![]() Such motifs can be a few identical amino acids or amino acids with similar physical properties. Any changes to this distribution signifies an expansion of cells with a particular immune receptor.ĭifferent receptors specific for the same epitope can be expected to share motifs. Therefore, analyzing the properties of this region is of great interest.ĭue to the randomness in addition and deletion of nucleotides during the rearrangement of the receptor, CDR3 lengths will be distributed around a mean value (Fig. , and is a key contributor to the overall specificity of the receptor. Is the most variable part of the rearranged IG The distribution of V-J and V-D-J usage can be compared between two repertoires using the functions compareVJDistributions and compareVDJDistributions in sumrep.įull size image 3.2 Properties of the CDR3 Skewing of the V-J usage can be revealed by plotting the V-J combination as a heatmap (Fig. The D and J gene strongly contributes to the CDR3Īnd can be compared using compareDGeneDistributions and compareJGeneDistributions. Differences in the distribution of V genes used in the rearranged repertoire can indicate an antigen-specific response or unusual clonal expansions and can be evaluated with the function compareVGeneDistributions of the sumrep R package ( ). This is driven especially by variation in the first and second complementarity-determining regions (CDR1 and CDR2) of the genes, which contribute to the specificity and affinity of the immune receptor. The V gene is the most diverse gene of the TR Heavy Chain Gene Rearrangements for Detection and Analysis of B-Cell Clone Distribution,” “Bulk Sequencing From mRNA With UMIĪnd Clonal Evolution and Single-Cell Analysis,” and “Tracking of Antigen-Specific T Cells: Integrating Paired-ChainĪIRR-Seq and Transcriptome Sequencing,” all in this volume. The theoretical framework presented here can be used to interpret the results of the practical methods detailed in the AIRR Community chapters “Bulk gDNA Sequencing of Antibody In addition, the selection of the method and the interpretation of the results can depend on the specific biological state for instance, some samples might be expanded from solid tumors, others from antigen-specific cells isolated from peripheral blood or from whole blood from healthy and diseased patients. Some of the methods are applicable to both IG In this section we introduce some of the most frequently used methods to analyze AIRRs and suggest computational tools that can perform such analysis. These are highlighted in Table 1, and several are discussed in more detail below and in other chapters in this volume, where we demonstrate their application to common analytical tasks. Here we focus on a small selection of commonly used tools, especially those which comply with AIRR Community guidelines for reproducibility and interoperability ( ). ![]() In addition, thought must be given to the computational resources necessary for repertoire analysis, including both storage and processing.Ī comprehensive listing of the available software is out of the scope of this conceptual introduction, but the interested reader is directed to some recent reviews. ![]() ![]() Moreover, most tools have a narrow scope of the types of analysis they can perform, so matching the implementation to the desired goal is also a critical consideration. Thus, a key factor in choosing which programs to use will be the skill level and comfort of the user. These range from bespoke command line tools written in various programming languages that require facility in a Linux terminal to software with fully developed graphical interfaces and no requirement for programming skills of any kind. A breathtaking array of computational tools are available for repertoire analysis. ![]()
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