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

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

14.4K
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...
14.4K
Genetic Screens02:46

Genetic Screens

5.1K
Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...
5.1K
Next-generation Sequencing03:00

Next-generation Sequencing

92.7K
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....
92.7K
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

17.9K
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%...
17.9K
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

6.2K
Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
6.2K

You might also read

Related Articles

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

Sort by
Same author

Frailty in Children and Adolescents With Acute Lymphoblastic Leukemia or Lymphoblastic Lymphoma Receiving Maintenance Chemotherapy-A Pilot Study.

Journal of pediatric hematology/oncology·2026
Same author

Identifying research priorities for post-collision care in the United Kingdom: outcomes and methodological adaptations from the final prioritisation workshop.

Scandinavian journal of trauma, resuscitation and emergency medicine·2026
Same author

Linkage-aware inference of fitness from short-read time-series genomic data.

Virus evolution·2026
Same author

Multi-omics analysis reveals LARP1 as a key integrator of translation and metabolism in AML.

Oncogenesis·2026
Same author

Dual role of glycosylation in resistance to CD4-binding site broadly neutralizing antibodies.

Journal of virology·2026
Same author

Historic 1994 influenza vaccine cohorts define breadth of antibody and B cell responses toward future influenza A and B viruses.

Science translational medicine·2026
Same journal

A human-specific genetic modifier reconfigures large-scale cortical network dynamics underlying behavioral performance.

bioRxiv : the preprint server for biology·2026
Same journal

<i>Staphylococcus aureus</i> uses a eukaryotic-like uridyltransferase to make UDP-GlcNAc for cell wall synthesis.

bioRxiv : the preprint server for biology·2026
Same journal

Dynamic redistribution of eIF4F controls cap-dependent translation initiation.

bioRxiv : the preprint server for biology·2026
Same journal

When does additional information improve accuracy of RNA secondary structure prediction?

bioRxiv : the preprint server for biology·2026
Same journal

Normative brain-state trajectories reveal deviation from healthy aging in Alzheimer's disease.

bioRxiv : the preprint server for biology·2026
Same journal

Noradrenergic infraslow rhythm during sleep is the critical link between heart-rate dynamics and memory consolidation.

bioRxiv : the preprint server for biology·2026
See all related articles

Related Experiment Video

Updated: Sep 16, 2025

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

15.3K

Back-projection improves inference from sparsely sampled genomic surveillance data.

Elizabeth E Finney1, Brian Lee1, Syed Faraz Ahmed2,3

  • 1Department of Physics and Astronomy, University of California, Riverside, USA.

Biorxiv : the Preprint Server for Biology
|July 9, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new model to accurately estimate SARS-CoV-2 infection times, improving the identification of highly transmissible variants and their mutations.

More Related Videos

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

2.3K
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.4K

Related Experiment Videos

Last Updated: Sep 16, 2025

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

15.3K
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

2.3K
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.4K

Area of Science:

  • Virology
  • Epidemiology
  • Genomic Surveillance

Background:

  • Emergence of highly transmissible SARS-CoV-2 variants has driven COVID-19 waves.
  • Genomic surveillance offers insights into viral evolution but faces challenges.
  • Delayed and limited regional data introduce biases in epidemiological models.

Purpose of the Study:

  • To develop a method that accounts for uncertainty in infection timing.
  • To reliably estimate selection for increased transmission of SARS-CoV-2 variants.
  • To improve the identification of mutations and variants with higher transmission rates.

Main Methods:

  • Utilized a novel, variant-specific back-projection model to estimate infection time distributions.
  • Integrated this with epidemiological modeling to infer transmission selection.
  • Validated the approach through simulations and application to real-world SARS-CoV-2 data.

Main Results:

  • The back-projection model accurately estimates infection times from sample collection times.
  • The integrated approach reliably infers selection for increased transmission.
  • The method effectively smoothed and extended data from regions or times with poor sampling.

Conclusions:

  • The developed method enhances the reliability of identifying SARS-CoV-2 variants with higher transmissibility.
  • It addresses biases caused by delayed and limited genomic surveillance data.
  • This approach aids in understanding viral evolution and informing public health strategies.