Biology subjects

Medema, M. H.

Publications and source records attributed to Medema, M. H..

At least 19 recordsLinked to original sources

bioRxiv2026

Elucidating biosynthetic pathways related to the synthesis of small halogenated peptidic natural products in marine sponge microbiomes

Marine sponges are known sources of bioactive natural products (NPs), many of which are produced by associated bacterial symbionts via encoded biosynthetic gene clusters (BGCs). A particularly interesting subclass of sponge-derived NPs is comprised of small, brominated alkaloids, which are recovered from diverse habitats and host sponge taxonomies. Despite having been described decades ago, most of these NPs do not have an elucidated biosynthetic origin. We queried metagenomes of several sponge species by making use of a minimal set of core enzymes that we postulate to be necessary to produce these small peptidic NPs: an FADH2-dependent halogenase and an AMP-binding adenylation enzyme. This revealed a variety of novel BGC architectures, many of which showed conservation among sponge host phylogenies and were encoded in the genomes of diverse sponge-associated bacteria. Furthermore, we identified a BGC in the sponge G. barretti that is potentially linked to the production of the iconic barettins, given its enzymatic machinery and specific acidobacterial origin. The present work contributes to the challenging quest to link orphan brominated NPs to their parent BGCs in the sponge holobiont and beyond.

bioinformatics↗

bioRxiv2026

PlantMetWiki: a FAIR knowledge graph for plant metabolic pathway cross-species representation and integration

Plants produce a vast diversity of specialized metabolites with extensive potential ecological, agro-industrial, and pharmaceutical applications. Discovery of novel plant natural products relies on combining multi-omics evidence with biochemical transformations. However, pathway-level annotations are fragmented across individual species, databases, and publications, limiting comparative cross-species pathway analyses and systematic generation of hypotheses. To support integration and reuse of plant metabolic knowledge, we developed PlantMetWiki, a FAIR Linked Open Data semantically enriched knowledge graph built on infrastructure adapted from WikiPathways. Our approach extends on the highly curated pathway information from Plant Metabolic Network with crosslinks to biosynthetic gene clusters resources (MIBiG and plantiSMASH), metabolite annotations, and cross-species modelling. This way, our resource captures pathway genomic context, increases metabolomics data interoperability via federated queries, and supports cross-species analysis to identify annotation gaps. PlantMetWiki represents 1,162 plant metabolic pathways as Resource Description Framework (RDF) graphs, preserving pathway structure, literature provenance, annotations, and taxonomic information from PlantCyc 17.0. PlantMetWiki is distributed through a public SPARQL endpoint with open-source reproducible data transformation and validation workflows. By modelling pathways as a multispecies graph, PlantMetWiki enables comparative analyses across taxa, integration with external chemical knowledge resources through federated queries, and identification of metabolic, genomic, and chemical annotation gaps. As a result, PlantMetWiki provides a foundation for FAIR reuse and integration of plant pathway knowledge.

bioinformatics↗

bioRxiv2026

Distinct taxonomic groups of sponge symbionts have the biosynthetic capacity for the production of ether lipids

The marine sponge holobiont, composed of the sponge host and its microbial symbionts, is a known source of abundant and diverse ether lipids (ELs). Apart from their structural role in the cytoplasmic membrane of archaea and some bacteria, ELs have often been linked to signaling functions and defense against pathogens. Despite the relevance of ELs, their biosynthesis, as well as the identity of their producers, remain elusive. Here, we report the analysis of potential ether lipid producing genes and gene clusters, detected in marine sponge metagenomes as well as public sponge genomes. We show that the sponge holobiont has the capacity to synthesize ELs via several pathways, and suggest the ability of the sponge holobiont to synthesize ELs under different O2 levels. Finally, targeted lipidome analysis confirmed that ELs are present in the lipid profiles of all of the studied sponge holobiont samples, and indicates that the biosynthesis of the plasmalogens detected is likely restricted to the sponge host itself, based on the detected hydrocarbon chain lengths. This work provides a basis for the challenging quest to decipher intricate EL biosynthesis in marine sponges and their associated microbes.

microbiology↗

bioRxiv2026

RetroMol: Parsing a shared encoding from natural products and their biosynthetic gene clusters

