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High-throughput immune repertoire analysis with IGoR

Quentin Marcou, Thierry Mora, Aleksandra M. Walczak

Nature Communications · 2018 · CC BY

doi:10.1101/141143

Abstract

High-throughput immune repertoire sequencing is promising to lead to new statistical diagnostic tools for medicine and biology. Successful implementations of these methods require a correct characterization, analysis, and interpretation of these data sets. We present IGoR (Inference and Generation Of Repertoires)—a comprehensive tool that takes B or T cell receptor sequence reads and quantitatively characterizes the statistics of receptor generation from both cDNA and gDNA. It probabilistically annotates sequences and its modular structure can be used to investigate models of increasing biological complexity for different organisms. For B cells, IGoR returns the hypermutation statistics, which we use to reveal co-localization of hypermutations along the sequence. We demonstrate that IGoR outperforms existing tools in accuracy and estimate the sample sizes needed for reliable repertoire characterization.

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References 38

  1. 1.

    Warren EH, Matsen Fa, Chou J. High-throughput sequencing of B- and T-lymphocyte antigen receptors in hematology. Blood. 2013;122:19-22.

    Not in our library10.1182/blood-2013-03-453142

  2. 2.
  3. 3.

    Woodsworth DJ, Castellarin M, Holt Ra. Sequence analysis of T-cell repertoires in health and disease. Genome Med. 2013;5:98.

    Not in our library10.1186/gm502

  4. 4.

    Georgiou G, et al. The promise and challenge of high-throughput sequencing of the antibody repertoire. Nat. Biotechnol. 2014;32:158-168.

    Not in our library10.1038/nbt.2782

  5. 5.

    Brochet X, Lefranc MP, Giudicelli V. IMGT/V-QUEST: the highly customized and integrated system for IG and TR standardized V-J and V-D-J sequence analysis. Nucleic Acids Res. 2008;36:503-508.

    Not in our library10.1093/nar/gkn316

  6. 6.

    Thomas N, Heather J, Ndifon W, Shawe-Taylor J, Chain B. Decombinator: a tool for fast, efficient gene assignment in T cell receptor sequences using a finite state machine. Bioinformatics. 2013;29:1-11.

    Not in our library10.1093/bioinformatics/btt004

  7. 7.

    Gupta NT, et al. Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data. Bioinformatics. 2015;31:3356-3358.

    Not in our library10.1093/bioinformatics/btv359

  8. 8.

    Bolotin DA, et al. Mixcr: software for comprehensive adaptive immunity profiling. Nat. Methods. 2015;12:380-381.

    Not in our library10.1038/nmeth.3364

  9. 9.

    Duez M, et al. Vidjil: a web platform for analysis of high-throughput repertoire sequencing. PLoS ONE. 2016;11:1-12.

    Not in our library10.1371/journal.pone.0166126

  10. 10.
  11. 11.

    Venturi V, et al. Sharing of T cell receptors in antigen-specific responses is driven by convergent recombination. Proc. Natl Acad. Sci. USA. 2006;103:18691-18696.

    Not in our library10.1073/pnas.0608907103

  12. 12.

    Pogorelyy MV, et al. Persisting fetal clonotypes influence the structure and overlap of adult human T cell receptor repertoires. PLoS Comput. Biol. 2017;13:1-18.

    Not in our library10.1371/journal.pcbi.1005572

  13. 13.

    Robins HS, et al. Comprehensive assessment of t-cell receptor β-chain diversity in αβ t cells. Blood. 2009;114:4099-4107.

    Not in our library10.1182/blood-2009-04-217604

  14. 14.

    Larimore K, McCormick MW, Robins HS, Greenberg PD. Shaping of human germline IgH repertoires revealed by deep sequencing. J. Immunol. 2012;189:3221-3230.

    Not in our library10.4049/jimmunol.1201303

  15. 15.

    Murugan A, Mora T, Walczak AM, Callan CG. Statistical inference of the generation probability of T-cell receptors from sequence repertoires. Proc. Natl Acad. Sci. USA. 2012;109:16161-16166.

    Not in our library10.1073/pnas.1212755109

  16. 16.
  17. 17.

    Elhanati Y, Marcou Q, Mora T, Walczak AM. repgenhmm: a dynamic programming tool to infer the rules of immune receptor generation from sequence data. Bioinformatics. 2016;32:1943-1951.

    Not in our library10.1093/bioinformatics/btw112

  18. 18.

    Jackson KJL, Gaeta B, Sewell W, Collins AM. Exonuclease activity and P nucleotide addition in the generation of the expressed immunoglobulin repertoire. BMC Immunol. 2004;5:19.

    Not in our library10.1186/1471-2172-5-19

  19. 19.

