MADAP

MADAP performs one-dimensional clustering of genome annotation data to identify peaks within noisy signal profiles and characterize their volumes, extensions, and center positions from genome coordinates associated with counts or intensities.

Key Features:

• Flexible Clustering: Defines discrete peaks characterized by volume, extension, and center position from genome coordinates linked to counts or intensities, enabling tasks such as transcription start site (TSS) definition and mapping transcription factor binding sites from ChIP-chip data.
• Customizable Parameters: Exposes adjustable parameters to tailor the clustering process to different genomic datasets and analysis goals.
• Noise-tolerant Peak Detection: Identifies peaks and clusters within noisy signal profiles typical of high-throughput genomic experiments.
• Integration with Biological Databases: Produces outputs compatible with external resources, including the Eukaryotic Promoter Database (EPD) of annotated eukaryotic POL II promoters defined by TSS.

Scientific Applications:

• Transcription Start Site Identification: Defines 5' and 3' ends of mRNA to pinpoint TSS for gene expression and promoter studies.
• ChIP-chip Data Analysis: Clusters binding-site signals to map transcription factor–DNA interactions from ChIP-chip experiments.
• Quantitative Trait Loci (QTL) Mapping: Analyzes whole-genome SNP profiles to detect QTLs associated with phenotypic traits.

Methodology:

Processes input of genome coordinates linked to counts or intensities and applies a one-dimensional clustering algorithm to identify significant peaks and report each peak's characteristics, with parameter settings to adjust detection sensitivity.