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Background T and B cell receptor (TCR, BCR) repertoires constitute the foundation of adaptive immunity. Adaptive immune receptor repertoire sequencing (AIRR-seq) is a common approach to study immune system dynamics. Understanding the genetic factors influencing the composition and dynamics of these repertoires is of major scientific and clinical importance. The chromosomal loci encoding for the variable regions of TCRs and BCRs are challenging to decipher due to repetitive elements and undocumented structural variants. Methods To confront this challenge, AIRR-seq-based methods have recently been developed for B cells, enabling genotype and haplotype inference and discovery of undocumented alleles. However, this approach relies on complete coverage of the receptors’ variable regions, whereas most T cell studies sequence a small fraction of that region. Here, we adapted a B cell pipeline for undocumented alleles, genotype, and haplotype inference for full and partial AIRR-seq TCR data sets. The pipeline also deals with gene assignment ambiguities, which is especially important in the analysis of data sets of partial sequences. Results From the full and partial AIRR-seq TCR data sets, we identified 39 undocumented polymorphisms in T cell receptor Beta V (TRBV) and 31 undocumented 5 ′ UTR sequences. A subset of these inferences was also observed using independent genomic approaches. We found that a single nucleotide polymorphism differentiating between the two documented T cell receptor Beta D2 (TRBD2) alleles is strongly associated with dramatic changes in the expressed repertoire. Conclusions We reveal a rich picture of germline variability and demonstrate how a single nucleotide polymorphism dramatically affects the composition of the whole repertoire. Our findings provide a basis for annotation of TCR repertoires for future basic and clinical studies.

VDJbase is a publicly available database that offers easy searching of data describing the complete sets of gene sequences (genotypes and haplotypes) inferred from adaptive immune receptor repertoire sequencing datasets. VDJbase is designed to act as a resource that will allow the scientific community to explore the genetic variability of the immunoglobulin (Ig) and T cell receptor (TR) gene loci. It can also assist in the investigation of Ig- and TR-related genetic predispositions to diseases. Our database includes web-based query and online tools to assist in visualization and analysis of the genotype and haplotype data. It enables users to detect those alleles and genes that are significantly over-represented in a particular population, in terms of genotype, haplotype and gene expression. The database website can be freely accessed at https://www.vdjbase.org/, and no login is required. The data and code use creative common licenses and are freely downloadable from https://bitbucket.org/account/user/yaarilab/projects/GPHP.

Celiac disease (CeD) is a common autoimmune disorder caused by an abnormal immune response to dietary gluten proteins. The disease has high heritability. HLA is the major susceptibility factor, and the HLA effect is mediated via presentation of deamidated gluten peptides by disease-associated HLA-DQ variants to CD4+ T cells. In addition to gluten-specific CD4+ T cells the patients have antibodies to transglutaminase 2 (autoantigen) and deamidated gluten peptides. These disease-specific antibodies recognize defined epitopes and they display common usage of specific heavy and light chains across patients. Interactions between T cells and B cells are likely central in the pathogenesis, but how the repertoires of naïve T and B cells relate to the pathogenic effector cells is unexplored. To this end, we applied machine learning classification models to naïve B cell receptor (BCR) repertoires from CeD patients and healthy controls. Strikingly, we obtained a promising classification performance with an F1 score of 85%. Clusters of heavy and light chain sequences were inferred and used as features for the model, and signatures associated with the disease were then characterized. These signatures included amino acid (AA) 3-mers with distinct bio-physiochemical characteristics and enriched V and J genes. We found that CeD-associated clusters can be identified and that common motifs can be characterized from naïve BCR repertoires. The results may indicate a genetic influence by BCR encoding genes in CeD. Analysis of naïve BCRs as presented here may become an important part of assessing the risk of individuals to develop CeD. Our model demonstrates the potential of using BCR repertoires and in particular, naïve BCR repertoires, as disease susceptibility markers.

