A novel approach for investigating the microbiome in extraordinary detail has been described by scientists.
In comparison to previous methodologies, the technology is simpler and easier to apply.
The researchers exhibit an increased capacity to detect physiologically relevant factors such as a subject's age and sex depending on microbial samples using the new approach.
Microbiome ecosystem
Researchers have just recently begun to investigate the large array of microorganisms that live on and within the human body.
Protists, archaea, fungus, viruses, and large numbers of bacteria living in symbiotic habitats are among them, as per ScienceDaily.
The human microbiome is a collection of microorganisms that impact a wide variety of functions, from metabolism to behavior, and play a critical role in health and illness.
In a never-ending, intertwined whirlpool, 39 trillion non-human bacteria thrive on and within humans.
They make up more than half of the cells in the human body while having 500 times the number of genes contained in human cells.
For researchers, identifying and making sense of this microbial mix has been a major difficulty.
Qiyun Zhu and colleagues discuss in detail a new approach for exploring the microbiome in recent research.
In comparison to previous methods, the procedure is simpler and easier to apply, as per News Prepare.
The researchers showed that using the new approach, they can better detect physiologically relevant traits such as a subject's age and gender from microbiome data.
Breakthrough research promises rapid advancements in the study of the microbiome's secrets.
The researchers sought to get a better understanding of how these bacteria work together to protect human health and how their malfunction might lead to a variety of illnesses with this information.
Medications and other therapy can be tailored to a patient's microbiome profile over time.
Also Read: New Study Reveals How Microbiome From Young Mice 'Reverse Aging' in Mouse Brains
The tools used for the trade
By sequencing the microbial DNA contained in a sample, two powerful technologies have been employed to assist researchers to unveil the richness and complexity of the microbiome.
16S and metagenomic sequencing are two examples. The strategy proposed in this work combines the advantages of both strategies to offer a new way of analyzing microbiome data.
We use part of the knowledge gained from 16S RNA sequencing in metagenomics, according to Zhu.
Metagenomics, unlike other sequencing approaches such as 16S, allows researchers to sequence all of the DNA information in a microbiome sample.
However, the current research suggests that the metagenomic technique might be improved.
The present methods for analyzing metagenomic data are restricted even though data from the whole genome must first be converted into the taxonomy.
Operational Genomic Units (OGUs) are a novel approach that avoids the time-consuming and frequently inaccurate practice of assigning taxonomic categories like genus and species to the myriad of microorganisms present in a sample.
Instead, the technique employs individual genomes as the fundamental units of statistical analysis, attempting to match sequences identified in the sample with sequences derived from existing genomic datasets.
Researchers may get significantly finer resolution this way, which is especially advantageous when bacteria with closely similar DNA sequences are present.
Because most taxonomy classifications are based on sequence similarity, this is true.
If two sequences differ by less than a particular amount, they are classified as the same taxonomic group; nonetheless, the OGU method can assist researchers to distinguish between them.
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