New method improves accuracy of microbiome measurement
Research from the University of Minnesota Genomics Center limits biases, expands accuracy
An improved method for measuring the microbiome could lead to more clear and accurate results, providing better data for a rapidly expanding research area.
Researchers at the University of Minnesota Genomics Center assessed commonly used DNA-sequencing-based measurement methods for microbiome science. They identified a number of factors contributing to errors and biases potentially leading to irreproducible results or erroneous conclusions. Understanding how biases arise in the data allowed the researchers to design a method that minimizes amplification bias and improves accuracy.
The research is published online in the journal Nature Biotechnology.
“The microbiome shows incredible promise for treating disease and improving the environment,” said Daryl Gohl, Ph.D., Research and Development Lead at the UMN Genomics Center and corresponding author on the paper. “However, in order to fulfill this promise, the science must be accurate and reproducible, and the underlying measurements must be more robust, particularly for clinical applications.”
Microbiomes are the communities of microorganisms residing on or inside humans and animals, or in the environment. Understanding the microbiome is important as it can play a key role in the healthy function of the body or of an ecosystem. Focus on this line of research has grown dramatically in recent years, and the White House has recently announced a Microbiome Initiative charged with utilizing interdisciplinary research to create new technologies.
Despite attempts to standardize research practices, current protocols are still lacking, putting the accuracy of many microbiome studies in question.
“When looking at the literature available, there are dozens of methods currently being used to classify microbes based on their ribosomal RNA gene sequences,” said Kenneth Beckman, Ph.D., director of the UMN Genomics Center and senior author. “Results are highly dependent on your experimental setup, and the large number of variables affecting accuracy and methods in the field have made it so data is difficult or impossible to compare across studies.”
This study improves one important step in the process, but making measurements of the microbiome is a multi-step process involving sample collection, storage, DNA extraction, DNA amplification, and sequencing. Each step presents an opportunity to introduce bias.
“There is much work to be done to standardize this process, and we are exploring new ideas to make these measurements more accurate,” said Gohl. “The search continues.”
Funding for this project was provided through the Minnesota Partnership for Biomedical Genomics grant and the University of Minnesota Genomics Center.