Quantifying selection in high-throughput Immunoglobulin sequencing data sets

• Published in: Nucleic acids research Vol. 40; no. 17; p. e134
• Main Authors: Yaari, Gur, Uduman, Mohamed, Kleinstein, Steven H.
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01.09.2012
• Subjects: Animals; Autoimmunity; Bayes Theorem; Bayesian analysis; Cancer; Computer Simulation; Data processing; DNA; DNA Mutational Analysis; Genes, Immunoglobulin; High-Throughput Nucleotide Sequencing; Humans; Immune response; Immunity; Immunoglobulin Heavy Chains - genetics; Immunoglobulins; Immunoglobulins - genetics; Lymphocytes B; Memory cells; Methods Online; Mice; Mutation; Negative selection; Pathogens; somatic hypermutation; Somatic Hypermutation, Immunoglobulin; Statistics
• ISSN: 0305-1048; 1362-4962; 1362-4962
• DOI: 10.1093/nar/gks457

High-throughput immunoglobulin sequencing promises new insights into the somatic hypermutation and antigen-driven selection processes that underlie B-cell affinity maturation and adaptive immunity. The ability to estimate positive and negative selection from these sequence data has broad application...