repgenHMM: a dynamic programming tool to infer the rules of immune receptor generation from sequence data

Motivation: The diversity of the immune repertoire is initially generated by random rearrangements of the receptor gene during early T and B cell development. Rearrangement scenarios are composed of random events—choices of gene templates, base pair deletions and insertions—described by probability...

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Vydané v:	Bioinformatics Ročník 32; číslo 13; s. 1943 - 1951
Hlavní autori:	Elhanati, Yuval, Marcou, Quentin, Mora, Thierry, Walczak, Aleksandra M.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	England Oxford University Press (OUP) 01.07.2016 Oxford University Press
Predmet:	Adaptive immunology Algorithms Bioinformatics Computer programs Computer Science Deletion Dynamic tests Gene Rearrangement, T-Lymphocyte Genes Humans Immune systems Immunology Life Sciences Mathematical models Original Papers Probability Receptors Receptors, Antigen, T-Cell - genetics Sequence Alignment Software V(D)J Recombination
ISSN:	1367-4803, 1367-4811, 1367-4811, 1460-2059
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Abstract	Motivation: The diversity of the immune repertoire is initially generated by random rearrangements of the receptor gene during early T and B cell development. Rearrangement scenarios are composed of random events—choices of gene templates, base pair deletions and insertions—described by probability distributions. Not all scenarios are equally likely, and the same receptor sequence may be obtained in several different ways. Quantifying the distribution of these rearrangements is an essential baseline for studying the immune system diversity. Inferring the properties of the distributions from receptor sequences is a computationally hard problem, requiring enumerating every possible scenario for every sampled receptor sequence. Results: We present a Hidden Markov model, which accounts for all plausible scenarios that can generate the receptor sequences. We developed and implemented a method based on the Baum–Welch algorithm that can efficiently infer the parameters for the different events of the rearrangement process. We tested our software tool on sequence data for both the alpha and beta chains of the T cell receptor. To test the validity of our algorithm, we also generated synthetic sequences produced by a known model, and confirmed that its parameters could be accurately inferred back from the sequences. The inferred model can be used to generate synthetic sequences, to calculate the probability of generation of any receptor sequence, as well as the theoretical diversity of the repertoire. We estimate this diversity to be ≈1023 for human T cells. The model gives a baseline to investigate the selection and dynamics of immune repertoires. Availability and implementation: Source code and sample sequence files are available at https://bitbucket.org/yuvalel/repgenhmm/downloads. Contact: elhanati@lpt.ens.fr or tmora@lps.ens.fr or awalczak@lpt.ens.fr
AbstractList	Motivation: The diversity of the immune repertoire is initially generated by random rearrangements of the receptor gene during early T and B cell development. Rearrangement scenarios are composed of random events—choices of gene templates, base pair deletions and insertions—described by probability distributions. Not all scenarios are equally likely, and the same receptor sequence may be obtained in several different ways. Quantifying the distribution of these rearrangements is an essential baseline for studying the immune system diversity. Inferring the properties of the distributions from receptor sequences is a computationally hard problem, requiring enumerating every possible scenario for every sampled receptor sequence. Results: We present a Hidden Markov model, which accounts for all plausible scenarios that can generate the receptor sequences. We developed and implemented a method based on the Baum–Welch algorithm that can efficiently infer the parameters for the different events of the rearrangement process. We tested our software tool on sequence data for both the alpha and beta chains of the T cell receptor. To test the validity of our algorithm, we also generated synthetic sequences produced by a known model, and confirmed that its parameters could be accurately inferred back from the sequences. The inferred model can be used to generate synthetic sequences, to calculate the probability of generation of any receptor sequence, as well as the theoretical diversity of the repertoire. We estimate this diversity to be ≈1023 for human T cells. The model gives a baseline to investigate the selection and dynamics of immune repertoires. Availability and implementation: Source code and sample sequence files are available at https://bitbucket.org/yuvalel/repgenhmm/downloads. Contact: elhanati@lpt.ens.fr or tmora@lps.ens.fr or awalczak@lpt.ens.fr Motivation: The diversity of the immune repertoire is initially generated by random rearrangements of the receptor gene during early T and B cell development. Rearrangement scenarios are composed of random events—choices of gene templates, base pair deletions and insertions—described by probability distributions. Not all scenarios are equally likely, and the same receptor sequence may be obtained in several different ways. Quantifying the distribution of these rearrangements is an essential baseline for studying the