B cell affinity maturation enables B cells to generate high-affinity antibodies. This process involves somatic hypermutation of B cell immunoglobulin receptor (BCR) genes and selection by their ability to bind antigens. Lineage trees are used to describe this microevolution of B cell immunoglobulin genes. In a lineage tree, each node is one BCR sequence that mutated from the germinal center and each directed edge represents a single base mutation, insertion or deletion. In BCR sequencing data, the observed data only contains a subset of BCR sequences in this microevolution process. Therefore, reconstructing the lineage tree from experimental data requires algorithms to build the tree based on partially observed tree nodes. We developed a new algorithm named Grow Lineages along Minimum Spanning Tree (GLaMST), which efficiently reconstruct the lineage tree given observed BCR sequences that correspond to a subset of the tree nodes. Through comparison using simulated and real data, GLaMST outperforms existing algorithms in simulations with high rates of mutation, insertion and deletion, and generates lineage trees with smaller size and closer to ground truth according to tree features that highly correlated with selection pressure. GLaMST outperforms state-of-art in reconstruction of the BCR lineage tree in both efficiency and accuracy. Integrating it into existing BCR sequencing analysis frameworks can significant improve lineage tree reconstruction aspect of the analysis.

中文翻译：

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GLaMST：沿最小生成树生长谱系，以获得b细胞受体测序数据。

B细胞亲和力成熟使B细胞能够生成高亲和力抗体。此过程涉及B细胞免疫球蛋白受体（BCR）基因的体细胞超突变以及通过其结合抗原的能力进行选择。世系树用于描述B细胞免疫球蛋白基因的这种微进化。在谱系树中，每个节点都是一个从生发中心突变的BCR序列，每个有向边代表单个碱基的突变，插入或缺失。在BCR测序数据中，在此微进化过程中，观察到的数据仅包含BCR序列的子集。因此，从实验数据重建谱系树需要基于部分观察到的树节点构建树的算法。我们开发了一种新算法，即沿最小生成树增长谱系（GLaMST），在给定观察到的BCR序列对应于树节点的子集的情况下，它可以有效地重建谱系树。通过使用模拟数据和真实数据进行比较，GLaMST在突变，插入和删除率较高的模拟中表现优于现有算法，并根据与选择压力高度相关的树特征生成了较小的谱系和更接近地面真实性的谱系树。在重建BCR谱系树方面，GLaMST在效率和准确性方面均优于最新技术。将其集成到现有的BCR测序分析框架中可以显着改善分析的谱系树重建方面。GLaMST在模拟中具有较高的突变，插入和删除率，其性能优于现有算法，并根据与选择压力高度相关的树特征生成了较小的谱系，并更接近于地面真实性。在重建BCR谱系树方面，GLaMST在效率和准确性方面均优于最新技术。将其集成到现有的BCR测序分析框架中可以显着改善分析的谱系树重建方面。GLaMST在模拟中具有较高的突变，插入和删除率，其性能优于现有算法，并根据与选择压力高度相关的树特征生成了较小的谱系，并更接近于地面真实性。在重建BCR谱系树方面，GLaMST在效率和准确性方面均优于最新技术。将其集成到现有的BCR测序分析框架中可以显着改善分析的谱系树重建方面。