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Public sequence data represents a major opportunity for viral discovery, but its exploration has been inhibited by a lack of efficient methods for searching this corpus, which is currently at the petabase scale and growing exponentially. To address the ongoing pandemic caused by Severe Acute Respiratory Syndrome Coronavirus 2 and expand the known sequence diversity of viruses, we aligned pangenomes for coronaviruses (CoV) and other viral families to 5.6 petabases of public sequencing data from 3.8 million biologically diverse samples. To implement this strategy, we developed a cloud computing architecture, Serratus, tailored for ultra-high throughput sequence alignment at the petabase scale. From this search, we identified and assembled thousands of CoV and CoV-like genomes and genome fragments ranging from known strains to putatively novel genera. We generalise this strategy to other viral families, identifying several novel deltaviruses and huge bacteriophages. To catalyse a new era of viral discovery we made millions of viral alignments and family identifications freely available to the research community. Expanding the known diversity and zoonotic reservoirs of CoV and other emerging pathogens can accelerate vaccine and therapeutic developments for the current pandemic, and help us anticipate and mitigate future ones.

Recent advances in long-read sequencing technologies enabled accuratede novoassembly of large genomes and metagenomes. However, even long and accurate high-fidelity (HiFi) reads do not resolve repeats that are longer than the read lengths. This limitation significantly affects the contiguity of diploid genome assemblies since two haplomes share many long identical regions. To generate telomere-to-telomere assemblies of diploid genomes, biologists use additional experimental technologies, such as linked reads or ultralong Oxford Nanopore reads. In particular, the barcoded linked-reads, generated using an inexpensive TELL-Seq technology, provide an attractive way to bridge unresolved repeats in phased assemblies of diploid genomes.Here, we present a SpLitteR tool for haplotype phasing and scaffolding in an assembly graph using barcoded linked-reads. We benchmark SpLitteR on assembly graphs produced by various long-read assemblers and TELL-Seq reads and show that it improves upon the state-of-the-art linked-read scaffolders in the accuracy and contiguity metrics.

MotivationSARS-CoV-2 wastewater samples are extensively collected and studied because it allows quantitatively assess a viral load in surrounding populations. Additionally, SARS-CoV-2 strain deconvolution gives more insights into pandemic dynamics, and the uprising of new strains. Usually, the solution to the strain deconvolution problem starts with read alignment of wastewater short read sequencing data to the SARS-CoV-2 reference genome. After variants are called and their abundances are estimated, a reference database is used to assign variants to strains, select a subset of strains, and infer relative abundance of these strains based on some mathematical model. Assembly-based methods have its own strengths, but currently reside in the shadow of alignment-based methods.ResultsIn this paper we propose a new assembly-based approach based on SPAdes toolkit codebase ––– wastewaterSPAdes, that is able to deconvolve wastewater without a need of read alignment. Our results show that watewaterSPAdes is able to accurately identify strains presented in a sample, and correctly estimate abundances for most of the samples.Availabilityhttps://cab.spbu.ru/software/wastewaterspades/Contacta.korobeynikov@spbu.ruSupplementary informationSupplementary data are available atBioinformatics

The MGnify platform (https://www.ebi.ac.uk/metagenomics) facilitates the assembly, analysis and archiving of microbiome-derived nucleic acid sequences. The platform provides access to taxonomic assignments and functional annotations for nearly half a million analyses covering metabarcoding, metatranscriptomic, and metagenomic datasets, which are derived from a wide range of different environments. Over the past 3 years, MGnify has not only grown in terms of the number of datasets contained but also increased the breadth of analyses provided, such as the analysis of long-read sequences. The MGnify protein database now exceeds 2.4 billion non-redundant sequences predicted from metagenomic assemblies. This collection is now organised into a relational database making it possible to understand the genomic context of the protein through navigation back to the source assembly and sample metadata, marking a major improvement. To extend beyond the functional annotations already provided in MGnify, we have applied deep learning-based annotation methods. The technology underlying MGnify's Application Programming Interface (API) and website has been upgraded, and we have enabled the ability to perform downstream analysis of the MGnify data through the introduction of a coupled Jupyter Lab environment.

