scholarly journals Synthaser: A CD-search Enabled Python Toolkit for Analysing Domain Architecture of Fungal Secondary Metabolite Megasynth(Et)ases and Beyond

2021 ◽  
Author(s):  
Cameron LM Gilchrist ◽  
Yit Heng Chooi
Keyword(s):  
Web Application ◽  
Large Scale ◽  
Polyketide Synthase ◽  
Genome Mining ◽  
Domain Architecture ◽  
Fungal Genome ◽  
Nonribosomal Peptide ◽  
Conserved Domain ◽  
Search Tool

Abstract Background: Fungi are prolific producers of secondary metabolites (SMs), which are bioactive small molecules with important applications in medicine, agriculture and other industries. The backbones of a large proportion of fungal SMs are generated through the action of large, multi-domain megasynth(et)ases such as polyketide synthases (PKSs) and nonribosomal peptide synthetases (NRPSs). The structure of these backbones is determined by the domain architecture of the corresponding megasynth(et)ase, and thus accurate annotation and classification of these architectures is an important step in linking SMs to their biosynthetic origins in the genome. Results: Here we report synthaser, a Python package leveraging the NCBI's conserved domain search tool for remote prediction and classification of fungal megasynth(et)ase domain architectures. synthaser is capable of batch sequence analysis, and produces rich textual output and interactive visualisations which allow for quick assessment of the megasynth(et)ase diversity of a fungal genome. synthaser uses a hierarchical rule-based classification system, which can be extensively customised by the user through a web application (http://gamcil.github.io/synthaser). We show that synthaser provides more accurate domain architecture predictions than comparable tools which rely on curated profile hidden Markov model (pHMM)-based approaches; the utilisation of the NCBI conserved domain database also allows for significantly greater flexibility compared to pHMM approaches. In addition, we demonstrate how synthaser can be applied to large scale genome mining pipelines through the construction of an Aspergillus PKS similarity network. Conclusions: synthaser is an easy to use tool that represents a significant upgrade to previous domain architecture analysis tools. synthaser is freely available under a MIT license from PyPI (https://pypi.org/project/synthaser) and GitHub (https://github.com/gamcil/synthaser). Keywords: secondary metabolism, domain architecture, polyketide synthase, nonribosomal peptide synthetase, bioinformatics, software

scholarly journals Synthaser: a CD-Search enabled Python toolkit for analysing domain architecture of fungal secondary metabolite megasynth(et)ases

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Cameron L. M. Gilchrist ◽  
Yit-Heng Chooi
Keyword(s):  
Web Application ◽  
Large Scale ◽  
Genome Mining ◽  
Domain Architecture ◽  
Fungal Genome ◽  
Peptide Synthetases ◽  
Link Type ◽  
Conserved Domain ◽  
Search Tool

Abstract Background Fungi are prolific producers of secondary metabolites (SMs), which are bioactive small molecules with important applications in medicine, agriculture and other industries. The backbones of a large proportion of fungal SMs are generated through the action of large, multi-domain megasynth(et)ases such as polyketide synthases (PKSs) and nonribosomal peptide synthetases (NRPSs). The structure of these backbones is determined by the domain architecture of the corresponding megasynth(et)ase, and thus accurate annotation and classification of these architectures is an important step in linking SMs to their biosynthetic origins in the genome. Results Here we report synthaser, a Python package leveraging the NCBI’s conserved domain search tool for remote prediction and classification of fungal megasynth(et)ase domain architectures. Synthaser is capable of batch sequence analysis, and produces rich textual output and interactive visualisations which allow for quick assessment of the megasynth(et)ase diversity of a fungal genome. Synthaser uses a hierarchical rule-based classification system, which can be extensively customised by the user through a web application (http://gamcil.github.io/synthaser). We show that synthaser provides more accurate domain architecture predictions than comparable tools which rely on curated profile hidden Markov model (pHMM)-based approaches; the utilisation of the NCBI conserved domain database also allows for significantly greater flexibility compared to pHMM approaches. In addition, we demonstrate how synthaser can be applied to large scale genome mining pipelines through the construction of an Aspergillus PKS similarity network. Conclusions Synthaser is an easy to use tool that represents a significant upgrade to previous domain architecture analysis tools. It is freely available under a MIT license from PyPI (https://pypi.org/project/synthaser) and GitHub (https://github.com/gamcil/synthaser).

scholarly journals Characterization of transglutaminase homologues found in andrimid biosynthesis

