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

Next-generation Sequencing03:00

Next-generation Sequencing

88.5K
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....
88.5K
Sanger Sequencing01:57

Sanger Sequencing

753.9K
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...
753.9K
Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

11.1K
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...
11.1K
Genomics02:02

Genomics

36.2K
Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
36.2K
RNA-seq03:21

RNA-seq

9.9K
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...
9.9K
Genome Annotation and Assembly03:36

Genome Annotation and Assembly

18.8K
The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
18.8K

You might also read

Related Articles

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

Sort by
Same author

Electronic Control of Emission Behavior in Atomically Precise Copper Nanoclusters.

JACS Au·2026
Same author

Unravelling the reactions between a hydride-protected Ag<sub>18</sub> nanocluster and thiol by the crystallization of intermediates.

Nanoscale·2026
Same author

Decoding d- and l-Amino Acids: Data-Driven Recognition of Enantiomers and Post-Translational Modifications via Quantum Tunneling.

The journal of physical chemistry letters·2026
Same author

Quantum-Transport Informed Machine Learning for Identifying Tobacco-Induced Regioisomeric DNA Adducts.

Analytical chemistry·2026
Same author

Unraveling Scaling Relationships in Dual-Atom Catalysts with Electronic Descriptors: A Machine Learning Investigation for OER/ORR Activity.

The journal of physical chemistry letters·2026
Same author

Deciphering Key Descriptors for Scaling Relationships in Graphene-Supported Pt<sub>n</sub> Clusters via Machine Learning.

Small (Weinheim an der Bergstrasse, Germany)·2026

Related Experiment Video

Updated: Jun 17, 2025

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
09:34

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

33.7K

Machine learning empowered next generation DNA sequencing: perspective and prospectus.

Sneha Mittal1, Milan Kumar Jena1, Biswarup Pathak1

  • 1Department of Chemistry, Indian Institute of Technology (IIT) Indore Indore Madhya Pradesh 453552 India biswarup@iiti.ac.in.

Chemical Science
|August 9, 2024
PubMed
Summary
This summary is machine-generated.

Machine learning (ML) offers a promising approach for ultra-rapid, cost-effective, and accurate DNA sequencing. This framework explores ML-aided next-generation sequencing, highlighting opportunities and challenges for artificial intelligence in genomics.

More Related Videos

Collection and Extraction of Saliva DNA for Next Generation Sequencing
06:58

Collection and Extraction of Saliva DNA for Next Generation Sequencing

Published on: August 27, 2014

39.3K
Targeted DNA Methylation Analysis by Next-generation Sequencing
08:38

Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

37.1K

Related Experiment Videos

Last Updated: Jun 17, 2025

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
09:34

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

33.7K
Collection and Extraction of Saliva DNA for Next Generation Sequencing
06:58

Collection and Extraction of Saliva DNA for Next Generation Sequencing

Published on: August 27, 2014

39.3K
Targeted DNA Methylation Analysis by Next-generation Sequencing
08:38

Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

37.1K

Area of Science:

  • Genomics and Bioinformatics
  • Nanotechnology
  • Personalized Medicine

Background:

  • The demand for rapid, affordable, and precise DNA sequencing is critical for advancing personalized medicine.
  • Machine learning (ML) algorithms have shown significant potential in various scientific fields, including nanoscience.
  • Current applications of ML in DNA sequencing are nascent but hold promise for high-throughput analysis.

Purpose of the Study:

  • To present a comprehensive framework for ML-aided next-generation DNA sequencing.
  • To guide the development of artificial intelligence-driven DNA sequencing technologies.
  • To explore the current landscape, opportunities, and challenges in ML-enhanced DNA sequencing.

Main Methods:

  • Reviewing state-of-the-art ML algorithms applicable to DNA sequencing.
  • Integrating domain knowledge with ML approaches for enhanced sequencing accuracy.
  • Analyzing complex datasets generated from DNA sequencing.

Main Results:

  • ML algorithms can potentially decipher intricate patterns in DNA sequencing data.
  • A holistic framework integrating ML and domain knowledge is proposed.
  • Identified key opportunities and challenges in the field of ML-aided DNA sequencing.

Conclusions:

  • ML holds immense potential to revolutionize DNA sequencing, enabling faster and more accurate results.
  • Further research and development are needed to overcome challenges and fully realize AI's capabilities in genomics.
  • Addressing critical issues is essential for the advancement of artificially intelligent DNA sequencers.