We assumed a binomial distribution for a number of “junction coverage” events in trials numbered as the sum of numbers of all “junction coverage” and all “continuous coverage” events. Since the “continuous coverage” was high, the 95% confidence interval for the “junction coverage” frequency was evaluated via a chi-square distribution for binomial log-likelihood ratio (Wilks theorem). In this way, the significance of the deletion, i.e., its deviation from zero, can be measured in standard deviation (SD) units of the normal distribution (z-score of the elementary deletion). This SD-unit measurement is based on the consideration that the 95% confidence interval of the normal distribution nearly equals to four SDs.

Significance of a cluster of deletions in a neighborhood was inferred by calculating the z-score of the neighborhood. For each passage, the neighborhood z-score was calculated as the sum of z-scores of its deletions divided by the square root of the total number of deletions in the neighborhood. Since significance of each deletion is measured in SD and therefore distributed normally N(0,1), the calculated z-score for total significance of all deletions in the neighborhood is also distributed normally N(0,1) and therefore easily translated to a p value. Thus, the dynamics of the neighborhood z-scores across passages show how the total significance of the neighborhood’s set of deletion frequencies increases or decreases.

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