Sebastiaan Valkiers

The paper introduces a computational framework to efficiently identify significantly neighbor enriched TCR sequences, demonstrating its utility in analyzing memory T cells and T cell responses to vaccination and viral infections.

The paper identifies TCR subsequence motifs associated with type 1 diabetes using deep learning and shows their enrichment in T1D individuals based on HLA risk alleles.

The paper analyzes 6 million T cells from 91 studies to identify TCR sequence convergence patterns and their association with functional and cellular T cell phenotypes.

A TCR-first approach to single-cell data analysis improves identification of disease-specific TCR features and reveals previously unknown T-cell clusters and arrangements.

This study analyzes T cell receptor repertoires in MS patients and controls to explore the relationship between EBV-specific and myelin-specific immune responses, finding distinct patterns associated with MS.

The paper analyzes the sequence distribution of epitope-specific TCR repertoires and shows how this affects machine learning model performance.

RapTCR provides a tool for rapid visualization and analysis of T-cell receptor repertoires, capturing both global and local sequence similarities.

clusTCR is a Python tool for fast clustering of large CDR3 sequences, offering improved speed over existing methods while maintaining comparable accuracy.