VDJtools: Unifying Post-analysis of T Cell Receptor Repertoires.
Ontology highlight
ABSTRACT:
SUBMITTER: Shugay M
PROVIDER: S-EPMC4659587 | biostudies-literature | 2015 Nov
REPOSITORIES: biostudies-literature
Publications
VDJtools: Unifying Post-analysis of T Cell Receptor Repertoires.
Shugay Mikhail M Bagaev Dmitriy V DV Turchaninova Maria A MA Bolotin Dmitriy A DA Britanova Olga V OV Putintseva Ekaterina V EV Pogorelyy Mikhail V MV Nazarov Vadim I VI Zvyagin Ivan V IV Kirgizova Vitalina I VI Kirgizov Kirill I KI Skorobogatova Elena V EV Chudakov Dmitriy M DM
PLoS computational biology 20151125 11
Despite the growing number of immune repertoire sequencing studies, the field still lacks software for analysis and comprehension of this high-dimensional data. Here we report VDJtools, a complementary software suite that solves a wide range of T cell receptor (TCR) repertoires post-analysis tasks, provides a detailed tabular output and publication-ready graphics, and is built on top of a flexible API. Using TCR datasets for a large cohort of unrelated healthy donors, twins, and multiple scleros ...[more]
Similar Datasets
Project description:B cell receptors (BCRs) and T cell receptors (TCRs) make up an essential network of defense molecules that, collectively, can distinguish self from non-self and facilitate destruction of antigen-bearing cells such as pathogens or tumors. The analysis of BCR and TCR repertoires plays an important role in both basic immunology as well as in biotechnology. Because the repertoires are highly diverse, specialized software methods are needed to extract meaningful information from BCR and TCR sequence data. Here, we review recent developments in bioinformatics tools for analysis of BCR and TCR repertoires, with an emphasis on those that incorporate structural features. After describing the recent sequencing technologies for immune receptor repertoires, we survey structural modeling methods for BCR and TCRs, along with methods for clustering such models. We review downstream analyses, including BCR and TCR epitope prediction, antibody-antigen docking and TCR-peptide-MHC Modeling. We also briefly discuss molecular dynamics in this context.
| S-EPMC7366105 | biostudies-literature
Project description:In modern molecular biology one of the standard ways of analyzing a vertebrate immune system is to sequence and compare the counts of specific antigen receptor clones (either immunoglobulins or T-cell receptors) derived from various tissues under different experimental or clinical conditions. The resulting statistical challenges are difficult and do not fit readily into the standard statistical framework of contingency tables primarily due to the serious under-sampling of the receptor populations. This under-sampling is caused, on one hand, by the extreme diversity of antigen receptor repertoires maintained by the immune system and, on the other, by the high cost and labor intensity of the receptor data collection process. In most of the recent immunological literature the differences across antigen receptor populations are examined via non-parametric statistical measures of the species overlap and diversity borrowed from ecological studies. While this approach is robust in a wide range of situations, it seems to provide little insight into the underlying clonal size distribution and the overall mechanism differentiating the receptor populations. As a possible alternative, the current paper presents a parametric method that adjusts for the data under-sampling as well as provides a unifying approach to a simultaneous comparison of multiple receptor groups by means of the modern statistical tools of unsupervised learning. The parametric model is based on a flexible multivariate Poisson-lognormal distribution and is seen to be a natural generalization of the univariate Poisson-lognormal models used in the ecological studies of biodiversity patterns. The procedure for evaluating a model's fit is described along with the public domain software developed to perform the necessary diagnostics. The model-driven analysis is seen to compare favorably vis a vis traditional methods when applied to the data from T-cell receptors in transgenic mice populations.
| S-EPMC3006491 | biostudies-literature
Project description: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.
