IntroductionChange-O is a collection of tools for analyzing immunoglobulin sequences.
Dramatic improvements in high-throughput sequencing technologies now enable large-scale characterization of immunoglobulin (Ig) repertoires, defined as the collection of trans-membrane antigen-receptor proteins located on the surface of T and B lymphocytes. Change-O is a suite of utilities to handle advanced analysis of Ig sequences following germline segment assignment. Change-O handles output from IMGT/High V-quest and works off of a tab-delimited database file. It includes features for creating a personalized genotype, identifying sequences that are from a single B cell clone and inferring its lineage tree, analyzing amino acid properties, calculating diversity, generating a model of somatic hypermutation, and quantifying selection pressure. Record sorting, grouping, and sampling operations are also included.
The Change-O commandline tools provide a set of utilities for automated processing of Ig repertoire data following germline segment assignment using a tools such as IMGT/HighV-QUEST.
Analyzes amino acid properties of the CDR3 region.
Reconstructs germline sequences from alignment information.
Assign Ig sequences into clonal groups.
Multiple align groups of sequence records.
Parses germline alignment output from IMGT/HighV-QUEST into a tab-delimited database file for import into other Change-O tools.
Performs basic database operations on tab-delimited files.
alakazam R package
- Infers maximum parsimony lineage trees for clonal groups.
- Calculates repertoire-level clonal diversity statistics.
shazam R package
- Calculates nearest neighbor distances for all sequences in a dataset.
- Generates SHM mutability and substitution profiles.
- Performs Bayesian estimation of antigen-driven selection.
tigger R package
- Infers an individual germline genotype from repertoire data.
- Identifies novel V-region polymorphisms.
CitationChange-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data.
Gupta NT*, Vander Heiden JA*, Uduman M, Gadala-Maria D, Yaari G, Kleinstein SH.
Bioinformatics 2015; doi: 10.1093/bioinformatics/btv359