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It’s About Time: Interpretable Methods and Associated Interactive Platforms to Uncover Regulatory Mechanisms from Temporal and Multi-Omics Data


Development is a complex process governed by coordinated gene regulation at a precise time and place. Uncovering how transcription factors (TFs) and their targets communicate at diverse molecular levels is critical to understanding signaling cascades of normal growth and disease. To provide insight into the relationships between molecular factors over time, researchers have generated vast amounts of temporal and multi-omics data that track function and physical interactions between DNA, RNA, and proteins. There is a lack of methods that adequately integrate these temporal multi-omics data to model these relations and form gene regulatory networks (GRNs). Identifying such processes would enable the targeted design of follow-up experiments and tailored treatment options. This dissertation addresses this gap through three computational methods and tools in interactive platforms to identify GRNs from temporal multi-omics data, with a focus on small temporal RNA-seq and protein-DNA data. 1) TIMEOR (Trajectory Inference and Mechanism Exploration with Omics in R) is the first automated temporal multi-omics analysis platform to infer GRNs between TFs and their order of action. Our results validate known findings and identify a novel link between the circadian rhythm cycle and insulin. 2) Time2splice identifies temporal and sex-specific alternative splicing from multi-omics data. Through our robust experimental design, computational method, and wet-lab validation, we show that widespread temporal sex-specific transcript diversity occurs much earlier than previously reported. Further, we highlight several mechanisms by which a maternal TF regulates sex-specific zygotic transcriptome diversity. 3) XvsY identifies shared and distinct molecular signatures given multiple high-dimensional temporal multi-omics datasets. We illustrate its utility to distinguish temporal and sex-specific roles of two vital TFs during Drosophila brain development. My current and future work entail building interpretable machine learning methods to analyze multi-omics data. Overall, these tools equip non-coding researchers to leverage interactive tools and platforms for their analyses, enabling them to focus their time designing and performing follow-up experiments. Researchers leveraging these tools report saving time and improving result interpretation. In the future, these types of tools will enable drug discovery researchers and clinicians to perturb a learned model to examine their context, thus bringing us closer to personalized therapeutics.
Thesis (Ph. D.)--Brown University, 2022


Conard, Ashley Mae, "It’s About Time: Interpretable Methods and Associated Interactive Platforms to Uncover Regulatory Mechanisms from Temporal and Multi-Omics Data" (2022). Center for Computational Molecular Biology Theses and Dissertations. Brown Digital Repository. Brown University Library.


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  • Supplement for Ashley Mae Conard Dissertation
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