@ARTICLE{Sun2017-dp,
  title     = "Specificity, privacy, and degeneracy in the {CD4} {T} cell
               receptor repertoire following immunization",
  author    = "Sun, Yuxin and Best, Katharine and Cinelli, Mattia and Heather,
               James M and Reich-Zeliger, Shlomit and Shifrut, Eric and
               Friedman, Nir and Shawe-Taylor, John and Chain, Benny",
  abstract  = "T cells recognize antigen using a large and diverse set of
               antigen-specific receptors created by a complex process of
               imprecise somatic cell gene rearrangements. In response to
               antigen-/receptor-binding-specific T cells then divide to form
               memory and effector populations. We apply high-throughput
               sequencing to investigate the global changes in T cell receptor
               sequences following immunization with ovalbumin (OVA) and
               adjuvant, to understand how adaptive immunity achieves
               specificity. Each immunized mouse contained a predominantly
               private but related set of expanded CDR3$\beta$ sequences. We
               used machine learning to identify common patterns which
               distinguished repertoires from mice immunized with adjuvant with
               and without OVA. The CDR3$\beta$ sequences were deconstructed
               into sets of overlapping contiguous amino acid triplets. The
               frequencies of these motifs were used to train the linear
               programming boosting (LPBoost) algorithm LPBoost to classify
               between TCR repertoires. LPBoost could distinguish between the
               two classes of repertoire with accuracies above 80\%, using a
               small subset of triplet sequences present at defined positions
               along the CDR3. The results suggest a model in which such motifs
               confer degenerate antigen specificity in the context of a highly
               diverse and largely private set of T cell receptors.",
  journal   = "Front. Immunol.",
  publisher = "Frontiers Media SA",
  volume    =  8,
  pages     = "430",
  month     =  apr,
  year      =  2017,
  keywords  = "CDR3; T cell receptor; machine learning; ovalbumin; repertoire
               analysis",
  language  = "en"
}

% The entry below contains non-ASCII chars that could not be converted
% to a LaTeX equivalent.

{"_id":"2yZSEd94728J47Csx","bibbaseid":"sun-best-cinelli-heather-reichzeliger-shifrut-friedman-shawetaylor-etal-specificityprivacyanddegeneracyinthecd4tcellreceptorrepertoirefollowingimmunization-2017","authorIDs":["5cc6a620aa96b4da010000d7","RxpzMWmPrvwrFLPRs","XTqeFBq8hj6RekJoE","XybFBYqD7cfyv4Gag","YAgo5nTspXhkCSz6J"],"author_short":["Sun, Y.","Best, K.","Cinelli, M.","Heather, J. M","Reich-Zeliger, S.","Shifrut, E.","Friedman, N.","Shawe-Taylor, J.","Chain, B."],"bibdata":{"bibtype":"article","type":"article","title":"Specificity, privacy, and degeneracy in the CD4 T cell receptor repertoire following immunization","author":[{"propositions":[],"lastnames":["Sun"],"firstnames":["Yuxin"],"suffixes":[]},{"propositions":[],"lastnames":["Best"],"firstnames":["Katharine"],"suffixes":[]},{"propositions":[],"lastnames":["Cinelli"],"firstnames":["Mattia"],"suffixes":[]},{"propositions":[],"lastnames":["Heather"],"firstnames":["James","M"],"suffixes":[]},{"propositions":[],"lastnames":["Reich-Zeliger"],"firstnames":["Shlomit"],"suffixes":[]},{"propositions":[],"lastnames":["Shifrut"],"firstnames":["Eric"],"suffixes":[]},{"propositions":[],"lastnames":["Friedman"],"firstnames":["Nir"],"suffixes":[]},{"propositions":[],"lastnames":["Shawe-Taylor"],"firstnames":["John"],"suffixes":[]},{"propositions":[],"lastnames":["Chain"],"firstnames":["Benny"],"suffixes":[]}],"abstract":"T cells recognize antigen using a large and diverse set of antigen-specific receptors created by a complex process of imprecise somatic cell gene rearrangements. In response to antigen-/receptor-binding-specific T cells then divide to form memory and effector populations. We apply high-throughput sequencing to investigate the global changes in T cell receptor sequences following immunization with ovalbumin (OVA) and adjuvant, to understand how adaptive immunity achieves specificity. Each immunized mouse contained a predominantly private but related set of expanded