Biology subjects

Blin, K.

Publications and source records attributed to Blin, K..

11 recordsLinked to original sources

bioRxiv2026

metaSMASH: Scalable Biosynthetic Gene Cluster Detection for Large Metagenomic Assemblies

antiSMASH is widely used for biosynthetic gene cluster (BGC) detection and annotation, but its standard workflow is poorly suited to large metagenomic assemblies, where massive contig counts create severe runtime bottlenecks and complicate downstream result exploration. We present metaSMASH, a re-engineered fork of antiSMASH for metagenome-scale BGC analysis. metaSMASH preserves the original antiSMASH detection and annotation logic while introducing streaming, memory-bounded execution, record-level parallelisation, optional output filtering, and an interactive dashboard for large result sets. Across 25 benchmark metagenome datasets, metaSMASH reproduced identical BGC detection results while dramatically reducing computational cost. Relative to the default antiSMASH configuration, metaSMASH was a geometric-mean 38x faster. It also outperformed an ad hoc chunked antiSMASH workflow: in the default configuration it achieved a geometric-mean 2.9 x speed-up and 1.7 x lower peak memory, and with extended-analysis modules enabled it was 2.7 x faster and used 3.1 x less memory while completing all datasets, whereas the ad hoc workflow ran out of memory on the two largest assemblies. By substantially reducing the computational burden of large-scale metagenome analysis without sacrificing result equivalence, metaSMASH makes routine mining of assembled metagenomes more practical and provides a scalable foundation for natural product discovery from complex microbial communities. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=78 SRC="FIGDIR/small/739405v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@d224ccorg.highwire.dtl.DTLVardef@6ddbdcorg.highwire.dtl.DTLVardef@7d5d40org.highwire.dtl.DTLVardef@7537e1_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2026

Using protein language models for pangenome construction

Current pangenome construction methods rely largely on nucleotide or protein sequence alignment, limiting their ability to detect remote orthologs and semantic relations. We introduce a novel method that leverages protein language model embeddings to capture functional and semantic relationships beyond sequence similarity. Our approach employs approximate nearest-neighbor search coupled with a clustering step utilizing HDBSCAN, DBSCAN, or weighted single-linkage clustering with multiple similarity thresholds. The method utilizes GPU acceleration, dynamic batching, and ONNX optimization to scale approximately linearly with the number of proteins, enabling the analysis of datasets containing millions of proteins. We evaluated our approach on a randomly sampled subset of OrthoDB and the CAFA5 dataset, benchmarking it against SCARAP. SCARAP is a recently published tool with similar performance to a variety of other common tools for computing pangenomics. Our benchmarking demonstrates that our method produces more specific clusters than SCARAP across both datasets. SCARAP excelled in term consistency within clusters on the OrthoDB dataset, where labels are inferred with sequence alignment (using MMseqs2). Both methods face a significant degradation in term consistency when transitioning to the experimentally validated CAFA5 dataset, ultimately resulting in similar term consistency scores for both approaches. Crucially, our approach yields superior cluster quality on both datasets and significantly outperforms SCARAP across all metrics of functional consistency and coherence on the experimental CAFA5 dataset. Finally, we demonstrate the methods scalability and utility by characterizing the pangenome of 1,034 Streptomyces genomes. The pipeline is available for use at our GitHub: https://github.com/jakob949/pan_genome

bioinformatics↗

bioRxiv2026

Computational pipeline reveals nature's untapped reservoir of halogenating enzymes

