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Quizizz merupakan sebuah web tool untuk membuat permainan kuis interaktif yang digunakan dalam pembelajaran di kelas. Penggunaannya sangat mudah, kuis interaktif dibuat dengan memiliki hingga 5 pilihan jawaban termasuk jawaban yang benar. Evaluasi dengan metode quizizz ini digunakan untuk mengukur pemahaman peserta pelatihan dasar sebelum dan sesudah proses pembelajaran. Dengan metode quizizz ini, peserta pelatihan akan lebih enjoy dalam melakukan evaluasi pembelajaran, karena metode ini menyenangkan seperti bermain games. Namun tentu tidak dapat dipungkiri bahwa setiap aplikasi memiliki keunggulan dan kelemahan termasuk aplikasi quizizz ini. Penggunaan aplikasi quizizz ini secara online sehingga sangat tergantung dengan ketersediaan jaringan internet. Tulisan ini menjelaskan tentang presepsi peserta pelatihan dasar (Latsar) terhadap penggunaan quizizz sebagai metode evaluasi pembelajaran khususnya mata pelatihan anti korupsi. Evaluasi pembelajaran dengan menggunakan quizizz ini sangat efektif, dimana hasil pre test terkait pemahaman peserta terhadap materi anti korupsi sebesar 44%, dan hasil post test meningkat menjadi 71% atau mengalami peningkatan sebesar 27 %. Itu berarti bahwa metode evaluasi pembelajaran dengan quizizz ini mampu meningkatkan hasil evaluasi, konsentrasi dan motivasi peserta ketika ada dalam proses evaluasi pembelajaran.

Genome-scale mechanistic models of pathways are gaining importance for genomic data interpretation because they provide a natural link between genotype measurements (transcriptomics or genomics data) and the phenotype of the cell (its functional behavior). Moreover, mechanistic models can be used to predict the potential effect of interventions, including drug inhibitions. Here, we present the implementation of a mechanistic model of cell signaling for the interpretation of transcriptomic data as an R/Bioconductor package, a Cytoscape plugin and a web tool with enhanced functionality which includes building interpretable predictors, estimation of the effect of perturbations and assessment of the effect of mutations in complex scenarios., This work is supported by grants SAF2017-88908-R from the Spanish Ministry of Economy and Competitiveness and PT17/0009/0006 and PI20/01305 from the ISCIII, both co-funded with European Regional Development Funds (ERDF) as well as H2020 Programme of the European Union grants Marie Curie Innovative Training Network “Machine Learning Frontiers in Precision Medicine” (MLFPM) (GA 813533) and “ELIXIR-EXCELERATE fast-track ELIXIR implementation and drive early user exploitation across the life sciences” (GA 676559) to JD. Funding for open access charge: from the Spanish Ministry of Economy and Competitiveness / SAF2017-88908-R.

Dos vários ambientes de aprendizagem virtual de ensino à distância existentes, o Moodle é a plataforma mais utilizada. No entanto, apesar de ser uma plataforma muito completa e flexível para o ensino à distância (EAD), ela não dispõe de ferramentas de fácil utilização para que os docentes possam aferir a interação dos alunos, dispondo unicamente de um módulo de visualização dos registros de utilização. A VAFAE - Visualização dos Acessos a Fóruns num Ambiente EAD é uma ferramenta web que mapeia visualmente esses dados e a sua respectiva análise numérica. De uma forma mais específica, a VAFAE visa auxiliar os docentes a perceber melhor o nível e qualidade de interação dos alunos com os módulos das unidades curriculares de EAD. Este artigo faz um resumo das ferramentas semelhantes existentes, aborda alguns conceitos subjacentes à teoria de visualização de informação e finalmente explica como foi construído o VAFAE e que tecnologias foram usadas.

Background The Gene Ontology (GO) knowledgebase is the world’s largest source of information on the functions of genes. Since the beginning of GO project, various tools have been developed to perform GO enrichment analysis experiments. GO enrichment analysis has become a commonly used method of gene function analysis. Existing GO enrichment analysis tools do not consider tissue-specific information, although this information is very important to current research. Results In this paper, we built an easy-to-use web tool called TS−GOEA that allows users to easily perform experiments based on tissue-specific GO enrichment analysis. TS−GOEA uses strict threshold statistical method for GO enrichment analysis, and provides statistical tests to improve the reliability of the analysis results. Meanwhile, TS−GOEA provides tools to compare different experimental results, which is convenient for users to compare the experimental results. To evaluate its performance, we tested the genes associated with platelet disease with TS−GOEA. Conclusions TS−GOEA is an effective GO analysis tool with unique features. The experimental results show that our method has better performance and provides a useful supplement for the existing GO enrichment analysis tools. TS−GOEA is available at http://120.77.47.2:5678.

