MicrobiomeStatPlots: Microbiome statistics plotting gallery for meta-omics and bioinformatics

Abstract

The rapid growth of microbiome research has generated an unprecedented amount of multi-omics data, presenting challenges in data analysis and visualization. To address these issues, we present MicrobiomeStatPlots, a comprehensive platform offering streamlined, reproducible tools for microbiome data analysis and visualization. This platform integrates essential bioinformatics workflows with multi-omics pipelines and provides 82 distinct visualization cases for interpreting microbiome datasets. By incorporating basic tutorials and advanced R-based visualization strategies, MicrobiomeStatPlots enhances accessibility and usability for researchers. Users can customize plots, contribute to the platform's expansion, and access a wealth of bioinformatics knowledge freely on GitHub (https://github.com/YongxinLiu/MicrobiomeStatPlot). Future plans include extending support for metabolomics, viromics, and metatranscriptomics, along with seamless integration of visualization tools into omics workflows. MicrobiomeStatPlots bridges gaps in microbiome data analysis and visualization, paving the way for more efficient, impactful microbiome research.

Keywords

bioinformatics / interpretation / microbiome / multi-omics / visualization

References

[1] Carlino, Niccolò, Aitor Blanco-Míguez, Michal Punčochář, Claudia Mengoni, Federica Pinto, Alessia Tatti, and Paolo Manghi, et al. 2024. “Unexplored Microbial Diversity From 2,500 Food Metagenomes and Links With the Human Microbiome.” Cell 187: 5775-5795.e15. https://doi.org/10.1016/j.cell.2024.07.039

[2] Wang, Yao, Huiyu Hou, Hao Luo, Jiani Xun, Chuang Ma, Haifei Yang, Defeng Bai, et al. 2024. “Imeta Conference 2024: Building an Innovative Scientific Research Ecosystem for Microbiome and One Health.” iMeta 3: e251. https://doi.org/10.1002/imt2.251

[3] Jin, Hao, Keyu Quan, Qiuwen He, Lai-Yu Kwok, Teng Ma, Yalin Li, Feiyan Zhao, et al. 2023. “A High-Quality Genome Compendium of the Human Gut Microbiome of Inner Mongolians.” Nature Microbiology 8: 150-161. https://doi.org/10.1038/s41564-022-01270-1

[4] Gao, Yunyun, Guoxing Zhang, Shunyao Jiang, and Yong-Xin Liu. 2024. “Wekemo Bioincloud: A User-Friendly Platform for Meta-Omics Data Analyses.” iMeta 3: e175. https://doi.org/10.1002/imt2.175

[5] Zhang, Xu, Leyuan Li, James Butcher, Alain Stintzi, and Daniel Figeys. 2019. “Advancing Functional and Translational Microbiome Research Using Meta-Omics Approaches.” Microbiome 7: 154. https://doi.org/10.1186/s40168-019-0767-6

[6] Gao, Yunyun, Danyi Li, and Yong-Xin Liu. 2023. “Microbiome Research Outlook: Past, Present, and Future.” Protein & Cell 14: 709-712. https://doi.org/10.1093/procel/pwad031

[7] Shi, Chun-Lin, Tong Chen, Canhui Lan, Ren-You Gan, Jun Yu, Fangqing Zhao, and Yong-Xin Liu. 2024. “iMetaOmics: Advancing Human and Environmental Health Through Integrated Meta-Omics.” iMetaOmics 1: e21. https://doi.org/10.1002/imo2.21

[8] Lu, Yao, Guangyan Zhou, Jessica Ewald, Zhiqiang Pang, Tanisha Shiri, and Jianguo Xia. 2023. “MicrobiomeAnalyst 2.0: Comprehensive Statistical, Functional and Integrative Analysis of Microbiome Data.” Nucleic Acids Research 51: W310-W318. https://doi.org/10.1093/nar/gkad407

[9] Xia, Han, Zhe Zhang, Chen Luo, Kangfei Wei, Xuming Li, Xiyu Mu, Meilin Duan, et al. 2023. “MultiPrime: A Reliable and Efficient Tool for Targeted Next-Generation Sequencing.” iMeta 2: e143. https://doi.org/10.1002/imt2.143

