Dante: genotyping of known complex and expanded short tandem repeats

Budiš, J.a,b,c, Kucharík, M.d, Duriš, F.b,c, Gazdarica, J.b,e, Zrubcová, M.e, Ficek, A.e, Szemes, T.b,c,e, Brejová, B.a, Radvanszky, J.b,f

aDepartment of Computer Science, Faculty of Mathematics, Physics and Informatics, Comenius University in Bratislava, Slovakia
bGeneton Ltd., Bratislava, Slovakia
cSlovak Centre of Scientific and Technical Information, Bratislava, Slovakia
dMedirex a.s., Bratislava, Slovakia
eDepartment of Molecular Biology, Faculty of Natural Sciences, Comenius University, Bratislava, Slovakia
fInstitute for Clinical and Translational Research, Biomedical Research Centre, Slovak Academy of Sciences, Bratislava, Slovakia

Abstract

Motivation

Short tandem repeats (STRs) are stretches of repetitive DNA in which short sequences, typically made of 2–6 nucleotides, are repeated several times. Since STRs have many important biological roles and also belong to the most polymorphic parts of the human genome, they became utilized in several molecular-genetic applications. Precise genotyping of STR alleles, therefore, was of high relevance during the last decades. Despite this, massively parallel sequencing (MPS) still lacks the analysis methods to fully utilize the information value of STRs in genome scale assays.

Results

We propose an alignment-free algorithm, called Dante, for genotyping and characterization of STR alleles at user-specified known loci based on sequence reads originating from STR loci of interest. The method accounts for natural deviations from the expected sequence, such as variation in the repeat count, sequencing errors, ambiguous bases and complex loci containing several different motifs. In addition, we implemented a correction for copy number defects caused by the polymerase induced stutter effect as well as a prediction of STR expansions that, according to the conventional view, cannot be fully captured by inherently short MPS reads. We tested Dante on simulated datasets and on datasets obtained by targeted sequencing of protein coding parts of thousands of selected clinically relevant genes. In both these datasets, Dante outperformed HipSTR and GATK genotyping tools. Furthermore, Dante was able to predict allele expansions in all tested clinical cases.

Availability and implementation

Dante is open source software, freely available for download at https://github.com/jbudis/dante.