A comprehensive understanding of how the human gut microbiome impacts systemic health and metabolic regulation necessitates advancing from traditional marker gene sequencing to sophisticated, data-driven systems biology approaches. The interpretation of high-dimensional datasets in nutritional microbiome research is often impeded by substantial genetic variability, statistical noise, and inherent compositional bias. This review provides an analysis of the current meta-omics technologies—covering metagenomics, metatranscriptomics, metaproteomics, and metabolomics—and their corresponding bioinformatic pipelines that convert raw biological samples into clinically meaningful nutritional information. We assess established analytical frameworks such as QIIME 2, bioBakery 3, and MaAsLin 3, together with multi-omics integration tools including DIABLO and MOFA+, which address compositional data complexities. Furthermore, this manuscript discusses innovative methodologies transforming the discipline, including single-cell metagenomics (SiC-seq), telomere-to-telomere (T2T) long-read sequencing, spatially resolved microbiomics, and the use of artificial intelligence through foundational large language models (LLMs). Through clinical case studies involving pediatric irritable bowel syndrome, epilepsy, and pharmacomicrobiomics (with a focus on GLP-1 agonists), the practical value of data-driven dietary intervention is demonstrated. Ongoing enhancement of analytical techniques and adherence to standardized reporting practices remain fundamental for resolving reproducibility challenges and progressing towards the clinical implementation of precision nutrition and the development of in silico digital twins for individualized healthcare.
References
[1]
Menkovska, M. and Raza, S. (2025) Human Gut Microbiome in Relation to Food, Nutrition, Health and Disease. AdvancesinBioscienceandBiotechnology, 16, 550-574. https://doi.org/10.4236/abb.2025.1612036
[2]
Galloway-Pe？a, J. and Hanson, B. (2020) Tools for Analysis of the Microbiome. DigestiveDiseasesandSciences, 65, 674-685. https://doi.org/10.1007/s10620-020-06091-y
[3]
Sirangelo, T.M. (2018) Human Gut Microbiome Analysis and Multi-Omics Approach. International Journal of Pharma Medicine and Biological Sciences, 7, 52-57.
[4]
Quince, C., Walker, A.W., Simpson, J.T., Loman, N.J. and Segata, N. (2017) Shotgun Metagenomics, from Sampling to Analysis. NatureBiotechnology, 35, 833-844. https://doi.org/10.1038/nbt.3935
[5]
Issa Isaac, N., Philippe, D., Nicholas, A., Raoult, D. and Eric, C. (2019) Metaproteomics of the Human Gut Microbiota: Challenges and Contributions to Other Omics. ClinicalMassSpectrometry, 14, 18-30. https://doi.org/10.1016/j.clinms.2019.06.001
[6]
Gibbons, H., O’Gorman, A. and Brennan, L. (2015) Metabolomics as a Tool in Nutritional Research. CurrentOpinioninLipidology, 26, 30-34. https://doi.org/10.1097/mol.0000000000000140
[7]
Bashiardes, S., Zilberman-Schapira, G. and Elinav, E. (2016) Use of Metatranscriptomics in Microbiome Research. Bioinformatics and Biology Insights, 10, 19-25. https://doi.org/10.4137/bbi.s34610
[8]
Zheng, T., Ni, Y., Li, J., Chow, B.K.C. and Panagiotou, G. (2017) Designing Dietary Recommendations Using System Level Interactomics Analysis and Network-Based Inference. FrontiersinPhysiology, 8, Article ID: 753. https://doi.org/10.3389/fphys.2017.00753
[9]
Gloor, G.B., Macklaim, J.M., Pawlowsky-Glahn, V. and Egozcue, J.J. (2017) Microbiome Datasets Are Compositional: And This Is Not Optional. FrontiersinMicrobiology, 8, Article ID: 2224. https://doi.org/10.3389/fmicb.2017.02224
[10]
