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dc.contributor.authorMiho, Enkelejda
dc.date.accessioned2020-02-06T10:34:19Z
dc.date.available2020-02-06T10:34:19Z
dc.date.issued2019-03-21
dc.identifier.doihttps://doi.org/10.1038/s41467-019-09278-8
dc.identifier.urihttps://irf.fhnw.ch/handle/11654/30421
dc.description.abstractThe architecture of mouse and human antibody repertoires is defined by the sequence similarity networks of the clones that compose them. The major principles that define the architecture of antibody repertoires have remained largely unknown. Here, we establish a high-performance computing platform to construct large-scale networks from comprehensive human and murine antibody repertoire sequencing datasets (>100,000 unique sequences). Leveraging a network-based statistical framework, we identify three fundamental principles of antibody repertoire architecture: reproducibility, robustness and redundancy. Antibody repertoire networks are highly reproducible across individuals despite high antibody sequence dissimilarity. The architecture of antibody repertoires is robust to the removal of up to 50–90% of randomly selected clones, but fragile to the removal of public clones shared among individuals. Finally, repertoire architecture is intrinsically redundant. Our analysis provides guidelines for the large-scale network analysis of immune repertoires and may be used in the future to define disease-associated and synthetic repertoires.en_US
dc.description.urihttps://www.nature.com/articles/s41467-019-09278-8.pdfen_US
dc.language.isoenen_US
dc.relation.ispartofNature Communicationsen_US
dc.accessRightsAnonymous*
dc.titleLarge-scale network analysis reveals the sequence space architecture of antibody repertoiresen_US
dc.type01 - Zeitschriftenartikel, Journalartikel oder Magazin*
dc.audienceScienceen_US
fhnw.publicationStatePublisheden_US
fhnw.ReviewTypeAnonymous ex ante peer review of a complete publicationen_US
fhnw.InventedHereYesen_US
fhnw.PublishedSwitzerlandYesen_US
fhnw.pagination1-11en_US
fhnw.IsStudentsWorknoen_US
fhnw.publicationOnlineJaen_US


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