Imperfections of existing metagenomic analyses. Credit: Serrano-Antón et al., CC-BY 4.0
Research research study of simulated microbial communities shows analyses are flawed by insufficient DNA databases.
Typical methods to examining DNA from a community of microbes, called a microbiome, can yield erroneous results, in large part due to the insufficient databases used to determine microbial DNA series. A team led by Aiese Cigliano of Sequentia Biotech SL, and Clemente Fernandez Arias and Federica Bertocchini of the Centro de Investigaciones Biologicas Margarita Salas, report these findings in a term paper released on February 8 in the open-access journal PLOS ONE.
Microbiomes have actually been the focus of extreme research efforts in current decades. These research studies vary from attempts to comprehend conditions such as obesity and autism by examining the human gut, to finding microbes that break down toxic substances or produce biofuels by studying ecological neighborhoods. The most typically utilized methods for studying microbial neighborhoods rely on comparing the DNA acquired from a biological sample to series in genome databanks. For that reason, researchers can just identify DNA sequences that are already in the databases– a reality that might badly jeopardize the dependability of microbiome data in unanticipated methods.
To test the consistency of existing methods of microbiome analysis, scientists used computer system simulations to develop virtual microbiome communities that mimic real-world bacterial populations. They utilized basic techniques to analyze the virtual neighborhoods and compared the results with the initial composition. The experiment showed that arise from DNA analyses can bear little similarity to the actual composition of the community, which a big number of the types “discovered” by the analysis are not in fact present in the community.
For the very first time, the study demonstrates significant defects in the techniques currently utilized to recognize microbial communities. The scientists conclude that there is a requirement for increased efforts to collect genome information from microorganisms and to make that information available in public databases to enhance the accuracy of microbiome analysis. In the meantime, the outcomes of microbiome studies should be analyzed with care, especially in cases where the available genomic details from those environments is still scarce.
The authors add: “This research study exposes intrinsic constraints in metagenomic analysis stemming from current database constraints and how genomic information is used. To improve the dependability of metagenomic data, a research effort is required to improve both database contents and analysis methods. Metagenomic data need to be approached with fantastic care.”
Reference: “The virtual microbiome: A computational framework to evaluate microbiome analyses” by Belén Serrano-Antón, Francisco Rodríguez-Ventura, Pere Colomer-Vidal, Riccardo Aiese Cigliano, Clemente F. Arias and Federica Bertocchini, 8 February 2023, PLOS ONE.DOI: 10.1371/ journal.pone.0280391.
Financing: FB and CFA gratefully acknowledge assistance by the Roechling structure. BS was partly supported by MINECO grant MTM2017-85020-P. The funders had no function in research study style, data collection and analysis, choice to publish, or preparation of the manuscript.
To check the consistency of existing approaches of microbiome analysis, researchers used computer simulations to create virtual microbiome neighborhoods that mimic real-world bacterial populations. The experiment showed that results from DNA analyses can bear little similarity to the real structure of the community, and that a large number of the species “discovered” by the analysis are not really present in the neighborhood.
The scientists conclude that there is a requirement for increased efforts to gather genome info from microorganisms and to make that information offered in public databases to improve the accuracy of microbiome analysis. The authors add: “This research study exposes intrinsic restraints in metagenomic analysis stemming from current database constraints and how genomic info is utilized.