Metagenomics is providing striking insights into the ecology of microbial communities. The recently developed massively parallel 454 pyrosequencing technique gives the opportunity to rapidly obtain metagenomic sequences at a low cost and without cloning bias. However, the phylogenetic analysis of the short reads produced represents a significant computational challenge. The phylogenetic algorithm CARMA for predicting the source organisms of environmental 454 reads is described. The algorithm searches for conserved Pfam domain and protein families in the unassembled reads of a sample. These gene fragments (environmental gene tags, EGTs), are classified into a higher-order taxonomy based on the reconstruction of a phylogenetic tree of each matching Pfam family. The method exhibits high accuracy for a wide range of taxonomic groups, and EGTs as short as 27 amino acids can be phylogenetically classified up to the rank of genus. The algorithm was applied in a comparative study of three aquatic microbial samples obtained by 454 pyrosequencing. Profound differences in the taxonomic composition of these samples could be clearly revealed.

Background: Metagenomics, or the sequencing and analysis of collective genomes (metagenomes) of microorganisms isolated from an environment, promises direct access to the "unculturable majority". This emerging field offers the potential to lay solid basis on our understanding of the entire living world. However, the taxonomic classification is an essential task in the analysis of metagenomics data sets that it is still far from being solved. We present a novel strategy to predict the taxonomic origin of environmental genomic fragments. The proposed classifier combines the idea of the k-nearest neighbor with strategies from kernel-based learning.

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