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- Title
OLGA: fast computation of generation probabilities of B- and T-cell receptor amino acid sequences and motifs.
- Authors
Sethna, Zachary; Elhanati, Yuval; Callan, Curtis G; Walczak, Aleksandra M; Mora, Thierry
- 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.
- Subjects
T cell receptors; AMINO acid sequence; PROBABILITY theory; NUCLEOTIDE sequence; MAGNITUDE (Mathematics); DYNAMIC programming
- Publication
Bioinformatics, 2019, Vol 35, Issue 17, p2974
- ISSN
1367-4803
- Publication type
Article
- DOI
10.1093/bioinformatics/btz035