Proteins are exposed to fluctuating environmental conditions in their cellular context and during their biotechnological production. Disordered regions are susceptible to these fluctuations and may experience solvent-dependent conformational switches that affect their local dynamism and activity. In a recent study, we modeled the influence of pH in the conformational state of IDPs by exploiting a charge–hydrophobicity diagram that considered the effect of solution pH on both variables. However, it was not possible to predict context-dependent transitions for multiple sequences, precluding proteome-wide analysis or the screening of collections of mutants. In this article, we present DispHScan, the first computational tool dedicated to predicting pH-induced disorder–order transitions in large protein datasets. The DispHScan web server allows the users to run pH-dependent disorder predictions of multiple sequences and identify context-dependent conformational transitions. It might provide new insights on the role of pH-modulated conditional disorder in the physiology and pathology of different organisms. The DispHScan web server is freely available for academic users, it is platform-independent and does not require previous registration.

The research addresses the problem of developing technologies enabling automated assessment and contrasting immediate recalls with an original expository text. The Corpus compiled for the study comprises an expository text of 252 tokens and 134 immediate recalls performed by Russian natives (over 1500 tokens). The study highlights three core issues: (i) automated multi-factor assessment of text parameters; (ii) developing algorithms of proportional analysis to contrast texts; (iii) identifying main patterns employed in immediate recalls, i.e. association, clustering, and prediction. We also demonstrate and expand the implementation area of a web tool RuLingva computing over 33 text metrics. Overall, the research advances our understanding of immediate recalls models and offers foundations for more effective algorithms to assess and profile immediate text recalls. The research findings can be useful in language teaching and text production as taken together, the models identified have a potential to assist in solving practical (essay scoring), and theoretical (information extraction) problems.

The debugging of database queries is a research topic of increasing interest in recent years. The Semantic Web query language SPARQL should be equipped with a debugger for helping users to detect bugs which usually cause empty results as well as wrong and missing answers. Declarative debugging is a well-known debugging method successfully used in other database query languages. In this paper we present the elements of a declarative debugger for SPARQL in which debugging is based on the construction of a debugging tree for a given answer, and the detection of buggy and failure nodes in the debugging tree causing empty results as well as wrong and missing answers. The debugger has been implemented and it is available as web tool.

This paper presents an expert system disease of the red-eye web-based using the decision tree with forward chaining algorithms. The aims are to design and to implement a web-based expert system of disorders of the red-eye (disease) and to provide a second opinion web tool based for diagnosing the red eye (disease). The system was built using framework CodeIgniter with php and html. The experimental results show that the system has run well which is indicated by valid black box achievements, 55% of conformity to experts from the Faculty of Medicine of the University of Mataram and 4.19 of five leakage scale of MOS parameter. It means that the expert system of the red-eye disease has been work properly and potentially to be implemented for the community.

Mass spectrometry is a widely applied technology with a strong impact in the proteomics field. MALDI-TOF is a combined technology in mass spectrometry with many applications in characterizing biological samples from different sources, such as the identification of cancer biomarkers, the detection of food frauds, the identification of doping substances in athletes’ fluids, and so on. The massive quantity of data, in the form of mass spectra, are often biased and altered by different sources of noise. Therefore, extracting the most relevant features that characterize the samples is often challenging and requires combining several computational methods. Here, we present GeenaR, a novel web tool that provides a complete workflow for pre-processing, analyzing, visualizing, and comparing MALDI-TOF mass spectra. GeenaR is user-friendly, provides many different functionalities for the analysis of the mass spectra, and supports reproducible research since it produces a human-readable report that contains function parameters, results, and the code used for processing the mass spectra. First, we illustrate the features available in GeenaR. Then, we describe its internal structure. Finally, we prove its capabilities in analyzing oncological datasets by presenting two case studies related to ovarian cancer and colorectal cancer. GeenaR is available at http://proteomics.hsanmartino.it/geenar/.

