OLGA: fast computation of generation probabilities of B- and T-cell receptor amino acid sequences and motifs

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 order...

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Vydáno v:Bioinformatics (Oxford, England) Ročník 35; číslo 17; s. 2974 - 2981
Hlavní autoři: Sethna, Zachary, Elhanati, Yuval, Callan, Curtis G, Walczak, Aleksandra M, Mora, Thierry
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Oxford University Press (OUP) 01.09.2019
Oxford University Press
Témata:
Biochemistry, Molecular Biology
Genomics
Life Sciences
Original Papers
Physics
ISSN:1367-4803, 1367-4811, 1367-4811
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Shrnutí: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. 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. Source code is available at https://github.com/zsethna/OLGA. Supplementary data are available at Bioinformatics online.
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The authors wish it to be known that, in their opinion, Zachary Sethna and Yuval Elhanati authors should be regarded as Joint Last Authors.
ISSN:1367-4803
1367-4811
1367-4811
DOI:10.1093/bioinformatics/btz035