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...

You might also read

Related Articles

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

Sort by

Same author

EpiATLAS - a reference for human epigenomic research.

bioRxiv : the preprint server for biology·2026

Same author

Regulatory landscape of widespread stop codon readthrough in <i>Drosophila</i>.

bioRxiv : the preprint server for biology·2026

Same author

Hierarchical PLGA/PEG Barrier Engineering of Alginate Hydrogels: Scale-Dependent Burst-Release Control in Beads and Microgels.

Biomimetics (Basel, Switzerland)·2026

Same author

Expansion Revealing of Pathology Resolves Nanostructures Associated with Inflammatory Phenotypes in COVID-19 Decedent Human Brain Tissue.

bioRxiv : the preprint server for biology·2026

Same author

Dissecting Alzheimer's disease heterogeneity by cross-trait polygenic prediction.

bioRxiv : the preprint server for biology·2026

Same author

Genotype epigenome phenotype integration reveals peripheral immune contributions to type I bipolar disorder.

Nature communications·2026

Same journal

SA-MTP: a structure-aware framework for multifunctional therapeutic peptide annotation.

Briefings in bioinformatics·2026

Same journal

Genome assemblies and annotations are not static and need support for tracking their evolution.

Briefings in bioinformatics·2026

Same journal

A historical journey of metabolite-protein interaction discovery: from data harmonization to AI-driven prediction.

Briefings in bioinformatics·2026

Same journal

Bridging local-global transmembrane protein contexts with contrastive pretraining for alignment-free pathogenicity prediction.

Briefings in bioinformatics·2026

Same journal

Prediction of drug hypersensitivity by comprehensive modeling of HLA-peptidomes.

Briefings in bioinformatics·2026

Same journal

EssTFNet: integration of adaptive time-frequency and DNA language models for interpretable human essential gene prediction.

Briefings in bioinformatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 27, 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

Single-cell RNA sequencing data imputation using bi-level feature propagation.

Junseok Lee¹, Sukwon Yun², Yeongmin Kim³

¹Department of Industrial and Systems Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea.

Briefings in Bioinformatics

|May 6, 2024

Summary

This summary is machine-generated.

This study introduces single-cell bilevel feature propagation (scBFP), a novel graph-based method to denoise single-cell RNA sequencing (scRNA-seq) data. scBFP effectively addresses noise and sparsity, improving downstream analysis and biological insight discovery.

Keywords:

feature propagation imputation scRNA-seq

More Related Videos

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level

RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level

Published on: May 19, 2019

Related Experiment Videos

Last Updated: Jun 27, 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

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level

RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level

Published on: May 19, 2019

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell RNA sequencing (scRNA-seq) is crucial for studying cellular heterogeneity.
scRNA-seq data are prone to technical noise, dropout events, and sparsity, complicating analysis.
Existing graph-based denoising methods may propagate noise and underutilize data relationships.

Purpose of the Study:

To develop a robust method for denoising scRNA-seq data.
To improve the accuracy of downstream analyses by mitigating noise and sparsity.
To leverage both cell-cell and gene-gene relationships for enhanced data processing.

Main Methods:

Introduced single-cell bilevel feature propagation (scBFP), a two-step graph-based approach.
Implemented an initial imputation step to handle zero values without affecting non-zero expression data.
Applied subsequent denoising leveraging both gene-gene and cell-cell associations.

Main Results:

scBFP effectively denoises scRNA-seq datasets, reducing technical noise and sparsity.
The method demonstrated superior performance in various downstream analytical tasks.
Experimental results confirmed the effectiveness of scBFP on real scRNA-seq data.

Conclusions:

scBFP offers a significant advancement in processing noisy scRNA-seq data.
The method enhances the reliability of gene expression profiles for biological interpretation.
scBFP facilitates the uncovering of deeper biological insights from single-cell studies.