Microbial halogenated natural products (hNPs) hold ecological, agricultural, and biomedical relevance. The hNP-producing potential of the organism can be assessed by the precise prediction of biosynthetic enzymes, yet the detailed annotations of halogenases are often missing from genomic and metagenomic data. We created a manually curated database (https://halogenases.secondarymetabolites.org/) containing information on the halide-specificity, role, and position of verified catalytic residues and results of the mutagenesis studies of more than 120 experimentally validated or in silico inferred halogenases. The collection of experimental data supports a computational pipeline that allows the family-, substrate-, and halide-scope-level annotation of halogenating enzymes by relying on catalytic residues, conserved motifs, and profile Hidden Markov Models (pHMMs). Our analysis with sequence similarity networks (SSNs) highlighted several underexplored clusters in the UniRef50 database. Such finding was a halogenase from Rhodopirellula baltica (RhobaVHPO) previously labelled as a hypothetical chloroperoxidase, which clustered apart from the known chloroperoxidases and bromoperoxidases, but accepted chloride and preferred bromide. Our database and workflow provide extensive and scalable solutions for the systematic and precise annotation of halogenating enzymes in genomic and metagenomic data. The in-depth categorization of halogenases will improve the chemical structure prediction of microbial hNPs, supporting ecological assessments and natural product discovery. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/700248v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@ebae51org.highwire.dtl.DTLVardef@10188f0org.highwire.dtl.DTLVardef@1c55684org.highwire.dtl.DTLVardef@b311bd_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

epsSMASH uncovers exopolysaccharide biosynthetic gene clusters in environmental and human microbiomes

Biofilms represent the default mode of bacterial life in natural and built environments, with extracellular polysaccharides (exoPS) serving as essential structural and functional components of the biofilm matrix. Despite their importance, exoPS production in these environments is largely unknown. Here we present epsSMASH, a bioinformatic tool and web service for predicting known and novel exoPS biosynthetic gene clusters (BGCs) in bacterial genomes. Benchmarking showed that comprehensive detection of exoPS gene clusters requires highly contiguous high-quality genome assemblies. We applied epsSMASH to high-quality bacterial genome catalogues representing four major ecosystems: Human gut, soil, ocean and activated sludge from wastewater treatment systems. In all catalogues, epsSMASH identified exoPS BGCs in most genomes (52.8-85.4%), with a median of 1-2 exoPS BGCs per genome. The number of exoPS BGC per genome was highly variable, with some taxa containing up to 19 distinct exoPS BGCs. Pel BGCs were abundant in human gut, ocean and activated sludge microbiomes, and were detected in 14 different phyla, making it the most phylogenetically widespread BGC in these environments. The vast majority (62-96%) of detected exoPS BGCs were uncharacterised. By constructing gene cluster families from uncharacterised systems, we identified novel and phylogenetically widespread exoPS BGCs. We investigated a novel exoPS gene cluster from the activated sludge microbiome and showed that it is conserved in most genera within the order Sphingomonadales. Our results highlight the remarkable number of uncharacterised exoPS gene clusters in environmental microbiomes and establish epsSMASH as an effective tool for identifying and classifying novel exoPS systems.

bioinformatics↗

bioRxiv2025

PhyloNaP: a user-friendly database of Phylogeny for Natural Product-producing enzymes

Phylogenetic analysis is widely used to predict enzyme function, yet building annotated and reusable trees is labor-intensive and requires extensive knowledge about the specific enzymes. Existing resources rarely cover biosynthetic enzymes and lack the context needed for meaningful analysis. We present PhyloNaP, the first large-scale resource dedicated to phylogenies of biosynthetic enzymes. PhyloNaP provides [~]18,500 annotated and interactive trees enriched with chemical, functional, and taxonomic information. Users can classify their own sequences via phylogenetic placement, enabling functional inference in an evolutionary context. A contribution portal allows the community to submit curated trees. By combining scale, breadth of annotation, and interactive functionality, PhyloNaP fills a major gap in bioinformatics resources for enzyme discovery and annotation, with immediate applications to secondary metabolism and beyond. Availabilityhttps://phylonap.cs.uni-tuebingen.de

bioinformatics↗

bioRxiv2024

StreptoCAD: An open-source software toolbox automating genome engineering workflows in streptomycetes