MotivationInferring biological phenotypes from genomic data and sample clusters is a routinely task usually performed with Gene-Set Enrichment Analysis (GSEA), a tool that queries gene-profiles. In previous work, we scrutinized the approach based on Mann-Witney for Gene-Sets test. We highlighted the Mann-Witney test-statistics sensitivity to uncover weak signals and the drastic decreasing of time complexity.ResultsWe propose web implementation of the Gene-sets testing based on the Mann-Witney procedure. The test-procedure has reshaped to decrease the computational expense, now about tens of seconds, even if a large collection of gene-sets queries the same gene-profile. The probabilistic interpretation of the normalized test-statistic has been investigated to a better understanding of the enrichment results. A novel prioritization method across enrichment-scores, gene-set dimensions, and p-values, draws attention to relevant gene-sets. The web tool provides both tabular and graphical enrichment results. A complimentary R function allows integrating the enrichment procedure in a complex context.Contactpagnotta@unisannio.itSupplementary informationExample data and guidelines are included in the supporting material of the web-site.

Motivation Drug immunomodulation modifies the response of the immune system and can be therapeutically exploited in pathologies such as cancer and autoimmune diseases. Results DREIMT is a new hypothesis-generation web tool, which performs drug prioritization analysis for immunomodulation. DREIMT provides significant immunomodulatory drugs targeting up to 70 immune cells subtypes through a curated database that integrates 4960 drug profiles and ∼2600 immune gene expression signatures. The tool also suggests potential immunomodulatory drugs targeting user-supplied gene expression signatures. Final output includes drug–signature association scores, FDRs and downloadable plots and results tables. Availabilityand implementation http://www.dreimt.org. Supplementary information Supplementary data are available at Bioinformatics online.

SummaryGene Expression Signatures (GES) have become instrumental for describing cellular phenotypes; however, they may have limited reproducibility across datasets. Structural Signatures (sGES) addresses this limitation by integrating protein structural information with GES data to improve signature robustness while reducing feature dimensionality. Here we present sGES, a web server for generating a range of protein features derived from transcriptomics data. sGES can describe biological phenomena ranging from tissue specific steady states to disease and drug perturbations. sGES also provides access to searchable signatures derived from GTEx, ARCHS4, and lung cancer single cell RNA sequencing data. The sGES server tool is freely available at structuralserver.kinametrix.com.

Forster resonance energy transfer (FRET) is a useful phenomenon in biomolecular investigations, as it can be leveraged for nanoscale measurements. The optical signals produced by such experiments can be analyzed by fitting a statistical model. Several software tools exist to fit such models in an unsupervised manner but lack the flexibility to adapt to different experimental setups and require local installations. Here, we propose to fit models to optical signals more intuitively by adopting a semisupervised approach, in which the user interactively guides the model to fit a given data set, and introduce FRETboard, a web tool that allows users to provide such guidance. We show that our approach is able to closely reproduce ground truth FRET statistics in a wide range of simulated single-molecule scenarios and correctly estimate parameters for up to 11 states. On in vitro data, we retrieve parameters identical to those obtained by laborious manual classification in a fraction of the required time. Moreover, we designed FRETboard to be easily extendable to other models, allowing it to adapt to future developments in FRET measurement and analysis.

Background Alternative sequence alignment algorithms yield different results. It is therefore useful to quantify the similarities and differences between alternative alignments of the same sequences. These measurements can identify regions of consensus that are likely to be most informative in downstream analysis. They can also highlight systematic differences between alignments that relate to differences in the alignment algorithms themselves. Results Here we present a simple method for aligning two alternative multiple sequence alignments to one another and assessing their similarity. Differences are categorised into merges, splits or shifts in one alignment relative to the other. A set of graphical visualisations allow for intuitive interpretation of the data. Conclusions AlignStat enables the easy one-off online use of MSA similarity comparisons or into R pipelines. The web-tool is available at AlignStat.Science.LaTrobe.edu.au. The R package, readme and example data are available on CRAN and GitHub.com/TS404/AlignStat. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1300-6) contains supplementary material, which is available to authorized users.

