Tax4Fun: predicting functional profiles from metagenomic 16S rRNA data - PubMed (original) (raw)

Tax4Fun: predicting functional profiles from metagenomic 16S rRNA data

Kathrin P Aßhauer et al. Bioinformatics. 2015.

Abstract

Motivation: The characterization of phylogenetic and functional diversity is a key element in the analysis of microbial communities. Amplicon-based sequencing of marker genes, such as 16S rRNA, is a powerful tool for assessing and comparing the structure of microbial communities at a high phylogenetic resolution. Because 16S rRNA sequencing is more cost-effective than whole metagenome shotgun sequencing, marker gene analysis is frequently used for broad studies that involve a large number of different samples. However, in comparison to shotgun sequencing approaches, insights into the functional capabilities of the community get lost when restricting the analysis to taxonomic assignment of 16S rRNA data.

Results: Tax4Fun is a software package that predicts the functional capabilities of microbial communities based on 16S rRNA datasets. We evaluated Tax4Fun on a range of paired metagenome/16S rRNA datasets to assess its performance. Our results indicate that Tax4Fun provides a good approximation to functional profiles obtained from metagenomic shotgun sequencing approaches.

Availability and implementation: Tax4Fun is an open-source R package and applicable to output as obtained from the SILVAngs web server or the application of QIIME with a SILVA database extension. Tax4Fun is freely available for download at http://tax4fun.gobics.de/.

Contact: kasshau@gwdg.de

Supplementary information: Supplementary data are available at Bioinformatics online.

© The Author 2015. Published by Oxford University Press.

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Figures

Fig. 1.

Fig. 1.

Spearman correlations between metagenomic and 16S-predicted functional profiles for comparison of Tax4Fun and PICRUSt on paired datasets from the human microbiome (HMP), mammalian guts, Guerrero Negro hypersaline microbial mat and soils

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References

    1. Aßhauer K.P., Meinicke P. (2013) On the estimation of metabolic profiles in metagenomics. In: Beißbarth T., Kollmar M., Leha A., Morgenstern B., Schultz A.-K., Waack S., Wingender E. (eds.) German Conference on Bioinformatics 2013, volume 34 of OpenAccess Series in Informatics (OASIcs). Schloss Dagstuhl–Leibniz-Zentrum fuer Informatik: Dagstuhl, Germany, pp. 1–13.
    1. Caporaso J.G., et al. (2010) QIIME allows analysis of high-throughput community sequencing data. Nat. Methods, 7, 335–336. - PMC - PubMed
    1. Fierer N., et al. (2012) Cross-biome metagenomic analyses of soil microbial communities and their functional attributes. Proc. Natl Acad. Sci. USA, 109, 21390–21395. - PMC - PubMed
    1. Harris J.K., et al. (2013) Phylogenetic stratigraphy in the Guerrero Negro hypersaline microbial mat. ISME J., 7, 50–60. - PMC - PubMed
    1. Human Microbiome Project Consortium. (2012) Structure, function and diversity of the healthy human microbiome. Nature, 486, 207–214. - PMC - PubMed

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