Aberrant DNA methylation plays an important role in cancer progression. However, no resource has been available that comprehensively provides DNA methylation-based diagnostic and prognostic models, expression–methylation quantitative trait loci (emQTL), pathway activity-methylation quantitative trait loci (pathway-meQTL), differentially variable and differentially methylated CpGs, and survival analysis, as well as functional epigenetic modules for different cancers. These provide valuable information for researchers to explore DNA methylation profiles from different aspects in cancer. To this end, we constructed a user-friendly database named DNA Methylation Interactive Visualization Database (DNMIVD), which comprehensively provides the following important resources: (i) diagnostic and prognostic models based on DNA methylation for multiple cancer types of The Cancer Genome Atlas (TCGA); (ii) meQTL, emQTL and pathway-meQTL for diverse cancers; (iii) Functional Epigenetic Modules (FEM) constructed from Protein-Protein Interactions (PPI) and Co-Occurrence and Mutual Exclusive (COME) network by integrating DNA methylation and gene expression data of TCGA cancers; (iv) differentially variable and differentially methylated CpGs and differentially methylated genes as well as related enhancer information; (v) correlations between methylation of gene promoter and corresponding gene expression and (vi) patient survival-associated CpGs and genes with different endpoints. DNMIVD is freely available at http://www.unimd.org/dnmivd/. We believe that DNMIVD can facilitate research of diverse cancers.

DNA methylation status is closely associated with diverse diseases, and is generally more stable than gene expression, thus abnormal DNA methylation could be important biomarkers for tumor diagnosis, treatment and prognosis. However, the signatures regarding DNA methylation changes for pan-cancer diagnosis and prognosis are less explored. Here we systematically analyzed the genome-wide DNA methylation patterns in diverse TCGA cancers with machine learning. We identified seven CpG sites that could effectively discriminate tumor samples from adjacent normal tissue samples for 12 main cancers of TCGA (1216 samples, AUC > 0.99). Those seven potential diagnostic biomarkers were further validated in the other 9 different TCGA cancers and 4 independent datasets (AUC > 0.92). Three out of the seven CpG sites were correlated with cell division, DNA replication and cell cycle. We also identified 12 CpG sites that can effectively distinguish 26 different cancers (7605 samples), and the result was repeatable in independent datasets as well as two disparate tumors with metastases (micro-average AUC > 0.89). Furthermore, a series of potential signatures that could significantly predict the prognosis of tumor patients for 7 different cancer were identified via survival analysis (p-value < 1e-4). Collectively, DNA methylation patterns vary greatly between tumor and adjacent normal tissues, as well as among different types of cancers. Our identified signatures may aid the decision of clinical diagnosis and prognosis for pan-cancer and the potential cancer-specific biomarkers could be used to predict the primary site of metastatic breast and prostate cancers.

Python has emerged as a robust programming language increasingly employed in genomics data analysis, largely due to its comprehensive deep learning libraries and proficiency in handling large-scale data, such as single-cell multi-omics datasets. Although Python has become a prominent data science ecosystem for bioinformatics, there remains a growing demand for advanced heatmap visualization and assembly tools, which are not sufficiently addressed by existing Python-based data visualization libraries. We present PyComplexHeatmap, an all-inclusive Python library for heatmap visualization, inspired by the ComplexHeatmap package currently available in R. PyComplexHeatmap is built upon the matplotlib library and features a versatile, modular interface that seamlessly integrates with other Python-based data science tools, such as Pandas, NumPy, and genomics tools, such as Scanpy, in a standard-compliant manner. This library caters to the requirements of exquisite rendering of multimodal matrix data, incorporating both textual and graphical annotations, thereby enabling efficient integrative analysis of multimodal data and associated metadata.

The arrival of the Infinium DNA methylation BeadChips for mice and other nonhuman mammalian species has outpaced the development of the informatics that supports their use for epigenetics study in model organisms. Here, we present informatics infrastructure and methods to allow easy DNA methylation analysis on multiple species, including domesticated animals and inbred laboratory mice (in SeSAMe version 1.16.0+). First, we developed a data-driven analysis pipeline covering species inference, genome-specific data preprocessing and regression modeling. We targeted genomes of 310 species and 37 inbred mouse strains and showed that genome-specific preprocessing prevents artifacts and yields more accurate measurements than generic pipelines. Second, we uncovered the dynamics of the epigenome evolution in different genomic territories and tissue types through comparative analysis. We identified a catalog of inbred mouse strain-specific methylation differences, some of which are linked to the strains’ immune, metabolic and neurological phenotypes. By streamlining DNA methylation array analysis for undesigned genomes, our methods extend epigenome research to broad species contexts.

The epigenome of human immune cells is shaped by both genetics and environmental factors, yet the relative contributions of these influences remain incompletely characterized. Here we use single-nucleus methylation sequencing and assay for transposase-accessible chromatin using sequencing (ATAC–seq) to systematically explore how pathogen and chemical exposures, along with genetic variation, are associated with changes in the immune cell epigenome. Distinct exposure-associated differentially methylated regions (eDMRs) and differentially accessible regions were identified, and a significant correlation between these two modalities was observed. Additionally, genotype-associated DMRs (gDMRs) were detected, indicating that eDMRs are enriched in regulatory regions, whereas gDMRs are preferentially located within gene body marks. Disease-associated single-nucleotide polymorphisms were frequently colocalized with methylation quantitative trait loci, providing cell-type-specific insights into the genetic basis of diseases. These findings highlight the complex interplay between genetic and environmental factors in shaping the immune cell epigenome and advance understanding of immune cell regulation in health and disease.