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Methods

Mapping of transcription factor binding regions in mammalian cells by ChIP: Comparison of array- and sequencing-based technologies

¹ Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, Connecticut 06520-8103, USA; ² Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut 06520-8114, USA; ³ Genome Institute of Singapore, Singapore 138672; ⁴ Department of Genetics, Yale University School of Medicine, New Haven, Connecticut 06520-8005, USA; ⁵ Center for Nanotechnology, NASA Ames Research Center, Moffett Field, California 94035, USA

Recent progress in mapping transcription factor (TF) binding regions can largely be credited to chromatin immunoprecipitation (ChIP) technologies. We compared strategies for mapping TF binding regions in mammalian cells using two different ChIP schemes: ChIP with DNA microarray analysis (ChIP-chip) and ChIP with DNA sequencing (ChIP-PET). We first investigated parameters central to obtaining robust ChIP-chip data sets by analyzing STAT1 targets in the ENCODE regions of the human genome, and then compared ChIP-chip to ChIP-PET. We devised methods for scoring and comparing results among various tiling arrays and examined parameters such as DNA microarray format, oligonucleotide length, hybridization conditions, and the use of competitor Cot-1 DNA. The best performance was achieved with high-density oligonucleotide arrays, oligonucleotides 50 bases (b), the presence of competitor Cot-1 DNA and hybridizations conducted in microfluidics stations. When target identification was evaluated as a function of array number, 80%–86% of targets were identified with three or more arrays. Comparison of ChIP-chip with ChIP-PET revealed strong agreement for the highest ranked targets with less overlap for the low ranked targets. With advantages and disadvantages unique to each approach, we found that ChIP-chip and ChIP-PET are frequently complementary in their relative abilities to detect STAT1 targets for the lower ranked targets; each method detected validated targets that were missed by the other method. The most comprehensive list of STAT1 binding regions is obtained by merging results from ChIP-chip and ChIP-sequencing. Overall, this study provides information for robust identification, scoring, and validation of TF targets using ChIP-based technologies.

⁷ Present addresses: PDL BioPharma, Inc., 34801 Campus Drive, Fremont, CA 94555, USA;

⁸ Stockholm Bioinformatics Center, AlbaNova University Center, Stockholm University, SE-10691 Stockholm, Sweden.

⁹ Corresponding author.

E-mail michael.snyder{at}yale.edu; fax (203) 432-6161.

[Supplemental material is available online at www.genome.org.]

Article is online at http://www.genome.org/cgi/doi/10.1101/gr.5583007

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