Statistics for ChIP-chip and DNase hypersensitivity experiments on NimbleGen arrays

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• High-density oligonucleotide tiling arrays generate complex data requiring specialized analysis.
• Existing methods for analyzing tiled array data present computational challenges for researchers.

Purpose of the Study:

• To introduce ACME (Algorithm for Capturing Microarray Enrichment), a novel computational program for analyzing NimbleGen-tiled microarray data.
• To provide a robust method for identifying significant genomic regions from ChIP-chip and DNase-chip experiments.

Main Methods:

• ACME employs a sliding window and threshold strategy to detect signal enrichment (peaks) in tiled array data.
• The program assigns a probability value (p-value) to each probe, enabling statistical assessment of identified signals.
• The software is implemented in the R language and is available through Bioconductor.

Main Results:

• ACME successfully identifies genomic signals in both ChIP-chip and DNase-chip applications.
• The algorithm provides a p-value for each probe, facilitating the statistical validation of enriched regions.
• Recommendations for data quality assessment and ACME optimization are provided.

Conclusions:

• ACME offers an effective solution for analyzing high-density oligonucleotide tiling array data.
• The program aids in the interpretation of ChIP-chip and DNase-chip data, advancing genomic research.
• ACME is a freely available bioinformatics tool for the scientific community.