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22-Feb-2022

CD Genomics Perspective: Bioinformatic Analysis for Microbial Metagenomics

Summary

In microbial ecology, metagenomic techniques are now widely utilized to research microbial communities in greater depth, including many strains that cannot be produced in the lab. Microbial bioinformatics may now be used to mine large metagenomic datasets for broad patterns that govern microbial communities. Typical metagenomic and bioinformatic investigations, on the other hand, do not fully characterize the ecology and evolution of microorganisms in their habitats. The majority of analyses still rely on basic sequence similarity searches against reference databases.
  • Author Name: Kiko Garcia
Editor: Fiona Bingly Last Updated: 22-Feb-2022

Introduction to Bioinformatics for Microbial Metagenomics

In microbial ecology, metagenomic techniques are now widely utilized to research microbial communities in greater depth, including many strains that cannot be produced in the lab. Microbial bioinformatics may now be used to mine large metagenomic datasets for broad patterns that govern microbial communities. Typical metagenomic and bioinformatic investigations, on the other hand, do not fully characterize the ecology and evolution of microorganisms in their habitats. The majority of analyses still rely on basic sequence similarity searches against reference databases.

Methods for Microbial Metagenomics & Its Applications

Despite the fact that microorganisms serve critical functions in a variety of ecosystems, the majority of them have yet to be fully defined. Bioinformatics, which uses large-scale data to identify novel biological concepts and principles, is now predicted to speed up discovery in previously unexplored sections of the microbial universe.

 

Bioinformatics has become indispensable in modern research because of the data flood; new breakthrough technologies are producing vast amounts of data at an unprecedented rate. Optical and electron microscopies, for example, are essential ways of observation when used in conjunction with various staining procedures. High-throughput DNA sequencing methods, for example, have swiftly produced large amounts of genetic information at minimal cost, making millions of microbial genomes available. These genome sequences help to weave a huge tapestry of microbial evolutionary histories by providing a comprehensive catalog of the microbial genetic components underlying different microbial physiology.

 

The majority of environmental microorganisms are uncultivable, which has hampered study in microbial ecology. Many microbial communities with no axenic culture have been identified in numerous investigations across a variety of natural habitats. Culture-independent methods, including DNA hybridization (e.g., microarray and fluorescent in situ hybridization), DNA cloning, and PCR, have been employed to detect individual members and/or functional genes in microbial communities in an attempt to overcome this fundamental issue.

 

 Shotgun metagenomic and amplicon sequencing methods, which detect members and/or functional genes at a faster rate and more detail, have lately gained popularity thanks to high-throughput sequencing technologies. Their application in a variety of settings has revealed the presence of extremophiles, established links between microorganisms and human diseases, and defined the feeding systems involved in symbiosis. Agriculture, food science and medicines, and forensics are all areas where these technologies are applied.

 

Several large-scale metagenomic initiatives are now producing extensive microbial sequence libraries for various settings. Time-series studies have grown popular because microbial communities fluctuate as they interact with other organisms and as the environment changes.

 

Bioinformatic Tools for Microbial Metagenomics

To evaluate metagenomic and amplicon sequence data, several bioinformatic tools have been created and popularized. Web servers and pipelines, such as MG-RAST, IMG/M, EBI Metagenomics, and SILVAngs, now allow researchers to do integrated metagenomic analyses and show results without command-line operations or extensive computational understanding.