Data Browser Matsch | Mazia: Web Application to access microclimatic time series of an ecological research site

Easily accessible data is an essential requirement for scientific data analysis. The Data Browser Matsch | Mazia was designed to provide a fast and comprehensible solution to access, visualize and download the microclimatic measurements of the IT 25 LT(S)ER Match | Mazia research site in South Tyrol, Northern Italy, with the overall aim to provide straightforward data accessibility and enhance dissemination. Data Browser Matsch | Mazia is a user-friendly web-based application to visualize and download micrometeorological and biophysical time series of the Long-Term Socio-Ecological Research site Matsch | Mazia in South Tyrol, Italy. It is designed both for the general public and researchers. The Data Browser Matsch | Mazia drop-down menus allow the user to query the InfluxDB database in the backend by selecting the measurements, time range, land use and elevation. Interactive Grafana dashboards show dynamic graphs of the time series.

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Meteo Browser South Tyrol: A Shiny App to download the meteorological time series from the Open Data Catalogue of the Province of Bolzano/Bozen - Italy

Research Ideas and Outcomes ◽

10.3897/rio.5.e35894 ◽

2019 ◽

Vol 5 ◽

Author(s):

Giulio Genova ◽

Mattia Rossi ◽

Georg Niedrist ◽

Stefano Della Chiesa

Keyword(s):

Time Series ◽

Open Data ◽

R Package ◽

Private Companies ◽

Web Based ◽

South Tyrol ◽

Wide Range ◽

Shiny App ◽

On Line ◽

User Friendly

Meteo Browser South Tyrol is a user-friendly web-based application that helps to visualize and download the hydro-meteorological time series freely available in South Tyrol, Italy. It is designed for a wide range of users, from common citizens to students as well as researchers, private companies and the public administration. Meteo Browser South Tyrol is a Shiny App inside an R package and can be used on a local machine or accessed on-line. Drop down menus allow the user to select hydro-meteorological station and measurements. A simple map shows where the monitoring stations are, the latest measurements available, and lets the user subset the selected stations geographically by drawing a polygon.

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An Interactive Web Application for the Statistical Analysis of Continuous Glucose Monitoring Data in Epidemiological Studies

Journal of Diabetes Science and Technology ◽

10.1177/1932296820985570 ◽

2021 ◽

pp. 193229682098557

Author(s):

Alysha M. De Livera ◽

Jonathan E. Shaw ◽

Neale Cohen ◽

Anne Reutens ◽

Agus Salim

Keyword(s):

Continuous Glucose Monitoring ◽

Web Application ◽

Diabetes Management ◽

Glucose Monitoring ◽

Epidemiological Studies ◽

Interactive Software ◽

Web Based ◽

Software Applications ◽

Visualization Of Data ◽

User Friendly

Motivation: Continuous glucose monitoring (CGM) systems are an essential part of novel technology in diabetes management and care. CGM studies have become increasingly popular among researchers, healthcare professionals, and people with diabetes due to the large amount of useful information that can be collected using CGM systems. The analysis of the data from these studies for research purposes, however, remains a challenge due to the characteristics and large volume of the data. Results: Currently, there are no publicly available interactive software applications that can perform statistical analyses and visualization of data from CGM studies. With the rapidly increasing popularity of CGM studies, such an application is becoming necessary for anyone who works with these large CGM datasets, in particular for those with little background in programming or statistics. CGMStatsAnalyser is a publicly available, user-friendly, web-based application, which can be used to interactively visualize, summarize, and statistically analyze voluminous and complex CGM datasets together with the subject characteristics with ease.

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G4Killer web application: a tool to design G-quadruplex mutations

Bioinformatics ◽

10.1093/bioinformatics/btaa057 ◽

2020 ◽

Vol 36 (10) ◽

pp. 3246-3247

Author(s):

Vaclav Brazda ◽

Jan Kolomaznik ◽

Jean-Louis Mergny ◽

Jiri Stastny

Keyword(s):

Dna Sequences ◽

Web Application ◽

Rational Design ◽

Therapeutic Potential ◽

Supplementary Information ◽

Web Tool ◽

Web Based ◽

Dna Structures ◽

G Quadruplex ◽

User Friendly

Abstract Motivation G-quadruplexes (G4) are important regulatory non-B DNA structures with therapeutic potential. A tool for rational design of mutations leading to decreased propensity for G4 formation should be useful in studying G4 functions. Although tools exist for G4 prediction, no easily accessible tool for the rational design of G4 mutations has been available. Results We developed a web-based tool termed G4Killer that is based on the G4Hunter algorithm. This new tool is a platform-independent and user-friendly application to design mutations crippling G4 propensity in a parsimonious way (i.e., keeping the primary sequence as close as possible to the original one). The tool is integrated into our DNA analyzer server and allows for generating mutated DNA sequences having the desired lowered G4Hunter score with minimal mutation steps. Availability and implementation The G4Killer web tool can be accessed at: http://bioinformatics.ibp.cz. Supplementary information Supplementary data are available at Bioinformatics online.