2014 ◽  
Vol 70 (a1) ◽  
pp. C476-C476
Author(s):  
Janice Reimer ◽  
T Schmeing
Keyword(s):  
Amino Acids ◽  
Polyketide Synthase ◽  
Genome Mining ◽  
Protein Domain ◽  
Amide Bond ◽  
Free Standing ◽  
Remarkable Feature ◽  
Nonribosomal Peptide ◽  
Peptide Carrier

Nonribosomal peptide synthetases (NRPSs) are multimodular enzymes that synthesize products with diverse structures and activities ranging from antibiotics to industrial solvents. They are arranged into an assembly line of modules where each module is responsible for incorporating a specific monomer into the final nonribosomal peptide (NRP). Diversity in NRPs stems from the fact that NRPSs utilize not only the 20 proteinogenic amino acids, but also include nonproteinogenic amino acids, fatty acids, and alpha-hydroxy acids as building block substrates. Andrimid is a NRP antibiotic that inhibits membrane biosynthesis by blocking bacterial acetyl coenzyme A carboxylases. It is synthesized in a hybrid NRPS-polyketide synthase (NRPS-PKS) using a fatty acid, phenylalanine, valine, and glycine. A remarkable feature of this synthetic system is that instead of a normal condensation domain, it uses two atypical free-standing proteins with homology to transglutaminases to catalyze the formation of the first and second amide bonds. We are characterizing the action of transglutaminase homologues (TGH) in andrimid synthesis using biochemical assays and X-ray crystallography. Initial investigations of the andrimid biosynthetic cluster found in Panteao agglomerans focused on the TGH, AdmF, which catalyzes the formation of the first amide bond. Crystallization trials have been initiated on AdmF in its apo form and in complex with its interacting binding partner, the peptide carrier protein domain AdmI. To date, only a few andrimid producing bacteria have been discovered. Using genome mining, a biosynthetic cluster homologous to the andrimid biosynthetic cluster found in Panteao agglomerans was identified in Vibrio coralliilyticus. The two TGHs, CoraF and CoraS, were cloned, expressed and purified, and crystallization trials are underway. Our progress in biochemical and biophysical characterization of AdmF, CoraF, and CoraS will be presented.

scholarly journals antiSMASH 5.0: updates to the secondary metabolite genome mining pipeline

2019 ◽  
Vol 47 (W1) ◽  
pp. W81-W87 ◽  
Author(s):  
Kai Blin ◽  
Simon Shaw ◽  
Katharina Steinke ◽  
Rasmus Villebro ◽  
Nadine Ziemert ◽  
...  
Keyword(s):  
Secondary Metabolite ◽  
Polybrominated Diphenyl Ethers ◽  
Polyketide Synthase ◽  
Genome Mining ◽  
Gene Clusters ◽  
Fungal Genome ◽  
Bacterial Genomes ◽  
Biosynthetic Gene Clusters ◽  
Bacteria And Fungi ◽  
New Compounds

Abstract Secondary metabolites produced by bacteria and fungi are an important source of antimicrobials and other bioactive compounds. In recent years, genome mining has seen broad applications in identifying and characterizing new compounds as well as in metabolic engineering. Since 2011, the ‘antibiotics and secondary metabolite analysis shell—antiSMASH’ (https://antismash.secondarymetabolites.org) has assisted researchers in this, both as a web server and a standalone tool. It has established itself as the most widely used tool for identifying and analysing biosynthetic gene clusters (BGCs) in bacterial and fungal genome sequences. Here, we present an entirely redesigned and extended version 5 of antiSMASH. antiSMASH 5 adds detection rules for clusters encoding the biosynthesis of acyl-amino acids, β-lactones, fungal RiPPs, RaS-RiPPs, polybrominated diphenyl ethers, C-nucleosides, PPY-like ketones and lipolanthines. For type II polyketide synthase-encoding gene clusters, antiSMASH 5 now offers more detailed predictions. The HTML output visualization has been redesigned to improve the navigation and visual representation of annotations. We have again improved the runtime of analysis steps, making it possible to deliver comprehensive annotations for bacterial genomes within a few minutes. A new output file in the standard JavaScript object notation (JSON) format is aimed at downstream tools that process antiSMASH results programmatically.

scholarly journals Palantir: a springboard for the analysis of secondary metabolite gene clusters in large-scale genome mining projects

2020 ◽  
Vol 36 (15) ◽  
pp. 4345-4347
Author(s):  
Loïc Meunier ◽  
Pierre Tocquin ◽  
Luc Cornet ◽  
Damien Sirjacobs ◽  
Valérie Leclère ◽  
...  
Keyword(s):  
Secondary Metabolite ◽  
Web Application ◽  
Large Scale ◽  
Genome Mining ◽  
Gene Clusters ◽  
Biosynthetic Gene Cluster ◽  
Supplementary Information ◽  
Easy Access ◽  
Mining Projects ◽  
Direct Use

