The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Tax4Fun: predicting functional profiles from metagenomic 16S rRNA data
Open accessAbstract
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.
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QIIME allows analysis of high-throughput community sequencing data
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The SILVA ribosomal RNA gene database project: improved data processing and web-based tools
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Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences
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Diet Drives Convergence in Gut Microbiome Functions Across Mammalian Phylogeny and Within Humans
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Cryan, J. F., O'Riordan, K. J., Cowan, C. S. M., Sandhu, K. V., Bastiaanssen, T. F. S., Boehme, M., Codagnone, M. G., Cussotto, S., Fulling, C., Golubeva, A. V., et al. (2019) · Physiological Reviews · doi:10.1152/physrev.00018.2018 · Cited by 5,286
Best practices for analysing microbiomes
Knight, R., Vrbanac, A., Taylor, B. C., Aksenov, A., Callewaert, C., Debelius, J., Gonzalez, A., Kosciolek, T., McCall, L.-I., McDonald, D., et al. (2018) · Nature Reviews Microbiology · doi:10.1038/s41579-018-0029-9 · Cited by 2,030
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Metadata
- Authors
- Aßhauer, K. P., Wemheuer, B., Daniel, R., & Meinicke, P.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2015-05-07
- Volume
- 31
- Issue
- 17
- Pages
- 2882-2884
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2110082875
- Cited by
- 1,578
- References
- 5
- FWCI
- 22.90
- Type
- journal article
- Language
- en
- Open access
- hybrid
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