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Forecasting the COVID-19 epidemic integrating symptom search behavior: an infodemiology study

10.1101/2021.03.09.21253186 ◽

2021 ◽

Author(s):

Alessandro Rabiolo ◽

Eugenio Alladio ◽

Esteban Morales ◽

Andrew I McNaught ◽

Francesco Bandello ◽

...

Keyword(s):

Time Series ◽

Web Application ◽

Moving Average ◽

Time Lag ◽

Search Behavior ◽

Google Trends ◽

Optimal Time ◽

Time Range ◽

Prediction Ability ◽

Time Series Modelling

ABSTRACTBackgroundPrevious studies have suggested associations between trends of web searches and COVID-19 traditional metrics. It remains unclear whether models incorporating trends of digital searches lead to better predictions.MethodsAn open-access web application was developed to evaluate Google Trends and traditional COVID-19 metrics via an interactive framework based on principal components analysis (PCA) and time series modelling. The app facilitates the analysis of symptom search behavior associated with COVID-19 disease in 188 countries. In this study, we selected data of eight countries as case studies to represent all continents. PCA was used to perform data dimensionality reduction, and three different time series models (Error Trend Seasonality, Autoregressive integrated moving average, and feed-forward neural network autoregression) were used to predict COVID-19 metrics in the upcoming 14 days. The models were compared in terms of prediction ability using the root-mean-square error (RMSE) of the first principal component (PC1). Predictive ability of models generated with both Google Trends data and conventional COVID-19 metrics were compared with those fitted with conventional COVID-19 metrics only.FindingsThe degree of correlation and the best time-lag varied as a function of the selected country and topic searched; in general, the optimal time-lag was within 15 days. Overall, predictions of PC1 based on both searched termed and COVID-19 traditional metrics performed better than those not including Google searches (median [IQR]: 1.43 [0.74-2.36] vs. 1.78 [0.95-2.88], respectively), but the improvement in prediction varied as a function of the selected country and timeframe. The best model varied as a function of country, time range, and period of time selected. Models based on a 7-day moving average led to considerably smaller RMSE values as opposed to those calculated with raw data (median [IQR]: 0.74 [0.47-1.22] vs. 2.15 [1.55-3.89], respectively).InterpretationThe inclusion of digital online searches in statistical models may improve the prediction of the COVID-19 epidemic.FundingEOSCsecretariat.eu has received funding from the European Union’s Horizon Programme call H2020-INFRAEOSC-05-2018-2019, grant Agreement number 831644.

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RaDMaX online: a web-based program for the determination of strain and damage profiles in irradiated crystals using X-ray diffraction

Journal of Applied Crystallography ◽

10.1107/s1600576720002514 ◽

2020 ◽

Vol 53 (2) ◽

pp. 587-593

Author(s):

A. Boulle ◽

V. Mergnac

Keyword(s):

Web Application ◽

The Internet ◽

Programming Environment ◽

X Ray Diffraction ◽

Web Browser ◽

Web Based ◽

X Ray ◽

Depth Profiles ◽

User Friendly

RaDMaX online is a major update to the previously published RaDMaX (radiation damage in materials analysed with X-ray diffraction) software [Souilah, Boulle & Debelle (2016). J. Appl. Cryst. 49, 311–316]. This program features a user-friendly interface that allows retrieval of strain and disorder depth profiles in irradiated crystals from the simulation of X-ray diffraction data recorded in symmetrical θ/2θ mode. As compared with its predecessor, RaDMaX online has been entirely rewritten in order to be able to run within a simple web browser, therefore avoiding the necessity to install any programming environment on the users' computers. The RaDMaX online web application is written in Python and developed within a Jupyter notebook implementing graphical widgets and interactive plots. RaDMaX online is free and open source and can be accessed on the internet at https://aboulle.github.io/RaDMaX-online/.

