Inferring Regulatory Information from DNA Methylation Data
DNA methylation is an epigenetic mark that influences gene regulation by adding a methyl group to cytosine. MIRA is an R package designed to analyze genome-wide methylation data to infer regulatory information by aggregating data in specific genomic regions. The package overcomes sparse data by leveraging publicly available region sets and provides a novel approach to understanding system-level regulatory activity. With a workflow that includes binning, aggregation, scoring, and comparing, MIRA allows for the exploration of DNA methylation patterns and their impact on gene activity.
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Presentation Transcript
MIRA: an R Package for Inferring Regulatory Information from DNA Methylation Data Presented by: John Lawson Developed by: John Lawson, Nathan Sheffield
DNA Methylation DNA methylation An epigenetic mark that affects gene regulation Addition of a methyl (CH3) group to a cytosine Methylation can interfere with transcription factor binding Lower DNA methylation of enhancers and promoters is associated with more protein binding and activity DNA methylation data contains regulatory information
Purpose of MIRA Use genome-wide methylation data to infer regulatory information Aggregates methylation data in regions of interest across the genome to make a meta-profile from which you can infer system-level activity of these regions Overcomes sparse methylation data through aggregation of genome- wide data Takes advantage of publicly available region sets
MIRA Workflow: Inputs Input 1: Genome wide single nucleotide resolution DNA methylation data Input 2: A set of genomic regions that share a biological annotation
MIRA Workflow: Binning and Aggregation Average methylation within each region by bin Collapse regions into meta-profile
MIRA Workflow: Scoring and Comparing Score profile based on the deepness of dip Assumption that deeper dip equals more activity Compare scores between samples, within and between groups
Conclusion MIRA package provides a novel way to infer systems-level regulatory information from DNA methylation data
Acknowledgments Nathan Sheffield: advisor and creator of package UVA Biomedical Engineering Graduate Program
