While metagenomics has emerged as a technology of choice for analyzing bacterial populations, the assembly of metagenomic data remains challenging, thus stifling biological discoveries. Moreover, recent studies revealed that complex bacterial populations may be composed from dozens of related strains, thus further amplifying the challenge of metagenomic assembly. metaSPAdes addresses various challenges of metagenomic assembly by capitalizing on computational ideas that proved to be useful in assemblies of single cells and highly polymorphic diploid genomes. We benchmark metaSPAdes against other state-of-the-art metagenome assemblers and demonstrate that it results in high-quality assemblies across diverse data sets.

Recent advances in single-cell genomics provide an alternative to largely gene-centric metagenomics studies, enabling whole-genome sequencing of uncultivated bacteria. However, single-cell assembly projects are challenging due to (i) the highly nonuniform read coverage and (ii) a greatly elevated number of chimeric reads and read pairs. While recently developed single-cell assemblers have addressed the former challenge, methods for assembling highly chimeric reads remain poorly explored. We present algorithms for identifying chimeric edges and resolving complex bulges in de Bruijn graphs, which significantly improve single-cell assemblies. We further describe applications of the single-cell assembler SPAdes to a new approach for capturing and sequencing "microbial dark matter" that forms small pools of randomly selected single cells (called a mini-metagenome) and further sequences all genomes from the mini-metagenome at once. On single-cell bacterial datasets, SPAdes improves on the recently developed E+V-SC and IDBA-UD assemblers specifically designed for single-cell sequencing. For standard (cultivated monostrain) datasets, SPAdes also improves on A5, ABySS, CLC, EULER-SR, Ray, SOAPdenovo, and Velvet. Thus, recently developed single-cell assemblers not only enable single-cell sequencing, but also improve on conventional assemblers on their own turf. SPAdes is available for free online download under a GPLv2 license.

SPAdes—St. Petersburg genome Assembler—was originally developed for de novo assembly of genome sequencing data produced for cultivated microbial isolates and for single‐cell genomic DNA sequencing. With time, the functionality of SPAdes was extended to enable assembly of IonTorrent data, as well as hybrid assembly from short and long reads (PacBio and Oxford Nanopore). In this article we present protocols for five different assembly pipelines that comprise the SPAdes package and that are used for assembly of metagenomes and transcriptomes as well as assembly of putative plasmids and biosynthetic gene clusters from whole‐genome sequencing and metagenomic datasets. In addition, we present guidelines for understanding results with use cases for each pipeline, and several additional support protocols that help in using SPAdes properly. © 2020 Wiley Periodicals LLC. Basic Protocol 1: Assembling isolate bacterial datasets Basic Protocol 2: Assembling metagenomic datasets Basic Protocol 3: Assembling sets of putative plasmids Basic Protocol 4: Assembling transcriptomes Basic Protocol 5: Assembling putative biosynthetic gene clusters Support Protocol 1: Installing SPAdes Support Protocol 2: Providing input via command line Support Protocol 3: Providing input data via YAML format Support Protocol 4: Restarting previous run Support Protocol 5: Determining strand‐specificity of RNA‐seq data

MGnify (http://www.ebi.ac.uk/metagenomics) provides a free to use platform for the assembly, analysis and archiving of microbiome data derived from sequencing microbial populations that are present in particular environments. Over the past 2 years, MGnify (formerly EBI Metagenomics) has more than doubled the number of publicly available analysed datasets held within the resource. Recently, an updated approach to data analysis has been unveiled (version 5.0), replacing the previous single pipeline with multiple analysis pipelines that are tailored according to the input data, and that are formally described using the Common Workflow Language, enabling greater provenance, reusability, and reproducibility. MGnify's new analysis pipelines offer additional approaches for taxonomic assertions based on ribosomal internal transcribed spacer regions (ITS1/2) and expanded protein functional annotations. Biochemical pathways and systems predictions have also been added for assembled contigs. MGnify's growing focus on the assembly of metagenomic data has also seen the number of datasets it has assembled and analysed increase six-fold. The non-redundant protein database constructed from the proteins encoded by these assemblies now exceeds 1 billion sequences. Meanwhile, a newly developed contig viewer provides fine-grained visualisation of the assembled contigs and their enriched annotations.

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supplementary data are available at Bioinformatics online.

Error correction of sequenced reads remains a difficult task, especially in single-cell sequencing projects with extremely non-uniform coverage. While existing error correction tools designed for standard (multi-cell) sequencing data usually come up short in single-cell sequencing projects, algorithms actually used for single-cell error correction have been so far very simplistic.We introduce several novel algorithms based on Hamming graphs and Bayesian subclustering in our new error correction tool BAYESHAMMER. While BAYESHAMMER was designed for single-cell sequencing, we demonstrate that it also improves on existing error correction tools for multi-cell sequencing data while working much faster on real-life datasets. We benchmark BAYESHAMMER on both k-mer counts and actual assembly results with the SPADES genome assembler.

An innovative approach was developed for the discovery of new natural products by combining mass spectrometric metabolic profiling with genomic analysis, and resulted in the discovery of the columbamides, a new class of di- and tri-chlorinated acyl amides with cannabinomimetic activity. Three species of cultured marine cyanobacteria, Moorea producens 3L, Moorea producens JHB and Moorea bouillonii PNG, were subjected to genome sequencing and analysis for their recognizable biosynthetic pathways, and this information was then compared with their respective metabolomes as detected by MS-profiling. By genome analysis, a presumed regulatory domain was identified upstream of several previously described biosynthetic gene clusters in two of these cyanobacteria, M. producens 3L and M. producens JHB. A similar regulatory domain was identified in the M. bouillonii PNG genome, and a corresponding downstream biosynthetic gene cluster was located and carefully analyzed. Subsequently, MS-based molecular networking identified a series of candidate products, and these were isolated and their structures rigorously established. Based on their distinctive acyl amide structure, the most prevalent metabolite was evaluated for cannabinomimetic properties and found to be a moderate affinity ligand for CB1.