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  • Title: OLGA: fast computation of generation probabilities of B- and T-cell receptor amino acid sequences and motifs.
    Author: Sethna Z, Elhanati Y, Callan CG, Walczak AM, Mora T.
    Journal: Bioinformatics; 2019 Sep 01; 35(17):2974-2981. PubMed ID: 30657870.
    Abstract:
    MOTIVATION: High-throughput sequencing of large immune repertoires has enabled the development of methods to predict the probability of generation by V(D)J recombination of T- and B-cell receptors of any specific nucleotide sequence. These generation probabilities are very non-homogeneous, ranging over 20 orders of magnitude in real repertoires. Since the function of a receptor really depends on its protein sequence, it is important to be able to predict this probability of generation at the amino acid level. However, brute-force summation over all the nucleotide sequences with the correct amino acid translation is computationally intractable. The purpose of this paper is to present a solution to this problem. RESULTS: We use dynamic programming to construct an efficient and flexible algorithm, called OLGA (Optimized Likelihood estimate of immunoGlobulin Amino-acid sequences), for calculating the probability of generating a given CDR3 amino acid sequence or motif, with or without V/J restriction, as a result of V(D)J recombination in B or T cells. We apply it to databases of epitope-specific T-cell receptors to evaluate the probability that a typical human subject will possess T cells responsive to specific disease-associated epitopes. The model prediction shows an excellent agreement with published data. We suggest that OLGA may be a useful tool to guide vaccine design. AVAILABILITY AND IMPLEMENTATION: Source code is available at https://github.com/zsethna/OLGA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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