immune system diversity. Inferring the properties of the distributions from receptor sequences is a computationally hard problem, requiring enumerating every possible scenario for every sampled receptor sequence. Results: We present a Hidden Markov model, which accounts for all plausible scenarios that can generate the receptor sequences. We developed and implemented a method based on the Baum–Welch algorithm that can efficiently infer the parameters for the different events of the rearrangement process. We tested our software tool on sequence data for both the alpha and beta chains of the T cell receptor. To test the validity of our algorithm, we also generated synthetic sequences produced by a known model, and confirmed that its parameters could be accurately inferred back from the sequences. The inferred model can be used to generate synthetic sequences, to calculate the probability of generation of any receptor sequence, as well as the theoretical diversity of the repertoire. We estimate this diversity to be ≈1023 for human T cells. The model gives a baseline to investigate the selection and dynamics of immune repertoires. Availability and implementation: Source code and sample sequence files are available at https://bitbucket.org/yuvalel/repgenhmm/downloads. Contact: elhanati@lpt.ens.fr or tmora@lps.ens.fr or awalczak@lpt.ens.fr Motivation: The diversity of the immune repertoire is initially generated by random rearrangements of the receptor gene during early T and B cell development. Rearrangement scenarios are composed of random events-choices of gene templates, base pair deletions and insertions-described by probability distributions. Not all scenarios are equally likely, and the same receptor sequence may be obtained in several different ways. Quantifying the distribution of these rearrangements is an essential baseline for studying the immune system diversity. Inferring the properties of the distributions from receptor sequences is a computationally hard problem, requiring enumerating every possible scenario for every sampled receptor sequence.Results: We present a Hidden Markov model, which accounts for all plausible scenarios that can generate the receptor sequences. We developed and implemented a method based on the Baum-Welch algorithm that can efficiently infer the parameters for the different events of the rearrangement process. We tested our software tool on sequence data for both the alpha and beta chains of the T cell receptor. To test the validity of our algorithm, we also generated synthetic sequences produced by a known model, and confirmed that its parameters could be accurately inferred back from the sequences. The inferred model can be used to generate synthetic sequences, to calculate the probability of generation of any receptor sequence, as well as the theoretical diversity of the repertoire. We estimate this diversity to be for human T cells. The model gives a baseline to investigate the selection and dynamics of immune repertoires.Availability and implementation: Source code and sample sequence files are available at https://bitbucket.org/yuvalel/repgenhmm/downloads. Abstract Motivation: The diversity of the immune repertoire is initially generated by random rearrangements of the receptor gene during early T and B cell development. Rearrangement scenarios are composed of random events—choices of gene templates, base pair deletions and insertions—described by probability distributions. Not all scenarios are equally likely, and the same receptor sequence may be obtained in several different ways. Quantifying the distribution of these rearrangements is an essential baseline for studying the immune system diversity. Inferring the properties of the distributions from receptor sequences is a computationally hard problem, requiring enumerating every possible scenario for every sampled receptor sequence. Results: We present a Hidden Markov model, which accounts for all plausible scenarios that can generate the receptor sequences. We developed and implemented a method based on the Baum–Welch algorithm that can efficiently infer the parameters for the different events of the rearrangement process. We tested our software tool on sequence data for both the alpha and beta chains of the T cell receptor. To test the validity of our algorithm, we also generated synthetic sequences produced by a known model, and confirmed that its parameters could be accurately inferred back from the sequences. The inferred model can be used to generate synthetic sequences, to calculate the probability of generation of any receptor sequence, as well as the theoretical diversity of the repertoire. We estimate this diversity to be ≈1023 for human T cells. The model gives a baseline to investigate the selection and dynamics of immune repertoires. Availability and implementation: Source code and sample sequence files are available at https://bitbucket.org/yuvalel/repgenhmm/downloads. Contact: elhanati@lpt.ens.fr or tmora@lps.ens.fr or awalczak@lpt.ens.fr The diversity of the immune repertoire is initially generated by random rearrangements of the receptor gene during early T and B cell development. Rearrangement scenarios are composed of random events-choices of gene templates, base pair deletions and insertions-described by probability distributions. Not all scenarios are equally likely, and the same receptor sequence may be obtained in several different ways. Quantifying the distribution of