Phymm and PhymmBL: metagenomic phylogenetic classification with interpolated Markov models

Abstract

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.

Figure 1. Percent accuracy of Phymm, with species-level matches masked, for read lengths from 100–1000 bp

Colored dots show classification accuracy reported for PhyloPythia at 1000 bp for genus-through phylum-level predictions, and for CARMA at 100 bp (as apercentage of the entire input data set) for genus-and phylum-level predictions.

Figure 2. PhymmBL’s phylum-level population characterization of the AMD data

using (A) the RefSeq-generated IMMs plus IMMs generated from the draft genomes of the three dominant species in the AMD set, and (B) the RefSeq-generated IMMs on their own.

Figure 3. PhymmBL’s species-level population characterization of the AMD data

using the RefSeq-generated IMMs plus IMMs generated from the draft genomes of the three dominant species in the AMD set.