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 Experiment Videos

Haplotype reconstruction from genotype data using Imperfect Phylogeny.

Eran Halperin1, Eleazar Eskin

  • 1CS Division, University of California Berkeley, Berkeley, CA 92093-0114, USA. eran@eecs.berkeley.edu

Bioinformatics (Oxford, England)
|February 28, 2004
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Automated implementation of the SwabSeq COVID-19 diagnostic assay on the opentrons flex liquid-handling robot.

Diagnostic microbiology and infectious disease·2026
Same author

Single-cell profiling of DNA methylation in autism spectrum disorder prefrontal cortex reveals distinct regulatory and aging signatures.

Cell genomics·2026
Same author

A deep learning model for automated identification of age-related macular degeneration atrophy.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie·2026
Same author

Systematic evaluation of 24 extraction and library preparation combinations for metagenomic sequencing of SARS-CoV-2 in saliva.

bioRxiv : the preprint server for biology·2026
Same author

One Size Might Not Fit All: A Tailored Approach to Psychological Intergroup Interventions.

Personality & social psychology bulletin·2026
Same author

Broken Promises: Betrayal and Support for Violence in Intergroup Relations.

Personality & social psychology bulletin·2026
Same journal

Cross-Domain Transfer Learning from Peptides to Metabolites Using a Multi-Property Fine-Tuned LLM.

Bioinformatics (Oxford, England)·2026
Same journal

Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Bioinformatics (Oxford, England)·2026
Same journal

TEDLH: Domain HMMs for sensitive detection of remote homologues.

Bioinformatics (Oxford, England)·2026
Same journal

PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026
See all related articles

This study introduces a novel, efficient method for accurately resolving human haplotypes from genotype data by identifying blocks of single nucleotide polymorphisms (SNPs). The approach achieves near-perfect prediction of common haplotypes with a low error rate for all haplotypes.

Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Understanding human genetic variation is crucial for complex disease research.
  • Single nucleotide polymorphisms (SNPs) are key markers of genetic variation.
  • Haplotype resolution is essential for characterizing individual genetic makeup.

Purpose of the Study:

  • To develop a highly accurate and efficient method for haplotype resolution from genotype data.
  • To leverage the block structure of single nucleotide polymorphisms (SNPs) for improved haplotype prediction.
  • To provide a scalable solution for analyzing large-scale genetic datasets.

Main Methods:

  • A novel algorithm that partitions SNPs into blocks based on haplotype structure.
  • Prediction of common haplotypes within each block.

Related Experiment Videos

  • Individual haplotype prediction using the identified block structure.
  • Evaluation on biological data, including handling of missing data.

    • Main Results:

    • Perfect prediction of common haplotypes within blocks.
    • Low overall haplotype prediction error rate (<2%).
    • Significantly improved computational efficiency compared to existing methods (e.g., PHASE, HAPLOTYPER).

    Conclusions:

    • The developed method provides accurate and efficient haplotype resolution.
    • The block-based approach effectively models human genetic variation.
    • The algorithm's efficiency enables analysis of large datasets and accommodates missing genotype data.