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

SNPtoGO: characterizing SNPs by enriched GO terms.

Daniel F Schwarz1, Oliver Hädicke, Jeanette Erdmann

  • 1Institut für Medizinische Biometrie und Statistik, Lübeck , Germany. schwarz@imbs.uni-luebeck.de

Bioinformatics (Oxford, England)
|November 21, 2007
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

In Reply.

Deutsches Arzteblatt international·2026
Same author

Interpretation of Pharmacometabolomics Results: Fingerprint of Drug Exposure or Confounder Effects? Insights from a Urinary Metabolomics Study with Voriconazole in Healthy Participants.

International journal of molecular sciences·2026
Same author

The phenotypic spectrum and genetic determinants of severe spinal muscular atrophy in individuals with a single <i>SMN2</i> copy: an international retrospective observational study.

EClinicalMedicine·2026
Same author

Urinary Metabolomics Predict Acute Kidney Injury in Very-Low-Birth-Weight Infants with Patent Ductus Arteriosus.

Biomolecules·2026
Same author

Confidence Intervals for Comparing Two Independent Folded Normals: A Case Study in Bunion Surgery.

Statistics in medicine·2026
Same author

Emulated Effects of Glucagon-Like Peptide 1 Receptor Agonist Therapy in the General Population.

Journal of the American College of Cardiology·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
Same journal

EDEL: Enhancing Dense Retrievers for Curation of Biomedical Knowledge Bases.

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

This study introduces a new tool that enhances the analysis of complex polygenic diseases by integrating Gene Ontology (GO) terms. This approach improves the interpretation of single nucleotide polymorphism (SNP) data, especially for intergenic regions.

Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Complex polygenic diseases involve variations across many genes, with no single set of alleles shared by all patients.
  • Assigning single nucleotide polymorphisms (SNPs) to specific genes is often ambiguous, particularly for intergenic SNPs.

Purpose of the Study:

  • To develop a tool that integrates external information, specifically Gene Ontology (GO) terms, into SNP data analysis.
  • To improve the interpretation of SNP data for complex polygenic diseases by leveraging functional gene annotations.

Main Methods:

  • The study presents a novel tool that incorporates Gene Ontology (GO) terms into the analysis of single nucleotide polymorphism (SNP) data.
  • The tool addresses the ambiguity in assigning intergenic SNPs to genes by adding functional context from GO.

Related Experiment Videos

Main Results:

  • The integration of GO terms provides additional layers of information for understanding the molecular processes associated with disease-risk SNPs.
  • This method enhances the ability to identify overlapping sets of genes and molecular pathways involved in polygenic diseases across different patients.

Conclusions:

  • The developed tool offers a valuable approach for the analysis of complex polygenic diseases by enriching SNP data with functional gene annotations.
  • This integration of GO terms aids in deciphering the genetic architecture of complex diseases and identifying shared biological mechanisms.