A Beginner’s Guide

Microbiome analysis is the investigation of the vast communities of microbes that live and interact with each other in particular environments. Researchers perform microbiome analysis with metagenomics, which includes sequencing-based approaches such as 16S rRNA sequencing and shotgun metagenome sequencing.

Metagenomic sequencing methods allow researchers to identify microbial species in microbiomes and to identify shifts in the proportions of microbes crucial for human health or disease. This knowledge could be used to develop future microbiome-based medicines. However, various metagenomic approaches are available, each requiring different analysis approaches, so which approach might be best for your next microbiome analysis?

This article aims to help you understand microbiome analysis with metagenomics by outlining the different types of popular metagenomic approaches and some important considerations for your future analyses.

What is the microbiome?

The microbiome is the vast community of microorganisms, including bacteria, viruses, archaea, and fungi, that are found within a given environment. Rich microbial communities occupy different niches and are adapted to the conditions present in that particular location, such as the human gut, mouth, vagina, and skin. The entire human microbiome includes around 39 trillion bacteria, which dwarfs the estimated 30 trillion human cells of the average person when combined with viruses, fungi, and archaea (1).

The human microbiome provides many benefits for human health, such as the biosynthesis of bioactive molecules, protection against pathogens, and regulation of the immune response (2). Thanks to the increasing accessibility and scalability of metagenomic methods, a continual wave of research indicates that changes in the composition of microbiomes are associated with a growing number of diseases, such as cancer, diabetes, and neurological disorders (3, 4).

What is metagenomics?

Metagenomics is the genetic analysis of uncultured microorganisms contained within microbiomes. Next-generation sequencing-based metagenomic methods sequence the collective DNA or RNA fragments from all microbes in an environment. The detected DNA or RNA sequences are mapped to the genomes of the species they originated from, and the relative quantity of these sequences indicates the abundance of microbes of different genera or species (5, 6).

One frequent application of metagenomics is comparing the amounts and types of microbes in healthy and diseased patients. This approach has allowed researchers to detect shifts in the proportions of microbes present in disease patient microbiomes and has uncovered microbiome changes associated with an ever-growing number of diseases (4, 5, 6).

Compared to historic culture-based methods, this high-throughput, culture-free snapshot of entire microbial communities, combined with advanced bioinformatic analyses, continually increases our understanding of their essential role in all aspects of human health.

Metagenomic sequencing methods for microbiome analysis

Researchers commonly use two main metagenomic methods, each with advantages and disadvantages.

1. 16s ribosomal RNA sequencing

16S ribosomal RNA (16S rRNA) sequencing is a popular and cost-effective type of amplicon sequencing used to identify bacteria and archaea in microbiomes (7). The technology targets a region of the 16S rRNA gene present in all bacteria and archaea but absent from other microorganisms. Because of this, no information is provided for viruses or fungi.

16S rRNA sequencing mainly provides information to identify and characterize the genus (e.g., Lactobacillus) of a bacterium but struggles to identify the species (e.g., Lactobacillus acidophilus) due to its targeted nature and reliance on one gene. However, the appearance of long-read sequencing methods now enables targeted sequencing of the entire 16S rRNA gene and provides better taxonomic classification than traditional 16S rRNA sequencing but you still might want to use the next approach in our list if your budget allows (7).

2. Shotgun metagenomic sequencing

Shotgun metagenomic sequencing is a non-targeted and more expensive metagenomic approach than 16S rRNA sequencing, but it allows scientists to comprehensively assess all genomic DNA in a microbiome sample for all types of microorganisms, including viruses and fungi (8).

All DNA is fragmented into small pieces and sequenced, so the unbiased nature of shotgun metagenomic sequencing provides researchers with higher-resolution insights into the composition of microbiomes, even down to the strain level. Researchers can use the sequencing reads generated with shotgun sequencing to evaluate microbial abundance and diversity in its entirety in complex environments containing countless species in all taxa (8).

