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

Ogive Graph01:07

Ogive Graph

6.8K
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...
6.8K
Graphing Antiderivatives01:30

Graphing Antiderivatives

66
The concept of an antiderivative is fundamental in calculus, describing how a function's values accumulate over time. This process is closely related to physical motion, such as the movement of a rolling ball. As the ball progresses, its position changes in response to variations in velocity, just as an antiderivative graph reflects the cumulative effect of the original function's values.Graphing an antiderivative requires interpreting how a function's values influence the shape of its...
66
Bar Graph01:07

Bar Graph

22.0K
A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
22.0K
Graphs of Functions01:30

Graphs of Functions

321
Graphs of functions provide a visual representation of how output values change in response to varying inputs. Each point on the graph corresponds to an ordered pair, where the x-coordinate (independent variable) determines the horizontal position and the y-coordinate (dependent variable) determines the vertical position. Linear functions like y = x give a straight line, indicating a constant rate of change.Nonlinear functions display more complex behaviors. Even power functions generate...
321
Time-Series Graph00:54

Time-Series Graph

5.1K
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...
5.1K
Multiple Bar Graph01:07

Multiple Bar Graph

9.2K
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...
9.2K

You might also read

Related Articles

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

Sort by
Same author

Primer Design through Submodular Function Estimation.

Bioinformatics (Oxford, England)·2026
Same author

Accelerating String Comparison in RLZ Compressed Sequences via LCE Jumps.

bioRxiv : the preprint server for biology·2026
Same author

STORM: spatial transcriptomics optimization by resolution via matrix factorization.

Briefings in bioinformatics·2026
Same author

Building genomic data structures from compressed representations using prefix-free parsing.

Genome research·2026
Same author

SPACT: A clustering-driven multi-modal framework for survival prediction using genomic and histopathology data.

Medical image analysis·2026
Same author

Response to: "best practices when benchmarking CATCH for the design of genome enrichment probes".

Bioinformatics (Oxford, England)·2026
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

Related Experiment Video

Updated: Jan 30, 2026

Optical Mapping of Intra-Sarcoplasmic Reticulum Ca2+ and Transmembrane Potential in the Langendorff-perfused Rabbit Heart
09:26

Optical Mapping of Intra-Sarcoplasmic Reticulum Ca2+ and Transmembrane Potential in the Langendorff-perfused Rabbit Heart

Published on: September 10, 2015

9.8K

Aligning optical maps to de Bruijn graphs.

Kingshuk Mukherjee1, Bahar Alipanahi1, Tamer Kahveci1

  • 1Department of Computer and Information Science and Engineering, College of Engineering, University of Florida, Gainesville, USA.

Bioinformatics (Oxford, England)
|January 31, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces omGraph, a novel algorithm for aligning optical maps (Rmaps) to de Bruijn graphs, improving genome assembly. The method achieves high accuracy in aligning Rmaps to sequence data for bacterial and human genomes.

More Related Videos

High-Throughput Analysis of Optical Mapping Data Using ElectroMap
07:36

High-Throughput Analysis of Optical Mapping Data Using ElectroMap

Published on: June 4, 2019

10.1K
Optical Mapping of Langendorff-perfused Rat Hearts
11:48

Optical Mapping of Langendorff-perfused Rat Hearts

Published on: August 11, 2009

21.4K

Related Experiment Videos

Last Updated: Jan 30, 2026

Optical Mapping of Intra-Sarcoplasmic Reticulum Ca2+ and Transmembrane Potential in the Langendorff-perfused Rabbit Heart
09:26

Optical Mapping of Intra-Sarcoplasmic Reticulum Ca2+ and Transmembrane Potential in the Langendorff-perfused Rabbit Heart

Published on: September 10, 2015

9.8K
High-Throughput Analysis of Optical Mapping Data Using ElectroMap
07:36

High-Throughput Analysis of Optical Mapping Data Using ElectroMap

Published on: June 4, 2019

10.1K
Optical Mapping of Langendorff-perfused Rat Hearts
11:48

Optical Mapping of Langendorff-perfused Rat Hearts

Published on: August 11, 2009

21.4K

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Optical maps (Rmaps) are high-resolution genome representations valuable for assembly and variation detection.
  • Current methods primarily use optical maps post-assembly, limiting their integration into the assembly process.
  • Aligning optical maps directly to sequence graphs remains an unexplored challenge.

Purpose of the Study:

  • To define and address the problem of aligning optical maps (Rmaps) to de Bruijn graphs.
  • To develop the first algorithm for Rmap-to-de Bruijn graph alignment.
  • To facilitate the use of optical maps within the genome assembly process.

Main Methods:

  • Developed a seed-and-extend approach for Rmap-to-de Bruijn graph alignment.
  • Implemented the algorithm in C++ software named omGraph.
  • Validated the method on bacterial (Escherichia coli) and human genome data.

Main Results:

  • Successfully aligned 73% of Rmaps to the de Bruijn graph for E. coli with 99.6% accuracy.
  • Demonstrated scalability to larger genomes, aligning 76% of Rmaps for human data.
  • The omGraph software is publicly available.

Conclusions:

  • The developed algorithm effectively aligns optical maps to de Bruijn graphs.
  • This alignment enables better integration of optical maps into genome assembly pipelines.
  • The omGraph tool offers a significant advancement for genomic analysis and structural variation discovery.