Benchmarking computational methods for B-cell receptor reconstruction from single-cell RNA-seq data

Abstract

Multiple methods have recently been developed to reconstruct full-length B-cell receptors (BCRs) from single-cell RNA-seq (scRNA-seq) data. This need emerged from the expansion of scRNA-seq techniques, the increasing interest in antibody-based drug development and the importance of BCR repertoire changes in cancer and autoimmune disease progression. However, a comprehensive assessment of performance-influencing factors like the sequencing depth, read length or the number of somatic hypermutations (SHMs) as well as guidance regarding the choice of methodology are still lacking. In this work, we evaluated the ability of six available methods to reconstruct full-length BCRs using one simulated and three real SMART-seq datasets. In addition, we validated that the BCRs assembled in silico recognize their intended targets when expressed as monoclonal antibodies. We observed that methods like BALDR, BASIC and BRACER showed the best overall performance across the tested datasets and conditions whereas only BASIC demonstrated acceptable results on very short read libraries. Furthermore, the de novo assembly-based methods BRACER and BALDR were the most accurate in reconstructing BCRs harboring different degrees of SHMs in the variable domain, while TRUST4, MiXCR and BASIC were the fastest. Finally, we propose guidelines to select the best method based on the given data characteristics.