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

Interpreting R Charts01:22

Interpreting R Charts

R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum values—of a sample...
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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...
Genomics02:02

Genomics

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...
Multiple Bar Graph01:07

Multiple Bar Graph

As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
Time-Series Graph00:54

Time-Series Graph

A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
Ogive Graph01:07

Ogive Graph

An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this type...

You might also read

Related Articles

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

Sort by
Same author

Real-world genetic testing data of ovarian cancer patients: Informing counseling and risk reduction in patients over age seventy.

Gynecologic oncology·2026
Same author

UNLOCKING MULTI-SAMPLE DIFFERENTIAL EXPRESSION FOR SPATIAL TRANSCRIPTOMICS DATA WITH TESSERA.

bioRxiv : the preprint server for biology·2026
Same author

Advancing precision health discovery in a genetically diverse health system.

Cell·2026
Same author

Genome-wide classification of tumor-derived reads from bulk long-read sequencing.

bioRxiv : the preprint server for biology·2026
Same author

A latent activated olfactory stem cell state revealed by single-cell transcriptomic and epigenomic profiling.

Stem cell reports·2026
Same author

Molecular Reactivity in Maternal Pregnancy Blood and Neonatal Dried Blood Spots Is Associated with the Risk of Pediatric Acute Lymphoblastic Leukemia.

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology·2025
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026
Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026
Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026
See all related articles

Related Experiment Video

Updated: Jun 26, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

GenomeGraphs: integrated genomic data visualization with R.

Steffen Durinck1, James Bullard, Paul T Spellman

  • 1Life Sciences Department, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Berkeley, CA 94720, USA. steffen@stat.berkeley.edu

BMC Bioinformatics
|January 7, 2009
PubMed
Summary
This summary is machine-generated.

GenomeGraphs is a new R software package for visualizing diverse genomic datasets. It integrates experimental data with genomic annotation for comprehensive biological study analysis.

More Related Videos

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study
07:50

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study

Published on: April 18, 2025

Related Experiment Videos

Last Updated: Jun 26, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study
07:50

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study

Published on: April 18, 2025

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • High-throughput biological studies generate diverse genomic datasets.
  • Existing methods lack versatile visualization for multiple genomic data types.
  • Genomic data analysis requires integrated visualization of experimental data, annotation, and statistical analyses.

Purpose of the Study:

  • To develop a versatile software package for integrated visualization of genomic datasets.
  • To facilitate the combination of experimental data with genomic annotation and statistical analyses.

Main Methods:

  • Developed GenomeGraphs as an add-on package for the R statistical programming environment.
  • Utilized the biomaRt package for online annotation queries to Ensembl.
  • Integrated gene/transcript structures with experimental data using the grid graphics package.

Main Results:

  • GenomeGraphs enables plotting genomic annotation alongside experimental data.
  • The package supports visualization of custom annotation tracks with various experimental data types.
  • All data is plotted within a unified genomic coordinate system for coherent analysis.

Conclusions:

  • GenomeGraphs provides a flexible and extensible solution for visualizing multiple genomic datasets.
  • The software package enhances integrated data analysis within the R environment.
  • Facilitates comprehensive characterization of biological samples through advanced visualization.