MotivationA recently published article in BMC Genomics by Fuentes-Trillo et al (2021) contains a comparison of assembly approaches of several Noroviral samples via different tools and preprocessing strategies. Unfortunately the study used outdated versions of tools as well as tools that were not designed for the viral assembly task. In order to improve the suboptimal assemblies the authors suggested different sophisticated preprocessing strategies that seem to make only minor contributions to the results. We redone the analysis using state-of-the art tools designed for viral assembly.ResultsHere we demonstrate that tools from the SPAdes toolkit (rnaviralSPAdes and coronaSPAdes) allows one to assemble the samples from the original study into a single contig without any additional preprocessing.

Evaluating metagenomic software is key for optimizing metagenome interpretation and focus of the Initiative for the Critical Assessment of Metagenome Interpretation (CAMI). The CAMI II challenge engaged the community to assess methods on realistic and complex datasets with long- and short-read sequences, created computationally from around 1,700 new and known genomes, as well as 600 new plasmids and viruses. Here we analyze 5,002 results by 76 program versions. Substantial improvements were seen in assembly, some due to long-read data. Related strains still were challenging for assembly and genome recovery through binning, as was assembly quality for the latter. Profilers markedly matured, with taxon profilers and binners excelling at higher bacterial ranks, but underperforming for viruses and Archaea. Clinical pathogen detection results revealed a need to improve reproducibility. Runtime and memory usage analyses identified efficient programs, including top performers with other metrics. The results identify challenges and guide researchers in selecting methods for analyses., Nature Methods, 19 (8), ISSN:1548-7105, ISSN:1548-7091

Evaluating metagenomic software is key for optimizing metagenome interpretation and focus of the community-driven initiative for the Critical Assessment of Metagenome Interpretation (CAMI). In its second challenge, CAMI engaged the community to assess their methods on realistic and complex metagenomic datasets with long and short reads, created from ∼1,700 novel and known microbial genomes, as well as ∼600 novel plasmids and viruses. Altogether 5,002 results by 76 program versions were analyzed, representing a 22x increase in results.Substantial improvements were seen in metagenome assembly, some due to using long-read data. The presence of related strains still was challenging for assembly and genome binning, as was assembly quality for the latter. Taxon profilers demonstrated a marked maturation, with taxon profilers and binners excelling at higher bacterial taxonomic ranks, but underperforming for viruses and archaea. Assessment of clinical pathogen detection techniques revealed a need to improve reproducibility. Analysis of program runtimes and memory usage identified highly efficient programs, including some top performers with other metrics. The CAMI II results identify current challenges, but also guide researchers in selecting methods for specific analyses.

Microbial communities might include distinct lineages of closely related organisms that complicate metagenomic assembly and prevent the generation of complete metagenome-assembled genomes (MAGs). Here we show that deep sequencing using long (HiFi) reads combined with Hi-C binning can address this challenge even for complex microbial communities. Using existing methods, we sequenced the sheep fecal metagenome and identified 428 MAGs with more than 90% completeness, including 44 MAGs in single circular contigs. To resolve closely related strains (lineages), we developed MAGPhase, which separates lineages of related organisms by discriminating variant haplotypes across hundreds of kilobases of genomic sequence. MAGPhase identified 220 lineage-resolved MAGs in our dataset. The ability to resolve closely related microbes in complex microbial communities improves the identification of biosynthetic gene clusters and the precision of assigning mobile genetic elements to host genomes. We identified 1,400 complete and 350 partial biosynthetic gene clusters, most of which are novel, as well as 424 (298) potential host–viral (host–plasmid) associations using Hi-C data.

