bugsigdbr 1.15.3
BugSigDB is a manually curated database of microbial signatures from the published literature of differential abundance studies of human and other host microbiomes.
BugSigDB provides:
The bugsigdbr package implements convenient access to BugSigDB from within R/Bioconductor. The goal of the package is to facilitate import of BugSigDB data into R/Bioconductor, provide utilities for extracting microbe signatures, and enable export of the extracted signatures to plain text files in standard file formats such as GMT.
The bugsigdbr package is primarily a data package. For descriptive statistics and comprehensive analysis of BugSigDB contents, please see the BugSigDBStats package and analysis vignette.
We start by loading the package.
library(bugsigdbr)The function importBugSigDB can be used to import the complete collection of
curated signatures from BugSigDB. The dataset is downloaded once and
subsequently cached. Use cache = FALSE to force a fresh download of BugSigDB
and overwrite the local copy in your cache.
bsdb <- importBugSigDB()
dim(bsdb)
#> [1] 8163 50
colnames(bsdb)
#> [1] "BSDB ID" "Study"
#> [3] "Study design" "PMID"
#> [5] "DOI" "URL"
#> [7] "Authors list" "Title"
#> [9] "Journal" "Year"
#> [11] "Keywords" "Experiment"
#> [13] "Location of subjects" "Host species"
#> [15] "Body site" "UBERON ID"
#> [17] "Condition" "EFO ID"
#> [19] "Group 0 name" "Group 1 name"
#> [21] "Group 1 definition" "Group 0 sample size"
#> [23] "Group 1 sample size" "Antibiotics exclusion"
#> [25] "Sequencing type" "16S variable region"
#> [27] "Sequencing platform" "Statistical test"
#> [29] "Significance threshold" "MHT correction"
#> [31] "LDA Score above" "Matched on"
#> [33] "Confounders controlled for" "Pielou"
#> [35] "Shannon" "Chao1"
#> [37] "Simpson" "Inverse Simpson"
#> [39] "Richness" "Signature page name"
#> [41] "Source" "Curated date"
#> [43] "Curator" "Revision editor"
#> [45] "Description" "Abundance in Group 1"
#> [47] "MetaPhlAn taxon names" "NCBI Taxonomy IDs"
#> [49] "State" "Reviewer"Each row of the resulting data.frame corresponds to a microbe signature from
differential abundance analysis, i.e. a set of microbes that has been found
with increased or decreased abundance in one sample group when compared to
another sample group (eg. in a case-vs.-control setup).
The curated signatures are richly annotated with additional metadata columns
providing information on study design, antibiotics exclusion criteria,
sample size, and experimental and statistical procedures, among others.
Subsetting the full dataset to certain conditions, body sites, or other
metadata columns of interest can be done along the usual lines for
subsetting data.frames.
For example, the following subset command restricts the dataset to signatures
obtained from microbiome studies on obesity, based on fecal samples from
participants in the US.
us.obesity.feces <- subset(bsdb,
`Location of subjects` == "United States of America" &
Condition == "obesity" &
`Body site` == "feces")Given the full BugSigDB collection (or a subset of interest), the function
getSignatures can be used to obtain the microbes annotated to each signature.
Microbes annotated to a signature are returned following the NCBI Taxonomy nomenclature per default.
