Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Genomics02:02

Genomics

36.5K
Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
36.5K
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

13.6K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
13.6K

You might also read

Related Articles

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

Sort by
Same author

OrganoidPortal: Web server and single-cell transcriptome database featuring reference atlases of organoids.

Clinical and translational medicine·2024
Same author

DNA Topoisomerase Iα Affects the Floral Transition.

Plant physiology·2016
Same author

Machine Learning Based Single-Frame Super-Resolution Processing for Lensless Blood Cell Counting.

Sensors (Basel, Switzerland)·2016
Same author

Autophagy in long propriospinal neurons is activated after spinal cord injury in adult rats.

Neuroscience letters·2016
Same author

Human Lysozyme Synergistically Enhances Bactericidal Dynamics and Lowers the Resistant Mutant Prevention Concentration for Metronidazole to Helicobacter pylori by Increasing Cell Permeability.

Molecules (Basel, Switzerland)·2016
Same author

Therapeutic effect of apatinib on overall survival is mediated by prolonged progression-free survival in advanced gastric cancer patients.

Oncotarget·2016
Same journal

Correction to 'SyMetrics: an integrated machine learning model for evaluating the pathogenicity of synonymous variants in the human genome'.

NAR genomics and bioinformatics·2026
Same journal

asms: finding allele-specific methylation in human genomes without phasing.

NAR genomics and bioinformatics·2026
Same journal

An epigenetic clock for chronological age estimation in East Asian populations.

NAR genomics and bioinformatics·2026
Same journal

The role of ATF4 in neurons under mitochondrial stress.

NAR genomics and bioinformatics·2026
Same journal

Distinct repeat architecture landscapes in the proteomes of protozoan parasites.

NAR genomics and bioinformatics·2026
Same journal

Long non-coding RNA triplex-dependent regulation of melanoma gene networks.

NAR genomics and bioinformatics·2026
See all related articles

Related Experiment Video

Updated: Jul 27, 2025

Transcriptome Analysis of Single Cells
07:27

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

30.0K

GRACE: a comprehensive web-based platform for integrative single-cell transcriptome analysis.

Hao Yu1,2,3,4, Yuqing Wang1,2, Xi Zhang1,2,4

  • 1Medical Center of Hematology, Second Affiliated Hospital, Army Medical University, Chongqing 400037, China.

NAR Genomics and Bioinformatics
|June 12, 2023
PubMed
Summary
This summary is machine-generated.

A new online platform, GRACE, simplifies massive single-cell RNA sequencing (scRNA-seq) data analysis for researchers. This user-friendly tool enhances data exploration and reproducibility, bridging the gap between experimental and computational biology.

More Related Videos

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

18.6K
Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq
06:24

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

3.7K

Related Experiment Videos

Last Updated: Jul 27, 2025

Transcriptome Analysis of Single Cells
07:27

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

30.0K
Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

18.6K
Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq
06:24

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

3.7K

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Single-cell RNA sequencing (scRNA-seq) is crucial for understanding cellular heterogeneity.
  • Existing computational demands limit accessibility for non-programming researchers.
  • A need exists for user-friendly, scalable platforms for scRNA-seq data analysis.

Purpose of the Study:

  • To develop GRACE, a web-based platform for accessible, large-scale scRNA-seq data analysis.
  • To enhance interactivity and reproducibility in scRNA-seq data exploration.
  • To bridge the gap between experimental and bioinformatics research.

Main Methods:

  • Development of a web-based platform (GRACE) for online scRNA-seq analysis.
  • Integration of preprocessing, clustering, trajectory inference, and cell-cell communication analysis.
  • Provision of interactive visualization, customized parameters, and publication-quality graphs.
  • Availability of a Docker version for private server deployment and freely available source code.

Main Results:

  • GRACE enables interactive visualization and customized analysis of massive scRNA-seq datasets.
  • The platform integrates comprehensive analysis modules including developmental trajectory inference and cell-type annotation.
  • GRACE improves reproducibility and accessibility of scRNA-seq data analysis for the scientific community.
  • A Docker version and accessible source code facilitate flexible deployment and collaboration.

Conclusions:

  • GRACE provides a user-friendly, scalable solution for analyzing large-scale scRNA-seq data.
  • The platform democratizes complex genomic data analysis for researchers without extensive programming expertise.
  • GRACE effectively addresses the need for integrated tools in single-cell transcriptomics research.