CDR3$β$ sequences. We used machine learning to identify common patterns which distinguished repertoires from mice immunized with adjuvant with and without OVA. The CDR3$β$ sequences were deconstructed into sets of overlapping contiguous amino acid triplets. The frequencies of these motifs were used to train the linear programming boosting (LPBoost) algorithm LPBoost to classify between TCR repertoires. LPBoost could distinguish between the two classes of repertoire with accuracies above 80%, using a small subset of triplet sequences present at defined positions along the CDR3. The results suggest a model in which such motifs confer degenerate antigen specificity in the context of a highly diverse and largely private set of T cell receptors.","journal":"Front. Immunol.","publisher":"Frontiers Media SA","volume":"8","pages":"430","month":"April","year":"2017","keywords":"CDR3; T cell receptor; machine learning; ovalbumin; repertoire analysis","language":"en","bibtex":"@ARTICLE{Sun2017-dp,\n title = \"Specificity, privacy, and degeneracy in the {CD4} {T} cell\n receptor repertoire following immunization\",\n author = \"Sun, Yuxin and Best, Katharine and Cinelli, Mattia and Heather,\n James M and Reich-Zeliger, Shlomit and Shifrut, Eric and\n Friedman, Nir and Shawe-Taylor, John and Chain, Benny\",\n abstract = \"T cells recognize antigen using a large and diverse set of\n antigen-specific receptors created by a complex process of\n imprecise somatic cell gene rearrangements. In response to\n antigen-/receptor-binding-specific T cells then divide to form\n memory and effector populations. We apply high-throughput\n sequencing to investigate the global changes in T cell receptor\n sequences following immunization with ovalbumin (OVA) and\n adjuvant, to understand how adaptive immunity achieves\n specificity. Each immunized mouse contained a predominantly\n private but related set of expanded CDR3$\\beta$ sequences. We\n used machine learning to identify common patterns which\n distinguished repertoires from mice immunized with adjuvant with\n and without OVA. The CDR3$\\beta$ sequences were deconstructed\n into sets of overlapping contiguous amino acid triplets. The\n frequencies of these motifs were used to train the linear\n programming boosting (LPBoost) algorithm LPBoost to classify\n between TCR repertoires. LPBoost could distinguish between the\n two classes of repertoire with accuracies above 80\\%, using a\n small subset of triplet sequences present at defined positions\n along the CDR3. The results suggest a model in which such motifs\n confer degenerate antigen specificity in the context of a highly\n diverse and largely private set of T cell receptors.\",\n journal = \"Front. Immunol.\",\n publisher = \"Frontiers Media SA\",\n volume = 8,\n pages = \"430\",\n month = apr,\n year = 2017,\n keywords = \"CDR3; T cell receptor; machine learning; ovalbumin; repertoire\n analysis\",\n language = \"en\"\n}\n\n% The entry below contains non-ASCII chars that could not be converted\n% to a LaTeX equivalent.\n","author_short":["Sun, Y.","Best, K.","Cinelli, M.","Heather, J. M","Reich-Zeliger, S.","Shifrut, E.","Friedman, N.","Shawe-Taylor, J.","Chain, B."],"key":"Sun2017-dp","id":"Sun2017-dp","bibbaseid":"sun-best-cinelli-heather-reichzeliger-shifrut-friedman-shawetaylor-etal-specificityprivacyanddegeneracyinthecd4tcellreceptorrepertoirefollowingimmunization-2017","role":"author","urls":{},"keyword":["CDR3; T cell receptor; machine learning; ovalbumin; repertoire analysis"],"metadata":{"authorlinks":{"shifrut, e":"https://822588431-atari-embeds.googleusercontent.com/embeds/16cb204cf3a9d4d223a0a3fd8b0eec5d/inner-frame-minified.html"}},"downloads":0},"bibtype":"article","biburl":"https://bibbase.org/network/files/xos2PRkrJXRcypZwA","creationDate":"2019-04-29T07:28:37.047Z","downloads":0,"keywords":["cdr3; t cell receptor; machine learning; ovalbumin; repertoire analysis"],"search_terms":["specificity","privacy","degeneracy","cd4","cell","receptor","repertoire","following","immunization","sun","best","cinelli","heather","reich-zeliger","shifrut","friedman","shawe-taylor","chain"],"title":"Specificity, privacy, and degeneracy in the CD4 T cell receptor repertoire following immunization","year":2017,"dataSources":["HKMAdWbuCdpcjTfsQ","AHkfRF5sD5CBc7YbG","WGzqbPDoqXMkKdgJ7","wmRjNkA5ZsjNtvjhZ"]}