Predicting biosynthetic gene clusters (BGCs) is critically important for discovery of antibiotics and other natural products. While BGC prediction from complete genomes is a well-studied problem, predicting BGCs in fragmented genomic assemblies remains challenging. The existing BGC prediction tools often assume that each BGC is encoded within a single contig in the genome assembly, a condition that is violated for most sequenced microbial genomes where BGCs are often scattered through several contigs, making it difficult to reconstruct them. The situation is even more severe in shotgun metagenomics, where the contigs are often short, and the existing tools fail to predict a large fraction of long BGCs. While it is difficult to assemble BGCs in a single contig, the structure of the genome assembly graph often provides clues on how to combine multiple contigs into segments encoding long BGCs. We describe biosyntheticSPAdes, a tool for predicting BGCs in assembly graphs and demonstrate that it greatly improves the reconstruction of BGCs from genomic and metagenomics data sets.

We describe fast and accurate algorithm for IonTorrent read error correction capable of significantly reducing the number of sequencing errors over the wide range of data sets. IonHammer is implemented in C++ and is freely available as part of the SPAdes genome assembler package.

Metagenomics is a segment of conventional microbial genomics dedicated to the sequencing and analysis of combined genomic DNA of entire environmental samples. The most critical step of the metagenomic data analysis is the reconstruction of individual genes and genomes of the microorganisms in the communities using metagenomic assemblers – computational programs that put together small fragments of sequenced DNA generated by sequencing instruments. Here, we describe the challenges of metagenomic assembly, a wide spectrum of applications in which metagenomic assemblies were used to better understand the ecology and evolution of microbial ecosystems, and present one of the most efficient microbial assemblers, SPAdes that was upgraded to become applicable for metagenomics.

Motivation The COVID-19 pandemic has ignited a broad scientific interest in viral research in general and coronavirus research in particular. The identification and characterization of viral species in natural reservoirs typically involves de novo assembly. However, existing genome, metagenome and transcriptome assemblers often are not able to assemble many viruses (including coronaviruses) into a single contig. Coverage variation between datasets and within dataset, presence of close strains, splice variants and contamination set a high bar for assemblers to process viral datasets with diverse properties. Results We developed coronaSPAdes, a novel assembler for RNA viral species recovery in general and coronaviruses in particular. coronaSPAdes leverages the knowledge about viral genome structures to improve assembly extending ideas initially implemented in biosyntheticSPAdes. We have shown that coronaSPAdes outperforms existing SPAdes modes and other popular short-read metagenome and viral assemblers in the recovery of full-length RNA viral genomes. Availability and implementation coronaSPAdes version used in this article is a part of SPAdes 3.15 release and is freely available at http://cab.spbu.ru/software/spades. Supplementary information Supplementary data are available at Bioinformatics online.

MotivationThe COVID-19 pandemic has ignited a broad scientific interest in viral research in general and coronavirus research in particular. The identification and characterization of viral species in natural reservoirs typically involves de novo assembly. However, existing genome, metagenome and transcriptome assemblers often are not able to assemble many viruses (including coronaviruses) into a single contig. Coverage variation between datasets and within dataset, presence of close strains, splice variants and contamination set a high bar for assemblers to process viral datasets with diverse properties.ResultsWe developed coronaSPAdes, a novel assembler for RNA viral species recovery in general and coronaviruses in particular. coronaSPAdes leverages the knowledge about viral genome structures to improve assembly extending ideas initially implemented in biosyntheticSPAdes. We have shown that coronaSPAdes outperforms existing SPAdes modes and other popular short-read metagenome and viral assemblers in the recovery of full-length RNA viral genomes.AvailabilitycoronaSPAdes version used in this article is a part of SPAdes 3.15 release and is freely available at http://cab.spbu.ru/software/spades.Contacta.korobeynikov@spbu.ruSupplementary informationSupplementary data are available at Bioinformatics

