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qKAT: Quantitative Semi-automated Typing of Killer-cell Immunoglobulin-like Receptor Genes
Published on: March 6, 2019
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Efficient Sequencing, Assembly, and Annotation of Human KIR Haplotypes
David Roe1, Jonathan Williams2, Keyton Ivery2
1Bioinformatics and Computational Biology, University of Minnesota, Rochester, MN, United States.
Frontiers in Immunology
|November 9, 2020
Summary
This study introduces a novel long-read sequencing method for accurately assembling complex human natural killer-cell immunoglobulin-like receptor (KIR) haplotypes. This efficient approach enables full diploid KIR haplotype sequencing for population-scale studies and clinical applications.
Area of Science:
- Genetics
- Immunology
- Bioinformatics
Background:
- The natural killer-cell immunoglobulin-like receptor (KIR) region is characterized by high homology, recombination, variation, and repetitive elements.
- These complex genetic features have historically hindered accurate, high-throughput DNA interpretation of full KIR haplotypes.
Purpose of the Study:
- To develop and validate a new method for efficient, high-throughput sequencing and assembly of diploid human KIR haplotypes.
- To overcome the limitations of previous methods in interpreting the complex KIR region.
Main Methods:
- Targeted hybridization probe capture of 2-8 kb DNA fragments from the KIR region.
- Long-read sequencing using PacBio Sequel technology.
- Bioinformatic pipeline involving error correction, binning, and assembly with Canu, including gene and exon/intron boundary annotation.
Main Results:
- The developed method achieved 97% coverage of the GenBank reference and 99.97% concordance.
- Assemblies required only 1.8 haplotigs to cover 75% of the reference, demonstrating high efficiency.
- Successfully assembled diploid KIR haplotypes from long-read whole-genome sequencing (WGS) for the first time.
Conclusions:
- This targeted probe capture and sequencing approach is the first to fully sequence and phase all diploid human KIR haplotypes.
- The method is efficient and suitable for population-scale studies and clinical use.
- Open-source software is available for the described workflow.
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