Research article

BMC Bioinformatics

, 13:271

First online:

Open Access This content is freely available online to anyone, anywhere at any time.

dsPIG: a tool to predict imprinted genes from the deep sequencing of whole transcriptomes

  • Hua LiAffiliated withShanghai Center for Systems Biomedicine, Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Jiao Tong UniversityDepartment of Stem Cell Transplantation and Cellular Therapy, The University of Texas M D Anderson Cancer Center
  • , Xiao SuAffiliated withDivision of Biostatistics, The University of Texas School of Public Health at Houston Email author 
  • , Juan GallegosAffiliated withDepartment of Molecular and Human Genetics, Baylor College of Medicine Email author 
  • , Yue LuAffiliated withDepartment of Leukemia, The University of Texas MD Anderson Cancer Center
  • , Yuan JiAffiliated withCenter for Clinical and Research Informatics, NorthShore University HealthSystem
  • , Jeffrey J MolldremAffiliated withDepartment of Stem Cell Transplantation and Cellular Therapy, The University of Texas M D Anderson Cancer Center
  • , Shoudan LiangAffiliated withDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center Email author 



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.


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.


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/​.


Prediction of imprinted genes Transcriptome deep sequencing mRNA-Seq Bayesian model Analysis of allelic expression