{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/olga-fast-computation-of-generation","title":"OLGA: fast computation of generation probabilities of B- and T-cell receptor amino acid sequences and motifs","arxiv_id":"1807.04425","date":"2018-11-13","proceeding":null,"authors":[],"abstract":"Motivation: High-throughput sequencing of large immune repertoires has\nenabled the development of methods to predict the probability of generation by\nV(D)J recombination of T- and B-cell receptors of any specific nucleotide\nsequence. These generation probabilities are very non-homogeneous, ranging over\n20 orders of magnitude in real repertoires. Since the function of a receptor\nreally depends on its protein sequence, it is important to be able to predict\nthis probability of generation at the amino acid level. However, brute-force\nsummation over all the nucleotide sequences with the correct amino acid\ntranslation is computationally intractable. The purpose of this paper is to\npresent a solution to this problem.\n  Results: We use dynamic programming to construct an efficient and flexible\nalgorithm, called OLGA (Optimized Likelihood estimate of immunoGlobulin\nAmino-acid sequences), for calculating the probability of generating a given\nCDR3 amino acid sequence or motif, with or without V/J restriction, as a result\nof V(D)J recombination in B or T cells. We apply it to databases of\nepitope-specific T-cell receptors to evaluate the probability that a typical\nhuman subject will possess T cells responsive to specific disease-associated\nepitopes. The model prediction shows an excellent agreement with published\ndata. We suggest that OLGA may be a useful tool to guide vaccine design.\n  Availability: Source code is available at https://github.com/zsethna/OLGA","url_abs":"http://arxiv.org/abs/1807.04425v2","url_pdf":"http://arxiv.org/pdf/1807.04425v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"olga-fast-computation-of-generation","repo_url":"https://github.com/zsethna/OLGA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"olga-fast-computation-of-generation","repo_url":"https://github.com/statbiophys/olga","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}