these rearrangements is an essential baseline for studying the immune system diversity. Inferring the properties of the distributions from receptor sequences is a computationally hard problem, requiring enumerating every possible scenario for every sampled receptor sequence. We present a Hidden Markov model, which accounts for all plausible scenarios that can generate the receptor sequences. We developed and implemented a method based on the Baum-Welch algorithm that can efficiently infer the parameters for the different events of the rearrangement process. We tested our software tool on sequence data for both the alpha and beta chains of the T cell receptor. To test the validity of our algorithm, we also generated synthetic sequences produced by a known model, and confirmed that its parameters could be accurately inferred back from the sequences. The inferred model can be used to generate synthetic sequences, to calculate the probability of generation of any receptor sequence, as well as the theoretical diversity of the repertoire. We estimate this diversity to be [Formula: see text] for human T cells. The model gives a baseline to investigate the selection and dynamics of immune repertoires. Source code and sample sequence files are available at https://bitbucket.org/yuvalel/repgenhmm/downloads elhanati@lpt.ens.fr or tmora@lps.ens.fr or awalczak@lpt.ens.fr. The diversity of the immune repertoire is initially generated by random rearrangements of the receptor gene during early T and B cell development. Rearrangement scenarios are composed of random events-choices of gene templates, base pair deletions and insertions-described by probability distributions. Not all scenarios are equally likely, and the same receptor sequence may be obtained in several different ways. Quantifying the distribution of these rearrangements is an essential baseline for studying the immune system diversity. Inferring the properties of the distributions from receptor sequences is a computationally hard problem, requiring enumerating every possible scenario for every sampled receptor sequence.MOTIVATIONThe diversity of the immune repertoire is initially generated by random rearrangements of the receptor gene during early T and B cell development. Rearrangement scenarios are composed of random events-choices of gene templates, base pair deletions and insertions-described by probability distributions. Not all scenarios are equally likely, and the same receptor sequence may be obtained in several different ways. Quantifying the distribution of these rearrangements is an essential baseline for studying the immune system diversity. Inferring the properties of the distributions from receptor sequences is a computationally hard problem, requiring enumerating every possible scenario for every sampled receptor sequence.We present a Hidden Markov model, which accounts for all plausible scenarios that can generate the receptor sequences. We developed and implemented a method based on the Baum-Welch algorithm that can efficiently infer the parameters for the different events of the rearrangement process. We tested our software tool on sequence data for both the alpha and beta chains of the T cell receptor. To test the validity of our algorithm, we also generated synthetic sequences produced by a known model, and confirmed that its parameters could be accurately inferred back from the sequences. The inferred model can be used to generate synthetic sequences, to calculate the probability of generation of any receptor sequence, as well as the theoretical diversity of the repertoire. We estimate this diversity to be [Formula: see text] for human T cells. The model gives a baseline to investigate the selection and dynamics of immune repertoires.RESULTSWe present a Hidden Markov model, which accounts for all plausible scenarios that can generate the receptor sequences. We developed and implemented a method based on the Baum-Welch algorithm that can efficiently infer the parameters for the different events of the rearrangement process. We tested our software tool on sequence data for both the alpha and beta chains of the T cell receptor. To test the validity of our algorithm, we also generated synthetic sequences produced by a known model, and confirmed that its parameters could be accurately inferred back from the sequences. The inferred model can be used to generate synthetic sequences, to calculate the probability of generation of any receptor sequence, as well as the theoretical diversity of the repertoire. We estimate this diversity to be [Formula: see text] for human T cells. The model gives a baseline to investigate the selection and dynamics of immune repertoires.Source code and sample sequence files are available at https://bitbucket.org/yuvalel/repgenhmm/downloadsAVAILABILITY AND IMPLEMENTATIONSource code and sample sequence files are available at https://bitbucket.org/yuvalel/repgenhmm/downloadselhanati@lpt.ens.fr or tmora@lps.ens.fr or awalczak@lpt.ens.fr.CONTACTelhanati@lpt.ens.fr or tmora@lps.ens.fr or awalczak@lpt.ens.fr.
Author	Walczak, Aleksandra M. Mora, Thierry Marcou, Quentin Elhanati, Yuval
Author_xml	– sequence: 1 givenname: Yuval surname: Elhanati fullname: Elhanati, Yuval – sequence: 2 givenname: Quentin surname: Marcou fullname: Marcou, Quentin – sequence: 3 givenname: Thierry surname: Mora fullname: Mora, Thierry – sequence: 4 givenname: Aleksandra M. surname: Walczak fullname: Walczak, Aleksandra M.