| S-EPMC8006302 | biostudies-literature
Project description:T cells are defined by a heterodimeric surface receptor, the T cell receptor (TCR), that mediates recognition of pathogen-associated epitopes through interactions with peptide and major histocompatibility complexes (pMHCs). TCRs are generated by genomic rearrangement of the germline TCR locus, a process termed V(D)J recombination, that has the potential to generate marked diversity of TCRs (estimated to range from 1015 (ref. 1) to as high as 1061 (ref. 2) possible receptors). Despite this potential diversity, TCRs from T cells that recognize the same pMHC epitope often share conserved sequence features, suggesting that it may be possible to predictively model epitope specificity. Here we report the in-depth characterization of ten epitope-specific TCR repertoires of CD8+ T cells from mice and humans, representing over 4,600 in-frame single-cell-derived TCR?? sequence pairs from 110 subjects. We developed analytical tools to characterize these epitope-specific repertoires: a distance measure on the space of TCRs that permits clustering and visualization, a robust repertoire diversity metric that accommodates the low number of paired public receptors observed when compared to single-chain analyses, and a distance-based classifier that can assign previously unobserved TCRs to characterized repertoires with robust sensitivity and specificity. Our analyses demonstrate that each epitope-specific repertoire contains a clustered group of receptors that share core sequence similarities, together with a dispersed set of diverse 'outlier' sequences. By identifying shared motifs in core sequences, we were able to highlight key conserved residues driving essential elements of TCR recognition. These analyses provide insights into the generalizable, underlying features of epitope-specific repertoires and adaptive immune recognition.
| S-EPMC5616171 | biostudies-literature
Project description:Following antigenic challenge, activated B cells rapidly expand and undergo somatic hypermutation, yielding groups of clonally related B cells with diversified immunoglobulin receptors. Inference of clonal relationships based on the receptor sequence is an essential step in many adaptive immune receptor repertoire sequencing studies. These relationships are typically identified by a multi-step process that involves: (i) grouping sequences based on shared V and J gene assignments, and junction lengths and (ii) clustering these sequences using a junction-based distance. However, this approach is sensitive to the initial gene assignments, which are error-prone, and fails to identify clonal relatives whose junction length has changed through accumulation of indels. Through defining a translation-invariant feature space in which we cluster the sequences, we develop an alignment free clonal identification method that does not require gene assignments and is not restricted to a fixed junction length. This alignment free approach has higher sensitivity compared to a typical junction-based distance method without loss of specificity and PPV. While the alignment free procedure identifies clones that are broadly consistent with the junction-based distance method, it also identifies clones with characteristics (multiple V or J gene assignments or junction lengths) that are not detectable with the junction-based distance method.
| S-EPMC7913774 | biostudies-literature
Project description:The sequencing of immunoglobulin (Ig) transcripts from single B cells yields essential information about Ig heavy:light chain pairing, which is lost in conventional bulk sequencing experiments. The previously limited throughput of single-cell approaches has recently been overcome by the introduction of multiple next-generation sequencing (NGS)-based platforms. Furthermore, single-cell techniques allow the assignment of additional data types (e.g. cell surface marker expression), which are crucial for biological interpretation. However, the currently available computational tools are not designed to handle single-cell data and do not provide integral solutions for linking of sequence data to other biological data.Here we introduce sciReptor, a flexible toolkit for the processing and analysis of antigen receptor repertoire sequencing data at single-cell level. The software combines bioinformatics tools for immunoglobulin sequence annotation with a relational database, where raw data and analysis results are stored and linked. sciReptor supports attribution of additional data categories such as cell surface marker expression or immunological metadata. Furthermore, it comprises a quality control module as well as basic repertoire visualization tools.sciReptor is a flexible framework for standardized sequence analysis of antigen receptor repertoires on single-cell level. The relational database allows easy data sharing and downstream analyses as well as immediate comparisons between different data sets.
