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Systematic evaluation of variability in ChIP-chip experiments using predefined DNA targets

    • 1 Department of Genetics, Stanford University Medical Center, Stanford, California 94305, USA;
    • 2 Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Harvard School of Public Health, Boston, Massachusetts 02115, USA;
    • 3 Agilent Technologies, Inc., Santa Clara, California 95051, USA;
    • 4 Cancer Research UK, Cambridge Research Institute, Cambridge, CB2 0RE, United Kingdom;
    • 5 Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts 02115, USA;
    • 6 EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom;
    • 7 European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom;
    • 8 Department of Pharmacology and the Genome Center, University of California–Davis, Davis, California 95616, USA;
    • 9 Affymetrix, Inc., Santa Clara, California 95051, USA;
    • 10 HCI Bio Informatics, Huntsman Cancer Institute, Salt Lake City, Utah 84112, USA;
    • 11 Roche NimbleGen, Inc., Madison, Wisconsin 53719, USA;
    • 12 Whitehead Institute, Cambridge, Massachusetts 02142, USA;
    • 13 SwitchGear Genomics, Menlo Park, California 94025, USA;
    • 14 Ludwig Institute for Cancer Research, Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, California 92093-0653, USA;
    • 15 Department of Genetics, Case Western Reserve University, Cleveland, Ohio 44106, USA;
    • 16 Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut 06520, USA;
    • 17 Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, Connecticut 06520, USA;
    • 18 Department of Genetics, Yale University, New Haven, Connecticut 06520, USA;
    • 19 Genentech Inc., South San Francisco, California 94080-4990, USA;
    • 20 Department of Biological Chemistry & Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts 02115-5730, USA;
    • 21 Biomedical Engineering Department, Boston University, Boston, Massachusetts 02215, USA;
    • 22 Bioinformatics Program, Boston University, Boston, Massachusetts 02215, USA;
    • 23 Department of Biology and Carolina Center for Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-3280, USA
    • 24 These authors contributed equally to this work.
    • 25 Present address: Division of Biostatistics, Dan L. Duncan Cancer Center, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA.
    • 26 Corresponding authors. E-mail [email protected]; fax (617) 632-2444. E-mail [email protected]; fax (617) 432-2529. E-mail [email protected]; fax (650) 725-9687. E-mail [email protected]; fax (919) 962-1625.
Published February 7, 2008. Vol 18 Issue 3, pp. 393-403. https://doi.org/10.1101/gr.7080508
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Abstract

The most widely used method for detecting genome-wide protein–DNA interactions is chromatin immunoprecipitation on tiling microarrays, commonly known as ChIP-chip. Here, we conducted the first objective analysis of tiling array platforms, amplification procedures, and signal detection algorithms in a simulated ChIP-chip experiment. Mixtures of human genomic DNA and “spike-ins” comprised of nearly 100 human sequences at various concentrations were hybridized to four tiling array platforms by eight independent groups. Blind to the number of spike-ins, their locations, and the range of concentrations, each group made predictions of the spike-in locations. We found that microarray platform choice is not the primary determinant of overall performance. In fact, variation in performance between labs, protocols, and algorithms within the same array platform was greater than the variation in performance between array platforms. However, each array platform had unique performance characteristics that varied with tiling resolution and the number of replicates, which have implications for cost versus detection power. Long oligonucleotide arrays were slightly more sensitive at detecting very low enrichment. On all platforms, simple sequence repeats and genome redundancy tended to result in false positives. LM-PCR and WGA, the most popular sample amplification techniques, reproduced relative enrichment levels with high fidelity. Performance among signal detection algorithms was heavily dependent on array platform. The spike-in DNA samples and the data presented here provide a stable benchmark against which future ChIP platforms, protocol improvements, and analysis methods can be evaluated.

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