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Cites_doi	10.1073/pnas.1319389111 10.4049/jimmunol.160.5.2360 10.3109/08830189609061755 10.1038/nmeth.2960 10.1186/1471-2105-9-S12-S20 10.1186/s12859-015-0589-x 10.1098/rstb.2014.0240 10.1182/blood-2009-04-217604 10.1016/j.imbio.2012.04.003 10.1111/j.1365-2567.2006.02431.x 10.1038/nmeth.3364 10.1038/nbt.2782 10.1093/bioinformatics/btm147 10.1073/pnas.1212755109 10.4049/jimmunol.166.2.892 10.4049/jimmunol.172.11.6790 10.1098/rstb.2014.0243 10.4049/jimmunol.1003898 10.1017/CBO9780511790492 10.1073/pnas.1417683112 10.1093/bioinformatics/btt004 10.1093/nar/gkn316 10.3389/fimmu.2013.00466 10.15252/embj.201489643 10.4049/jimmunol.1400119 10.4049/jimmunol.166.4.2597 10.1093/nar/gki010 10.1093/bioinformatics/btq056 10.1016/S0092-8674(04)00039-X 10.4049/jimmunol.164.4.1971 10.1371/journal.pone.0118192 10.1093/nar/gkt382 10.4049/jimmunol.177.6.3857 10.1038/nri2941 10.1146/annurev-genet-110410-132552 10.1016/j.coi.2013.09.017 10.1002/eji.201242517 10.1073/pnas.0608248104 10.4049/jimmunol.175.8.5170
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References	Bolotin (2023020112335656500_btw112-B2) 2012; 42 Bishop (2023020112335656500_btw112-B1) 2006 Volpe (2023020112335656500_btw112-B40) 2006; 22 Gapin (2023020112335656500_btw112-B13) 2014; 192 Venturi (2023020112335656500_btw112-B39) 2011; 186 Bonissone (2023020112335656500_btw112-B4) 2015 Warmflash (2023020112335656500_btw112-B42) 2006; 177 Cowell (2023020112335656500_btw112-B6) 2000; 164 Brochet (2023020112335656500_btw112-B5) 2008; 36 Robins (2023020112335656500_btw112-B30) 2009; 114 Georgiou (2023020112335656500_btw112-B14) 2014; 32 Munshaw (2023020112335656500_btw112-B23) 2010; 26 Elhanati (2023020112335656500_btw112-B9) 2015; 370 Frost (2023020112335656500_btw112-B10) 2015; 370 Huang (2023020112335656500_btw112-B19) 2001; 166 Zvyagin (2023020112335656500_btw112-B44) 2014; 111 Gouge (2023020112335656500_btw112-B16) 2015; 34 Ye (2023020112335656500_btw112-B43) 2013; 41 Giudicelli (2023020112335656500_btw112-B15) 2005; 33 Ralph (2023020112335656500_btw112-B28) 2015 Oprea (2023020112335656500_btw112-B26) 2001; 166 Shugay (2023020112335656500_btw112-B35) 2014; 11 Robins (2023020112335656500_btw112-B29) 2013; 25 Gaëta (2023020112335656500_btw112-B12) 2007; 23 Ohm-Laursen (2023020112335656500_btw112-B25) 2006; 119 Bolotin (2023020112335656500_btw112-B3) 2015; 12 Greenaway (2023020112335656500_btw112-B17) 2013; 218 Komori (2023020112335656500_btw112-B21) 1996; 13 Thomas (2023020112335656500_btw112-B38) 2013; 29 Gadala-Maria (2023020112335656500_btw112-B11) 2015; 112 Jung (2023020112335656500_btw112-B20) 2004; 116 Schatz (2023020112335656500_btw112-B32) 2011; 11 Schatz (2023020112335656500_btw112-B33) 2011; 45 Hawwari (2023020112335656500_btw112-B18) 2007; 104 Durbin (2023020112335656500_btw112-B8) 1998 Paciello (2023020112335656500_btw112-B27) 2015; 10 Wang (2023020112335656500_btw112-B41) 2008; 9 Souto-Carneiro (2023020112335656500_btw112-B36) 2004; 172 Murugan (2023020112335656500_btw112-B24) 2012; 109 Russ (2023020112335656500_btw112-B31) 2015; 16 Shugay (2023020112335656500_btw112-B34) 2013; 4 Lefranc (2023020112335656500_btw112-B22) 2001 Spencer (2023020112335656500_btw112-B37) 2005; 175 Dunn-Walters (2023020112335656500_btw112-B7) 1998; 160 26001675 - BMC Bioinformatics. 2015 May 23;16:170 25799103 - PLoS One. 2015 Mar 23;10(3):e0118192 23303508 - Bioinformatics. 2013 Mar 1;29(5):542-50 8884428 - Int Rev Immunol. 1996;13(4):317-25 25675496 - Proc Natl Acad Sci U S A. 2015 Feb 24;112(8):E862-70 24711416 - Proc Natl Acad Sci U S A. 2014 Apr 22;111(16):5980-5 16357034 - Bioinformatics. 2006 Feb 15;22(4):438-44 16210621 - J Immunol. 2005 Oct 15;175(8):5170-7 11145665 - J Immunol. 2001 Jan 15;166(2):892-9 22988065 - Proc Natl Acad Sci U S A. 2012 Oct 2;109(40):16161-6 19706884 - Blood. 2009 Nov 5;114(19):4099-107 15608191 - Nucleic Acids Res. 2005 Jan 1;33(Database issue):D256-61 17463026 - Bioinformatics. 