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Microbiome refers to the total number of microorganisms in a particular environment, such as the total number of bacteria in the human gut. Microbiome research is a new frontier of scientific exploration. Research that uses big data techniques to examine the entire genomes of hundreds of organisms simultaneously corresponds to a field called metagenomics. As the field matures, scientists increasingly recognize the need for advanced tools and techniques to decipher the complexities hidden in microbial ecosystems.
To this end, on April 2, Mihai Popp, professor in the Department of Computer Science and director of the Advanced Computing Institute at the University of Maryland, will discuss the analytical challenges of microbiome science and how they can be achieved. I gave a lecture about It is countered by computational methods. The talk focused on the pivotal role of computational tools in unlocking the secrets of the microbiome and addressing the challenges associated with analyzing the vast datasets generated by these studies.
The main focus of metagenomics is the taxonomic classification of various microorganisms. The main method of organizing and classifying microorganisms is by comparing them to databases of known biological sequences. These similarity-based methods are particularly effective when the organisms in the sample are well represented in the database. Pop mentioned the Basic Local Alignment Search Tool (BLAST), one of the most common similarity search techniques used for microbial classification. However, BLAST often incorrectly identifies the closest microorganism to the microorganism of interest. The “most similar” organisms according to BLAST may not actually be the closest relatives.
“How can I find the real thing? [closest hit] What if there is a hit? E-values are misleading,” Popp explained during his talk, suggesting that BLAST does not always accurately identify the microorganisms most similar to the target microbiome.
The E value that Pop mentioned refers to a parameter in BLAST that describes the number of hits you “expect” to see by chance when searching a database of a certain size. Pop also emphasized that many of these issues were only discovered years after BLAST came into general use.
“These are things we learned 20, 30, 40 years later.” [the computational tool] It said…even if something has been in use for years, there is [are] There’s still a lot to learn about that,” Popp explained.
One of the other key challenges Popp highlighted is how the structure of biological databases impacts scientists’ ability to reliably uncover insights about the microbiome. Reference databases are not meant to be exhaustive. Many microorganisms cannot be cultivated in the laboratory; can It has not been sequenced or added to these reference databases. Therefore, not all environmental organisms are included in sequence databases, limiting the accuracy of similarity-based methods.
These problems are Consecutive Information available for most sequence datasets. Many sequence analyzes must begin by combining many sequence fragments and piecing together the entire related sequence. Construction of sequence data is also a non-standardized process, as new techniques used to construct genomes are constantly being developed. These limitations can severely limit precision and reduce the accuracy of reference databases, thereby preventing researchers from drawing meaningful insights and associations from microbiome datasets.
Popp then discussed algorithmic and software approaches to sequence similarity. Many of the current software used for classification employ the most recent common ancestor (MRCA) method. MRCA provides annotation (marking of specific features of a DNA sequence) in the broadest taxonomic class that encompasses all possible markings within a sequence. However, this means that software using MRCA only performs some classifications at the genus or species level. That is, a stronger relationship between two microorganisms cannot be determined at the family, class, or phylum level.
To address this challenge, Popp shared his lab’s efforts to develop advanced computational tools specifically for microbiome analysis. He specifically focused on Ambiguous Taxonomy eLucidation by Apportionment of Sequences (ATLAS). ATLAS is a data-driven database partitioning technique that aims to divide large datasets into smaller datasets that are easier to analyze. ATLAS groups sequences into biologically meaningful partitions by querying sequences against reference databases and identifying and clustering hits that are considered significant. ATLAS also represents a transition from MRCA techniques.
Concluding his talk, Popp emphasized that interdisciplinary collaboration is essential to advance microbiome research. Integrating expertise from fields such as biology, computer science, and statistics is essential to developing innovative solutions to microbiome-related challenges. This interdisciplinary approach allows researchers to harness the power of computational tools to extract meaningful patterns and associations from microbiome datasets.
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