Detect differentially methylated regions using non-homogeneous hidden Markov model for methylation array 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.

Reliable genotyping of recombinant genomes using a robust hidden Markov model

Meiotic recombination is an essential mechanism during sexual reproduction and includes the exchange of chromosome segments between homologous chromosomes. New allelic combinations are transmitted to the new generation, introducing novel genetic variation in the offspring genomes. With the improvement of high-throughput whole-genome sequencing technologies, large numbers of recombinant individuals can now be sequenced with low sequencing depth at low costs, necessitating computational methods for reconstructing their haplotypes. The main challenge is the uncertainty in haplotype calling that arises from the low information content of a single genomic position. Straightforward sliding window-based approaches are difficult to tune and fail to place recombination breakpoints precisely. Hidden Markov model (HMM)-based approaches, on the other hand, tend to over-segment the genome. Here, we present RTIGER, an HMM-based model that exploits in a mathematically precise way the fact that true chromosome segments typically have a certain minimum length. We further separate the task of identifying the correct haplotype sequence from the accurate placement of haplotype borders, thereby maximizing the accuracy of border positions. By comparing segmentations based on simulated data with known underlying haplotypes, we highlight the reasons for RTIGER outperforming traditional segmentation approaches. We then analyze the meiotic recombination pattern of segregants of 2 Arabidopsis (Arabidopsis thaliana) accessions and a previously described hyper-recombining mutant. RTIGER is available as an R package with an efficient Julia implementation of the core algorithm.

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

DNA methylation plays an important role in many biological processes and diseases. With the rise of the whole genome bisulfite sequencing technique, aberrant methylation patterns can now be detected by comparing paired normal and disease samples at the single nucleotide level. We develop a novel Bayesian method for detecting differentially methylated regions from paired bisulfite sequencing data, and implement it as a R package called BSDMR. Based on a non-homogeneous hidden Markov model, BSDMR provides a better modeling strategy for the spatial correlation between CpG sites and takes into consideration the relationship between methylation signals from normal and disease samples. Simulations show that BSDMR performs well even under low read depth and has a smaller false discovery rates than existing methods. We also apply BSDMR to the colon cancer data from Gene Expression Omnibus. The detected DMRs are well supported by existing biomedical literatures.

Molecular characterization and diagnostic criteria of renal cell carcinoma with emphasis on liquid biopsies

Introduction: Over the past 6 years, important genomic and transcriptomic studies performed on RCC reported a comprehensive molecular description of RCC pathogenic alterations. Such molecular findings pave the way for an integrated classification, based on histopathology aspects and molecular alterations in order to personalize the clinical management of RCC.Areas covered: The aim of this review is to evaluate the current knowledge and the potential value of liquid biopsy in RCC. Studies on presence and analysis of circulating tumor DNA (ctDNA), circulating RNA, specific microRNA, long non-coding RNA, and circulating tumor cells are reported for each phase of disease, from the diagnostic setting to the localized disease and, lastly, in the metastatic stage.Expert opinion: Advantages of liquid biopsies compared to serial tissue sampling are numerous. However, some limitations must be addressed before considering liquid biopsy as a noninvasive biomarker of clinical utility. The suboptimal sensitivity depends on the assessment technique and genetic platforms used, the tumor organ, the tumor stage, tumor heterogeneity, and clonality. The rate of discordance with tumor tissue genotyping may depends on temporal heterogeneity, spatial heterogeneity, and/or assay error (false-negative or false-positive genotyping).

DNA methylation markers in obesity, metabolic syndrome, and weight loss

The fact that not all individuals exposed to the same environmental risk factors develop obesity supports the hypothesis of the existence of underlying genetic and epigenetic elements. There is suggestive evidence that environmental stimuli, such as dietary pattern, particularly during pregnancy and early life, but also in adult life, can induce changes in DNA methylation predisposing to obesity and related comorbidities. In this context, the DNA methylation marks of each individual have emerged not only as a promising tool for the prediction, screening, diagnosis, and prognosis of obesity and metabolic syndrome features, but also for the improvement of weight loss therapies in the context of precision nutrition. The main objectives in this field are to understand the mechanisms involved in transgenerational epigenetic inheritance, and featuring the nutritional and lifestyle factors implicated in the epigenetic modifications. Likewise, DNA methylation modulation caused by diet and environment may be a target for newer therapeutic strategies concerning the prevention and treatment of metabolic diseases.

Assessing genome-wide significance for the detection of differentially methylated regions

DNA methylation plays an important role in human health and disease, and methods for the identification of differently methylated regions are of increasing interest. There is currently a lack of statistical methods which properly address multiple testing, i.e. control genome-wide significance for differentially methylated regions. We introduce a scan statistic (DMRScan), which overcomes these limitations. We benchmark DMRScan against two well established methods (bumphunter, DMRcate), using a simulation study based on real methylation data. An implementation of DMRScan is available from Bioconductor. Our method has higher power than alternative methods across different simulation scenarios, particularly for small effect sizes. DMRScan exhibits greater flexibility in statistical modeling and can be used with more complex designs than current methods. DMRScan is the first dynamic approach which properly addresses the multiple-testing challenges for the identification of differently methylated regions. DMRScan outperformed alternative methods in terms of power, while keeping the false discovery rate controlled.