2007 Jul 1;23(13):1580-7 21394103 - Nat Rev Immunol. 2011 Apr;11(4):251-63 24441474 - Nat Biotechnol. 2014 Feb;32(2):158-68 24795465 - J Immunol. 2014 May 15;192(10):4475-80 24793455 - Nat Methods. 2014 Jun;11(6):653-5 23671333 - Nucleic Acids Res. 2013 Jul;41(Web Server issue):W34-40 24400005 - Front Immunol. 2013 Dec 25;4:466 21854230 - Annu Rev Genet. 2011;45:167-202 17210914 - Proc Natl Acad Sci U S A. 2007 Jan 16;104(3):903-7 22647874 - Immunobiology. 2013 Feb;218(2):213-24 19091020 - BMC Bioinformatics. 2008 Dec 12;9 Suppl 12:S20 21383244 - J Immunol. 2011 Apr 1;186(7):4285-94 25762590 - EMBO J. 2015 Apr 15;34(8):1126-42 11160321 - J Immunol. 2001 Feb 15;166(4):2597-601 20147303 - Bioinformatics. 2010 Apr 1;26(7):867-72 25924071 - Nat Methods. 2015 May;12(5):380-1 17005006 - Immunology. 2006 Oct;119(2):265-77 18503082 - Nucleic Acids Res. 2008 Jul 1;36(Web Server issue):W503-8 26194754 - Philos Trans R Soc Lond B Biol Sci. 2015 Sep 5;370(1676):null 9498777 - J Immunol. 1998 Mar 1;160(5):2360-4 22806588 - Eur J Immunol. 2012 Nov;42(11):3073-83 14744439 - Cell. 2004 Jan 23;116(2):299-311 26751373 - PLoS Comput Biol. 2016 Jan 11;12(1):e1004409 10657647 - J Immunol. 2000 Feb 15;164(4):1971-6 24140071 - Curr Opin Immunol. 2013 Oct;25(5):646-52 16951348 - J Immunol. 2006 Sep 15;177(6):3857-64 15153497 - J Immunol. 2004 Jun 1;172(11):6790-802 26194757 - Philos Trans R Soc Lond B Biol Sci. 2015 Sep 5;370(1676):null
References_xml	– volume: 111 start-page: 5980 year: 2014 ident: 2023020112335656500_btw112-B44 article-title: Distinctive properties of identical twins’ TCR repertoires revealed by high-throughput sequencing publication-title: Proc. Natl. Acad. Sci. U. S. A doi: 10.1073/pnas.1319389111 – volume: 160 start-page: 2360 year: 1998 ident: 2023020112335656500_btw112-B7 article-title: Base-specific sequences that bias somatic hypermutation deduced by analysis of out-of-frame human IgVH genes publication-title: J. Immunol. (Baltimore, MD.: 1950) doi: 10.4049/jimmunol.160.5.2360 – volume: 13 start-page: 317 year: 1996 ident: 2023020112335656500_btw112-B21 article-title: Repertoires of antigen receptors in Tdt congenitally deficient mice publication-title: Int. Rev. Immunol doi: 10.3109/08830189609061755 – volume: 11 start-page: 653 year: 2014 ident: 2023020112335656500_btw112-B35 article-title: Towards error-free profiling of immune repertoires publication-title: Nat. Methods doi: 10.1038/nmeth.2960 – volume: 9 start-page: S20. year: 2008 ident: 2023020112335656500_btw112-B41 article-title: Ab-origin: an enhanced tool to identify the sourcing gene segments in germline for rearranged antibodies publication-title: BMC Bioinf doi: 10.1186/1471-2105-9-S12-S20 – volume: 16 start-page: 1 year: 2015 ident: 2023020112335656500_btw112-B31 article-title: HTJoinSolver: Human immunoglobulin VDJ partitioning using approximate dynamic programming constrained by conserved motifs publication-title: BMC Bioinf doi: 10.1186/s12859-015-0589-x – volume: 370 start-page: 20140240. year: 2015 ident: 2023020112335656500_btw112-B10 article-title: Assigning and visualizing germline genes in antibody repertoires publication-title: Philos. Trans. R. Soc. Lond. Ser. B: Biol. Sci doi: 10.1098/rstb.2014.0240 – volume-title: The T Cell Receptor FactsBook year: 2001 ident: 2023020112335656500_btw112-B22 – volume: 114 start-page: 4099 year: 2009 ident: 2023020112335656500_btw112-B30 article-title: Comprehensive assessment of T-cell receptor beta-chain diversity in alphabeta T cells publication-title: Blood doi: 10.1182/blood-2009-04-217604 – volume: 218 start-page: 213 year: 2013 ident: 2023020112335656500_btw112-B17 article-title: NKT and MAIT invariant TCRα sequences can be produced efficiently by VJ gene recombination publication-title: Immunobiology doi: 10.1016/j.imbio.2012.04.003 – volume: 119 start-page: 265 year: 2006 ident: 2023020112335656500_btw112-B25 article-title: No evidence for the use of DIR, D-D fusions, chromosome 15 open reading frames or VHreplacement