Summary Inference of germline polymorphisms in immunoglobulin genes from B cell receptor repertoires is complicated by somatic hypermutations, sequencing/PCR errors, and by varying length of reference alleles. The light chain inference is particularly challenging owing to large gene duplications and absence of D genes. We analyzed the light chain cDNA sequences from naïve B cell receptor repertoires from 100 individuals. We optimized light chain allele inference by tweaking parameters of the TIgGER functions, extending the germline reference sequences, and establishing mismatch frequency patterns at polymorphic positions to filter out false-positive candidates. We identified 48 previously unreported variants of light chain variable genes. We selected 14 variants for validation and successfully validated 11 by Sanger sequencing. Clustering of light chain 5′UTR, L-PART1, and L-PART2 revealed partial intron retention in 11 kappa and 9 lambda V alleles. Our results provide insight into germline variation in human light chain immunoglobulin loci., Graphical abstract, Highlights • Inference of germline light chain Ig V alleles and filtering of sequencing artefacts • 48 previously unreported V gene alleles, 25 in kappa and 23 in lambda genes • Partial intron detected in some upstream sequences suggesting alternative splicing • Novel polymorphisms in the upstream regions: 5′UTR, L-PART1, L-PART2, Genetics; Genomics; Molecular genetics; Immunology

With the advent of high-throughput sequencing technologies, the fields of immunogenomics and adaptive immune receptor repertoire research are facing both opportunities and challenges. Adaptive immune receptor repertoire sequencing (AIRR-seq) has become an increasingly important tool to characterize T and B cell responses in settings of interest. However, the majority of AIRR-seq studies conducted so far were performed in individuals of European ancestry, restricting the ability to identify variation in human adaptive immune responses across populations and limiting their applications. As AIRR-seq studies depend on the ability to assign VDJ sequence reads to the correct germline gene segments, efforts to characterize the genomic loci that encode adaptive immune receptor genes in different populations are urgently needed. The availability of comprehensive germline gene databases and further applications of AIRR-seq studies to individuals of non-European ancestry will substantially enhance our understanding of human adaptive immune responses, promote the development of effective diagnostics and treatments, and eventually advance precision medicine., 22 pages,1 table

Immunoglobulin genes are rarely considered as disease susceptibility genes despite their obvious and central contributions to immune function. This appears to be a consequence of historical views on antibody repertoire formation that no longer stand, and of difficulties that until recently surrounded the documentation of the suite of antibody genes in any individual. If these important genes are to be accessible to GWAS studies, allelic variation within the human population needs to be better documented, and a curated set of genomic variations associated with antibody genes needs to be formulated. Repertoire studies arising from the COVID-19 pandemic provide an opportunity to meet these needs, and may provide insights into the profound variability that is seen in outcomes to this infection.

Recent advances in data acquiring technologies in biology have led to major challenges in mining relevant information from large datasets. For example, single-cell RNA sequencing technologies are producing expression and sequence information from tens of thousands of cells in every single experiment. A common task in analyzing biological data is to cluster samples or features (e.g., genes) into groups sharing common characteristics. This is an NP-hard problem for which numerous heuristic algorithms have been developed. However, in many cases, the clusters created by these algorithms do not reflect biological reality. To overcome this, a Networks Based Clustering (NBC) approach was recently proposed, by which the samples or genes in the dataset are first mapped to a network and then community detection (CD) algorithms are used to identify clusters of nodes.Here, we created an open and flexible python-based toolkit for NBC that enables easy and accessible network construction and community detection. We then tested the applicability of NBC for identifying clusters of cells or genes from previously published large-scale single-cell and bulk RNA-seq datasets.We show that NBC can be used to accurately and efficiently analyze large-scale datasets of RNA sequencing experiments.

Germline variations in immunoglobulin genes influence the repertoire of B cell receptors and antibodies, and such polymorphisms may impact disease susceptibility. However, the knowledge of the genomic variation of the immunoglobulin loci is scarce. Here, we report 25 novel germlineIGHValleles as inferred from rearranged naïve B cell cDNA repertoires of 98 individuals. Thirteen novel alleles were selected for validation, out of which ten were successfully confirmed by targeted amplification and Sanger sequencing of non-B cell DNA. Moreover, we detected a high degree of variability upstream of the V-region in the 5’UTR, leader 1, and leader 2 sequences, and found that identical V-region alleles can differ in upstream sequences. Thus, we have identified a large genetic variation not only in the V-region but also in the upstream sequences ofIGHVgenes. Our findings challenge current approaches used for annotating immunoglobulin repertoire sequencing data.

Germline variations in immunoglobulin genes influence the repertoire of B cell receptors and antibodies, and such polymorphisms may impact disease susceptibility. However, the knowledge of the genomic variation of the immunoglobulin loci is scarce. Here, we report 25 potential novel germline IGHV alleles as inferred from rearranged naïve B cell cDNA repertoires of 98 individuals. Thirteen novel alleles were selected for validation, out of which ten were successfully confirmed by targeted amplification and Sanger sequencing of non-B cell DNA. Moreover, we detected a high degree of variability upstream of the V-REGION in the 5′UTR, L-PART1 and L-PART2 sequences, and found that identical V-REGION alleles can differ in upstream sequences. Thus, we have identified a large genetic variation not only in the V-REGION but also in the upstream sequences of IGHV genes. Our findings provide a new perspective for annotating immunoglobulin repertoire sequencing data.

