Utility of q-value over p-value
The p-value, the probability that a negative control would appear positive, must be used with great care because genomes are vast relative to regulatory sequence elements. For instance, in many other situations a p-value of 10-6 is considered excellent, but when there are on the order of 109 places where a transcription factor binding site is not likely to bind, such a "strong" p-value can leave us with 1,000 false positives – or even more, in the usual case that some of the biology has not been incorporated into the statistical model. Thus, to properly interpret a p-value, the researcher must be on guard to quantify the number of negative cases.
The q-value (or False Discovery Rate [16]) explicitly incorporates the vastness of the genome in the calculation. The q-value of a transcription factor binding site tells us the proportion of sites of that strength or better that we expect to be false positives. Under ideal circumstances, the researcher who chooses a q-value threshold of 0.001 expects only one in 1,000 of the reported sites to be a false positive regardless of the genome size. (However, because we do not pretend to have statistically modeled all the relevant biology, the false discovery rate will generally be higher than the specified threshold.)