Identification and utilization of arbitrary correlations in models of recombination signal sequences.

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Cowell, LG; Davila, M; Kepler, TB; Kelsoe, G

Published Date

  • 2002

Published In

Volume / Issue

  • 3 / 12

Start / End Page

  • RESEARCH0072 -

PubMed ID

  • 12537561

Pubmed Central ID

  • 12537561

Electronic International Standard Serial Number (EISSN)

  • 1474-760X

Language

  • eng

Conference Location

  • England