PhyloPythiaS+: A self-training method for the rapid reconstruction of low-ranking taxonomic bins from metagenomes

Abstract

Metagenomics is an approach for characterizing environmental microbial communities in situ, it allows their functional and taxonomic characterization and to recover sequences from uncultured taxa. For communities of up to medium diversity, e.g. excluding environments such as soil, this is often achieved by a combination of sequence assembly and binning, where sequences are grouped into 'bins' representing taxa of the underlying microbial community from which they originate. Assignment to low-ranking taxonomic bins is an important challenge for binning methods as is scalability to Gb-sized datasets generated with deep sequencing techniques. One of the best available methods for the recovery of species bins from an individual metagenome sample is the expert-trained PhyloPythiaS package, where a human expert decides on the taxa to incorporate in a composition-based taxonomic metagenome classifier and identifies the 'training' sequences using marker genes directly from the sample. Due to the manual effort involved, this approach does not scale to multiple metagenome samples and requires substantial expertise, which researchers who are new to the area may not have. With these challenges in mind, we have developed PhyloPythiaS+, a successor to our previously described method PhyloPythia(S). The newly developed + component performs the work previously done by the human expert. PhyloPythiaS+ also includes a new k-mer counting algorithm, which accelerated k-mer counting 100-fold and reduced the overall execution time of the software by a factor of three. Our software allows to analyze Gb-sized metagenomes with inexpensive hardware, and to recover species or genera-level bins with low error rates in a fully automated fashion.