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

Thomas Konstantinovsky1,2, Ayelet Peres1,2,3, Ran Eisenberg4

  • 1Department of Bioengineering, Faculty of Engineering, Bar Ilan University, 5290002 Ramat Gan, Israel.

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Summary
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AlignAIR, a new deep learning tool, accurately aligns immunoglobulin sequences for adaptive immunity research. It improves V(D)J recombination and somatic hypermutation analysis, advancing immunogenetics and antibody engineering.

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Area of Science:

  • Immunology
  • Bioinformatics
  • Computational Biology

Background:

  • Sequence alignment of immunoglobulin (Ig) sequences is crucial for adaptive immune receptor repertoire sequencing (AIRR-seq) data analysis.
  • Existing aligners face challenges with V(D)J recombination and somatic hypermutation (SHM), impacting accuracy in allele assignment and sequence segmentation.

Purpose of the Study:

  • To introduce AlignAIR, a novel deep learning-based sequence aligner for immunoglobulin sequences.
  • To overcome limitations of traditional aligners in handling V(D)J recombination and SHM complexities.
  • To enhance accuracy, productivity, and speed in AIRR-seq data analysis.

Main Methods:

  • Development of a deep learning model incorporating advanced simulation approaches.
  • Utilization of a multi-task learning framework for comprehensive analysis.
  • Integration of a latent space to capture somatic hypermutation characteristics.

Main Results:

  • AlignAIR achieves state-of-the-art results in allele assignment accuracy, productivity, sequence segmentation, and processing speed.
  • The model's latent space provides deeper insights into sequence variability and SHM patterns.
  • Demonstrated efficiency in processing millions of sequences.

Conclusions:

  • AlignAIR offers a significant advancement for immunogenetics research and antibody engineering.
  • The tool provides a critical resource for analyzing adaptive immune receptor repertoires.
  • AlignAIR is designed for seamless integration into existing AIRR-seq pipelines, with accessible web and container interfaces.