Luo Jiaqi, Zou Yiping, Li Shuai Cheng
Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong, China.
City University of Hong Kong Shenzhen Research Institute, Nanshan District, Shenzhen, 518000, China.
Bioinformatics. 2025 Jul 1;41(7). doi: 10.1093/bioinformatics/btaf346.
B-cell lineage trees describe the evolutionary process of immunoglobulin genes during affinity maturation. Existing methods for building B-cell lineage trees generally do not guarantee the parent-to-child inheritance and accumulation of advantageous mutations under successive rounds of somatic hypermutation (SHM) and selection, and are often incompatible with repertoire input.
To address previous limitations, we developed AffMB (Affinity Maturation of B-cell receptor), a comprehensive toolkit for tracking affinity maturation through the generation and visualization of SHM-ordered, inheritance-based B-cell lineage trees from single-cell or bulk B-cell receptor sequencing data. The SHM-ordered inheritance tree algorithm outperformed state-of-the-art benchmarks in simulations. When applied to single-cell data from BNT162b2 vaccination (n = 42), AffMB demonstrated the ability to infer immunization responses and showed the feasibility of identifying potential high-affinity antibody sequences.
AffMB is an open-source Python package that supports contig FASTA or AIRR rearrangement TSV inputs. The source code for AffMB is freely available at https://github.com/deepomicslab/AffMB.
B细胞谱系树描述了亲和力成熟过程中免疫球蛋白基因的进化过程。现有的构建B细胞谱系树的方法通常不能保证在连续几轮体细胞高频突变（SHM）和选择下优势突变从亲代到子代的遗传和积累，并且往往与序列库输入不兼容。
为了解决先前的局限性，我们开发了AffMB（B细胞受体亲和力成熟），这是一个综合工具包，用于通过从单细胞或批量B细胞受体测序数据生成和可视化按SHM排序的、基于遗传的B细胞谱系树来追踪亲和力成熟。在模拟中，按SHM排序的遗传树算法优于现有最佳基准。当应用于来自BNT162b2疫苗接种的单细胞数据（n = 42）时，AffMB展示了推断免疫反应的能力，并显示了识别潜在高亲和力抗体序列的可行性。
AffMB是一个开源的Python包，支持重叠群FASTA或AIRR重排TSV输入。AffMB的源代码可在https://github.com/deepomicslab/AffMB上免费获取。
