HMST-Seq-Analyzer
HMST-Seq-Analyzer analyzes DNA methylation (5mC) and hydroxymethylation (5hmC) sequencing data to detect, quantify, and annotate differential methylation and hydroxymethylation at single-base resolution.
Key Features:
- Simultaneous detection: Processes Hydroxymethylation-and Methylation-Sensitive Tag sequencing (HMST-seq) data to detect 5mC and 5hmC at single base-pair resolution.
- Differential analysis: Performs differential methylation analysis and identifies Differentially Methylated Regions (DMRs) with genomic annotation.
- Data compatibility: Accepts HMST-seq, whole-genome bisulfite sequencing (WGBS), and reduced representation bisulfite sequencing (RRBS) datasets.
- Visualization and quality control: Produces visual summaries of methylation status and performs preliminary quality checks.
- Computational performance: Implemented in Python and supports parallel processing for large datasets.
Scientific Applications:
- Epigenetic profiling: Enables exploration of 5mC and 5hmC distributions and their roles in gene regulation.
- Developmental biology: Supports analysis of methylation and hydroxymethylation dynamics across developmental stages.
- Disease and cancer research: Identifies methylation and hydroxymethylation changes associated with disease processes such as cancer.
Methodology:
Analyzes HMST-seq (Hydroxymethylation-and Methylation-Sensitive Tag sequencing) and accepts WGBS and RRBS inputs; performs differential methylation analysis to identify DMRs with annotation, provides visualization and preliminary quality checks, and is implemented in Python with parallel processing.
Topics
Details
- Tool Type:
- library
- Programming Languages:
- Python
- Added:
- 1/18/2021
- Last Updated:
- 1/30/2021
Operations
Publications
Farooq A, Grønmyr S, Ali O, Rognes T, Scheffler K, Bjørås M, Wang J. HMST-Seq-Analyzer: A new python tool for differential methylation and hydroxymethylation analysis in various DNA methylation sequencing data. Computational and Structural Biotechnology Journal. 2020;18:2877-2889. doi:10.1016/j.csbj.2020.09.038. PMID:33163148. PMCID:PMC7593523.