Background Graph-based representation of genome assemblies has been recently used in different contexts — from improved reconstruction of plasmid sequences and refined analysis of metagenomic data to read error correction and reference-free haplotype reconstruction. While many of these applications heavily utilize the alignment of long nucleotide sequences to assembly graphs, first general-purpose software tools for finding such alignments have been released only recently and their deficiencies and limitations are yet to be discovered. Moreover, existing tools can not perform alignment of amino acid sequences, which could prove useful in various contexts — in particular the analysis of metagenomic sequencing data. Results In this work we present a novel SPAligner (Saint-Petersburg Aligner) tool for aligning long diverged nucleotide and amino acid sequences to assembly graphs. We demonstrate that SPAligner is an efficient solution for mapping third generation sequencing reads onto assembly graphs of various complexity and also show how it can facilitate the identification of known genes in complex metagenomic datasets. Conclusions Our work will facilitate accelerating the development of graph-based approaches in solving sequence to genome assembly alignment problem. SPAligner is implemented as a part of SPAdes tools library and is available on Github.

Background Illumina paired-end reads are often used for 16S analysis in metagenomic studies. Since DNA fragment size is usually smaller than the sum of lengths of paired reads, reads can be merged for downstream analysis. In spite of development of several tools for merging of paired-end reads, poor quality at the 3′ ends within the overlapping region prevents the accurate combining of significant portion of read pairs. Recently CD-HIT-OTU-Miseq was presented as a new approach for 16S analysis using the paired-end reads, it completely avoids the reads merging process due to separate clustering of paired reads. CD-HIT-OTU-Miseq is a set of tools which are supposed to be successively launched by auxiliary shell scripts. This launch mode is not suitable for processing of big amounts of data generated in modern omics experiments. To solve this issue we created CDSnake – Snakemake pipeline utilizing CD-HIT tools for easier consecutive launch of CD-HIT-OTU-Miseq tools for complete processing of paired end reads in metagenomic studies. Usage of pipeline make 16S analysis easier due to one-command launch and helps to yield reproducible results. Results We benchmarked our pipeline against two commonly used pipelines for OTU retrieval, incorporated into popular workflow for microbiome analysis, QIIME2 - DADA2 and deblur. Three mock datasets having highly overlapping paired-end 2 × 250 bp reads were used for benchmarking - Balanced, HMP, and Extreme. CDSnake outputted less OTUs than DADA2 and deblur. However, on Balanced and HMP datasets number of OTUs outputted by CDSnake was closer to real number of strains which were used for mock community generation, than those outputted by DADA2 and deblur. Though generally slower than other pipelines, CDSnake outputted higher total counts, preserving more information from raw data. Inheriting this properties from original CD-HIT-OTU-MiSeq utilities, CDSnake made their usage handier due to simple scalability, easier automated runs and other Snakemake benefits. Conclusions We developed Snakemake pipeline for OTU-MiSeq utilities, which simplified and automated data analysis. Benchmarking showed that this approach is capable to outperform popular tools in certain conditions.

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.

Natural products have traditionally been rich sources for drug discovery. In order to clear the road toward the discovery of unknown natural products, biologists need dereplication strategies that identify known ones. Here we report DEREPLICATOR+, an algorithm that improves on the previous approaches for identifying peptidic natural products, and extends them for identification of polyketides, terpenes, benzenoids, alkaloids, flavonoids, and other classes of natural products. We show that DEREPLICATOR+ can search all spectra in the recently launched Global Natural Products Social molecular network and identify an order of magnitude more natural products than previous dereplication efforts. We further demonstrate that DEREPLICATOR+ enables cross-validation of genome-mining and peptidogenomics/glycogenomics results., New natural products can be identified via mass spectrometry by excluding all known ones from the analysis, a process called dereplication. Here, the authors extend a previously published dereplication algorithm to different classes of secondary metabolites.