| S-EPMC4743164 | biostudies-literature
Project description:The human genome contains approximately 20 thousand protein-coding genes1, but the size of the collection of antigen receptors of the adaptive immune system that is generated by the recombination of gene segments with non-templated junctional additions (on B cells) is unknown-although it is certainly orders of magnitude larger. It has not been established whether individuals possess unique (or private) repertoires or substantial components of shared (or public) repertoires. Here we sequence recombined and expressed B cell receptor genes in several individuals to determine the size of their B cell receptor repertoires, and the extent to which these are shared between individuals. Our experiments revealed that the circulating repertoire of each individual contained between 9 and 17 million B cell clonotypes. The three individuals that we studied shared many clonotypes, including between 1 and 6% of B cell heavy-chain clonotypes shared between two subjects (0.3% of clonotypes shared by all three) and 20 to 34% of λ or κ light chains shared between two subjects (16 or 22% of λ or κ light chains, respectively, were shared by all three). Some of the B cell clonotypes had thousands of clones, or somatic variants, within the clonotype lineage. Although some of these shared lineages might be driven by exposure to common antigens, previous exposure to foreign antigens was not the only force that shaped the shared repertoires, as we also identified shared clonotypes in umbilical cord blood samples and all adult repertoires. The unexpectedly high prevalence of shared clonotypes in B cell repertoires, and identification of the sequences of these shared clonotypes, should enable better understanding of the role of B cell immune repertoires in health and disease.
| S-EPMC6949180 | biostudies-literature
Project description:The collection of T cell receptors (TCRs) generated by somatic recombination is large but unknown. We generate large TCR repertoire datasets as a resource to facilitate detailed studies of the role of TCR clonotypes and repertoires in health and disease. We estimate the size of individual human recombined and expressed TCRs by sequence analysis and determine the extent of sharing between individual repertoires. Our experiments reveal that each blood sample contains between 5 million and 21 million TCR clonotypes. Three individuals share 8% of TCRβ- or 11% of TCRα-chain clonotypes. Sorting by T cell phenotypes in four individuals shows that 5% of naive CD4+ and 3.5% of naive CD8+ subsets share their TCRβ clonotypes, whereas memory CD4+ and CD8+ subsets share 2.3% and 0.4% of their clonotypes, respectively. We identify the sequences of these shared TCR clonotypes that are of interest for studies of human T cell biology.
| S-EPMC7433715 | biostudies-literature
Project description:Many experimental and bioinformatics approaches have been developed to characterize the human T cell receptor (TCR) repertoire. However, the unknown functional relevance of TCR profiling hinders unbiased interpretation of the biology of T cells. To address this inadequacy, we developed tessa, a tool to integrate TCRs with gene expression of T cells to estimate the effect that TCRs confer on the phenotypes of T cells. Tessa leveraged techniques combining single-cell RNA-sequencing with TCR sequencing. We validated tessa and showed its superiority over existing approaches that investigate only the TCR sequences. With tessa, we demonstrated that TCR similarity constrains the phenotypes of T cells to be similar and dictates a gradient in antigen targeting efficiency of T cell clonotypes with convergent TCRs. We showed this constraint could predict a functional dichotomization of T cells postimmunotherapy treatment and is weakened in tumor contexts.
| S-EPMC7799492 | biostudies-literature
Project description:High-throughput T cell receptor (TCR) sequencing allows the characterization of an individual's TCR repertoire and directly queries their immune state. However, it remains a non-trivial task to couple these sequenced TCRs to their antigenic targets. In this paper, we present a novel strategy to annotate full TCR sequence repertoires with their epitope specificities. The strategy is based on a machine learning algorithm to learn the TCR patterns common to the recognition of a specific epitope. These results are then combined with a statistical analysis to evaluate the occurrence of specific epitope-reactive TCR sequences per epitope in repertoire data. In this manner, we can directly study the capacity of full TCR repertoires to target specific epitopes of the relevant vaccines or pathogens. We demonstrate the usability of this approach on three independent datasets related to vaccine monitoring and infectious disease diagnostics by independently identifying the epitopes that are targeted by the TCR repertoire. The developed method is freely available as a web tool for academic use at tcrex.biodatamining.be.
| S-EPMC6896208 | biostudies-literature