scholarly journals CalcUS: An Open-Source Computational Chemistry Web Platform

2021 ◽  
Author(s):  
Raphaël Robidas ◽  
Claude Legault
Keyword(s):  
Computational Chemistry ◽  
Open Source ◽  
State Of The Art ◽  
Quantum Mechanical ◽  
Quantum Mechanical Calculations ◽  
Web Interface ◽  
Software Packages ◽  
Technical Challenges ◽  
User Friendly ◽  
Web Platform

Computational chemistry is an increasingly active field due to the improvement of computing resources and theoretical tools. However, its use remains usually limited to technically-inclined users due to the technical challenges of preparing, launching and analyzing calculations. In this context, we have developed CalcUS, an open-source platform to streamline computational chemistry studies. Its objective is to democratize access to computational chemistry by providing a user-friendly web interface to simplify running and analyzing quantum mechanical calculations. It is freely available, expandable and customizable. It promotes connectivity to multiple software packages and algorithms, thus providing state-of-the-art techniques to all practitioners. We propose CalcUS as a standalone tool and infrastructure to support other open-source packages.

DMAKit: A user-friendly web platform for bringing state-of-the-art data analysis techniques to non-specific users

2020 ◽  
Vol 93 ◽  
pp. 101557 ◽  
Author(s):  
David Medina-Ortiz ◽  
Sebastián Contreras ◽  
Cristofer Quiroz ◽  
Juan A. Asenjo ◽  
Álvaro Olivera-Nappa
Keyword(s):  
Data Analysis ◽  
State Of The Art ◽  
Analysis Techniques ◽  
User Friendly ◽  
Web Platform

scholarly journals MOSGA: Modular Open-Source Genome Annotator

2020 ◽  
Author(s):  
Roman Martin ◽  
Thomas Hackl ◽  
Georges Hattab ◽  
Matthias G Fischer ◽  
Dominik Heider
Keyword(s):  
Open Source ◽  
Source Code ◽  
Supplementary Information ◽  
Web Interface ◽  
Fully Integrated ◽  
Sequencing Technologies ◽  
A Genome ◽  
Wide Range ◽  
User Friendly ◽  
Eukaryotic Genomes

Abstract Motivation The generation of high-quality assemblies, even for large eukaryotic genomes, has become a routine task for many biologists thanks to recent advances in sequencing technologies. However, the annotation of these assemblies—a crucial step toward unlocking the biology of the organism of interest—has remained a complex challenge that often requires advanced bioinformatics expertise. Results Here, we present MOSGA (Modular Open-Source Genome Annotator), a genome annotation framework for eukaryotic genomes with a user-friendly web-interface that generates and integrates annotations from various tools. The aggregated results can be analyzed with a fully integrated genome browser and are provided in a format ready for submission to NCBI. MOSGA is built on a portable, customizable and easily extendible Snakemake backend, and thus, can be tailored to a wide range of users and projects. Availability and implementation We provide MOSGA as a web service at https://mosga.mathematik.uni-marburg.de and as a docker container at registry.gitlab.com/mosga/mosga: latest. Source code can be found at https://gitlab.com/mosga/mosga Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Open-source Δ-quantum machine learning for medicinal chemistry

2021 ◽  
Author(s):  
Kenneth Atz ◽  
Clemens Isert ◽  
Markus N. A. Böcker ◽  
José Jiménez-Luna ◽  
Gisbert Schneider
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Density Functional ◽  
Large Scale ◽  
Molecular Design ◽  
State Of The Art ◽  
Computational Cost ◽  
Quantum Mechanical ◽  
Quantum Observables ◽  
Graph Neural Networks

Certain molecular design tasks benefit from fast and accurate calculations of quantum-mechanical (QM) properties. However, the computational cost of QM methods applied to drug-like compounds currently makes large-scale applications of quantum chemistry challenging. In order to mitigate this problem, we developed DelFTa, an open-source toolbox for predicting small-molecule electronic properties at the density functional (DFT) level of theory, using the Δ-machine learning principle. DelFTa employs state-of-the-art E(3)-equivariant graph neural networks that were trained on the QMugs dataset of QM properties. It provides access to a wide array of quantum observables by predicting approximations to ωB97X-D/def2-SVP values from a GFN2-xTB semiempirical baseline. Δ-learning with DelFTa was shown to outperform direct DFT learning for most of the considered QM endpoints. The software is provided as open-source code with fully-documented command-line and Python APIs.

scholarly journals DERIVING 3D POINT CLOUDS FROM TERRESTRIAL PHOTOGRAPHS - COMPARISON OF DIFFERENT SENSORS AND SOFTWARE

2016 ◽  
Vol XLI-B5 ◽  
pp. 685-692 ◽  
Author(s):  
Robert Niederheiser ◽  
Martin Mokroš ◽  
Julia Lange ◽  
Helene Petschko ◽  
Günther Prasicek ◽  
...  
Keyword(s):  
Point Cloud ◽  
State Of The Art ◽  
User Interaction ◽  
Point Clouds ◽  
Rock Face ◽  
3D Point Clouds ◽  
Software Packages ◽  
User Friendly ◽  
Insight Into

