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

• Published in: BMC bioinformatics Vol. 13; no. 1; p. 271
• Main Authors: Li, Hua, Su, Xiao, Gallegos, Juan, Lu, Yue, Ji, Yuan, Molldrem, Jeffrey J, Liang, Shoudan
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 19.10.2012; BioMed Central; BMC
• Subjects: Alleles; Allelic Imbalance; Analysis; Analysis of allelic expression; Base Sequence; Bayes Theorem; Bayesian analysis; Bayesian model; Binomial distribution; Bioinformatics; Cancer; Cerebellum; Colleges & universities; Computer programs; Data processing; DNA sequencing; Gene Expression; Gene Expression Profiling - statistics & numerical data; Gene Frequency; Genes; Genetic research; Genome, Human - genetics; Genomes; Genomic Imprinting - genetics; Genomics; High-Throughput Nucleotide Sequencing - statistics & numerical data; Humans; Internet; Mathematical models; Methods; Models, Genetic; mRNA-Seq; Nucleotide sequencing; Polymorphism, Single Nucleotide; Prediction of imprinted genes; Probability; RNA; RNA, Messenger - genetics; RNA, Messenger - metabolism; Science; Sequence Analysis, DNA - methods; Transcriptome - genetics; Transcriptome deep sequencing; United States
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-13-271

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/.