Hickman, B. and Korpela, K. (2025) Impact of Data Compositionality on the Detection of Microbiota Responses. GutMicrobes, 17, Article 2590841. https://doi.org/10.1080/19490976.2025.2590841
Patnode, M.L., Beller, Z.W., Han, N.D., Cheng, J., Peters, S.L., Terrapon, N., et al. (2019) Interspecies Competition Impacts Targeted Manipulation of Human Gut Bacteria by Fiber-Derived Glycans. Cell, 179, 59-73.E13. https://doi.org/10.1016/j.cell.2019.08.011
[13]
Singh, A., Shannon, C.P., Gautier, B., Rohart, F., Vacher, M., Tebbutt, S.J., et al. (2019) DIABLO: An Integrative Approach for Identifying Key Molecular Drivers from Multi-Omics Assays. Bioinformatics, 35, 3055-3062. https://doi.org/10.1093/bioinformatics/bty1054
[14]
Argelaguet, R., Arnol, D., Bredikhin, D., Deloro, Y., Velten, B., Marioni, J.C., et al. (2020) MOFA+: A Statistical Framework for Comprehensive Integration of Multi-modal Single-Cell Data. GenomeBiology, 21, Article No. 111. https://doi.org/10.1186/s13059-020-02015-1
[15]
Caporaso, J.G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F.D., Costello, E.K., et al. (2010) QIIME Allows Analysis of High-Throughput Community Sequencing Data. NatureMethods, 7, 335-336. https://doi.org/10.1038/nmeth.f.303
[16]
Beghini, F., McIver, L.J., Blanco-Míguez, A., Dubois, L., Asnicar, F., Maharjan, S., et al. (2021) Integrating Taxonomic, Functional, and Strain-Level Profiling of Diverse Microbial Communities with Biobakery 3. eLife, 10, e65088. https://doi.org/10.7554/elife.65088
[17]
Nickols, W.A., Kuntz, T., Shen, J., Maharjan, S., Mallick, H., Franzosa, E.A., Thompson, K.N., Nearing, J.T. and Huttenhower, C. (2024) MaAsLin 3: Refining and Extending Generalized Multivariable Linear Models for Meta-Omic Association Discovery. bioRxiv.
[18]
Douglas, G.M., Maffei, V.J., Zaneveld, J.R., Yurgel, S.N., Brown, J.R., Taylor, C.M., et al. (2020) PICRUSt2 for Prediction of Metagenome Functions. NatureBiotechnology, 38, 685-688. https://doi.org/10.1038/s41587-020-0548-6
[19]
Lu, Y., Zhou, G., Ewald, J., Pang, Z., Shiri, T. and Xia, J. (2023) Microbiomeanalyst 2.0: Comprehensive Statistical, Functional and Integrative Analysis of Microbiome Data. NucleicAcidsResearch, 51, W310-W318. https://doi.org/10.1093/nar/gkad407
[20]
Zhou, H., He, K., Chen, J. and Zhang, X. (2022) Linda: Linear Models for Differential Abundance Analysis of Microbiome Compositional Data. GenomeBiology, 23, Article No. 20. https://doi.org/10.1186/s13059-022-02655-5
Wirbel, J., Zych, K., Essex, M., Karcher, N., Kartal, E., Salazar, G., et al. (2021) Microbiome Meta-Analysis and Cross-Disease Comparison Enabled by the SIAMCAT Machine Learning Toolbox. GenomeBiology, 22, Article No. 93. https://doi.org/10.1186/s13059-021-02306-1
[23]
Lan, F., Demaree, B., Ahmed, N. and Abate, A.R. (2017) Single-Cell Genome Sequencing at Ultra-High-Throughput with Microfluidic Droplet Barcoding. NatureBiotechnology, 35, 640-646. https://doi.org/10.1038/nbt.3880
[24]
Li, H. and Durbin, R. (2024) Genome Assembly in the Telomere-to-Telomere Era. NatureReviewsGenetics, 25, 658-670. https://doi.org/10.1038/s41576-024-00718-w
[25]
Cheng, J., Jin, X., Smyth, G.K. and Chen, Y. (2025) Benchmarking Cell Type Annotation Methods for 10x Xenium Spatial Transcriptomics Data. BMCBioinformatics, 26, Article No. 22. https://doi.org/10.1186/s12859-025-06044-0
[26]
Mayassi, T., Li, C., Segerstolpe, ？., Brown, E.M., Weisberg, R., Nakata, T., et al. (2024) Spatially Restricted Immune and Microbiota-Driven Adaptation of the Gut. Nature, 636, 447-456. https://doi.org/10.1038/s41586-024-08216-z
[27]
