Resolving complex tandem repeats with long reads

• Published in: Bioinformatics (Oxford, England) Vol. 30; no. 24; pp. 3491 - 3498
• Main Authors: Ummat, Ajay, Bashir, Ali
• Format: Journal Article
• Language: English
• Published: England 15.12.2014
• Subjects: Genome, Human; Humans; Markov Chains; Microsatellite Repeats; Sequence Alignment; Sequence Analysis, DNA - methods; Software
• ISSN: 1367-4803; 1367-4811
• DOI: 10.1093/bioinformatics/btu437

Resolving tandemly repeated genomic sequences is a necessary step in improving our understanding of the human genome. Short tandem repeats (TRs), or microsatellites, are often used as molecular markers in genetics, and clinically, variation in microsatellites can lead to genetic disorders like Huntington's diseases. Accurately resolving repeats, and in particular TRs, remains a challenging task in genome alignment, assembly and variation calling. Though tools have been developed for detecting microsatellites in short-read sequencing data, these are limited in the size and types of events they can resolve. Single-molecule sequencing technologies may potentially resolve a broader spectrum of TRs given their increased length, but require new approaches given their significantly higher raw error profiles. However, due to inherent error profiles of the single-molecule technologies, these reads presents a unique challenge in terms of accurately identifying and estimating the TRs. Here we present PacmonSTR, a reference-based probabilistic approach, to identify the TR region and estimate the number of these TR elements in long DNA reads. We present a multistep approach that requires as input, a reference region and the reference TR element. Initially, the TR region is identified from the long DNA reads via a 3-stage modified Smith-Waterman approach and then, expected number of TR elements is calculated using a pair-Hidden Markov Models-based method. Finally, TR-based genotype selection (or clustering: homozygous/heterozygous) is performed with Gaussian mixture models, using the Akaike information criteria, and coverage expectations.