Prospective estimation of recombination signal efficiency and identification of functional cryptic signals in the genome by statistical modeling.
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The recombination signals (RS) that guide V(D)J recombination are phylogenetically conserved but retain a surprising degree of sequence variability, especially in the nonamer and spacer. To characterize RS variability, we computed the position-wise information, a measure correlated with sequence conservation, for each nucleotide position in an RS alignment and demonstrate that most position-wise information is present in the RS heptamers and nonamers. We have previously demonstrated significant correlations between RS positions and here show that statistical models of the correlation structure that underlies RS variability efficiently identify physiologic and cryptic RS and accurately predict the recombination efficiencies of natural and synthetic RS. In scans of mouse and human genomes, these models identify a highly conserved family of repetitive DNA as an unexpected source of frequent, cryptic RS that rearrange both in extrachromosomal substrates and in their genomic context.
Molecular Sequence Data
Sequence Homology, Nucleic Acid
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Adjunct Assistant Professor in the Department of Biostatistics and Bioinformatics
Somatic Diversification of Lymphocyte Antigen Receptor Genes * V(D)J Recombination * Somatic Hypermutation Biomedical Ontology * Ontological Representation of Cells of Hematopoietic Lineage Biomedical Text Mining Logic-based Reasoning
James B. Duke Distinguished Professor of Immunology
1. Lymphocyte development and antigen-driven diversification of immunoglobulin and T cell antigen receptor genes. 2. The germinal center reaction and mechanisms for clonal selection and self - tolerance. The origins of autoimmunity. 3. Interaction of innate- and adaptive immunity and the role of inflammation in lymphoid organogenesis. 4. The role of secondary V(D)J gene rearrangment in lymphocyte development and malignancies. 5. Mathematical modeling of immune responses,
Adjunct Professor in the Department of Immunology
Computational and Systems Immunology, Theoretical and Evolutionary Medicine
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