sigs <- getSignatures(bsdb)
length(sigs)
#> [1] 7910
sigs[1:3]
#> $`bsdb:11/1/1_Cervical-glandular-intraepithelial-neoplasia:HIV-patients-with-lesion-at-12-months_vs_HIV-pateints-with-normal-tissue-at-6-months_DOWN`
#> [1] "2702"
#>
#> $`bsdb:39/1/1_Body-odor-measurement:youth-underarm-after-exercise_vs_youth-underarm-before_UP`
#> [1] "1282"
#>
#> $`bsdb:39/1/2_Body-odor-measurement:youth-underarm-after-exercise_vs_youth-underarm-before_DOWN`
#> [1] "265"It is also possible obtain signatures based on the full taxonomic classification in MetaPhlAn format …
mp.sigs <- getSignatures(bsdb, tax.id.type = "metaphlan")
mp.sigs[1:3]
#> $`bsdb:11/1/1_Cervical-glandular-intraepithelial-neoplasia:HIV-patients-with-lesion-at-12-months_vs_HIV-pateints-with-normal-tissue-at-6-months_DOWN`
#> [1] "d__Bacteria|k__Bacillati|p__Actinomycetota|c__Actinomycetes|o__Bifidobacteriales|f__Bifidobacteriaceae|g__Gardnerella|s__Gardnerella vaginalis"
#>
#> $`bsdb:39/1/1_Body-odor-measurement:youth-underarm-after-exercise_vs_youth-underarm-before_UP`
#> [1] "d__Bacteria|k__Bacillati|p__Bacillota|c__Bacilli|o__Bacillales|f__Staphylococcaceae|g__Staphylococcus|s__Staphylococcus epidermidis"
#>
#> $`bsdb:39/1/2_Body-odor-measurement:youth-underarm-after-exercise_vs_youth-underarm-before_DOWN`
#> [1] "d__Bacteria|k__Pseudomonadati|p__Pseudomonadota|c__Alphaproteobacteria|o__Rhodobacterales|f__Paracoccaceae|g__Paracoccus"… or using the taxonomic name only:
tn.sigs <- getSignatures(bsdb, tax.id.type = "taxname")
tn.sigs[1:3]
#> $`bsdb:11/1/1_Cervical-glandular-intraepithelial-neoplasia:HIV-patients-with-lesion-at-12-months_vs_HIV-pateints-with-normal-tissue-at-6-months_DOWN`
#> [1] "Gardnerella vaginalis"
#>
#> $`bsdb:39/1/1_Body-odor-measurement:youth-underarm-after-exercise_vs_youth-underarm-before_UP`
#> [1] "Staphylococcus epidermidis"
#>
#> $`bsdb:39/1/2_Body-odor-measurement:youth-underarm-after-exercise_vs_youth-underarm-before_DOWN`
#> [1] "Paracoccus"As metagenomic profiling with 16S RNA sequencing or whole-metagenome shotgun sequencing is typically conducted on a certain taxonomic level, it is also possible to obtain signatures restricted to eg. the genus level …
gn.sigs <- getSignatures(bsdb,
tax.id.type = "taxname",
tax.level = "genus")
gn.sigs[1:3]
#> $`bsdb:39/1/2_Body-odor-measurement:youth-underarm-after-exercise_vs_youth-underarm-before_DOWN`
#> [1] "Paracoccus"
#>
#> $`bsdb:39/2/1_Body-odor-measurement:youth-Neck-before_vs_after-excercise_DOWN`
#> [1] "Acinetobacter"
#>
#> $`bsdb:39/3/1_Body-odor-measurement:Children-underarm-before_vs_after-excercise_DOWN`
#> [1] "Paracoccus"… or the species level:
gn.sigs <- getSignatures(bsdb,
tax.id.type = "taxname",
tax.level = "species")
gn.sigs[1:3]
#> $`bsdb:11/1/1_Cervical-glandular-intraepithelial-neoplasia:HIV-patients-with-lesion-at-12-months_vs_HIV-pateints-with-normal-tissue-at-6-months_DOWN`
#> [1] "Gardnerella vaginalis"
#>
#> $`bsdb:39/1/1_Body-odor-measurement:youth-underarm-after-exercise_vs_youth-underarm-before_UP`
#> [1] "Staphylococcus epidermidis"
#>
#> $`bsdb:39/2/1_Body-odor-measurement:youth-Neck-before_vs_after-excercise_DOWN`
#> [1] "Cutibacterium granulosum" "Acinetobacter schindleri"Note that restricting signatures to microbes given at the genus level, will per default exclude microbes given at a more specific taxonomic rank such as species or strain.
For certain applications, it might be desirable to not exclude microbes given
at a more specific taxonomic rank, but rather extract the more general
tax.level for microbes given at a more specific taxonomic level.
