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

Wenxing Gao, Weili Lin, Sheng Gao, Lei Liu, Qiang Li, Wanning Chen, Wenjing Yin, Xinyue Zhu, Wenjie Li, Dingfeng Wu, Guoqing Zhang, Ruixin Zhu, Na Jiao

Published: 2024-05-13 DOI: 10.1038/s41596-024-00999-9

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Supplementary Figs. 1 and 2 and Table 1.

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Supplementary Data 1

Example data for the execution of xMarkerFinder.

Using high-resolution microscopy data to generate realistic structures for electromagnetic FDTD simulations from complex biological models

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Mitochondrial single-cell ATAC-seq for high-throughput multi-omic detection of mitochondrial genotypes and chromatin accessibility

Identification, isolation and analysis of human gut-associated lymphoid tissues

Development of RNA G-quadruplex (rG4)-targetingl-RNA aptamers by rG4-SELEX

Self-assembled ultraflexible probes for long-term neural recordings and neuromodulation

Hyperpolarized water as universal sensitivity booster in biomolecular NMR

Improved sampling and DNA extraction procedures for microbiome analysis in food-processing environments

An analytical workflow for dynamic characterization and quantification of metal-bearing nanomaterials in biological matrices

A flow cytometry-based protocol for syngenic isolation of neurovascular unit cells from mouse and human tissues

