GBScleanR: Robust genotyping error correction using hidden Markov model with error pattern recognition

Reduced-representation sequencing (RRS) provides cost-effective and time-saving genotyping platforms. Although the outstanding advantage of RRS in throughput, the obtained genotype data usually contains a large number of errors. Several error correction methods employing hidden Morkov model (HMM) have been developed to overcome these issues. Those methods assume that markers have a uniform error rate with no bias in the allele read ratio. However, bias does occur because of uneven amplification of genomic fragments and read mismapping. In this paper we introduce an error correction tool, GBScleanR, which enables robust and precise error correction for noisy RRS-based genotype data by incorporating marker-specific error rates into the HMM. The results indicate that GBScleanR improves the accuracy by more than 25 percentage points at maximum as compared to the existing tools in simulation datasets and achieves the most reliable genotype estimation in real data even with error prone markers. ### Competing Interest Statement The authors have declared no competing interest.