Identification and utilization of arbitrary correlations in models of recombination signal sequences.
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BACKGROUND: A significant challenge in bioinformatics is to develop methods for detecting and modeling patterns in variable DNA sequence sites, such as protein-binding sites in regulatory DNA. Current approaches sometimes perform poorly when positions in the site do not independently affect protein binding. We developed a statistical technique for modeling the correlation structure in variable DNA sequence sites. The method places no restrictions on the number of correlated positions or on their spatial relationship within the site. No prior empirical evidence for the correlation structure is necessary. RESULTS: We applied our method to the recombination signal sequences (RSS) that direct assembly of B-cell and T-cell antigen-receptor genes via V(D)J recombination. The technique is based on model selection by cross-validation and produces models that allow computation of an information score for any signal-length sequence. We also modeled RSS using order zero and order one Markov chains. The scores from all models are highly correlated with measured recombination efficiencies, but the models arising from our technique are better than the Markov models at discriminating RSS from non-RSS. CONCLUSIONS: Our model-development procedure produces models that estimate well the recombinogenic potential of RSS and are better at RSS recognition than the order zero and order one Markov models. Our models are, therefore, valuable for studying the regulation of both physiologic and aberrant V(D)J recombination. The approach could be equally powerful for the study of promoter and enhancer elements, splice sites, and other DNA regulatory sites that are highly variable at the level of individual nucleotide positions.
Gene Rearrangement, B-Lymphocyte
Gene Rearrangement, T-Lymphocyte
Immunoglobulin Joining Region
Immunoglobulin Variable Region
Nucleic Acid Conformation
Regulatory Sequences, 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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