Tax4Fun: predicting functional profiles from metagenomic 16S rRNA data

Aßhauer, Kathrin Petra; Wemheuer, Bernd; Daniel, Rolf; Meinicke, Peter

doi:10.1093/bioinformatics/btv287

Cited by 1,294 publications

(874 citation statements)

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“…It has to be emphasized that we did not apply a metagenomics approach to assess the entire set of functional genes but used the in silico tool Tax4Fun [30] to predict functional genes derived from our 16S rRNA amplicon dataset (universal primer set). In total, we obtained 6558 predicted single genes and 281 pathways (KEGG3 level).…”

Section: Resultsmentioning

confidence: 99%

“…After merging paired reads and chimera filtering, the sequences were assigned to a taxonomy using the RDP classifier and the SILVA v.14 trainset. The visualization was carried out using the R package phyloseq [28, 29], and metabolic pathways were predicted using the R package Tax4Fun [30]. Biostatistical analyses were performed using STAMP [31].…”

Section: Methodsmentioning

confidence: 99%

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Resilient microorganisms in dust samples of the International Space Station—survival of the adaptation specialists

et al. 2016

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BackgroundThe International Space Station (ISS) represents a unique biotope for the human crew but also for introduced microorganisms. Microbes experience selective pressures such as microgravity, desiccation, poor nutrient-availability due to cleaning, and an increased radiation level. We hypothesized that the microbial community inside the ISS is modified by adapting to these stresses.For this reason, we analyzed 8–12 years old dust samples from Russian ISS modules with major focus on the long-time surviving portion of the microbial community. We consequently assessed the cultivable microbiota of these samples in order to analyze their extremotolerant potential against desiccation, heat-shock, and clinically relevant antibiotics. In addition, we studied the bacterial and archaeal communities from the stored Russian dust samples via molecular methods (next-generation sequencing, NGS) and compared our new data with previously derived information from the US American ISS dust microbiome.ResultsWe cultivated and identified in total 85 bacterial, non-pathogenic isolates (17 different species) and 1 fungal isolate from the 8–12 year old dust samples collected in the Russian segment of the ISS. Most of these isolates exhibited robust resistance against heat-shock and clinically relevant antibiotics. Microbial 16S rRNA gene and archaeal 16S rRNA gene targeting Next Generation Sequencing showed signatures of human-associated microorganisms (Corynebacterium, Staphylococcus, Coprococcus etc.), but also specifically adapted extremotolerant microorganisms. Besides bacteria, the detection of archaeal signatures in higher abundance was striking.ConclusionsOur findings reveal (i) the occurrence of living, hardy microorganisms in archived Russian ISS dust samples, (ii) a profound resistance capacity of ISS microorganisms against environmental stresses, and (iii) the presence of archaeal signatures on board. In addition, we found indications that the microbial community in the Russian segment dust samples was different to recently reported US American ISS microbiota.Electronic supplementary materialThe online version of this article (doi:10.1186/s40168-016-0217-7) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Resilient microorganisms in dust samples of the International Space Station—survival of the adaptation specialists

et al. 2016

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“…It requires an association matrix between the prokaryotic organisms in the KEGG database and the SILVA SSU Ref NR database as well as pre-computation of functional profiles for all prokaryotic genomes in KEGG. In addition, the input sequence data needs to be converted to a SILVA-based profile [8]. …”

Section: Introductionmentioning

confidence: 99%

Piphillin: Improved Prediction of Metagenomic Content by Direct Inference from Human Microbiomes

et al. 2016

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Functional analysis of a clinical microbiome facilitates the elucidation of mechanisms by which microbiome perturbation can cause a phenotypic change in the patient. The direct approach for the analysis of the functional capacity of the microbiome is via shotgun metagenomics. An inexpensive method to estimate the functional capacity of a microbial community is through collecting 16S rRNA gene profiles then indirectly inferring the abundance of functional genes. This inference approach has been implemented in the PICRUSt and Tax4Fun software tools. However, those tools have important limitations since they rely on outdated functional databases and uncertain phylogenetic trees and require very specific data pre-processing protocols. Here we introduce Piphillin, a straightforward algorithm independent of any proposed phylogenetic tree, leveraging contemporary functional databases and not obliged to any singular data pre-processing protocol. When all three inference tools were evaluated against actual shotgun metagenomics, Piphillin was superior in predicting gene composition in human clinical samples compared to both PICRUSt and Tax4Fun (p<0.01 and p<0.001, respectively) and Piphillin’s ability to predict disease associations with specific gene orthologs exhibited a 15% increase in balanced accuracy compared to PICRUSt. From laboratory animal samples, no performance advantage was observed for any one of the tools over the others and for environmental samples all produced unsatisfactory predictions. Our results demonstrate that functional inference using the direct method implemented in Piphillin is preferable for clinical biospecimens. Piphillin is publicly available for academic use at http://secondgenome.com/Piphillin.

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“…To better understand plant-microbe interactions with respect to management regimes, correlation-based indicator species analyses were performed18. In addition, functional profiles (artificial metagenomes) were calculated from obtained 16S rRNA gene data using Tax4Fun19 to investigate functional responses of endophyte communities to applied management regimes. This is of particular importance as differences in community function between various grass species and the functional responses towards management regimes have not been addressed so far.…”

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confidence: 99%

Bacterial endophyte communities of three agricultural important grass species differ in their response towards management regimes

Wemheuer

Kaiser

Karlovský

et al. 2017

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Endophytic bacteria are critical for plant growth and health. However, compositional and functional responses of bacterial endophyte communities towards agricultural practices are still poorly understood. Hence, we analyzed the influence of fertilizer application and mowing frequency on bacterial endophytes in three agriculturally important grass species. For this purpose, we examined bacterial endophytic communities in aerial plant parts of Dactylis glomerata L., Festuca rubra L., and Lolium perenne L. by pyrotag sequencing of bacterial 16S rRNA genes over two consecutive years. Although management regimes influenced endophyte communities, observed responses were grass species-specific. This might be attributed to several bacteria specifically associated with a single grass species. We further predicted functional profiles from obtained 16S rRNA data. These profiles revealed that predicted abundances of genes involved in plant growth promotion or nitrogen metabolism differed between grass species and between management regimes. Moreover, structural and functional community patterns showed no correlation to each other indicating that plant species-specific selection of endophytes is driven by functional rather than phylogenetic traits. The unique combination of 16S rRNA data and functional profiles provided a holistic picture of compositional and functional responses of bacterial endophytes in agricultural relevant grass species towards management practices.

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Tax4Fun: predicting functional profiles from metagenomic 16S rRNA data

Cited by 1,294 publications

References 13 publications

Resilient microorganisms in dust samples of the International Space Station—survival of the adaptation specialists

Resilient microorganisms in dust samples of the International Space Station—survival of the adaptation specialists

Piphillin: Improved Prediction of Metagenomic Content by Direct Inference from Human Microbiomes

Bacterial endophyte communities of three agricultural important grass species differ in their response towards management regimes

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