Motivation The field of metagenomics has provided valuable insights into the structure, diversity and ecology within microbial communities. One key step in metagenomics analysis is to assemble reads into longer contigs which are then binned into groups of contigs that belong to different species present in the metagenomic sample. Binning of contigs plays an important role in metagenomics and most available binning algorithms bin contigs using genomic features such as oligonucleotide/k-mer composition and contig coverage. As metagenomic contigs are derived from the assembly process, they are output from the underlying assembly graph which contains valuable connectivity information between contigs that can be used for binning. Results We propose GraphBin, a new binning method that makes use of the assembly graph and applies a label propagation algorithm to refine the binning result of existing tools. We show that GraphBin can make use of the assembly graphs constructed from both the de Bruijn graph and the overlap-layout-consensus approach. Moreover, we demonstrate improved experimental results from GraphBin in terms of identifying mis-binned contigs and binning of contigs discarded by existing binning tools. To the best of our knowledge, this is the first time that the information from the assembly graph has been used in a tool for the binning of metagenomic contigs. Availability and implementation The source code of GraphBin is available at https://github.com/Vini2/GraphBin. Contact vijini.mallawaarachchi@anu.edu.au or yu.lin@anu.edu.au Supplementary information Supplementary data are available at Bioinformatics online.

Motivation Metagenomics studies have provided key insights into the composition and structure of microbial communities found in different environments. Among the techniques used to analyse metagenomic data, binning is considered a crucial step to characterize the different species of micro-organisms present. The use of short-read data in most binning tools poses several limitations, such as insufficient species-specific signal, and the emergence of long-read sequencing technologies offers us opportunities to surmount them. However, most current metagenomic binning tools have been developed for short reads. The few tools that can process long reads either do not scale with increasing input size or require a database with reference genomes that are often unknown. In this article, we present MetaBCC-LR, a scalable reference-free binning method which clusters long reads directly based on their k-mer coverage histograms and oligonucleotide composition. Results We evaluate MetaBCC-LR on multiple simulated and real metagenomic long-read datasets with varying coverages and error rates. Our experiments demonstrate that MetaBCC-LR substantially outperforms state-of-the-art reference-free binning tools, achieving ∼13% improvement in F1-score and ∼30% improvement in ARI compared to the best previous tools. Moreover, we show that using MetaBCC-LR before long-read assembly helps to enhance the assembly quality while significantly reducing the assembly cost in terms of time and memory usage. The efficiency and accuracy of MetaBCC-LR pave the way for more effective long-read-based metagenomics analyses to support a wide range of applications. Availability and implementation The source code is freely available at: https://github.com/anuradhawick/MetaBCC-LR. Supplementary information Supplementary data are available at Bioinformatics online.

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Summary With recent advances in sequencing technologies, it is now possible to obtain near-perfect complete bacterial chromosome assemblies cheaply and efficiently by combining a long-read-first assembly approach with short-read polishing. However, existing methods for assembling bacterial plasmids from long-read-first assemblies often misassemble or even miss bacterial plasmids entirely and accordingly require manual curation. Plassembler was developed to provide a tool that automatically assembles and outputs bacterial plasmids using a hybrid assembly approach. It achieves increased accuracy and computational efficiency compared to the existing gold standard tool Unicycler by removing chromosomal reads from the input read sets using a mapping approach. Availability Plassembler is implemented in Python and is installable as a bioconda package using ‘conda install -c bioconda plassembler’. The source code is available on GitHub at https://github.com/gbouras13/plassembler. The full benchmarking pipeline can be found at https://github.com/gbouras13/plassembler_simulation_benchmarking, while the benchmarking input FASTQ and output files can be found at https://doi.org/10.5281/zenodo.7996690. Supplementary information Supplementary data are available at Bioinformatics online.

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