Enhancing sequence alignment of adaptive immune receptors through multi-task deep learning

Sequence alignment of immunoglobulin (Ig) sequences is central to the computational analysis of adaptive immune receptor repertoire sequencing (AIRR-seq) data, impacting adaptive immunity research and antibody engineering. Traditional Ig sequence aligners often struggle to handle the complexities of V(D)J recombination and somatic hypermutation (SHM), resulting in suboptimal allele assignment accuracy and sequence segmentation. We introduce AlignAIR, a novel deep learning-based aligner that leverages advanced simulation approaches and a multi-task learning framework. AlignAIR sets new state-of-the-art results in allele assignment accuracy, productivity assessments, sequence segmentation, and speed. The model's latent space captures SHM characteristics, offering more profound insights into sequence variability. AlignAIR is designed for seamless integration with existing AIRR-seq pipelines and includes a user-friendly web interface and a container image for efficient local processing of millions of sequences. AlignAIR represents a significant advancement in immunogenetics research and antibody engineering, providing a critical resource for analyzing adaptive immune receptor repertoires.