For any genome-wide analysis, reporting individual p-values can be misleading, because the p-value does not correct for the large number of tests performed. The q-value is an analog of the p-value that incorporates multiple testing correction. The q-value is defined as the minimum false discovery rate at which an observed score is deemed significant. Thus, the q-value attempts to control the percentage of false positives among a collection of scores. This contrasts with a traditional Bonferroni correction (or E-value), which controls the probability of one or more false positives in a collection of scores.

Software for computing q-values from a collection of p-values is available at:

http://genomics.princeton.edu/storeylab/qvalue

For a good introduction to false discovery rate estimation and the q-value see:

Storey JD, Tibshirani R. Statistical significance for genomewide studies. Proc Natl Acad Sci. 2003 Aug 5;100(16):9440-5.