BugSigDB captures patterns of differential abundance across a broad range of host-associated microbial signatures.
Journal
Nature biotechnology
ISSN: 1546-1696
Titre abrégé: Nat Biotechnol
Pays: United States
ID NLM: 9604648
Informations de publication
Date de publication:
11 Sep 2023
11 Sep 2023
Historique:
received:
24
10
2022
accepted:
20
06
2023
medline:
12
9
2023
pubmed:
12
9
2023
entrez:
11
9
2023
Statut:
aheadofprint
Résumé
The literature of human and other host-associated microbiome studies is expanding rapidly, but systematic comparisons among published results of host-associated microbiome signatures of differential abundance remain difficult. We present BugSigDB, a community-editable database of manually curated microbial signatures from published differential abundance studies accompanied by information on study geography, health outcomes, host body site and experimental, epidemiological and statistical methods using controlled vocabulary. The initial release of the database contains >2,500 manually curated signatures from >600 published studies on three host species, enabling high-throughput analysis of signature similarity, taxon enrichment, co-occurrence and coexclusion and consensus signatures. These data allow assessment of microbiome differential abundance within and across experimental conditions, environments or body sites. Database-wide analysis reveals experimental conditions with the highest level of consistency in signatures reported by independent studies and identifies commonalities among disease-associated signatures, including frequent introgression of oral pathobionts into the gut.
Identifiants
pubmed: 37697152
doi: 10.1038/s41587-023-01872-y
pii: 10.1038/s41587-023-01872-y
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
ID : 5R01CA230551
Organisme : U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
ID : 5U24CA180996
Organisme : U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
ID : 5R01CA230551
Organisme : U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
ID : 5R01CA230551
Organisme : U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
ID : 5R01CA230551
Organisme : U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
ID : 5R01CA230551
Organisme : U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
ID : 5R01CA230551
Organisme : U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
ID : 5R01CA230551
Organisme : U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
ID : 5R01CA230551
Organisme : U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
ID : 5R01CA230551
Organisme : U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
ID : 5R01CA230551
Organisme : U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
ID : 5R01CA230551
Organisme : U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
ID : 5R01CA230551
Organisme : U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
ID : 5R01CA230551
Organisme : U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
ID : 5R01CA230551
Organisme : U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
ID : 5R01CA230551
Informations de copyright
© 2023. The Author(s).
Références
Jonsson, A. L. & Bäckhed, F. Role of gut microbiota in atherosclerosis. Nat. Rev. Cardiol. 14, 79–87 (2017).
pubmed: 27905479
Tang, W. H. W., Kitai, T. & Hazen, S. L. Gut microbiota in cardiovascular health and disease. Circ. Res. 120, 1183–1196 (2017).
pubmed: 28360349
pmcid: 5390330
Schwabe, R. F. & Jobin, C. The microbiome and cancer. Nat. Rev. Cancer 13, 800–812 (2013).
pubmed: 24132111
pmcid: 3986062
Gurung, M. et al. Role of gut microbiota in type 2 diabetes pathophysiology. EBioMedicine 51, 102590 (2020).
pubmed: 31901868
pmcid: 6948163
Paulson, J. N., Stine, O. C., Bravo, H. C. & Pop, M. Differential abundance analysis for microbial marker-gene surveys. Nat. Methods 10, 1200–1202 (2013).
pubmed: 24076764
pmcid: 4010126
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).
pubmed: 16199517
pmcid: 1239896
Geistlinger, L. et al. Toward a gold standard for benchmarking gene set enrichment analysis. Brief. Bioinform. 22, 545–556 (2020).
pmcid: 7820859
Huang, D. W., Sherman, B. T. & Lempicki, R. A. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37, 1–13 (2009).
pubmed: 19033363
Geistlinger, L., Csaba, G., Küffner, R., Mulder, N. & Zimmer, R. From sets to graphs: towards a realistic enrichment analysis of transcriptomic systems. Bioinformatics 27, i366–i373 (2011).
