Evidence map›Paper›PMID 42478776›Full record

ArticleBioinformatics (Oxford, England)2026

Graph-KIR: graph-based KIR copy number estimation and allele calling using short-read sequencing data.

Hong-Ye Lin, Ting-Jian Wang, Ting-Yu Chang, Hui-Wen Chuang, Tsung-Kai Hung, Ching-Jim Lin, Jacob Shujui Hsu, Chia-Lang Hsu, Ya-Chien Yang, Pei-Lung Chen and 1 more

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Article in Bioinformatics (Oxford, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Hong-Ye LinDepartment of Biomechatronics Engineering, National Taiwan University, Taipei 10617, Taiwan.
Ting-Jian WangDepartment of Biomechatronics Engineering, National Taiwan University, Taipei 10617, Taiwan.
Ting-Yu ChangGraduate Institute of Medical Genomics and Proteomics, National Taiwan University, Taipei 10055, Taiwan.
Hui-Wen ChuangGraduate Institute of Medical Genomics and Proteomics, National Taiwan University, Taipei 10055, Taiwan.
Tsung-Kai HungGraduate Institute of Medical Genomics and Proteomics, National Taiwan University, Taipei 10055, Taiwan.
Ching-Jim LinGraduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 10617, Taiwan.
Jacob Shujui HsuGraduate Institute of Medical Genomics and Proteomics, National Taiwan University, Taipei 10055, Taiwan.
Chia-Lang HsuDepartment of Medical Research, National Taiwan University Hospital, Taipei 10002, Taiwan.ORCID 0000-0002-7447-8045
Ya-Chien YangDepartment of Clinical Laboratory Sciences and Medical Biotechnology, National Taiwan University College of Medicine, Taipei 10051, Taiwan.
Pei-Lung ChenGraduate Institute of Medical Genomics and Proteomics, National Taiwan University, Taipei 10055, Taiwan.
Chien-Yu ChenDepartment of Biomechatronics Engineering, National Taiwan University, Taipei 10617, Taiwan.ORCID 0000-0002-6940-6389

Funding

MOST 111-2221-E-002-166-MY3National Science and Technology CouncilNSTC 112-2221-E-002-184-MY3
6 · The paper itself

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 Graph-KIR, a tool designed to estimate gene copy numbers and predict full-resolution (7-digit, encompassing both coding and non-coding sequence variations) from a whole genome sequencing (WGS) sample.

resultsGraph-KIR 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 99.2% accuracy in copy number estimation and high F1-score of allele typing: 91.79% at 7-digit resolution, 97.37% at 5-digit resolution, and 97.11% at 3-digit resolution. Graph-KIR outperforms existing tools such as Geny (96.39% F1-score), PING's WGS version (92.77% F1-score), and T1K (90.44% F1-score) at 5-digit resolution. By analyzing the results on 44 HPRC samples, Graph-KIR achieves better F1-score than Geny and PING at 7-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. AVAILABILITY AND IMPLEMENTATION: The Graph-KIR and paper-related pipeline codes are available at https://github.com/linnil1/KIR_graph.

Indexed as

AllelesGene DosageReceptors, KIRSoftwareDNA Copy Number VariationsGenome, HumanHumansSequence Analysis, DNAWhole Genome SequencingReceptors, KIR

Identifiers

PMID42478776
PMCPMC13423236

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.