Tax4Fun: Predicting Functional Profiles From Metagenomic 16S rRNA Data

Bioinformatics. 2015 Sep 1;31(17):2882-4. doi: 10.1093/bioinformatics/btv287. Epub 2015 May 7.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bacteria / classification
  • Bacteria / genetics*
  • Computational Biology / methods*
  • Databases, Factual
  • Databases, Nucleic Acid
  • Genes, Bacterial / genetics*
  • Genetic Markers
  • Metagenome
  • Metagenomics / methods*
  • Phylogeny
  • RNA, Bacterial / genetics
  • RNA, Ribosomal, 16S / genetics*
  • Sequence Analysis, RNA / methods*
  • Software*

Substances

  • Genetic Markers
  • RNA, Bacterial
  • RNA, Ribosomal, 16S