The immune rejection of allografts is mediated by T cells via two distinct pathways: the direct and the indirect pathways. Direct alloresponse to intact donor MHC molecules is ensured by T cells which are polyclonal and directed toward a variety of antigens. This response is highly sensitive to treatment by immunosuppressive drugs including Cyclosporin A. Indirect alloresponse is oligoclonal and involves a few dominant antigen peptides on donor MHC. In contrast to its direct counterpart, indirect allorecognition is thought to be poorly sensitive to blockade by cyclosporin A. It is likely that indirect and direct types of alloresponses play different roles in the physiology of the rejection process. T cell responses occurring via direct allorecognition play a critical role during the early phase of acute graft rejection by sensitizing the host to graft antigens. Alternatively, once such sensitization has taken place, indirect type of alloresponse may become predominant and presumably represent the driving force in the actual destruction of transplanted tissues. In addition, we and others have provided strong circumstantial evidence indicating that secondary T cell responses via indirect allorecognition spread to new determinants on donor MHC and tissue-specific antigens. This phenomenon is likely to play an important role in late and chronic rejection, a major obstacle to long-term graft acceptance in clinical transplantation. Finally, a series of studies have demonstrated that early, pre-transplant treatment with tolerogenic donor-derived MHC peptides can protect the graft from rejection in rodents. Although the mechanisms involved in MHC-peptide-induced tolerance are ill defined, this strategy represents a promising approach for ensuring long-lasting graft acceptance in the absence of widespread immunosuppression. It is now crucial to further explore the mechanims involved in immunogenicity and tolerogenicity of MHC peptides and to initiate clinical studies to evaluate the efficacy of blocking indirect alloresponses in transplanted patients.

The increasing worldwide prevalence of Hepatocellular carcinoma (HCC), characterized by resistance to conventional chemotherapy, poor prognosis and eventually mortality, place it as a prime target for new modes of prevention and treatment. Hepatitis C Virus (HCV) is the predominant risk factor for HCC in the US and Europe. Multiple epidemiological studies showed that sustained virological responses (SVR) following treatment with the powerful direct acting antivirals (DAAs), which have replaced interferon-based regimes, do not eliminate tumor development. We aimed to identify an HCV-specific pathogenic mechanism that persists post SVR following DAAs treatment. We demonstrate that HCV infection induces genome-wide epigenetic changes by performing chromatin immunoprecipitation followed by next-generation sequencing (ChIP-seq) for histone post-translational modifications that are epigenetic markers for active and repressed chromatin. The changes in histone modifications correlate with reprogramed host gene expression and alter signaling pathways known to be associated with HCV life cycle and HCC. These epigenetic alterations require the presence of HCV RNA or/and expression of the viral proteins in the cells. Importantly, the epigenetic changes induced following infection persist as an "epigenetic signature" after virus eradication by DAAs treatment, as detected using in vitro HCV infection models. These observations led to the identification of an 8 gene signature that is associated with HCC development and demonstrate persistent epigenetic alterations in HCV infected and post SVR liver biopsy samples. The epigenetic signature was reverted in vitro by drugs that inhibit epigenetic modifying enzyme and by the EGFR inhibitor, Erlotinib. This epigenetic “scarring” of the genome, persisting following HCV eradication, suggest a novel mechanism for the persistent pathogenesis of HCV after its eradication by DAAs. Our study offers new avenues for prevention of the persistent oncogenic effects of chronic hepatitis infections using specific drugs to revert the epigenetic changes to the genome., Author summary Hepatitis C virus (HCV) is a leading cause of hepatocellular carcinoma (HCC) in western countries. While direct acting antivirals (DAAs) therapy for HCV efficiently eradicates the infection, sustained virological response (SVR) following anti-HCV treatment does not eliminate the risk for HCC development. With the 3–4 million newly infected cases each year, it is estimated that HCV-associated disease burden remains high in the next decade and the population of post-DAA-based SVR patients is expected to grow exponentially in the near future. Therefore, Post-SVR HCC is an emerging problem with an urgent unmet need for the elucidation of its molecular mechanisms for therapeutic target and biomarker discovery. We demonstrate that HCV-induce epigenetic missregulation. These HCV-induced epigenetic changes reprogram host gene expression and persist as an "epigenetic signature" following virus eradication. Treatment of HCV-cured cells with specific inhibitors reverted the epigenetic signature. These results suggest a "hit and run" scenario that may explain why some chronic HCV infected patients do proceed to develop HCC after HCV eradication. Our discoveries provide an insight into the outcomes of HCV infection and HCC development, also following SVR, and novel approaches for its prevention.

