Machine learning approaches to mine evolving 3D genomes

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The evolutionary process that led to the diversity of genomes and species known today can be thought of as a 3 Billion-year long, massively parallel selection experiment. The outcome of this experiment, the genomes of modern species, can today be sequenced at low cost, and the genomes of hundreds of vertebrates are now publicly available. The long-term objective of my research program is to develop and apply computational methodologies to best take advantage of that incredibly rich data set to better understand the regulatory function of modern genomes such as the human genome.******We stand at the convergence of three major trends in science: (i) the increased ability to sequence genomes; (ii) the development of deep learning approaches that can take advantage of large data sets to identify patterns and predict function with an unprecedented accuracy. (iii) The realization that epigenomics, and in particular 3D epigenomics play a driver role in regulating gene expression. In this exciting conjuncture, our short-term objectives are:******Aim 1: Develop machine-learning approaches that can take advantage of multi-species comparison to boost accuracy of prediction of various types of regulatory elements, both transcriptional and post-transcriptional. We will develop machine learning approaches that can combine predictions made by existing bioinformatics tools on multiple orthologous sequence to boost accuracy. We will then develop new semi-supervised machine learning approaches train deep learning approaches directly on a combination of labeled genomic data and unlabeled orthologous regions. ******Aim 2: Use ML to improve the reconstruction of mammalian evolutionary history. We will improve the accuracy of whole-genome alignment, ancestral genome inference using deep learning and reinforcement learning. We will also focus on developing machine learning approaches to predict cell-type specific 3D genome organization (compartments, TADs) from DNA sequence, and apply the approach to both modern genomes and inferred ancestral genomes.******Aim 3: Study how changes in genomic context impact regulatory functions, and how best to take this into consideration for prediction. Taking advantage of the approaches developed in Aims 1 and 2, we will study how genome rearrangements and transposable element insertion alter (predicted) 3D genome conformation and the activity of regulatory regions. We will then revise the machine learning models developed in Aim 1 to augment them with information about 3D genome context. ******In summary, this research program will develop cutting-edge machine learning to help study genome evolution and learn to recognize function from studying evolving genomes. By bringing together approaches from evolution, genomics, bioinformatics, and machine learning, it not only offers the opportunity for significant conceptual steps forward in both biology and computer science, but will also serve as a great conduit for training in interdisciplinary research.