deepNGS navigator: exploring antibody NGS datasets using deep contrastive learning.

Abstract

Motivation: High-throughput sequencing uncovers how B-cells adapt in response to antigens by generating B-cell-receptor (BCR) sequences at an unprecedented scale. As BCR datasets grow to millions of sequences, using efficient computational methods becomes crucial. One important aspect of antibody sequence analysis is detecting clonal families or clusters of related sequences, whether they come from immunization, synthetic-libraries or even ML-generated datasets.

Results: We introduce deepNGS Navigator, a computational tool that leverages language models and contrastive learning to transform antibody sequences into intuitive 2D representations. The resulting 2D maps offer a visualization of overall diversity of input datasets, which can be clustered based on the sequence distances and their densities across the map. Beyond grouping related sequences, the 2D maps also represent mutational patterns inferred from sequence embeddings, enabling trajectory analysis and clustering within the projected space. By overlaying properties such as charge, the map helps identify clusters of interest for further investigation while also flagging potentially noisy or non-specific sequences with higher risk. We demonstrate deepNGS Navigator's utilities on several datasets, including: (i) a synthetic-library from a yeast-display targeting HER2, (ii) a machine learning-generated dataset with a hierarchical structure, (iii) NGS sequences from a llama immunized against COVID RBD, (iv) human naive and memory B-cell sequences, and (v) an in silico dataset simulating B-cell clonal lineages.

Availability and implementation: The deepNGS Navigator source code is available at: github.com/prescient-design/deepngs-navigator and github.com/prescient-design/deepngs-navigator-panel-app.