DOI: 10.6076/d1530h
title: Data from: A scalable model for simulating multi-round antibody evolution and benchmarking of clonal tree reconstruction methods

description: Affinity maturation (AM) of B cells through somatic hypermutations (SHMs)
 enables the immune system to evolve to recognize diverse pathogens. The
 accumulation of SHMs leads to the formation of clonal lineages of
 antibody-secreting b cells that have evolved from a common naïve B cell.
 Advances in high-throughput sequencing have enabled deep scans of B cell
 receptor repertoires, paving the way for reconstructing clonal trees.
 However, it is not clear if clonal trees, which capture microevolutionary
 time scales, can be reconstructed using traditional phylogenetic
 reconstruction methods with adequate accuracy. In fact, several clonal
 tree reconstruction methods have been developed to fix supposed
 shortcomings of phylogenetic methods. Nevertheless, no consensus has been
 reached regarding the relative accuracy of these methods, partially
 because evaluation is challenging. Benchmarking the performance of
 existing methods and developing better methods would both benefit from
 realistic models of clonal lineage evolution specifically designed for
 emulating B cell evolution. In this paper, we propose a model for modeling
 B cell clonal lineage evolution and use this model to benchmark several
 existing clonal tree reconstruction methods. Our model, designed to be
 extensible, has several features: by evolving the clonal tree and
 sequences simultaneously, it allows modeling selective pressure due to
 changes in affinity binding; it enables scalable simulations of large
 numbers of cells; it enables several rounds of infection by an evolving
 pathogen; and, it models building of memory. In addition, we also suggest
 a set of metrics for comparing clonal trees and measuring their
 properties. Our results show that while maximum likelihood phylogenetic
 reconstruction methods can fail to capture key features of clonal tree
 expansion if applied naively, a simple post-processing of their results,
 where short branches are contracted, leads to inferences that are better
 than alternative methods.
description: The data was created using the simulation method DimSIM. As
 described in the paper, the analyses includes two sets of simulations, one
 based on real target antibodies (SARS-Cov2) and the other based on
 flu.  SARS-CoV2 simulations had
 3-5 rounds with 50 replicates. For targets, we first selected all heavy
 chain sequences of human antibodies with IGHV1-58 and IGHJ3 from the
 Coronavirus Antibody Database that neutralize some variants of SARS-CoV2
 and have 16 amino acids in their CDR3. Per upload date, we chose the
 antibody that neutralizes the most variants of SARS-CoV2 resulting in 14
 sequences. We then randomly chose targets among them. The infection start
 date was set to be the upload date. Each round of infections is set to
 last 50 days. At the end of simulations, we sample <em>ς</em>
 = 50, 100, 200, 500 antibody-coding nucleotide sequences from the last
 round of infection and built the clonal tree.  For flu simulations, we performed several
 simulations with <em>r</em> = 56 rounds of flu, using
 sequences of hemagglutinin (HA) protein. We used the NCBI Influenza Virus
 Resource which includes HA sequences from influenza B virus. We selected
 59 HA sequences belonging to flu infections in Hong Kong among which 56
 had the same length (584 aa). These were used to find the targets
 using and evolutionary approach described in the paper. We set up four
 experiments, varying one or two parameters in each experiment and setting
 the remaining ones to default values. Details of these conditions are
 given in Tables 1 and 3 of the paper.