in the peripheral repertoire was found on application of an improved algorithm, JointML, to 6329 human immunoglobulin H rearrangements publication-title: Immunology doi: 10.1111/j.1365-2567.2006.02431.x – volume: 12 start-page: 380 year: 2015 ident: 2023020112335656500_btw112-B3 article-title: MiXCR: software for comprehensive adaptive immunity profiling publication-title: Nat. Methods doi: 10.1038/nmeth.3364 – volume: 32 start-page: 158 year: 2014 ident: 2023020112335656500_btw112-B14 article-title: The promise and challenge of high-throughput sequencing of the antibody repertoire publication-title: Nat. Biotechnol doi: 10.1038/nbt.2782 – volume: 23 start-page: 1580 year: 2007 ident: 2023020112335656500_btw112-B12 article-title: iHMMune-align: hidden Markov model-based alignment and identification of germline genes in rearranged immunoglobulin gene sequences publication-title: Bioinformatics doi: 10.1093/bioinformatics/btm147 – volume: 109 start-page: 16161 year: 2012 ident: 2023020112335656500_btw112-B24 article-title: Statistical inference of the generation probability of T-cell receptors from sequence repertoires publication-title: Proc. Natl. Acad. Sci. U. S. A doi: 10.1073/pnas.1212755109 – volume: 166 start-page: 892 year: 2001 ident: 2023020112335656500_btw112-B26 article-title: The targeting of somatic hypermutation closely resembles that of meiotic mutation publication-title: J. Immunol doi: 10.4049/jimmunol.166.2.892 – volume: 172 start-page: 6790 year: 2004 ident: 2023020112335656500_btw112-B36 article-title: Characterization of the human Ig heavy chain antigen binding complementarity determining region 3 using a newly developed software algorithm, JOINSOLVER publication-title: J. Immunol. (Baltimore, MD.: 1950) doi: 10.4049/jimmunol.172.11.6790 – volume: 370 start-page: 20140243. year: 2015 ident: 2023020112335656500_btw112-B9 article-title: Inferring processes underlying B-cell repertoire diversity publication-title: Philos. Trans. R. Soc. Lond. Ser. B: Biol. Sci doi: 10.1098/rstb.2014.0243 – start-page: e1004409 volume-title: PLoS computational biology year: 2015 ident: 2023020112335656500_btw112-B28 article-title: Consistency of VDJ rearrangement and substitution parameters enables accurate B cell receptor sequence annotation – volume: 186 start-page: 4285 year: 2011 ident: 2023020112335656500_btw112-B39 article-title: A mechanism for TCR sharing between T cell subsets and individuals revealed by pyrosequencing publication-title: J. Immunol. (Baltimore, MD.: 1950) doi: 10.4049/jimmunol.1003898 – volume-title: Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids year: 1998 ident: 2023020112335656500_btw112-B8 doi: 10.1017/CBO9780511790492 – volume: 112 start-page: E862 year: 2015 ident: 2023020112335656500_btw112-B11 article-title: Automated analysis of high-throughput B-cell sequencing data reveals a high frequency of novel immunoglobulin V gene segment alleles publication-title: Proc. Natl. Acad. Sci. U. S. A doi: 10.1073/pnas.1417683112 – volume: 29 start-page: 542 year: 2013 ident: 2023020112335656500_btw112-B38 article-title: Decombinator: a tool for fast, efficient gene assignment in T-cell receptor sequences using a finite state machine publication-title: Bioinformatics (Oxford, England) doi: 10.1093/bioinformatics/btt004 – volume: 36 start-page: 503 year: 2008 ident: 2023020112335656500_btw112-B5 article-title: IMGT/V-QUEST: the highly customized and integrated system for IG and TR standardized V-J and V-D-J sequence analysis publication-title: Nucleic Acids Res doi: 10.1093/nar/gkn316 – volume: 4 start-page: 466. year: 2013 ident: 2023020112335656500_btw112-B34 article-title: Huge overlap of individual TCR beta repertoires publication-title: Front. Immunol doi: 10.3389/fimmu.2013.00466 – volume: 34 start-page: 1126 year: 2015 ident: 2023020112335656500_btw112-B16 article-title: Structural basis for a novel mechanism of DNA bridging and alignment in eukaryotic DSB DNA repair publication-title: