ClassGraph: Improving Metagenomic Read Classification with Overlap Graphs

Abstract

Current technologies allow the sequencing of microbial communities directly from the environment without prior culturing. One of the major problems when analyzing a microbial sample is to taxonomically annotate its reads to identify the species it contains. Most methods that are currently available focus on the classification of reads using a set of reference genomes and their k-mers. While in terms of precision these methods have reached percentages of correctness close to perfection, in terms of sensitivity (the actual number of classified reads), the performance is often poor. One reason is that the reads in a sample can be very different from the corresponding reference genomes; for example, viral genomes are usually highly mutated. To address this issue, in this article, we propose ClassGraph, a new taxonomic classification method that makes use of the read overlap graph and applies a label propagation algorithm to refine the results of existing tools. We evaluated its performance on simulated and real datasets with several taxonomic classification tools, and the results showed an improved sensitivity and F-measure, while maintaining high precision. ClassGraph is capable of improving the classification accuracy, especially in difficult cases such as virus and real datasets, where traditional tools can classify <40% of reads.