BRILIA: Integrated Tool for High-Throughput Annotation and Lineage Tree Assembly of B-Cell Repertoires

Abstract

The somatic diversity of antigen-recognizing B-cell receptors (BCRs) arises from Variable (V), Diversity (D), and Joining (J) (VDJ) recombination and somatic hypermutation (SHM) during B-cell development and affinity maturation. The VDJ junction of the BCR heavy chain forms the highly variable complementarity determining region 3 (CDR3), which plays a critical role in antigen specificity and binding affinity. Tracking the selection and mutation of the CDR3 can be useful in characterizing humoral responses to infection and vaccination. Although tens to hundreds of thousands of unique BCR genes within an expressed B-cell repertoire can now be resolved with high-throughput sequencing, tracking SHMs is still challenging because existing annotation methods are often limited by poor annotation coverage, inconsistent SHM identification across the VDJ junction, or lack of B-cell lineage data. Here, we present B-cell repertoire inductive lineage and immunosequence annotator (BRILIA), an algorithm that leverages repertoire-wide sequencing data to globally improve the VDJ annotation coverage, lineage tree assembly, and SHM identification. On benchmark tests against simulated human and mouse BCR repertoires, BRILIA correctly annotated germline and clonally expanded sequences with 94 and 70% accuracy, respectively, and it has a 90% SHM-positive prediction rate in the CDR3 of heavily mutated sequences; these are substantial improvements over existing methods. We used BRILIA to process BCR sequences obtained from splenic germinal center B cells extracted from C57BL/6 mice. BRILIA returned robust B-cell lineage trees and yielded SHM patterns that are consistent across the VDJ junction and agree with known biological mechanisms of SHM. By contrast, existing BCR annotation tools, which do not account for repertoire-wide clonal relationships, systematically underestimated both the size of clonally related B-cell clusters and yielded inconsistent SHM frequencies. We demonstrate BRILIA’s utility in B-cell repertoire studies related to VDJ gene usage, mechanisms for adenosine mutations, and SHM hot spot motifs. Furthermore, we show that the complete gene usage annotation and SHM identification across the entire CDR3 are essential for studying the B-cell affinity maturation process through immunosequencing methods.

IgEvolution: clonal analysis of antibody repertoires

“…Although clonal analysis of immunosequencing data has important applications in monitoring treatment, measuring vaccine efficacy, and designing antibody drugs, the existing tools for constructing clonal trees have limitations. Moreover, it remains unclear how to pre-process immunosequencing datasets for followup clonal tree reconstruction: constructing clonal trees on all reads faces the challenge of dealing with high error rates in Rep-seq dataset (Lee et al, 2017), while constructing clonal trees on error-corrected antibody repertoires faces the challenge of constructing accurate antibody repertoires in the case of stimulated Rep-seq datasets (Shlemov et al, 2017). Although the latter challenge can potentially be addressed by molecular barcoding techniques (Horns et al, 2017), these techniques are still rarely used (most publicly available Rep-seq datasets do not contain molecular barcodes).…”

“…Emergence of Rep-seq technologies triggered developments of various clonal tree reconstruction approaches (Barak et al, 2008, Davidsen andMatsen, 2018) such as likelihood-based ImmuniTree (Sok et al, 2013) and IgPhyML (Hoehn et al, 2017) algorithms. Horns et al, 2016 proposed a minimum spanning tree (MST) algorithm for clonal tree reconstruction and applied it to barcoded Rep-seq datasets that were error-corrected using unique molecular identifiers (a similar MST-based BRILIA tool was developed by Lee et al, 2017).…”

“…To integrate these problems, we developed the IgEvolution algorithm for simultaneous error correction and clonal tree reconstruction. Extending the BRILIA algorithm (Lee et al, 2017), IgEvolution decomposes a Rep-seq dataset into clonal lineages, constructs an MST for each clonal lineage, and analyzes leaves in the constructed trees to identify erroneous sequences. In difference from BRILIA that arbitrarily selects one of many MSTs (and does not correct some erroneous sequences), IgEvolution finds an MST that maximizes the number of leaves (leading to a more comprehensive error correction), and further performs an iterative (rather than a single round as in BRILIA) error correction.…”

The Bayesian optimist's guide to adaptive immune receptor repertoire analysis

“…The rearrangement processes change with age, a phenomenon recently quantified in mouse (61). In addition to the usual rearrangement process, oddities such as VH replacement (62–64) and inverted and multiple D genes do occur, although recent analyses indicate that these are rare in the overall repertoire (65,66). …”

“…61 In addition to the usual rearrangement process, oddities such as VH replacement 62-64 and inverted and multiple D genes do occur, although recent analyses indicate that these are rare in the overall repertoire. 65,66 This process is greatly deserving of complex models, as all variables determining the rearrangement process are both interesting and decidedly non-uniform. Indeed, many probabilistic models have been formulated, with the hidden Markov model (HMM) framework being particularly popular.…”