NanoMethViz

TENET identifies key transcription factors (TFs) and regulatory elements (REs) linked to a specific cell type by finding significantly correlated differences in gene expression and RE DNA methylation between case and control input datasets, and identifying the top genes by number of significant RE DNA methylation site links. It also includes many tools for visualization and analysis of the results, including plots displaying and comparing methylation and expression data and methylation site link counts, survival analysis, TF motif searching in the vicinity of linked RE DNA methylation sites, custom TAD and peak overlap analysis, and UCSC Genome Browser track file generation. A utility function is also provided to download methylation, expression, and patient survival data from The Cancer Genome Atlas (TCGA) for use in TENET or other analyses.

Bioconductor-native infrastructure for handling large nanoporetech modkit bedMethyl pileup files from ONT data using HDF5Array and DelayedArray.

RnBeads facilitates comprehensive analysis of various types of DNA methylation data at the genome scale.

The cfTools R package provides methods for cell-free DNA (cfDNA) methylation data analysis to facilitate cfDNA-based studies. Given the methylation sequencing data of a cfDNA sample, for each cancer marker or tissue marker, we deconvolve the tumor-derived or tissue-specific reads from all reads falling in the marker region. Our read-based deconvolution algorithm exploits the pervasiveness of DNA methylation for signal enhancement, therefore can sensitively identify a trace amount of tumor-specific or tissue-specific cfDNA in plasma. cfTools provides functions for (1) cancer detection: sensitively detect tumor-derived cfDNA and estimate the tumor-derived cfDNA fraction (tumor burden); (2) tissue deconvolution: infer the tissue type composition and the cfDNA fraction of multiple tissue types for a plasma cfDNA sample. These functions can serve as foundations for more advanced cfDNA-based studies, including cancer diagnosis and disease monitoring.

The Cancer Genome Atlas (TCGA) Data Portal provides a platform for researchers to search, download, and analyze data sets generated by TCGA. It contains clinical information, genomic characterization data, and high level sequence analysis of the tumor genomes. The key is to understand genomics to improve cancer care. RTCGA package offers download and integration of the variety and volume of TCGA data using patient barcode key, what enables easier data possession. This may have an benefcial infuence on impact on development of science and improvement of patients' treatment. Furthermore, RTCGA package transforms TCGA data to tidy form which is convenient to use.

mist (Methylation Inference for Single-cell along Trajectory) is a hierarchical Bayesian framework for modeling DNA methylation trajectories and performing differential methylation (DM) analysis in single-cell DNA methylation (scDNAm) data. It estimates developmental-stage-specific variations, identifies genomic features with drastic changes along pseudotime, and, for two phenotypic groups, detects features with distinct temporal methylation patterns. mist uses Gibbs sampling to estimate parameters for temporal changes and stage-specific variations.