Human Microbiome Analysis: Sequencing Methods, Technological Advances, Bioinformatics Tools, and Future Challenges

Human Microbiome Analysis: Sequencing Methods, Technological Advances, Bioinformatics Tools, and Future Challenges

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Definition of the Human Microbiome

Technological Advances, Bioinformatics Tools, and Future Challenges

The human microbiome includes the entire range of microorganisms present in and on the human body, including bacteria, archaea, fungi, viruses, and other microorganisms. These microorganisms interact with human cells and play critical roles in maintaining host health, regulating metabolism, modulating immune responses, and defending against pathogens. Because of their profound impact on human health and disease, they have received much attention in recent years. The human microbiome is extremely diverse and its study involves understanding the composition, diversity and functional potential of microbial communities associated with various body sites such as the gut, skin, mouth and nasal cavity. Understanding the composition and functional dynamics of these microbial communities is critical to understanding their impact on human health and disease.

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Technologies for Studying the Human Microbiome

Sequencing approaches have revolutionized the field of human microbiome research through comprehensive analysis of microbial communities. There are many different techniques available for studying the microbiome.

Amplicon Sequencing

Amplicon sequencing targets specific genomic regions, typically the 16S rRNA genes of bacteria and archaea or the internal transcribed spacer (ITS) region of fungi. This approach provides insight into the composition of microbial communities, allowing the identification and quantification of the different taxa present in a sample. Amplicon sequencing is cost effective, high throughput, and provides valuable information about community diversity and relative abundance. Explore with our 16S / 18S / ITS Amplicon Sequencing Analysis for more information.

Shotgun Metagenomics Sequencing

Shotgun metagenome sequencing involves sequencing total DNA extracted from a microbial community without targeting specific genes. This technique provides a more comprehensive view of the microbiome, enabling characterization of microbial taxa, functional genes and potential interactions within the community. Shotgun metagenomics allows the reconstruction of microbial genomes and the identification of specific functional pathways and genes. Explore with our Shotgun Metagenomics Data Analysis for more information.

Metatranscriptomics, Metabolomics and Metaproteomics

In addition to metagenomics, which provides insights into the genetic composition of microbial communities, other histological techniques such as metatranscriptomics, metabolomics and metaproteomics have emerged as powerful tools for understanding functional activities and interactions within the human microbiome. These approaches enable researchers to explore the gene expression, metabolite and protein profiles of microbes and elucidate their role in health and disease.

Metatranscriptomics, a cutting-edge field, focuses on the comprehensive capture and analysis of RNA transcripts originating from microbial cells within the microbiome. By employing high-throughput sequencing technologies such as Illumina's HiSeq or NovaSeq platforms, researchers can decipher the active gene expression profiles and functional activities of the diverse microorganisms present in the microbiome. Expanding the horizons of metatranscriptomic analysis, alternative sequencing platforms like PacBio and Oxford Nanopore are being explored, offering extended read lengths that facilitate gene calling and mapping to reference genomes.

  • Metabolomics: Probing the Intricacies of Microbial Metabolites

The field of metabolomics delves deep into the intricate realm of small molecules called metabolites produced by the microbiome, as well as their interactions with the microbiota and host metabolism. Through the analysis of metabolite profiles using techniques such as mass spectrometry, metabolomics elucidates the metabolic activities of the microbiome and sheds light on their impact on overall health.

Employing state-of-the-art mass spectrometry techniques, researchers can identify and quantify various metabolites present in microbiome samples. Metabolomics allows for a granular understanding of the diverse metabolic pathways and intricate interactions that transpire within the microbiome. It enables the quantification of small molecules, including antibiotics, antibiotic byproducts, and metabolites implicated in both host and bacterial metabolism.

  • Metaproteomics: Decoding Microbial Protein Profiles

Similar to metatranscriptomics, metaproteomics offers a panoramic view of the functional activities transpiring within the microbiome, but at the protein level. By employing advanced techniques to identify and quantify proteins within microbiome samples, metaproteomics provides unparalleled insights into the active microbial protein profiles and their roles in microbial interactions and host-microbiome crosstalk.

Metaproteomics enables researchers to explore the diverse protein constituents present in the microbiome, shedding light on their functional activities and interactions. By deciphering the complex protein networks, researchers gain a deeper understanding of the molecular mechanisms underlying microbial community dynamics and their interplay with the host.