Natural products such as polyketides and nonribosomal peptides (NRPs) are important sources of bioactive compounds, including many antibiotics. Many of them are assembled by modular enzyme complexes and further modified and diversified by tailoring reactions encoded by biosynthetic gene clusters (BGCs). Although natural products and their coding BGCs describe different data modalities of the same biochemical process, a unified language to jointly describe their biochemistry is lacking. Here we introduce a sequence-based representation of the core biosynthesis of modular natural products, which we call primary sequences, that bridges chemical structures and BGCs. We also present RetroMol, an algorithm that parses either natural product structures or their encoding BGCs into their primary sequences of natural product building blocks. RetroMol allows for similarity scoring between natural products and BGCs, enabling the retrieval of compounds, BGCs, and a combination of the two, based on their biosynthetic similarity. This can, for instance, be used to retrieve biosynthetically similar but structurally dissimilar compounds, or link natural products to candidate coding BGCs in large experimental datasets. We demonstrate the latter by rediscovering the nocardichelin B BGC as a proof of principle. We also exemplify the utility of biosynthetic similarity by showing various pairs of biosynthetically similar compounds with low structural similarity. Together, these results establish primary sequences as a shared biosynthetic encoding for natural product comparison and BGC prioritization.

bioinformatics↗

bioRxiv2026

A global regulatory atlas of Streptomyces reveals conserved and diversified transcriptional networks across actinomycetes

Transcriptional regulatory networks determine how bacteria integrate environmental signals with growth, metabolism and stress adaptation. Actinomycetes encode exceptionally large repertoires of transcription factors (TFs) coordinating morphological development, environmental adaptation and specialized metabolism, yet their regulatory networks remain poorly defined. Here, we apply DNA affinity purification sequencing (DAP-seq) to 789 predicted TFs of Streptomyces coelicolor, generating genome-wide binding maps for 393 regulators and expanding the experimentally supported regulome from [~]8% to [~]50%. Integration with ChIP-seq reveals pleiotropic regulators and hierarchical network architecture linking primary metabolism, development and biosynthetic gene clusters (BGCs). Multiplexed DAP-seq (multiDAP) across 16 additional actinomycetes uncovered deeply conserved regulatory circuits alongside widespread divergence in TF-target interactions. Together, these data establish a regulatory atlas for Streptomyces and related actinomycetes, enabling researchers to explore control of genes, pathways, BGCs and conserved network modules. This provides a foundation for predictive analysis and engineering of complex bacterial phenotypes in biotechnology and medicine.

microbiology↗

bioRxiv2026

Multi-omics reveals mechanisms behind pathogen inhibition by a microalgal microbiome

Aquaculture is an essential food production sector for meeting the global demand for high-quality protein. However, the sector faces significant challenges from bacterial pathogens, particularly Vibrio anguillarum, which causes vibriosis in numerous commercially important fish species. Current disease management strategies rely heavily on antibiotics, leading to antimicrobial resistance and environmental concerns. Microalgal microbiomes represent promising alternatives for sustainable pathogen control, yet the molecular mechanisms underlying their inhibitory activity remain poorly understood. Here, we employed an integrated multi-omics approach to elucidate the mechanisms by which the microbiome of microalga Isochrysis galbana inhibits the highly virulent fish pathogen V. anguillarum strain 90-11-286. Using a GFP-based inhibition assay, we confirmed potent pathogen suppression by the algal microbiome, achieving complete inhibition at a 1:1000 ratio of pathogen to microbiome. Through 16S rRNA gene amplicon sequencing, metagenomics, metatranscriptomics, and metabolomics, we characterized community composition, genomic potential, gene expression patterns, and metabolite production during pathogen challenge. The inhibitory microbiome was dominated by Alteromonas macleodii and Vreelandella alkaliphila, with high-quality metagenome-assembled genomes revealing substantial secondary metabolite biosynthetic potential. Metatranscriptomic analysis revealed active expression of biosynthetic gene clusters encoding, for example, non-ribosomal peptide synthetases, particularly a siderophore gene cluster in V. alkaliphila. Metabolomic profiling confirmed the production of hydroxamate siderophores in the microbiome, including desferrioxamine analogues, proferrioxamine G1t, and tenacibactin D, which accumulated during pathogen inhibition, as well as 10 putative new compounds. Notably, siderophore production was constitutive rather than pathogen-induced, suggesting iron competition as the primary inhibitory mechanism. Our findings demonstrate that iron sequestration through siderophore production represents a key strategy for pathogen suppression in marine microbial communities. This work provides molecular evidence for microbiome-mediated disease control and establishes a foundation for developing rationally designed multi-strain probiotic consortia for sustainable aquaculture applications, offering an environmentally friendly alternative to antibiotic-based pathogen management strategies.

