2017
DOI: 10.1101/133462
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

BugBase predicts organism-level microbiome phenotypes

Abstract: Shotgun metagenomics and marker gene amplicon sequencing can be used to directly measure or predict the functional repertoire of the microbiota en masse, but current methods do not readily estimate the functional capability of individual microorganisms. Here we present BugBase, an algorithm that predicts organism-level coverage of functional pathways as well as biologically interpretable phenotypes such as oxygen tolerance, Gram staining and pathogenic potential, within complex microbiomes using either whole-g… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
222
0

Year Published

2018
2018
2022
2022

Publication Types

Select...
6
1

Relationship

0
7

Authors

Journals

citations
Cited by 270 publications
(228 citation statements)
references
References 40 publications
0
222
0
Order By: Relevance
“…The functional differences between groups were determined with Linear Discriminant Analysis Effect Size (LEfSe). Organism level microbiome phenotypes were predicted and compared with BugBase . The proportions of six phenotypic categories, including Gram staining, oxygen tolerance, ability to form biofilms, mobile element content, pathogenicity, and oxidative stress tolerance, were compared among different groups of patients with gastritis.…”
Section: Methodsmentioning
confidence: 99%
“…The functional differences between groups were determined with Linear Discriminant Analysis Effect Size (LEfSe). Organism level microbiome phenotypes were predicted and compared with BugBase . The proportions of six phenotypic categories, including Gram staining, oxygen tolerance, ability to form biofilms, mobile element content, pathogenicity, and oxidative stress tolerance, were compared among different groups of patients with gastritis.…”
Section: Methodsmentioning
confidence: 99%
“…To obtain the phenotypic traits, OTU tables were introduced into the METAGENassist server . Before prediction of potentially pathogenic bacteria by the BugBase server, the OTU table was re‐picked against the Greengenes database (version 13.8) with a closed‐reference approach in QIIME …”
Section: Methodsmentioning
confidence: 99%
“…24,36 To obtain the phenotypic traits, OTU tables were introduced into the METAGENassist server. 37 Before prediction of potentially pathogenic bacteria by the BugBase server, 38 the OTU table was re-picked against the Greengenes database (version 13.8) with a closed-reference approach in QIIME. 39 For comparison, currently available 16S rRNA amplicon data sets based on Illumina sequencing (Lymantria dispar, SRP030624; Helicoverpa armigera and Ostrinia furnacalis, SRP152395; Plutella xylostella, SRP091798; Spodoptera frugiperda, SRP185622) were downloaded from the NCBI SRA (www.ncbi.nlm.nih.gov).…”
Section: Bacterial Community Analysis and Functional Predictionmentioning
confidence: 99%
“…For bacterial functional potential, we used the BugBase (Ward et al. ) tool that groups organisms into functional groups based on KEGG pathways (Ogata et al. ) compiled by PICRUSt (Langille et al.…”
Section: Methodsmentioning
confidence: 99%
“…We analyzed outputs at the trophic mode level to understand the proportion of the root communities composed of mutualists (symbiotrophs), pathogens (pathotrophs), and likely commensalists (saprotrophs). For bacterial functional potential, we used the BugBase (Ward et al 2017) tool that groups organisms into functional groups based on KEGG pathways (Ogata et al 1999) compiled by PICRUSt (Langille et al 2013). This tool allowed us to view bacterial communities by their oxygen requirements and potential for stress tolerance.…”
Section: Functional Assignmentmentioning
confidence: 99%