Fast clonal family inference from large-scale B cell repertoire sequencing data

Area of Science:

• Immunology
• Bioinformatics
• Computational Biology

Background:

• High-throughput sequencing enables large-scale B cell receptor (BCR) repertoire characterization.
• The vast scale and diversity of BCR sequences present analytical challenges.
• Efficient methods are needed for biologically meaningful analysis of BCR repertoires.

Purpose of the Study:

• Introduce fastBCR, an efficient computational approach for inferring B cell clonal families from massive BCR heavy chain sequences.
• Demonstrate the speed and accuracy of fastBCR on simulated and real-world datasets.
• Facilitate the identification of disease-associated antibodies and enhance understanding of B cell immune responses.

Main Methods:

• Development of fastBCR, a computational tool for B cell clonal family inference.
• Validation using simulated datasets with varying lineage numbers and mutation rates.
• Application to real BCR sequencing data from COVID-19 patient peripheral blood samples.

Main Results:

• fastBCR significantly reduces running time while maintaining high accuracy.
• Inferred clonal families from COVID-19 data exhibit disease-associated features.
• Analysis reveals antigen-binding specificity and affinity of identified clonal families.

Conclusions:

• fastBCR offers an efficient and accurate method for analyzing large-scale BCR repertoire data.
• The tool aids in identifying disease-specific B cell responses.
• This facilitates discovery of disease-associated antibodies and deepens insights into B cell immunity.