Detecting tandem repeat variants in coding regions using code-adVNTR
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Updated: Aug 31, 2025

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
Published on: April 4, 2018
Jonghun Park1, Mehrdad Bakhtiari1, Bernt Popp2,3
1Department of Computer Science & Engineering, University of California, San Diego, La Jolla, CA 92093, USA.
Abstract:
The human genome contains more than one million tandem repeats (TRs), DNA sequences containing multiple approximate copies of a motif repeated contiguously. TRs account for significant genetic variation, with 50 + diseases attributed to changes in motif number. A few diseases have been to be caused by small indels in variable number tandem repeats (VNTRs) including poly-cystic kidney disease type 1 (MCKD1) and monogenic type 1 diabetes. However, small indels in VNTRs are largely unexplored mainly due to the long and complex structure of VNTRs with multiple motifs. We developed a method, code-adVNTR, that utilizes multi-motif hidden Markov models to detect both, motif count variation and small indels, within VNTRs. In simulated data, code-adVNTR outperformed GATK-HaplotypeCaller in calling small indels within large VNTRs. We used code-adVNTR to characterize coding VNTRs in the 1000 genomes data identifying many population-specific variants, and to reliably call MUC1 mutations for MCKD1.
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