qKAT: Quantitative Semi-automated Typing of Killer-cell Immunoglobulin-like Receptor Genes.

Killer cell immunoglobulin-like receptors (KIRs) are a set of inhibitory and activating immune receptors, on natural killer (NK) and T cells, encoded by a polymorphic cluster of genes on chromosome 19. Their best-characterized ligands are the human leukocyte antigen (HLA) molecules that are encoded within the major histocompatibility complex (MHC) locus on chromosome 6. There is substantial evidence that they play a significant role in immunity, reproduction, and transplantation, making it crucial to have techniques that can accurately genotype them. However, high-sequence homology, as well as allelic and copy number variation, make it difficult to design methods that can accurately and efficiently genotype all KIR genes. Traditional methods are usually limited in the resolution of data obtained, throughput, cost-effectiveness, and the time taken for setting up and running the experiments. We describe a method called quantitative KIR semi-automated typing (qKAT), which is a high-throughput multiplex real-time polymerase chain reaction method that can determine the gene copy numbers for all genes in the KIR locus. qKAT is a simple high-throughput method that can provide high-resolution KIR copy number data, which can be further used to infer the variations in the structurally polymorphic haplotypes that encompass them. This copy number and haplotype data can be beneficial for studies on large-scale disease associations, population genetics, as well as investigations on expression and functional interactions between KIR and HLA.

VDJtools: Unifying Post-analysis of T Cell Receptor Repertoires.

Despite the growing number of immune repertoire sequencing studies, the field still lacks software for analysis and comprehension of this high-dimensional data. Here we report VDJtools, a complementary software suite that solves a wide range of T cell receptor (TCR) repertoires post-analysis tasks, provides a detailed tabular output and publication-ready graphics, and is built on top of a flexible API. Using TCR datasets for a large cohort of unrelated healthy donors, twins, and multiple sclerosis patients we demonstrate that VDJtools greatly facilitates the analysis and leads to sound biological conclusions. VDJtools software and documentation are available at https://github.com/mikessh/vdjtools.