STRsensor: a computationally efficient method for STR allele-typing from massively parallel sequencing data

Short tandem repeats (STRs) represent one of the most polymorphic variations in the human genome, finding extensive applications in forensics, population genetics and medical genetics. In contrast to the traditional capillary electrophoresis (CE) method, genotyping STRs using massive parallel sequencing technology offers enhanced sensitivity and accuracy. However, current methods are mainly designed for target sequencing with higher coverage for a specific STR locus, thereby constraining the utility of STRs in low- and medium-coverage whole genome sequencing (WGS) data. Here, we introduce STRsensor, a method designed to type STR alleles in low-coverage WGS data and target sequencing data, achieving a significant high detection ratio and accuracy. STRsensor employs two methods for STR allele-typing: the Kmers-based method and the CIGAR-based method. Furthermore, by incorporating a model for PCR stutters, STRsensor greatly enhances the accuracy of STR allele typing. With simulation data, we demonstrate that STRsensor achieves a detection ratio of 100$\%$ and an accuracy of 99.37$\%$ for a 30$\times $ WGS data, outperforming the existing methods, such as STRait Razor, STRinNGS, and HipSTR. When applied to real target sequencing data from 687 individuals, STRsensor achieves a detection ratio of 99.64$\%$ and an accuracy of 99.99$\%$. Moreover, STRsensor is a computationally efficient method that runs 79 times faster than HipSTR and 10 000 times faster than STRinNGS. STRsensor is freely available on GitHub: https://github.com/ChenHuaLab/STRsensor.