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

Dynamic variable selection in SNP genotype autocalling from APEX microarray data.

Mohua Podder1, William J Welch, Ruben H Zamar

  • 1Department of Statistics, University of British Columbia, Vancouver, BC, Canada. mpodder@mrl.ubc.ca <mpodder@mrl.ubc.ca>

BMC Bioinformatics
|December 2, 2006
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

Newborns with sickle cell trait have distinct peripheral blood molecular signatures across the first week of life.

Blood. Red cells & iron·2026
Same author

Pilot longitudinal integrated transcriptomic-metabolomic study reveals immune and metabolic signatures in non-hospitalized healthcare workers with long COVID.

Frontiers in cellular and infection microbiology·2026
Same author

Using cell-specific late-phase asthma mRNA biomarkers to repurpose drugs that concurrently reverse disease signatures across multiple immune cell-types.

PLoS computational biology·2026
Same author

Donor-derived cell-free DNA associated with increased risk of chronic lung allograft dysfunction and mortality: Implications of immunosuppression and retransplantation.

The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation·2026
Same author

A randomized prospective study of neonatal hepatitis B vaccine immunogenicity in The Gambia and Papua New Guinea.

The journal of allergy and clinical immunology. Global·2026
Same author

2024-2025 Covid-19 Vaccine Outcomes in U.S. Veterans.

The New England journal of medicine·2026
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026
Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026
Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026
See all related articles

An automated genotyping algorithm was developed for single nucleotide polymorphisms (SNPs), improving accuracy and efficiency. This method leverages arrayed primer extension (APEX) data for reliable SNP genotyping.

Area of Science:

  • Genetics
  • Bioinformatics
  • Molecular Biology

Background:

  • Single nucleotide polymorphisms (SNPs) represent over 90% of human genetic variation.
  • Arrayed primer extension (APEX) is a mini-sequencing method combining microarrays and Sanger sequencing for SNP genotyping.
  • Current manual genotype calling from APEX data is time-consuming and prone to user bias.

Purpose of the Study:

  • To develop a fully-automated genotyping algorithm for SNP analysis using APEX data.
  • To overcome the limitations of manual genotype calling, enhancing efficiency and reducing subjectivity.

Main Methods:

  • Developed a genotyping algorithm using linear discriminant analysis (LDA) with dynamic variable selection.
  • The algorithm integrates data from multiple probes per SNP to calculate a final genotype probability.

Related Experiment Videos

  • Utilized a training dataset of 32 DNA samples and validated on an independent set of 270 DNA samples.

    • Main Results:

    • Achieved a 98.9% concordance rate and 99.6% call rate on an independent dataset of 96 SNPs.
    • Adjusting the posterior probability threshold improved concordance to 99.6% with a 94.9% call rate.
    • Reversed training and testing datasets yielded a high concordance rate of up to 99.8%.

    Conclusions:

    • The APEX platform's redundancy (multiple probes per SNP) is a key strength.
    • The model-based algorithm effectively utilizes redundancy, down-weighting 'bad data' and sample-specific probe performance issues.
    • This automated method offers robust and adaptable SNP genotype calling for any number of SNPs.