Model-based analysis of tiling-arrays for ChIP-chip.
We propose a fast and powerful analysis algorithm, titled Model-based Analysis of Tiling-arrays (MAT), to reliably detect regions enriched by transcription factor chromatin immunoprecipitation (ChIP) on Affymetrix tiling arrays (ChIP-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 ChIP 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 ChIP-chip to test their protocols and antibodies and allows established ChIP-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/ approximately wli/MAT. The general framework presented here can be extended to other oligonucleotide microarrays and tiling array platforms.
Johnson
W Evan
WE
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 44 Binney Street, Boston, MA 02115, USA.
Li
Wei
W
Meyer
Clifford A
CA
Gottardo
Raphael
R
Carroll
Jason S
JS
Brown
Myles
M
Liu
X Shirley
XS
D000465:Algorithms; D006801:Humans; D020407:Internet; D008962:Models, Theoretical; D020411:Oligonucleotide Array Sequence Analysis / Q000295:instrumentation / Q000379:methods; D011960:Receptors, Estrogen / Q000235:genetics / Q000378:metabolism; D012984:Software; D014157:Transcription Factors / Q000235:genetics / Q000378:metabolism
D011960:Receptors, Estrogen; D014157:Transcription Factors