Improving Metagenomic Binning of Long Reads via Feature Fusion and Graph SAGE

The advent of advanced metagenome sequencing techniques has enabled the direct study of microbial communities in their natural habitats, thereby facilitating the acquisition of crucial insights into their composition and structure. As sequencing technology has advanced, the quality and length of sequences have continuously improved, making long-read segments and contigs more similar. Consequently, long-read segments can now be binned before assembly. However, a limited number of long-read binning methods exist, and some rely directly on composition and inaccurate coverage information. Additionally, while some existing methods attempt to improve the accuracy of their coverage estimation for individual long reads, their strategies remain imprecise, and fusion strategies of composition and coverage information also show unreliability. In this paper, we present improving metagenomic binning of long reads via feature fusion and Graph SAGE named as MBLR-GS, a scalable reference-free binning method. Specifically, it employs a new coverage estimation strategy and a feature fusion strategy that first enables fast and accurate estimation of coverage information using an overlap graph and utilizes a new feature fusion strategy that leverages composition and coverage information for binning during the binning process. Moreover, MBLR-GS incorporates sequence overlap information into the binning process using Graph SAGE. The proposed MBLR-GS outperforms existing methods in terms of binning accuracy on both simulated and real datasets, with an average improvement of about 6.32% in F1 score and 13.55% in ARI. In addition, MBLR-GS improves the accuracy without increasing the computation time. Furthermore, our method has been demonstrated to significantly improve binning accuracy without increasing computation time, thus facilitating subsequent downstream analyses.