Provides a suite of utilities that cover a range of complex analysis tasks for immunoglobulin (Ig) repertoire sequencing data. Change-O is a suite of utilities that (i) processes the output of V(D)J alignment tools, (ii) assigns clonal clusters to Ig sequences and (iii) reconstructs germline sequences. It also offers applications to import data from the frequently used IMGT/HighV-QUEST tool and a set of utilities to perform basic database operations, such as sorting, filtering and modifying annotations.
Identifies novel immunoglobulin (Ig) variable (V) segment alleles based on the analysis of mutation patterns in Rep-Seq data. TIgGER is an automated method that infers the genotype of a subject and corrects the initial allele assignments. It detects novel alleles by analyzing the apparent mutation frequency pattern at each nucleotide position as a function of the sequence-wide mutation count.
Involves data processing, clustering, assembly, and optimization. IMPre is a method that provides a comprehensive approach for identification of novel B- and T-cell receptor (BCR/TCR) genes and alleles in certain species with greatly improved speed, cost, and accuracy. This de novo package comprises four main steps: data processing, clustering, assembly, and optimization. IMPre is stable with animal and long-sequence data.
Identifies germline V genes from expressed repertoires to a specificity of 100%. IgDiscover uses a cluster identification process to produce candidate sequences that, once filtered, results in individualized germline V gene databases. IgDiscover was tested in multiple species, validated by genomic cloning and cross library comparisons and produces comprehensive gene databases even where limited genomic sequence is available. IgDiscover analysis of the allelic content of the Indian and Chinese-origin rhesus macaques reveals high levels of immunoglobulin gene diversity in this species.
Allows users to reconstruct the native T-cell receptors (TCR)αβ from single cell RNA-seq data of Ag-specific T cells and to link these with the gene expression profile of individual cells. VDJPuzzle enables analysis about TCR diversity and its relationship with the transcriptional profile of different clones. Moreover, single-cell transcriptome analysis can successfully distinguish Ag-specific T cell populations sorted directly from resting memory cells in peripheral blood and sorted after ex vivo stimulation. Moreover, it has been adapted for B-cell receptor (BCRs) and includes additional features to reliably characterizes somatic hypermutation (SHMs).
A collection of tools for downstream analysis of Rep-Seq data, including clustering and phylogenetic analysis. TRigS assists with the determination and analysis of B-cell lineage trees from next-generation sequencing data. TRigS consists in various tools, including 1) AnnotateTree creates annotated lineage trees and sequence alignments showing the point at which amino acid substitutions occur 2) RevertToGermline uses a simple approach to infer the germline ancestor of a B-cell variable region sequence, given the IMGT junction analysis. 3) Clustering tools support the clustering of sequences for clonal analysis, and their large-scale depiction. 4) Tools for junction parsing and results manipluation support the integration of IgBLAST into an IMGT-style pipeline, and ease the processing of tab-separated analysis files.
A computational method to reconstruct full-length, paired T cell receptor (TCR) sequences from T lymphocyte single-cell RNA sequence data. TraCeR links T cell specificity with functional response by revealing clonal relationships between cells alongside their transcriptional profiles. TraCeR extracts TCR-derived sequencing reads for each cell by alignment against ‘combinatorial recombinomes’ comprising all possible combinations of V and J segments. Reads are then assembled into contiguous sequences that are analyzed to find full-length, recombined TCR sequences. Importantly, the reconstructed recombinant sequences typically contain nearly the complete length of the TCR V(D)J region and so allow high-confidence discrimination between closely related gene segments. Our method is sensitive, accurate and easy to adapt to any species for which annotated TCR gene sequences are available.