals cellular heterogeneity and function.
Advanced experimental methods extend scRNA-Seq to include cell surface proteins and genomic perturbations.
These 'feature barcoding' strategies convert cellular features into detectable sequence barcodes.

Purpose of the Study:

Introduce FBA, a software package designed for feature barcoding assays.
Provide a streamlined workflow for analyzing complex single-cell data.

Main Methods:

FBA package implementation for quality control and data processing.
Utilizes sequence barcodes to quantify and demultiplex feature barcoding data.
Includes algorithms for multiplet detection, clustering, and visualization.

Main Results:

FBA offers a flexible and streamlined approach to feature barcoding data analysis.
Enables comprehensive analysis from quality control to visualization.
Facilitates deeper insights into cellular heterogeneity and function.

Conclusions:

FBA is a valuable tool for researchers utilizing feature barcoding strategies.
Enhances the analysis of advanced scRNA-Seq experiments.
Supports the interpretation of multi-modal single-cell data.