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EpiCompare is used to compare and analyse epigenetic datasets for quality control and benchmarking purposes. The package outputs an HTML report consisting of three sections: (1. General metrics) Metrics on peaks (percentage of blacklisted and non-standard peaks, and peak widths) and fragments (duplication rate) of samples, (2. Peak overlap) Percentage and statistical significance of overlapping and non-overlapping peaks. Also includes upset plot and (3. Functional annotation) functional annotation (ChromHMM, ChIPseeker and enrichment analysis) of peaks. Also includes peak enrichment around TSS.

KnowYourCG (KYCG) is a supervised learning framework designed for the functional analysis of DNA methylation data. Unlike existing tools that focus on genes or genomic intervals, KnowYourCG directly targets CpG dinucleotides, featuring automated supervised screenings of diverse biological and technical influences, including sequence motifs, transcription factor binding, histone modifications, replication timing, cell-type-specific methylation, and trait-epigenome associations. KnowYourCG addresses the challenges of data sparsity in various methylation datasets, including low-pass Nanopore sequencing, single-cell DNA methylomes, 5-hydroxymethylation profiles, spatial DNA methylation maps, and array-based datasets for epigenome-wide association studies and epigenetic clocks (<doi:10.1126/sciadv.adw3027>).

Integrates various levels of epigenomic information, including ChIP-seq, histone modification, ATAC-seq, and RNA-seq data. Regulatory network analysis uses combinatory approaches to infer regions of significance, such as enhancers. Downstream analysis identifies co-occurrence of epigenomic data at regions of interest. Visualization functions display multi-track genomic views with signal overlays. Please contact <ammawla@ucdavis.edu> for suggestions, feedback, or bug reporting.

MotifPeeker is used to compare and analyse datasets from epigenomic profiling methods with motif enrichment as the key benchmark. The package outputs an HTML report consisting of three sections: (1. General Metrics) Overview of peaks-related general metrics for the datasets (FRiP scores, peak widths and motif-summit distances). (2. Known Motif Enrichment Analysis) Statistics for the frequency of user-provided motifs enriched in the datasets. (3. Motif Discovery Enrichment Analysis) Statistics for the frequency of ab-initio discovered motifs enriched in the datasets and compared with known motifs.

The package prepares input scATAC-seq data and adapts for copy number variance profiling with InferCNV package usage. It has also various paramters to control the analysis (e.g. external normal reference usage, meta-cells, bin size, etc) and custom plot visualizations.

A quasi-simulation based approach to performing power analysis for EWAS (Epigenome-wide association studies) with continuous or binary outcomes. 'EpipwR' relies on empirical EWAS datasets to determine power at specific sample sizes while keeping computational cost low. EpipwR can be run with a variety of standard statistical tests, controlling for either a false discovery rate or a family-wise type I error rate.

Capture Hi-C is a set of techniques that enable the detection of genomic interactions involving regions of interest, known as baits. By focusing on selected loci, these approaches reduce sequencing costs while maintaining high resolution at the level of restriction fragments. HiCaptuRe provides tools to import, annotate, manipulate, and export Capture Hi-C data. The package accounts for the specific structure of bait–otherEnd interactions, facilitates integration with other omics datasets, and enables comparison across samples and conditions.

G-quadruplexes (G4s) are unique nucleic acid secondary structures predominantly found in guanine-rich regions and have been shown to be involved in various biological regulatory processes. G4SNVHunter is an R package designed to rapidly identify genomic sequences with G4-forming propensity and to accurately screen user-provided single nucleotide variants—as well as other small-scale variants such as indels and MNVs—for their potential to destabilize these structures. This allows researchers to then screen these critical variants for deeper study, digging into how they might influence biological functions—think gene regulation, for instance—by impairing G4 formation propensity.

Computational evaluation of variability across DNA or RNA sequencing datasets is a crucial step in genomics, as it allows both to evaluate reproducibility of replicates, and to compare different datasets to identify potential correlations. fCCAC applies functional Canonical Correlation Analysis to allow the assessment of: (i) reproducibility of biological or technical replicates, analyzing their shared covariance in higher order components; and (ii) the associations between different datasets. fCCAC represents a more sophisticated approach that complements Pearson correlation of genomic coverage.

This package estimates epigenetic age in skeletal muscle, using DNA methylation data generated with the Illumina Infinium technology (HM27, HM450 and HMEPIC).

cTTN is a set of R scripts for analyzing genetic constraint in the TTN gene by adjusting for sequencing depth and calculating constraint metrics with confidence intervals. It includes tools for obtaining observed and expected variant counts from gnomAD exome data.