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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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Updated: Aug 31, 2025

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
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Detecting tandem repeat variants in coding regions using code-adVNTR.

Jonghun Park1, Mehrdad Bakhtiari1, Bernt Popp2,3

  • 1Department of Computer Science & Engineering, University of California, San Diego, La Jolla, CA 92093, USA.

Iscience
|August 19, 2022
PubMed
Summary
This summary is machine-generated.

We developed code-adVNTR, a novel method using hidden Markov models to accurately detect genetic variations, including small insertions/deletions, within complex tandem repeats (TRs). This tool enhances our understanding of TR-associated diseases.

Keywords:
GeneticsGenomicsHuman geneticsMolecular genetics

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Area of Science:

  • Genomics
  • Bioinformatics
  • Human Genetics

Background:

  • Tandem repeats (TRs) are abundant in the human genome and contribute to genetic variation.
  • Alterations in TR motif numbers are linked to over 50 diseases.
  • Small insertions and deletions (indels) in variable number tandem repeats (VNTRs) are implicated in diseases like MCKD1 but are difficult to detect.

Purpose of the Study:

  • To develop a robust method for detecting both motif count variations and small indels within VNTRs.
  • To overcome the challenges posed by the complex structure of VNTRs.
  • To improve the characterization of genetic variations in VNTR regions.

Main Methods:

  • Development of code-adVNTR, a novel computational method.
  • Utilizing multi-motif hidden Markov models for variant detection.
  • Application to simulated data and the 1000 Genomes Project data.

Main Results:

  • code-adVNTR demonstrated superior performance in calling small indels within large VNTRs compared to GATK-HaplotypeCaller in simulations.
  • Characterization of coding VNTRs in the 1000 Genomes data revealed numerous population-specific variants.
  • Reliable identification of *MUC1* mutations associated with MCKD1.

Conclusions:

  • code-adVNTR is an effective tool for analyzing complex VNTR regions, enabling the detection of both copy number variations and small indels.
  • The method facilitates the discovery of population-specific VNTR variants and aids in diagnosing VNTR-related genetic disorders.
  • This advancement opens new avenues for exploring the role of VNTRs in human genetic diversity and disease.