TRIg: a robust alignment pipeline for non-regular T-cell receptor and immunoglobulin sequences

BackgroundT cells and B cells are essential in the adaptive immunity via expressing T cell receptors and immunoglogulins respectively for recognizing antigens. To recognize a wide variety of antigens, a highly diverse repertoire of receptors is generated via complex recombination of the receptor genes. Reasonably, frequencies of the recombination events have been shown to predict immune diseases and provide insights into the development of immunity. The field is further boosted by high-throughput sequencing and several computational tools have been released to analyze the recombined sequences. However, all current tools assume regular recombination of the receptor genes, which is not always valid in data prepared using a RACE approach. Compared to the traditional multiplex PCR approach, RACE is free of primer bias, therefore can provide accurate estimation of recombination frequencies. To handle the non-regular recombination events, a new computational program is needed.ResultsWe propose TRIg to handle non-regular T cell receptor and immunoglobulin sequences. Unlike all current programs, TRIg does alignments to the whole receptor gene instead of only to the coding regions. This brings new computational challenges, e.g., ambiguous alignments due to multiple hits to repetitive regions. To reduce ambiguity, TRIg applies a heuristic strategy and incorporates gene annotation to identify authentic alignments. On our own and public RACE datasets, TRIg correctly identified non-regularly recombined sequences, which could not be achieved by current programs. TRIg also works well for regularly recombined sequences.ConclusionsTRIg takes into account non-regular recombination of T cell receptor and immunoglobulin genes, therefore is suitable for analyzing RACE data. Such analysis will provide accurate estimation of recombination events, which will benefit various immune studies directly. In addition, TRIg is suitable for studying aberrant recombination in immune diseases. TRIg is freely available at https://github.com/TLlab/trig.

[1]  B. Nadel,et al.  Different chromosomal breakpoints impact the level of LMO2 expression in T-ALL. , 2007, Blood.

[2]  Evan Bolton,et al.  Database resources of the National Center for Biotechnology Information , 2017, Nucleic Acids Res..

[3]  J. Seidman,et al.  Abnormal recombination products result from aberrant DNA rearrangement of the human T-cell antigen receptor beta-chain gene. , 1986, Proceedings of the National Academy of Sciences of the United States of America.

[4]  D. Schatz,et al.  Recombination centres and the orchestration of V(D)J recombination , 2011, Nature Reviews Immunology.

[5]  Marie-Paule Lefranc,et al.  IMGT/V-QUEST, an integrated software program for immunoglobulin and T cell receptor VJ and VDJrearrangement analysis , 2004, Nucleic Acids Res..

[6]  Gregory D. Schuler,et al.  Database resources of the National Center for Biotechnology Information: update , 2004, Nucleic acids research.

[7]  M. Egholm,et al.  Measurement and Clinical Monitoring of Human Lymphocyte Clonality by Massively Parallel V-D-J Pyrosequencing , 2009, Science Translational Medicine.

[8]  Y. Takagaki,et al.  Porcine T-cell receptor β-chain: A genomic sequence covering Dβ1.1 to Cβ2 gene segments and the diversity of cDNA expressed in piglets including novel alternative splicing products , 2007 .

[9]  B. Falini,et al.  Proteins encoded by genes involved in chromosomal alterations in lymphoma and leukemia: clinical value of their detection by immunocytochemistry. , 2002, Blood.

[10]  J. V. van Dongen,et al.  Recombination in the human IGK locus. , 2006, Critical reviews in immunology.

[11]  Richard A. Moore,et al.  Exhaustive T-cell repertoire sequencing of human peripheral blood samples reveals signatures of antigen selection and a directly measured repertoire size of at least 1 million clonotypes. , 2011, Genome research.

[12]  Ning Ma,et al.  IgBLAST: an immunoglobulin variable domain sequence analysis tool , 2013, Nucleic Acids Res..

[13]  S. Roman-Roman,et al.  Alternatively spliced T cell receptor transcripts expressed in human T lymphocytes. , 1993, Molecular immunology.

[14]  M. Eisenstein Personalized, sequencing-based immune profiling spurs startups , 2013, Nature Biotechnology.

