A scalable model for simulating multi-round antibody evolution and benchmarking of clonal tree reconstruction methods

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..., 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 ς = 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 r = 56 rounds of flu, using sequence...,