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Cullin, N., Azevedo Antunes, C., Straussman, R., Stein-Thoeringer, C. K. & Elinav, E. Microbiome and cancer. Cancer Cell 39, 1317–1341 (2021).
LaCourse, K. D., Johnston, C. D. & Bullman, S. The relationship between gastrointestinal cancers and the microbiota. Lancet Gastroenterol. Hepatol. 6, 498–509 (2021).
Fan, Y. & Pedersen, O. Gut microbiota in human metabolic health and disease. Nat. Rev. Microbiol. 19, 55–71 (2021).
Britton, G. J. et al. Microbiotas from humans with inflammatory bowel disease alter the balance of gut Th17 and RORγt+ regulatory T cells and exacerbate colitis in mice. Immunity 50, 212–224. e214 (2019).
Mima, K. et al. Fusobacterium nucleatum in colorectal carcinoma tissue and patient prognosis. Gut 65, 1973–1980 (2016).
McQuade, J. L., Daniel, C. R., Helmink, B. A. & Wargo, J. A. Modulating the microbiome to improve therapeutic response in cancer. Lancet Oncol. 20, e77–e91 (2019).
Wu, Y. et al. Identification of microbial markers across populations in early detection of colorectal cancer. Nat. Commun. 12, 3063 (2021).
Liu, N.-N. et al. Multi-kingdom microbiota analyses identify bacterial–fungal interactions and biomarkers of colorectal cancer across cohorts. Nat. Microbiol. 7, 238–250 (2022).
Ma, S. et al. Population structure discovery in meta-analyzed microbial communities and inflammatory bowel disease using MMUPHin. Genome Biol. 23, 208 (2022).
Gao, W. et al. Multimodal metagenomic analysis reveals microbial single nucleotide variants as superior biomarkers for early detection of colorectal cancer. Gut Microbes 15, 2245562 (2023).
Zhu, X. et al. Multi-kingdom microbial signatures in excess body weight colorectal cancer based on global metagenomic analysis. Commun. Biol. 7, 24 (2024).
Gao, S. et al. Microbial genes outperform species and SNVs as diagnostic markers for Crohn’s disease on multicohort fecal metagenomes empowered by artificial intelligence. Gut Microbes 15, 2221428 (2023).
Relman, D. A. The human microbiome and the future practice of medicine. JAMA 314, 1127–1128 (2015).
Gao, L. et al. Oral microbiomes: more and more importance in oral cavity and whole body. Protein Cell 9, 488–500 (2018).
Byrd, A. L., Belkaid, Y. & Segre, J. A. The human skin microbiome. Nat. Rev. Microbiol. 16, 143–155 (2018).
Wang, Z. et al. Inflammatory endotype-associated airway microbiome in chronic obstructive pulmonary disease clinical stability and exacerbations: a multicohort longitudinal analysis. Am. J. Respir. Crit. Care Med. 203, 1488–1502 (2021).
Zhou, J. et al. Signatures of mucosal microbiome in oral squamous cell carcinoma identified using a random forest model. Cancer Manag. Res. 12, 5353–5363 (2020).
Zhang, L., Liu, Y., Zheng, H. J. & Zhang, C. P. The oral microbiota may have influence on oral cancer. Front. Cell Infect. Microbiol. 9, 476 (2019).
Zhao, H. et al. Variations in oral microbiota associated with oral cancer. Sci. Rep. 7, 11773 (2017).
Sunagawa, S. et al. Structure and function of the global ocean microbiome. Science 348, 1261359 (2015).
Salazar, G. et al. Gene expression changes and community turnover differentially shape the global ocean metatranscriptome. Cell 179, 1068–1083 e1021 (2019).
Govaere, O. et al. Transcriptomic profiling across the nonalcoholic fatty liver disease spectrum reveals gene signatures for steatohepatitis and fibrosis. Sci. Transl. Med. 12, eaba4448 (2020).
Pantano, L. et al. Molecular characterization and cell type composition deconvolution of fibrosis in NAFLD. Sci. Rep. 11, 18045 (2021).
Gerhard, G. S. et al. Transcriptomic profiling of obesity-related nonalcoholic steatohepatitis reveals a core set of fibrosis-specific genes. J. Endocr. Soc. 2, 710–726 (2018).
Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011).
Xiao, L., Zhang, F. & Zhao, F. Large-scale microbiome data integration enables robust biomarker identification. Nat. Comput. Sci. 2, 307–316 (2022).
Wirbel, J. et al. Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine learning toolbox. Genome Biol. 22, 93 (2021).
Jiang, J. MIIDL: a Python package for microbial biomarkers identification powered by interpretable deep learning. Preprint at https://arxiv.org/abs/2109.12204 (2021).
Wang, Y. & LeCao, K. A. Managing batch effects in microbiome data. Brief. Bioinform. 21, 1954–1970 (2020).
Ling, W. et al. Batch effects removal for microbiome data via conditional quantile regression. Nat. Commun. 13, 5418 (2022).
Gibbons, S. M., Duvallet, C. & Alm, E. J. Correcting for batch effects in case-control microbiome studies. PLoS Comput. Biol. 14, e1006102 (2018).
Dai, Z., Wong, S. H., Yu, J. & Wei, Y. Batch effects correction for microbiome data with Dirichlet-multinomial regression. Bioinformatics 35, 807–814 (2018).
Knight, R. et al. Best practices for analysing microbiomes. Nat. Rev. Microbiol. 16, 410–422 (2018).
Coelho, L. P. et al. NG-meta-profiler: fast processing of metagenomes using NGLess, a domain-specific language. Microbiome 7, 84 (2019).
Faul, F., Erdfelder, E., Lang, A. G. & Buchner, A. G. Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav. Res. Methods 39, 175–191 (2007).
Faul, F., Erdfelder, E., Buchner, A. & Lang, A.-G. Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses. Behav. Res. Methods 41, 1149–1160 (2009).
Kelly, B. J. et al. Power and sample-size estimation for microbiome studies using pairwise distances and PERMANOVA. Bioinformatics 31, 2461–2468 (2015).
Casals-Pascual, C. et al. Microbial diversity in clinical microbiome studies: sample size and statistical power considerations. Gastroenterology 158, 1524–1528 (2020).
Pasolli, E. et al. Accessible, curated metagenomic data through ExperimentHub. Nat. Methods 14, 1023–1024 (2017).
Dai, D. et al. GMrepo v2: a curated human gut microbiome database with special focus on disease markers and cross-dataset comparison. Nucleic Acids Res. 50, D777–D784 (2022).
Janssens, Y. et al. Disbiome database: linking the microbiome to disease. BMC Microbiol. 18, 50 (2018).
Shoaie, S. et al. Global and temporal state of the human gut microbiome in health and disease. Preprint at Research Square https://doi.org/10.21203/rs.3.rs-339282/v1 (2021).
Weiss, S. et al. Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome 5, 27 (2017).
Molder, F. et al. Sustainable data analysis with Snakemake. F1000Res 10, 33 (2021).

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

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