Comparison of methods for phylogenetic B-cell lineage inference using time-resolved antibody repertoire simulations (AbSim)

Abstract

Supplementary data are available at Bioinformatics online.

Somatic hypermutation analysis for improved identification of B cell clonal families from next-generation sequencing data

“…Local scaling is especially useful when the classification of the B cell repertoire contains multiple scales (e.g., if one clone is tight, while another Table 1. Overview of 25 simulated datasets generated by the AbSim R package (Yermanos et al, 2017). Each B cell clone is generated by one set of randomly selected unmutated human H-chain germline gene sequences (Giudicelli et al, 2004) to produce the V(D)J recombination event.…”

“…Each simulated dataset was generated using the AbSim R package (version 0.2.6) in a B cell single-lineage fashion (Yermanos et al, 2017). Each B cell clone simulation begins with a random selection from sets of IGHV, IGHD, and IGHJ germline sequences (Giudicelli et al, 2004) to produce a unique V(D)J recombination event.…”

“…For each simulated dataset, the pair-wise shared SHM matrix H was generated for each B cell clone by comparing the IGHV and IGHJ regions of each pair of sequences with the relevant germline sequence. Then, the average of the upper clustering-based framework was applied to identify clonally-related sequences in 25 simulated datasets (diamonds) generated via AbSim R package (Yermanos et al, 2017) (Table 1). Performance was assessed by calculating (A) sensitivity, (B) specificity, and (C) precision via applying the distance-based approach on the junction (ham-junc) and CDR3 (ham-cdr3) regions, as well as the integrated distance-and stress-based approaches on the junction (ham-shm-junc) and CDR3 (ham-shm-cdr3) regions.…”

Integrating high‐throughput screening and sequencing for monoclonal antibody discovery and engineering

“…Furthermore, new screening platforms that combine genome editing and mammalian surface display are emerging, which not only enable the screening of full‐length antibodies for antigen binding but also allow for the integration of Ig‐seq to predict biophysical properties more optimal for downstream therapeutic development. Finally, new advanced approaches are emerging for identifying antigen‐specific clones based on sequence clustering algorithms, as was recently shown for T‐cell receptors; these methods may eventually also be employed for discovering large panels of monoclonal antibodies from Ig‐seq data sets.…”

The influence of the phylogenetic inference pipeline on murine antibody repertoire sequencing data following viral infection

“…Furthermore, there exist several methods to construct phylogenetic trees, including distance-based metrics, maximum likelihood (ML), maximum parsimony (MP), and Bayesian inference (Stamatakis, 2006;Gascuel, 2006;Bouckaert et al, 2014). While there exist multiple simulation tools capable of exploring how inference method impacts the resulting phylogenetic trees derived from simulated B cell data (Yermanos et al, 2017;Davidsen and Matsen IV, 2018;Safonova et al, 2015;Weber et al, 2019), the extent of this influence on the evolutionary conclusions on experimental data remains largely unexplored. It remains unknown, for example, the extent of which the phylogenetic inference strategy impacts the biological conclusions pertaining to the evolutionary landscape across various infection cohorts.…”