University of Virginia School of Medicine scientists have developed important new resources that will aid the battle against cancer and advance cutting-edge genomics research.

UVA's Chongzhi Zang, Ph.D., and his colleagues and students have developed a new computational method to map the folding patterns of our chromosomes in three dimensions from experimental data. This is important because the configuration of genetic material inside our chromosomes actually affects how our genes work. In cancer, that configuration can go wrong, so scientists want to understand the genome architecture of both healthy cells and cancerous ones. This will help them develop better ways to treat and prevent cancer, in addition to advancing many other areas of medical research. 

Using their new approaches, Zang and his colleagues and students have already unearthed a treasure trove of useful data, and they are making their techniques and findings available to their fellow scientists. To advance cancer research, they've even built an interactive website that brings together their findings with vast amounts of data from other resources. They say their new website, bartcancer.org, can provide "unique insights" for cancer researchers.

"The folding pattern of the genome is highly dynamic; it changes frequently and differs from cell to cell. Our new method aims to link this dynamic pattern to the control of gene activities," said Zang, a computational biologist with UVA's Center for Public Health Genomics and UVA Cancer Center. "A better understanding of this link can help unravel the genetic cause of cancer and other diseases and can guide future drug development for precision medicine." 

Bet on BART 

Zang's new approach to mapping the folding of our genome is called BART3D. Essentially, it compares available three-dimensional configuration data about one region of a chromosome with many of its neighbors. It can then extrapolate from this comparison to fill in blanks in the blueprints of genetic material using "Binding Analysis for Regulation of Transcription", or BART, a novel algorithm they recently developed. The result is a map that offers unprecedented insights into how our genes interact with the "transcriptional regulators" that control their activity. Identifying these regulators helps scientists understand what turns particular genes on and off - information they can use in the battle against cancer and other diseases.

The researchers have built a web server, BARTweb, to offer the BART tool to their fellow scientists. It's available, for free, at http://bartweb.org. The source code is available at https://github.com/zanglab/bart2. Test runs demonstrated that the server outperformed several existing tools for identifying the transcriptional regulators that control particular sets of genes, the researchers report.

The UVA team also built the BART Cancer database to advance research into 15 different types of cancer, including breast, lung, colorectal, and prostate cancer. Scientists can search the interactive database to see which regulators are more active and which are less active in each cancer.

"While a cancer researcher can browse our database to screen potential drug targets, any biomedical scientist can use our webserver to analyze their own genetic data," Zang said. "We hope that the tools and resources we develop can benefit the whole biomedical research community by accelerating scientific discoveries and future therapeutic development."

Robert Weigel, Associate Professor, Physics and Astronomy, and his collaborators are developing a suite of Python tools that will allow scientists to easily view and explore heliophysics data in 3-D.

For the software development side of the project, the researchers will (1) create a set of Python functions that use existing SpacePy and HAPI libraries to read in measurements and simulation output and use the Python/ParaView API to render the data; (2) create a library of functions that produce 3-D renderings of data in a way that is useful for domain scientists; and (3) develop a set of functions that allow for common and basic 3-D data reduction and manipulation that can be used for exploring 3-D data renderings.

The researchers' initial target for the types of simulation visualizations that can be created will be magnetosphere magnetohydrodynamic simulation output in the Community Coordinated Modeling Center CDF format, native Space Weather Modeling Framework simulation output, and Enlil solar wind model simulation output. {module INSIDE STORY}

The software will also have the ability to read in spacecraft measurements from NASA's CDAWeb from and SSCWeb using an existing HAPI library and overlay the spacecraft time series data on a 3-D rendering of simulation output.

The second part of this project will involve the development of a set of recommendations and conventions for 3-D visualization tools in Python in collaboration with members of the PyHC (Python Heliophysics) community.

Weigel received $74,996 from NASA Goddard Space Flight Center for this work. Funding began in January 2021 and will conclude in late December 2021.