Strategies for analyzing highly enriched IP-chip datasets

Rif1 regulates initiation timing of late replication origins throughout the S. cerevisiae genome

Chromosomal DNA replication involves the coordinated activity of hundreds to thousands of replication origins. Individual replication origins are subject to epigenetic regulation of their activity during S-phase, resulting in differential efficiencies and timings of replication initiation during S-phase. This regulation is thought to involve chromatin structure and organization into timing domains with differential ability to recruit limiting replication factors. Rif1 has recently been identified as a genome-wide regulator of replication timing in fission yeast and in mammalian cells. However, previous studies in budding yeast have suggested that Rif1’s role in controlling replication timing may be limited to subtelomeric domains and derives from its established role in telomere length regulation. We have analyzed replication timing by analyzing BrdU incorporation genome-wide, and report that Rif1 regulates the timing of late/dormant replication origins throughout the S. cerevisiae genome. Analysis of pfa4Δ cells, which are defective in palmitoylation and membrane association of Rif1, suggests that replication timing regulation by Rif1 is independent of its role in localizing telomeres to the nuclear periphery. Intra-S checkpoint signaling is intact in rif1Δ cells, and checkpoint-defective mec1Δ cells do not comparably deregulate replication timing, together indicating that Rif1 regulates replication timing through a mechanism independent of this checkpoint. Our results indicate that the Rif1 mechanism regulates origin timing irrespective of proximity to a chromosome end, and suggest instead that telomere sequences merely provide abundant binding sites for proteins that recruit Rif1. Still, the abundance of Rif1 binding in telomeric domains may facilitate Rif1-mediated repression of non-telomeric origins that are more distal from centromeres.

The level of origin firing inversely affects the rate of replication fork progression

Cells with reduced origin firing have an increased rate of replication fork progression, whereas fork progression is slowed in cells with excess origins.

DNA damage slows DNA synthesis at replication forks; however, the mechanisms remain unclear. Cdc7 kinase is required for replication origin activation, is a target of the intra-S checkpoint, and is implicated in the response to replication fork stress. Remarkably, we found that replication forks proceed more rapidly in cells lacking Cdc7 function than in wild-type cells. We traced this effect to reduced origin firing, which results in fewer replication forks and a consequent decrease in Rad53 checkpoint signaling. Depletion of Orc1, which acts in origin firing differently than Cdc7, had similar effects as Cdc7 depletion, consistent with decreased origin firing being the source of these defects. In contrast, mec1-100 cells, which initiate excess origins and also are deficient in checkpoint activation, showed slower fork progression, suggesting the number of active forks influences their rate, perhaps as a result of competition for limiting factors.

HAT: a novel statistical approach to discover functional regions in the genome

Tiling arrays are useful for exploring local functions of regions of the genome in an unbiased fashion. The exact determination of those genomic regions based on tiling-array data, e.g., generated by means of hybridization with immunopreciptated DNA-fragments to the arrays is a challenge. Many different statistical methodologies have been developed to find biological relevant regions-of-interest (ROI) by using the quantitative signal intensity of each probe. We previously developed a method called Hypergeometric Analysis of Tiling arrays (HAT) for the analysis of tiling-array data, but it is developed such that it can also be used to study data derived by genome-wide deep sequencing approaches. Here we applied HAT to analyze two publicly available tiling-array data sets. After the detection of statistically significant ROI, these are often used in additional analysis for hypothesis testing. We therefore discuss, by using the results of the tiling-array experiment, pathway and motif analyses.

Forkhead transcription factors establish origin timing and long-range clustering in S. cerevisiae

The replication of eukaryotic chromosomes is organized temporally and spatially within the nucleus through epigenetic regulation of replication origin function. The characteristic initiation timing of specific origins is thought to reflect their chromatin environment or sub-nuclear positioning, however the mechanism remains obscure. Here we show that the yeast Forkhead transcription factors, Fkh1 and Fkh2, are global determinants of replication origin timing. Forkhead regulation of origin timing is independent of local levels or changes of transcription. Instead, we show that Fkh1 and Fkh2 are required for the clustering of early origins and their association with the key initiation factor Cdc45 in G1 phase, suggesting that Fkh1 and Fkh2 selectively recruit origins to emergent replication factories. Fkh1 and Fkh2 bind Fkh-activated origins, and interact physically with ORC, providing a plausible mechanism to cluster origins. These findings add a new dimension to our understanding of the epigenetic basis for differential origin regulation and its connection to chromosomal domain organization.

Normalization of high dimensional genomics data where the distribution of the altered variables is skewed

Genome-wide analysis of gene expression or protein binding patterns using different array or sequencing based technologies is now routinely performed to compare different populations, such as treatment and reference groups. It is often necessary to normalize the data obtained to remove technical variation introduced in the course of conducting experimental work, but standard normalization techniques are not capable of eliminating technical bias in cases where the distribution of the truly altered variables is skewed, i.e. when a large fraction of the variables are either positively or negatively affected by the treatment. However, several experiments are likely to generate such skewed distributions, including ChIP-chip experiments for the study of chromatin, gene expression experiments for the study of apoptosis, and SNP-studies of copy number variation in normal and tumour tissues. A preliminary study using spike-in array data established that the capacity of an experiment to identify altered variables and generate unbiased estimates of the fold change decreases as the fraction of altered variables and the skewness increases. We propose the following work-flow for analyzing high-dimensional experiments with regions of altered variables: (1) Pre-process raw data using one of the standard normalization techniques. (2) Investigate if the distribution of the altered variables is skewed. (3) If the distribution is not believed to be skewed, no additional normalization is needed. Otherwise, re-normalize the data using a novel HMM-assisted normalization procedure. (4) Perform downstream analysis. Here, ChIP-chip data and simulated data were used to evaluate the performance of the work-flow. It was found that skewed distributions can be detected by using the novel DSE-test (Detection of Skewed Experiments). Furthermore, applying the HMM-assisted normalization to experiments where the distribution of the truly altered variables is skewed results in considerably higher sensitivity and lower bias than can be attained using standard and invariant normalization methods.