Quantifying the functional and evolutionary relationships among sequences, transcription factor binding and gene expression

A central challenge in regulatory genomics today is to understand the precise relationship between regulatory sequences, transcription factor (TF) binding and gene expression. Many studies have discussed how TFs recognize their DNA binding sites. However, it is not well understood how the various factors that influence TF-DNA binding alter the cascade of gene expression. Moreover, mutations in regulatory sequences are a key driving force of evolution and diseases. A number of studies have examined the sequence motif turnover and divergence in TF binding across species. However, there is currently a lack of clarity on what these changes mean to enhancer function. In this thesis, we used computational and statistical methods to quantitatively and systematically examine the relationships among regulatory sequences, TF binding, and gene expression, from both functional and evolutionary perspectives. At the functional level, we extended thermodynamics-based statistical models of the genetic sequence-to-function relationship to accurately predict gene expression. We incorporated chromatin accessibility and structural biological data into the models, described in Chapter 2 and 3. In doing so, we aimed to better identify transcription factor binding sites likely to influence gene expression, and thus, enhance the models’ capacity to predict gene expression. We demonstrated these improvements to gene expression modeling in Drosophila melanogaster by integrating DNaseI hypersensitivity assays and DNA shape. At the evolutionary level, we focused on regulatory variations between two distant Drosophila species to access inherent properties of enhancers, as described in Chapter 4. We used statistical and computational approaches to quantitatively examine the extent to which sequence and accessibility variations can predict TF occupancy divergence and enhancer activity change. We also found combinatorial TF binding can buffer variations at individual TF level to avoid drastic gene expression changes