This can be achieved by setting the argument exact.tax.level to FALSE,
which will here extract genus level taxon names, for taxa given at the species
or strain level.
gn.sigs <- getSignatures(bsdb,
tax.id.type = "taxname",
tax.level = "genus",
exact.tax.level = FALSE)
gn.sigs[1:3]
#> $`bsdb:11/1/1_Cervical-glandular-intraepithelial-neoplasia:HIV-patients-with-lesion-at-12-months_vs_HIV-pateints-with-normal-tissue-at-6-months_DOWN`
#> [1] "Gardnerella"
#>
#> $`bsdb:39/1/1_Body-odor-measurement:youth-underarm-after-exercise_vs_youth-underarm-before_UP`
#> [1] "Staphylococcus"
#>
#> $`bsdb:39/1/2_Body-odor-measurement:youth-underarm-after-exercise_vs_youth-underarm-before_DOWN`
#> [1] "Paracoccus"Once signatures have been extracted using a taxonomic identifier type of
choice, the function writeGMT allows to write the signatures to plain text
files in GMT format.
writeGMT(sigs, gmt.file = "bugsigdb_signatures.gmt")This is the standard file format for gene sets used by MSigDB and GeneSigDB and is compatible with most enrichment analysis software.
Leveraging BugSigDB’s semantic MediaWiki web interface, we can also programmatically access annotations for individual microbes and microbe signatures.
The browseSignature function can be used to display BugSigDB signature pages
in an interactive session. For programmatic access in a non-interactive
setting, the URL of the signature page is returned.
browseSignature(names(sigs)[1])
#> [1] "https://bugsigdb.org/Study_11/Experiment_1/Signature_1"Analogously, the browseTaxon function displays BugSigDB taxon pages in an
interactive session, or the URL of the corresponding taxon page otherwise.
browseTaxon(sigs[[1]][1])
#> [1] "https://bugsigdb.org/Special:RunQuery/Taxon?Taxon%5BNCBI%5D=2702&_run=1"The Semantic MediaWiki curation interface at bugsigdb.org enforces metadata annotation of signatures to follow established ontologies such as the Experimental Factor Ontology (EFO) for condition, and the Uber-Anatomy Ontology (UBERON) for body site.
The getOntology function can be used to import both ontologies into R.
The result is an object of class ontology_index from the
ontologyIndex
package.
efo <- getOntology("efo")
#> Loading required namespace: ontologyIndex
#> Using cached version from 2025-04-07 23:09:44
efo
#> Ontology with 59992 terms
#>
#> format-version: 1.2
#> data-version: http://www.ebi.ac.uk/efo/releases/v3.78.0/efo.owl
#> ontology: http://www.ebi.ac.uk/efo/efo.owl
#>
#> Properties:
#> id: character
#> name: character
#> parents: list
#> children: list
#> ancestors: list
#> obsolete: logical
#> equivalent_to: list
#> Roots:
#> efo:EFO_0000001 - experimental factor
#> efo:EFO_0000824 - relationship
#> RO:0000053 - bearer_of
#> RO:0000057 - has_participant
#> RO:0000056 - participates_in
#> RO:0002233 - has input
#> RO:0002323 - mereotopologically related to
#> RO:0002502 - depends on
#> genomically_related_to - genomically_related_to
#> located_in - located_in
#> ... 64 moreuberon <- getOntology("uberon")
#> Using cached version from 2025-04-07 23:10:26
uberon
#> Ontology with 14107 terms
#>
#> format-version: 1.2
#> data-version: releases/2020-09-16
#> default-namespace: uberon
#> ontology: uberon
#>
#> Properties:
#> id: character
#> name: character
#> parents: list
#> children: list
#> ancestors: list
#> obsolete: logical
#> Roots:
#> part_of - part of
#> has_part - has part
#> functionally_related_to - functionally related to
#> UBERON:0001062 - anatomical entity
#> adjacent_to - adjacent_to
#> UBERON:0000000 - processual entity
#> anterior_to - anterior_to
#> posterior_to - posterior_to
#> attaches_to_part_of - attaches_to_part_of
#> bearer_of - bearer of
#> ... 127 moreAs demonstrated above, subsets of BugSigDB signatures can be obtained for signatures
associated with certain experimental factors or specific body sites of interest.
Higher-level queries can be performed with the subsetByOntology function, which
implements subsetting by more general ontology terms. This facilitates grouping
of signatures that semantically belong together.
