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

Genomics02:02

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

Individual identification of dairy cows with occluded camera views using open-set contrastive learning model.

Journal of dairy science·2026
Same author

Real-time milk traits and wearable sensor-derived rumination and feeding behaviors for assessing heat stress effects in dairy cattle.

Journal of dairy science·2026
Same author

2NPLGBM: a genomic model that merges the strengths of classical and machine learning methods in genomic prediction.

Plant methods·2026
Same author

Editorial: Insights in livestock genomics.

Frontiers in genetics·2026
Same author

Corrigendum to "Handling errors in the response: Considerations for leveraging unsupervised or incomplete data for genetic evaluations" (JDS Commun. 5:675-680).

JDS communications·2026
Same author

Cattle grow taller: Implications of outdated ordinal scores for genetic evaluations and selection?

JDS communications·2026
Same journal

Potential role of the <i>Trpv4 c.1491+1G>A</i> mutation in pulmonary fibrosis in a gene-edited mouse model.

Frontiers in genetics·2026
Same journal

Utilization of whole exome sequencing to identify hereditary mutations in Palestinian families with hereditary cancers.

Frontiers in genetics·2026
Same journal

Research of N-acetyl-L-cysteine on CD40-CD40L pathway in pulmonary fibrosis induced by silicon dioxide.

Frontiers in genetics·2026
Same journal

Novel variants in LSS related hypotrichosis simplex 14.

Frontiers in genetics·2026
Same journal

Network-based analysis identifies shared mechanisms between ischemic stroke and myocardial infarction and therapeutic ingredients of Buyang Huanwu Decoction.

Frontiers in genetics·2026
Same journal

GWAS analysis of a depression cohort defined by an EHR-phenotyping algorithm reveals the role of immune regulations in depression risk.

Frontiers in genetics·2026
See all related articles

Related Experiment Video

Updated: May 25, 2026

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

A primer on high-throughput computing for genomic selection.

Xiao-Lin Wu1, Timothy M Beissinger, Stewart Bauck

  • 1Department of Dairy Science, University of Wisconsin Madison, WI, USA.

Frontiers in Genetics
|February 4, 2012
PubMed
Summary
This summary is machine-generated.

High-throughput computing (HTC) accelerates genomic selection by using computer clusters for complex calculations. This approach significantly reduces computation time, increasing output for genetic trait prediction in animals and plants.

Keywords:
Bayesian modelsgeneral-purpose computinggenomic selectionhigh-throughput computingparallel programmingpipelining

More Related Videos

Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Related Experiment Videos

Last Updated: May 25, 2026

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

Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Genomic selection (GS) requires computationally intensive models to predict genetic merit.
  • Current methods face long computing times and low output due to sequential trait evaluation.
  • High-throughput computing (HTC) offers a solution for complex computational problems.

Purpose of the Study:

  • To present principles and scenarios for applying HTC in genomic selection.
  • To demonstrate how HTC can improve computational efficiency and throughput in GS.
  • To explore the potential of HTC in advancing animal and plant breeding.

Main Methods:

  • Implementation of HTC using batch processing and pipelining on distributed computer clusters.
  • Utilization of scripting languages (shell, Perl, R) for pipeline development.
  • Leveraging graphics processing units (GPUs) for massive parallel computing in GS.

Main Results:

  • Pipelining reduces total computing time and increases throughput compared to traditional methods.
  • HTC enables the use of more sophisticated statistical models for genomic selection.
  • Existing institutional HTC infrastructures can be readily adapted for GS.

Conclusions:

  • HTC significantly enhances computational efficiency and output in genomic selection.
  • The adoption of HTC is crucial for handling increasing genomic data demands.
  • HTC promises to revolutionize data analysis and decision-making in post-genomic selection programs.