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

6.7K
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...
6.7K
Graphing Antiderivatives01:30

Graphing Antiderivatives

58
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...
58
Bar Graph01:07

Bar Graph

21.9K
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...
21.9K
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

9.1K
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...
9.1K
Parallel Resonance01:23

Parallel Resonance

544
The parallel RLC circuit is an arrangement where the resistor (R), inductor (L), and capacitor (C) are all connected to the same nodes and, as a result, share the same voltage across them. The parallel RLC circuit is analyzed in terms of admittance (Y), which reflects the ease with which current can flow. The admittance is given by:
544

You might also read

Related Articles

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

Sort by
Same author

Population-scale Y chromosome assemblies reveal recurrent remodeling within constrained architectures.

bioRxiv : the preprint server for biology·2026
Same author

Complete genomes of a multi-generational pedigree to expand studies of genetic and epigenetic inheritance.

bioRxiv : the preprint server for biology·2025
Same author

Sequence-to-graph alignment based copy number calling using a network flow formulation.

bioRxiv : the preprint server for biology·2025
Same author

Locityper enables targeted genotyping of complex polymorphic genes.

Nature genetics·2025
Same author

A complete diploid human genome benchmark for personalized genomics.

bioRxiv : the preprint server for biology·2025
Same author

Author Correction: Complex genetic variation in nearly complete human genomes.

Nature·2025
Same journal

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

Bioinformatics (Oxford, England)·2026
Same journal

SpaMFG: a Spatial Multi-omics Integration Method based on Feature Grouping.

Bioinformatics (Oxford, England)·2026
Same journal

CSCN: Inference of Cell-Specific Causal Networks Using Single-Cell RNA-Seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

Sparse CCA-Based Mediation Analysis with High-Dimensional Exposures and Mediators.

Bioinformatics (Oxford, England)·2026
Same journal

Enhancing Cross-Context Generalization in Drug Perturbation Prediction with a Multimodal Conditional Diffusion Framework.

Bioinformatics (Oxford, England)·2026
Same journal

Primer Design through Submodular Function Estimation.

Bioinformatics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Jan 28, 2026

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation
16:02

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

Published on: February 10, 2023

3.2K

Bit-parallel sequence-to-graph alignment.

Mikko Rautiainen1,2,3, Veli Mäkinen4, Tobias Marschall1,2

  • 1Center for Bioinformatics, Saarland University, Saarland Informatics Campus E2.1, 66123 Saarbrücken, Germany.

Bioinformatics (Oxford, England)
|March 10, 2019
PubMed
Summary
This summary is machine-generated.

We developed a novel graph alignment algorithm that speeds up sequence alignment to graphs by 3-20x. This method enhances genome assembly and variant calling by efficiently processing sequencing reads against complex genomic structures.

More Related Videos

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
11:02

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Published on: October 18, 2013

19.9K
Isolation of Adult Spinal Cord Nuclei for Massively Parallel Single-nucleus RNA Sequencing
06:38

Isolation of Adult Spinal Cord Nuclei for Massively Parallel Single-nucleus RNA Sequencing

Published on: October 12, 2018

19.6K

Related Experiment Videos

Last Updated: Jan 28, 2026

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation
16:02

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

Published on: February 10, 2023

3.2K
Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
11:02

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Published on: October 18, 2013

19.9K
Isolation of Adult Spinal Cord Nuclei for Massively Parallel Single-nucleus RNA Sequencing
06:38

Isolation of Adult Spinal Cord Nuclei for Massively Parallel Single-nucleus RNA Sequencing

Published on: October 12, 2018

19.6K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Graphs are essential for representing complex sequence data, including genome assemblies and pan-genomes.
  • Aligning sequencing reads to these graphs is crucial for applications like genome assembly, error correction, and variant calling.

Purpose of the Study:

  • To generalize linear sequence-to-sequence algorithms for efficient alignment on graph structures.
  • To develop a fast and scalable algorithm for aligning sequencing reads to variation graphs.

Main Methods:

  • Generalized the Shift-And and Myers' bitvector algorithms for sequence-to-graph alignment.
  • Developed a bitvector-based graph alignment algorithm with optimized runtime complexities for acyclic and cyclic graphs.
  • Implemented and evaluated the algorithm on five different graph types.

Main Results:

  • Achieved significant speedups (3-fold to 20-fold) compared to previous alignment algorithms.
  • The algorithm demonstrates efficient performance on both acyclic and cyclic graphs.
  • Provided a publicly available implementation (GraphAligner).

Conclusions:

  • The new graph alignment algorithm offers substantial performance improvements for sequence-to-graph alignment.
  • This advancement facilitates more efficient and scalable genomic analyses, including genome assembly and variant calling.
  • The generalized algorithms provide a robust framework for sequence analysis in graph-based representations.