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

Genome Annotation and Assembly

Genome Annotation and Assembly

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.

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...

Evolutionary Relationships through Genome Comparisons

Evolutionary Relationships through Genome Comparisons

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...

Sanger Sequencing

Sanger Sequencing

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...

Genomics

Genomics

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...

You might also read

Related Articles

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

Sort by

Same author

Regulatory mechanisms driven by functional 3'-UTR variants in alcohol use disorder and related traits.

Genome biology·2026

Same author

Tenascin-C Drives Inflammatory VSMC Phenotypic Switching Through NF-κB Signaling in Saphenous Vein Graft Restenosis.

International journal of molecular sciences·2026

Same author

Comparison of nanopore sequencing, MethylationEPIC array, and EM-Seq for DNA methylation detection.

Computational biology and chemistry·2026

Same author

Prognostic analysis of autophagy-related differentially expressed genes in oral squamous cell carcinoma.

Journal of stomatology, oral and maxillofacial surgery·2026

Same author

Multimodal deep learning model integrating electronic medical records and CT images for gallbladder cancer diagnosis: a retrospective multicenter study in China.

BMC cancer·2026

Same author

CEMTNet: a cognitive emotion modulated network for multimodal depression detection.

Neural networks : the official journal of the International Neural Network Society·2026

Search research articles

Related Experiment Video

Updated: May 28, 2026

Novel Sequence Discovery by Subtractive Genomics

Novel Sequence Discovery by Subtractive Genomics

Published on: January 25, 2019

Graph accordance of next-generation sequence assemblies.

Guohui Yao¹, Liang Ye, Hongyu Gao

¹The Genome Institute, Washington University School of Medicine, 4444 Forest Park Avenue, St Louis, MO 63108, USA.

Bioinformatics (Oxford, England)

|October 26, 2011

Summary

This summary is machine-generated.

This study introduces a new algorithm to improve next-generation sequencing (NGS) assemblies by combining different methods. The Graph Accordance Assembly (GAA) program enhances contig length and genome coverage for better sequence assembly.

More Related Videos

Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies

Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies

Published on: August 20, 2021

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms

Published on: May 9, 2017

Related Experiment Videos

Last Updated: May 28, 2026

Novel Sequence Discovery by Subtractive Genomics

Novel Sequence Discovery by Subtractive Genomics

Published on: January 25, 2019

Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies

Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies

Published on: August 20, 2021

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms

Published on: May 9, 2017

Area of Science:

Bioinformatics
Genomics
Computational Biology

Background:

Existing assembly algorithms struggle with short-read sequencing data, leading to fragmented contigs.
Improved contiguity is crucial for effective utilization of next-generation sequencing (NGS) assemblies.
Limitations in current assembly methods hinder comprehensive genomic analysis.

Purpose of the Study:

To develop a novel algorithm that overcomes limitations of individual assembly approaches.
To enhance the contiguity and quality of NGS assemblies by integrating diverse data.
To improve genome and gene coverage in assembled sequences.

Main Methods:

Developed the Graph Accordance Assembly (GAA) program.
Constructed an accordance graph to map relationships between target and query assemblies.
Utilized constraints like gap sizes, mate pairs, and scaffold orientation for accurate assembly operations.

Main Results:

GAA successfully extended, merged, and bridged contigs and scaffolds.
Applied to chicken NGS assemblies, GAA demonstrated significant improvements in contiguity statistics.
Achieved higher genome and gene coverage compared to existing methods.

Conclusions:

The GAA algorithm effectively integrates information from different assembly algorithms and sequencing platforms.
GAA provides a robust solution for improving the contiguity of short-read NGS assemblies.
The developed tool offers enhanced genome and gene coverage, advancing genomic research.