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

Abstract

Summary 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. ImmuneSIM enables the tuning of the immune receptor features: (i) species and chain type (BCR, TCR, single, 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. Availability The package is available via https://github.com/GreiffLab/immuneSIM and will also be available at CRAN (submitted). The documentation is hosted at https://immuneSIM.readthedocs.io. Contact victor.greiff@medisin.uio, sai.reddy@ethz.ch

Stitchr: stitching coding TCR nucleotide sequences from V/J/CDR3 information

“…While these published datasets allowed us to benchmark basic run results, they do not contain complete variable domains, and were thus unable to confirm that Stitchr and Thimble were generating the correct nucleotide sequences. To generate gold-standard TCR sequences spanning the entirety of the variable domain necessary to rigorously assess this, we used the immuneSIM tool (16) to simulate V(D)J recombination, creating known TCR sequences from the IMGT germline reference database and predetermined generation probabilities (Figure 3B). This produces a repertoire with a normal distribution of variable domain lengths (Supplementary Figure 5A).…”

Reference-based comparison of adaptive immune receptor repertoires

“…Of note, the concept of diversity measures creating equivalence classes has been noted previously for Hill diversity measures (Greiff et al, 2015b) and is here extended to include additional repertoire features immuneREF unifies single and composite features, frequency-dependent, and sequence-dependent similarity measures into one computational framework. Beyond quantifying the repertoire similarity of experimental immune repertoires, immuneREF also enables the comparison of simulated (Han et al, 2021; Marcou et al, 2017; Safonova et al, 2015; Weber et al, 2020)(Marcou et al, 2017; Safonova et al, 2015; Weber et al, 2019) and in vitro synthetic immune repertoires used for therapeutic antibody discovery (Mason et al, 2018). Furthermore, immuneREF may be used for data curation purposes in immune repertoire databases such as iReceptor (Corrie et al, 2018), VDJserver (Cowell et al, 2015), PIRD (Zhang et al, 2019), and Observed Antibody Space (Kovaltsuk et al, 2018).…”

Echidna: integrated simulations of single-cell immune receptor repertoires and transcriptomes

“…Here, we developed a simulation framework and further exemplified how simulated repertoires can be leveraged to benchmark and develop bioinformatics software relating to experimental datasets. Although multiple simulation frameworks have been previously developed (Yermanos et al, 2017;Weber et al, 2020;Safonova et al, 2015;Davidsen and Matsen, 2018), there is a lack of software specifically tailored to generating adaptive immune receptors at the single-cell resolution and with corresponding transcriptome information. Echidna bridges the gap between single-cell transcriptome and immune repertoire sequencing, providing information relevant to both fields at the single-cell resolution.…”

“…Interpreting such datasets, however, remains challenging as the accompanying computational pipelines and software are still in their infancy (Yermanos, Agrafiotis, et al, 2021b;Borcherding et al, 2020;Sturm et al, 2020). Although multiple tools have been developed to simulate immune receptors and single-cell transcriptomes (Marcou et al, 2018;Weber et al, 2020;Yermanos et al, 2017;Davidsen et al, 2019;Safonova et al, 2015), these represent separate platforms and thus there remains a lack of software capable of simulating scSeq data of immune repertoires and transcriptomes. We therefore developed Echidna, an R package that simulates immune repertoires and their corresponding transcriptomes.…”