Model-Based Analysis of Tiling-Arrays for ChIP-Chip

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 103; no. 33; pp. 12457 - 12462
• Main Authors: Johnson, W. Evan, Li, Wei, Meyer, Clifford A., Gottardo, Raphael, Carroll, Jason S., Brown, Myles, Liu, X. Shirley
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 15.08.2006; National Acad Sciences
• Subjects: Algorithms; Antibodies; Behavior modeling; Biological Sciences; Chromatin; Chromosomes; Data analysis; DNA probes; Field programmable gate arrays; Genetic algorithms; Genomes; Genomics; Humans; Internet; Modeling; Models, Theoretical; Nucleotides; Oligonucleotide Array Sequence Analysis - instrumentation; Oligonucleotide Array Sequence Analysis - methods; Physical Sciences; Receptors, Estrogen - genetics; Receptors, Estrogen - metabolism; Software; Standardization; Statistical variance; Tiling; Transcription Factors - genetics; Transcription Factors - metabolism
• ISSN: 0027-8424; 1091-6490
• DOI: 10.1073/pnas.0601180103

We propose a fast and powerful analysis algorithm, titled Modelbased Analysis of Tiling-arrays (MAT), to reliably detect regions enriched by transcription factor chromatin immunoprecipitation (ChIP) on Affymetrix tiling arrays (ChlP-chip). MAT models the baseline probe behavior by considering probe sequence and copy number on each array. It standardizes the probe value through the probe model, eliminating the need for sample normalization. MAT uses an innovative function to score regions for ChIP enrichment, which allows robust P value and false discovery rate calculations. MAT can detect ChIP regions from a single ChIP sample, multiple ChlP samples, or multiple ChIP samples with controls with increasing accuracy. The single-array ChIP region detection feature minimizes the time and monetary costs for laboratories newly adopting ChlP-chip to test their protocols and antibodies and allows established ChlP-chip laboratories to identify samples with questionable quality that might contaminate their data. MAT is developed in open-source Python and is available at http://chip. dfci.harvard.edu/∼wli/MAT. The general framework presented here can be extended to other oligonucleotide microarrays and tiling array platforms.