[10] Bolyen, Evan, Jai Ram Rideout, Matthew R. Dillon, Nicholas A. Bokulich, Christian C. Abnet, Gabriel A. Al-Ghalith, Harriet Alexander, et al. 2019. “Reproducible, Interactive, Scalable and Extensible Microbiome Data Science Using QIIME 2.” Nature Biotechnology 37: 852-857. https://doi.org/10.1038/s41587-019-0209-9

[11] Edgar, Robert C. 2018. “Accuracy of Taxonomy Prediction for 16S rRNA and Fungal ITS Sequences.” PeerJ 6: e4652. https://doi.org/10.7717/peerj.4652

[12] Zhou, Yuanping, Yong-Xin Liu, and Xuemeng Li. 2024. “USEARCH 12: Open-Source Software for Sequencing Analysis in Bioinformatics and Microbiome.” iMeta 3: e236. https://doi.org/10.1002/imt2.236

[13] Bolger, Anthony M., Marc Lohse, and Bjoern Usadel. 2014. “Trimmomatic: A Flexible Trimmer for Illumina Sequence Data.” Bioinformatics 30: 2114-2120. https://doi.org/10.1093/bioinformatics/btu170

[14] Chen, Shifu, Yanqing Zhou, Yaru Chen, and Jia Gu. 2018. “Fastp: An Ultra-Fast All-In-One FASTQ Preprocessor.” Bioinformatics 34: i884-i890. https://doi.org/10.1093/bioinformatics/bty560

[15] Chen, Shifu. 2023. “Ultrafast One-Pass FASTQ Data Preprocessing, Quality Control, and Deduplication Using Fastp.” iMeta 2: e107. https://doi.org/10.1002/imt2.107

[16] Wood, Derrick E., Jennifer Lu, and Ben Langmead. 2019. “Improved Metagenomic Analysis With Kraken 2.” Genome Biology 20: 257. https://doi.org/10.1186/s13059-019-1891-0

[17] Beghini, Francesco, Lauren J. McIver, Aitor Blanco-Míguez, Leonard Dubois, Francesco Asnicar, Sagun Maharjan, Ana Mailyan, 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

[18] Chong, Jasmine, Peng Liu, Guangyan Zhou, and Jianguo Xia. 2020. “Using MicrobiomeAnalyst for Comprehensive Statistical, Functional, and Meta-Analysis of Microbiome Data.” Nature Protocols 15: 799-821. https://doi.org/10.1038/s41596-019-0264-1

[19] Zhai, Peng, Longshu Yang, Xiao Guo, Zhe Wang, Jiangtao Guo, Xiaoqi Wang, and Huaiqiu Zhu. 2017. “MetaComp: Comprehensive Analysis Software for Comparative Meta-Omics Including Comparative Metagenomics.” BMC Bioinformatics 18: 434. https://doi.org/10.1186/s12859-017-1849-8

[20] Han, Chang, Caiping Shi, Linmeng Liu, Jichen Han, Qianqian Yang, Yan Wang, Xiaodan Li, et al. 2024. “Majorbio Cloud 2024: Update Single-Cell and Multiomics Workflows.” iMeta 3: e217. https://doi.org/10.1002/imt2.217

[21] Chen, Di, Lixia Xu, Huiwu Xing, Weitao Shen, Ziguang Song, Hongjiang Li, Xuqiang Zhu, et al. 2024. “Sangerbox 2: Enhanced Functionalities and Update for a Comprehensive Clinical Bioinformatics Data Analysis Platform.” iMeta 3: e238. https://doi.org/10.1002/imt2.238

[22] Kaur, Parampreet, Ashima Singh, and Inderveer Chana. 2021. “Computational Techniques and Tools for Omics Data Analysis: State-of-the-Art, Challenges, and Future Directions.” Archives of Computational Methods in Engineering 28: 4595-4631. https://doi.org/10.1007/s11831-021-09547-0

[23] Parks, Donovan H., Gene W. Tyson, Philip Hugenholtz, and Robert G. Beiko. 2014. “STAMP: Statistical Analysis of Taxonomic and Functional Profiles.” Bioinformatics 30: 3123-3124. https://doi.org/10.1093/bioinformatics/btu494