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The Framework for Web-Based Automated Online Question Paper Generator through JEE

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.13573 ◽

2018 ◽

Vol 7 (3) ◽

pp. 1415

Author(s):

Vinayak Hegde ◽

Lavanya V Rao ◽

Shivali B S

Keyword(s):

Web Application ◽

Large Scale ◽

Educational Institution ◽

Faculty Members ◽

Educational Institutions ◽

Small Scale ◽

Web Based ◽

Educational Institute ◽

User Friendly ◽

Word Format

Examinations are an indispensable part of a student’s life. In the conventional mechanism, the question paper generation is time-consuming work for the faculty members of the educational institution. Every educational institute mandatorily expects exam setters to follow its own typesetting format. We have designed the automated question paper setting software to be user-friendly so that, paper setters can overcome from the typographic problem. Presently in most of the educational institutions question papers are set manually. It is time-consuming work and there may be chances of repetition of the same questions. So, in order to make the question paper generation more convenient to use, the web application is developed using Java Enterprise Edition (JEE) that can be accessed from LAN/Intranet.The application comes with the Admin Module and Teachers Module. The Admin grants access to the users by registering them. The faculty can access the system once they are registered. The faculty can enter questions in the database daily as per their free time. In this way, the question pool can be generated. The questions are approved by the chairperson and substandard questions are discarded. The question paper is then generated by selected course experts. The Fisher-Yates Shuffling algorithm used to choose questions randomly from the pool of questions from the database. Text Mining Algorithm aids in duplicity removal from the paper. The generated question paper will be in Word Format. In our application, we assure better security, removal of duplicity, cost-effectiveness, and human intervention avoidance. It can be used by small-scale and large-scale institutions.

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G4Hunter web application: a web server for G-quadruplex prediction

Bioinformatics ◽

10.1093/bioinformatics/btz087 ◽

2019 ◽

Vol 35 (18) ◽

pp. 3493-3495 ◽

Cited By ~ 22

Author(s):

Václav Brázda ◽

Jan Kolomazník ◽

Jiří Lýsek ◽

Martin Bartas ◽

Miroslav Fojta ◽

...

Keyword(s):

Data Storage ◽

Web Application ◽

Data Representation ◽

Complete Characterization ◽

Supplementary Information ◽

Web Based ◽

Rna Molecules ◽

Dna And Rna ◽

Sorting Data ◽

User Friendly

Abstract Motivation Expanding research highlights the importance of guanine quadruplex structures. Therefore, easy-accessible tools for quadruplex analyses in DNA and RNA molecules are important for the scientific community. Results We developed a web version of the G4Hunter application. This new web-based server is a platform-independent and user-friendly application for quadruplex analyses. It allows retrieval of gene/nucleotide sequence entries from NCBI databases and provides complete characterization of localization and quadruplex propensity of quadruplex-forming sequences. The G4Hunter web application includes an interactive graphical data representation with many useful options including visualization, sorting, data storage and export. Availability and implementation G4Hunter web application can be accessed at: http://bioinformatics.ibp.cz. Supplementary information Supplementary data are available at Bioinformatics online.

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MALBoost: a web-based application for gene regulatory network analysis in Plasmodium falciparum

Malaria Journal ◽

10.1186/s12936-021-03848-2 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Roelof van Wyk ◽

Riëtte van Biljon ◽

Lyn-Marie Birkholtz

Keyword(s):

Gene Regulatory Network ◽

Regulatory Network ◽

Web Application ◽

Regulatory Networks ◽

Web Based ◽

Time Estimates ◽

Gene Regulatory ◽

Validation Set ◽

User Friendly ◽

Case Validation

Abstract Background Gene Regulatory Networks (GRN) produce powerful insights into transcriptional regulation in cells. The power of GRNs has been underutilized in malaria research. The Arboreto library was incorporated into a user-friendly web-based application for malaria researchers (http://malboost.bi.up.ac.za). This application will assist researchers with gaining an in depth understanding of transcriptomic datasets. Methods The web application for MALBoost was built in Python-Flask with Redis and Celery workers for queue submission handling, which execute the Arboreto suite algorithms. A submission of 5–50 regulators and total expression set of 5200 genes is permitted. The program runs in a point-and-click web user interface built using Bootstrap4 templates. Post-analysis submission, users are redirected to a status page with run time estimates and ultimately a download button upon completion. Result updates or failure updates will be emailed to the users. Results A web-based application with an easy-to-use interface is presented with a use case validation of AP2-G and AP2-I. The validation set incorporates cross-referencing with ChIP-seq and transcriptome datasets. For AP2-G, 5 ChIP-seq targets were significantly enriched with seven more targets presenting with strong evidence of validated targets. Conclusion The MALBoost application provides the first tool for easy interfacing and efficiently allows gene regulatory network construction for Plasmodium. Additionally, access is provided to a pre-compiled network for use as reference framework. Validation for sexually committed ring-stage parasite targets of AP2-G, suggests the algorithm was effective in resolving “traditionally” low-level signatures even in bulk RNA datasets.

