Molecular and computational tools for regulatory genomics

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Transcription of genomic DNA is a central step in regulating how much each gene is expressed, and integral to development, cellular identity, and response to stimuli. Cells use proteins called transcription factors that work together in complex ways to interpret DNA sequences and regulate transcription in a process termed “cis-regulatory logic”. Although we can study cis-regulation using the reference human genome, it likely doesn't contain sufficient examples of the rarely-used regulatory from which to learn. However, we can learn arbitrarily complex mechanisms by creating new examples. The long-term objective of my research program is to predict gene expression in any cell type from DNA sequence alone. In the short term, I will learn cis-regulatory logic in select cell types with the next generation of experimental and computational tools. We previously showed that random DNA makes ideal training data for learning cis-regulatory logic. Random DNA can be synthesized and assayed in very high throughput. Each experiment measured an entire human genome's worth of regulatory DNA. Random DNA was comparable in activity to actual regulatory DNA due to the abundant cis-regulatory elements included by chance. The scale and diversity of these data allowed us to learn complex regulatory mechanisms from scratch, significantly advancing our understanding of cis-regulation. We hypothesize that random DNA will yield similar insights into human cis-regulation. The assortment of -regulatory elements in each random sequence will provide rich data from which to learn the roles of the transcription factors active in each cell type. (1) We will learn complex cis-regulatory mechanisms in a controlled system. We will develop experimental tools for measuring the regulatory activity of tens of millions of DNA sequences per experiment in a controlled system and in many cell types, giving us the scale of training data needed to learn complex regulatory mechanisms. (2) We will adapt our models to the genome. By randomizing genomic DNA and measuring gene expression, we will learn how cis-regulatory sequences affect the expression of endogenous genes. This will allow us to predict gene expression from sequence genome-wide. (3) Learning more complex rules. As we generate data through the first two objectives, we will develop our models to capture more complex and rare gene regulatory rules. The approaches we create in Objectives 1 and 2 will act as gene regulation sentinels, providing a direct readout of the transcriptional cell state. We will make our computational and experimental tools accessible to others in the field, maximizing our impact. As a mixed computational and experimental lab, the 9 HQP included in this application will be trained in machine learning, “big data”, statistics, genome editing, synthetic biology, molecular biology, gene regulation, project management, and communication.