EMBO J doi: 10.15252/embj.201489643 – volume: 192 start-page: 4475 year: 2014 ident: 2023020112335656500_btw112-B13 article-title: Check MAIT publication-title: J. Immunol. (Baltimore, MD.: 1950) doi: 10.4049/jimmunol.1400119 – volume: 166 start-page: 2597 year: 2001 ident: 2023020112335656500_btw112-B19 article-title: Ordered and coordinated rearrangement of the TCR locus: role of secondary rearrangement in thymic selection publication-title: J. Immunol doi: 10.4049/jimmunol.166.4.2597 – volume: 33 start-page: D256 year: 2005 ident: 2023020112335656500_btw112-B15 article-title: IMGT/GENE-DB: a comprehensive database for human and mouse immunoglobulin and T cell receptor genes publication-title: Nucleic Acids Res doi: 10.1093/nar/gki010 – volume: 26 start-page: 867 year: 2010 ident: 2023020112335656500_btw112-B23 article-title: SoDA2: a Hidden Markov Model approach for identification of immunoglobulin rearrangements publication-title: Bioinformatics doi: 10.1093/bioinformatics/btq056 – volume: 116 start-page: 299 year: 2004 ident: 2023020112335656500_btw112-B20 article-title: Unraveling V(D)J recombination; insights into gene regulation publication-title: Cell doi: 10.1016/S0092-8674(04)00039-X – volume: 164 start-page: 1971 year: 2000 ident: 2023020112335656500_btw112-B6 article-title: The nucleotide-replacement spectrum under somatic hypermutation exhibits microsequence dependence that is strand-symmetric and distinct from that under germline mutation publication-title: J. Immunol doi: 10.4049/jimmunol.164.4.1971 – volume: 10 start-page: e0118192. year: 2015 ident: 2023020112335656500_btw112-B27 article-title: VDJSeq-Solver: in silico V(D)J recombination detection tool publication-title: Plos One doi: 10.1371/journal.pone.0118192 – volume: 41 start-page: 1 year: 2013 ident: 2023020112335656500_btw112-B43 article-title: IgBLAST: an immunoglobulin variable domain sequence analysis tool publication-title: Nucleic Acids Res doi: 10.1093/nar/gkt382 – volume: 22 start-page: 438 year: 2006 ident: 2023020112335656500_btw112-B40 article-title: SoDA: implementation of a 3D alignment algorithm for inference of antigen receptor recombinations publication-title: Bioinformatics (Oxford, England) – volume: 177 start-page: 3857 year: 2006 ident: 2023020112335656500_btw112-B42 article-title: A model for TCR gene segment use publication-title: J. Immunol doi: 10.4049/jimmunol.177.6.3857 – volume: 11 start-page: 251 year: 2011 ident: 2023020112335656500_btw112-B32 article-title: Recombination centres and the orchestration of V(D)J recombination publication-title: Nat. Rev. Immunol doi: 10.1038/nri2941 – volume: 45 start-page: 167 year: 2011 ident: 2023020112335656500_btw112-B33 article-title: V(D)J recombination: mechanisms of initiation publication-title: Annu. Rev. Genet doi: 10.1146/annurev-genet-110410-132552 – volume: 25 start-page: 646 year: 2013 ident: 2023020112335656500_btw112-B29 article-title: Immunosequencing: applications of immune repertoire deep sequencing publication-title: Curr. Opin. Immunol doi: 10.1016/j.coi.2013.09.017 – volume: 42 start-page: 3073 year: 2012 ident: 2023020112335656500_btw112-B2 article-title: Next generation sequencing for TCR repertoire profiling: platform-specific features and correction algorithms publication-title: Eur. J. Immunol doi: 10.1002/eji.201242517 – start-page: 44 volume-title: Research in Computational Molecular Biology SE - 7, volume 9029 of Lecture Notes in Computer Science year: 2015 ident: 2023020112335656500_btw112-B4 – volume: 104 start-page: 903 year: 2007 ident: 2023020112335656500_btw112-B18 article-title: Role for rearranged variable gene segments in directing secondary T cell receptor alpha recombination publication-title: Proc. Natl. Acad. Sci. U. S. A doi: 10.1073/pnas.0608248104 – volume: 175 start-page: 5170 year: 2005 ident: 2023020112335656500_btw112-B37 article-title: Hypermutation at A-T base pairs: the a nucleotide replacement spectrum is affected by adjacent nucleotides and there is no reverse complementarity of sequences flanking mutated A and T nucleotides publication-title: J. Immunol doi: 10.4049/jimmunol.175.8.5170 – volume-title: Pattern Recognition and Machine Learning year: 2006 ident: 2023020112335656500_btw112-B1 – reference: 24795465 - J Immunol. 