This track displays the 2-way regulatory potential (RP) score computed from alignments of human (hg16, Jul. '03) and mouse (mm4, Oct. '03). RP scores compare frequencies of short alignment patterns between regulatory elements and neutral DNA.
Score values at or below 0 indicate resemblance to alignment patterns typical of neutral DNA. Score values at or above 0.1 indicate very marked resemblance to alignment patterns typical of regulatory elements. Absence of a score value at a given location indicates lack of alignment.
Preliminary results from a calibration study investigating sensitivity and specificity of the 2-way RP score on the hemoglobin beta gene cluster suggest the use of a threshold just above 0 for identifying new putative regulatory elements.
(A 3-way RP score, computed from alignments of human, mouse and rat is also available on this browser).
This track may be configured in a variety of ways to highlight different aspects of the displayed information. Click the "Graph configuration help" link for an explanation of the configuration options.
The comparison employs log-ratios of transitions probabilities from two Markov models. Training the score entails selecting appropriate alphabet (alignment column symbols) and order (length of the patterns = order + 1) for the Markov models, and estimating their transition probabilities, based on alignment data from known regulatory elements and ancestral repeats. The 2-way RP score uses a 5-symbol alphabet and order 5.
In the track, score values are displayed using a system of overlapping windows of size 100 bp along aligned portions of the human sequence Log-ratios are added over positions in a window, and the sum normalized for length.
Work on RP scores is performed by members of the Comparative Genomics and Bioinformatics Center at Penn State University. More information on this research and the collection of known regulatory elements used in training the score can be found at this site.
Mouse sequence data were provided by the Mouse Sequencing Consortium. The alignment data were created in collaboration with the UCSC Genome Bioinformatics group.
Elnitski L, Hardison R, Li J, Yang S, Kolbe D, Eswara P, O'Connor M, Schwartz S, Miller W and Chiaromonte F. (2003). Distinguishing regulatory DNA from neutral sites. Genome Res. 13(1):64-72.
Schwartz S, Kent WJ, Smit A, Zhang Z, Baertsch R, Hardison R, Haussler D, and Miller W. (2003). Human-Mouse Alignments with BLASTZ. Genome Res. 13(1):103-7.