Abstract Summary To support small and large-scale genome mining projects, we present Post-processing Analysis tooLbox for ANTIsmash Reports (Palantir), a dedicated software suite for handling and refining secondary metabolite biosynthetic gene cluster (BGC) data annotated with the popular antiSMASH pipeline. Palantir provides new functionalities building on NRPS/PKS predictions from antiSMASH, such as improved BGC annotation, module delineation and easy access to sub-sequences at different levels (cluster, gene, module and domain). Moreover, it can parse user-provided antiSMASH reports and reformat them for direct use or storage in a relational database. Availability and implementation Palantir is released both as a Perl API available on CPAN (https://metacpan.org/release/Bio-Palantir) and as a web application (http://palantir.uliege.be). As a practical use case, the web interface also features a database built from the mining of 1616 cyanobacterial genomes, of which 1488 were predicted to encode at least one BGC. Supplementary information Supplementary data are available at Bioinformatics online.

Classification of woody-shrub vegetation of the forest-steppe complex of Privolzhskaya Upland

2009 ◽  
pp. 27-53
Author(s):  
A. Yu. Kudryavtsev
Keyword(s):  
Plant Community ◽  
Plant Communities ◽  
Dominant Species ◽  
Large Scale ◽  
Nature Reserve ◽  
Vegetation Mapping ◽  
Vegetation Classification ◽  
Forest Steppe ◽  
Mapping Data

Diversity of plant communities in the nature reserve “Privolzhskaya Forest-Steppe”, Ostrovtsovsky area, is analyzed on the basis of the large-scale vegetation mapping data from 2000. The plant community classi­fication based on the Russian ecologic-phytocoenotic approach is carried out. 12 plant formations and 21 associations are distinguished according to dominant species and a combination of ecologic-phytocoenotic groups of species. A list of vegetation classification units as well as the characteristics of theshrub and woody communities are given in this paper.

Classification of vegetation in Cucphuong National Park with the aim of large-scale mapping, Vietnam

1996 ◽  
pp. 64-67 ◽  
Author(s):  
Nguen Nghia Thin ◽  
Nguen Ba Thu ◽  
Tran Van Thuy
Keyword(s):  
National Park ◽  
Large Scale ◽  
Satellite Images ◽  
Field Work ◽  
Aerial Photographs ◽  
Vegetation Map ◽  
Dense Forest ◽  
Broad Leaved Forest ◽  
Leaved Forest

The tropical seasonal rainy evergreen broad-leaved forest vegetation of the Cucphoung National Park has been classified and the distribution of plant communities has been shown on the map using the relations of vegetation to geology, geomorphology and pedology. The method of vegetation mapping includes: 1) the identifying of vegetation types in the remote-sensed materials (aerial photographs and satellite images); 2) field work to compile the interpretation keys and to characterize all the communities of a study area; 3) compilation of the final vegetation map using the combined information. In the classification presented a number of different level vegetation units have been identified: formation classes (3), formation sub-classes (3), formation groups (3), formations (4), subformations (10) and communities (19). Communities have been taken as mapping units. So in the vegetation map of the National Park 19 vegetation categories has been shown altogether, among them 13 are natural primary communities, and 6 are the secondary, anthropogenic ones. The secondary succession goes through 3 main stages: grassland herbaceous xerophytic vegetation, xerophytic scrub, dense forest.

The Swiss Prison Study (SWIPS): Protocol for Establishing a Public Health Registry of Prisoners in Switzerland (Preprint)

2020 ◽  
Author(s):  
Thomas Gaisl ◽  
Naser Musli ◽  
Patrick Baumgartner ◽  
Marc Meier ◽  
Silvana K Rampini ◽  
...  
Keyword(s):  
Public Health ◽  
Medical History ◽  
Large Scale ◽  
International Classification Of Diseases ◽  
Disease Prevalence ◽  
Vulnerable Population ◽  
International Classification ◽  
Classification Of Diseases ◽  
Health Registry

