AI Article Synopsis

  • B cell receptor genes undergo somatic hypermutations and natural selection, leading to a diverse and specific B cell lineage that enhances antibody affinity through a process called affinity maturation.
  • Phylogenetic tree inference is crucial for reconstructing these B cell lineages; however, existing methods often struggle with balancing accuracy and computational efficiency, particularly when analyzing large datasets.
  • The newly proposed method, ClonalTree, offers a faster and more efficient way to build B-cell lineage trees by incorporating cellular abundances and outperforms traditional algorithms while maintaining accuracy and reducing computation time significantly.

Article Abstract

B cell receptor (BCR) genes exposed to an antigen undergo somatic hypermutations and Darwinian antigen selection, generating a large BCR-antibody diversity. This process, known as B cell affinity maturation, increases antibody affinity, forming a specific B cell lineage that includes the unmutated ancestor and mutated variants. In a B cell lineage, cells with a higher antigen affinity will undergo clonal expansion, while those with a lower affinity will not proliferate and probably be eliminated. Therefore, cellular (genotype) abundance provides a valuable perspective on the ongoing evolutionary process. Phylogenetic tree inference is often used to reconstruct B cell lineage trees and represents the evolutionary dynamic of BCR affinity maturation. However, such methods should process B-cell population data derived from experimental sampling that might contain different cellular abundances. There are a few phylogenetic methods for tracing the evolutionary events occurring in B cell lineages; best-performing solutions are time-demanding and restricted to analysing a reduced number of sequences, while time-efficient methods do not consider cellular abundances. We propose ClonalTree, a low-complexity and accurate approach to construct B-cell lineage trees that incorporates genotype abundances into minimum spanning tree (MST) algorithms. Using both simulated and experimental data, we demonstrate that ClonalTree outperforms MST-based algorithms and achieves a comparable performance to a method that explores tree-generating space exhaustively. Furthermore, ClonalTree has a lower running time, being more convenient for building B-cell lineage trees from high-throughput BCR sequencing data, mainly in biomedical applications, where a lower computational time is appreciable. It is hundreds to thousands of times faster than exhaustive approaches, enabling the analysis of a large set of sequences within minutes or seconds and without loss of accuracy. The source code is freely available at github.com/julibinho/ClonalTree.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9972711PMC
http://dx.doi.org/10.1186/s12859-022-05112-zDOI Listing

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