MagCluster: a Tool for Identification, Annotation, and Visualization of Magnetosome Gene Clusters.
Ontology highlight
ABSTRACT:
SUBMITTER: Ji R
PROVIDER: S-EPMC8759365 | biostudies-literature | 2022 Jan
REPOSITORIES: biostudies-literature
Publications
MagCluster: a Tool for Identification, Annotation, and Visualization of Magnetosome Gene Clusters.
Ji Runjia R Zhang Wensi W Pan Yongxin Y Lin Wei W
Microbiology resource announcements 20220113 1
Magnetosome gene clusters (MGCs), which are responsible for magnetosome biosynthesis and organization in magnetotactic bacteria (MTB), are the key to deciphering the mechanisms and evolutionary origin of magnetoreception, organelle biogenesis, and intracellular biomineralization in bacteria. Here, we report the development of MagCluster, a Python stand-alone tool for efficient exploration of MGCs from large-scale (meta)genomic data. ...[more]
Similar Datasets
Project description:MotivationGenes involved in coordinated biological pathways, including metabolism, drug resistance and virulence, are often collocalized as gene clusters. Identifying homologous gene clusters aids in the study of their function and evolution, however, existing tools are limited to searching local sequence databases. Tools for remotely searching public databases are necessary to keep pace with the rapid growth of online genomic data.ResultsHere, we present cblaster, a Python-based tool to rapidly detect collocated genes in local and remote databases. cblaster is easy to use, offering both a command line and a user-friendly graphical user interface. It generates outputs that enable intuitive visualizations of large datasets and can be readily incorporated into larger bioinformatic pipelines. cblaster is a significant update to the comparative genomics toolbox.Availability and implementationcblaster source code and documentation is freely available from GitHub under the MIT license (github.com/gamcil/cblaster).Supplementary informationSupplementary data are available at Bioinformatics Advances online.
| S-EPMC9710679 | biostudies-literature
Project description:SummaryMolArt fills the gap between sequence and structure visualization by providing a light-weight, interactive environment enabling exploration of sequence annotations in the context of available experimental or predicted protein structures. Provided a UniProt ID, MolArt downloads and displays sequence annotations, sequence-structure mapping and relevant structures. The sequence and structure views are interlinked, enabling sequence annotations being color overlaid over the mapped structures, thus providing an enhanced understanding and interpretation of the available molecular data.Availability and implementationMolArt is released under the Apache 2 license and is available at https://github.com/davidhoksza/MolArt. The project web page https://davidhoksza.github.io/MolArt/ features examples and applications of the tool.
| S-EPMC6247942 | biostudies-literature
Project description:We introduce a web-based tool, Peak Annotation and Visualization (PAVIS), for annotating and visualizing ChIP-seq peak data. PAVIS is designed with non-bioinformaticians in mind and presents a straightforward user interface to facilitate biological interpretation of ChIP-seq peak or other genomic enrichment data. PAVIS, through association with annotation, provides relevant genomic context for each peak, such as peak location relative to genomic features including transcription start site, intron, exon or 5'/3'-untranslated region. PAVIS reports the relative enrichment P-values of peaks in these functionally distinct categories, and provides a summary plot of the relative proportion of peaks in each category. PAVIS, unlike many other resources, provides a peak-oriented annotation and visualization system, allowing dynamic visualization of tens to hundreds of loci from one or more ChIP-seq experiments, simultaneously. PAVIS enables rapid, and easy examination and cross-comparison of the genomic context and potential functions of the underlying genomic elements, thus supporting downstream hypothesis generation.