BACKGROUND The health aspects, disease frequencies, and specific health interests of prisoners and refugees are poorly understood. Importantly, access to the health care system is limited for this vulnerable population. There has been no systematic investigation to understand the health issues of inmates in Switzerland. Furthermore, little is known on how recent migration flows in Europe may have affected the health conditions of inmates. OBJECTIVE The Swiss Prison Study (SWIPS) is a large-scale observational study with the aim of establishing a public health registry in northern-central Switzerland. The primary objective is to establish a central database to assess disease prevalence (ie, International Classification of Diseases-10 codes [German modification]) among prisoners. The secondary objectives include the following: (1) to compare the 2015 versus 2020 disease prevalence among inmates against a representative sample from the local resident population, (2) to assess longitudinal changes in disease prevalence from 2015 to 2020 by using cross-sectional medical records from all inmates at the Police Prison Zurich, Switzerland, and (3) to identify unrecognized health problems to prepare successful public health strategies. METHODS Demographic and health-related data such as age, sex, country of origin, duration of imprisonment, medication (including the drug name, brand, dosage, and release), and medical history (including the International Classification of Diseases-10 codes [German modification] for all diagnoses and external results that are part of the medical history in the prison) have been deposited in a central register over a span of 5 years (January 2015 to August 2020). The final cohort is expected to comprise approximately 50,000 to 60,000 prisoners from the Police Prison Zurich, Switzerland. RESULTS This study was approved on August 5, 2019 by the ethical committee of the Canton of Zurich with the registration code KEK-ZH No. 2019-01055 and funded in August 2020 by the “Walter and Gertrud Siegenthaler” foundation and the “Theodor and Ida Herzog-Egli” foundation. This study is registered with the International Standard Randomized Controlled Trial Number registry. Data collection started in August 2019 and results are expected to be published in 2021. Findings will be disseminated through scientific papers as well as presentations and public events. CONCLUSIONS This study will construct a valuable database of information regarding the health of inmates and refugees in Swiss prisons and will act as groundwork for future interventions in this vulnerable population. CLINICALTRIAL ISRCTN registry ISRCTN11714665; http://www.isrctn.com/ISRCTN11714665 INTERNATIONAL REGISTERED REPORT DERR1-10.2196/23973

Deep Learning-Based Classification of Large-Scale Airborne LiDAR Point Cloud

2021 ◽  
pp. 1-15
Author(s):  
Mathieu Turgeon-Pelchat ◽  
Samuel Foucher ◽  
Yacine Bouroubi
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Large Scale ◽  
Airborne Lidar

scholarly journals Uncertainty-Aware Deep Learning-Based Cardiac Arrhythmias Classification Model of Electrocardiogram Signals

Computers ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 82
Author(s):  
Ahmad O. Aseeri
Keyword(s):  
Deep Learning ◽  
Cardiac Arrhythmias ◽  
Large Scale ◽  
Clinical Decision Making ◽  
Probabilistic Approach ◽  
Classification Model ◽  
Gating Mechanism ◽  
Uncertainty Estimates ◽  
Wide Range

Deep Learning-based methods have emerged to be one of the most effective and practical solutions in a wide range of medical problems, including the diagnosis of cardiac arrhythmias. A critical step to a precocious diagnosis in many heart dysfunctions diseases starts with the accurate detection and classification of cardiac arrhythmias, which can be achieved via electrocardiograms (ECGs). Motivated by the desire to enhance conventional clinical methods in diagnosing cardiac arrhythmias, we introduce an uncertainty-aware deep learning-based predictive model design for accurate large-scale classification of cardiac arrhythmias successfully trained and evaluated using three benchmark medical datasets. In addition, considering that the quantification of uncertainty estimates is vital for clinical decision-making, our method incorporates a probabilistic approach to capture the model’s uncertainty using a Bayesian-based approximation method without introducing additional parameters or significant changes to the network’s architecture. Although many arrhythmias classification solutions with various ECG feature engineering techniques have been reported in the literature, the introduced AI-based probabilistic-enabled method in this paper outperforms the results of existing methods in outstanding multiclass classification results that manifest F1 scores of 98.62% and 96.73% with (MIT-BIH) dataset of 20 annotations, and 99.23% and 96.94% with (INCART) dataset of eight annotations, and 97.25% and 96.73% with (BIDMC) dataset of six annotations, for the deep ensemble and probabilistic mode, respectively. We demonstrate our method’s high-performing and statistical reliability results in numerical experiments on the language modeling using the gating mechanism of Recurrent Neural Networks.

scholarly journals Research on Methods of Parsing and Classification of Internet Super Large-scale Texts

2021 ◽  
Vol 1757 (1) ◽  
pp. 012121
Author(s):  
Mengting Song ◽  
Hang Zheng ◽  
Zhen Tao ◽  
Jia Jiang ◽  
Bin Pan
Keyword(s):  
Large Scale
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