A phylogenetic Gibbs sampler that yields centroid solutions of cis-regulatory sites

Journal Article · Sun Jul 15 04:00:00 EDT 2007 · Bioinformatics, 23(14):1718-1727

DOI:https://doi.org/10.1093/bioinformatics/btm241· OSTI ID:919290

Newberg, Lee A; Thompson, William A; Conlan, Sean; Smith, Thomas M; McCue, Lee Ann; Lawrence, Charles E

Identification of functionally conserved regulatory elements in sequence data from closely related organisms is becoming feasible, due to the rapid growth of public sequence databases. Closely related organisms are most likely to have common regulatory motifs, however the recent speciation of such organisms results in the high degree of correlation in their genome sequences, confounding the detection of functional elements. Additionally, alignment algorithms that use optimization techniques are limited to the detection of a single alignment that may not be representative. Comparative-genomics studies must be able to address the phylogenetic correlation in the data and efficiently explore the alignment space, in order to make specific and biologically relevant predictions. Results: We describe here a Gibbs sampler that employs a full phylogenetic model and reports an ensemble centroid solution. We describe regulatory motif detection using both simulated and real data, and demonstrate that this approach achieves improved specificity, sensitivity, and positive predictive value over non-phylogenetic algorithms, and over phylogenetic algorithms that report a maximum likelihood solution.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (US)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 919290
• Report Number(s): PNWD-SA-7675
• Journal Information: Bioinformatics, 23(14):1718-1727, Journal Name: Bioinformatics, 23(14):1718-1727 Journal Issue: 14 Vol. 23
• Country of Publication: United States
• Language: English

Related Subjects

59 BASIC BIOLOGICAL SCIENCES
97 MATHEMATICS AND COMPUTING
ALGORITHMS
DNA SEQUENCING
MATHEMATICAL MODELS
SAMPLERS
SPECIFICITY