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

• Type of Material: Article
• Authors: Xingyu Yang, Georgia Institute of Technology; Christopher Tipton, Emory University; Matthew Woodruff, Emory University; Enlu Zhou, Georgia Institute of Technology; Frances Eun-Hyung Lee, Emory University; Ignacio Sanz, Emory University; Peng Qiu, Georgia Institute of Technology
• Language: English
• Date: 2020-09-09
• Publisher: BMC
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s) 2020
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1186/s12864-020-06936-w
• Title of Journal or Parent Work: BMC GENOMICS
• Volume: 21
• Issue: Suppl 9
• Start Page: 583
• End Page: 583
• Grant/Funding Information: This work was partially supported by funding from the National Science Foundation (CCF1552784). PQ is an ISAC Marylou Ingram Scholar and a Carol Ann and David D. Flanagan Faculty Fellow. Publication costs are funded by PQ’s Faculty Fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
• Keywords: Biotechnology & Applied Microbiology; Science & Technology; Genetics & Heredity; MOLECULAR PHYLOGENETICS; Life Sciences & Biomedicine; HIERARCHY; REPERTOIRE; B cell receptor gene; Lineage tree
• Research Categories: Biology, Microbiology; Biology, Cell; Biology, Genetics

Abstract

Background: 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. Results: 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. Conclusions: 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.