[15]  J. V. van Dongen,et al.  Breakpoint sites disclose the role of the V(D)J recombination machinery in the formation of T-cell receptor (TCR) and non-TCR associated aberrations in T-cell acute lymphoblastic leukemia , 2013, Haematologica.

[16]  S. Salzberg,et al.  Fast algorithms for large-scale genome alignment and comparison. , 2002, Nucleic acids research.

[17]  M. Saito,et al.  Unbiased Analysis of TCRα/β Chains at the Single-Cell Level in Human CD8+ T-Cell Subsets , 2012, PloS one.

[18]  J. Kearney,et al.  Terminal deoxynucleotidyl transferase and repertoire development , 2000, Immunological reviews.

[19]  John Shawe-Taylor,et al.  Decombinator: a tool for fast, efficient gene assignment in T-cell receptor sequences using a finite state machine , 2013, Bioinform..

[20]  Stephan M. Winkler,et al.  ImmunExplorer (IMEX): a software framework for diversity and clonality analyses of immunoglobulins and T cell receptors on the basis of IMGT/HighV-QUEST preprocessed NGS data , 2015, BMC Bioinformatics.

[21]  Mikhail Pogorelyy,et al.  tcR: an R package for T cell receptor repertoire advanced data analysis , 2015, BMC Bioinformatics.

[22]  M Hummel,et al.  Design and standardization of PCR primers and protocols for detection of clonal immunoglobulin and T-cell receptor gene recombinations in suspect lymphoproliferations: Report of the BIOMED-2 Concerted Action BMH4-CT98-3936 , 2003, Leukemia.

[23]  H. Spits,et al.  Complex rearrangements within the human J delta‐C delta/J alpha‐C alpha locus and aberrant recombination between J alpha segments. , 1988, The EMBO journal.

[24]  Craig H. Bassing,et al.  Antigen Receptor Allelic Exclusion: An Update and Reappraisal , 2010, The Journal of Immunology.

[25]  K. P. Murphy,et al.  Janeway's immunobiology , 2007 .

[26]  A. Sewell,et al.  alphabeta T cell receptors as predictors of health and disease alphabeta T cell receptors as predictors of health and disease , 2017 .

[27]  Yusuke Nakamura,et al.  Quantitative T cell repertoire analysis by deep cDNA sequencing of T cell receptor α and β chains using next-generation sequencing (NGS) , 2014, Oncoimmunology.

[28]  A. Sewell,et al.  αβ T cell receptors as predictors of health and disease , 2015, Cellular and Molecular Immunology.

[29]  Andrew P. Stubbs,et al.  Integrated transcript and genome analyses reveal NKX2-1 and MEF2C as potential oncogenes in T cell acute lymphoblastic leukemia. , 2011, Cancer cell.

[30]  Patrice Duroux,et al.  IMGT/HIGHV-QUEST: THE IMGT® WEB PORTAL FOR IMMUNOGLOBULIN (IG) OR ANTIBODY AND T CELL RECEPTOR (TR) ANALYSIS FROM NGS HIGH THROUGHPUT AND DEEP SEQUENCING , 2012 .

[31]  S. Salzberg,et al.  Alignment of whole genomes. , 1999, Nucleic acids research.

[32]  H. Robins Immunosequencing: applications of immune repertoire deep sequencing. , 2013, Current opinion in immunology.

[33]  R. Holt,et al.  Profiling the T-cell receptor beta-chain repertoire by massively parallel sequencing. , 2009, Genome research.

[34]  R. Emerson,et al.  Using synthetic templates to design an unbiased multiplex PCR assay , 2013, Nature Communications.

[35]  Mikhail Shugay,et al.  MiTCR: software for T-cell receptor sequencing data analysis , 2013, Nature Methods.

[36]  L. Hood,et al.  The complete 685-kilobase DNA sequence of the human beta T cell receptor locus. , 1996, Science.

[37]  Janice L Abbey,et al.  Detection of spliced and unspliced forms of germline TCR-Vbeta transcripts in extrathymic lymphoid sites. , 2008, Molecular immunology.

[38]  L. Hood,et al.  The Complete 685-Kilobase DNA Sequence of the Human β T Cell Receptor Locus , 1996, Science.