An illustration of targeted amplicon and metagenomic sequencing approaches.Fig. 1. An illustration of targeted amplicon and metagenomic sequencing approaches. (Bharti R, et al, 2021)

Human Microbiome Bioinformatics Analysis

Microbial Diversity Measurement

  • Alpha Diversity: It quantifies the diversity within a sample and allows comparison across groups. Common metrics include the observed OTU, Chao1 index, Shannon index and Inverse Simpson index. These metrics measure species richness, evenness, or both, and provide insight into the diversity of microbial communities within a sample.
  • Beta diversity: Compare diversity between samples, calculated by comparing dissimilarity of features, to obtain a matrix of distances between all sample pairs. Metrics such as Bray-Curtis dissimilarity, weighted Unifrac, unweighted Unifrac and Jaccard's index were used to assess differences in microbial composition between samples. These metrics take into account the abundance of taxonomic units, phylogenetic correlation or the presence/absence of taxonomic units.

Functional Analysis

  • 16S sequencing data: Functional profiles can be predicted using programs such as PICRUSt or Tax4Fun, which use the relative abundance of taxa to predict gene content and potential function. However, these methods are approximations and do not take protein expression into account.
  • Shotgun metagenome approaches: Assembly of metagenomes, gene prediction, and functional annotation of databases of immediate homologs, enzymes or protein domains using homology-based searches. Pathway enrichment analysis and metabolic network derivation can be performed to assess functional pathways and gene families in a community.
  • Metabolomics, metatranscriptomics and metaproteomics: Metabolites can be measured using liquid chromatography and mass spectrometry, while metatranscriptomics and metaproteomics allow the characterization of transcribed RNA and expressed proteins, respectively. These methods provide insights into the actual metabolic activity and protein expression of the microbial community.

Data Visualization and Statistical Methods

  • Downscaling and visualization: Principal coordinate analysis (PCoA), principal component analysis (PCA) and heat maps can be used to visualize microbiome data and explore clustering patterns or differences between groups.
  • Statistical tests: Traditional statistical tests such as t-tests, Wilcoxon rank sum tests, ANOVA or Kruskal-Wallis tests can be used to compare alpha diversity, abundance of specific taxa, or known functional components. Methods such as ANOSIM, PERMANOVA, and ANCOM are used to test for differences in community structure, differential abundance, or log-ratios between groups.

Graphical Networks and Machine Learning

  • Network analysis: Correlation networks can be inferred to visualize interactions within microbial communities. Programs such as SparCC, CCLasso and SPEIC-EASI are used to infer correlation networks based on similarity or correlation coefficients. Regression correlation methods and association rule mining can also be used to identify associations between taxa or clinical features.
  • Machine learning algorithms: Random forests, CART analysis and other machine learning techniques can be used for classification, regression and feature selection. These methods help identify important taxonomic units, clinical covariates, or predict specific outcomes based on microbiome data.

Abedside, to bench, and back to bedside approach to microbiome investigation.Fig. 2. A "bedside, to bench, and back to bedside" approach to microbiome investigation. (Galloway-Peña J, et al, 2020)

Future Challenges for Human Microbiome Analysis

One of the most pressing challenges in the realm of human microbiome analysis lies in the realm of standardization. To achieve meaningful progress, it is imperative to establish rigorous protocols and guidelines governing the collection, storage, and processing of samples. By adhering to standardized practices, we pave the way for comparative analyses across studies, ensuring reproducibility and facilitating the identification of consistent microbial signatures associated with health and disease. Robust meta-analyses, empowered by the foundation of standardized methodologies, will guide us towards unlocking the secrets held within the vast realm of the human microbiome.

As we plunge deeper into the era of big data, another formidable challenge arises on the horizon. The exponential growth in volume and intricacy of microbiome data necessitates innovative approaches to data management and analysis. Here, the marriage of cutting-edge techniques such as data integration, machine learning, and artificial intelligence becomes indispensable. Only through harnessing these transformative tools can we extract meaningful insights from the colossal datasets generated by microbiome studies. By employing advanced computational methodologies, we can discern hidden patterns, unearth novel biomarkers, and identify elusive therapeutic targets, propelling us closer to personalized medicine and tailored interventions.


In conclusion, the study of the human microbiome is a rapidly evolving field, made possible by sequencing methods, technological advances and bioinformatics tools. These tools and methods provide unprecedented insights into the composition, diversity, and functional potential of microbial communities associated with the human body. Model systems and future advances will further enhance our understanding of the intricate interactions between the human microbiome and human health, paving the way for targeted interventions and personalized medicine.


  1. Galloway-Peña J, Hanson B. Tools for Analysis of the Microbiome. Dig Dis Sci. 2020 Mar;65(3):674-685.
  2. Bharti R, Grimm D G. Current challenges and best-practice protocols for microbiome analysis[J]. Briefings in bioinformatics, 2021, 22(1): 178-193.
  3. Morgan X C, Huttenhower C. Chapter 12: Human microbiome analysis[J]. PLoS computational biology, 2012, 8(12): e1002808.
* For Research Use Only. Not for use in diagnostic procedures.
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