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In molecular biology, an oscillating gene is a gene expressed in a rhythmic pattern or in periodic cycles.
Oscillating genes are usually circadian and identified by periodic changes in the state of an organism.
Circadian rhythms, controlled by oscillating genes, have a period of approximately 24 hours.
Image Credit: Wikipedia

Algorithm identifies gene movement around the clock

An algorithm gives scientists a new way to identify dynamic patterns in oscillating genes, helping define when genes begin to function during fetal growth.

Genes that turn on in precisely timed patterns are known as oscillatory genes. They are essential to cell division, limb formation, and control our circadian rhythms. However, without us having a time-lapse view of when these genes begin to function, oscillatory genes can go unidentified and delay our understanding of molecular functions impacting development.

Now, a study published in Nature Methods describes a new statistical approach, called "Oscope," to help identify oscillating genes. The key to Oscope is examining unsynchronized cells during different cell states - compare results and average when the possibility for oscillation will begin in each. Identifying oscillatory genes using traditional RNA-sequencing requires scientists to choose a known system and identify cells as they synchronize to that system. But this process may mask different types of oscillatory signals developing later, from other signals sychronizing sooner.

With Oscope "we study unsynchronized single cell data so none of the oscillatory systems are disturbed," says Ning Leng, computational biologist with the Morgridge Institute at the University of Madison Wisconsin-Madison (UW-Madison) and co-author on the paper. "This enables us to discover unknown oscillatory signals."

Oscope identifies independent groups of cyclic genes by capturing one "base cycle" oscillation within each. Such technology could open research into genes at the heart of basic development. This type of research needed an interdisciplinary collaboration to solve the question of when genes begin to function, thus combining statistical expertise with cell biology.

Statistical expertise in this project was provided by professor Christina Kendziorski from the Department of Biostatistics and Medical Informatics at UW-Madison. Her group worked in collaboration with the cell biology strengths of stem cell research pioneer — James Thomson — and his group at the Morgridge Institute for Research.

The Oscope software package is offered free to scientists at: https://www.biostat.wisc.edu/~kendzior/OSCOPE/.

"We view this as the first step to understanding oscillatory genes in their relationship to development. What's most interesting is how do genetic regulatory networks govern oscillatory gene expression? How are these signals coordinated in a timely fashion to execute a developmental task?"

Li-Fang Chu PhD, postdoctoral research associate, James Thomson group, Morgridge Regenerative Biology, University of Wisconsin-Madison

The goal of the Thomson lab is to establish a system to detect timing-related genes important in development. The most well understood oscillatory genes are those related to our circadian clock. These genes become active or inactive in tune with the sun cycle and tightly regulate cell proliferation and metabolism over a 24-hour period. They affect disease, the most notable being cancer where cell division is hijacked by cells proliferating out of control. The Oscope technique may help scientists identify the connections between gene cycles and disease cycles.

Although single-cell RNA sequencing is an extremely powerful tool for getting a snapshot of when genes become active, it is less able to trace a single gene over time. Oscope uses high-resolution snapshots of data to identify different gene groups that are potentially changing over time and possibly capture oscillating genes in that group.

"We need a combination of an average signal across many cells for the big picture. We also need single-cell resolution techniques to compare the two in order to figure out which signal may represent true biological variables. The data analysis is very challenging and requires new algorithms, such as Oscope, to perform such complex tasks."

Li-Fang Chu PhD

Abstract
Oscillatory gene expression is fundamental to development, but technologies for monitoring expression oscillations are limited. We have developed a statistical approach called Oscope to identify and characterize the transcriptional dynamics of oscillating genes in single-cell RNA-seq data from an unsynchronized cell population. Applying Oscope to a number of data sets, we demonstrated its utility and also identified a potential artifact in the Fluidigm C1 platform.

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