Binning long reads in metagenomics datasets using composition and coverage information

Abstract

Background Advancements in metagenomics sequencing allow the study of microbial communities directly from their environments. Metagenomics binning is a key step in the species characterisation of microbial communities. Next-generation sequencing reads are usually assembled into contigs for metagenomics binning mainly due to the limited information within short reads. Third-generation sequencing provides much longer reads that have lengths similar to the contigs assembled from short reads. However, existing contig-binning tools cannot be directly applied on long reads due to the absence of coverage information and the presence of high error rates. The few existing long-read binning tools either use only composition or use composition and coverage information separately. This may ignore bins that correspond to low-abundance species or erroneously split bins that correspond to species with non-uniform coverages. Here we present a reference-free binning approach, LRBinner, that combines composition and coverage information of complete long-read datasets. LRBinner also uses a distance-histogram-based clustering algorithm to extract clusters with varying sizes. Results The experimental results on both simulated and real datasets show that LRBinner achieves the best binning accuracy in most cases while handling the complete datasets without any sampling. Moreover, we show that binning reads using LRBinner prior to assembly reduces computational resources required for assembly while attaining satisfactory assembly qualities. Conclusion LRBinner shows that deep-learning techniques can be used for effective feature aggregation to support the metagenomics binning of long reads. Furthermore, accurate binning of long reads supports improvements in metagenomics assembly, especially in complex datasets. Binning also helps to reduce the resources required for assembly. Source code for LRBinner is freely available at https://github.com/anuradhawick/LRBinner.

Exploring high-quality microbial genomes by assembling short-reads with long-range connectivity

“…The VAE was successfully applied to contigs and long-reads binning 25,26 and showed better binning performance than classical dimensional reduction algorithms such as principal component analysis (PCA; Supplementary Figure 3 ). For clustering linked-reads in the latent space of VAE, the classical k-means was not optimized to process the highly imbalanced metagenomic data due to its instability to choose proper initial centroids.…”

Viruses of sulfur oxidizing phototrophs encode genes for pigment, carbon, and sulfur metabolisms

“…The resulting bins were refined using MetaWRAP v1.3 bin_refinement module 77 and refined bins were assessed for contamination and completion with CheckM v1.2.0 78 . In the second approach, the binning program LRBinner v.2.1 79 , which is specialized in long reads, was utilized to bin metagenomic contigs. The third approach applied the long-read binning pipeline Nano-Phase v.0.2, which utilizes MetaBAT2 and MaxBin2, and has been validated on the ZymoBIOMICS gut microbiome standard 80 .…”

Unraveling metagenomics through long-read sequencing: a comprehensive review

“…Binning of long-reads presents a set of challenges such as a lack of coverage information, which is the information of an average number of reads that is mapped to a position in a reference genome, relatively high error rates, and varying degree of species coverage [ 53 ]. When compared to contigs from short-reads, the read length is significantly longer, which requires a unique binning algorithm.…”

“…Then, samples are taken from latent space for the decoder to produce an output that is ideally identical to the original input. When compared to MetaBBC-LR, LRBinner is capable of producing bins with better completeness, lower contamination, better estimation of the number of bins, and overall higher precisions [ 53 ] (Fig. 4 C).…”