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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| Published in: | Bioinformatics (Oxford, England) Vol. 35; no. 17; pp. 2974 - 2981 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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England
Oxford University Press (OUP)
01.09.2019
Oxford University Press |
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| ISSN: | 1367-4803, 1367-4811, 1367-4811 |
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| Abstract | 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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| AbstractList | 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.MOTIVATIONHigh-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.RESULTSWe 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.AVAILABILITY AND IMPLEMENTATIONSource code is available at https://github.com/zsethna/OLGA.Supplementary data are available at Bioinformatics online.SUPPLEMENTARY INFORMATIONSupplementary data are available at Bioinformatics online. 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. |
| Author | Walczak, Aleksandra M Callan, Curtis G Mora, Thierry Sethna, Zachary Elhanati, Yuval |
| AuthorAffiliation | 1 Joseph Henry Laboratories, Princeton University , Princeton, NJ, USA 2 Laboratoire de physique de l'Ecole normale supérieure (PSL University), Centre national de la recherche scientifique, Sorbonne University, University Paris-Diderot , Paris, France |
| AuthorAffiliation_xml | – name: 1 Joseph Henry Laboratories, Princeton University , Princeton, NJ, USA – name: 2 Laboratoire de physique de l'Ecole normale supérieure (PSL University), Centre national de la recherche scientifique, Sorbonne University, University Paris-Diderot , Paris, France |
| Author_xml | – sequence: 1 givenname: Zachary surname: Sethna fullname: Sethna, Zachary – sequence: 2 givenname: Yuval surname: Elhanati fullname: Elhanati, Yuval – sequence: 3 givenname: Curtis G surname: Callan fullname: Callan, Curtis G – sequence: 4 givenname: Aleksandra M surname: Walczak fullname: Walczak, Aleksandra M – sequence: 5 givenname: Thierry surname: Mora fullname: Mora, Thierry |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30657870$$D View this record in MEDLINE/PubMed https://hal.sorbonne-universite.fr/hal-02344638$$DView record in HAL |
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| ContentType | Journal Article |
| Copyright | The Author(s) 2019. Published by Oxford University Press. Distributed under a Creative Commons Attribution 4.0 International License The Author(s) 2019. Published by Oxford University Press. 2019 |
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