Identification and utilization of arbitrary correlations in models of recombination signal sequences.
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.
Cowell
Lindsay G
LG
Department of Immunology, Duke University Medical Center, Durham, NC 27710, USA.
Davila
Marco
M
Kepler
Thomas B
TB
Kelsoe
Garnett
G
D000818:Animals; D001402:B-Lymphocytes / Q000737:chemistry / Q000378:metabolism; D002874:Chromosome Mapping; D002875:Chromosomes / Q000235:genetics; D019295:Computational Biology / Q000379:methods / Q000706:statistics & numerical data; D017124:Conserved Sequence / Q000235:genetics; D021901:DNA, Intergenic / Q000235:genetics / Q000502:physiology; D015322:Gene Rearrangement, B-Lymphocyte; D015329:Gene Rearrangement, T-Lymphocyte; D014644:Genetic Variation / Q000235:genetics / Q000502:physiology; D007133:Immunoglobulin Joining Region / Q000235:genetics / Q000502:physiology; D007135:Immunoglobulin Variable Region / Q000235:genetics / Q000502:physiology; D008390:Markov Chains; D051379:Mice; D008957:Models, Genetic; D009690:Nucleic Acid Conformation; D011995:Recombination, Genetic / Q000235:genetics* / Q000502:physiology; D012045:Regulatory Sequences, Nucleic Acid / Q000235:genetics*; D013601:T-Lymphocytes / Q000737:chemistry / Q000378:metabolism
D021901:DNA, Intergenic; D007133:Immunoglobulin Joining Region; D007135:Immunoglobulin Variable Region