Metagenomic sequencing data analysis

Both metagenomic sequencing techniques require similar approaches to data analysis to identify and classify the microorganisms present in a sample, but shotgun metagenomic sequencing analysis is substantially more complex due to the amount and diversity of reads present. In both cases, sequencing generates short reads that must be aligned to databases containing the genetic information of known microorganisms to identify which species the reads belong to.

Specialized bioinformatic tools like QIIME2, mothur, HUMAnN 3.0, and many other tools with various analysis options are developed for metagenomic sequencing data and require varying levels of bioinformatics experience. Typically, taxonomic assignment of reads occurs where sequences from the microbiome samples are compared to known microbial marker gene sequences in reference databases, such as SILVA or the National Center for Biotechnology Information (NCBI) database (9).

The (very) simplified pipeline is as follows (9):

1. Preprocessing of raw data

Sequencing data must first be cleaned by removing adapters, PCR primers, low-quality reads, and sequencing errors.

2. Identification of identical and unique sequences

All reads with identical sequences must be combined to calculate the read abundance of that unique sequence.

3. Taxonomic assignment

These sequences are then mapped to the representative sequences in publicly available reference genome databases. The tools used for mapping may vary depending on the microbiome analysis technology used.

Strengths and limitations of 16S rRNA sequencing and shotgun metagenomic sequencing

Shotgun metagenomic sequencing results provide insight into the relative abundance of bacteria, fungi, viruses, and other microbes in samples down to the strain level, whereas 16S rRNA sequencing only includes information about the abundance of bacteria and archaea at the genus and sometimes species level (8, 9).

Shotgun sequencing also generates information for all microbial genes in a sample, which is necessary to detect functionally or clinically relevant genes like those conferring antibiotic resistance. In 16S rRNA sequencing, this genome-wide information is lost, but the method is considerably cheaper due to its targeted nature and the lower amount of data generated (8, 9).

Ultimately, the type of metagenomic sequencing technology you use for your next microbiome analysis experiment will depend on the amount of detail you need about the microbes and genomes to answer your particular research question.

At Nexco Analytics, we have extensive experience in the bespoke analysis of metagenomic sequencing data for microbiome analysis. If you have any questions, we’ll be happy to help you explore the microbial worlds within and around us.

References

1. Sender R, Fuchs S, Milo R. Revised estimates for the number of human and bacteria cells in the body. PLoS biology. 2016 Aug 19;14(8):e1002533.

2. Ogunrinola GA, Oyewale JO, Oshamika OO, Olasehinde GI. The human microbiome and its impacts on health. International journal of microbiology. 2020;2020(1):8045646.

3. Sepich-Poore GD, Zitvogel L, Straussman R, Hasty J, Wargo JA, Knight R. The microbiome and human cancer. Science. 2021 Mar 26;371(6536):eabc4552.

4. Manos J. The human microbiome in disease and pathology. Apmis. 2022 Dec;130(12):690–705.

5. Zhang L, Chen F, Zeng Z, Xu M, Sun F, Yang L, Bi X, Lin Y, Gao Y, Hao H, Yi W. Advances in metagenomics and its application in environmental microorganisms. Frontiers in microbiology. 2021 Dec 17;12:766364.

6. Kim N, Ma J, Kim W, Kim J, Belenky P, Lee I. Genome-resolved metagenomics: a game changer for microbiome medicine. Experimental & Molecular Medicine. 2024 Jul 1:1–2.

7. Johnson JS, Spakowicz DJ, Hong BY, Petersen LM, Demkowicz P, Chen L, Leopold SR, Hanson BM, Agresta HO, Gerstein M, Sodergren E. Evaluation of 16S rRNA gene sequencing for species and strain-level microbiome analysis. Nature communications. 2019 Nov 6;10(1):5029.

8. Wensel CR, Pluznick JL, Salzberg SL, Sears CL. Next-generation sequencing: insights to advance clinical investigations of the microbiome. The Journal of clinical investigation. 2022 Apr 1;132(7).

9. Regueira‐Iglesias A, Balsa‐Castro C, Blanco‐Pintos T, Tomás I. Critical review of 16S rRNA gene sequencing workflow in microbiome studies: From primer selection to advanced data analysis. Molecular Oral Microbiology. 2023 Oct;38(5):347–99.