Terrestrial photogrammetry nowadays offers a reasonably cheap, intuitive and effective approach to 3D-modelling. However, the important choice, which sensor and which software to use is not straight forward and needs consideration as the choice will have effects on the resulting 3D point cloud and its derivatives. <br><br> We compare five different sensors as well as four different state-of-the-art software packages for a single application, the modelling of a vegetated rock face. The five sensors represent different resolutions, sensor sizes and price segments of the cameras. The software packages used are: (1) Agisoft PhotoScan Pro (1.16), (2) Pix4D (2.0.89), (3) a combination of Visual SFM (V0.5.22) and SURE (1.2.0.286), and (4) MicMac (1.0). We took photos of a vegetated rock face from identical positions with all sensors. Then we compared the results of the different software packages regarding the ease of the workflow, visual appeal, similarity and quality of the point cloud. <br><br> While PhotoScan and Pix4D offer the user-friendliest workflows, they are also “black-box” programmes giving only little insight into their processing. Unsatisfying results may only be changed by modifying settings within a module. The combined workflow of Visual SFM, SURE and CloudCompare is just as simple but requires more user interaction. MicMac turned out to be the most challenging software as it is less user-friendly. However, MicMac offers the most possibilities to influence the processing workflow. The resulting point-clouds of PhotoScan and MicMac are the most appealing.

scholarly journals Tribe: The collaborative platform for reproducible web-based analysis of gene sets

2016 ◽  
Author(s):  
René A. Zelaya ◽  
Aaron K. Wong ◽  
Alex T. Frase ◽  
Marylyn D. Ritchie ◽  
Casey S. Greene
Keyword(s):  
Open Source ◽  
Web Interface ◽  
Web Based ◽  
Gene Set ◽  
Collaborative Editing ◽  
Gene Sets ◽  
Collaborative Platform ◽  
Set Up ◽  
User Friendly ◽  
Multi Server

AbstractBackgroundThe adoption of new bioinformatics webservers provides biological researchers with new analytical opportunities but also raises workflow challenges. These challenges include sharing collections of genes with collaborators, translating gene identifiers to the most appropriate nomenclature for each server, tracking these collections across multiple analysis tools and webservers, and maintaining effective records of the genes used in each analysis.DescriptionIn this paper, we present the Tribe webserver (available at https://tribe.greenelab.com), which addresses these challenges in order to make multi-server workflows seamless and reproducible. This allows users to create analysis pipelines that use their own sets of genes in combinations of specialized data mining webservers and tools while seamlessly maintaining gene set version control. Tribe’s web interface facilitates collaborative editing: users can share with collaborators, who can then view, download, and edit these collections. Tribe’s fully-featured API allows users to interact with Tribe programmatically if desired. Tribe implements the OAuth 2.0 standard as well as gene identifier mapping, which facilitates its integration into existing servers. Access to Tribe’s resources is facilitated by an easy-to-install Python application called tribe-client. We provide Tribe and tribe-client under a permissive open-source license to encourage others to download the source code and set up a local instance or to extend its capabilities.ConclusionsThe Tribe webserver addresses challenges that have made reproducible multi-webserver workflows difficult to implement until now. It is open source, has a user-friendly web interface, and provides a means for researchers to perform reproducible gene set based analyses seamlessly across webservers and command line tools.

scholarly journals DERIVING 3D POINT CLOUDS FROM TERRESTRIAL PHOTOGRAPHS - COMPARISON OF DIFFERENT SENSORS AND SOFTWARE

2016 ◽  
Vol XLI-B5 ◽  
pp. 685-692 ◽  
Author(s):  
Robert Niederheiser ◽  
Martin Mokroš ◽  
Julia Lange ◽  
Helene Petschko ◽  
Günther Prasicek ◽  
...  
Keyword(s):  
Point Cloud ◽  
State Of The Art ◽  
User Interaction ◽  
Point Clouds ◽  
Rock Face ◽  
3D Point Clouds ◽  
Software Packages ◽  
User Friendly ◽  
Insight Into

Terrestrial photogrammetry nowadays offers a reasonably cheap, intuitive and effective approach to 3D-modelling. However, the important choice, which sensor and which software to use is not straight forward and needs consideration as the choice will have effects on the resulting 3D point cloud and its derivatives. &lt;br&gt;&lt;br&gt; We compare five different sensors as well as four different state-of-the-art software packages for a single application, the modelling of a vegetated rock face. The five sensors represent different resolutions, sensor sizes and price segments of the cameras. The software packages used are: (1) Agisoft PhotoScan Pro (1.16), (2) Pix4D (2.0.89), (3) a combination of Visual SFM (V0.5.22) and SURE (1.2.0.286), and (4) MicMac (1.0). We took photos of a vegetated rock face from identical positions with all sensors. Then we compared the results of the different software packages regarding the ease of the workflow, visual appeal, similarity and quality of the point cloud. &lt;br&gt;&lt;br&gt; While PhotoScan and Pix4D offer the user-friendliest workflows, they are also “black-box” programmes giving only little insight into their processing. Unsatisfying results may only be changed by modifying settings within a module. The combined workflow of Visual SFM, SURE and CloudCompare is just as simple but requires more user interaction. MicMac turned out to be the most challenging software as it is less user-friendly. However, MicMac offers the most possibilities to influence the processing workflow. The resulting point-clouds of PhotoScan and MicMac are the most appealing.

A quantum mechanical study of optical excitations in nanodisk plasmonic oligomers

2019 ◽  
Vol 21 (48) ◽  
pp. 26540-26548
Author(s):  
Junais Habeeb Mokkath
Keyword(s):  
State Of The Art ◽  
Quantum Mechanical ◽  
Quantum Mechanical Calculations ◽  
Mechanical Study ◽  
Quantum Mechanical Study ◽  
Optical Excitations

Using state-of-the-art quantum-mechanical calculations, we investigate the optical excitations in plasmonic nanodisk oligomers.

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