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

13.2K
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...
13.2K
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

17.6K
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%...
17.6K
Next-generation Sequencing03:00

Next-generation Sequencing

88.4K
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....
88.4K
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

14.8K
A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
14.8K
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

5.7K
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...
5.7K
Genetic Variation01:25

Genetic Variation

268
Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
Genes exist in different versions called alleles,...
268

You might also read

Related Articles

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

Sort by
Same author

DinoFlow: Self-supervised pretraining in flow cytometry enables accurate detection of common hematopathological disorders.

Cytometry. Part B, Clinical cytometry·2026
Same author

Efficacy of universal genome sequencing in infant extracorporeal membrane oxygenation.

Journal of perinatology : official journal of the California Perinatal Association·2026
Same author

Improving Meat Quality and Lipid Metabolism of Finishing Pigs by Replacing Dietary Soybean Meal with Enzyme-Bacteria Co-Fermented Rapeseed Meal.

Foods (Basel, Switzerland)·2026
Same author

Molecular Characterization of Vascular Anomalies Tissues Guides Clinical Diagnosis.

Lymphatic research and biology·2026
Same author

Venous thromboembolism laboratory testing (factor V Leiden and factor II c.∗97G>A), 2025 revision: A technical standard of the American College of Medical Genetics and Genomics (ACMG).

Genetics in medicine : official journal of the American College of Medical Genetics·2025
Same author

The Utah NeoSeq Project: a collaborative multidisciplinary program to facilitate genomic diagnostics in the neonatal intensive care unit.

NPJ genomic medicine·2025

Related Experiment Video

Updated: Jun 12, 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

15.2K

Generative haplotype prediction outperforms statistical methods for small variant detection in next-generation

Brendan O'Fallon1,2, Ashini Bolia1, Jacob Durtschi1,2

  • 1Institute for Research and Innovation, ARUP Labs, Salt Lake City, UT 84108, United States.

Bioinformatics (Oxford, England)
|September 19, 2024
PubMed
Summary

A new deep generative model, Jenever, accurately detects germline variants in whole genome sequencing data. This advanced approach surpasses existing tools in sensitivity and precision for small variant genotyping, reducing manual review burdens.

More Related Videos

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
11:35

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Published on: August 21, 2016

12.9K
Fluorescence-microscopy Screening and Next-generation Sequencing: Useful Tools for the Identification of Genes Involved in Organelle Integrity
12:42

Fluorescence-microscopy Screening and Next-generation Sequencing: Useful Tools for the Identification of Genes Involved in Organelle Integrity

Published on: April 13, 2012

12.3K

Related Experiment Videos

Last Updated: Jun 12, 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

15.2K
Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
11:35

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Published on: August 21, 2016

12.9K
Fluorescence-microscopy Screening and Next-generation Sequencing: Useful Tools for the Identification of Genes Involved in Organelle Integrity
12:42

Fluorescence-microscopy Screening and Next-generation Sequencing: Useful Tools for the Identification of Genes Involved in Organelle Integrity

Published on: April 13, 2012

12.3K

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Germline variant detection is crucial for genomics.
  • Current tools face challenges with false positives in whole genome data.
  • Manual review of variant calls creates a significant burden.

Purpose of the Study:

  • To introduce a novel deep generative model for germline variant detection.
  • To improve accuracy and reduce false positives in whole genome sequencing analysis.
  • To provide a more efficient alternative to existing variant callers.

Main Methods:

  • Developed a transformer-based encoder and double-decoder deep generative model.
  • Trained the model on 37 whole genome sequences from Genome-in-a-Bottle samples.
  • Implemented the model as a Python-based command-line tool named Jenever.

Main Results:

  • Jenever accurately constructs diploid germline haplotypes with correct phase and genotype.
  • Demonstrated superior overall accuracy compared to FreeBayes, GATK HaplotypeCaller, Clair3, and DeepVariant.
  • Achieved the highest sensitivity, precision, and fewest genotyping errors for indel variants, and the highest F1 score for SNVs.

Conclusions:

  • Jenever offers a significant advancement in germline variant detection accuracy.
  • The deep generative model approach effectively addresses limitations of traditional statistical methods.
  • Jenever provides a powerful and accurate tool for analyzing whole genome sequencing data.