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Identification and validation of microbial biomarkers from cross-cohort datasets using xMarkerFinder

Nature Protocols (2024)Cite this article

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Abstract

Microbial signatures have emerged as promising biomarkers for disease diagnostics and prognostics, yet their variability across different studies calls for a standardized approach to biomarker research. Therefore, we introduce xMarkerFinder, a four-stage computational framework for microbial biomarker identification with comprehensive validations from cross-cohort datasets, including differential signature identification, model construction, model validation and biomarker interpretation. xMarkerFinder enables the identification and validation of reproducible biomarkers for cross-cohort studies, along with the establishment of classification models and potential microbiome-induced mechanisms. Originally developed for gut microbiome research, xMarkerFinder’s adaptable design makes it applicable to various microbial habitats and data types. Distinct from existing biomarker research tools that typically concentrate on a singular aspect, xMarkerFinder uniquely incorporates a sophisticated feature selection process, specifically designed to address the heterogeneity between different cohorts, extensive internal and external validations, and detailed specificity assessments. Execution time varies depending on the sample size, selected algorithm and computational resource. Accessible via GitHub (https://github.com/tjcadd2020/xMarkerFinder), xMarkerFinder supports users with diverse expertise levels through different execution options, including step-to-step scripts with detailed tutorials and frequently asked questions, a single-command execution script, a ready-to-use Docker image and a user-friendly web server (https://www.biosino.org/xmarkerfinder).

Key points

  • The authors describe xMarkerFinder, a four-stage computational framework for microbial biomarker identification with comprehensive validations from cross-cohort datasets.

  • xMarkerFinder is the first computational framework aggregating meta-analyses and machine learning models for the establishment and validation of universally robust microbial biomarkers across multiple cohorts.

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Fig. 1: The procedure for selecting biomarkers from cross-cohort microbial profiles.
Fig. 2: The performance of microbial SNV biomarkers for the detection of adenoma10.
Fig. 3: Validation of the microbial biomarkers for adenoma diagnosis10.
Fig. 4: Overview of the xMarkerFinder workflow.
Fig. 5: Example output files for model construction and validation7.
Fig. 6: The initial interface of Gephi.
Fig. 7: Gephi import report.
Fig. 8: The interface for network construction.
Fig. 9: An example microbial network8.

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Data availability

Example input/output files, codes, frequently asked questions and a user message board for xMarkerFinder are provided in our GitHub repository (https://github.com/tjcadd2020/xMarkerFinder). Source data used for generating Figs. 3 and 9 can be accessed as Supplementary information.

Code availability

The single-command execution option and the step-to-step scripts for xMarkerFinder can be obtained at https://github.com/tjcadd2020/xmarkerfinder. The ready-to-use Docker image can be pulled from Docker Hub (https://hub.docker.com/r/tjcadd2022/xmarkerfinder) and the dockerfile used for creating this Docker image is also provided. The user-friendly web server for xMarkerFinder is available at https://www.biosino.org/xmarkerfinder/. The code in this protocol has been peer reviewed.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (grant numbers 92251307 to R.Z. and G.Z., 82170542 to R.Z., 82000536 to N.J.), and the National Key Research and Development Program of China (grant number 2021YFF0703702 to R.Z.). We thank X. Huang, K. Chen and Y. Huang for testing xMarkerFinder and providing their constructive feedback.

Author information

Author notes

  1. These authors contributed equally: Wenxing Gao, Weili Lin, Qiang Li.

Authors and Affiliations

  1. The Shanghai Tenth People’s Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, P. R. China

    Wenxing Gao, Weili Lin, Wanning Chen, Wenjing Yin, Xinyue Zhu, Sheng Gao, Lei Liu & Ruixin Zhu

  2. National Genomics Data Center & Bio-Med Big Data Center, Chinese Academy of Sciences Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of the Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, P. R. China

    Qiang Li & Guoqing Zhang

  3. Shanghai Southgene Technology Co., Ltd., Shanghai, P. R. China

    Wenjie Li

  4. National Clinical Research Center for Child Health, the Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, P. R. China

    Dingfeng Wu & Na Jiao

  5. State Key Laboratory of Genetic Engineering, Fudan Microbiome Center, School of Life Sciences, Fudan University, Shanghai, P. R. China

    Na Jiao

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Contributions

N.J., R.Z. and G.Z. conceived and designed the study. W.G. and W.Lin wrote the codes and step-to-step protocol. Q.L. and W.Li designed the user interface and wrote related codes. W.G., W.Lin and N.J. drafted the manuscript. W.C., W.Y., X.Z. and S.G. tested the protocol. L.L., D.W., G.Z., R.Z. and N.J. reviewed and edited the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Guoqing Zhang, Ruixin Zhu or Na Jiao.

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Nature Protocols thanks Kang Ning, Jiachao Zhang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Related links

Key references using this protocol

Wu, Y. et al. Nat. Commun. 12, 3063 (2021): https://doi.org/10.1038/s41467-021-23265-y

Liu, N.-N. et al. Nat. Microbiol. 7, 238–250 (2022): https://doi.org/10.1038/s41564-021-01030-7

Gao, S. et al. Gut Microbes 15, 2221428 (2023): https://doi.org/10.1080/19490976.2023.2221428

Gao, W et al. Gut Microbes 15, 2245562 (2023): https://doi.org/10.1080/19490976.2023.2245562

Zhu, X et al. Commun. Biol. 7, 24 (2024): https://doi.org/10.1038/s42003-023-05714-0

Key data used in this protocol

Liu, N.-N. et al. Nat. Microbiol. 7, 238–250 (2022): https://doi.org/10.1038/s41564-021-01030-7

Extended data

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2 and Table 1.

Reporting Summary

Supplementary Data 1

Example data for the execution of xMarkerFinder.

Source data

Source Data Fig. 3

Statistical source data.

Source Data Fig. 9

Statistical source data.

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Gao, W., Lin, W., Li, Q. et al. Identification and validation of microbial biomarkers from cross-cohort datasets using xMarkerFinder. Nat Protoc (2024). https://doi.org/10.1038/s41596-024-00999-9

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