Benchmarking computational methods for B-cell receptor reconstruction from single-cell RNA-seq data
NAR Genomics and Bioinformatics, vol. 4, pp. lqac049
Abstract
Abstract Multiple methods have recently been developed to reconstruct full-length B-cell receptors (BCRs) from single-cell RNA sequencing (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 such as the sequencing depth, read length or number of somatic hypermutations (SHMs) as well as guidance regarding the choice of methodology is still lacking. In this work, we evaluated the ability of six available methods to reconstruct full-length BCRs using one simulated and three experimental 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 such as 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.
Authors 11
-
Tommaso Andreani corresponding
Affiliation as printed
AI & Deep Analytics—Omics Data Science, Sanofi , Frankfurt am Main 65926,
AI & Deep Analytics—Omics Data Science, Sanofi , Frankfurt am Main 65926, Germany
-
Leiden University Medical Center
Affiliation as printed
Department of Rheumatology, Leiden University Medical Center , 2333 RC Leiden,
Department of Rheumatology, Leiden University Medical Center , 2333 RC Leiden, The Netherlands
-
Affiliation as printed
Life & Soft , Le Plessis-Robinson, Paris 92260,
Life & Soft , Le Plessis-Robinson, Paris 92260, France
-
Affiliation as printed
Immunology & Inflammation Research, Sanofi , Frankfurt am Main 65926,
Immunology & Inflammation Research, Sanofi , Frankfurt am Main 65926, Germany
-
Affiliation as printed
Molecular Biology & Genomics, Translational Science Unit, Sanofi , Chilly-Mazarin 91385,
Molecular Biology & Genomics, Translational Science Unit, Sanofi , Chilly-Mazarin 91385, France
-
Leiden University Medical Center
Affiliation as printed
Department of Rheumatology, Leiden University Medical Center , 2333 RC Leiden,
Department of Rheumatology, Leiden University Medical Center , 2333 RC Leiden, The Netherlands
-
Affiliation as printed
AI & Deep Analytics, Sanofi , Cambridge, MA 02142,
AI & Deep Analytics, Sanofi , Cambridge, MA 02142, USA
-
Leiden University Medical Center
Affiliation as printed
Department of Rheumatology, Leiden University Medical Center , 2333 RC Leiden,
Department of Rheumatology, Leiden University Medical Center , 2333 RC Leiden, The Netherlands
-
Leiden University Medical Center
Affiliation as printed
Department of Rheumatology, Leiden University Medical Center , 2333 RC Leiden,
Department of Rheumatology, Leiden University Medical Center , 2333 RC Leiden, The Netherlands
-
Affiliation as printed
AI & Deep Analytics—Omics Data Science, Sanofi , Paris 91385,
AI & Deep Analytics—Omics Data Science, Sanofi , Paris 91385, France
-
Affiliation as printed
AI & Deep Analytics—Omics Data Science, Sanofi , Frankfurt am Main 65926,
AI & Deep Analytics—Omics Data Science, Sanofi , Frankfurt am Main 65926, Germany
Cited by 13 stored of 14
13 results
No patents citing this paper on Lens.org (checked 2026-10-11).