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Inferring short tandem repeat variation from paired-end short reads.

ABSTRACT: The advances of high-throughput sequencing offer an unprecedented opportunity to study genetic variation. This is challenged by the difficulty of resolving variant calls in repetitive DNA regions. We present a Bayesian method to estimate repeat-length variation from paired-end sequence read data. The method makes variant calls based on deviations in sequence fragment sizes, allowing the analysis of repeats at lengths of relevance to a range of phenotypes. We demonstrate the method's ability to detect and quantify changes in repeat lengths from short read genomic sequence data across genotypes. We use the method to estimate repeat variation among 12 strains of Arabidopsis thaliana and demonstrate experimentally that our method compares favourably against existing methods. Using this method, we have identified all repeats across the genome, which are likely to be polymorphic. In addition, our predicted polymorphic repeats also included the only known repeat expansion in A. thaliana, suggesting an ability to discover potential unstable repeats.

SUBMITTER: Cao MD

PROVIDER: S-EPMC3919575 | biostudies-literature | 2014 Feb

REPOSITORIES: biostudies-literature

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Inferring short tandem repeat variation from paired-end short reads.

Cao Minh Duc MD Tasker Edward E Willadsen Kai K Imelfort Michael M Vishwanathan Sailaja S Sureshkumar Sridevi S Balasubramanian Sureshkumar S Bodén Mikael M

Nucleic acids research 20131217 3

The advances of high-throughput sequencing offer an unprecedented opportunity to study genetic variation. This is challenged by the difficulty of resolving variant calls in repetitive DNA regions. We present a Bayesian method to estimate repeat-length variation from paired-end sequence read data. The method makes variant calls based on deviations in sequence fragment sizes, allowing the analysis of repeats at lengths of relevance to a range of phenotypes. We demonstrate the method's ability to d ...[more]

PMID: 24353318

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Project description:BackgroundWith the advent of paired-end high throughput sequencing, it is now possible to identify various types of structural variation on a genome-wide scale. Although many methods have been proposed for structural variation detection, most do not provide precise boundaries for identified variants. In this paper, we propose a new method, Distribution Based detection of Duplication Boundaries (DB2), for accurate detection of tandem duplication breakpoints, an important class of structural variation, with high precision and recall.ResultsOur computational experiments on simulated data show that DB2 outperforms state-of-the-art methods in terms of finding breakpoints of tandem duplications, with a higher positive predictive value (precision) in calling the duplications' presence. In particular, DB2's prediction of tandem duplications is correct 99% of the time even for very noisy data, while narrowing down the space of possible breakpoints within a margin of 15 to 20?bps on the average. Most of the existing methods provide boundaries in ranges that extend to hundreds of bases with lower precision values. Our method is also highly robust to varying properties of the sequencing library and to the sizes of the tandem duplications, as shown by its stable precision, recall and mean boundary mismatch performance. We demonstrate our method's efficacy using both simulated paired-end reads, and those generated from a melanoma sample and two ovarian cancer samples. Newly discovered tandem duplications are validated using PCR and Sanger sequencing.ConclusionsOur method, DB2, uses discordantly aligned reads, taking into account the distribution of fragment length to predict tandem duplications along with their breakpoints on a donor genome. The proposed method fine tunes the breakpoint calls by applying a novel probabilistic framework that incorporates the empirical fragment length distribution to score each feasible breakpoint. DB2 is implemented in Java programming language and is freely available at http://mendel.gene.cwru.edu/laframboiselab/software.php.

Dataset Information

Inferring short tandem repeat variation from paired-end short reads.

Publications

Inferring short tandem repeat variation from paired-end short reads.

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