![]() ![]() ![]() In FANTOM, the abundance can be specified at different levels in hierarchical databases, which are called nodes (e.g. The custom database can be easily imported as a tabular input file to analyze the abundances of corresponding database levels. Moreover, FANTOM provides the option that allows the user to create and use a custom made hierarchical database. ![]() 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. Metadata can either be numerical or categorical and the software will automatically recognize the format and display options for selecting and filtering samples. 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. ![]() The software was tested successfully on Windows, Linux and OSX operating systems and the installers are provided for the different platforms.įANTOM 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). 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 installer, user manual and demonstration videos can be found and downloaded at the website įANTOM 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. We believe that this tool will be highly useful for a broad community of scientists desiring to analyze metagenomics data. 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 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. MEGAN, SmashCommunity, STAMP, shotgunFunctionalizeR, VEGAN, QIIME and Mothur. There are several standalone software tools available for statistical analysis and visualization of annotated metagenomics data, e.g. Īlthough, 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. Further analysis of the hereby obtained quantitative abundance data of metagenomics features, in particular together with sample meta data is important for biological interpretation. 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. 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. Another approach is based on mapping high quality reads on reference genomes or well annotated genes by short read aligners. The high quality reads can be annotated to reference taxonomic and functional features using sequence similarity based alignment methods i.e. 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). 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. This allows for determining the ecosystems taxonomic diversity, functional capacity, dynamics and comparison with other environments. 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. ![]()
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