Effective drug discovery contributes to the treatment of numerous diseases but is limited by high costs and long cycles. The Quantitative Structure–Activity Relationship (QSAR) method was introduced to evaluate the activity of a large number of compounds virtually, reducing the time and labor costs required for chemical synthesis and experimental determination. Hence, this method increases the efficiency of drug discovery. To meet the needs of researchers to utilize this technology, numerous QSAR-related web servers, such as Web-4D-QSAR and DPubChem, have been developed in recent years. However, none of the servers mentioned above can perform a complete QSAR modeling and supply activity prediction functions. We introduce Cloud 3D-QSAR by integrating the functions of molecular structure generation, alignment, molecular interaction field (MIF) computing and results analysis to provide a one-stop solution. We rigidly validated this server, and the activity prediction correlation was R2 = 0.934 in 834 test molecules. The sensitivity, specificity and accuracy were 86.9%, 94.5% and 91.5%, respectively, with AUC = 0.981, AUCPR = 0.971. The Cloud 3D-QSAR server may facilitate the development of good QSAR models in drug discovery. Our server is free and now available at http://chemyang.ccnu.edu.cn/ccb/server/cloud3dQSAR/ and http://agroda.gzu.edu.cn:9999/ccb/server/cloud3dQSAR/.

With accumulating dysregulated circular RNAs (circRNAs) in pathological processes, the regulatory functions of circRNAs, especially circRNAs as microRNA (miRNA) sponges and their interactions with RNA-binding proteins (RBPs), have been widely validated. However, the collected information on experimentally validated circRNA-disease associations is only preliminary. Therefore, an updated CircR2Disease database providing a comprehensive resource and web tool to clarify the relationships between circRNAs and diseases in diverse species is necessary. Here, we present an updated CircR2Disease v2.0 with the increased number of circRNA-disease associations and novel characteristics. CircR2Disease v2.0 provides more than 5-fold experimentally validated circRNA-disease associations compared to its previous version. This version includes 4201 entries between 3077 circRNAs and 312 disease subtypes. Secondly, the information of circRNA-miRNA, circRNA-miRNA-target, and circRNA-RBP interactions has been manually collected for various diseases. Thirdly, the gene symbols of circRNAs and disease name IDs can be linked with various nomenclature databases. Detailed descriptions such as samples and journals have also been integrated into the updated version. Thus, CircR2Disease v2.0 can serve as a platform for users to systematically investigate the roles of dysregulated circRNAs in various diseases and further explore the posttranscriptional regulatory function in diseases. Finally, we propose a computational method named circDis based on the graph convolutional network (GCN) and gradient boosting decision tree (GBDT) to illustrate the applications of the CircR2Disease v2.0 database. CircR2Disease v2.0 is available at http://bioinfo.snnu.edu.cn/CircR2Disease_v2.0 and https://github.com/bioinforlab/CircR2Disease-v2.0.

Background Protein microarray is a well-established approach for characterizing activity levels of thousands of proteins in a parallel manner. Analysis of protein microarray data is complex and time-consuming, while existing solutions are either outdated or challenging to use without programming skills. The typical data analysis pipeline consists of a data preprocessing step, followed by differential expression analysis, which is then put into context via functional enrichment. Normally, biologists would need to assemble their own workflow by combining a set of unrelated tools to analyze experimental data. Provided that most of these tools are developed independently by various bioinformatics groups, making them work together could be a real challenge. Results Here we present PAWER, the online web tool dedicated solely to protein microarray analysis. PAWER enables biologists to carry out all the necessary analysis steps in one go. PAWER provides access to state-of-the-art computational methods through the user-friendly interface, resulting in publication-ready illustrations. We also provide an R package for more advanced use cases, such as bespoke analysis workflows. Conclusions PAWER is freely available at https://biit.cs.ut.ee/pawer.

The significant evolution of smartphones has given ordinary people the power to create good-quality content which can then be spread, by the press, over multiple platforms. Citizens are almost always the first ones to arrive at a breaking news location and can provide the initial images of the scene. However, existing crowdsourced tools and platforms are predominantly centralized and are usually fed with unreliable and untrustworthy information.This work introduces a Crowd Journalism ecosystem whose core is a video marketplace web tool based on an organization-level decentralized system that can store, visualize, rate, and execute transactions of live-made videos. Smart contracts ensure that all the transactions are transparent and secure.This approach to Crowd Journalism exploits the inherent features of a blockchain such as offering trustful, anonymized, and immutable transactions, which has the potential to revolutionize the way news content is shared and commercially exploited.

Computational thinking (CT) is a set of abilities, which has lately acquired great relevance because it brings to students the components to handle in-formation and develop analytical skills, which allows them to perform ac-cording to today’s society. In this study, the authors describe the development of a formative assessment tool for Scratch programming. This web tool allows school teachers to design challenges for their students, supports the assessment of CT dimensions, gives feedback to students through the challenges’ results and a gamified strategy, and keeps a track of the development of CT for each student.