microbiology↗

bioRxiv2026

SynCom101: A web-based platform for the standardized design of functionally tailored synthetic microbial communities

BackgroundSynthetic microbial communities (SynComs) are essential tools for dissecting the causal mechanisms in host-microbiota interactions. To date, however, SynCom design suffers from a lack of standardization, typically oscillating between arbitrary strain selection and computational pipelines that misalign with experimental design. As microbiome research transitions toward functionally defined community systems with reproducible experimental outcomes, there is a strong need for a user-friendly platform that integrates multi-dimensional genomic and/or biological data into a standardized and tailored SynComs design. ResultsHere, we present SynCom101, a web-based platform that democratizes the design of reproducible, hypothesis-driven SynComs. SynCom101 accommodates diverse input formats including genomic annotations and laboratory-obtained phenotypic traits, allowing users to customize their design criteria with high flexibility. The platform utilizes a parsimony algorithm to ensure computational scalability for large datasets, complemented by an optional correlation-aware mode to account for microbial compatibility and co-occurrence patterns when ecological interactions among strains are available. A core innovation of SynCom101 is its suite of trait-weighting modules, which empowers researchers to strategically guide the selection algorithm toward maximal functional trait coverage, the emulation of natural community architectures, or the enrichment of positively correlated microbial assemblages to enhance community stability. We showcase the functionalities of the platform by in silico design of communities from different datasets, demonstrating its capacity to generate concise, functionally prioritized SynComs aligned with targeted design objectives. ConclusionBy providing a transparent, parameter-documented workflow, SynCom101 ensures that community design is no longer a "black box" but a reproducible scientific record. This platform establishes a necessary standard for in silico community assembly, facilitating the transition from descriptive microbiome studies toward high-throughput, predictive functional screening and cross-study comparability. AvailabilitySynCom101 can be accessed via the web interface (https://syncom101.bioinformatics.nl/). The datasets used for case studies are available on Zenodo (https://doi.org/10.5281/zenodo.18310451). The source code is available at Git (https://git.wur.nl/jiayi.jing/syncom101).

bioinformatics↗

bioRxiv2026

Systematic mass-spectrometry-guided metabolic fingerprinting elucidates diversity of specialized metabolites across the Brassicaceae

O_LIPlants produce diverse bouquets of specialized metabolites (SMs), yet only a fraction of the vast phytochemical space has been explored to date. Comparative analysis of SM profiles can reveal hotspots of biochemical novelty, while systematic profiling across taxonomic levels does presently not cover large plant families. C_LIO_LITo study core and accessory SM profiles in the Brassicaceae plant family, we fingerprinted 14 species by Liquid-Chromatography Mass-Spectrometry (LCMS/MS). We develop standardized experimental and computational workflows integrating in silico annotation tools to study consensus compound class and substructure distributions of SMs. Furthermore, we investigate the congruence of chemotaxonomy and species phylogeny across an extended panel of 17 species. C_LIO_LIUnique metabolite profiles were outstanding in Camelina sativa, Capsella rubella, and B. vulgaris, with the largest unique terpenoid profile annotated in C. sativa, accounting for 33.5% and 55.6% in positive and negative ionization mode, respectively. Substructure motifs were found to overlap with compound class predictions, highlighted for triterpenoids in Camelinodae. Furthermore, dual-tissue chemotaxonomic clustering resembled relationships of Brassica subgenomes across tissues. C_LIO_LIWe anticipate that our systematic approach can serve as a blueprint for investigating biochemical diversity in other plant lineages and can boost the characterization of plant natural product pathways. C_LI

plant biology↗

bioRxiv2026

Metabolic fingerprinting of 17 Brassicaceae species across three tissues

Plants produce the most diverse blends of specialized metabolites on earth. Natural products derived from plants are valuable resources for drug development, food chemistry, and crop resistance breeding. Phenotypes of specialized metabolite profiles can be captured by untargeted mass-spectrometry across species phylogeny, tissues, and genotypes. Here, we collected metabolic fingerprints of 17 Brassicaceae species across three tissues (paired leaf and root; flower) using liquid chromatography-tandem mass spectrometry (LC-MS/MS) in positive and negative ionization mode. Corresponding metadata has been refined for reuse according to ReDU guidelines, and for integration with public genomic and transcriptomic data. Standardization of in vitro growth conditions, and data processing workflows enables integration of acquired raw and processed data across platforms for single- and multi-omics analysis. Further, the inclusion of tissue-specific metabolic profiles across ploidy levels, as well as across crop species and wild relatives, makes this dataset a valuable resource for natural product discovery.

