POSMM: an efficient alignment-free metagenomic profiler that complements alignment-based profiling

We present here POSMM (pronounced 'Possum'), Python-Optimized Standard Markov Model classifier, which is a new incarnation of the Markov model approach to metagenomic sequence analysis. Built on the top of a rapid Markov model based classification algorithm SMM, POSMM reintroduces high sensitivity associated with alignment-free taxonomic classifiers to probe whole genome or metagenome datasets of increasingly prohibitive sizes. Logistic regression models generated and optimized using the Python sklearn library, transform Markov model probabilities to scores suitable for thresholding. Featuring a dynamic database-free approach, models are generated directly from genome fasta files per run, making POSMM a valuable accompaniment to many other programs. By combining POSMM with ultrafast classifiers such as Kraken2, their complementary strengths can be leveraged to produce higher overall accuracy in metagenomic sequence classification than by either as a standalone classifier. POSMM is a user-friendly and highly adaptable tool designed for broad use by the metagenome scientific community.

Automated summary

POSMM is a new Markov model-based classifier that reintroduces high sensitivity associated with alignment-free taxonomic classifiers. It is built on the top of a rapid Markov model based classification algorithm and generates logistic regression models using the Python sklearn library. POSMM is a valuable accompaniment to other programs as it features a dynamic database-free approach and is user-friendly and highly adaptable. By combining POSMM with ultrafast classifiers like Kraken2, higher overall accuracy in metagenomic sequence classification can be achieved.