pubmed: 21685094
pmcid: 3117393
Alexa, A., Rahnenführer, J. & Lengauer, T. Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. Bioinformatics 22, 1600–1607 (2006).
pubmed: 16606683
Goeman, J. J. & Bühlmann, P. Analyzing gene expression data in terms of gene sets: methodological issues. Bioinformatics 23, 980–987 (2007).
pubmed: 17303618
McLean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010).
pubmed: 20436461
pmcid: 4840234
Chagoyen, M., López-Ibáñez, J. & Pazos, F. Functional analysis of metabolomics data. Methods Mol. Biol. 1415, 399–406 (2016).
pubmed: 27115644
Ried, J. S. et al. PSEA: phenotype set enrichment analysis—a new method for analysis of multiple phenotypes. Genet. Epidemiol. 36, 244–252 (2012).
pubmed: 22714936
Ma, W., Huang, C., Zhou, Y., Li, J. & Cui, Q. MicroPattern: a web-based tool for microbe set enrichment analysis and disease similarity calculation based on a list of microbes. Sci. Rep. 7, 40200 (2017).
pubmed: 28071710
pmcid: 5223220
Dhariwal, A. et al. MicrobiomeAnalyst: a web-based tool for comprehensive statistical, visual and meta-analysis of microbiome data. Nucleic Acids Res. 45, W180–W188 (2017).
pubmed: 28449106
pmcid: 5570177
Kou, Y., Xu, X., Zhu, Z., Dai, L. & Tan, Y. Microbe-set enrichment analysis facilitates functional interpretation of microbiome profiling data. Sci. Rep. 10, 21466 (2020).
pubmed: 33293650
pmcid: 7722755
Nguyen, Q. P., Hoen, A. G. & Frost, H. R. CBEA: competitive balances for taxonomic enrichment analysis. PLoS Comput. Biol. 18, e1010091 (2022).
pubmed: 35584140
pmcid: 9154102
Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–29 (2000).
pubmed: 10802651
pmcid: 3037419
Kanehisa, M. & Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30 (2000).
pubmed: 10592173
pmcid: 102409
Liberzon, A. et al. Molecular Signatures Database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).
pubmed: 21546393
pmcid: 3106198
Liberzon, A. et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 1, 417–425 (2015).
pubmed: 26771021
pmcid: 4707969
Culhane, A. C. et al. GeneSigDB: a manually curated database and resource for analysis of gene expression signatures. Nucleic Acids Res. 40, D1060–D1066 (2012).
pubmed: 22110038
Wattam, A. R. et al. Improvements to PATRIC, the all-bacterial bioinformatics database and analysis resource center. Nucleic Acids Res. 45, D535–D542 (2017).
pubmed: 27899627
Shaaban, H. et al. The Microbe Directory: an annotated, searchable inventory of microbes’ characteristics. Gates Open Res. 2, 3 (2018).
pubmed: 29630066
pmcid: 5883067
Bergey, D. H. & Holt J. G. Bergey’s Manual of Systematic Bacteriology, Vol. 1 (Williams & Wilkins, 1984).
Reimer, L. C. et al. BacDive in 2022: the knowledge base for standardized bacterial and archaeal data. Nucleic Acids Res. 50, D741–D746 (2022).
pubmed: 34718743
Mungall, C. J., Torniai, C., Gkoutos, G. V., Lewis, S. E. & Haendel, M. A. Uberon, an integrative multi-species anatomy ontology. Genome Biol. 13, R5 (2012).
pubmed: 22293552
pmcid: 3334586
Federhen, S. The NCBI Taxonomy Database. Nucleic Acids Res. 40, D136–D143 (2012).
pubmed: 22139910
Krötzsch, M., Vrandečić, D. & Völkel, M. Semantic MediaWiki. In Proc. 5th International Semantic Web Conference, ISWC 2006 (ed. Cruz, I. et al.) 935–942 (Springer, 2006).
