COCACOLA: binning metagenomic contigs using sequence COmposition, read CoverAge, CO-alignment, and paired-end read LinkAge

TL;DR

COCACOLA is a novel binning framework for metagenomic contigs that leverages sequence composition, coverage, co-alignment, and paired-end linkage, outperforming existing methods in accuracy and speed.

Contribution

It introduces a new binning approach combining $L_{1}$ distance, sparsity regularization, and customizable knowledge integration, improving accuracy and scalability.

Findings

COCACOLA outperforms state-of-the-art binning tools in accuracy.

Incorporating co-alignment and linkage information enhances binning results.

The method is scalable and faster than existing approaches.

Abstract

The advent of next-generation sequencing (NGS) technologies enables researchers to sequence complex microbial communities directly from environment. Since assembly typically produces only genome fragments, also known as contigs, instead of entire genome, it is crucial to group them into operational taxonomic units (OTUs) for further taxonomic profiling and down-streaming functional analysis. OTU clustering is also referred to as binning. We present COCACOLA, a general framework automatically bin contigs into OTUs based upon sequence composition and coverage across multiple samples. The effectiveness of COCACOLA is demonstrated in both simulated and real datasets in comparison to state-of-art binning approaches such as CONCOCT, GroopM, MaxBin and MetaBAT. The superior performance of COCACOLA relies on two aspects. One is employing $L_{1}$ distance instead of Euclidean distance for better taxonomic identification during initialization. More importantly, COCACOLA takes advantage of both hard clustering and soft clustering by sparsity regularization. In addition, the COCACOLA framework seamlessly embraces customized knowledge to facilitate binning accuracy. In our study, we have investigated two types of additional knowledge, the co-alignment to reference genomes and linkage of contigs provided by paired-end reads, as well as the ensemble of both. We find that both co-alignment and linkage information further improve binning in the majority of cases. COCACOLA is scalable and faster than CONCOCT ,GroopM, MaxBin and MetaBAT. The software is available at https://github.com/younglululu/COCACOLA