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

Hardy-Weinberg Principle01:49

Hardy-Weinberg Principle

Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.In the early 20th century,...
Genome-wide Association Studies-GWAS01:11

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...
What is Population Genetics?01:25

What is Population Genetics?

A population is composed of members of the same species that simultaneously live and interact in the same area. When individuals in a population breed, they pass down their genes to their offspring. Many of these genes are polymorphic, meaning that they occur in multiple variants. Such variations of a gene are referred to as alleles. The collective set of all the alleles within a population is known as the gene pool.While some alleles of a given gene might be observed commonly, other variants...
Next-generation Sequencing03:00

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.

You might also read

Related Articles

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

Sort by
Same author

An inter-specific Amaranthus pangenome captures genetic variation potentially underlying key leafy vegetable traits in this underutilised crop.

The New phytologist·2026
Same author

A novel Leishmania infantum reference strain for gene editing and the study of visceral leishmaniasis.

PloS one·2025
Same author

Whole-genome sequences provide insights into the formation and adaptation of human populations in the Himalayas.

Current biology : CB·2025
Same author

Molecular Insights into Cell-Mediated Immunity in Atypical Non-Ulcerated Cutaneous Leishmaniasis.

Microorganisms·2025
Same author

Microchip Based Isolation and Drug Delivery of Patient-Derived Extracellular Vesicles Against Their Homologous Tumor.

Advanced healthcare materials·2024
Same author

Enhancing lymph node metastasis prediction in adenocarcinoma of the esophagogastric junction: A study combining radiomic with clinical features.

Medical physics·2024

Related Experiment Video

Updated: Jun 5, 2026

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

PoolHap: inferring haplotype frequencies from pooled samples by next generation sequencing.

Quan Long1, Daniel C Jeffares, Qingrun Zhang

  • 1Gregor Mendel Institute, Vienna, Austria. quan.long@gmi.oeaw.ac.at

Plos One
|January 26, 2011
PubMed
Summary
This summary is machine-generated.

PoolHap accurately estimates haplotype frequencies in pooled samples using next-generation sequencing data. This computational tool enables biological insight into complex, heterogeneous samples without experimental partitioning, achieving high accuracy even with low coverage.

More Related Videos

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Related Experiment Videos

Last Updated: Jun 5, 2026

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

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Next-generation sequencing (NGS) enables analysis of complex biological samples.
  • Sequencing pooled samples is challenging due to the difficulty of experimental partitioning of subtypes.
  • Inferring subtype frequencies is crucial for understanding heterogeneous samples like mixed pathogen strains or cancerous tissues.

Purpose of the Study:

  • To introduce PoolHap, a novel computational tool for inferring haplotype frequencies from pooled samples.
  • To address the challenge of analyzing heterogeneous samples where experimental separation is not feasible.
  • To provide a cost-effective method for genetic mapping using pooled samples, such as in bulked segregant analysis.

Main Methods:

  • PoolHap utilizes the abundance of single nucleotide polymorphisms (SNPs) from genome-wide coverage to infer haplotype frequencies.
  • The method leverages uneven genomic coverage to compensate for potential biases.
  • Performance is validated using both simulated and real Arabidopsis thaliana whole genome polymorphism data.

Main Results:

  • PoolHap accurately estimates haplotype proportions in pooled samples.
  • The tool achieved less than 2% error in estimating proportions for 34-strain mixtures with 2X total coverage.
  • Demonstrated effectiveness on both simulated and real-world genomic data.

Conclusions:

  • PoolHap offers a robust computational solution for analyzing pooled samples.
  • The method enhances biological insight into heterogeneous samples.
  • The freely available software facilitates broader application in genetic and genomic research.