This file format is a default output of the MiTCR software that is widely used for TR NGS data extraction and raw clonotype set generation (see the package vignette for the detailed information on valid input file formats). Input data and data manipulation: The input data for tcR are tab-delimited files with rows representing clonotypes and columns representing read counts, nucleotide and amino acid sequences of the CDR3, names and borders of the identified V(ariable), D(iversity) and J(oining) genes and the number of insertions at gene junctions. The R package vignette presents a more detailed overview of methods included in tcR. This section describes the input data format, methods and procedures implemented in tcR. Here, we introduce tcR: an R package for the analysis of TR repertoires that integrates widely used methods for individual repertoires analyses and TR repertoires comparison: gene usage comparison, customisable search for clonotypes shared among repertoires, spectratyping, random TR repertoire generation, various repertoire diversity measures and other commonly used approaches to the repertoire analysis. Only two software tools that apply a limited number of the analysis methods - MiTCRViewer and ViDJiL are available. In order to examine TR repertoires of different individuals a number of strategies can be employed such as quantifying the number of shared nucleotide and amino acid sequences between repertoires, comparisons of gene usage frequencies and repertoire diversity estimation. However, the interpretation of TR repertoires (i.e., lists of TR clonotypes with their quantities) in terms of biological relevance requires further downstream analysis of the resultant clonotype sets. In addition to standard IMGT/HighV-QUEST recent studies provided powerful tools for processing raw IG/TR NGS data: extraction of complementarity determining regions (CDR) from reads and generation of clonotype (hereafter clonotype is a group of sequencing reads with identical aminoacid or nucleotide CDR3 sequence and V/J genes) sets, as well as advanced algorithms for the correction of PCR and sequencing errors. Next-generation sequencing (NGS) technologies have opened a new era in the field of IG and TR repertoires research, which includes the studies on adaptive immune system ageing, immune repertoire reconstitution after therapy, response to vaccines and subpopulation repertoire structure. Until recently, studies on the structural composition of immune repertoires, receptor sequence sharing and quantitative estimation of particular B or T cell clones abundance have remained a challenge due to an extremely high diversity of IG and TR sequences: the maximal theoretical diversity of the most variable TR beta chains is estimated as 1 × 10 14 and 1 × 10 18 for the heterodimeric T cell receptor consisting of α and β chains. The power of the human adaptive immunity is realised throughout the immunoglobulins (IG) and T cell receptors (TR): the highly diverse antigen receptors which recognise pathogens and provide specific immune responses. The source code and development version are available at tcR GitHub ( ) along with the full documentation and typical usage examples. The stable version can be directly installed from The Comprehensive R Archive Network ( ). TcR is an R package for the advanced analysis of T cell receptor repertoires after primary TR sequences extraction from raw sequencing reads. The tool has proven its utility in recent research studies. Here we introduce tcR, a new R package, representing a platform for the advanced analysis of T cell receptor repertoires, which includes diversity measures, shared T cell receptor sequences identification, gene usage statistics computation and other widely used methods. However, a specialised software is required for the rational analysis of massive data generated by next-generation sequencing. Recent progress in next-generation sequencing technologies has provided an opportunity for the deep T cell receptor repertoire profiling. The Immunoglobulins (IG) and the T cell receptors (TR) play the key role in antigen recognition during the adaptive immune response.
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