A team led by UCLA researchers says it has developed a faster and more accurate way to determine where the many bacteria that live in, and on, humans come from. Broadly, the tool can deduce the origins of any microbiome, noted the scientists.

The new computational tool, called “FEAST,” reportedly can analyze large amounts of genetic information in just a few hours, compared to tools that take days or weeks. The software program could be used in health care, public health, environmental studies, and agriculture, according to the study (“FEAST: fast expectation-maximization for microbial source tracking”) published online in Nature Methods.

“A major challenge of analyzing the compositional structure of microbiome data is identifying its potential origins. Here, we introduce fast expectation-maximization microbial source tracking (FEAST), a ready-to-use scalable framework that can simultaneously estimate the contribution of thousands of potential source environments in a timely manner, thereby helping unravel the origins of complex microbial communities (https://github.com/cozygene/FEAST),” wrote the investigators.

“The information gained from FEAST may provide insight into quantifying contamination, tracking the formation of developing microbial communities, as well as distinguishing and characterizing bacteria-related health conditions.”

Knowing where microbial species come from and how these communities form can give scientists a more detailed picture of the unseen ecological processes that affect human health. The researchers developed the program to give doctors and scientists a more effective tool to investigate these phenomena.

The source-tracking program gives the percentage of the microbiome that came from somewhere else. It’s similar in concept to a census that reveals the countries that its immigrant population came from, and what percentage each group is of the total population.

For example, using the source-tracking tool on a kitchen counter sample can indicate how much of that sample came from humans, how much came from food, and specifically which types of food.

Armed with this information, doctors will be able to distinguish a healthy person from one who has a particular disease by simply analyzing their microbiome, explained UCLA’s Eran Halperin, PhD. Scientists could use the tool to detect contamination in water resources or in food supply chains.

“The microbiome has been linked to many aspects of human physiology and health, yet we are just in the early stages of understanding the clinical implications of this dynamic web of many species and how they interact with each other,” said Halperin, the study’s principal investigator who holds faculty appointments in the Samueli School of Engineering and in the David Geffen School of Medicine.

“There has been an unprecedented expansion of microbiome data, which has rapidly increased our knowledge of the diverse functions and distributions of microbial life,” Halperin added. “Nonetheless, such big and complex datasets pose statistical and computational challenges.”

Compared to other source-tracking tools, FEAST is up to 300 times faster, and is significantly more accurate, the researchers said.

Also, current tools can only analyze smaller datasets, or only target specific microorganisms that are deemed to be harmful contaminants. The new tool can process much larger datasets and offer a more complete picture of the microorganisms that are present and where they came from, said Halperin.

The researchers confirmed FEAST’s viability by comparing it against analyses of previously published datasets.

For example, they used the tool to determine the types of microorganisms on a kitchen counter and it provided much more detail than previous tools that analyzed the same dataset.

They also used the tool to compare the gut microbiomes of infants delivered by cesarean section to the microbiomes of babies who were delivered vaginally.

“My hope is that scientists will use FEAST to diagnose bacteria-related health conditions,” said UCLA computer science graduate student Liat Shenhav, the study’s first author. “For example, if a particular cancer has a microbial signature, FEAST can potentially be utilized for early diagnosis.”