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Phymm and PhymmBL: metagenomic phylogenetic classification with interpolated Markov models


Metagenomics projects collect DNA from uncharacterized environments that may contain thousands of species per sample. One main challenge facing metagenomic analysis is phylogenetic classification of raw sequence reads into groups representing the same or similar taxa, a prerequisite for genome assembly and for analyzing the biological diversity of a sample. New sequencing technologies have made metagenomics easier, by making sequencing faster, and more difficult, by producing shorter reads than previous technologies. Classifying sequences from reads as short as 100 base pairs has until now been relatively inaccurate, requiring researchers to use older, long-read technologies. We present Phymm, a classifier for metagenomic data, that has been trained on 539 complete, curated genomes and can accurately classify reads as short as 100 base pairs, a substantial improvement over previous composition-based classification methods. We also describe how combining Phymm with sequence alignment algorithms improves accuracy.

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Figure 1: Accuracy of Phymm, with species-level matches masked.
Figure 2: PhymmBL's phylum-level population characterization of the AMD data.
Figure 3: PhymmBL's species-level population characterization of the AMD data.


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We thank A. Delcher for helpful discussions regarding IMM configuration. This work was supported in part by US National Institutes of Health grants R01-LM006845 and R01-GM083873.

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Authors and Affiliations



A.B. performed the experiments and subsequent analysis. A.B. and S.L.S. designed the experiments and wrote the paper.

Corresponding author

Correspondence to Arthur Brady.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–10 and Supplementary Tables 1–11 (PDF 702 kb)

Supplementary Software

Open-source installer package for Phymm/PhymmBL, including all algorithms used during setup and scoring. (ZIP 5454 kb)

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Brady, A., Salzberg, S. Phymm and PhymmBL: metagenomic phylogenetic classification with interpolated Markov models. Nat Methods 6, 673–676 (2009).

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