f688dc8b0b0b4cff6962e70ebf6a16b4422b65ea
gperez2
  Tue Nov 17 10:54:53 2020 -0800
Fixed broken url

diff --git src/hg/htdocs/goldenPath/help/qValue.html src/hg/htdocs/goldenPath/help/qValue.html
index 6c5da82..22d25dc 100755
--- src/hg/htdocs/goldenPath/help/qValue.html
+++ src/hg/htdocs/goldenPath/help/qValue.html
@@ -5,24 +5,24 @@
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 <h1>Q-Value</h1>
 <p>
 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.</p>
 <p>
 Software for computing q-values from a collection of p-values is available at:
-<a href="http://genomics.princeton.edu/storeylab/qvalue/" 
-target="_blank">http://genomics.princeton.edu/storeylab/qvalue</a></p>
+<a href="https://github.com/StoreyLab/qvalue" 
+target="_blank">https://github.com/StoreyLab/qvalue</a></p>
 <p>
 For a good introduction to false discovery rate estimation and the q-value see: Storey JD, 
 Tibshirani R. <a href="http://www.pnas.org/content/100/16/9440.abstract" 
 target="_blank">Statistical significance for genomewide studies</a>. <em>Proc Natl Acad Sci</em>. 
 2003 Aug 5;100(16):9440-5.</p>
 
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