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Related Experiment Videos

dbVar structural variant cluster set for data analysis and variant comparison.

Lon Phan1, Jeffrey Hsu2, Le Quang Minh Tri3

  • 1National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.

F1000Research
|April 4, 2017
PubMed
Summary

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This summary is machine-generated.

This study introduces structural variant clusters (SVCs) to standardize the analysis of millions of human structural variants (SSVs) from dbVar. SVCs improve data comparison and interpretation across diverse studies and platforms.

Area of Science:

  • Genomics
  • Bioinformatics
  • Human Genetics

Background:

  • dbVar contains over 3 million human structural variants (SSVs) from 120 studies, including copy number variations (CNVs), insertions, deletions, inversions, and translocations.
  • Inconsistent genomic location reporting for SSVs hinders cross-study analysis, comparison, and annotation.
  • Standardization is needed to facilitate the interpretation and exchange of structural variant data.

Purpose of the Study:

  • To generate a non-redundant set of genomic regions, termed structural variant clusters (SVCs), for all human SSVs.
  • To standardize SSV data on the RefSeq assembly GRCh38 (GCF_000001405.26).
  • To facilitate analysis, annotation, and exchange of structural variant data, improving variant interpretation.

Main Methods:

Keywords:
EducationGVFGenome AnnotationGenomicsNCBIOpen-SourceSoftwareStructural Variation ClusterdbVar

Related Experiment Videos

  • Generated structural variant clusters (SVCs) by grouping concordant SSVs from 120 human studies.
  • Utilized RefSeq assembly GRCh38 for genomic placement of SSVs.
  • Developed utilities for annotating, searching, and filtering SVC data in GVF format.

    • Main Results:

    • Processed over 3.64 million SSVs to create non-redundant SVCs.
    • Generated 2.5 million SVCs by variant type for individual studies and 3.4 million SVCs across all studies (count >=1).
    • Developed data processing utilities for enhanced SVC analysis and visualization.

    Conclusions:

    • Structural variant clusters (SVCs) provide a standardized, non-redundant dataset for human SSVs.
    • SVCs facilitate improved analysis, annotation, and exchange of structural variant data.
    • The developed utilities enhance the utility of SVCs for genomic viewers and external data annotation.