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bioRxiv · 10.1101/2023.11.29.568665

Graph-KIR: Graph-based KIR Copy Number Estimation and Allele Calling Using Short-read Sequencing Data

Lin, H.-Y.·Chuang, H.-W.·Hung, T.-K.·Wang, T.-J.·Lin, C.-J.·Hsu, J. S.·Hsu, C.-L.·Yang, Y.-C.·Chen, P.-L.·Chen, C.-Y.
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Abstract

MotivationThe Killer-cell Immunoglobulin-like Receptor (KIR) is a highly polymorphic region in the human genome, associated with autoimmune diseases and organ transplantation. The sequences of KIR genes are highly similar among star alleles as well as in between individual genes, with the copy number of each KIR gene typically ranging from 0 to 4. In this study, we introduce a tool Graph-KIR that aims to estimate the copy number of genes and to call full-resolution (7-digit) KIR alleles from a whole genome sequencing (WGS) sample. ResultsGraph-KIR, unlike most KIR tools, is capable of independently typing KIR alleles per sample with no reliance on the distribution of any framework gene in a cohort. In a set of 100 simulated samples, Graph-KIR demonstrated 100% accuracy in copy number estimation and high accuracy of allele typing: 91.2% at 7-digit resolution, 97.0% at 5-digit resolution, 97.2% at 3-digit resolution, and 99.6% at gene-level resolution. Graph-KIR outperforms existing tools such as PINGs WGS version (91.9% accuracy) and T1K (84.6% accuracy) at 5-digit resolution. By analyzing the results on 44 HPRC samples, Graph-KIR achieves an accuracy of 85.0%, better than PINGs WGS version (75.5% accuracy) at 5-digit resolution. The release of Graph-KIR adds another valuable tool to assist users in accurately estimating copy numbers and calling alleles of KIR genes from WGS samples, ensuring reliable performance. AvailabilityThe Graph-KIR and paper-related pipeline codes are available at https://github.com/linnil1/KIR_graph.

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Lin, H.-Y., Chuang, H.-W., Hung, T.-K., Wang, T.-J., Lin, C.-J., Hsu, J. S., Hsu, C.-L., Yang, Y.-C., Chen, P.-L., Chen, C.-Y.. 2023-12-01. Graph-KIR: Graph-based KIR Copy Number Estimation and Allele Calling Using Short-read Sequencing Data. https://doi.org/10.1101/2023.11.29.568665

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