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.
Allows summary, interrogation, and further processing of IMGT/HighV-QUEST output files. IgAT is a Microsoft Excel based software for the extensive analysis and graphical presentation of very large collections of immunoglobulin (Ig) transcripts which have been pre-analyzed by the web-based IMGT/HighV-QUEST program. It additionally calculates the probability of antigen-driven selection within Ig repertoires and predicts structural properties of the antigen-binding site.
Estimates the repertoire differences. RDI is a computational approach allowing comparison between different types of repertoires. It is useful for all analysis of immune sequences and it can be extended to realize any repertoire experiment. This method assists understanding for the variance-inflating effects of low sequencing depth.
A computational framework for Bayesian estimation of antigen-driven selection in immunoglobulin sequences based on the analysis of somatic mutation patterns. BASELINe represents a fundamental advance over previous methods by shifting the problem from one of simply detecting selection to one of quantifying selection. Along with providing a more intuitive means to assess and visualize selection, BASELINe allows comparative analysis between groups of sequences derived from different germline V(D)J segments.
Assists users in analyzing T-cell receptor (TCR) repertoire utilizing a sequence analysis approach inspired by phylogenetics. ImmunoMap is a program able to display and quantify immune repertoire diversity and enables assessment of similarity between TCR sequences. Moreover, it can supply insight into the biological impact of tumors on T-cell responses and TCR usage by studying TCR repertoire changes in the presence of tumor.
A dynamic programming approach to learn the distribution of rearrangement scenarios from large numbers of non-productive sequences in an efficient way. This approach is based on a Hidden Markov Models (HMM) formulation of the problem, and learns its parameters using a modified BaumWelch (BW) algorithm to avoid the full enumerations of all scenarios. We tested our software tool on sequence data for both the alpha and beta chains of the T cell receptor. To test the validity of our algorithm, we also generated synthetic sequences produced by a known model, and confirmed that its parameters could be accurately inferred back from the sequences. The inferred model can be used to generate synthetic sequences, to calculate the probability of generation of any receptor sequence, as well as the theoretical diversity of the repertoire. We estimate this diversity to be ≈ 1023 for human T cells. The model gives a baseline to investigate the selection and dynamics of immune repertoires.
Characterizes the full distribution of the T cell receptor (TCR) repertoire. powerTCR offers an overview of the state of the immune repertoire. It simulates large clones that are above the threshold, where the power law begins, using the generalized Pareto distribution (GPD). This tool also models the small clones below the threshold using a truncated Gamma distribution. It is useful to discover descriptors of strengths and deficiencies in the immune system.