iepimutacions

iepimutacions detects epimutations in DNA methylation data using statistical outlier approaches to identify rare deviations from normal methylation patterns relevant to rare disease research and clinical diagnostics.

Key Features:

• Statistical Approaches: Implements two published methods and four novel statistical approaches, including MANOVA, multivariate linear models, isolation forest, robust Mahalanobis distance, quantile analysis, and beta distribution-based methods.
• Validation: Methods validated against publicly available datasets with experimentally confirmed epimutations.
• Performance: Demonstrates superior performance relative to ramr, particularly at low sample sizes relevant to rare disease studies.
• Experimental Design and Preprocessing Guidelines: Provides guidelines and preprocessing recommendations based on analyses of the INMA and HELIX cohorts to identify technical and biological factors affecting detection accuracy.
• Annotation and Visualization: Provides functions for annotating and visualizing detected epimutations to support interpretation.
• Clinical Application: Applied to a cohort of children with autism to identify novel recurrent epimutations in candidate autism genes.

Scientific Applications:

• Rare Disease Research: Enables detection and prioritization of epimutations associated with rare diseases.
• Clinical Diagnostics: Supports identification of clinically relevant epimutations in patient cohorts, as demonstrated in autism cohort analyses.
• Epigenetic Studies: Facilitates genome-wide analyses of DNA methylation outliers to study epigenetic modifications and their biological implications.

Methodology:

Detection is performed via statistical outlier analyses (MANOVA, multivariate linear models, isolation forest, robust Mahalanobis distance, quantile analysis, beta distribution-based methods); methods were validated against publicly available datasets with experimentally confirmed epimutations and include functions for annotation and visualization.