TY - JOUR
T1 - Benchmarking computational methods for B-cell receptor reconstruction from single-cell RNA-seq data
AU - Andreani, Tommaso
AU - Slot, Linda M.
AU - Gabillard, Samuel
AU - Strübing, Carsten
AU - Reimertz, Claus
AU - Yaligara, Veeranagouda
AU - Bakker, Aleida M.
AU - Olfati-Saber, Reza
AU - Toes, Rene E. M.
AU - Scherer, Hans U.
AU - Auge, Franck
AU - Šimaitė, Deimantė
N1 - Publisher Copyright: © 2022 The Author(s). Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.
PY - 2022/9/1
Y1 - 2022/9/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85134588202&partnerID=8YFLogxK
U2 - https://doi.org/10.1093/nargab/lqac049
DO - https://doi.org/10.1093/nargab/lqac049
M3 - Article
C2 - 35855325
SN - 2631-9268
VL - 4
JO - NAR Genomics and Bioinformatics
JF - NAR Genomics and Bioinformatics
IS - 3
M1 - lqac049
ER -