- Open Access
FANTOM: Functional and taxonomic analysis of metagenomes
© Sanli et al.; licensee BioMed Central Ltd. 2013
Received: 27 September 2012
Accepted: 29 January 2013
Published: 1 February 2013
Interpretation of quantitative metagenomics data is important for our understanding of ecosystem functioning and assessing differences between various environmental samples. There is a need for an easy to use tool to explore the often complex metagenomics data in taxonomic and functional context.
Here we introduce FANTOM, a tool that allows for exploratory and comparative analysis of metagenomics abundance data integrated with metadata information and biological databases. Importantly, FANTOM can make use of any hierarchical database and it comes supplied with NCBI taxonomic hierarchies as well as KEGG Orthology, COG, PFAM and TIGRFAM databases.
The software is implemented in Python, is platform independent, and is available at http://www.sysbio.se/Fantom
Metagenomics  is the culture independent study of an environmental sample by sequencing of the recovered genetic materials of targeted ribosomal RNAs (16S) through amplicon sequencing or whole genomic DNA. This allows for determining the ecosystems taxonomic diversity, functional capacity, dynamics and comparison with other environments. Typically for whole genome based metagenomics, extracted DNA from an environmental sample is a starting material to generate short reads of DNA through next generation sequencing (NGS) technologies that represent the microbiota of the sample. The generated raw sequence reads data typically contain errors that need to be eliminated before further steps using trimming and filtering processes based on a base calling quality score (Phred) [2, 3]. The high quality reads can be annotated to reference taxonomic and functional features using sequence similarity based alignment methods i.e. BLAST , HMMER , etc. against reference databases. Another approach is based on mapping high quality reads on reference genomes or well annotated genes by short read aligners . There are web services such as CAMERA , IMG/M  and MG-RAST , available for performing the above mentioned pipeline of NGS processing and annotation in an automated fashion. Depending on user-given parameters such as percentage similarity or e-value thresholds, each of these individual software tools or web services are able to report the annotated sequences in terms of abundance data for each feature in the subjected database. Further analysis of the hereby obtained quantitative abundance data of metagenomics features, in particular together with sample meta data is important for biological interpretation [10, 11].
Although, the above mentioned web-services can to some extent provide both analysis tools for the comparative analysis of metagenomes, these methods have some limitations; 1) statistical and visual analysis capabilities are limited, 2) functional annotation sources might not satisfy user’s demand, and 3) users may simply not want to upload their sequencing data to an online service. There are several standalone software tools available for statistical analysis and visualization of annotated metagenomics data, e.g. MEGAN , SmashCommunity , STAMP , shotgunFunctionalizeR , VEGAN , QIIME  and Mothur .
We identified the requirement for a user-friendly comparative analysis and data visualization tool where annotated metagenomics data can meet sample metadata and be analyzed at different hierarchy levels using a built-in or user provided biological database. This tool, FANTOM for F unctional AN notation and T axonomic analysis O f M etagenomes, is an easy installed, standalone software tool that is accessed through a graphical user interface to analyze abundance of metagenomics features that are easily integrated with NCBI taxonomy, KEGG , COG  and protein family databases PFAM  and TIGRFAM  with hierarchy information. We believe that this tool will be highly useful for a broad community of scientists desiring to analyze metagenomics data.
The software installer, user manual and demonstration videos can be found and downloaded at the website http://www.sysbio.se/Fantom
FANTOM was implemented in Python allowing it to operate platform independent in addition to the utilization of core scientific packages including numpy, scipy and matplotlib to implement statistical functions and various plotting options. wxPython was incorporated to provide graphical user interface components and storm package was used for object relational mapping of data from the local SQLite database. The software was tested successfully on Windows, Linux and OSX operating systems and the installers are provided for the different platforms.
FANTOM requires two input files; a metagenomics abundance file, which could be derived from annotation of metagenomics data, including either taxonomic or functional annotations and another file containing the samples’ metadata (see user manual and demonstration videos). Besides, there are web services such as CAMERA , IMG/M  and MG-RAST  that allow the users to easily obtain metagenomics abundance from their metagenome data. Metadata can either be numerical or categorical and the software will automatically recognize the format and display options for selecting and filtering samples. Functional hierarchy information was downloaded from KEGG Orthology, COG, PFAM and TIGRFAM databases and taxonomic lineage information was downloaded from the NCBI taxonomy database and constitute the standards feature databases in the software package. Moreover, FANTOM provides the option that allows the user to create and use a custom made hierarchical database. The custom database can be easily imported as a tabular input file to analyze the abundances of corresponding database levels.
In FANTOM, the abundance can be specified at different levels in hierarchical databases, which are called nodes (e.g. pathways or Genera), the abundance of a higher node in the hiearchy is calculated by summing the abundance of all member nodes further down in the hierarchy structure (e.g. orthologs or species). The abundance of nodes that are members of more than one higher level nodes are split equally between higher nodes.
The metadata file can include both categorical and numerical properties of each sample, which can then be used in FANTOM to filter and select sample groups of interest for comparative analysis. Numerical variables can further be used for correlation analysis with the annotated features. Taxonomic or functional feature abundances can be displayed and processed either as absolute counts or as normalized relative values. After selecting relevant subsets of metagenomics data, principal component analysis can be applied to reduce the dimensionality. Furthermore, hierarchical clustering, another multivariate analysis method is implemented to evaluate high dimensional metagenomics data by drawing dendograms for features and samples as well as a heatmap with 2-dimensional clustering, reflecting abundance values.
Results and discussion
The software was evaluated using metagenomics data from the gut microbiome of 124 subjects in the MetaHIT  project. Sequences were quality trimmed (SolexaQA -p 0.05) and sequences shorter than 35 bp were filtered out. High quality reads were aligned to a reference catalogue of 440 genomes to obtain taxonomic abundance. Moreover, the reads were aligned to the MetaHIT gene catalogue of 3.3 million genes to get the abundance of genes. The genes were annotated to the KEGG and COG database and this information was used to transform gene abundance to KEGG KO and COG abundances. This data are available as example files together with metadata included bundled with the software.
The MetaHIT study focused on two human diseases, obesity and inflammatory bowel disease (Crohn’s disease and ulcerative colitis), which we make use of here as example capabilities of FANTOM.
We provide an open source standalone user-friendly software tool, FANTOM, for data analyses and data mining of read counts from whole shotgun metagenomics or amplicon sequencing studies. FANTOM allows the user to integrate sample metadata, taxonomy and gene functional profiling in the analysis, and FANTOM is supplied with access to biological databases as well as the possibility to upload custom made databases.
Availability and requirements
Project name: FANTOM : Functional and taxonomic analysis of metagenomes
Project home page: http://www.sysbio.se/Fantom
Operating system(s): Windows, Linux, Mac OSX
Programming language: python
Other requirements: -
License: GNU-GPL version 3 software license
Any restrictions to use by non-academics: No
We would like to thank Chalmers Foundation, Knut and Alice Wallenberg Foundation and Bioinformatics Infrastructure for Life Sciences (BILS) for financial support. The open access charge is funded by Chalmers Library.
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