More specifically, subsetting BugSigDB signatures by an EFO term then involves
subsetting the Condition column to the term itself and all descendants of that
term in the EFO ontology and that are present in the Condition column. Here,
we demonstrate the usage by subsetting to signatures associated with cancer.
sdf <- subsetByOntology(bsdb,
column = "Condition",
term = "cancer",
ontology = efo)
dim(sdf)
#> [1] 283 50
table(sdf[,"Condition"])
#>
#> Bladder carcinoma
#> 2
#> Breast cancer
#> 40
#> Cervical cancer
#> 22
#> Cervical glandular intraepithelial neoplasia,Cervical cancer
#> 2
#> Digestive system cancer
#> 2
#> Endometrial cancer
#> 19
#> Esophageal cancer
#> 10
#> Gastric cancer
#> 91
#> Head and neck carcinoma
#> 7
#> Human papilloma virus infection,Cervical cancer
#> 2
#> Lung cancer
#> 26
#> Mycobacterium tuberculosis,Lung cancer
#> 1
#> Oral cavity carcinoma
#> 19
#> Ovarian cancer
#> 36
#> Prostate cancer
#> 4And analogously, subsetting by an UBERON term involves subsetting the
Body site column to the term itself and all descendants of that term in the
UBERON ontology and that are present in the Body site column. For example,
we can use subsetByOntology to subset to signatures for which microbiome
samples have been obtained from parts of the digestive system.
sdf <- subsetByOntology(bsdb,
column = "Body site",
term = "digestive system",
ontology = uberon)
dim(sdf)
#> [1] 1205 50
table(sdf[,"Body site"])
#>
#> Actinopterygian pyloric caecum
#> 2
#> Bronchus,Mouth
#> 2
#> Buccal mucosa
#> 16
#> Caecum
#> 41
#> Cardia of stomach
#> 2
#> Cavity of pharynx
#> 2
#> Cecum mucosa
#> 31
#> Cecum mucosa,Colonic mucosa,Mucosa of rectum,Ileal mucosa
#> 5
#> Colon
#> 70
#> Colon,Blood serum
#> 1
#> Colon,Feces
#> 2
#> Colonic mucosa
#> 8
#> Colorectal mucosa
#> 18
#> Colorectal mucosa,Feces
#> 2
#> Colorectum
#> 3
#> Dental plaque
#> 17
#> Dental plaque,Internal cheek pouch,Saliva
#> 2
#> Digestive tract
#> 8
#> Duodenal mucosa
#> 6
#> Duodenum
#> 18
#> Duodenum,Bile duct
#> 1
#> Epithelium of oropharynx
#> 3
#> Esophagus
#> 40
#> Feces,Colon
#> 4
#> Feces,Colonic mucosa
#> 1
#> Feces,Colorectal mucosa
#> 2
#> Feces,Duodenum
#> 6
#> Feces,Mucosa of descending colon
#> 5
#> Feces,Mucosa of small intestine
#> 4
#> Feces,Oral cavity
#> 9
#> Feces,Stomach,Caecum,Small intestine,Colon
#> 2
#> Gastric pit
#> 4
#> Gingiva
#> 5
#> Gingival groove
#> 9
#> Hindgut
#> 4
#> Hypopharynx
#> 1
#> Ileum
#> 23
#> Ileum,Colon
#> 2
#> Ileum,Feces
#> 2
#> Ileum,Feces,Rectum
#> 2
#> Ileum,Jejunum
#> 2
#> Ileum,Rectum
#> 2
#> Internal cheek pouch
#> 21
#> Intestinal mucosa
#> 12
#> Intestine
#> 59
#> Jejunum
#> 29
#> Lower lip,Buccal mucosa
#> 8
#> Lumen of duodenum
#> 2
#> Midgut
#> 2
#> Midgut,Ovary
#> 2
#> Midgut,Saliva-secreting gland
#> 2