[24] Zhang, Jingying, Yong-Xin Liu, Xiaoxuan Guo, Yuan Qin, Ruben Garrido-Oter, Paul Schulze-Lefert, and Yang Bai. 2021. “High-Throughput Cultivation and Identification of Bacteria From the Plant Root Microbiota.” Nature Protocols 16: 988-1012. https://doi.org/10.1038/s41596-020-00444-7

[25] Liu, Yong-Xin, Lei Chen, Tengfei Ma, Xiaofang Li, Maosheng Zheng, Xin Zhou, Liang Chen, et al. 2023. “EasyAmplicon: An Easy-To-Use, Open-Source, Reproducible, and Community-Based Pipeline for Amplicon Data Analysis in Microbiome Research.” iMeta 2: e83. https://doi.org/10.1002/imt2.83

[26] Yousuf, Salsabeel, Hao Luo, Meiyin Zeng, Lei Chen, Tengfei Ma, Xiaofang Li, Maosheng Zheng, et al. 2024. “Unveiling Microbial Communities With EasyAmplicon: A User-Centric Guide to Perform Amplicon Sequencing Data Analysis.” iMetaOmics 1: e42. https://doi.org/10.1002/imo2.42

[27] Schiebenhoefer, Henning, Kay Schallert, Bernhard Y. Renard, Kathrin Trappe, Emanuel Schmid, Dirk Benndorf, Katharina Riedel, Thilo Muth, and Stephan Fuchs. 2020. “A Complete and Flexible Workflow for Metaproteomics Data Analysis Based on MetaProteomeAnalyzer and Prophane.” Nature Protocols 15: 3212-3239. https://doi.org/10.1038/s41596-020-0368-7

[28] Klåvus, Anton, Marietta Kokla, Stefania Noerman, Ville M. Koistinen, Marjo Tuomainen, Iman Zarei, Topi Meuronen, et al. 2020. “ ‘Notame’: Workflow for Non-Targeted LC-MS Metabolic Profiling.” Metabolites 10: 135. https://doi.org/10.3390/metabo10040135

[29] Nayfach, Stephen, David Páez-Espino, Lee Call, Soo Jen Low, Hila Sberro, Natalia N. Ivanova, Amy D. Proal, et al. 2021. “Metagenomic Compendium of 189,680 DNA Viruses From the Human Gut Microbiome.” Nature Microbiology 6: 960-970. https://doi.org/10.1038/s41564-021-00928-6

[30] Zhou, Zhichao, Cody Martin, James C. Kosmopoulos, and Karthik Anantharaman. 2023. “ViWrap: A Modular Pipeline to Identify, Bin, Classify, and Predict Viral-Host Relationships for Viruses From Metagenomes.” iMeta 2: e118. https://doi.org/10.1002/imt2.118

[31] Roach, Michael J., Sarah J. Beecroft, Kathie A. Mihindukulasuriya, Leran Wang, Anne Paredes, Luis Alberto Chica Cárdenas, Kara Henry-Cocks, et al. 2024. “Hecatomb: An Integrated Software Platform for Viral Metagenomics.” Gigascience 13: giae020. https://doi.org/10.1093/gigascience/giae020

[32] Chen, Tong, Yong-Xin Liu, and Luqi Huang. 2022. “ImageGP: An Easy-To-Use Data Visualization Web Server for Scientific Researchers.” iMeta 1: e5. https://doi.org/10.1002/imt2.5

[33] Chen, Tong, Yong-Xin Liu, Tao Chen, Mei Yang, Siqing Fan, Minglei Shi, Buqing Wei, et al. 2024. “ImageGP 2 for Enhanced Data Visualization and Reproducible Analysis in Biomedical Research.” iMeta 3: e239. https://doi.org/10.1002/imt2.239

[34] Lyu, Fengye, Feiran Han, Changli Ge, Weikang Mao, Li Chen, Huipeng Hu, Guoguo Chen, Qiulei Lang, and Chao Fang. 2023. “OmicStudio: A Composable Bioinformatics Cloud Platform With Real-Time Feedback That Can Generate High-Quality Graphs for Publication.” iMeta 2: e85. https://doi.org/10.1002/imt2.85

