immuneSIM: tunable multi-feature simulation of B- and T-cell receptor repertoires for immunoinformatics benchmarking

Abstract

<jats:title>Abstract</jats:title> <jats:sec> <jats:title>Summary</jats:title> <jats:p>B- and T-cell receptor repertoires of the adaptive immune system have become a key target for diagnostics and therapeutics research. Consequently, there is a rapidly growing number of bioinformatics tools for immune repertoire analysis. Benchmarking of such tools is crucial for ensuring reproducible and generalizable computational analyses. Currently, however, it remains challenging to create standardized ground truth immune receptor repertoires for immunoinformatics tool benchmarking. Therefore, we developed immuneSIM, an R package that allows the simulation of native-like and aberrant synthetic full-length variable region immune receptor sequences by tuning the following immune receptor features: (i) species and chain type (BCR, TCR, single and paired), (ii) germline gene usage, (iii) occurrence of insertions and deletions, (iv) clonal abundance, (v) somatic hypermutation and (vi) sequence motifs. Each simulated sequence is annotated by the complete set of simulation events that contributed to its in silico generation. immuneSIM permits the benchmarking of key computational tools for immune receptor analysis, such as germline gene annotation, diversity and overlap estimation, sequence similarity, network architecture, clustering analysis and machine learning methods for motif detection.</jats:p> </jats:sec> <jats:sec> <jats:title>Availability and implementation</jats:title> <jats:p>The package is available via https://github.com/GreiffLab/immuneSIM and on CRAN at https://cran.r-project.org/web/packages/immuneSIM. The documentation is hosted at https://immuneSIM.readthedocs.io.</jats:p> </jats:sec> <jats:sec> <jats:title>Contact</jats:title> <jats:p>sai.reddy@ethz.ch or victor.greiff@medisin.uio.no</jats:p> </jats:sec> <jats:sec> <jats:title>Supplementary information</jats:title> <jats:p>Supplementary data are available at Bioinformatics online.</jats:p> </jats:sec>

Human antibody immune responses are personalized by selective removal of MHC-II peptide epitopes

“…Repertoire modeling was carried out with immuneSIM for V(D)J recombination modeling, and ShaZam for SHM modeling [62,63]. First, V gene frequency was extracted from donor data and used to build V gene distribution table.…”

A compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding

“…Ground truth datasets are defined by the property that the link between the class of a sequence (class = disease/antigen specificity) or repertoire and the underlying sequence structure is known a priori. Thus, by definition, ground-truth datasets are those that are synthetically generated in silico (Weber et al, 2019a(Weber et al, , 2019b. One of the current bottlenecks for generating nature-like synthetic datasets for predicting, for example, antigenbinding from the immune receptor sequence alone is the lack of knowledge on the complexity of paratope and epitope interaction motifs.…”

“…Our research resolves this important knowledge gap. Specifically, our atlas structural interaction motifs now allows for the faithful simulation of nativelike ground truth immune receptor datasets for the development of immune-receptor based machine learning methods (Weber et al, 2019a). For example, paratope and epitope sequence motifs may be implanted into synthetic 3D antibody-antigen structures (Robert et al, 2018) to subsequently perform benchmarking of different ML architectures with regard to their capacity to (i) predict paratope-epitope binding and (ii) recover the exact sites of antibody-antigen interaction (native-like antigen-specific sequence motifs).…”

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.…”