RepBin: Constraint-based Graph Representation Learning for Metagenomic Binning

TL;DR

RepBin introduces a novel graph-based approach for metagenomic binning that incorporates biological constraints and heterophilous signals, significantly improving clustering accuracy in complex microbial communities.

Contribution

This paper presents a new constraint-based graph representation learning and clustering framework specifically designed for metagenomic binning, addressing biological constraints and skewed cluster sizes.

Findings

Outperforms existing binning methods on real and synthetic datasets.

Effectively incorporates biological constraints into graph learning.

Advances state-of-the-art in both metagenomics and graph representation learning.

Abstract

Mixed communities of organisms are found in many environments (from the human gut to marine ecosystems) and can have profound impact on human health and the environment. Metagenomics studies the genomic material of such communities through high-throughput sequencing that yields DNA subsequences for subsequent analysis. A fundamental problem in the standard workflow, called binning, is to discover clusters, of genomic subsequences, associated with the unknown constituent organisms. Inherent noise in the subsequences, various biological constraints that need to be imposed on them and the skewed cluster size distribution exacerbate the difficulty of this unsupervised learning problem. In this paper, we present a new formulation using a graph where the nodes are subsequences and edges represent homophily information. In addition, we model biological constraints providing heterophilous signal about nodes that cannot be clustered together. We solve the binning problem by developing new algorithms for (i) graph representation learning that preserves both homophily relations and heterophily constraints (ii) constraint-based graph clustering method that addresses the problems of skewed cluster size distribution. Extensive experiments, on real and synthetic datasets, demonstrate that our approach, called RepBin, outperforms a wide variety of competing methods. Our constraint-based graph representation learning and clustering methods, that may be useful in other domains as well, advance the state-of-the-art in both metagenomics binning and graph representation learning.