Jove
Visualize
Contact Us

Related Concept Videos

RNA-seq03:21

RNA-seq

12.2K
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...
12.2K
Next-generation Sequencing03:00

Next-generation Sequencing

99.2K
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....
99.2K
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

15.8K
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...
15.8K

You might also read

Related Articles

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

Sort by
Same author

High quality genome assemblies of African cattle breeds using PacBio HiFi sequencing.

Scientific data·2026
Same author

Genetic prediction with ARG-powered linear algebra.

Genetics·2026
Same author

Leptin receptor gene influences pig gut microbiota both through feed intake and independently.

Animal microbiome·2025
Same author

On ARGs, pedigrees, and genetic relatedness matrices.

Genetics·2025
Same author

Accessible, Realistic Genome Simulation with Selection Using stdpopsim.

Molecular biology and evolution·2025
Same author

Accessible, realistic genome simulation with selection using stdpopsim.

bioRxiv : the preprint server for biology·2025
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 Experiment Video

Updated: Feb 20, 2026

Comparative Lesions Analysis Through a Targeted Sequencing Approach
08:16

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Published on: November 5, 2019

7.3K

A method for allocating low-coverage sequencing resources by targeting haplotypes rather than individuals.

Roger Ros-Freixedes1, Serap Gonen1, Gregor Gorjanc1

  • 1The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush, Midlothian, Scotland, UK.

Genetics, Selection, Evolution : GSE
|October 27, 2017
PubMed
Summary

This study introduces AlphaSeqOpt, a novel heuristic method for optimizing low-coverage sequencing resource allocation. By targeting haplotypes, it improves phasing and imputation accuracy, ensuring more genomic data meets desired coverage levels efficiently.

More Related Videos

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

15.8K
Targeted DNA Methylation Analysis by Next-generation Sequencing
08:38

Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

38.1K

Related Experiment Videos

Last Updated: Feb 20, 2026

Comparative Lesions Analysis Through a Targeted Sequencing Approach
08:16

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Published on: November 5, 2019

7.3K
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

15.8K
Targeted DNA Methylation Analysis by Next-generation Sequencing
08:38

Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

38.1K

Area of Science:

  • Genomics
  • Bioinformatics
  • Population Genetics

Background:

  • Low-coverage sequencing aims to assemble high-coverage data by leveraging shared genomic segments into consensus haplotypes.
  • Accurate derivation of consensus haplotypes is crucial for high phasing and imputation accuracy in population genomics.
  • Efficient allocation of sequencing resources is essential for maximizing the utility of low-coverage data.

Purpose of the Study:

  • To develop a method for allocating low-coverage sequencing resources to maximize the proportion of haplotypes sequenced at sufficient coverage.
  • To improve phasing and imputation accuracy for population-based studies using low-coverage sequencing data.
  • To reduce sequencing redundancy and optimize resource distribution in large-scale genomic projects.

Main Methods:

  • A heuristic method, AlphaSeqOpt, was developed to prioritize haplotypes for sequencing based on their frequency and target coverage.
  • The method involves an iterative selection of individuals and a refinement step for set optimization.
  • An approach for filtering rare haplotypes based on flanking haplotypes was incorporated to target recombination events.

Main Results:

  • AlphaSeqOpt effectively distributes fixed sequencing resources, reducing the proportion of haplotypes below target coverage.
  • The method achieves a higher proportion of haplotypes at target coverage by sequencing fewer individuals compared to frequency-based methods.
  • Refinement of the selected set increases diversity and the number of unique individuals, beneficial for low-coverage sequencing.

Conclusions:

  • AlphaSeqOpt enhances the proportion of haplotypes sequenced at adequate coverage for population-based imputation with low-coverage sequencing.
  • The integrated haplotype scoring, refinement, and rare haplotype filtering contribute to its superior performance.
  • The method offers a more effective solution for reducing sequencing redundancy than prior approaches.