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

Sanger Sequencing01:57

Sanger Sequencing

DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...
Variability: Analysis01:11

Variability: Analysis

Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
Next-generation Sequencing03:00

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features.
RNA-seq03:21

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 microarray-based...
Sample Preparation for Analysis: Advanced Techniques01:08

Sample Preparation for Analysis: Advanced Techniques

Accurate analysis of complex samples often requires advanced preparation techniques to achieve reliable and reproducible results. Samples containing inorganic or organic materials can be challenging to dissolve or decompose effectively. Standard sample preparation methods include acid digestion, fusion, dry ashing, and wet digestion.
Acid digestion with strong acids is commonly used to dissolve inorganic materials that are insoluble (do not dissolve) in water. This method can be useful for...

You might also read

Related Articles

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

Sort by
Same author

The tree labeling polytope: A unified approach to ancestral reconstruction problems.

Cell systems·2026
Same author

Spatial Mapping of the Precancer-to-Cancer Transition in Breast and Prostate.

Cancer discovery·2026
Same author

LAML-Pro: Joint Maximum Likelihood Inference of Cell Genotypes and Cell Lineage Trees.

bioRxiv : the preprint server for biology·2026
Same author

Multimodal spatial alignment and morphology mapping with MOSAICField.

bioRxiv : the preprint server for biology·2026
Same author

Genomic evolution of pancreatic cancer at single-cell resolution.

Nature genetics·2026
Same author

Riemannian Metric Learning for Alignment of Spatial Multiomics.

bioRxiv : the preprint server for biology·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: Jun 14, 2026

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Structural variation analysis with strobe reads.

Anna Ritz1, Ali Bashir, Benjamin J Raphael

  • 1Department of Computer Science, Brown University, Providence, RI 02912, USA.

Bioinformatics (Oxford, England)
|April 10, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a new algorithm for identifying structural variations in DNA using strobe sequencing. The method improves accuracy and efficiency compared to traditional paired-read approaches for complex genomic regions.

More Related Videos

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Genotyping of Staphylococcus aureus by Ribosomal Spacer PCR (RS-PCR)
08:51

Genotyping of Staphylococcus aureus by Ribosomal Spacer PCR (RS-PCR)

Published on: November 4, 2016

Related Experiment Videos

Last Updated: Jun 14, 2026

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Genotyping of Staphylococcus aureus by Ribosomal Spacer PCR (RS-PCR)
08:51

Genotyping of Staphylococcus aureus by Ribosomal Spacer PCR (RS-PCR)

Published on: November 4, 2016

Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Structural variations, including deletions, duplications, and rearrangements, are key drivers of genome diversity.
  • Identifying structural variants in complex, repetitive genomic regions remains a significant challenge.
  • Strobe sequencing, generating reads with multiple subreads from single DNA fragments, offers a novel approach to overcome these limitations.

Purpose of the Study:

  • To develop and evaluate an algorithm for structural variant identification using strobe sequencing data.
  • To address the challenges posed by ambiguous alignments in repetitive genomic regions.

Main Methods:

  • Formulated the identification of structural variants as a combinatorial optimization problem.
  • Developed an integer linear programming approach to solve the optimization problem.
  • Utilized simulated strobe sequencing data for algorithm validation.

Main Results:

  • The proposed algorithm demonstrates improved sensitivity and specificity for structural variant identification compared to paired-read methods.
  • Successfully identified structural variants in simulated data, highlighting the efficacy of strobe sequencing.

Conclusions:

  • The developed algorithm effectively leverages strobe sequencing data for accurate structural variant detection.
  • This approach shows significant promise for unraveling complex structural variants in challenging genomic regions.