TIgGER

High-throughput sequencing of B cell immunoglobulin receptors is providing unprecedented insight into adaptive immunity. A key step in analyzing these data involves assignment of the germline V, D and J gene segment alleles that comprise each immunoglobulin sequence by matching them against a database of known V(D)J alleles. However, this process will fail for sequences that utilize previously undetected alleles, whose frequency in the population is unclear. We have created TIgGER, a computational method that significantly improves V(D)J allele assignments by first determining the complete set of gene segments carried by an individual, including novel alleles. The application of TIgGER identifies a surprisingly high frequency of novel alleles, highlighting the critical need for this approach.

Supplementary Data:

• NovelAlleles.fasta - The novel V segment alleles identified by TIgGER (and presented in Table 2 of the paper cited below)
• AlleleEvidence.pdf - Summaries of evidence for the V segment alleles identified by TIgGER

Download:

TIgGER is available as a package in the R programming language. The download below contains the source package and can be loaded into R with the following command: install.packages("YOUR-FILE-PATH/tigger_0.2.3.tar.gz", repos=NULL, type="source") (Note that the newest version, 0.2.3, succeeded version 2.1. This is so that the TIgGER version numbers will follow those of of the other Change-O packages.)

• TIgGER v0.2.3
• Vignette (usage example)
• Reference manual (function documentation)

Citing TIgGER:

When including the results of the TIgGER package in a publication, please cite the following paper:

Daniel Gadala-Maria, Gur Yaari, Mohamed Uduman, Steven H. Kleinstein (2015) "Automated analysis of high-throughput B cell sequencing data reveals a high frequency of novel immunoglobulin V gene segment alleles." PNAS 112(8), E862-E870

Other Ig Tools

• pRESTO - Toolkit for processing raw reads from high-throughput sequencing of lymphocyte repertoires
  
• Change-O - Toolkit for clonal assignment, lineage reconstruction, diversity analysis, mutation profiling and selection analysis.
  
• BASELINe - Bayesian estimation of antigen-driven selection
  
• S5F - A 5-mer microsequence context model of somatic hypermutation targeting and substitution rates
  
• UNSWIg repertoire - The University of New South Wales database, which classifies alleles according to certainty and contains alleles not in the IMGT database
• IMGT/HighV-QUEST - The IMGT tool for aligning samples to the IMGT germline database alleles
• IMGT/GENE-DB - The IMGT database of germline alleles