Ribosomally synthesized and post-translationally modified peptides (RiPPs) are an important class of natural products that contain antibiotics and a variety of other bioactive compounds. The existing methods for discovery of RiPPs by combining genome mining and computational mass spectrometry are limited to discovering specific classes of RiPPs from small datasets, and these methods fail to handle unknown post-translational modifications. Here, we present MetaMiner, a software tool for addressing these challenges that is compatible with large-scale screening platforms for natural product discovery. After searching millions of spectra in the Global Natural Products Social (GNPS) molecular networking infrastructure against just eight genomic and metagenomic datasets, MetaMiner discovered 31 known and seven unknown RiPPs from diverse microbial communities, including human microbiome and lichen microbiome, and microorganisms isolated from the International Space Station.

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.

Peptidic natural products (PNPs) include many antibiotics and other bioactive compounds. While the recent launch of the Global Natural Products Social (GNPS) molecular networking infrastructure is transforming PNP discovery into a high-throughput technology, PNP identification algorithms are needed to realize the potential of the GNPS project. GNPS relies on the assumption that each connected component of a molecular network (representing related metabolites) illuminates the 'dark matter of metabolomics' as long as it contains a known metabolite present in a database. We reveal a surprising diversity of PNPs produced by related bacteria and show that, contrary to the 'comparative metabolomics' assumption, two related bacteria are unlikely to produce identical PNPs (even though they are likely to produce similar PNPs). Since this observation undermines the utility of GNPS, we developed a PNP identification tool, VarQuest, that illuminates the connected components in a molecular network even if they do not contain known PNPs and only contain their variants. VarQuest reveals an order of magnitude more PNP variants than all previous PNP discovery efforts and demonstrates that GNPS already contains spectra from 41% of the currently known PNP families. The enormous diversity of PNPs suggests that biosynthetic gene clusters in various microorganisms constantly evolve to generate a unique spectrum of PNP variants that differ from PNPs in other species.

Genome sequencing of microorganisms has revealed a greatly increased capacity for natural products biosynthesis than was previously recognized from compound isolation efforts alone. Hence, new methods are needed for the discovery and description of this hidden secondary metabolite potential. Here we show that provision of heavy nitrogen 15N-nitrate to marine cyanobacterial cultures followed by single filament MALDI analysis over a period of days was highly effective in identifying a new natural product with an exceptionally high nitrogen content. The compound, named cryptomaldamide, was subsequently isolated using MS to guide the purification process, and its structure determined by 2D NMR and other spectroscopic and chromatographic methods. Bioinformatic analysis of the draft genome sequence identified a 28.7 kB gene cluster which putatively encodes for cryptomaldamide biosynthesis. Notably, an amidinotransferase is proposed to initiate the biosynthetic process by transferring an amidino group from arginine to serine to produce the first residue to be incorporated by the hybrid NRPS-PKS pathway. The maldiisotopic approach presented here is thus demonstrated to provide an orthogonal method by which to discover novel chemical diversity from Nature.

Cyanobacteria are major sources of oxygen, nitrogen, and carbon in nature. In addition to the importance of their primary metabolism, some cyanobacteria are prolific producers of unique and bioactive secondary metabolites. Chemical investigations of the cyanobacterial genus Moorea have resulted in the isolation of over 190 compounds in the last two decades. However, preliminary genomic analysis has suggested that genome-guided approaches can enable the discovery of novel compounds from even well-studied Moorea strains, highlighting the importance of obtaining complete genomes. We report a complete genome of a filamentous tropical marine cyanobacterium, Moorea producens PAL, which reveals that about one-fifth of its genome is devoted to production of secondary metabolites, an impressive four times the cyanobacterial average. Moreover, possession of the complete PAL genome has allowed improvement to the assembly of three other Moorea draft genomes. Comparative genomics revealed that they are remarkably similar to one another, despite their differences in geography, morphology, and secondary metabolite profiles. Gene cluster networking highlights that this genus is distinctive among cyanobacteria, not only in the number of secondary metabolite pathways but also in the content of many pathways, which are potentially distinct from all other bacterial gene clusters to date. These findings portend that future genome-guided secondary metabolite discovery and isolation efforts should be highly productive.

