“In clinical samples, for example, microbial profiling can provide important information about microorganisms that may be relevant for an infection,” explains Prof. Alice McHardy, head of the research group “Computational Biology for Infection Research” at HZI. “Such information can complement established diagnostic approaches and help researchers and clinicians investigate potential pathogens. But taxonomic profiling is equally important far beyond clinical applications, from human microbiome research to environmental monitoring.”
Most taxonomic profilers compare sequencing reads from a sample with reference genomes stored in databases. This seemingly straightforward task is complicated by the fact that different microorganisms can share highly similar DNA sequences. Reference genomes may also contain contaminating sequences. In addition, traces of microbial DNA can sometimes come from laboratory reagents used during sample preparation — a source of contamination often referred to as the “kitome”. As a result, sequencing reads may sometimes match reference genomes of microorganisms that are not actually present in the sample.
“An important piece of information is where these matching reads occur across the genome,” says Dr. Zhi-Luo Deng, scientist in McHardy’s research group and first author of the study. “If a microorganism is truly present, we generally expect reads to be distributed across multiple regions of its genome. False-positive signals, in contrast, are often restricted to only one or a few local regions, with little or no support elsewhere.”
Metax was designed to look beyond a simple sequence match. It also considers how sequencing reads are distributed across a microorganism’s genome. By using this genome-wide coverage information, Metax can more reliably distinguish genuine microbial signals from false-positive results.
Metax can analyze a wide range of microorganisms, including bacteria, archaea, fungi and other eukaryotic microorganisms, as well as viruses. It therefore provides a more complete picture of the microbial community in a sample and can estimate how abundant the different microorganisms are.
The researchers tested Metax on simulated and real-world samples, including samples from the human microbiome, the environment, wastewater and clinical settings. “Overall, Metax identified microorganisms more accurately and estimated their abundances more reliably than the other methods tested. This is particularly important for low-biomass samples, where microbial signals can be overwhelmed by large amounts of other genetic material, e.g. from the host or contaminants,” says McHardy. “Metax can, in a sense, better find the microbial needle in the haystack.”
Accurately identifying which microorganisms are present in a sample and how abundant they are can be useful in many areas. In the human microbiome, more reliable profiling can help uncover microbial patterns linked to health and disease, including potential biomarkers. In wastewater surveillance, it can help track pathogens circulating in a community and provide early warning of outbreaks that might otherwise go unnoticed. In environmental research, it can help scientists better understand microbial diversity and how microorganisms interact with each other and their surroundings. And in clinical metagenomics, more accurate profiling can support the detection of potential pathogens, particularly in samples containing only very small amounts of microbial material.
The researchers now plan to further develop Metax and evaluate its performance and potential applications in additional microbiome and clinical studies. The Computational Biology for Infection Research group is based at the Braunschweig Integrated Centre of Systems Biology (BRICS), a joint research centre of the Helmholtz Centre for Infection Research (HZI) and Technische Universität Braunschweig. Alice McHardy is also a scientist at the German Center for Infection Research (DZIF) and within the RESIST Cluster of Excellence.
Metax is freely available for research use.