Streptomycetes hold immense potential for discovering novel bioactive molecules for applications in medicine or sustainable agriculture. However, high-throughput exploration is hampered by the current Streptomyces genetic engineering methods that involve the manual design of complex experimental molecular biological engineering strategies for each targeted gene. Here, we introduce StreptoCAD, an open-source software toolbox that automates and streamlines the design of genome engineering strategies in Streptomyces, supporting various CRISPR-based and gene overexpression methods. Once initiated, StreptoCAD designs all necessary DNA primers and CRISPR guide sequences, simulates plasmid assemblies (cloning) and the resulting modification of the genomic target(s), and further summarizes the information needed for laboratory implementation and documentation. StreptoCAD currently offers six design workflows, including the construction of overexpression libraries, base-editing, including multiplexed CRISPR-BEST plasmid generation, and genome engineering using CRISPR-Cas9, CRISPR-Cas3, and CRISPRi systems. In addition to automating the design process, StreptoCAD further secures compliance with the FAIR principles, ensuring reproducibility and ease of data management via standardized output files. To experimentally demonstrate the design process and output of StreptoCAD, we designed and constructed a series of gene overexpression strains in Streptomyces Go40/10, underscoring the tools efficiency and user-friendliness. This tool simplifies complex genetic engineering tasks and promotes collaboration through standardized workflows and design parameters. StreptoCAD is set to transform genome engineering in Streptomyces, making sophisticated genetic manipulations accessible for all and accelerating natural product discovery. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=170 SRC="FIGDIR/small/629370v1_ufig1.gif" ALT="Figure 1000"> View larger version (56K): org.highwire.dtl.DTLVardef@42a7deorg.highwire.dtl.DTLVardef@1aef57org.highwire.dtl.DTLVardef@574e59org.highwire.dtl.DTLVardef@1d8dd3d_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2024

BGC Atlas: A Web Resource for Exploring the Global Chemical Diversity Encoded in Bacterial Genomes

Secondary metabolites are compounds not essential for an organisms development, but provide significant ecological and physiological benefits. These compounds have applications in medicine, biotechnology, and agriculture. Their production is encoded in biosynthetic gene clusters (BGCs), groups of genes collectively directing their biosynthesis. The advent of metagenomics has allowed researchers to study BGCs directly from environmental samples, identifying numerous previously unknown BGCs encoding unprecedented chemistry. Here, we present the BGC Atlas (https://bgc-atlas.cs.uni-tuebingen.de), a web resource that facilitates the exploration and analysis of BGC diversity in metagenomes. The BGC Atlas identifies and clusters BGCs from publicly available datasets, offering a centralized database and a web interface for metadata-aware exploration of BGCs and gene cluster families (GCFs). We analyzed over 35,000 datasets from MGnify, identifying nearly 1.8 million BGCs, which were clustered into GCFs. The analysis showed that ribosomally synthesized and post-translationally modified peptides (RiPPs) are the most abundant compound class, with most GCFs exhibiting high environmental specificity. We believe that our tool will enable researchers to easily explore and analyze the BGC diversity in environmental samples, significantly enhancing our understanding of bacterial secondary metabolites, and promote the identification of ecological and evolutionary factors shaping the biosynthetic potential of microbial communities.

bioinformatics↗

bioRxiv2024

A treasure trove of 1,034 actinomycete genomes

Filamentous Actinobacteria, recently renamed Actinomycetia, are the most prolific source of microbial bioactive natural products. Studies on biosynthetic gene clusters benefit from or require chromosome-level assemblies. Here, we provide DNA sequences from more than 1,000 isolates: 881 complete genomes and 153 near-complete genomes, representing 28 genera and 389 species, including 244 likely novel species. All genomes are from filamentous isolates of the class Actinomycetia from the NBC culture collection. The largest genus is Streptomyces with 886 genomes including 742 complete assemblies. We use this data to show that analysis of complete genomes can bring biological understanding not previously derived from more fragmented sequences or less systematic datasets. We document the central and structured location of core genes and distal location of specialized metabolite biosynthetic gene clusters and duplicate core genes on the linear Streptomyces chromosome, and analyze the content and length of the terminal inverted repeats which are characteristic for Streptomyces. We then analyze the diversity of trans-AT polyketide synthase biosynthetic gene clusters, which encodes the machinery of a biotechnologically highly interesting compound class. These insights have both ecological and biotechnological implications in understanding the importance of high quality genomic resources and the complex role synteny plays in Actinomycetia biology.