Tarca, A. L., Draghici, S., Bhatti, G. & Romero, R. Down-weighting overlapping genes improves gene set analysis. BMC Bioinformatics 13, 136 (2012).
pubmed: 22713124
pmcid: 3443069
Malone, J. et al. Modeling sample variables with an Experimental Factor Ontology. Bioinformatics 26, 1112–1118 (2010).
pubmed: 20200009
pmcid: 2853691
Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011).
pubmed: 21702898
pmcid: 3218848
Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome datasets are compositional: and this is not optional. Front. Microbiol. 8, 2224 (2017).
pubmed: 29187837
pmcid: 5695134
Chung, N. C., Miasojedow, B., Startek, M. & Gambin, A. Jaccard/Tanimoto similarity test and estimation methods for biological presence–absence data. BMC Bioinformatics 20, 644 (2019).
pubmed: 31874610
pmcid: 6929325
Pesquita, C., Faria, D., Falcão, A. O., Lord, P. & Couto, F. M. Semantic similarity in biomedical ontologies. PLoS Comput. Biol. 5, e1000443 (2009).
pubmed: 19649320
pmcid: 2712090
Fouhy, F. et al. High-throughput sequencing reveals the incomplete, short-term recovery of infant gut microbiota following parenteral antibiotic treatment with ampicillin and gentamicin. Antimicrob. Agents Chemother. 56, 5811–5820 (2012).
pubmed: 22948872
pmcid: 3486619
O’Sullivan, O. et al. Alterations in intestinal microbiota of elderly Irish subjects post-antibiotic therapy. J. Antimicrob. Chemother. 68, 214–221 (2013).
Arat, S. et al. Microbiome changes in healthy volunteers treated with GSK1322322, a novel antibiotic targeting bacterial peptide deformylase. Antimicrob. Agents Chemother. 59, 1182–1192 (2015).
pubmed: 25487798
pmcid: 4335841
de Gunzburg, J. et al. Protection of the human gut microbiome from antibiotics. J. Infect. Dis. 217, 628–636 (2018).
Zou, Z.-H. et al. Prenatal and postnatal antibiotic exposure influences the gut microbiota of preterm infants in neonatal intensive care units. Ann. Clin. Microbiol. Antimicrob. 17, 9 (2018).
pubmed: 29554907
pmcid: 5858143
Zhang, M. et al. Association of prenatal antibiotics with measures of infant adiposity and the gut microbiome. Ann. Clin. Microbiol. Antimicrob. 18, 18 (2019).
pubmed: 31226994
pmcid: 6587281
Coker, M. O. et al. Specific class of intrapartum antibiotics relates to maturation of the infant gut microbiota: a prospective cohort study. BJOG 127, 217–227 (2020).
pubmed: 31006170
McHardy, I. H. et al. HIV infection is associated with compositional and functional shifts in the rectal mucosal microbiota. Microbiome 1, 26 (2013).
pubmed: 24451087
pmcid: 3971626
Ling, Z. et al. Alterations in the fecal microbiota of patients with HIV-1 infection: an observational study in a Chinese population. Sci. Rep. 6, 30673 (2016).
pubmed: 27477587
pmcid: 4967929
Zhou, Y. et al. Alterations in the gut microbiota of patients with acquired immune deficiency syndrome. J. Cell. Mol. Med. 22, 2263–2271 (2018).
pubmed: 29411528
pmcid: 5867062
Kaur, U. S. et al. High abundance of genus Prevotella in the gut of perinatally HIV-infected children is associated with IP-10 levels despite therapy. Sci. Rep. 8, 17679 (2018).
pubmed: 30518941
pmcid: 6281660
Sainz, T. et al. Effect of a nutritional intervention on the intestinal microbiota of vertically HIV-infected children: The Pediabiota Study. Nutrients 12, 2112 (2020).
pubmed: 32708743
pmcid: 7400861
Rashid, M.-U. et al. Determining the long-term effect of antibiotic administration on the human normal intestinal microbiota using culture and pyrosequencing methods. Clin. Infect. Dis. https://doi.org/10.1093/cid/civ137 (2015).
