Rebekah Loving is researching RNA-Seq, and her work is getting noticed. The UH Hilo senior is one of 41 finalists for a prestigious Hertz Foundation Fellowship and has received acceptance letters offering full funding to doctoral programs in biostatistics, computational biology, and computer science from a veritable “who’s who” of top research universities.
By Leah Sherwood.
A research scientist at the University of Hawai‘i at Hilo is under consideration for a coveted national fellowship and is being courted by some of the most prestigious research universities in the country… and she hasn’t yet completed her undergraduate studies.
Rebekah Loving, currently in her final semester at UH Hilo where she is dual-majoring in computer science and mathematics, is one of 41 finalists for a prestigious 2019 Hertz Foundation Fellowship. According to the foundation’s press release naming this year’s finalists, the Fannie and John Hertz Foundation grants five-year fellowships “empowering the nation’s most promising young scientists, engineers and mathematicians.”
Loving also has received acceptance letters with offers of full funding to doctoral programs in biostatistics, computational biology, and computer science from a veritable “who’s who” of top research universities including Harvard, Columbia University, University of California Berkeley, and the California Institute of Technology (Caltech).
These outstanding opportunities are because Loving is already establishing herself as a serious research scientist. Building on a 2017 summer research project at Caltech in Pasadena, California, Loving is doing ongoing research in RNA sequencing (RNA-Seq), a new molecular technology that uses a computationally intensive “shotgun” sequencing approach to reveal the presence and quantity of RNA in a biological sample.
Loving is a native of the Hāmākua Coast on Hawai‘i Island, where she grew up with 11 siblings, and was homeschooled by her mother. As a preteen, she was already becoming interested in the intersections of biology, applied mathematics, and computer science. By the time she was applying to UH Hilo, she had already settled on computer science as a major, but soon added math as a double major.
“After finishing my first math class, I thought math was pretty cool, too,” says Loving, whose father is a public school math teacher.
Loving’s summer research experiences at Harvard University, Caltech, UC Berkeley, and research at UH Hilo allowed her to explore different ways to apply statistical and mathematical methods and computer science techniques to some of the hardest questions in science across a wide range of scientific disciplines, from physics to the biomedical sciences.
She also participated in the 2018 Heidelberg Laureate Forum, a conference held in Heidelberg, Germany, which brings together laureates and the most innovative young researchers from around the world.
Though her projects have all had different foci, they have all involved the application of computer science and statistical approaches to finding solutions and uncovering patterns hidden deep within scientific data.
The research opportunity that most piqued her interest was her 2017 summer research project at Caltech, where Loving was introduced to RNA-Seq while working under the mentorship of Lior Pachter, a leading computational biologist. The RNA-Seq technique relies on powerful computers to process large amounts of data to analyze an organism’s continuously changing cellular transcriptome, which is the sum total of all the RNA molecules expressed from the genes of the organism.
RNA-Seq has three major steps: preparing a sequencing library, sequencing, and data analysis. Loving’s passion and expertise lies in the last step, where she hopes to develop new RNA-Seq analysis methods.
“What I’m focused on is the methods side of it— building a statistical model and implementing it within a program,” says Loving.
The current state of RNA-Seq technology is far from perfect. Researchers face myriad challenges when interpreting the results of analysis because mistakes can be made during sequencing or the signal may not be strong enough. At Caltech, Loving worked on techniques that provide higher resolution in quantification of single-cell expression levels than current standards and that provide quantitative insight into the bias of different library preparations of cell samples.
Loving is specifically interested in developing new methods for looking at RNA-Seq data both at the single-cell level and in bulk. Earlier methods, which utilized only single-cell RNA-Seq data, could not account for the high drop-out of expressed RNA due to either the bias of library preparation, the stochasticity of the genetic pattern, or true low expression levels. Loving spent her time at Caltech developing a novel method for improving the single-cell transcriptome quantifications by joint quantification of bulk and single-cell RNA-Seq data.
“I formulated a statistical model in the form of a likelihood for modeling DNA expression levels by jointly quantifying bulk and single-cell RNA-Seq data,” says Loving, “and then optimized the parameters of the model using an expectation-maximization approach.”
Loving implemented her model in the C++ programming language and ran it within kallisto, a program developed in Pachter’s lab for the purpose of rapidly quantifying abundances of transcripts from bulk and single-cell RNA-Seq data.
“This required learning the architecture of kallisto, which has approximately 30,000 lines of source code,” says Loving.
Looking forward, the budding scientist plans to explore how to incorporate the information from single-cell RNA-Seq and spatial transcriptomics, a new frontier of research mapping the differential expression of genes to their histology in the analysis of cells. This will require the identification of which cells are associated with each other from one time point to another with high sensitivity.
“One goal I hope to accomplish during my PhD is to produce a comprehensive, fully integrated framework for quantifying time-course single-cell RNA-Seq data,” says Loving. “I hope to develop a systematic statistical model and algorithmic method for associating the time points of single-cell RNA-Seq.”
The ultimate goal is to capture what is occurring at the RNA and protein level of the individual cells for a better understanding of how the genes are being expressed in the course of cell decisions.
“This has various implications for different analyses of cancer at the molecular level and for different analyses of tissue development,” explains Loving.
Better statistical models, such as the one Loving is working on, would help give researchers a clearer picture of what is happening at the individual cell level and help them measure gene expression in normal cells versus mutated or cancerous cells.
“Recently developed sequencing-based technologies like RNA-Seq have allowed for unprecedented probing of cells to study disease, gene regulation, and biological evolution,” says Loving. She hopes that by developing and implementing new statistical models for RNA sequencing she will help facilitate the study of cells beyond the microscopic level, revealing the answers to current mysteries surrounding cell decisions, cell differentiation, tissue growth, and disease.
About the author of this story: Leah Sherwood is a graduate student in the tropical conservation biology and environmental science program at UH Hilo. She received her bachelor of science in biology and bachelor of arts in English from Boise State University.