Large-scale paired chain BCR analysis reveals antibody clonal family inference bias and enhances resolution with machine learning.

A fundamental question in immunology is how the adaptive immune system encodes antigen specificity while maintaining repertoire diversity. B cell receptor (BCR) or antibody clonal families, defined by groups of B cells descending from a common ancestor, are key to deciphering this encoding. Although paired heavy and light chains jointly determine antibody specificity, most repertoire analyses have historically relied on heavy-chain-only data due to the loss of native pairing information in bulk BCR sequencing. This reliance introduces potential biases in computational clonal cluster inference, which may complicate efforts to resolve disease-associated immune signatures. Here, we leverage large-scale paired-chain BCR sequencing data to demonstrate that heavy-chain-based clustering may misrepresent true clonal architecture, and identify two major artifacts: chain-mixed clusters, in which similar heavy chains are paired with distinct light chains, and naive-like pseudo-clonal clusters, which are detected in an individual's naive B cell repertoire and exhibit highly similar heavy and light chains without reflecting true clonal expansion. To address these limitations, we present fastBCR-p, an optimized framework that integrates light-chain-informed subclustering, with public sequence aware refinement to improve clonal family inference. By resolving both technical artifacts and biological convergence, fastBCR-p improves the chain concordance and overall clustering quality of clonal inference in real-world datasets. This enables more accurate tracking of immune dynamics in health and disease and facilitates the identification of clinically relevant antibody lineages.