Dataset Information

Detecting tandem repeat variants in coding regions using code-adVNTR.

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.

SUBMITTER: Park J

PROVIDER: S-EPMC9379575 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Detecting tandem repeat variants in coding regions using code-adVNTR.

Park Jonghun J Bakhtiari Mehrdad M Popp Bernt B Wiesener Michael M Bafna Vineet V

iScience 20220719 8

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 mainl ...[more]

PMID: 35982790

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Project description:BackgroundIn the estimation of repeat numbers in a short tandem repeat (STR) region from high-throughput sequencing data, two types of strategies are mainly taken: a strategy based on counting repeat patterns included in sequence reads spanning the region and a strategy based on estimating the difference between the actual insert size and the insert size inferred from paired-end reads. The quality of sequence alignment is crucial, especially in the former approaches although usual alignment methods have difficulty in STR regions due to insertions and deletions caused by the variations of repeat numbers.ResultsWe proposed a new dynamic programming based realignment method named STR-realigner that considers repeat patterns in STR regions as prior knowledge. By allowing the size change of repeat patterns with low penalty in STR regions, accurate realignment is expected. For the performance evaluation, publicly available STR variant calling tools were applied to three types of aligned reads: synthetically generated sequencing reads aligned with BWA-MEM, those realigned with STR-realigner, those realigned with ReviSTER, and those realigned with GATK IndelRealigner. From the comparison of root mean squared errors between estimated and true STR region size, the results for the dataset realigned with STR-realigner are better than those for other cases. For real data analysis, we used a real sequencing dataset from Illumina HiSeq 2000 for a parent-offspring trio. RepeatSeq and lobSTR were applied to the sequence reads for these individuals aligned with BWA-MEM, those realigned with STR-realigner, ReviSTER, and GATK IndelRealigner. STR-realigner shows the best performance in terms of consistency of the size of estimated STR regions in Mendelian inheritance. Root mean squared error values were also calculated from the comparison of these estimated results with STR region sizes obtained from high coverage PacBio sequencing data, and the results from the realigned sequencing data with STR-realigner showed the least (the best) root mean squared error value.ConclusionsThe effectiveness of the proposed realignment method for STR regions was verified from the comparison with an existing method on both simulation datasets and real whole genome sequencing dataset.

Dataset Information

Detecting tandem repeat variants in coding regions using code-adVNTR.

Publications

Detecting tandem repeat variants in coding regions using code-adVNTR.

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