plant biology↗

bioRxiv2026

A global metagenomic atlas uncovers ubiquitous biosynthetic potential linked to adaptation in extreme environments

Extreme environments impose severe physicochemical stresses that drive microorganisms to evolve specialized survival strategies. Microbial secondary metabolites determined by biosynthetic gene clusters (BGCs) are recognized as important mediators of microbial adaptation to environmental stress. However, their ecological roles, particularly habitat-dependent preferences across different environments, remain poorly understood. Although extreme environments provide opportunities to mine microbiomes for unique adaptations, such research is hampered by a lack of systematic overview of its genomic diversity, BGC diversity, and the relationships between them. Here, we constructed a standardized extremophilic genomic catalogue (SEGC) from 1,462 metagenomic samples spanning seven representative extreme habitats. The catalogue comprised 54,661 metagenome-assembled genomes representing 21,805 species, 66.1% of which were previously uncharacterized. With this catalogue, we identified 162,855 BGCs distributed across 81.5% of MAGs. Gene cluster family analysis showed the strong habitat dependence largely explained by species-level habitat specificity. Terpene biosynthetic pathways illustrated habitat-linked adaptive strategies, with hopan-22-ol biosynthesis enriched in acid mine, deep sea and hydrothermal plume environments, while retinal-based phototrophy predominated in cryosphere and saline-alkaline habitats. Metatranscriptomic analyses supported in situ activity of these pathways. In conclusion, we presented a global atlas of biosynthetic potential across extreme-environment microbiota and revealed habitat-dependent patterns of secondary metabolism linked to microbial survival.

microbiology↗

bioRxiv2026

Reconstructing plant beneficial bacterial consortia by integrating dilution-to-extinction microbiome perturbation with genome-resolved synthetic ecology

Across the biosphere, microbiomes play essential roles in shaping the health of their host. One notable example of such a microbiome-associated phenotype is disease-suppressive soils, where susceptible plant hosts enrich and activate specific rhizosphere microbial consortia for protection against fungal root pathogens. However, identifying and reconstructing microbial consortia responsible for host protection remains challenging, given the inherent taxonomic and functional complexity of microbiomes. Here, we integrated metagenomic profiling of disease-suppressive microbiomes perturbed by dilution-to-extinction (DTE) with comprehensive culturing and synthetic ecology to identify the key bacterial taxa conferring suppressiveness to the fungal wheat pathogen Fusarium culmorum. Metagenomics of wheat rhizosphere samples along the DTE trajectory revealed bacterial taxa and functions associated with the disease-suppressive phenotype. Crosslinking these DTE metagenome data with a genome-sequenced collection of 336 rhizobacterial isolates from the suppressive soil allowed the reconstruction of synthetic communities (SynComs) of 11 de-replicated strains negatively associated with disease severity. Upon re-introduction in sterilized suppressive soils, this SynCom consistently reproduced the disease-suppressive phenotype. Paired time-series metagenomics and metatranscriptomics of the SynComs pinpointed candidate biosynthetic gene clusters, including a novel non-alpha poly-amino-acid (NAPAA) gene cluster from Arthrobacter, upregulated in presence of F. culmorum. Chemically synthesized NAPAA variants {varepsilon}-poly-L-lysine and {delta}-poly-L-ornithine significantly inhibited F. culmorum hyphal growth. Collectively, our work establishes a transformative strategy for reconstructing microbial consortia that recapitulates beneficial microbiome-associated phenotypes in plant and animal kingdoms.

microbiology↗

bioRxiv2026

Bootstrap resampling of mass spectral pairs with SpecReBoot reveals hidden molecular relationships

