Databases and Systems

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Figure 2: Whitehead Radiation Hybrid Map vs. Stanford RH Map of Chr.3
(The nonlinear relationship evident between the maps is somewhat
surprising; this is to be expected between linkage and physical maps because
recombination is inhibited near the centromeres and telomeres, resulting in a
compression of genetic distances in those areas and a relative expansion in the
chromosome arms, where most recombination takes place. In the figure both maps
are radiation hybrid maps, which might have been expected to be more linearly
correlated. The nonlinearity may be a function of the different radiation doses used to
create the panels the maps were based on, which affects the density of breakpoints. If
the centromere is nonrandomly retained in radiation hybrid cells, then the different
panels might experience different degrees of centromeric distortion. However these
are just guesses; to my knowledge this effect has not been well studied.)
Although linear transformation provides a practical solution to the map alignment
problem, it is by no means perfect. With any universal coordinate scheme a locus
which appears on several maps may be assigned slightly different universal
coordinates in each of them, and so will appear several times in several different
places in the universal coordinate space. Ideally these places should be close to each
other, unless the locus was incorrectly placed in one of the maps, or a locus
identification error was made during entry of the map into GDB. Figure 3 shows one
way to visualize this effect, which we call a dispersion plot: each x,y point represents
a locus having a universal coordinate x from one map, and y from another. (The same
pair of maps will also generate a point for this locus at y,x, so the plot is symmetric.)
In the ideal case where every locus ended up at a unique universal coordinate all the
points would fall along the line y=x. This distance of points from this line is a
measure of the variability in the assignment of universal coordinates, and the overall
thickness of the distribution intuitively represents how good the linear transformation
approach is.