Many microorganisms produce natural products that form the basis of antimicrobials, antivirals, and other drugs. Genome mining is routinely used to complement screening-based workflows to discover novel natural products. Since 2011, the "antibiotics and secondary metabolite analysis shell—antiSMASH" (https://antismash.secondarymetabolites.org/) has supported researchers in their microbial genome mining tasks, both as a free-to-use web server and as a standalone tool under an OSI-approved open-source license. It is currently the most widely used tool for detecting and characterising biosynthetic gene clusters (BGCs) in bacteria and fungi. Here, we present the updated version 6 of antiSMASH. antiSMASH 6 increases the number of supported cluster types from 58 to 71, displays the modular structure of multi-modular BGCs, adds a new BGC comparison algorithm, allows for the integration of results from other prediction tools, and more effectively detects tailoring enzymes in RiPP clusters.

Microorganisms produce small bioactive compounds as part of their secondary or specialised metabolism. Often, such metabolites have antimicrobial, anticancer, antifungal, antiviral or other bio-activities and thus play an important role for applications in medicine and agriculture. In the past decade, genome mining has become a widely-used method to explore, access, and analyse the available biodiversity of these compounds. Since 2011, the ‘antibiotics and secondary metabolite analysis shell—antiSMASH’ (https://antismash.secondarymetabolites.org/) has supported researchers in their microbial genome mining tasks, both as a free to use web server and as a standalone tool under an OSI-approved open source licence. It is currently the most widely used tool for detecting and characterising biosynthetic gene clusters (BGCs) in archaea, bacteria, and fungi. Here, we present the updated version 7 of antiSMASH. antiSMASH 7 increases the number of supported cluster types from 71 to 81, as well as containing improvements in the areas of chemical structure prediction, enzymatic assembly-line visualisation and gene cluster regulation.

SUMMARY Actinobacteria are Gram-positive bacteria with high G+C DNA content that constitute one of the largest bacterial phyla, and they are ubiquitously distributed in both aquatic and terrestrial ecosystems. Many Actinobacteria have a mycelial lifestyle and undergo complex morphological differentiation. They also have an extensive secondary metabolism and produce about two-thirds of all naturally derived antibiotics in current clinical use, as well as many anticancer, anthelmintic, and antifungal compounds. Consequently, these bacteria are of major importance for biotechnology, medicine, and agriculture. Actinobacteria play diverse roles in their associations with various higher organisms, since their members have adopted different lifestyles, and the phylum includes pathogens (notably, species of Corynebacterium, Mycobacterium, Nocardia, Propionibacterium, and Tropheryma), soil inhabitants (e.g., Micromonospora and Streptomyces species), plant commensals (e.g., Frankia spp.), and gastrointestinal commensals (Bifidobacterium spp.). Actinobacteria also play an important role as symbionts and as pathogens in plant-associated microbial communities. This review presents an update on the biology of this important bacterial phylum.

Protecting plants from the inside out: Some soils show a remarkable ability to suppress disease caused by plant pathogens, an ability that is attributed to plant-associated microbiota. Carrión et al. investigated the role of endophytes, the intimate microbial community found within roots, in fungal disease suppression (see the Perspective by Tringe). The wilt fungus Rhizoctonia solani infects sugar beets, whereupon transcriptional analysis shows that several bacterial endophyte species activate biosynthetic gene clusters to cause disease suppression. These organisms produce antifungal effectors, including enzymes that can digest fungal cell walls, and secondary metabolites, including phenazines, polyketides, and siderophores, which may contribute to the antifungal phenotype. Science , this issue p. 606 ; see also p. 568

The first community competition designed to objectively compare the performance of particle tracking algorithms provides valuable practical information for both users and developers. Particle tracking is of key importance for quantitative analysis of intracellular dynamic processes from time-lapse microscopy image data. Because manually detecting and following large numbers of individual particles is not feasible, automated computational methods have been developed for these tasks by many groups. Aiming to perform an objective comparison of methods, we gathered the community and organized an open competition in which participating teams applied their own methods independently to a commonly defined data set including diverse scenarios. Performance was assessed using commonly defined measures. Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, leading to notable practical conclusions for users and developers.

Microorganisms synthesize small bioactive compounds through their secondary or specialized metabolism. Those compounds play an important role in microbial interactions and soil health, but are also crucial for the development of pharmaceuticals or agrochemicals. Over the past decades, advancements in genome sequencing have enabled the identification of large numbers of biosynthetic gene clusters directly from microbial genomes. Since its inception in 2011, antiSMASH (https://antismash.secondarymetabolites.org/), has become the leading tool for detecting and characterizing these gene clusters in bacteria and fungi. This paper introduces version 8 of antiSMASH, which has increased the number of detectable cluster types from 81 to 101, and has improved analysis support for terpenoids and tailoring enzymes, as well as improvements in the analysis of modular enzymes like polyketide synthases and nonribosomal peptide synthetases. These modifications keep antiSMASH up-to-date with developments in the field and extend its overall predictive capabilities for natural product genome mining.