microbiology↗

bioRxiv2021

Identification of the 216kbp gene cluster and structure elucidation of gargantulides B and C, new complex 52-membered macrolides from Amycolatopsis sp.

Gargantulides B and C, two new and highly complex 52-membered glycosylated macrolactones, were isolated from Amycolatopsis sp. strain CA-230715 during an antibacterial screening campaign. The structures of these giant macrolides were elucidated by 2D NMR spectroscopy and shown to be related to gargantulide A, although containing additional {beta}-glucopyranose and/or -arabinofuranose monosaccharides separately attached to their backbones. Genome sequencing allowed the identification of a strikingly large 216 kbp biosynthetic gene cluster, among the largest type I PKS clusters described so far, and the proposal of a biosynthetic pathway for gargantulides A-C. Additionally, genes putatively responsible for the biosynthesis of the amino sugar {beta}-3,6-deoxy-3-methylamino glucose, reported exclusively in gargantulide macrolides, were also found in the cluster and described in this work. The absolute configurations of gargantulides B and C were assigned based on a combination of NMR and bioinformatics analysis of ketoreductase and enoylreductase domains within the multimodular type I PKS. Furthermore, the absolute stereochemistry of the related macrolide gargantulide A has now been revised and completed. Gargantulides B and C display potent antibacterial activity against a set of drug-resistant Gram-positive bacteria and moderate activity against the clinically relevant Gram-negative pathogen Acinetobacter baumannii.

microbiology↗

bioRxiv2019

CRISPR-BEST: a highly efficient DSB-free base editor for filamentous actinomycetes

Filamentous actinomycetes serve as major producers of various natural products including antimicrobial compounds. Although CRISPR-Cas9 systems have been developed for more robust genetic manipulations, concerns of genome instability caused by the DNA double-strand breaks (DSB) and the toxicity of Cas9 remain. To overcome these limitations, here we report development of the DSB-free, single-nucleotide resolution genome editing system CRISPR-BEST (CRISPR-Base Editing SysTem). Specifically targeted by an sgRNA, the cytidine deaminase component of CRISPR-BEST efficiently converts C:G to T:A within a window of approximately seven-nucleotides. The system was validated and successfully used in different Streptomyces species.

synthetic biology↗

bioRxiv2016

plantiSMASH: automated identification, annotation and expression analysis of plant biosynthetic gene clusters

Plant specialized metabolites are chemically highly diverse, play key roles in host-microbe interactions, have important nutritional value in crops and are frequently applied as medicines. It has recently become clear that plant biosynthetic pathway-encoding genes are sometimes densely clustered in specific genomic loci: biosynthetic gene clusters (BGCs). Here, we introduce plantiSMASH, a versatile online analysis platform that automates the identification of candidate plant BGCs. Moreover, it allows integration of transcriptomic data to prioritize candidate BGCs based on the coexpression patterns of predicted biosynthetic enzyme-coding genes, and facilitates comparative genomic analysis to study the evolutionary conservation of each cluster. Applied on 48 high-quality plant genomes, plantiSMASH identifies a rich diversity of candidate plant BGCs. These results will guide further experimental exploration of the nature and dynamics of gene clustering in plant metabolism. Moreover, spurred by the continuing decrease in costs of plant genome sequencing, they will allow genome mining technologies to be applied to plant natural product discovery.\n\nThe plantiSMASH web server, precalculated results and source code are freely available from http://plantismash.secondarymetabolites.org.

bioinformatics↗