Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Next-generation Sequencing

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....

Maxam-Gilbert Sequencing

Maxam-Gilbert Sequencing

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...

You might also read

Related Articles

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

Sort by

Same author

Multi-ancestry, trans-generational GWAS meta-analysis of gestational diabetes and glycaemic traits during pregnancy reveals limited evidence of pregnancy-specific genetic effects.

Nature communications·2026

Same author

Genome-wide meta-analysis identifies genetic drivers of bile acid metabolism in intrahepatic cholestasis of pregnancy.

Nature communications·2026

Same author

Global multi-ancestry genome-wide analyses identify genes and biological pathways associated with thyroid cancer and benign thyroid diseases.

Nature genetics·2026

Same author

Development and validation of a neural network survival prediction model for ischemic heart disease.

Cardiovascular diabetology·2026

Same author

COMPREHENSIVE GENETIC INVESTIGATION REVEALS HETEROGENEOUS PATHWAYS TO OBSTRUCTIVE SLEEP APNEA.

medRxiv : the preprint server for health sciences·2026

Same author

De novo and inherited dominant variants in U4 and U6 snRNA genes cause retinitis pigmentosa.

Nature genetics·2026

Same journal

PCSK5 promotes angiogenesis and cardiac repair after myocardial infarction.

Nature communications·2026

Same journal

PfApiAT2 is a proline transporter essential for the transmission of Plasmodium falciparum by the mosquito vector.

Nature communications·2026

Same journal

Transient distortions of the South Atlantic Anomaly radiation environments driven by electric fields.

Nature communications·2026

Same journal

Structural basis of the regulation by CDK11 kinase of early spliceosome activation and evidence for its proofreading by DHX15 helicase.

Nature communications·2026

Same journal

Structural and mechanistic insights into primer synthesis initiation by DNA primase.

Nature communications·2026

Same journal

Changes in heritability and shared environmentality of educational attainment across twentieth-century Norway.

Nature communications·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 3, 2026

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Brain age prediction using deep learning uncovers associated sequence variants.

B A Jonsson^1,2, G Bjornsdottir¹, T E Thorgeirsson¹

¹deCODE Genetics/Amgen, Inc., 101, Reykjavik, Iceland.

Nature Communications

|November 29, 2019

Summary

This summary is machine-generated.

We developed a deep learning method to predict brain age from MRI scans. This approach identified genetic variants associated with brain aging and structural differences in the brain.

More Related Videos

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Identification and Classification of Position-specific GABA_A Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

Author Spotlight: Automated Lifespan Monitoring – Discovering Aging Dynamics with the Lifespan Machine

Author Spotlight: Automated Lifespan Monitoring – Discovering Aging Dynamics with the Lifespan Machine

Published on: January 26, 2024

Related Experiment Videos

Last Updated: Jan 3, 2026

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Identification and Classification of Position-specific GABA_A Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

Author Spotlight: Automated Lifespan Monitoring – Discovering Aging Dynamics with the Lifespan Machine

Author Spotlight: Automated Lifespan Monitoring – Discovering Aging Dynamics with the Lifespan Machine

Published on: January 26, 2024

Area of Science:

Neuroimaging
Genetics
Artificial Intelligence

Background:

Machine learning models can estimate chronological age from brain structural MRI.
Predicted age difference (PAD) is a relevant phenotype for aging and brain diseases.

Purpose of the Study:

To present a novel deep learning approach for brain age prediction using T1-weighted MRI.
To identify genetic variants associated with PAD using genome-wide association studies.

Main Methods:

A deep learning model was trained on brain MRI data from healthy Icelanders.
Transfer learning was employed to enhance model accuracy on the IXI and UK Biobank datasets.
Genome-wide association study (GWAS) was performed on PAD in UK Biobank data.

Main Results:

The deep learning model accurately predicted brain age across different datasets.
GWAS identified two significant sequence variants (rs1452628-T and rs2435204-G) associated with PAD.
rs1452628-T correlated with reduced sulcal width, and rs2435204-G with reduced white matter surface area.

Conclusions:

Deep learning provides an effective method for brain age estimation from MRI.
Genetic variants near KCNK2 and at 17q21.31 (H2) are associated with brain age and structural differences.