GLaMST: grow lineages along minimum spanning tree for b cell receptor sequencing data

Area of Science:

• Immunology
• Bioinformatics
• Computational Biology

Background:

• B cell affinity maturation generates high-affinity antibodies through somatic hypermutation and antigen selection.
• Lineage trees model the microevolution of B cell immunoglobulin receptor (BCR) genes.
• Reconstructing BCR lineage trees from partial sequencing data requires specialized algorithms.

Purpose of the Study:

• To develop an efficient and accurate algorithm for reconstructing BCR lineage trees from partially observed sequence data.
• To compare the performance of the new algorithm against existing methods.

Main Methods:

• Developed the Grow Lineages along Minimum Spanning Tree (GLaMST) algorithm.
• Evaluated GLaMST using simulated datasets with high mutation, insertion, and deletion rates.
• Compared GLaMST with existing algorithms on both simulated and real BCR sequencing data.

Main Results:

• GLaMST efficiently reconstructs lineage trees from observed BCR sequences.
• The algorithm outperforms existing methods in simulations, especially with high rates of genetic variation.
• GLaMST generates smaller lineage trees that are closer to the ground truth, reflecting selection pressure.

Conclusions:

• GLaMST offers superior efficiency and accuracy in BCR lineage tree reconstruction compared to state-of-the-art methods.
• Integrating GLaMST into BCR sequencing analysis workflows can significantly enhance lineage tree reconstruction capabilities.