MaGIC

MaGIC identifies genes subject to monoallelic expression (MAE) by using a sequence-independent chromatin signature derived from ChIP-seq chromatin mark enrichment to map MAE across tissues and enable model training with additional chromatin mark datasets.

Key Features:

• Sequence-independent chromatin signature: Identifies MAE based on chromatin mark patterns rather than DNA sequence.
• ChIP-seq input: Uses chromatin mark enrichment data derived from ChIP-seq experiments.
• Sensitivity and specificity: Demonstrated high sensitivity and specificity across multiple tissue types.
• Open-source pipeline: Implemented as an open-source software pipeline for computational analysis of chromatin marks.
• Model training and adaptability: Supports use of existing predictive models or training of new models with additional chromatin mark datasets.
• Multi-tissue applicability: Applicable to diverse cell types and tissue datasets for mapping MAE.

Scientific Applications:

• MAE mapping: Maps monoallelic expression across tissues and cell lineages to reveal cell-type-specific MAE patterns.
• Epigenetic regulation studies: Aids investigation of regulatory mechanisms of allele-specific gene expression in clonal cell lineages.

Methodology:

Analyzes chromatin mark enrichment data from ChIP-seq and infers MAE using a sequence-independent signature based on the presence and patterns of chromatin marks; the pipeline supports application to existing datasets or training of new models.