Liang, Q., Bible, P.W., Liu, Y., Zou, B. and Wei, L. (2020) Deepmicrobes: Taxonomic Classification for Metagenomics with Deep Learning. NARGenomicsandBioinformatics, 2, lqaa009. https://doi.org/10.1093/nargab/lqaa009
[28]
Zhang, H., Zhang, Y., Kang, Z., Xiong, J., Yang, R. and Ning, K. (2026) MGM as a Large‐Scale Pretrained Foundation Model for Microbiome Analyses in Diverse Contexts. AdvancedScience, 13, e13333. https://doi.org/10.1002/advs.202513333
[29]
Kang, D.D., Li, F., Kirton, E., Thomas, A., Egan, R., An, H., etal. (2019) Metabat 2: An Adaptive Binning Algorithm for Robust and Efficient Genome Reconstruction from Metagenome Assemblies. PeerJ, 7, e7359. https://doi.org/10.7717/peerj.7359
[30]
Wu, Y., Simmons, B.A. and Singer, S.W. (2016) Maxbin 2.0: An Automated Binning Algorithm to Recover Genomes from Multiple Metagenomic Datasets. Bioinformatics, 32, 605-607. https://doi.org/10.1093/bioinformatics/btv638
[31]
Alneberg, J., Bjarnason, B.S., de Bruijn, I., Schirmer, M., Quick, J., Ijaz, U.Z., etal. (2014) Binning Metagenomic Contigs by Coverage and Composition. NatureMethods, 11, 1144-1146. https://doi.org/10.1038/nmeth.3103
[32]
Uritskiy, G.V., DiRuggiero, J. and Taylor, J. (2018) Metawrap—A Flexible Pipeline for Genome-Resolved Metagenomic Data Analysis. Microbiome, 6, Article No. 158. https://doi.org/10.1186/s40168-018-0541-1
[33]
Hollister, E.B., Oezguen, N., Chumpitazi, B.P., Luna, R.A., Weidler, E.M., Rubio-Gonzales, M., etal. (2019) Leveraging Human Microbiome Features to Diagnose and Stratify Children with Irritable Bowel Syndrome. TheJournalofMolecularDiagnostics, 21, 449-461. https://doi.org/10.1016/j.jmoldx.2019.01.006
[34]
Chumpitazi, B.P., Cope, J.L., Hollister, E.B., Tsai, C.M., McMeans, A.R., Luna, R.A., etal. (2015) Randomised Clinical Trial: Gut Microbiome Biomarkers Are Associated with Clinical Response to a Low FODMAP Diet in Children with the Irritable Bowel Syndrome. AlimentaryPharmacology&Therapeutics, 42, 418-427. https://doi.org/10.1111/apt.13286
[35]
Lindefeldt, M., Eng, A., Darban, H., Bjerkner, A., Zetterstr？m, C.K., Allander, T., etal. (2019) The Ketogenic Diet Influences Taxonomic and Functional Composition of the Gut Microbiota in Children with Severe Epilepsy. npjBiofilmsandMicrobiomes, 5, Article No. 5. https://doi.org/10.1038/s41522-018-0073-2
[36]
Kamath, S., Chan, N.S.L. and Joyce, P. (2026) GLP‐1 Agonists and the Gut Microbiome: A Bidirectional Relationship. BritishJournalofClinicalPharmacology, 92, 1309-1325. https://doi.org/10.1002/bcp.70487
[37]
Wang, H., Huang, R., Nelson, J., Gao, C., Tran, M., Yeaton, A., etal. (2025) Systematic Benchmarking of Imaging Spatial Transcriptomics Platforms in FFPE Tissues. NatureCommunications, 16, Article No. 10215. https://doi.org/10.1038/s41467-025-64990-y
[38]
Mirzayi, C., Renson, A., Furlanello, C., Sansone, S., Zohra, F., Elsafoury, S., et al. (2021) Reporting Guidelines for Human Microbiome Research: The STORMS Checklist. NatureMedicine, 27, 1885-1892.
[39]
Kelliher, J.M., Mirzayi, C., Bordenstein, S.R., Oliver, A., Kellogg, C.A., Hatcher, E.L., et al. (2025) STREAMS Guidelines: Standards for Technical Reporting in Environmental and Host-Associated Microbiome Studies. NatureMicrobiology, 10, 3059-3068. https://doi.org/10.1038/s41564-025-02186-2
[40]
Katsoulakis, E., Wang, Q., Wu, H., Shahriyari, L., Fletcher, R., Liu, J., et al. (2024) Digital Twins for Health: A Scoping Review. npjDigitalMedicine, 7, Article No. 77. https://doi.org/10.1038/s41746-024-01073-0
[41]
Pribyl, A.L., Hugenholtz, P. and Cooper, M.A. (2025) A Decade of Advances in Human Gut Microbiome-Derived Biotherapeutics. NatureMicrobiology, 10, 301-312. https://doi.org/10.1038/s41564-024-01896-3