Ramirez, J. et al. Antibiotics as major disruptors of gut microbiota. Front. Cell. Infect. Microbiol. 10, 572912 (2020).
Alzahrani, J. et al. Inflammatory and immunometabolic consequences of gut dysfunction in HIV: parallels with IBD and implications for reservoir persistence and non-AIDS comorbidities. EBioMedicine 46, 522–531 (2019).
pubmed: 31327693
pmcid: 6710907
Faiela, C. & Sevene, E. Antibiotic prescription for HIV-positive patients in primary health care in Mozambique: a cross-sectional study. S. Afr. J. Infect. Dis. 37, 340 (2022).
pubmed: 35284563
pmcid: 8905412
Szychowiak, P., Villageois-Tran, K., Patrier, J., Timsit, J.-F. & Ruppé, É. The role of the microbiota in the management of intensive care patients. Ann. Intensive Care 12, 3 (2022).
pubmed: 34985651
pmcid: 8728486
Pasolli, E. et al. Accessible, curated metagenomic data through ExperimentHub. Nat. Methods 14, 1023–1024 (2017).
pubmed: 29088129
pmcid: 5862039
Geistlinger, L., Csaba, G. & Zimmer, R. Bioconductor’s EnrichmentBrowser: seamless navigation through combined results of set- & network-based enrichment analysis. BMC Bioinformatics 17, 45 (2016).
pubmed: 26791995
pmcid: 4721010
Thomas, A. M. et al. Metagenomic analysis of colorectal cancer datasets identifies cross-cohort microbial diagnostic signatures and a link with choline degradation. Nat. Med. 25, 667–678 (2019).
pubmed: 30936548
pmcid: 9533319
Wirbel, J. et al. Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer. Nat. Med. 25, 679–689 (2019).
pubmed: 30936547
pmcid: 7984229
Wu, N. et al. Dysbiosis signature of fecal microbiota in colorectal cancer patients. Microb. Ecol. 66, 462–470 (2013).
pubmed: 23733170
Allali, I. et al. Gut microbiome of Moroccan colorectal cancer patients. Med. Microbiol. Immunol. 207, 211–225 (2018).
pubmed: 29687353
pmcid: 6096775
Koliarakis, I. et al. Oral bacteria and intestinal dysbiosis in colorectal cancer. Int. J. Mol. Sci. 20, 4146 (2019).
pubmed: 31450675
pmcid: 6747549
Irfan, M., Delgado, R. Z. R. & Frias-Lopez, J. The oral microbiome and cancer. Front. Immunol. 11, 591088 (2020).
pubmed: 33193429
pmcid: 7645040
Lo, C.-H. et al. Periodontal disease, tooth loss, and risk of serrated polyps and conventional adenomas. Cancer Prev. Res. 13, 699–706 (2020).
Wu, D. & Smyth, G. K. Camera: a competitive gene set test accounting for inter-gene correlation. Nucleic Acids Res. 40, e133 (2012).
pubmed: 22638577
pmcid: 3458527
Falcon, S. & Gentleman, R. Using GOstats to test gene lists for GO term association. Bioinformatics 23, 257–258 (2007).
pubmed: 17098774
Tarca, A. L., Bhatti, G. & Romero, R. A comparison of gene set analysis methods in terms of sensitivity, prioritization and specificity. PLoS ONE 8, e79217 (2013).
pubmed: 24260172
pmcid: 3829842
Nguyen, T.-M., Shafi, A., Nguyen, T. & Draghici, S. Identifying significantly impacted pathways: a comprehensive review and assessment. Genome Biol. 20, 203 (2019).
pubmed: 31597578
pmcid: 6784345
McLaren, M. R., Willis, A. D. & Callahan, B. J. Consistent and correctable bias in metagenomic sequencing experiments. eLife 8, e46923 (2019).
The Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).
pmcid: 3564958
Qin, J. et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59–65 (2010).
pubmed: 20203603
pmcid: 3779803
Falony, G. et al. Population-level analysis of gut microbiome variation. Science 352, 560–564 (2016).
pubmed: 27126039
Costea, P. I. et al. Enterotypes in the landscape of gut microbial community composition. Nat. Microbiol. 3, 8–16 (2017).
pubmed: 29255284
pmcid: 5832044
Krzyściak, W., Pluskwa, K. K., Jurczak, A. & Kościelniak, D. The pathogenicity of the Streptococcus genus. Eur. J. Clin. Microbiol. Infect. Dis. 32, 1361–1376 (2013).
pubmed: 24141975
pmcid: 3824240
Fiore, E., Van Tyne, D. & Gilmore, M. S. Pathogenicity of enterococci. Microbiol. Spectr. https://doi.org/10.1128/microbiolspec.GPP3-0053-2018 (2019).
Harty, D. W., Oakey, H. J., Patrikakis, M., Hume, E. B. & Knox, K. W. Pathogenic potential of lactobacilli. Int. J. Food Microbiol. 24, 179–189 (1994).
pubmed: 7703012
Actor, J. K. Elsevier’s Integrated Review Immunology and Microbiology (Elsevier Health Sciences, 2011).
Nie, K. et al. Roseburia intestinalis: a beneficial gut organism from the discoveries in genus and species. Front. Cell. Infect. Microbiol. 11, 757718 (2021).
pubmed: 34881193
pmcid: 8647967
Parker, B. J., Wearsch, P. A., Veloo, A. C. M. & Rodriguez-Palacios, A. The genus Alistipes: gut bacteria with emerging implications to inflammation, cancer, and mental health. Front. Immunol. 11, 906 (2020).
pubmed: 32582143
pmcid: 7296073
Faust, K. et al. Microbial co-occurrence relationships in the human microbiome. PLoS Comput. Biol. 8, e1002606 (2012).
pubmed: 22807668
pmcid: 3395616
Herrera, S., Martínez-Sanz, J. & Serrano-Villar, S. HIV, cancer, and the microbiota: common pathways influencing different diseases. Front. Immunol. 10, 1466 (2019).
pubmed: 31316514
pmcid: 6610485
Caubit, X. et al. TSHZ3 deletion causes an autism syndrome and defects in cortical projection neurons. Nat. Genet. 48, 1359–1369 (2016).
pubmed: 27668656
pmcid: 5083212
Sanna-Cherchi, S. et al. Copy-number disorders are a common cause of congenital kidney malformations. Am. J. Hum. Genet. 91, 987–997 (2012).
pubmed: 23159250
pmcid: 3516596
Peralta-Marzal, L. N. et al. The impact of gut microbiota-derived metabolites in autism spectrum disorders. Int. J. Mol. Sci. 22, 10052 (2021).
pubmed: 34576216
pmcid: 8470471
Clothier, J. & Absoud, M. Autism spectrum disorder and kidney disease. Pediatr. Nephrol. 36, 2987–2995 (2021).
pubmed: 33340339
Suvisaari, J., Keinänen, J., Eskelinen, S. & Mantere, O. Diabetes and schizophrenia. Curr. Diab. Rep. 16, 16 (2016).
pubmed: 26803652
Knezevic, J., Starchl, C., Tmava Berisha, A. & Amrein, K. Thyroid–gut–axis: how does the microbiota influence thyroid function? Nutrients 12, 1769 (2020).
pubmed: 32545596
pmcid: 7353203
Ruiz-Núñez, B., Tarasse, R., Vogelaar, E. F., Janneke Dijck-Brouwer, D. A. & Muskiet, F. A. J. Higher prevalence of ‘low T3 syndrome’ in patients with chronic fatigue syndrome: a case–control study. Front. Endocrinol. 9, 97 (2018).
