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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.
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