Common Microbiome Analysis Techniques Might be Flawed

Scientists have found that commonly used methods to analyze DNA from microbiomes can produce inaccurate results. The study, led by Aiese Cigliano of Sequentia Biotech SL and Clemente Fernandez Arias and Federica Bertocchini of the Centro de Investigaciones Biologicas Margarita Salas, was recently published in the journal PLoS ONE.

The research findings highlight a significant issue with the methods used in microbiome analysis, which relies on comparing the DNA obtained from a biological sample to sequences in genome databanks. This means that researchers can only identify DNA sequences already in the databases, which may severely compromise the reliability of microbiome data.

To test the consistency of the current methods, the researchers used computer simulations to create virtual microbiome communities and then analyzed them using standard techniques. The results showed that the results from DNA analysis could bear little resemblance to the actual composition of the community, with a large number of the species “detected” by the analysis not actually present in the community. This study is the first to demonstrate the significant flaws in the techniques used to identify microbial communities.

The researchers suggest that there is a need for increased efforts to collect genome information from microbes and make it available in public databases to improve the accuracy of microbiome analysis. They advise that the results of microbiome studies should be interpreted with caution, especially in cases where the available genomic information from those environments is still scarce.

Some of the limitations of microbiome analysis result from intrinsic features of microbial populations, such as the loss of species due to sampling, which is greater in highly diverse communities. Other constraints arise from the lack of genomic information in the databanks used in metagenomic studies, which can lead to an increase in the detection of false positives, severely distorting the identification of species present in a microbiome.

The study also found that applying 16S and WGS methods to characterize the same microbial community is not necessarily a guarantee of accuracy. The observed overlap between the results of the two methods can consist mainly of false positives. Better results can be obtained at the genus level, but accuracy still depends on the amount of information present in the databases.

The team highlights the need for increased efforts to improve the database contents and analysis methods, and to approach metagenomic data with great care. The increasing amount of genomic information gathered in recent decades should be channeled towards improving the accuracy of microbiome analysis and reducing the uncertainty of microbiome data.