    Mora, T. & Walczak, A. in Systems Immunology: An Introduction to Modeling Methods for Scientists (eds Das, J. & Jayaprakash, C.) (CRC Press, Boca Raton, 2017).

    Not in our library

  20. 20.

    Elhanati Y, Murugan A, Callan CG, Mora T, Walczak AM. Quantifying selection in immune receptor repertoires. Proc. Natl Acad. Sci. USA. 2014;111:9875-9880.

    Not in our library10.1073/pnas.1409572111

  21. 21.

    Murphy, K. & Weaver, C. Janeway’s Immunobiology (Garland Science, New York, 2016).

    Not in our library

  22. 22.

    Berg OG, von Hippel PH. Selection of DNA binding sites by regulatory proteins: Statistical-mechanical theory and application to operators and promoters. J. Mol. Biol. 1987;193:723-743.

    Not in our library10.1016/0022-2836(87)90354-8

  23. 23.
  24. 24.

    Cui A, et al. A model of somatic hypermutation targeting in mice based on high-throughput Ig sequencing data. J. Immunol. 2016;197:3566-3574.

    Not in our library10.4049/jimmunol.1502263

  25. 25.

    Rogozin IB, Diaz M. Cutting edge: Dgyw/wrch is a better predictor of mutability at g: C bases in ig hypermutation than the widely accepted rgyw/wrcy motif and probably reflects a two-step activation-induced cytidine deaminase-triggered process. J. Immunol. 2004;172:3382-3384.

    Not in our library10.4049/jimmunol.172.6.3382

  26. 26.

    Betz AG, Rada C, Pannell R, Milstein C, Neuberger MS. Passenger transgenes reveal intrinsic specificity of the antibody hypermutation mechanism: clustering, polarity, and specific hot spots. Proc. Natl Acad. Sci. USA. 1993;90:2385-2388.

    Not in our library10.1073/pnas.90.6.2385

  27. 27.

    Shapiro GS, Aviszus K, Ikle D, Wysocki LJ. Predicting regional mutability in antibody v genes based solely on di-and trinucleotide sequence composition. J. Immunol. 1999;163:259-268.

    Not in our library

  28. 28.

    Bransteitter R, Pham P, Calabrese P, Goodman MF. Biochemical analysis of hypermutational targeting by wild type and mutant activation-induced cytidine deaminase. J. Biol. Chem. 2004;279:51612-51621.

    Not in our library10.1074/jbc.m408135200

  29. 29.

    Kenter AL, Kumar S, Wuerffel R, Grigera F. AID hits the jackpot when missing the target. Curr. Opin. Immunol. 2016;39:96-102.

    Not in our library10.1016/j.coi.2016.01.008

  30. 30.

    Steele EJ. Somatic hypermutation in immunity and cancer: critical analysis of strand-biased and codon-context mutation signatures. DNA Repair. 2016;45:1-24.

    Not in our library10.1016/j.dnarep.2016.07.001

  31. 31.

    Chandra V, Bortnick A, Murre C. AID targeting: old mysteries and new challenges. Trends Immunol. 2015;36:527-535.

    Not in our library10.1016/j.it.2015.07.003

  32. 32.

    Unniraman S, Schatz DG. Strand-biased spreading of mutations during somatic hypermutation. Science. 2007;317:1227-1230.

    Not in our library10.1126/science.1145065

  33. 33.

    Pogorelyy, M. V. et al. Method for identification of condition-associated public antigen receptor sequences. Preprint at https://arxiv.org/abs/1709.09703 (2017).

    Not in our library10.7554/elife.33050

  34. 34.

    Emerson RO, et al. Immunosequencing identifies signatures of cytomegalovirus exposure history and HLA-mediated effects on the T cell repertoire. Nat. Genet. 2017;49:659-665.

    Not in our library10.1038/ng.3822

  35. 35.

    Yeap LS, et al. Sequence-intrinsic mechanisms that target AID mutational outcomes on antibody genes. Cell. 2015;163:1124-1137.

    Not in our library10.1016/j.cell.2015.10.042

  36. 36.

    Gadala-maria, D., Yaari, G., Uduman, M. & Kleinstein, S. H. Automated analysis of high-throughput B-cell sequencing data reveals a high frequency of novel immunoglobulin V gene segment alleles. Proc. Natl Acad. Sci. USA112, E862–70 (2015).

    Not in our library10.1073/pnas.1417683112

  37. 37.

    Smith TF, Waterman MS. Identification of common molecular subsequences. J. Mol. Biol. 1981;147:195-197.

    Not in our library10.1016/0022-2836(81)90087-5

  38. 38.

    Larimore K, McCormick MW, Robins HS, Greenberg PD. Shaping of human germline IgH repertoires revealed by deep sequencing. J. Immunol. 2012;189:3221-3230.

    Not in our library10.4049/jimmunol.1201303

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