Gur Yaari

The paper integrates gene expression and B cell receptor data to capture complementary B cell information, revealing substructure supported by both transcriptional and receptor features.

The paper introduces BEAM, a probability model for TCRβ sequences that enables depth-aware sampling calculations and interpretable repertoire comparison.

This paper constructs HUSA, a comprehensive germline resource linking allele diversity to expressed antibody repertoires by integrating long-read sequencing and AIRR-seq data.

The paper identifies structural variants and coding polymorphisms in the human IGHC locus, revealing population-specific diversity and potential functional impacts.

The paper uses single-cell profiling to show CMV drives clonal expansion of GZMK CD8 and Th1 cells in humans.

This study shows that genetic variations in immunoglobulin light chain loci influence the diversity of expressed light chain antibodies, affecting amino acid sequences and gene usage.

This paper introduces LIgO, a software suite that simulates adaptive immune receptor repertoires with immune information to aid in developing and benchmarking AIRR machine learning methods.

The paper develops methodologies to generate and analyze immunoglobulin loci variation in rhesus macaques, revealing extensive diversity and expanding existing allele sets by 60%.

The paper introduces GenAIRR, a simulation framework for Ig sequences, and evaluates aligners to identify their performance characteristics, establishing a benchmarking standard for Ig sequence alignment.

The paper introduces nf-core/airrflow, a Nextflow-based workflow for processing AIRR-seq data, validated on simulated and real datasets to analyze immune receptor repertoires.

The paper uses long-read sequencing to resolve haplotype variation and genetic architecture in the human IGK locus across diverse ancestries, identifying novel variants and population-specific allele frequencies.

The paper curates high-quality human immunoglobulin germline gene reference sets to improve AIRR-seq analysis accuracy and facilitate immunoglobulin gene studies.

immuneML provides an open-source ecosystem for reproducible and interoperable machine learning analysis of adaptive immune receptor repertoires, demonstrating its utility through benchmarking and novel methods.

The paper identifies 48 novel light chain variable gene polymorphisms in a Norwegian cohort and finds partial intron retention in alternative splice variants of low-expressed genes.

The paper introduces a python-based toolkit for Networks Based Clustering (NBC) and demonstrates its effectiveness in analyzing large-scale RNA-seq datasets.

This study improves interneuron classification by using axonal tree morphology and activity patterns, showing better classification than dendritic tree methods.

The paper improves the TIgGER algorithm to accurately identify subject-specific immunoglobulin alleles from AIRR-seq data, even when alleles differ by more than 5 SNPs from known database alleles.

The paper develops a Bayesian haplotyping method to infer V-D-J haplotypes and deletion patterns in the human antibody heavy chain gene locus using antibody repertoire sequencing data.