[35] Mu, Hongyan, Jianzhou Chen, Wenjie Huang, Gui Huang, Meiying Deng, Shimiao Hong, Peng Ai, Chuan Gao, and Huangkai Zhou. 2024. “OmicShare Tools: A Zero-Code Interactive Online Platform for Biological Data Analysis and Visualization.” iMeta 3: e228. https://doi.org/10.1002/imt2.228

[36] Peng, Xi, Kai Feng, Xingsheng Yang, Qing He, Bo Zhao, Tong Li, Shang Wang, and Ye Deng. 2024. “iNAP 2.0: Harnessing Metabolic Complementarity in Microbial Network Analysis.” iMeta 3: e235. https://doi.org/10.1002/imt2.235

[37] Li, Taibo, April Kim, Joseph Rosenbluh, Heiko Horn, Liraz Greenfeld, David An, Andrew Zimmer, et al. 2018. “GeNets: A Unified Web Platform for Network-Based Genomic Analyses.” Nature Methods 15: 543-546. https://doi.org/10.1038/s41592-018-0039-6

[38] Liu, Dongmei, Yifei Zhang, Guomei Fan, Dingzhong Sun, Xingjiao Zhang, Zhengfei Yu, Jinfeng Wang, et al. 2022. “IPGA: A Handy Integrated Prokaryotes Genome and Pan-Genome Analysis Web Service.” iMeta 1: e55. https://doi.org/10.1002/imt2.55

[39] Yang, Mei, Tong Chen, Yong-Xin Liu, and Luqi Huang. 2024. “Visualizing Set Relationships: EVenn's Comprehensive Approach to Venn Diagrams.” iMeta 3: e184. https://doi.org/10.1002/imt2.184

[40] Li, Yahui, Hujie Lyu, Haifei Yang, Zhicheng Ju, Chuang Ma, Huiyu Hou, Yao Wang, et al. 2024. “FoodMicroDB: A Microbiome Database for Composition and Time-Series Research in Food.” iMetaOmics 1: e40. https://doi.org/10.1002/imo2.40

[41] Liu, Chi, Yaoming Cui, Xiangzhen Li, and Minjie Yao. 2021. “Microeco: An R Package for Data Mining in Microbial Community Ecology.” FEMS Microbiology Ecology 97: fiaa255. https://doi.org/10.1093/femsec/fiaa255

[42] Gao, Chun-Hui, Guangchuang Yu, and Peng Cai. 2021. “ggVennDiagram: An Intuitive, Easy-To-Use, and Highly Customizable R Package to Generate Venn Diagram.” Frontiers in Genetics 12: 706907. https://doi.org/10.3389/fgene.2021.706907

[43] Gao, Chun-Hui, Chengjie Chen, Turgut Akyol, Adrian Dusa, Guangchuang Yu, Bin Cao, and Peng Cai. 2024. “ggVennDiagram: Intuitive Venn Diagram Software Extended.” iMeta 3: e177. https://doi.org/10.1002/imt2.177

[44] Wen, Tao, Penghao Xie, Shengdie Yang, Guoqing Niu, Xiaoyu Liu, Zhexu Ding, Chao Xue, et al. 2022. “ggClusterNet: An R Package for Microbiome Network Analysis and Modularity-Based Multiple Network Layouts.” iMeta 1: e32. https://doi.org/10.1002/imt2.32

[45] Cambiaghi, Alice, Manuela Ferrario, and Marco Masseroli. 2017. “Analysis of Metabolomic Data: Tools, Current Strategies and Future Challenges for Omics Data Integration.” Briefings in Bioinformatics 18: 498-510. https://doi.org/10.1093/bib/bbw031

[46] Li, Wenjun, Likun Wang, Xiaofang Li, Xin Zheng, Michael F. Cohen, and Yong-Xin Liu. 2023. “Sequence-Based Functional Metagenomics Reveals Novel Natural Diversity of Functional CopA in Environmental Microbiomes.” Genomics Proteomics Bioinformatics 21: 1182-1194. https://doi.org/10.1016/j.gpb.2022.08.006

