Monoallelic gene inference from chromatin (MaGIC)

Monoallelic gene inference from chromatin (MaGIC) infers monoallelically expressed (MAE) versus biallelically expressed (BAE) genes by analyzing chromatin-mark enrichment from ChIP-seq using machine learning.

Key Features:

• Sequence-independent chromatin signature: Uses a sequence-independent chromatin signature derived from chromatin-mark enrichment to identify MAE with high sensitivity and specificity across tissue types.
• ChIP-seq input: Operates on ChIP-seq chromatin mark enrichment data as the primary input for inference.
• Model flexibility and machine learning: Applies machine learning classification with support for applying pre-existing models or training new models using additional chromatin marks.

Scientific Applications:

• Random monoallelic expression analysis: Enables study of epigenetic mechanisms such as random MAE involving allele-specific expression that varies between clonal cell lineages.
• MAE mapping across tissues: Facilitates mapping of MAE across diverse cell and tissue types to investigate cell-type-specific gene expression and epigenetic regulation.
• Biological and translational research: Provides data and classifications relevant to developmental biology, cancer research, and personalized medicine.

Methodology:

MaGIC analyzes chromatin marks from ChIP-seq to derive a sequence-independent chromatin signature and applies machine learning models to classify genes as MAE or BAE, with options to use pre-existing models or train new models using additional chromatin marks.