Short tandem repeats in the human, cow, mouse, chicken, and lizard genomes are concentrated in the terminal regions of chromosomes

Misawa, Kazuharu

doi:10.1016/j.genrep.2016.01.004

Cited by 1 publication

(1 citation statement)

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“…Irregular repeat patterns are often contaminated in the provided STR regions detected by some Bioinformatics tools [16, 17], and those irregular repeat patterns worsen the quality of the alignment of the proposed algorithm due to the difference of the actual sequence and the assumed repeat pattern. In order to address this issue, we extract maximal regions containing repeat patterns consecutively with some pre-specified error rate from the target STR region.…”

Section: Methodsmentioning

confidence: 99%

STR-realigner: a realignment method for short tandem repeat regions

et al. 2016

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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.

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Section: Methodsmentioning

confidence: 99%