Sponsored technical content from Novogene

Inside your digestive tract lives a bustling microscopic city, trillions of bacteria, fungi, viruses, and other microorganisms working, competing, and communicating around the clock. Researchers have now cataloged over 200,000 reference genomes and 170 million predicted protein sequences from this community, yet what most of these genes actually do remains unknown.^[1] For decades, microbiome research could describe much of this community, but one question remained stubbornly difficult: what are these microbes actually doing?

That is where modern multi-omics changes the conversation. Shotgun metagenomics provides the foundation. It reveals the microbial blueprint: who is present, which genes they carry, and what functions they may be capable of. Metatranscriptomics adds the active voice. Metabolomics captures the chemical messages. Integrative analysis brings these signals together into a more complete biological story.^[2][3][4][5]

1. The Foundation: Shotgun Metagenomics

Traditional 16S rRNA sequencing offers a useful community overview. But it is still a little like identifying the buildings in a city without seeing what happens inside them.

Shotgun metagenomics takes a broader view by sequencing DNA directly from the microbial community. It can help researchers investigate:

• Community composition at higher taxonomic resolution.
• Genes, pathways, and functional modules.
• Antimicrobial-resistance and virulence-associated features.
• Genomic variation across samples.
• Species- or strain-level differences when sequencing depth, sample quality, community complexity, and reference databases are suitable.^[6][7][8]

By avoiding the primer design required for targeted amplicon workflows, shotgun sequencing can reduce certain primer-related biases. It is not bias-free: extraction method, host-DNA contamination, sequencing depth, reference databases, and computational choices all shape the final picture.^[7]

The key output is functional potential, what the microbial community may be equipped to do.

2. The Supporting Cast: RNA and Metabolites

A blueprint tells us what could happen. It does not tell us what is happening under a particular set of conditions. That is where the next layers of omics enter the story.

• Metatranscriptomics: the active voice
  • Possessing a gene does not mean that gene is switched on. Metatranscriptomic RNA sequencing provides a snapshot of microbial gene expression at the moment of sampling.^[4][5]
  • It can help researchers investigate how microbial functions respond to disease states, diets, treatments, environmental conditions, or experimental interventions. But transcripts are not the same as proteins, metabolic flux, or biological outcomes. RNA stabilization, extraction, normalization, and validation remain essential parts of the investigation.
• Metabolomics: the chemical messenger
  • Microbes communicate with their surroundings through chemistry. Metabolomics helps researchers measure the small molecules involved in that conversation, including short-chain fatty acids, bile acids, tryptophan metabolites, and other compounds linked to host metabolism.^[9][10]
  • Untargeted metabolomics scans broadly for discovery. Targeted metabolomics focuses on selected compounds when precise quantification is required. A detected metabolite should not automatically be labelled microbial; host metabolism, diet, medication, and environmental factors may also leave their fingerprints.

Together, the layers create a richer picture:

Metagenomics shows what may be possible. Metatranscriptomics shows what may be expressed. Metabolomics shows what chemical signals are present.

3. The Integrative Layer: Bringing the Data Together

The real power of multi-omics appears when these data layers stop being viewed as separate reports and start answering the same research question. Shotgun metagenomics may identify a microbial pathway. Metatranscriptomics may show whether related genes are being expressed. Metabolomics may reveal a chemical feature that changes alongside them. The next question is whether these signals are connected.

To answer that, integrative workflows generate several layers of evidence:

• Correlation heatmaps and scatterplots reveal which microbes and metabolites move in lockstep, the first hint that a biological relationship exists.
• Chord diagrams, networks, and Sankey diagrams turn those pairwise links into visual maps of cross-kingdom crosstalk, making complex patterns readable at a glance.
• Canonical correlation analysis (CCA) identifies coordinated variation across entire datasets, surfacing shared axes of biological change rather than isolated correlations.
• O2PLS data integration separates shared biological signals from dataset-specific noise, helping researchers distinguish true coupling from technical artifact.