| S-EPMC3834791 | biostudies-literature
Project description:Magnetosomes are complex membrane organelles synthesized by magnetotactic bacteria (MTB) for navigation in the Earth's magnetic field. In the alphaproteobacterium Magnetospirillum gryphiswaldense, all steps of magnetosome formation are tightly controlled by >30 specific genes arranged in several gene clusters. However, the transcriptional organization of the magnetosome gene clusters has remained poorly understood. Here, by applying Cappable-seq and whole-transcriptome shotgun RNA sequencing, we show that mamGFDCop and feoAB1op are transcribed as single transcriptional units, whereas multiple transcription start sites (TSS) are present in mms6op, mamXYop, and the long (>16 kb) mamABop. Using a bioluminescence reporter assay and promoter knockouts, we demonstrate that most of the identified TSS originate from biologically meaningful promoters which mediate production of multiple transcripts and are functionally relevant for proper magnetosome biosynthesis. In addition, we identified a strong promoter in a large intergenic region within mamXYop, which likely drives transcription of a noncoding RNA important for gene expression in this operon. In summary, our data suggest a more complex transcriptional architecture of the magnetosome operons than previously recognized, which is largely conserved in other magnetotactic Magnetospirillum species and, thus, is likely fundamental for magnetosome biosynthesis in these organisms. IMPORTANCE Magnetosomes have emerged as a model system to study prokaryotic organelles and a source of biocompatible magnetic nanoparticles for various biomedical applications. However, the lack of knowledge about the transcriptional organization of magnetosome gene clusters has severely impeded the engineering, manipulation, and transfer of this highly complex biosynthetic pathway into other organisms. Here, we provide a high-resolution image of the previously unappreciated transcriptional landscape of the magnetosome operons. Our findings are important for further unraveling the complex genetic framework of magnetosome biosynthesis. In addition, they will facilitate the rational reengineering of magnetic bacteria for improved bioproduction of tunable magnetic nanoparticles, as well as transplantation of magnetosome biosynthesis into foreign hosts by synthetic biology approaches. Overall, our study exemplifies how a genetically complex pathway is orchestrated at the transcriptional level to ensure the balanced expression of the numerous constituents required for the proper assembly of one of the most intricate prokaryotic organelles.
| S-EPMC8547445 | biostudies-literature
Project description: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. The plantiSMASH web server, precalculated results and source code are freely available from http://plantismash.secondarymetabolites.org.
| S-EPMC5570173 | biostudies-literature
Project description:In this report, we describe the selective cloning of large DNA fragments from magnetotactic metagenomes from various aquatic habitats. This was achieved by a two-step magnetic enrichment which allowed the mass collection of environmental magnetotactic bacteria (MTB) virtually free of nonmagnetic contaminants. Four fosmid libraries were constructed and screened by end sequencing and hybridization analysis using heterologous magnetosome gene probes. A total of 14 fosmids were fully sequenced. We identified and characterized two fosmids, most likely originating from two different alphaproteobacterial strains of MTB that contain several putative operons with homology to the magnetosome island (MAI) of cultivated MTB. This is the first evidence that uncultivated MTB exhibit similar yet differing organizations of the MAI, which may account for the diversity in biomineralization and magnetotaxis observed in MTB from various environments.
| S-EPMC2698345 | biostudies-literature
Project description:The Network Makeup Artist (NORMA) is a web tool for interactive network annotation visualization and topological analysis, able to handle multiple networks and annotations simultaneously. Precalculated annotations (e.g., Gene Ontology, Pathway enrichment, community detection, or clustering results) can be uploaded and visualized in a network, either as colored pie-chart nodes or as color-filled areas in a 2D/3D Venn-diagram-like style. In the case where no annotation exists, algorithms for automated community detection are offered. Users can adjust the network views using standard layout algorithms or allow NORMA to slightly modify them for visually better group separation. Once a network view is set, users can interactively select and highlight any group of interest in order to generate publication-ready figures. Briefly, with NORMA, users can encode three types of information simultaneously. These are 1) the network, 2) the communities or annotations of interest, and 3) node categories or expression values. Finally, NORMA offers basic topological analysis and direct topological comparison across any of the selected networks. NORMA service is available at http://norma.pavlopouloslab.info, whereas the code is available at https://github.com/PavlopoulosLab/NORMA.
