MMDiff2

MMDiff2 detects statistically significant differences between read enrichment profiles across ChIP-Seq samples using kernel-based Maximum Mean Discrepancy to quantify shape differences in read distributions.

Key Features:

• Read enrichment profile analysis: Analyzes read enrichment profiles derived from ChIP-Seq samples.
• Statistical detection of differences: Performs statistical detection of significant variations between ChIP-Seq datasets.
• Kernel-based Maximum Mean Discrepancy (MMD): Employs kernel methods and MMD to compare distributions and quantify shape differences.
• Non-parametric comparison: Uses a non-parametric framework that does not assume specific parametric forms for underlying distributions.
• Integration with Bioconductor: Integrates with the Bioconductor ecosystem for genomic analysis workflows.
• Interoperability with R: Implements functionality in R to interoperate with other Bioconductor packages.

Scientific Applications:

• Differential binding analysis: Detects differential binding between conditions or samples from ChIP-Seq experiments.
• Epigenetic and chromatin studies: Compares enrichment shapes to characterize epigenetic modifications and chromatin state differences.
• Protein–DNA interaction profiling: Assists in identifying changes in protein–DNA interactions by comparing read enrichment profiles across samples.

Methodology:

Computes Maximum Mean Discrepancy (MMD) using kernel methods to compare distributions of ChIP-Seq read enrichment profiles in a non-parametric framework.