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BMC Biology

, 13:92

First Online: 05 November 2015Received: 06 July 2015Accepted: 02 October 2015DOI: 10.1186-s12915-015-0195-4

Cite this article as: Petrenko, P., Lobb, B., Kurtz, D.A. et al. BMC Biol 2015 13: 92. doi:10.1186-s12915-015-0195-4


BackgroundMetagenomes provide access to the taxonomic composition and functional capabilities of microbial communities. Although metagenomic analysis methods exist for estimating overall community composition or metabolic potential, identifying specific taxa that encode specific functions or pathways of interest can be more challenging. Here we present MetAnnotate, which addresses the common question: -which organisms perform my function of interest within my metagenomes of interest?- MetAnnotate uses profile hidden Markov models to analyze shotgun metagenomes for genes and pathways of interest, classifies retrieved sequences either through a phylogenetic placement or best hit approach, and enables comparison of these profiles between metagenomes.

ResultsBased on a simulated metagenome dataset, the tool achieves high taxonomic classification accuracy for a broad range of genes, including both markers of community abundance and specific biological pathways. Lastly, we demonstrate MetAnnotate by analyzing for cobalamin vitamin B12 synthesis genes across hundreds of aquatic metagenomes in a fraction of the time required by the commonly used Basic Local Alignment Search Tool top hit approach.

ConclusionsMetAnnotate is multi-threaded and installable as a local web application or command-line tool on Linux systems. Metannotate is a useful framework for general and-or function-specific taxonomic profiling and comparison of metagenomes.

KeywordsMetagenomics Microbiome Next-generation sequencing Taxonomic profiling AbbreviationsNCBINational Center for Biotechnology Information

BLASTBasic Local Alignment Search Tool

HMMHidden Markov Model

simHCSimulated High Complexity Metagenome

GOGene Ontology

Electronic supplementary materialThe online version of this article doi:10.1186-s12915-015-0195-4 contains supplementary material, which is available to authorized users.

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Autor: Pavel Petrenko - Briallen Lobb - Daniel A. Kurtz - Josh D. Neufeld - Andrew C. Doxey


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