CD Genomics Perspective: Sequencing Methods and Bioinformatics for Microbial Genomics

Metagenomic sequencing of microbial specimens is progressively employed to define novel mobile genetic elements (MGEs), which are crucial to our knowledge of how genes (and their associated components) move within a community through horizontal gene transfer (HGT) within a community, even after having to surmount significant obstacles. To recognize and explore novel as well as recognized MGEs, both targeted and the whole metagenomic techniques are now being utilized. Targeted metagenomics involves a process that primarily chooses a type of MGE before sequencing, as opposed to whole metagenomic techniques where all DNA extracts are sequenced.

16S rRNA gene sequencing is an accurate technique for questioning the taxonomic concentration of bacterial diversity. Within the prokaryotic domain, this gene is prevalent and can be efficiently PCR-amplified from even earlier unidentified organisms. Long before the surge of high-throughput sequencing (HTS) info, the assessment of bacterial diversity via the sequencing of the 16S rRNA gene was prevalent, creating this gene one of the most highly represented within GenBank. Usually, targeted Illumina or 454 reads of up to a few hundred nucleotides are HTS methods to 16S rRNA sequence evaluation, each focusing on uniquely identifiable variable gene areas that can be utilized as unique microbial identifiers. By averaging 5,000 454 FLX 16S rRNA gene sequences from all 300 subjects, 18 body sites, and multiple time points, the HMP was scheduled to adequately categorize the microbiome's taxonomic proportion. Integrated with more than a 1,000-fold rise in sequencing throughput over the duration of the HMP, this layout obligated the consortium to create novel tools for accessing large 16S rRNA gene databases, addressing issues particular to the quality of 454 sequence info, and acknowledging novel biological concerns that were initially unreachable because of limited sample sizes.

There is a progressively long bioinformatic history of assessing the bacterial communities of a group using unassembled short metagenomic reads. In classifying the microbe(s) of source for individual short reads, computational methods have been and are progressively effective. Even so, in evaluating both the structure of the human microbiome and its genetic diversity using read-based mapping for microbial reference genomes, the HMP asked a major question. Over 1,700 draft or finished microbial genomes were obtainable after the combination of new HMP microbial isolates with public databases, to which reads within each metagenome could be plotted.

An instant constraint of current computational approaches was confirmed by early attempts to read integration with this reference database: at the time this task was instituted, no evaluation of procedures for managing billions of reads targeting thousands of various genomes concurrently was released, and a comprehensive review of speed and accuracy was first required. The study findings by the HMP implied that since human-associated bacteria are phylogenetically well by sequenced genomes, a reliable population census was supplied by calculating the number of reads plotted to each genome. These complementary findings are based on 16S rRNA gene sequencing to evaluate community members, a job tough to accomplish concisely because of the differing copy number of the ribosomal operon in bacteria through the 16S rRNA gene. These mapping findings also show single nucleotide polymorphism and structural variations within the microbiomes of individuals, unlike techniques that immediately categorize each reading into a taxonomic bin. This was an impressive discovery, the implications of which remain to be investigated: not only does every human genome contain variations that can facilitate or inhibit disease, but every human microbiome could also contain personalized risk or protective microbial alleles.

References:

1. Moyes D, Carr V, Hill C, Shkoporov A. Probing the Mobilome: Discoveries in the Dynamic Microbiome. Trends in Microbiology. 2020 May 10.
2. Kim Y, Koh I, Rho M. Deciphering the human microbiome using next-generation sequencing data and bioinformatics approaches. Methods. 2015 Jun 1;79.

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