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BugSigDB — a database for identifying unusual abundance patterns in human microbiome studies

BugSigDB is a community-editable wiki that harmonizes how key microbial differential abundance methods and results are reported, identifying rare and common patterns across the literature of published host-associated microbiome studies.

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Fig. 1: Geography and health outcomes of BugSigDB.


  1. Krötzsch, M., Vrandečić, D. & Völkel, M. Semantic MediaWiki. in The Semantic Web — ISWC 2006 935–942 (Springer Berlin Heidelberg, 2006). This paper describes Semantic MediaWiki, the system we use for distributed data entry, review, storage and public visualization.

  2. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005). A seminal article on gene set enrichment analysis, a widely used method that helped to motivate development of BugSigDB for microbiome research.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Pesquita, C. et al. Metrics for GO based protein semantic similarity: a systematic evaluation. BMC Bioinformatics 9(Suppl. 5), S4 (2008). This paper presents the semantic similarity measure we use to calculate taxonomic similarity between two microbial signatures.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Falony, G. et al. Population-level analysis of gut microbiome variation. Science 352, 560–564 (2016). This paper describes recurrent patterns of taxonomic co-occurrence among healthy participants that we observe as being shared in many signatures of disease.

    Article  CAS  PubMed  Google Scholar 

  5. Pasolli, E. et al. Accessible, curated metagenomic data through ExperimentHub. Nat. Methods 14, 1023–1024 (2017). This paper describes the individual participant data resource we use to analyze healthy microbiomes and for meta-analysis of differential abundance in colorectal cancer.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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This is a summary of: Geistlinger, L. et al. BugSigDB captures patterns of differential abundance across a broad range of host-associated microbial signatures. Nat. Biotechnol. (2023).

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BugSigDB — a database for identifying unusual abundance patterns in human microbiome studies. Nat Biotechnol (2023).

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