2014 May 15;192(10):4475-80 – reference: 24793455 - Nat Methods. 2014 Jun;11(6):653-5 – reference: 20147303 - Bioinformatics. 2010 Apr 1;26(7):867-72 – reference: 15153497 - J Immunol. 2004 Jun 1;172(11):6790-802 – reference: 26751373 - PLoS Comput Biol. 2016 Jan 11;12(1):e1004409 – reference: 11145665 - J Immunol. 2001 Jan 15;166(2):892-9 – reference: 25924071 - Nat Methods. 2015 May;12(5):380-1 – reference: 23671333 - Nucleic Acids Res. 2013 Jul;41(Web Server issue):W34-40 – reference: 21854230 - Annu Rev Genet. 2011;45:167-202 – reference: 22988065 - Proc Natl Acad Sci U S A. 2012 Oct 2;109(40):16161-6 – reference: 26194754 - Philos Trans R Soc Lond B Biol Sci. 2015 Sep 5;370(1676):null – reference: 24711416 - Proc Natl Acad Sci U S A. 2014 Apr 22;111(16):5980-5 – reference: 16357034 - Bioinformatics. 2006 Feb 15;22(4):438-44 – reference: 21383244 - J Immunol. 2011 Apr 1;186(7):4285-94 – reference: 23303508 - Bioinformatics. 2013 Mar 1;29(5):542-50 – reference: 25675496 - Proc Natl Acad Sci U S A. 2015 Feb 24;112(8):E862-70 – reference: 21394103 - Nat Rev Immunol. 2011 Apr;11(4):251-63 – reference: 17210914 - Proc Natl Acad Sci U S A. 2007 Jan 16;104(3):903-7 – reference: 22806588 - Eur J Immunol. 2012 Nov;42(11):3073-83 – reference: 24441474 - Nat Biotechnol. 2014 Feb;32(2):158-68 – reference: 24400005 - Front Immunol. 2013 Dec 25;4:466 – reference: 16951348 - J Immunol. 2006 Sep 15;177(6):3857-64 – reference: 10657647 - J Immunol. 2000 Feb 15;164(4):1971-6 – reference: 25799103 - PLoS One. 2015 Mar 23;10(3):e0118192 – reference: 19091020 - BMC Bioinformatics. 2008 Dec 12;9 Suppl 12:S20 – reference: 18503082 - Nucleic Acids Res. 2008 Jul 1;36(Web Server issue):W503-8 – reference: 19706884 - Blood. 2009 Nov 5;114(19):4099-107 – reference: 14744439 - Cell. 2004 Jan 23;116(2):299-311 – reference: 26194757 - Philos Trans R Soc Lond B Biol Sci. 2015 Sep 5;370(1676):null – reference: 25762590 - EMBO J. 2015 Apr 15;34(8):1126-42 – reference: 11160321 - J Immunol. 2001 Feb 15;166(4):2597-601 – reference: 26001675 - BMC Bioinformatics. 2015 May 23;16:170 – reference: 8884428 - Int Rev Immunol. 1996;13(4):317-25 – reference: 22647874 - Immunobiology. 2013 Feb;218(2):213-24 – reference: 15608191 - Nucleic Acids Res. 2005 Jan 1;33(Database issue):D256-61 – reference: 17005006 - Immunology. 2006 Oct;119(2):265-77 – reference: 24140071 - Curr Opin Immunol. 2013 Oct;25(5):646-52 – reference: 16210621 - J Immunol. 2005 Oct 15;175(8):5170-7 – reference: 9498777 - J Immunol. 1998 Mar 1;160(5):2360-4 – reference: 17463026 - Bioinformatics. 2007 Jul 1;23(13):1580-7
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Snippet	Motivation: The diversity of the immune repertoire is initially generated by random rearrangements of the receptor gene during early T and B cell development.... The diversity of the immune repertoire is initially generated by random rearrangements of the receptor gene during early T and B cell development.... Abstract Motivation: The diversity of the immune repertoire is initially generated by random rearrangements of the receptor gene during early T and B cell...
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SubjectTerms	Adaptive immunology Algorithms Bioinformatics Computer programs Computer Science Deletion Dynamic tests Gene Rearrangement, T-Lymphocyte Genes Humans Immune systems Immunology Life Sciences Mathematical models Original Papers Probability Receptors Receptors, Antigen, T-Cell - genetics Sequence Alignment Software V(D)J Recombination
Title	repgenHMM: a dynamic programming tool to infer the rules of immune receptor generation from sequence data
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