A Bayesian hidden Markov model for detecting differentially methylated regions.

Alterations in DNA methylation have been linked to the development and progression of many diseases. The bisulfite sequencing technique presents methylation profiles at base resolution. Count data on methylated and unmethylated reads provide information on the methylation level at each CpG site. As more bisulfite sequencing data become available, these data are increasingly needed to infer methylation aberrations in diseases. Automated and powerful algorithms also need to be developed to accurately identify differentially methylated regions between treatment groups. This study adopts a Bayesian approach using the hidden Markov model to account for inherent dependence in read count data. Given the expense of sequencing experiments, few replicates are available for each treatment group. A Bayesian approach that borrows information across an entire chromosome improves the reliability of statistical inferences. The proposed hidden Markov model considers location dependence among genomic loci by incorporating correlation structures as a function of genomic distance. An iterative algorithm based on expectation-maximization is designed for parameter estimation. Methylation states are inferred by identifying the optimal sequence of latent states from observations. Real datasets and simulation studies that mimic the real datasets are used to illustrate the reliability and success of the proposed method.

Global assessment of imprinted gene expression in the bovine conceptus by next generation sequencing.

Genomic imprinting is an epigenetic mechanism that leads to parental-allele-specific gene expression. Approximately 150 imprinted genes have been identified in humans and mice but less than 30 have been described as imprinted in cattle. For the purpose of de novo identification of imprinted genes in bovine, we determined global monoallelic gene expression in brain, skeletal muscle, liver, kidney and placenta of day ∼105 Bos taurus indicus × Bos taurus taurus F1 conceptuses using RNA sequencing. To accomplish this, we developed a bioinformatics pipeline to identify parent-specific single nucleotide polymorphism alleles after filtering adenosine to inosine (A-to-I) RNA editing sites. We identified 53 genes subject to monoallelic expression. Twenty three are genes known to be imprinted in the cow and an additional 7 have previously been characterized as imprinted in human and/or mouse that have not been reported as imprinted in cattle. Of the remaining 23 genes, we found that 10 are uncharacterized or unannotated transcripts located in known imprinted clusters, whereas the other 13 genes are distributed throughout the bovine genome and are not close to any known imprinted clusters. To exclude potential cis-eQTL effects on allele expression, we corroborated the parental specificity of monoallelic expression in day 86 Bos taurus taurus × Bos taurus taurus conceptuses and identified 8 novel bovine imprinted genes. Further, we identified 671 candidate A-to-I RNA editing sites and describe random X-inactivation in day 15 bovine extraembryonic membranes. Our results expand the imprinted gene list in bovine and demonstrate that monoallelic gene expression can be the result of cis-eQTL effects.