An important part of AIRR-seq analysis is computationally reconstructing these relationships from the sequences obtained. As such, all cells in a clone contain the same set of rearrangements. Ī clone or a clonal lineage comprises a group of T or B cells descended from the same original naive ancestor. Length and a number of amino acid physicochemical properties. Properties of two repertoires, such as the CDR3 The R package sumrep provides functions to compare the CDR3 Atchley factors comprise five numerical descriptions, and Kidera factors comprise ten numerical descriptions. Apart from properties like size, charge, and polarity, the properties of amino acids can be described by different factors derived through dimensionality reduction of a larger number of properties.
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 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 ( ).
#William robinson immune repertoire capture software
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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