| S-EPMC9801029 | biostudies-literature
Project description:Thiopeptides are a growing class of sulfur-rich, highly modified heterocyclic peptides that are mainly active against Gram-positive bacteria including various drug-resistant pathogens. Recent studies also reveal that many thiopeptides inhibit the proliferation of human cancer cells, further expanding their application potentials for clinical use. Thiopeptide biosynthesis shares a common paradigm, featuring a ribosomally synthesized precursor peptide and conserved posttranslational modifications, to afford a characteristic core system, but differs in tailoring to furnish individual members. Identification of new thiopeptide gene clusters, by taking advantage of increasing information of DNA sequences from bacteria, may facilitate new thiopeptide discovery and enrichment of the unique biosynthetic elements to produce novel drug leads by applying the principle of combinatorial biosynthesis. In this study, we have developed a web-based tool ThioFinder to rapidly identify thiopeptide biosynthetic gene cluster from DNA sequence using a profile Hidden Markov Model approach. Fifty-four new putative thiopeptide biosynthetic gene clusters were found in the sequenced bacterial genomes of previously unknown producing microorganisms. ThioFinder is fully supported by an open-access database ThioBase, which contains the sufficient information of the 99 known thiopeptides regarding the chemical structure, biological activity, producing organism, and biosynthetic gene (cluster) along with the associated genome if available. The ThioFinder website offers researchers a unique resource and great flexibility for sequence analysis of thiopeptide biosynthetic gene clusters. ThioFinder is freely available at http://db-mml.sjtu.edu.cn/ThioFinder/.
| S-EPMC3454323 | biostudies-literature
Project description:Bacterial and fungal secondary metabolism is a rich source of novel bioactive compounds with potential pharmaceutical applications as antibiotics, anti-tumor drugs or cholesterol-lowering drugs. To find new drug candidates, microbiologists are increasingly relying on sequencing genomes of a wide variety of microbes. However, rapidly and reliably pinpointing all the potential gene clusters for secondary metabolites in dozens of newly sequenced genomes has been extremely challenging, due to their biochemical heterogeneity, the presence of unknown enzymes and the dispersed nature of the necessary specialized bioinformatics tools and resources. Here, we present antiSMASH (antibiotics & Secondary Metabolite Analysis Shell), the first comprehensive pipeline capable of identifying biosynthetic loci covering the whole range of known secondary metabolite compound classes (polyketides, non-ribosomal peptides, terpenes, aminoglycosides, aminocoumarins, indolocarbazoles, lantibiotics, bacteriocins, nucleosides, beta-lactams, butyrolactones, siderophores, melanins and others). It aligns the identified regions at the gene cluster level to their nearest relatives from a database containing all other known gene clusters, and integrates or cross-links all previously available secondary-metabolite specific gene analysis methods in one interactive view. antiSMASH is available at http://antismash.secondarymetabolites.org.
| S-EPMC3125804 | biostudies-literature
Project description:Bacterial and fungal secondary metabolism is a rich source of novel bioactive compounds with potential pharmaceutical applications as antibiotics, anti-tumor drugs or cholesterol-lowering drugs. To find new drug candidates, microbiologists are increasingly relying on sequencing genomes of a wide variety of microbes. However, rapidly and reliably pinpointing all the potential gene clusters for secondary metabolites in dozens of newly sequenced genomes has been extremely challenging, due to their biochemical heterogeneity, the presence of unknown enzymes and the dispersed nature of the necessary specialized bioinformatics tools and resources. Here, we present antiSMASH (antibiotics & Secondary Metabolite Analysis Shell), the first comprehensive pipeline capable of identifying biosynthetic loci covering the whole range of known secondary metabolite compound classes (polyketides, non-ribosomal peptides, terpenes, aminoglycosides, aminocoumarins, indolocarbazoles, lantibiotics, bacteriocins, nucleosides, beta-lactams, butyrolactones, siderophores, melanins and others). It aligns the identified regions at the gene cluster level to their nearest relatives from a database containing all other known gene clusters, and integrates or cross-links all previously available secondary-metabolite specific gene analysis methods in one interactive view. antiSMASH is available at http://antismash.secondarymetabolites.org.
| S-EPMC3125804 | biostudies-literature