The interactions between antibodies, SARS-CoV-2 and immune cells contribute to the pathogenesis of COVID-19 and protective immunity. To understand the differences between antibody responses in mild versus severe cases of COVID-19, we analyzed the B cell responses in patients 1.5 months post SARS-CoV-2 infection. Severe, and not mild, infection correlated with high titers of IgG against Spike receptor binding domain (RBD) that were capable of ACE2:RBD inhibition. B cell receptor (BCR) sequencing revealed that VH3-53 was enriched during severe infection. Of the 22 antibodies cloned from two severe donors, six exhibited potent neutralization against authentic SARS-CoV-2, and inhibited syncytia formation. Using peptide libraries, competition ELISA and mutagenesis of RBD, we mapped the epitopes of the neutralizing antibodies (nAbs) to three different sites on the Spike. Finally, we used combinations of nAbs targeting different immune-sites to efficiently block SARS-CoV-2 infection. Analysis of 49 healthy BCR repertoires revealed that the nAbs germline VHJH precursors comprise up to 2.7% of all VHJHs. We demonstrate that severe COVID-19 is associated with unique BCR signatures and multi-clonal neutralizing responses that are relatively frequent in the population. Moreover, our data support the use of combination antibody therapy to prevent and treat COVID-19., Author summary The correlates of effective durable antibody response to SARS-CoV-2 infection are still unclear. In this study, we compared B cell receptor signatures in 8 Severe versus 10 Mild SARS-CoV-2 infected Israeli donors, at 1.5 months post infection using molecular and bioinformatic approaches. We found distinct features between the two groups with higher anti-SARS-CoV-2 receptor binding domain (RBD) plasma IgG titers and increased B cell expansion in donors with severe disease manifestations. We further isolated 22 monoclonal antibodies from these donors, 6 of which were highly potent neutralizing the live virus and inhibited the fusion of infected cells. Using mutagenesis and peptide libraries we mapped the binding sites of the neutralizing antibodies on the RBD of the SARS-CoV-2 Spike. We next demonstrated that combinations of different classes of neutralizing mAbs can completely block the live virus from spreading in culture. Lastly, we performed a bioinformatic search in 49 healthy BCR repertoires identifying precursors for these neutralizing antibodies in the top 30 most common precursors, suggesting that these antibodies can be readily produced by the majority of the uninfected population upon antigenic stimulation.

The adaptive immune receptor repertoire (AIRR) contains information on an individuals’ immune past, present and potential in the form of the evolving sequences that encode the B cell receptor (BCR) repertoire. AIRR sequencing (AIRR-seq) studies rely on databases of known BCR germline variable (V), diversity (D) and joining (J) genes to detect somatic mutations in AIRR-seq data via comparison to the best-aligning database alleles. However, it has been shown that these databases are far from complete, leading to systematic misidentification of mutated positions in subsets of sample sequences. We previously presented TIgGER, a computational method to identify subject-specific V gene genotypes, including the presence of novel V gene alleles, directly from AIRR-seq data. However, the original algorithm was unable to detect alleles that differed by more than 5 single nucleotide polymorphisms (SNPs) from a database allele. Here we present and apply an improved version of the TIgGER algorithm which can detect alleles that differ by any number of SNPs from the nearest database allele, and can construct subject-specific genotypes with minimal prior information. TIgGER predictions are validated both computationally (using a leave-one-out strategy) and experimentally (using genomic sequencing), resulting in the addition of three new immunoglobulin heavy chain V (IGHV) gene alleles to the IMGT repertoire. Finally, we develop a Bayesian strategy to provide a confidence estimate associated with genotype calls. All together, these methods allow for much higher accuracy in germline allele assignment, an essential step in AIRR-seq studies.

