The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record. EnquiriesFor any further enquiries regarding the licence status of this document, please contact: researchsupport@kent.ac.uk If you believe this document infringes copyright then please contact the KAR admin team with the take-down information provided at http://kar.kent.ac.uk/contact.html We present three models of how transcription factors (TFs) bind to their specific binding sites on the DNA: a model based on statistical physics, a Markov-chain model and a computational simulation. Citation for published versionComparison of these models suggests that the effect of non-specific binding can be significant. We also investigate possible mechanisms for cooperativity. The simulation model suggests that direct interactions between TFs are unlikely to be the main source of cooperativity between specific binding sites, because such interactions tend to lead to the formation of clusters on the DNA with undesirable side-effects.

The classical example of nonadditive contributions of the two parents to allopolyploids is nucleolar dominance, which entails silencing of one parental set of ribosomal RNA genes. This has been observed for many other loci. The prevailing explanation for this genome-wide expression disparity is that the two merged genomes differ in their transposable element (TE) complement and in their level of TE-mediated repression of gene expression. Alternatively, and not exclusively, gene expression dominance may arise from mismatches between trans effectors and their targets. Here, we explore quantitative models of regulatory mismatches leading to gene expression dominance. We also suggest that, when pairs of merged genomes are similar from one allopolyploidization event to another, gene-level and genome dominance patterns should also be similar.

The organization of the genome into topologically associating domains (TADs) was shown to have a regulatory role in development and cellular function, but the mechanism involved in TAD establishment is still unclear. Here, we present the first high-resolution contact map of Drosophila neuronal cells (BG3) and identify different classes of TADs by comparing this to genome organization in embryonic cells (Kc167). We find that only some TADs are conserved in both cell lines, whereas the rest are cell-type-specific. This is supported by a change in the enrichment of architectural proteins at TAD borders, with BEAF-32 present in embryonic cells and CTCF in neuronal cells. Furthermore, we observe strong divergent transcription, together with RNA Polymerase II occupancy and an increase in DNA accessibility at the TAD borders. TAD borders that are specific to neuronal cells are enriched in enhancers controlled by neuronal-specific transcription factors. Our results suggest that TADs are dynamic across developmental stages and reflect the interplay between insulators, transcriptional states, and enhancer activities.

DNA methylation has been associated with transcriptional repression and detection of differential methylation is important in understanding the underlying causes of differential gene expression. Bisulfite-converted genomic DNA sequencing is the current gold standard in the field for building genome-wide maps at a base pair resolution of DNA methylation. Here we systematically investigate the underlying features of detecting differential DNA methylation in CpG and non-CpG contexts, considering both the case of mammalian systems and plants. In particular, we introduce DMRcaller, a highly efficient R/Bioconductor package, which implements several methods to detect differentially methylated regions (DMRs) between two samples. Most importantly, we show that different algorithms are required to compute DMRs and the most appropriate algorithm in each case depends on the sequence context and levels of methylation. Furthermore, we show that DMRcaller outperforms other available packages and we propose a new method to select the parameters for this tool and for other available tools. DMRcaller is a comprehensive tool for differential methylation analysis which displays high sensitivity and specificity for the detection of DMRs and performs entire genome wide analysis within a few hours.