Predicting B cell receptor substitution profiles using public repertoire data

descriptionPublicationkeyboard_double_arrow_right Article , Preprint 17 Oct 2018Embargo end date: 01 Jan 2018 United States English Publisher:Public Library of Science (PLoS)Journal:PLOS Computational Biology, volume 14, page e1006388 (eissn: 1553-7358,

Copyright policy )Funded by:NSF | IGERT-CIF21: Big Data U: ..., NIH | Leveraing deep sequencing..., NIH | Dynamics and Evolution of...

Authors: Dhar, Amrit; Davidsen, Kristian; Matsen, Frederick; Minin, Vladimir N.;

doi: 10.1371/journal.pcbi.1006388 , 10.5281/zenodo.1289983 , 10.5281/zenodo.1289984 , 10.48550/arxiv.1802.06406

pmid: 30332400

pmc: PMC6205660

arXiv: 1802.06406

Predicting B cell receptor substitution profiles using public repertoire data

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Abstract

Data for fitting, testing and validating the model described in the associated article.

{"references": ["https://arxiv.org/abs/1802.06406"]}

Country

United States

Related Organizations

University of California, San Francisco
United States
FRED HUTCHINSON CANCER RESEARCH CENTER
Fred Hutchinson Cancer Research Center
United States
University of Washington
University of California, Irvine
United States

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Keywords

Bioinformatics, QH301-705.5, Bioinformatics and Computational Biology, Receptors, Antigen, B-Cell, Bioengineering, Mathematical Sciences, Databases, q-bio.BM, Models, Information and Computing Sciences, Receptors, Genetics, Animals, Humans, Quantitative Biology - Genomics, Biology (General), Amino Acids, Databases, Protein, Genomics (q-bio.GN), B-Lymphocytes, Protein, B-Cell, Models, Immunological, Computational Biology, Biomolecules (q-bio.BM), Biological Sciences, Recombinant Proteins, Clone Cells, Immunological, Networking and Information Technology R&D (NITRD), Quantitative Biology - Biomolecules, Amino Acid Substitution, Antigen, FOS: Biological sciences, q-bio.GN, Research Article

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