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

754.8K
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...
754.8K
Next-generation Sequencing03:00

Next-generation Sequencing

91.5K
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....
91.5K
Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

11.2K
In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
Challenges of the Maxam-Gilbert Method
The...
11.2K
Parallel Processing01:20

Parallel Processing

181
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
181
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

94
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
94
Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

4.0K
Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved...
4.0K

You might also read

Related Articles

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

Sort by
Same author

ClairS: a deep-learning method for long-read tumor-normal pair somatic small variant calling.

Nature methods·2026
Same author

A complete human pancreatic cancer genome.

bioRxiv : the preprint server for biology·2026
Same author

Accelerated long-read variant calling with Clair3 for whole-genome sequencing.

Bioinformatics (Oxford, England)·2026
Same author

Gungnir codec enabling high error-tolerance and low-redundancy DNA storage through substantial computing power.

Nature communications·2026
Same author

MAN++: Scaling Momentum Auxiliary Network for Supervised Local Learning in Vision Tasks.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

EPInformer: scalable and integrative prediction of gene expression from promoter-enhancer sequences with multimodal epigenomic profiles.

Nature communications·2026
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026
Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026
Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026
See all related articles

Related Experiment Video

Updated: Jul 20, 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

Boosting variant-calling performance with multi-platform sequencing data using Clair3-MP.

Huijing Yu1, Zhenxian Zheng1, Junhao Su2

  • 1Department of Computer Science, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China.

BMC Bioinformatics
|August 3, 2023
PubMed
Summary
This summary is machine-generated.

Integrating Oxford Nanopore (ONT) and Illumina sequencing data improves variant calling accuracy, especially in challenging genomic regions. This multi-platform approach enhances genomic analysis reliability and efficiency for researchers.

Keywords:
Deep learningMulti-platform sequencing dataVariant calling

More Related Videos

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing
11:11

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Published on: August 24, 2017

16.9K
CIRCLE-Seq for Interrogation of Off-Target Gene Editing
08:23

CIRCLE-Seq for Interrogation of Off-Target Gene Editing

Published on: November 1, 2024

680

Related Experiment Videos

Last Updated: Jul 20, 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
Detection of Rare Mutations in CtDNA Using Next Generation Sequencing
11:11

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Published on: August 24, 2017

16.9K
CIRCLE-Seq for Interrogation of Off-Target Gene Editing
08:23

CIRCLE-Seq for Interrogation of Off-Target Gene Editing

Published on: November 1, 2024

680

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Increasing availability of sequencing data from diverse platforms like Oxford Nanopore (ONT) and Illumina.
  • Limited research on leveraging multi-platform data for enhanced variant calling performance.
  • Need to optimize genomic analysis by integrating strengths of different sequencing technologies.

Purpose of the Study:

  • To investigate the impact of multi-platform sequencing data (ONT and Illumina) on variant calling.
  • To develop and evaluate a deep learning-based variant caller, Clair3-MP, for multi-platform data integration.
  • To identify optimal scenarios and genomic regions benefiting from combined ONT-Illumina data.

Main Methods:

  • Designed experiments utilizing both ONT and Illumina sequencing data.
  • Employed a deep learning-based variant caller, Clair3-MP (Multi-Platform).
  • Incorporated reference genome stratification information within Clair3-MP.

Main Results:

  • Demonstrated improved variant calling performance using combined ONT-Illumina data.
  • Identified specific genomic regions, including low-complexity and duplication regions, that benefit most.
  • Achieved small but measurable improvements with reference genome stratification in Clair3-MP.

Conclusions:

  • Multi-platform data integration, particularly ONT-Illumina, significantly enhances variant calling accuracy.
  • Clair3-MP effectively leverages combined data, offering improved performance in complex genomic areas.
  • Findings provide guidance for more reliable and efficient genomic analyses across various applications.