Germline reconstruction software tools | Repertoire sequencing data analysis

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.

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 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.

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.

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).