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

ABSTRACT:

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 simu

SUBMITTER: Li H 

PROVIDER: S-EPMC3497615 | biostudies-literature | 2012 Oct

REPOSITORIES: biostudies-literature