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-wide Association Studies-GWAS

Genome-wide Association Studies-GWAS

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...

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.

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...

You might also read

Related Articles

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

Sort by

Same author

Respiratory pathogen dynamics in community fever cases: Jiangsu Province, China (2023-2024).

Virology journal·2024

Same author

Sly-miR398b Mediates Mature Leaf Flattening by Orchestrating Auxin and H<sub>2</sub>O<sub>2</sub> Signalling in Tomato.

Plant, cell & environment·2024

Same author

Serotonin release in the habenula during emotional contagion promotes resilience.

Science (New York, N.Y.)·2024

Same author

Needle-Shaped Biosensors for Precision Diagnoses: From Benchtop Development to In Vitro and In Vivo Applications.

Biosensors·2024

Same author

Identification and characterization of new Siberian subtype of tick-borne encephalitis virus isolates revealed genetic variations of the Chinese strains.

Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases·2024

Same author

Advances in the Clinical Application of High-throughput Proteomics.

Exploratory research and hypothesis in medicine·2024

Same journal

Chromosomal scale genome assembly of medicinal plant Sophora tonkinensis.

BMC genomics·2026

Same journal

Variant-specific RNA testing resolves variants of uncertain significance in exome testing.

BMC genomics·2026

Same journal

Kaiso overexpression promotes an interferon immune response in murine intestines.

BMC genomics·2026

Same journal

Genomic evidence of ecological flexibility and cross-niche CRISPR spacerome targeting phage-plasmid hybrids in Latilactobacillus curvatus.

BMC genomics·2026

Same journal

Fgf evolution in vertebrates: insights from cyclostomes.

BMC genomics·2026

Same journal

Metabolic reprogramming, oxidative stress, and mitophagy in JSRV Env-transformed BEAS-2B cells: insights from integrated transcriptomics and metabolomics.

BMC genomics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 13, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

A highly accurate heuristic algorithm for the haplotype assembly problem.

Fei Deng¹, Wenjuan Cui, Lusheng Wang

¹Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong.

|March 1, 2013

Summary

This summary is machine-generated.

This study introduces a new algorithm for haplotype assembly, accurately reconstructing DNA haplotypes even with high fragment error rates. The developed heuristic method offers precise solutions for complex genetic variations.

More Related Videos

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Published on: June 28, 2018

Related Experiment Videos

Last Updated: May 13, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Published on: June 28, 2018

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single nucleotide polymorphisms (SNPs) represent common human DNA variations.
Haplotypes, the sequence of SNPs on a chromosome, are crucial for disease diagnosis and drug design.
The haplotype assembly problem involves reconstructing two haplotypes from DNA fragments.

Purpose of the Study:

To develop an accurate algorithm for haplotype assembly, particularly effective with high fragment error rates.
To address limitations of existing algorithms when dealing with noisy sequencing data.

Main Methods:

Developed a dynamic programming algorithm for exact solutions to haplotype assembly.
Proposed a heuristic algorithm based on dynamic programming to improve efficiency for large datasets.
Evaluated algorithm performance on benchmark datasets.

Main Results:

The dynamic programming algorithm provides exact solutions but has high time complexity.
The heuristic algorithm achieves highly accurate haplotype assembly solutions.
The proposed algorithm demonstrates superior performance compared to existing methods, especially with high error rates.

Conclusions:

The developed algorithm provides accurate haplotype assembly solutions.
The heuristic approach effectively handles high fragment error rates, outperforming existing programs.
This work advances the field of haplotype reconstruction for genetic variation analysis.