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Related Experiment Videos

SPDI: data model for variants and applications at NCBI.

J Bradley Holmes1, Eric Moyer1, Lon Phan1

  • 1Information Engineering Branch, National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20894, USA.

Bioinformatics (Oxford, England)
|November 19, 2019
PubMed
Summary

Standardizing sequence variant representation is crucial for genetic analysis. The SPDI data model and NCBI services offer a robust solution for variant normalization and aggregation across diverse sequences.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Accurate representation of sequence variants is essential for understanding genetic disease and biological function.
  • Inconsistent variant data across different tools and databases complicates genetic analysis.
  • NCBI's genetic variation resources require standardized principles for managing sequence variants.

Purpose of the Study:

  • To introduce the Sequence Position Deletion Insertion (SPDI) data model for consistent variant representation.
  • To present NCBI web services that convert variant formats and aggregate diverse variant data.
  • To enable robust projection of variants across different sequence assemblies and versions.

Main Methods:

  • The SPDI data model represents variants using sequence, position, deletion, and insertion attributes.

Related Experiment Videos

  • NCBI web services convert between HGVS, VCF, and SPDI formats.
  • The NCBI Variant Overprecision Correction Algorithm generates a unique 'Contextual Allele' for normalized variant representation.
  • Variant projection across congruent sequences utilizes an alignment dataset of NCBI RefSeq sequences.

    • Main Results:

    • The SPDI model precisely defines the reference subsequence affected by a variant, including in repeat regions.
    • NCBI services provide functions to aggregate variants, returning a unique 'Canonical Allele' for each variant set.
    • Variants are projected to all congruent Contextual Alleles, with the latest assembly version's allele designated as the Canonical Allele.
    • These services facilitate robust variant projection across congruent sequences and assembly versions.

    Conclusions:

    • The SPDI data model and associated NCBI services provide a scalable and robust solution for managing and analyzing sequence variants.
    • Standardized variant representation improves the accuracy and consistency of genetic variation data.
    • These tools enhance the elucidation of the genetic basis of disease and biological function by simplifying variant analysis.