Xia, X. et al. Bacteria pathogens drive host colonic epithelial cell promoter hypermethylation of tumor suppressor genes in colorectal cancer. Microbiome 8, 108 (2020).
pubmed: 32678024
pmcid: 7367367
Sinha, R., Abnet, C. C., White, O., Knight, R. & Huttenhower, C. The microbiome quality control project: baseline study design and future directions. Genome Biol. 16, 276 (2015).
pubmed: 26653756
pmcid: 4674991
Schloss, P. D. Identifying and overcoming threats to reproducibility, replicability, robustness, and generalizability in microbiome research. mBio 9, e00525-18 (2018).
pubmed: 29871915
pmcid: 5989067
Quince, C., Walker, A. W., Simpson, J. T., Loman, N. J. & Segata, N. Shotgun metagenomics, from sampling to analysis. Nat. Biotechnol. 35, 833–844 (2017).
pubmed: 28898207
Schloissnig, S. et al. Genomic variation landscape of the human gut microbiome. Nature 493, 45–50 (2013).
pubmed: 23222524
Zhu, A., Sunagawa, S., Mende, D. R. & Bork, P. Inter-individual differences in the gene content of human gut bacterial species. Genome Biol. 16, 82 (2015).
pubmed: 25896518
pmcid: 4428241
McDonnell, L. et al. Association between antibiotics and gut microbiome dysbiosis in children: systematic review and meta-analysis. Gut Microbes 13, 1–18 (2021).
pubmed: 33651651
Mirzayi, C. et al. Reporting guidelines for human microbiome research: the STORMS checklist. Nat. Med. 27, 1885–1892 (2021).
pubmed: 34789871
pmcid: 9105086
Lee, J. et al. BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36, 1234–1240 (2020).
pubmed: 31501885
Wang, L. L. & Lo, K. Text mining approaches for dealing with the rapidly expanding literature on COVID-19. Brief. Bioinform. 22, 781–799 (2021).
pubmed: 33279995
Cook, R. R. et al. Alterations to the gastrointestinal microbiome associated with methamphetamine use among young men who have sex with men. Sci. Rep. https://doi.org/10.1038/s41598-019-51142-8 (2019).
Tsementzi, D. et al. Comparison of vaginal microbiota in gynecologic cancer patients pre- and post-radiation therapy and healthy women. Cancer Med. https://doi.org/10.1002/cam4.3027 (2020).
Ren, Z. et al. Alterations in the human oral and gut microbiomes and lipidomics in COVID-19. Gut 70, 1253–1265 (2021).
pubmed: 33789966
Gong, H. et al. Microbiota in the throat and risk factors for laryngeal carcinoma. Appl. Environ. Microbiol. https://doi.org/10.1128/AEM.02329-14 (2014).
Yang, C.-Y. et al. Oral microbiota community dynamics associated with oral squamous cell carcinoma staging. Front. Microbiol. https://doi.org/10.3389/fmicb.2018.00862 (2018).
Correa, J. D. et al. Oral microbial dysbiosis linked to worsened periodontal condition in rheumatoid arthritis patients. Sci. Rep. 9, 8379 (2019).
pubmed: 31182740
pmcid: 6557833
Sato, N. et al. The relationship between cigarette smoking and the tongue microbiome in an East Asian population. J. Oral Microbiol. https://doi.org/10.1080/20002297.2020.1742527 (2020).
Oku, S. et al. Disrupted tongue microbiota and detection of nonindigenous bacteria on the day of allogeneic hematopoietic stem cell transplantation. PLoS Pathog. https://doi.org/10.1371/journal.ppat.1008348 (2020).
Balan, P. et al. Subgingival microbiota during healthy pregnancy and pregnancy gingivitis. JDR Clin. Trans. Res. https://doi.org/10.1177/2380084420948779 (2021).
