Detect differentially methylated regions using non-homogeneous hidden Markov model for bisulfite sequencing data

Uncovering Alterations in Cancer Epigenetics via Trans-Dimensional Markov Chain Monte Carlo and Hidden Markov Models

Epigenetic alterations are key drivers in the development and progression of cancer. Identifying differentially methylated cytosines (DMCs) in cancer samples is a crucial step toward understanding these changes. In this paper, we propose a trans-dimensional Markov chain Monte Carlo (TMCMC) approach that uses hidden Markov models (HMMs) with binomial emission, and bisulfite sequencing (BS-Seq) data, called DMCTHM, to identify DMCs in cancer epigenetic studies. We introduce the Expander-Collider penalty to tackle under and over-estimation in TMCMC-HMMs. We address all known challenges inherent in BS-Seq data by introducing novel approaches for capturing functional patterns and autocorrelation structure of the data, as well as for handling missing values, multiple covariates, multiple comparisons, and family-wise errors. We demonstrate the effectiveness of DMCTHM through comprehensive simulation studies. The results show that our proposed method outperforms other competing methods in identifying DMCs. Notably, with DMCTHM, we uncovered new DMCs and genes in Colorectal cancer that were significantly enriched in the Tp53 pathway.

Single-Cell DNA Methylation Analysis of Chicken Lampbrush Chromosomes

DNA methylation is an essential epigenetic regulation mechanism implicated in transcription and replication control, developmental reprogramming, retroelements silencing and other genomic processes. During mammalian development, a specific DNA methylation pattern should be established in germ cells to allow embryonic development. Less is known about germ cell DNA methylation in other species. To close this gap, we performed a single-cell methylome analysis of chicken diplotene oocytes. We comprehensively characterized methylation patterns in these cells, obtained methylation-based chicken genome segmentation and identified oocyte-specific methylated gene promoters. Our data show that despite the formation of specific transcriptionally hyperactive genome architecture in chicken diplotene oocytes, methylation patterns in these cells closely resemble genomic distribution observed in somatic tissues.