Abstract
BackgroundGiven the lack of a complete and comprehensive library of microbial reference genomes, determining the functional profile of diverse microbial communities is challenging. The available functional analysis pipelines lack several key features: (i) an integrated alignment tool, (ii) operon-level analysis, and (iii) the ability to process large datasets.ResultsHere we introduce our open-sourced, stand-alone functional analysis pipeline for analyzing whole metagenomic and metatranscriptomic sequencing data, FMAP (Functional Mapping and Analysis Pipeline). FMAP performs alignment, gene family abundance calculations, and statistical analysis (three levels of analyses are provided: differentially-abundant genes, operons and pathways). The resulting output can be easily visualized with heatmaps and functional pathway diagrams. FMAP functional predictions are consistent with currently available functional analysis pipelines.ConclusionFMAP is a comprehensive tool for providing functional analysis of metagenomic/metatranscriptomic sequencing data. With the added features of integrated alignment, operon-level analysis, and the ability to process large datasets, FMAP will be a valuable addition to the currently available functional analysis toolbox. We believe that this software will be of great value to the wider biology and bioinformatics communities.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1278-0) contains supplementary material, which is available to authorized users.
Highlights
Given the lack of a complete and comprehensive library of microbial reference genomes, determining the functional profile of diverse microbial communities is challenging
Functional Mapping and Analysis Pipeline (FMAP) has been applied to a broad range of datasets, with specific details included in the relevant sections below
For the purposes of validating the accuracy of the software, we first compared the gene abundance results generated by FMAP to those generated by the well-established HUMAnN pipeline
Summary
FMAP has been applied to a broad range of datasets, with specific details included in the relevant sections below. To compare the performance of FMAP with comparable tools for pathway analysis, we performed functional analysis using (a) FMAP using the KFU alignment, (b) HUMAnN using the KEGG alignments and (c) ShotgunFunctionalizeR using raw counts of COGs calculated from the COG alignments. FMAP is able to complete a metagenomic pathway analysis five times faster than HUMAnN when analyzing the same data set (Fig. 6) These advantages hold when compared with the same analysis in ShotgunFunctionalizeR. The computation time for this pathway analysis is quite short for FMAP compared to HUMAnN (Fig. 6)
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