A major goal in understanding cellular behavior is to reveal the 'wiring' of transcriptional regulation, through which transcription factors (TFs) bind target-gene promoters to control gene expression. Promoter regions contain sequence elements - typically 5 to 12 nucleotides (nt) in length - at which TFs bind specifically. By enhancing/inhibiting transcription or recruiting complexes that remodel chromatin structure, TFs regulate expression of the genes whose promoters they bind. Chromatin immunoprecipitation (ChIP) is an experimental technique for identifying those regions of DNA bound by a particular protein, and is, therefore, a useful method for determining which genes have their promoters bound by a TF. In outline, the method consists of the following steps. The TF under study is crosslinked to DNA which is subsequently extracted and sheared into fragments approximately 400 nt long (1,000 nt resolution is usually sufficient to assign binding to the regulation of a specific gene, so it is rare to exceed this length [1
]). The fragments are immunoprecipitated with an antibody specific to that TF (or to a peptide affinity tag fused to that TF), whereupon the crosslinks are reversed, the DNA precipitate amplified, and the intergenic regions (IGRs) containing the binding site(s) are determined by examining the relative abundance of each immunoprecipitated DNA fragment. The combination of ChIP with microarray technology is often called 'ChIP-chip' [1
] and is referred to here as 'Chip2
'. It has turned ChIP into a high-throughput technique for efficiently mapping gene regulatory networks [2
Two-channel microarrays use hybridization to compare the abundance of specific nucleic acid sequences in one mixture to abundance of the same sequences in another control mixture. The choice of control mixture may greatly affect the outcome of the experiment. A typical choice is fragmented genomic DNA, which controls for the relative abundance and non-specific hybridization potential of genomic DNA fragments. Genomic DNA may be purified from 'whole-cell extract', which itself is sometimes used as a control. As some DNA fragments may be 'stickier' than others, a more stringent and laborious mock control (containing fragments recovered nonspecifically by immunoprecipitation (IP)) is sometimes performed, in which the TF does not have a fused affinity tag.
The change in abundance of a particular sequence between two mixtures is often measured in terms of 'fold-change' between the two channels (ratio) or, alternatively, the logarithm of fold-change (log-ratio). The IP channel serves as numerator, while the control is the denominator. The array surface between regions with spotted DNA is never completely 'dark', due to the combined effects of residual DNA fragments bound non-specifically to the array surface, and the experimentalist's control of the visual amplification ('gain') in the image analysis software. It is customary to subtract this 'background' from each spot because it reveals nothing about the protein-DNA binding. This subtraction raises the possibility, however, that the denominator could become negative or zero, in which case the log-ratio is not useful. Common strategies for handling zero or negative values are either to threshold or to discard data points altogether, neither of which is entirely satisfactory. A further, and perhaps more serious, problem is the practice of interpreting this fold-change as a measure of significance, when it provides no such statistical basis. Small random fluctuations in signals close to background, particularly in the denominator, are amplified, leading to spuriously high levels of 'fold-change' [10
]. In other words, we should reduce our confidence in a twofold change between signals that are each near the background noise, compared to a twofold change between strong signals. Because we are generally more interested in whether a region is specifically bound at all than we are in the degree of its binding (occupancy), there is a need for an accurate measure of confidence in each measurement.
A statistical approach for analysis of mRNA abundance microarrays has been developed in which a 'single-array' error model accounts for variation in the background level for each microarray, while a 'gene-specific' error model describes variation of a single gene across replicate arrays. These two complementary models can be combined to estimate the error in each log-ratio measurement [10
]. A variant of the single-array approach (in which there is gene-specific normalization) has been applied to transcription-factor binding site identification by means of Chip2
in yeast [2
]. Unfortunately, it requires one or more separate control experiments to determine error model parameters, in which identical nucleic acid mixtures are compared. This adds to the expense of the experiment; furthermore, error model parameters derived from a separate microarray are potential sources of systematic error, since quality can vary between microarrays.