Across a typical project, these tools compress taxonomic and functional profiles, differential feature tables, and correlation maps into a shortlist of integrated candidate pathways ready for experimental follow-up.

Correlation and multivariate integration identify associations and shared patterns. They do not, by themselves, prove that one microorganism produced a metabolite or caused a phenotype. Stronger mechanistic conclusions require perturbation, controlled models, orthogonal assays, independent replication, and, where appropriate, human studies.^[2]

4. From Signal to Hypothesis: Alistipes indistinctus and Hippuric Acid

One study offers a clear example of this progression. Researchers used integrative metagenomic and metabolomic analysis to investigate hyperuricemia, identifying a relationship involving Alistipes indistinctus and hippuric acid.^[11] The work then moved beyond the initial association. Mechanistic experiments linked hippuric acid with intestinal urate excretion through pathways involving PPARγ, ABCG2, and PDZK1.^[11]

The lesson is not that a single correlation has solved a disease. It is that multi-omics can help turn a broad microbial signal into a focused microbial–metabolite–host hypothesis, one that can be investigated experimentally.^[2]

5. Turning Microbiome Data into Discovery with Novogene

From sample preparation and sequencing to transcriptomics, metabolomics, visualization, and integration, every stage shapes the quality of the final biological story.

Novogene supports researchers across this multi-omics journey, from shotgun metagenomics and microbial genome analysis to metatranscriptomics, metabolomics, bioinformatics, visualization, and integrative analysis. The appropriate combination of technologies depends on the research question, sample type, cohort design, metadata, desired resolution, and validation plan.

The goal is not simply to generate more data. It is to help researchers identify the signals, relationships, and pathways that deserve a closer look.

Book a study-design consultation with Novogene to discuss your sample type, sequencing strategy, complementary omics requirements, analytical outputs, and validation plan.

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References

1. Almeida A, Nayfach S, Boland M, et al. A unified catalog of 204,938 reference genomes from the human gut microbiome. Nat Biotechnol. 2021;39(1):105–114.
2. Wu J, Singleton SS, Bhuiyan U, Krammer L, Mazumder R. Multi-omics approaches to studying gastrointestinal microbiome in the context of precision medicine and machine learning. Front Mol Biosci. 2024;10:1337373.
3. Valles-Colomer M, Menni C, Berry SE, et al. Cardiometabolic health, diet and the gut microbiome: a meta-omics perspective. Nat Med. 2023;29(3):551–561.
4. Van Hul M, Cani PD. From microbiome to metabolism: bridging a two-decade translational gap. Cell Metab. 2026;38(1):14–32.
5. Yang SY, Han SM, Lee JY, Kim KS, Lee JE, Lee DW. Advancing gut microbiome research: the shift from metagenomics to multi-omics and future perspectives. J Microbiol Biotechnol. 2025;35:e2412001.
6. Jovel J, Patterson J, Wang W, et al. Characterization of the gut microbiome using 16S or shotgun metagenomics. Front Microbiol. 2016;7:459.
7. Meyer F, Fritz A, Deng ZL, et al. Critical Assessment of Metagenome Interpretation: the second round of challenges. Nat Methods. 2022;19(4):429–440.
8. Li D, Liu CM, Luo R, et al. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct De Bruijn graph. Bioinformatics. 2015;31(10):1674–1676.
9. Pedersen HK, Gudmundsdottir V, Nielsen HB, et al. Human gut microbes impact host serum metabolome and insulin sensitivity. Nature. 2016;535(7612):376–381.
10. Wishart DS. Metabolomics for investigating physiological and pathophysiological processes. Physiol Rev. 2019;99(4):1819–1875.
11. Xu YX, Liu LD, Zhu JY, et al. Alistipes indistinctus-derived hippuric acid promotes intestinal urate excretion to alleviate hyperuricemia. Cell Host Microbe. 2024;32(3):366–381.