Wavelet Transformation of Genome Annotations

<div><p>(A) To illustrate the purpose of wavelet transformation, we show the original traces and continuous wavelet transformations using the derivative of Gaussian wavelet basis for gene content and divergence over a 2-Mb stretch of Chromosome 20. Colours indicate the magnitude (blue = low, red = high, white = zero) of the wavelet coefficients at each scale and location, with each level being normalised to have equal variance.</p><p>(B) Analysis of the correlation between the smoothed and detailed coefficients at each scale (see Text S2). The height of each bar is the value of the correlation coefficient and the boxes are the contributions from broader scales (top is the broadest scale), with colour intensity related to the magnitude of the effect (blue is negative, red is positive) and size proportional to the fraction of variance explained by a given level. The correlation between divergence and constraint in the original signal (−0.0823) can be decomposed into positive contributions from correlations between detail coefficients at broad scales and negative contributions from correlations between detail coefficients at fine scales.</p></div

Marginal Significance (−log₁₀ p-value as Determined by <i>t-</i>Test) of the Wavelet Coefficients from Four Annotations as Predictors of the Coefficients of the Decomposition of Human-Chimpanzee Divergence

<div><p>Red boxes highlight significant positive linear relationships, and blue boxes, negative. The intensity of the colour is proportional to the degree of significance.</p><p>(A) Smoothed coefficients.</p><p>(B) Detail coefficients.</p></div

Marginal Significance (−log₁₀ p-value as Determined by <i>t-</i>Test) of the Wavelet Coefficients from Four Annotations as Predictors of the Coefficients of the Decomposition of Ascertainment Panel Diversity

<div><p>Red boxes highlight significant positive linear relationships and blue negative. The intensity of the colour is proportional to the degree of significance.</p><p>(A) Smoothed coefficients.</p><p>(B) Detail coefficients.</p><p>Also shown is the adjusted <i>r</i>², which can be interpreted as the proportion of the variance in the signal explained by the linear model.</p></div

Quantile-Quantile Plots Showing the Difference in Allele Frequency Spectrum for AT→GC Mutations and GC→AT Mutations in Regions of Low and High Recombination

<p>If the two types of mutation were to have the same allele frequency distribution, we would expect to see a straight line. In both cases, AT→GC mutations are typically at higher frequencies than GC→AT mutations; however, the effect is more pronounced in regions of high recombination [(A), low recombination; (B), high recombination]. A quantification of the difference can be found in the text and supporting material.</p

Power Spectra and Pairwise Correlations of Detail Wavelet Coefficients

<p>Diagonal plots show the power spectrum of the wavelet decomposition of each factor on the long (red) and short (blue) arms of Chromosome 20. Off-diagonal plots show the rank correlation coefficient between pairs of detail wavelet coefficients at each scale on the long (top right) and short (bottom left) arms. Red crosses indicate significant correlations (<i>p</i>-value < 0.01; Kendall's rank correlation). Scale is shown in kilobases.</p