Recently large databases containing profile Hidden Markov Models (pHMMs) emerged. These pHMMs may represent the sequences of antibiotic resistance genes, or allelic variations amongst highly conserved housekeeping genes used for strain typing, etc. The typical application of such a database includes the alignment of contigs to pHMM hoping that the sequence of gene of interest is located within the single contig. Such a condition is often violated for metagenomes preventing the effective use of such databases.We present PathRacer — a novel standalone tool that aligns profile HMM directly to the assembly graph (performing the codon translation on fly for amino acid pHMMs). The tool provides the set of most probable paths traversed by a HMM through the whole assembly graph, regardless whether the sequence of interested is encoded on the single contig or scattered across the set of edges, therefore significantly improving the recovery of sequences of interest even from fragmented metagenome assemblies.Availabilityhttp://cab.spbu.ru/software/pathracer/

Motivation: Recent advances in single molecule real-time (SMRT) and nanopore sequencing technologies have enabled high-quality assemblies from long and inaccurate reads. However, these approaches require high coverage by long reads and remain expensive. On the other hand, the inexpensive short reads technologies produce accurate but fragmented assemblies. Thus, a hybrid approach that assembles long reads (with low coverage) and short reads has a potential to generate high-quality assemblies at reduced cost. Results: We describe hybridSPAdes algorithm for assembling short and long reads and benchmark it on a variety of bacterial assembly projects. Our results demonstrate that hybridSPAdes generates accurate assemblies (even in projects with relatively low coverage by long reads) thus reducing the overall cost of genome sequencing. We further present the first complete assembly of a genome from single cells using SMRT reads. Availability and implementation: hybridSPAdes is implemented in C++ as a part of SPAdes genome assembler and is publicly available at http://bioinf.spbau.ru/en/spades Contact: d.antipov@spbu.ru Supplementary information: supplementary data are available at Bioinformatics online.

In Chap. 2, the use of SSA for analyzing one-dimensional data is thoroughly examined. In this chapter, the use of models is minimal so that the main techniques can be considered as non-parametric and descriptory. Relations with algorithms of space rotation and many other methods aiming at achieving better separability of signal from noise are outlined. The common problems of smoothing, filtration, and splitting of a time series into identifiable components such as trend, seasonality, and noise are thoroughly discussed and illustrated on case studies with real data. An important issue of automatization of the SSA methods is also considered in detail.

Chapter 3 is devoted to applications of SSA for one-dimensional series for forecasting, gap filling, low-rank approximation, parameter estimation, and change-point detection. The SSA analysis of time series of Chap. 2 is model-free. Methods of Chap. 3, on the contrary, are model-based. The model is constructed on the base of the approximating subspace built in the process of performing the SSA analysis of Chap. 2. The main parametric model is a linear recurrence relation which the signal should approximately satisfy. Application of methods is illustrated on real-life data.

Ribosomally synthesized and post-translationally modified peptides (RiPPs) are an important class of natural products that include many antibiotics and a variety of other bioactive compounds. While recent breakthroughs in RiPP discovery raised the challenge of developing new algorithms for their analysis, peptidogenomic-based identification of RiPPs by combining genome/metagenome mining with analysis of tandem mass spectra remains an open problem. We present here MetaRiPPquest, a software tool for addressing this challenge that is compatible with large-scale screening platforms for natural product discovery. After searching millions of spectra in the Global Natural Products Social (GNPS) molecular networking infrastructure against just six genomic and metagenomic datasets, MetaRiPPquest identified 27 known and discovered 5 novel RiPP natural products.

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 trichlorinated 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. On the basis of their distinctive acyl amide structure, the most prevalent metabolite was evaluated for cannabinomimetic properties and found to be moderate affinity ligands for CB1.