[47] Pang, Zhiqiang, Jasmine Chong, Guangyan Zhou, David Anderson de Lima Morais, Le Chang, Michel Barrette, Carol Gauthier, et al. 2021. “MetaboAnalyst 5.0: Narrowing the Gap Between Raw Spectra and Functional Insights.” Nucleic Acids Research 49: W388-W396. https://doi.org/10.1093/nar/gkab382

[48] Wen, Tao, Guoqing Niu, Tong Chen, Qirong Shen, Jun Yuan, and Yong-Xin Liu. 2023. “The Best Practice for Microbiome Analysis Using R.” Protein & Cell 14: 713-725. https://doi.org/10.1093/procel/pwad024

[49] Chen, Tong, Haiyan Zhang, Yu Liu, Yong-Xin Liu, and Luqi Huang. 2021. “EVenn: Easy to Create Repeatable and Editable Venn Diagrams and Venn Networks Online.” Journal of Genetics and Genomics 48: 863-866. https://doi.org/10.1016/j.jgg.2021.07.007

[50] Yang, Qingxia, Yaguo Gong, and Feng Zhu. 2023. “Critical Assessment of the Biomarker Discovery and Classification Methods for Multiclass Metabolomics.” Analytical Chemistry 95: 5542-5552. https://doi.org/10.1021/acs.analchem.2c04402

[51] Li, Fengcheng, Jiayi Yin, Mingkun Lu, Qingxia Yang, Zhenyu Zeng, Bing Zhang, Zhaorong Li, et al. 2022. “ConSIG: Consistent Discovery of Molecular Signature From OMIC Data.” Briefings in Bioinformatics 23: 1-15. https://doi.org/10.1093/bib/bbac253

[52] Li, Fengcheng, Ying Zhou, Ying Zhang, Jiayi Yin, Yunqing Qiu, Jianqing Gao, and Feng Zhu. 2022. “POSREG: Proteomic Signature Discovered by Simultaneously Optimizing Its Reproducibility and Generalizability.” Briefings in Bioinformatics 23: 1-16. https://doi.org/10.1093/bib/bbac040

[53] Yang, Qingxia, Yi Li, Bo Li, and Yaguo Gong. 2022. “A Novel Multi-Class Classification Model for Schizophrenia, Bipolar Disorder and Healthy Controls Using Comprehensive Transcriptomic Data.” Computers in Biology and Medicine 148: 105956. https://doi.org/10.1016/j.compbiomed.2022.105956

[54] Mallick, Himel, Ali Rahnavard, Lauren J. McIver, Siyuan Ma, Yancong Zhang, Long H. Nguyen, Timothy L. Tickle, et al. 2021. “Multivariable Association Discovery in Population-Scale Meta-Omics Studies.” PLoS Computational Biology 17: e1009442. https://doi.org/10.1371/journal.pcbi.1009442

[55] Lin, Huang, and Shyamal Das Peddada. 2020. “Analysis of Compositions of Microbiomes With Bias Correction.” Nature Communications 11: 3514. https://doi.org/10.1038/s41467-020-17041-7

[56] Mei, Zhendong, Fenglei Wang, Amrisha Bhosle, Danyue Dong, Raaj Mehta, Andrew Ghazi, Yancong Zhang, et al. 2024. “Strain-Specific Gut Microbial Signatures in Type 2 Diabetes Identified in a Cross-Cohort Analysis of 8,117 Metagenomes.” Nature Medicine 30: 2265-2276. https://doi.org/10.1038/s41591-024-03067-7

[57] Friedman, Jonathan, and Eric J. Alm. 2012. “Inferring Correlation Networks From Genomic Survey Data.” PLoS Computational Biology 8: e1002687. https://doi.org/10.1371/journal.pcbi.1002687

[58] Su, Qi, Oscar W. H. Wong, Wenqi Lu, Yating Wan, Lin Zhang, Wenye Xu, Moses K. T. Li, et al. 2024. “Multikingdom and Functional Gut Microbiota Markers for Autism Spectrum Disorder.” Nature Microbiology 9: 2344-2355. https://doi.org/10.1038/s41564-024-01739-1

[59] Wirbel, Jakob, Konrad Zych, Morgan Essex, Nicolai Karcher, Ece Kartal, Guillem Salazar, Peer Bork, Shinichi Sunagawa, and Georg Zeller. 2021. “Microbiome Meta-Analysis and Cross-Disease Comparison Enabled by the SIAMCAT Machine Learning Toolbox.” Genome Biology 22: 93. https://doi.org/10.1186/s13059-021-02306-1