High-throughput sequencing of B-cell immunoglobulin repertoires is increasingly being applied to gain insights into the adaptive immune response in healthy individuals and in those with a wide range of diseases. Recent applications include the study of autoimmunity, infection, allergy, cancer and aging. As sequencing technologies continue to improve, these repertoire sequencing experiments are producing ever larger datasets, with tens- to hundreds-of-millions of sequences. These data require specialized bioinformatics pipelines to be analyzed effectively. Numerous methods and tools have been developed to handle different steps of the analysis, and integrated software suites have recently been made available. However, the field has yet to converge on a standard pipeline for data processing and analysis. Common file formats for data sharing are also lacking. Here we provide a set of practical guidelines for B-cell receptor repertoire sequencing analysis, starting from raw sequencing reads and proceeding through pre-processing, determination of population structure, and analysis of repertoire properties. These include methods for unique molecular identifiers and sequencing error correction, V(D)J assignment and detection of novel alleles, clonal assignment, lineage tree construction, somatic hypermutation modeling, selection analysis, and analysis of stereotyped or convergent responses. The guidelines presented here highlight the major steps involved in the analysis of B-cell repertoire sequencing data, along with recommendations on how to avoid common pitfalls.

The ability to detect selection by analyzing mutation patterns in experimentally derived immunoglobulin (Ig) sequences is a critical part of many studies. Such techniques are useful not only for understanding the response to pathogens, but also to determine the role of antigen-driven selection in autoimmunity, B cell cancers and the diversification of pre-immune repertoires in certain species. Despite its importance, quantifying selection in experimentally derived sequences is fraught with difficulties. The necessary parameters for statistical tests (such as the expected frequency of replacement mutations in the absence of selection) are non-trivial to calculate, and results are not easily interpretable when analyzing more than a handful of sequences. We have developed a web server that implements our previously proposed Focused binomial test for detecting selection. Several features are integrated into the web site in order to facilitate analysis, including V(D)J germline segment identification with IMGT alignment, batch submission of sequences and integration of additional test statistics proposed by other groups. We also implement a Z-score-based statistic that increases the power of detecting selection while maintaining specificity, and further allows for the combined analysis of sequences from different germlines. The tool is freely available at http://clip.med.yale.edu/selection.

Broadly neutralizing HIV antibodies (bnAbs) are typically highly somatically mutated, raising doubts as to whether they can be elicited by vaccination. We used 454 sequencing and designed a novel phylogenetic method to model lineage evolution of the bnAbs PGT121–134 and found a positive correlation between the level of somatic hypermutation (SHM) and the development of neutralization breadth and potency. Strikingly, putative intermediates were characterized that show approximately half the mutation level of PGT121–134 but were still capable of neutralizing roughly 40–80% of PGT121–134 sensitive viruses in a 74-virus panel at median titers between 15- and 3-fold higher than PGT121–134. Such antibodies with lower levels of SHM may be more amenable to elicitation through vaccination while still providing noteworthy coverage. Binding characterization indicated a preference of inferred intermediates for native Env binding over monomeric gp120, suggesting that the PGT121–134 lineage may have been selected for binding to native Env at some point during maturation. Analysis of glycan-dependent neutralization for inferred intermediates identified additional adjacent glycans that comprise the epitope and suggests changes in glycan dependency or recognition over the course of affinity maturation for this lineage. Finally, patterns of neutralization of inferred bnAb intermediates suggest hypotheses as to how SHM may lead to potent and broad HIV neutralization and provide important clues for immunogen design., National Institutes of Health (U.S.) (Bioinformatics and Integrative Genomics Fellowship), National Institutes of Health (U.S.) (Center for Excellence in Genomic Science)

Analyses of somatic hypermutation (SHM) patterns in B cell immunoglobulin (Ig) sequences contribute to our basic understanding of adaptive immunity, and have broad applications not only for understanding the immune response to pathogens, but also to determining the role of SHM in autoimmunity and B cell cancers. Although stochastic, SHM displays intrinsic biases that can confound statistical analysis, especially when combined with the particular codon usage and base composition in Ig sequences. Analysis of B cell clonal expansion, diversification, and selection processes thus critically depends on an accurate background model for SHM micro-sequence targeting (i.e., hot/cold-spots) and nucleotide substitution. Existing models are based on small numbers of sequences/mutations, in part because they depend on data from non-coding regions or non-functional sequences to remove the confounding influences of selection. Here, we combine high-throughput Ig sequencing with new computational analysis methods to produce improved models of SHM targeting and substitution that are based only on synonymous mutations, and are thus independent of selection. The resulting "S5F" models are based on 806,860 Synonymous mutations in 5-mer motifs from 1,145,182 Functional sequences and account for dependencies on the adjacent four nucleotides (two bases upstream and downstream of the mutation). The estimated profiles can explain almost half of the variance in observed mutation patterns, and clearly show that both mutation targeting and substitution are significantly influenced by neighboring bases. While mutability and substitution profiles were highly conserved across individuals, the variability across motifs was found to be much larger than previously estimated. The model and method source code are made available at http://clip.med.yale.edu/SHM.

