MetaVW: Large-scale machine learning for metagenomics sequence classification

Abstract

Metagenomics is the study of microbial community diversity, especially the uncultured microorganisms by shotgun sequencing environmental samples. As the sequencers throughput and the data volume increase, it becomes challenging to develop scalable bioinformatics tools that reconstruct microbiome structure by binning sequencing reads to reference genomes. Standard alignment-based methods, such as BWA-MEM, provide state-of-the-art performance, but we demonstrate in Vervier et al. (2016) that compositional approaches using nucleotides motifs have faster analysis time, for comparable accuracy. In this work, we describe how to use MetaVW, a scalable machine learning implementation for short sequencing reads binning, based on their k-mers profile. We provide a step-by-step guideline on how we trained the classification models and how it can easily generalize to user-defined reference genomes and specific applications. We also give additional details on what effect parameters in the algorithm have on performances.