[60] Liu, Yang, Guillaume Méric, Aki S. Havulinna, Shu Mei Teo, Fredrik Åberg, Matti Ruuskanen, Jon Sanders, et al. 2022. “Early Prediction of Incident Liver Disease Using Conventional Risk Factors and Gut-Microbiome-Augmented Gradient Boosting.” Cell Metabolism 34: 719-730.e4. https://doi.org/10.1016/j.cmet.2022.03.002

[61] Gu, Zuguang. 2022. “Complex Heatmap Visualization.” iMeta 1: e43. https://doi.org/10.1002/imt2.43

[62] Asnicar, Francesco, George Weingart, Timothy L. Tickle, Curtis Huttenhower, and Nicola Segata. 2015. “Compact Graphical Representation of Phylogenetic Data and Metadata With GraPhlAn.” PeerJ 3: e1029. https://doi.org/10.7717/peerj.1029

[63] Huang, Yiming, Ravi U. Sheth, Shijie Zhao, Lucas A. Cohen, Kendall Dabaghi, Thomas Moody, Yiwei Sun, et al. 2023. “High-Throughput Microbial Culturomics Using Automation and Machine Learning.” Nature Biotechnology 41: 1424-1433. https://doi.org/10.1038/s41587-023-01674-2

[64] Grant, Jason R., Eric Enns, Eric Marinier, Arnab Mandal, Emily K. Herman, Chih-yu Chen, Morag Graham, Gary Van Domselaar, and Paul Stothard. 2023. “Proksee: In-Depth Characterization and Visualization of Bacterial Genomes.” Nucleic Acids Research 51: W484-W492. https://doi.org/10.1093/nar/gkad326

[65] Anderson, Marti J. 2008. “A New Method for Non-Parametric Multivariate Analysis of Variance.” Austral Ecology 26: 32-46. https://doi.org/10.1111/j.1442-9993.2001.01070.pp.x

[66] Kelly, Brendan J., Robert Gross, Kyle Bittinger, Scott Sherrill-Mix, James D. Lewis, Ronald G. Collman, Frederic D. Bushman, and Hongzhe Li. 2015. “Power and Sample-Size Estimation for Microbiome Studies Using Pairwise Distances and PERMANOVA.” Bioinformatics 31: 2461-2468. https://doi.org/10.1093/bioinformatics/btv183

[67] Gómez-Rubio, Virgilio. 2017. “ggplot2 - Elegant Graphics for Data Analysis (2nd Edition).” Journal of Statistical Software 77: 1-3. https://doi.org/10.18637/jss.v077.b02

[68] Xu, Shuangbin, Lin Li, Xiao Luo, Meijun Chen, Wenli Tang, Li Zhan, Zehan Dai, et al. 2022. “Ggtree: A Serialized Data Object for Visualization of a Phylogenetic Tree and Annotation Data.” iMeta 1: e56. https://doi.org/10.1002/imt2.56

[69] Xu, Shuangbin, Zehan Dai, Pingfan Guo, Xiaocong Fu, Shanshan Liu, Lang Zhou, Wenli Tang, et al. 2021. “Ggtreeextra: Compact Visualization of Richly Annotated Phylogenetic Data.” Molecular Biology and Evolution 38: 4039-4042. https://doi.org/10.1093/molbev/msab166

[70] Chen, Meijun, Xiao Luo, Shuangbin Xu, Lin Li, Junrui Li, Zijing Xie, Qianwen Wang, et al. 2025. “Scalable Method for Exploring Phylogenetic Placement Uncertainty With Custom Visualizations Using Treeio and Ggtree.” iMeta 4: e269. https://doi.org/10.1002/imt2.269

[71] Chaumeil, Pierre-Alain, Aaron J. Mussig, Philip Hugenholtz, and Donovan H. Parks. 2022. “GTDB-Tk v2: Memory Friendly Classification With the Genome Taxonomy Database.” Bioinformatics 38: 5315-5316. https://doi.org/10.1093/bioinformatics/btac672