Enrichment analysis of gene sets is a popular approach that provides a functional interpretation of genome-wide expression data. Existing tests are affected by inter-gene correlations, resulting in a high Type I error. The most widely used test, Gene Set Enrichment Analysis, relies on computationally intensive permutations of sample labels to generate a null distribution that preserves gene–gene correlations. A more recent approach, CAMERA, attempts to correct for these correlations by estimating a variance inflation factor directly from the data. Although these methods generate P-values for detecting gene set activity, they are unable to produce confidence intervals or allow for post hoc comparisons. We have developed a new computational framework for Quantitative Set Analysis of Gene Expression (QuSAGE). QuSAGE accounts for inter-gene correlations, improves the estimation of the variance inflation factor and, rather than evaluating the deviation from a null hypothesis with a P-value, it quantifies gene-set activity with a complete probability density function. From this probability density function, P-values and confidence intervals can be extracted and post hoc analysis can be carried out while maintaining statistical traceability. Compared with Gene Set Enrichment Analysis and CAMERA, QuSAGE exhibits better sensitivity and specificity on real data profiling the response to interferon therapy (in chronic Hepatitis C virus patients) and Influenza A virus infection. QuSAGE is available as an R package, which includes the core functions for the method as well as functions to plot and visualize the results.

High-throughput immunoglobulin sequencing promises new insights into the somatic hypermutation and antigen-driven selection processes that underlie B-cell affinity maturation and adaptive immunity. The ability to estimate positive and negative selection from these sequence data has broad applications not only for understanding the immune response to pathogens, but is also critical to determining the role of somatic hypermutation in autoimmunity and B-cell cancers. Here, we develop a statistical framework for Bayesian estimation of Antigen-driven SELectIoN (BASELINe) based on the analysis of somatic mutation patterns. Our approach represents a fundamental advance over previous methods by shifting the problem from one of simply detecting selection to one of quantifying selection. Along with providing a more intuitive means to assess and visualize selection, our approach allows, for the first time, comparative analysis between groups of sequences derived from different germline V(D)J segments. Application of this approach to next-generation sequencing data demonstrates different selection pressures for memory cells of different isotypes. This framework can easily be adapted to analyze other types of DNA mutation patterns resulting from a mutator that displays hot/cold-spots, substitution preference or other intrinsic biases.

Summary We identify the interfollicular (IF) zone as the site where germinal center B cell and T follicular helper (Tfh) cell differentiation initiates. For the first 2 days postimmunization, antigen-specific T and B cells remained confined within the IF zone, formed long-lived interactions, and upregulated the transcriptional repressor Bcl6. T cells also acquired the Tfh cell markers CXCR5, PD-1, and GL7. Responding B and T cells migrated to the follicle interior directly from the IF zone, T cell immigration preceding B cells by 1 day. Notably, in the absence of cognate B cells, Tfh cells still formed and migrated to the follicle. However, without such B cells, PD-1, ICOS, and GL7 were no longer expressed on follicular Bcl6 hi T cells that nevertheless persisted in the follicle. Thus, Ag-specific B cells are required for the maintenance of the PD-1 hi ICOS hi GL7 hi Tfh cell phenotype within the follicle, but not for their initial differentiation in the IF zone.

Summary Antibody haplotype inference (chromosomal phasing) may have clinical implications for the identification of genetic predispositions to diseases. Yet, our knowledge of the genomic loci encoding for the variable regions of the antibody is only partial, mostly due to the challenge of aligning short reads from genome sequencing to these highly repetitive loci. A powerful approach to infer the content of these loci relies on analyzing repertoires of rearranged V(D)J sequences. We present here RAbHIT, an R Haplotype Antibody Inference Tool, that implements a novel algorithm to infer V(D)J haplotypes by adapting a Bayesian framework. RAbHIT offers inference of haplotype and gene deletions. It may be applied to sequences from naïve and non-naïve B-cells, sequenced by different library preparation protocols. Availability and implementation RAbHIT is freely available for academic use from comprehensive R archive network (CRAN) (https://cran.r-project.org/web/packages/rabhit/) under CC BY-SA 4.0 license. Supplementary information Supplementary data are available at Bioinformatics online.