Mass spectral molecular networking organizes tandem mass spectrometry (MS/MS) data by connecting spectra based on similarity scores. However, these deterministic metrics provide no measure of uncertainty, so molecular networks often retain edges arising from noise or missing fragments, while authentic chemical relationships may remain obscured. To address this gap, we present SpecReBoot, a statistical framework that adapts Felsensteins bootstrap principle to MS/MS similarity scoring. By resampling fragment-level features and recomputing similarity across replicates, SpecReBoot transforms a single score into a confidence distribution and assigns bootstrap support to network edges. Using large, curated MS/MS spectral datasets, we demonstrate that SpecReBoot systematically removes unreliable connections and recovers robust low-similarity links, refining networks and revealing hidden relationships. We highlight how this confidence-aware "rebooting" guided the discovery of an unprecedented macrolactone scaffold from the endophytic fungus Diaporthe caliensis. Altogether, SpecReBoot provides the first general framework for quantifying confidence in MS/MS similarity and molecular network analysis.

biochemistry↗

bioRxiv2025

plantiSMASH 2.0: improvements to detection, annotation, and prioritization of plant biosynthetic gene clusters

Plants produce bioactive compounds as part of their specialized metabolism, with applications in medicine, agriculture, and nutrition. The biosynthesis of a growing number of these specialized metabolites has been found to be encoded in biosynthetic gene clusters (BGCs), creating increasing demand for genome mining tools to automate their detection. plantiSMASH enables the identification of putative plant BGCs through a rule-based approach, available via both command-line and web interfaces. Here, we present plantiSMASH 2.0 (https://plantismash.bioinformatics.nl/), a major update that expands and improves the original framework with revised and additional BGC detection rules (now supporting 12 BGC types), substrate prediction for selected enzyme families, and regulatory analysis through transcription factor binding site detection. The updated plantiSMASH 2.0 database includes 30,423 putative BGCs across 430 genomes. Together, these improvements make plantiSMASH 2.0 a powerful and comprehensive platform for the detection and characterization of plant biosynthetic pathways, supporting and accelerating research in plant specialized metabolism and plant natural product discovery.

bioinformatics↗

bioRxiv2025

Charting the biosynthetic landscape of hybrid polyketide-nonribosomal peptide-specialized lipids

Polyunsaturated fatty acid (PUFA) synthase-like enzymes are best known for their role in membrane lipid biosynthesis in marine bacteria, but have also been repurposed for the assembly of specialized lipid metabolites with unique biological functions. Here, we illuminate their broader biosynthetic potential by charting the unexplored landscape of hybrid peptide-polyketide-specialized lipid biosynthesis in bacteria. Using a targeted genome mining strategy, we identified more than 60 biosynthetic gene clusters that combine PUFA synthase-like, polyketide synthase (PKS), and nonribosomal peptide synthetase (NRPS) enzymes across diverse bacterial lineages. Comparative analysis revealed extensive diversification of these triple hybrid pathways through gene fusion, domain reshuffling and recruitment of accessory enzymes. We further expand the known repertoire of peptide-polyketide-specialized lipid hybrids by identifying the chitinimines, a new family of amphiphilic metabolites produced by Chitinimonas koreensis featuring a C22 polyunsaturated lipid chain conjugated to a cyclic peptide-polyketide and a pyruvate-derived cyclic acetal moiety. The chitinimines exhibit surfactant properties, as well as moderate antibacterial activity against Gram-positive bacteria and contribute to a growth-promoting interaction between C. koreensis and Salmonella spp. Together, these findings demonstrate that PUFA synthase-like systems are far more versatile than previously appreciated, playing a key role in combinatorial biosynthetic innovation and serving as a rich, untapped source of chemically and functionally diverse specialized lipids.

microbiology↗

bioRxiv2025

BiG-SCAPE 2.0 and BiG-SLiCE 2.0: scalable, accurate and interactive sequence clustering of metabolic gene clusters

Microbial metabolic gene clusters encode the biosynthesis or catabolism of metabolites that facilitate ecological specialization, mediate microbiome interactions and constitute a major source of medicines and crop protection agents. Here, we present BiG-SCAPE and BiG-SLiCE 2.0, next-generation methods that facilitate scalable, accurate and interactive gene cluster analyses. BiG-SCAPE 2.0 updates its classification, alignment methods, and visualizations, enabling more accurate analysis, up to 8x faster runtimes and halved memory requirements. BiG-SLiCE 2.0 updates its distance metric, pHMM database, and classification logic, resulting in increased sensitivity nearing that of BiG-SCAPE. Analysis of 260,630 biosynthetic gene clusters from publicly available genomes reveals that both tools generate concurring estimates of gene cluster diversity, thus providing significantly extended methodological support for recent evidence indicating that the vast majority of natural product diversity remains unexplored. Together, these updates will facilitate global genome mining efforts for natural product discovery and microbiome analyses scalable with current data sizes.

