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�������ϲ���ʷ��¼ Researcher Receives $1.8 Million NIH Award

Raquel Assis, Ph.D., associate professor, Department of Electrical Engineering and Computer Science within the College of Engineering and Computer Science, and a fellow of �������ϲ���ʷ��¼’s Institute for Human Health and Disease Intervention (I-HEALTH).

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�������ϲ���ʷ��¼’s , Ph.D., associate professor, within the , and a fellow of �������ϲ���ʷ��¼’s Institute for Human Health and Disease Intervention (I-HEALTH), has received a five-year, $1.8 million “Maximizing Investigators’ Research Award” (MIRA) from the National Institutes of Health (NIH). The goal of this early career award is to enhance the ability of investigators to take on ambitious scientific projects and approach problems more creatively.

Assis will develop a suite of tailored, model-based statistical and machine-learning approaches for classifying the evolutionary outcomes and predicting the evolutionary parameters of structural variations arising from duplication, deletion, inversion, and translocation events. Structural variations are key drivers of both evolutionary adaptation and human disease.

For the project, she will make comparisons among different types of structural variations, their evolutionary outcomes, and taxonomic groups. The major goal of the research is to ascertain the general rules by which different types of structural variations contribute to evolutionary innovation. These studies will shed light on how gene duplication, deletion, inversion, and translocation work in concert to generate a diversity of complex adaptations across the tree of life.

“Our preliminary studies indicated that these techniques will be much more powerful and accurate than previous approaches and will therefore compose major advancements in evolutionary investigations of structural variations,” said Assis. “In addition to implementing our methods in open source software packages, we will apply them to assay the evolutionary implications of different types of structural variations in humans and several other animal and plant taxa.”

Follow-up analyses from this research will test targeted hypotheses about how structural variations drive evolutionary innovation, such as whether different types of structural variations and their evolutionary outcomes play roles in sex-specific adaptations. 

Assis’ group develops and applies computational and statistical approaches for understanding the evolution of structural variations from patterns in their genomic and transcriptomic data. During the past few years, her group’s studies have focused primarily on gene duplication, which represents the most common type of structural variation observed in nature. In particular, Assis has investigated the origins of evolutionary innovation after gene duplication, a problem of long-standing interest in the evolutionary genomics community.

As a postdoctoral researcher in the at the , Berkeley, Assis designed the first method for classifying evolutionary outcomes of duplicate genes from phylogenetic comparisons of their gene expression profiles. By applying this decision tree method to multi-tissue gene expression data, she and her group were able to classify evolutionary outcomes of duplicate genes in Drosophila (fruit fly), mammals, and grasses. These studies revealed frequent tissue-specific expression divergence after duplication, as well as sequence and expression differences within and among taxa that are consistent with natural selection.

“There are many limitations of decision tree methods, including sensitivity to gene expression stochasticity, lack of statistical support, and inability to predict parameters driving the evolution of structural variations,” said , Ph.D., dean, College of Engineering and Computer Science. “We are extremely proud of professor Assis for receiving this esteemed and highly-competitive NIH MIRA grant, which will help her overcome these obstacles with the design of new model-based statistical and machine-learning techniques for simultaneously classifying evolutionary outcomes and predicting evolutionary parameters of structural variations from genomic and transcriptomic data.”

In a follow-up population-genomic analysis, Assis’ group demonstrated that natural selection indeed plays an important role in the evolutionary outcomes of young duplicate genes in Drosophila. Her group later developed analogous decision tree classifiers for an additional type of structural variations: gene deletion. Application of this method to gene expression data from multiple tissues and developmental stages in Drosophila uncovered rapid divergence concordant with adaptation, suggesting that natural selection shapes the evolutionary trajectories of structural variations generated by deletion and translocation as well.

Assis worked with her collaborator, , Ph.D., associate professor, Department of Electrical Engineering and Computer Science at �������ϲ���ʷ��¼, to develop the first machine learning algorithm for classifying evolutionary outcomes and predicting parameters of duplicate genes from expression data. Much of Assis’ future work will focus on continuing these more sophisticated machine learning methods for other types of structural variations.

“Dr. Assis is an outstanding young researcher and this NIH MIRA grant is a testament to the bright academic career ahead of her,” said Gregg Fields, Ph.D., executive director, �������ϲ���ʷ��¼ I-HEALTH. “Structural variations have important implications in medicine and molecular biology and are associated with an increasing number of normal phenotypic variations. Outcomes from Dr. Assis’ work will help to paint a global picture of structural variations’ relative biological roles and importance in humans and other taxa.”

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