BACKGROUND: Dysregulation of imprinted genes, which are expressed in a 
parent-of-origin-specific manner, plays an important role in various human 
diseases, such as cancer and behavioral disorder. To date, however, fewer than 
100 imprinted genes have been identified in the human genome. The recent 
availability of high-throughput technology makes it possible to have large-scale 
prediction of imprinted genes. Here we propose a Bayesian model (dsPIG) to 
predict imprinted genes on the basis of allelic expression observed in mRNA-Seq 
data of independent human tissues.
RESULTS: Our model (dsPIG) was capable of identifying imprinted genes with high 
sensitivity and specificity and a low false discovery rate when the number of 
sequenced tissue samples was fairly large, according to simulations. By applying 
dsPIG to the mRNA-Seq data, we predicted 94 imprinted genes in 20 cerebellum 
samples and 57 imprinted genes in 9 diverse tissue samples with expected low 
false discovery rates. We also assessed dsPIG using previously validated 
imprinted and non-imprinted genes. With simulations, we further analyzed how 
imbalanced allelic expression of non-imprinted genes or different minor allele 
frequencies affected the predictions of dsPIG. Interestingly, we found that, 
among biallelically expressed genes, at least 18 genes expressed significantly 
more transcripts from one allele than the other among different individuals and 
tissues.
CONCLUSION: With the prevalence of the mRNA-Seq technology, dsPIG has become a 
useful tool for analysis of allelic expression and large-scale prediction of 
imprinted genes. For ease of use, we have set up a web service and also provided 
an R package for dsPIG at http://www.shoudanliang.com/dsPIG/.