bioinformatics↗

bioRxiv2025

Automated genome mining predicts structural diversity and taxonomic distribution of peptide metallophores across bacteria

Microbial competition for trace metals shapes their communities and interactions with humans and plants. Many bacteria scavenge trace metals with metallophores, small molecules that chelate environmental metal ions. Metallophore production may be predicted by genome mining, where genomes are scanned for homologs of known biosynthetic gene clusters (BGCs). However, accurately detecting non-ribosomal peptide (NRP) metallophore biosynthesis requires expert manual inspection, stymieing large-scale investigations. Here, we introduce automated identification of NRP metallophore BGCs through a comprehensive algorithm, implemented in antiSMASH, that detects chelator biosynthesis genes with 97% precision and 78% recall against manual curation. We showcase the utility of the detection algorithm by experimentally characterizing metallophores from several taxa. High-throughput NRP metallophore BGC detection enabled metallophore detection across 69,929 genomes spanning the bacterial kingdom. We predict that 25% of all bacterial non-ribosomal peptide synthetases encode metallophore production and that significant chemical diversity remains undiscovered. A reconstructed evolutionary history of NRP metallophores supports that some chelating groups may predate the Great Oxygenation Event. The inclusion of NRP metallophore detection in antiSMASH will aid non-expert researchers and continue to facilitate large-scale investigations into metallophore biology.

biochemistry↗

bioRxiv2025

Predicting Rhizosphere Competence Related Catabolic Gene Clusters in plant-associated bacteria with RhizoSMASH

Plants release a substantial fraction of their photosynthesized carbon into the rhizosphere as root exudates, a mix of chemically diverse compounds that drive microbiome assembly. Deciphering how plants modulate the composition and activities of rhizosphere microbiota through root exudates is challenging, as no dedicated computational methods exist to systematically identify microbial root exudate catabolic pathways. Here we used and integrated published information on catabolic genes in bacterial taxa that contribute to their rhizosphere competence. We developed the RhizoSMASH algorithm for genome-synteny-based annotation of rhizosphere-competence-related catabolic gene clusters (rCGCs) in bacteria by means of a set of 58 knowledge-based logic detection rules carefully curated through sequence similarity network analysis. Our analysis revealed large heterogeneity of rCGC prevalence both across and within plant-associated bacterial taxa, indicating extensive niche specialization. Furthermore, we validated that the presence or absence of rCGCs in bacterial genomes reflects their catabolic capacity and is predictive for their rhizosphere competence by aligning rhizoSMASH results with paired genome/metabolome datasets of rhizobacterial taxa. RhizoSMASH provides an extensible framework for studying rhizosphere bacterial catabolism, allowing targeted selection of beneficial bacterial taxa for microbiome-assisted breeding approaches for sustainable agriculture.

bioinformatics↗

bioRxiv2025

PARAS: high-accuracy machine-learning of substrate specificities in nonribosomal peptide synthetases

Nonribosomal peptides are diverse natural products with important applications in medicine and agriculture. Bacterial and fungal genomes contain thousands of nonribosomal peptide biosynthetic gene clusters (BGCs) of unknown function, providing a promising resource for peptide discovery. Core structural features of such peptides can be inferred by predicting the substrate(s) of adenylation (A) domains in nonribosomal peptide synthetases (NRPSs). However, existing approaches to A domain prediction rely on limited datasets and often struggle with domains selecting large substrates or from less-studied taxa. Here, we systematically curate and computationally analyse 3,653 A domains and present two high-accuracy specificity predictors, PARAS and PARASECT. A type of A domain with unusually high L-tryptophan specificity was identified through the application of PARAS, and intact protein mass spectrometry to the corresponding NRPS showed it to direct the production of tryptopeptin-related metabolites in Streptomyces species. Together, these technologies will accelerate the characterisation of novel NRPSs and their metabolic products. PARAS and PARASECT are available at https://paras.bioinformatics.nl. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=106 SRC="FIGDIR/small/631717v2_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@8e48aaorg.highwire.dtl.DTLVardef@144c78dorg.highwire.dtl.DTLVardef@890c11org.highwire.dtl.DTLVardef@1776de9_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