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BMC Bioinformatics. 2009 Sep 22;10:305. doi: 10.1186/1471-2105-10-305.

Strategies for analyzing highly enriched IP-chip datasets.

Author information

1
Molecular and Computational Biology Program, University of Southern California, Ray Irani Hall, University Park Campus, Los Angeles, CA 90089-2910, USA. knott@usc.edu

Abstract

BACKGROUND:

Chromatin immunoprecipitation on tiling arrays (ChIP-chip) has been employed to examine features such as protein binding and histone modifications on a genome-wide scale in a variety of cell types. Array data from the latter studies typically have a high proportion of enriched probes whose signals vary considerably (due to heterogeneity in the cell population), and this makes their normalization and downstream analysis difficult.

RESULTS:

Here we present strategies for analyzing such experiments, focusing our discussion on the analysis of Bromodeoxyruridine (BrdU) immunoprecipitation on tiling array (BrdU-IP-chip) datasets. BrdU-IP-chip experiments map large, recently replicated genomic regions and have similar characteristics to histone modification/location data. To prepare such data for downstream analysis we employ a dynamic programming algorithm that identifies a set of putative unenriched probes, which we use for both within-array and between-array normalization. We also introduce a second dynamic programming algorithm that incorporates a priori knowledge to identify and quantify positive signals in these datasets.

CONCLUSION:

Highly enriched IP-chip datasets are often difficult to analyze with traditional array normalization and analysis strategies. Here we present and test a set of analytical tools for their normalization and quantification that allows for accurate identification and analysis of enriched regions.

PMID:
19772646
PMCID:
PMC2759964
DOI:
10.1186/1471-2105-10-305
[Indexed for MEDLINE]
Free PMC Article

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