[72] Eren, A. Murat, Özcan C. Esen, Christopher Quince, Joseph H. Vineis, Hilary G. Morrison, Mitchell L. Sogin, and Tom O. Delmont. 2015. “Anvi'o: An Advanced Analysis and Visualization Platform for ‘Omics Data’.” PeerJ 3: e1319. https://doi.org/10.7717/peerj.1319

[73] Peters, Brandilyn A., Melissa Wilson, Una Moran, Anna Pavlick, Allison Izsak, Todd Wechter, Jeffrey S. Weber, Iman Osman, and Jiyoung Ahn. 2019. “Relating the Gut Metagenome and Metatranscriptome to Immunotherapy Responses in Melanoma Patients.” Genome Medicine 11: 61. https://doi.org/10.1186/s13073-019-0672-4

[74] Pu, Yanni, Zhonghan Sun, Hui Zhang, Qingxia Huang, Zhengdong Wang, Zhendong Mei, Peilu Wang, et al. 2024. “Gut Microbial Features and Circulating Metabolomic Signatures of Frailty in Older Adults.” Nature Aging 4: 1249-1262. https://doi.org/10.1038/s43587-024-00678-0

[75] Kong, Cheng, Lei Liang, Guang Liu, Lutao Du, Yongzhi Yang, Jianqiang Liu, Debing Shi, Xinxiang Li, and Yanlei Ma. 2023. “Integrated Metagenomic and Metabolomic Analysis Reveals Distinct Gut-Microbiome-Derived Phenotypes in Early-Onset Colorectal Cancer.” Gut 72: 1129-1142. https://doi.org/10.1136/gutjnl-2022-327156

[76] Szklarczyk, Damian, Annika L. Gable, David Lyon, Alexander Junge, Stefan Wyder, Jaime Huerta-Cepas, Milan Simonovic, et al. 2019. “STRING v11: Protein-Protein Association Networks With Increased Coverage, Supporting Functional Discovery in Genome-Wide Experimental Datasets.” Nucleic Acids Research 47: D607-D613. https://doi.org/10.1093/nar/gky1131

[77] Szklarczyk, Damian, Annika L. Gable, Katerina C. Nastou, David Lyon, Rebecca Kirsch, Sampo Pyysalo, Nadezhda T. Doncheva, et al. 2021. “The STRING Database in 2021: Customizable Protein-Protein Networks, and Functional Characterization of User-Uploaded Gene/Measurement Sets.” Nucleic Acids Research 49: D605-D612. https://doi.org/10.1093/nar/gkaa1074

[78] Johansen, Joachim, Koji Atarashi, Yasumichi Arai, Nobuyoshi Hirose, Søren J. Sørensen, Tommi Vatanen, Mikael Knip, et al. 2023. “Centenarians Have a Diverse Gut Virome With the Potential to Modulate Metabolism and Promote Healthy Lifespan.” Nature Microbiology 8: 1064-1078. https://doi.org/10.1038/s41564-023-01370-6

[79] Shen, Weitao, Ziguang Song, Xiao Zhong, Mei Huang, Danting Shen, Pingping Gao, Xiaoqian Qian, et al. 2022. “Sangerbox: A Comprehensive, Interaction-Friendly Clinical Bioinformatics Analysis Platform.” iMeta 1: e36. https://doi.org/10.1002/imt2.36

[80] Liu, Yong-Xin, Yuan Qin, Tong Chen, Meiping Lu, Xubo Qian, Xiaoxuan Guo, and Yang Bai. 2020. “A Practical Guide to Amplicon and Metagenomic Analysis of Microbiome Data.” Protein & Cell 12: 315-330. https://doi.org/10.1007/s13238-020-00724-8

[81] Bai, Defeng, Tong Chen, Jiani Xun, Chuang Ma, Hao Luo, Haifei Yang, Chen Cao, et al. 2025. “EasyMetagenome: A User-Friendly and Flexible Pipeline for Shotgun Metagenomic Analysis in Microbiome Research.” iMeta 4: e70001. https://doi.org/10.1002/imt2.70001

RIGHTS & PERMISSIONS

2025 The Author(s). iMeta published by John Wiley & Sons Australia, Ltd on behalf of iMeta Science.