Coit, P. et al. Sequencing of 16S rRNA reveals a distinct salivary microbiome signature in Behçet’s disease. Clin. Immunol. https://doi.org/10.1016/j.clim.2016.06.002 (2016).
Hannigan, G. D., Duhaime, M. B., Ruffin IV, M. T., Koumpouras, C. C. & Schloss, P. D. Diagnostic potential and interactive dynamics of the colorectal cancer virome. mBio https://doi.org/10.1128/mbio.02248-18 (2017).
Gupta, A. et al. Association of Flavonifractor plautii, a flavonoid-degrading bacterium, with the gut microbiome of colorectal cancer patients in India. mSystems https://doi.org/10.1128/msystems.00438-19 (2019).
Vogtmann, E. et al. Colorectal cancer and the human gut microbiome: reproducibility with whole-genome shotgun sequencing. PLoS ONE https://doi.org/10.1371/journal.pone.0155362 (2016).
Feng, Q. et al. Gut microbiome development along the colorectal adenoma–carcinoma sequence. Nat. Commun. https://doi.org/10.1038/ncomms7528 (2015).
Zeller, G. et al. Potential of fecal microbiota for early-stage detection of colorectal cancer. Mol. Syst. Biol. https://doi.org/10.15252/msb.20145645 (2014).
Yu, J. et al. Metagenomic analysis of faecal microbiome as a tool towards targeted non-invasive biomarkers for colorectal cancer. Gut https://doi.org/10.1136/gutjnl-2015-309800 (2015).
Yachida, S. et al. Metagenomic and metabolomic analyses reveal distinct stage-specific phenotypes of the gut microbiota in colorectal cancer. Nat. Med. https://doi.org/10.1038/s41591-019-0458-7 (2019).
Park, S.-J. & Nakai, K. OpenContami: a web-based application for detecting microbial contaminants in next-generation sequencing data. Bioinformatics 37, 3021–3022 (2021).
pubmed: 33576798
pmcid: 8479661
Salter, S. J. et al. Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biol. 12, 87 (2014).
pubmed: 25387460
pmcid: 4228153
Lin, D. et al. An information-theoretic definition of similarity. In Proc. 5th International Conference on Machine Learning (ed. Shavlik, J. W.) 296–304 (Morgan Kaufmann, 1998).
Greene, D., Richardson, S. & Turro, E. ontologyX: a suite of R packages for working with ontological data. Bioinformatics 33, 1104–1106 (2017).
pubmed: 28062448
Resnik, P. Semantic similarity in a taxonomy: an information-based measure and its application to problems of ambiguity in natural language. J. Artif. Intell. Res. 11, 95–130 (1999).
Lozupone, C. & Knight, R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71, 8228–8235 (2005).
pubmed: 16332807
pmcid: 1317376
Pesquita, C. et al. Metrics for GO based protein semantic similarity: a systematic evaluation. BMC Bioinformatics 9, S4 (2008).
pubmed: 18460186
pmcid: 2367622
Calgaro, M., Romualdi, C., Waldron, L., Risso, D. & Vitulo, N. Assessment of statistical methods from single cell, bulk RNA-seq, and metagenomics applied to microbiome data. Genome Biol. 21, 191 (2020).
pubmed: 32746888
pmcid: 7398076
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).
pubmed: 25605792
pmcid: 4402510
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).
pubmed: 19910308
Webber, W., Moffat, A. & Zobel, J. A similarity measure for indefinite rankings. ACM Trans. Inf. Syst. 28, 1–38 (2010).
Ihaka, R. & Gentleman, R. R: a language for data analysis and graphics. J. Comput. Graph. Stat. 5, 299–314 (1996).
Huber, W. et al. Orchestrating high-throughput genomic analysis with Bioconductor. Nat. Methods 12, 115–121 (2015).
pubmed: 25633503
pmcid: 4509590
Geistlinger, L. & Waldron, L. Analysis code for the BugSigDB manuscript. GitHub https://github.com/waldronlab/BugSigDBPaper (2023).