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Forecasting the COVID-19 Epidemic By Integrating Symptom Search Behavior Into Predictive Models: Infoveillance Study (Preprint)

10.2196/preprints.28876 ◽

2021 ◽

Author(s):

Alessandro Rabiolo ◽

Eugenio Alladio ◽

Esteban Morales ◽

Andrew Ian McNaught ◽

Francesco Bandello ◽

...

Keyword(s):

Time Series ◽

Predictive Models ◽

Web Application ◽

Moving Average ◽

Time Lag ◽

Search Behavior ◽

Principal Component ◽

Google Trends ◽

Time Range ◽

Prediction Ability

BACKGROUND Previous studies have suggested associations between trends of web searches and COVID-19 traditional metrics. It remains unclear whether models incorporating trends of digital searches lead to better predictions. OBJECTIVE The aim of this study is to investigate the relationship between Google Trends searches of symptoms associated with COVID-19 and confirmed COVID-19 cases and deaths. We aim to develop predictive models to forecast the COVID-19 epidemic based on a combination of Google Trends searches of symptoms and conventional COVID-19 metrics. METHODS An open-access web application was developed to evaluate Google Trends and traditional COVID-19 metrics via an interactive framework based on principal component analysis (PCA) and time series modeling. The application facilitates the analysis of symptom search behavior associated with COVID-19 disease in 188 countries. In this study, we selected the data of nine countries as case studies to represent all continents. PCA was used to perform data dimensionality reduction, and three different time series models (error, trend, seasonality; autoregressive integrated moving average; and feed-forward neural network autoregression) were used to predict COVID-19 metrics in the upcoming 14 days. The models were compared in terms of prediction ability using the root mean square error (RMSE) of the first principal component (PC1). The predictive abilities of models generated with both Google Trends data and conventional COVID-19 metrics were compared with those fitted with conventional COVID-19 metrics only. RESULTS The degree of correlation and the best time lag varied as a function of the selected country and topic searched; in general, the optimal time lag was within 15 days. Overall, predictions of PC1 based on both search terms and COVID-19 traditional metrics performed better than those not including Google searches (median 1.56, IQR 0.90-2.49 versus median 1.87, IQR 1.09-2.95, respectively), but the improvement in prediction varied as a function of the selected country and time frame. The best model varied as a function of country, time range, and period of time selected. Models based on a 7-day moving average led to considerably smaller RMSE values as opposed to those calculated with raw data (median 0.90, IQR 0.50-1.53 versus median 2.27, IQR 1.62-3.74, respectively). CONCLUSIONS The inclusion of digital online searches in statistical models may improve the nowcasting and forecasting of the COVID-19 epidemic and could be used as one of the surveillance systems of COVID-19 disease. We provide a free web application operating with nearly real-time data that anyone can use to make predictions of outbreaks, improve estimates of the dynamics of ongoing epidemics, and predict future or rebound waves.

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Xeno-miRNet: a comprehensive database and analytics platform to explore xeno-miRNAs and their potential targets

PeerJ ◽

10.7717/peerj.5650 ◽

2018 ◽

Vol 6 ◽

pp. e5650 ◽

Cited By ~ 12

Author(s):

Yannan Fan ◽

Maria Habib ◽

Jianguo Xia

Keyword(s):

Host Species ◽

Species Interactions ◽

Web Application ◽

Potential Functions ◽

Circulation System ◽

Multiple Sources ◽

Web Based ◽

User Friendly ◽

The Web ◽

Computational Predictions

Xeno-miRNAs are microRNAs originating from exogenous species detected in host biofluids. A growing number of studies have suggested that many of these xeno-miRNAs may be involved in cross-species interactions and manipulations. To date, hundreds of xeno-miRNAs have been reported in different hosts at various abundance levels. Based on computational predictions, many more miRNAs could be potentially transferred to human circulation system. There is a clear need for bioinformatics resources and tools dedicated to xeno-miRNA annotations and their potential functions. To address this need, we have systematically curated xeno-miRNAs from multiple sources, performed target predictions using well-established algorithms, and developed a user-friendly web-based tool—Xeno-miRNet—to allow researchers to search and explore xeno-miRNAs and their potential targets within different host species. Xeno-miRNet currently contains 1,702 (including both detected and predicted) xeno-miRNAs from 54 species and 98,053 potential gene targets in six hosts. The web application is freely available at http://xeno.mirnet.ca.

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