#> Midgut,Saliva-secreting gland,Ovary
#> 6
#> Mouth
#> 111
#> Mouth mucosa
#> 4
#> Mucosa of ascending colon
#> 2
#> Mucosa of body of stomach
#> 1
#> Mucosa of oral region
#> 2
#> Mucosa of oral region,Vagina,Skin of forehead,Skin of forearm
#> 1
#> Mucosa of oropharynx
#> 4
#> Mucosa of rectum
#> 5
#> Mucosa of sigmoid colon
#> 3
#> Mucosa of small intestine
#> 2
#> Mucosa of stomach
#> 3
#> Nasopharyngeal gland
#> 2
#> Nasopharyngeal gland,Saliva
#> 6
#> Nasopharynx
#> 82
#> Nasopharynx,Lung
#> 3
#> Nasopharynx,Oropharynx
#> 8
#> Nasopharynx,Throat
#> 12
#> Nose,Mouth
#> 2
#> Oral cavity
#> 60
#> Oral cavity,Esophagus
#> 2
#> Oral cavity,Feces
#> 2
#> Oral opening
#> 6
#> Oropharyngeal gland,Saliva
#> 6
#> Oropharynx
#> 32
#> Pharynx
#> 8
#> Posterior wall of oropharynx
#> 6
#> Proventriculus,Cloaca
#> 9
#> Rectal lumen
#> 2
#> Rectum
#> 31
#> Rumen
#> 13
#> Saliva,Esophagus,Gastric juice,Stomach,Feces
#> 3
#> Saliva,Esophagus,Stomach,Gastric juice,Feces
#> 3
#> Saliva,Subgingival dental plaque
#> 3
#> Saliva,Supragingival dental plaque
#> 6
#> Saliva-secreting gland,Ovary
#> 2
#> Sigmoid colon
#> 2
#> Small intestine
#> 19
#> Spleen
#> 2
#> Sputum,Mouth
#> 2
#> Stomach
#> 48
#> Subgingival dental plaque
#> 85
#> Subgingival dental plaque,Feces
#> 3
#> Superior surface of tongue
#> 2
#> Supragingival dental plaque
#> 8
#> Supragingival dental plaque,Saliva
#> 2
#> Surface of tongue
#> 16
#> Tongue
#> 33
#> Tonsillar fossa
#> 2
#> Wall of small intestine
#> 2sessionInfo()
#> R version 4.5.0 (2025-04-11 ucrt)
#> Platform: x86_64-w64-mingw32/x64
#> Running under: Windows Server 2022 x64 (build 20348)
#>
#> Matrix products: default
#> LAPACK version 3.12.1
#>
#> locale:
#> [1] LC_COLLATE=C
#> [2] LC_CTYPE=English_United States.utf8
#> [3] LC_MONETARY=English_United States.utf8
#> [4] LC_NUMERIC=C
#> [5] LC_TIME=English_United States.utf8
#>
#> time zone: America/New_York
#> tzcode source: internal
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] bugsigdbr_1.15.3 BiocStyle_2.37.0
#>
#> loaded via a namespace (and not attached):
#> [1] bit_4.6.0 jsonlite_2.0.0 dplyr_1.1.4
#> [4] compiler_4.5.0 BiocManager_1.30.25 filelock_1.0.3
#> [7] tidyselect_1.2.1 blob_1.2.4 jquerylib_0.1.4
#> [10] yaml_2.3.10 fastmap_1.2.0 R6_2.6.1
#> [13] generics_0.1.4 curl_6.2.3 httr2_1.1.2
#> [16] knitr_1.50 ontologyIndex_2.12 tibble_3.2.1
#> [19] bookdown_0.43 DBI_1.2.3 bslib_0.9.0
#> [22] pillar_1.10.2 rlang_1.1.6 cachem_1.1.0
#> [25] xfun_0.52 sass_0.4.10 bit64_4.6.0-1
#> [28] RSQLite_2.4.0 memoise_2.0.1 cli_3.6.5
#> [31] withr_3.0.2 magrittr_2.0.3 digest_0.6.37
#> [34] dbplyr_2.5.0 rappdirs_0.3.3 lifecycle_1.0.4
#> [37] BiocFileCache_2.99.5 vctrs_0.6.5 evaluate_1.0.3
#> [40] glue_1.8.0 purrr_1.0.4 rmarkdown_2.29
#> [43] tools_4.5.0 pkgconfig_2.0.3 htmltools_0.5.8.1