A universal framework that processes big immunome data from raw sequences to quantitated clonotypes. MiXCR efficiently handles paired- and single-end reads, considers sequence quality, corrects PCR errors and identifies germline hypermutations. The software supports both partial- and full-length profiling and employs all available RNA or DNA information, including sequences upstream of V and downstream of J gene segments.
Provides an Hidden Markov Model (HMM)-based framework for studying B-cell receptor sequence (BCRs). Partis is an open source software able to annotate, simulate, and infer clonal family of BCRs. The program deduces parameters about the rearrangement process before performing annotation inference on each sequence in the set. It intends to be effective for analyzing modern large sequencing data sets.
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.
Processes raw immune sequence reads from any source and learns unbiased statistics of recombination and somatic hypermutations. IGoR is a flexible computational method that outputs a whole list of potential recombination and hypermutation scenarios, with their corresponding likelihoods. It learns a context-dependent hypermutation model to identify hotspots, which allows for a comprehensive analysis of the mutational landscape of B cells receptor (BCR).