Summary We propose a flexible design for the identification of optimal dose combinations in dual-agent dose finding clinical trials. The design is called AAA, standing for three adaptations: adaptive model selection, adaptive dose insertion and adaptive cohort division. The adaptations highlight the need and opportunity for innovation for dual-agent dose finding and are supported by the numerical results presented in the proposed simulation studies. To our knowledge, this is the first design that allows for all three adaptations at the same time. We find that AAA enhances the chance of finding the optimal dose combinations and shortens the trial duration. A clinical trial is being planned to apply the AAA design and a Web tool is being developed for both statisticians and non-statisticians.

High-throughput screening (HTS) performs the experimental testing of a large number of chemical compounds aiming to identify those active in the considered assay. Alternatively, faster and cheaper methods of large-scale virtual screening are performed computationally through quantitative structure-activity relationship (QSAR) models. However, the vast amount of available HTS heterogeneous data and the imbalanced ratio of active to inactive compounds in an assay make this a challenging problem. Although different QSAR models have been proposed, they have certain limitations, e.g., high false positive rates, complicated user interface, and limited utilization options. Therefore, we developed DPubChem, a novel web tool for deriving QSAR models that implement the state-of-the-art machine-learning techniques to enhance the precision of the models and enable efficient analyses of experiments from PubChem BioAssay database. DPubChem also has a simple interface that provides various options to users. DPubChem predicted active compounds for 300 datasets with an average geometric mean and F1 score of 76.68% and 76.53%, respectively. Furthermore, DPubChem builds interaction networks that highlight novel predicted links between chemical compounds and biological assays. Using such a network, DPubChem successfully suggested a novel drug for the Niemann-Pick type C disease. DPubChem is freely available at www.cbrc.kaust.edu.sa/dpubchem.

Finding binding site motifs plays an important role in bioinformatics as it reveals the transcription factors that control the gene expression. The development for motif finders has flourished in the past years with many tools have been introduced to the research community. Although these tools possess exceptional features for detecting motifs, they report different results for an identical data set. Hence, using multiple tools is recommended because motifs reported by several tools are likely biologically significant. However, the results from multiple tools need to be compared for obtaining common significant motifs. MOTIFSIM web tool and command-line tool were developed for this purpose. In this work, we present several technical improvements as well as additional features to further support the motif analysis in our new release MOTIFSIM 2.1.

AbsatractAlong with the constant improvement in high-throughput sequencing technology, an increasing number of transcriptome sequencing projects are carried out in organisms without decoded genome information and even on environmental biological samples. To study the biological functions of novel transcripts, the very first task is to identify their potential functions. We present a web-based annotation tool, FunctionAnnotator, which offers comprehensive annotations, including GO term assignment, enzyme annotation, domain/motif identification and predictions for subcellular localization. To accelerate the annotation process, we have optimized the computation processes and used parallel computing for all annotation steps. Moreover, FunctionAnnotator is designed to be versatile, and it generates a variety of useful outputs for facilitating other analyses. Here, we demonstrate how FunctionAnnotator can be helpful in annotating non-model organisms. We further illustrate that FunctionAnnotator can estimate the taxonomic composition of environmental samples and assist in the identification of novel proteins by combining RNA-Seq data with proteomics technology. In summary, FunctionAnnotator can efficiently annotate transcriptomes and greatly benefits studies focusing on non-model organisms or metatranscriptomes. FunctionAnnotator, a comprehensive annotation web-service tool, is freely available online at: http://fa.cgu.edu.tw/. This new web-based annotator will shed light on field studies involving organisms without a reference genome.

Digital polymerase chain reaction (digital PCR, dPCR) is a direct nucleic acid quantification method, thus requiring no standard curves unlike quantitative real-time PCR (qPCR). Nevertheless, evaluation of the linear dynamic range, accuracy, and precision of an assay or platform is recommended, as there are several potential causes of important non-linearity, bias, and imprecision. Ignoring these quality issues may lead to erroneous quantification. This necessitates an approach akin to the construction of standard curves. We study the pitfalls associated with the evaluation of such an experiment, and provide guidelines for the assessment of linearity, accuracy, and precision in dPCR experiments. We present simulation results and a case study supporting the importance of a thorough evaluation. Further, typically presented plots and statistics may not reveal problems with linearity, accuracy, or precision. We find that a robust weighted least-squares approach is highly advisable, yet may also suffer from an inflated false-positive rate. The proposed assessments are also applicable to other analyses, such as the comparison of results obtained from qPCR and dPCR. A web tool for quality evaluation, dPCalibRate, is available.

By large group decision-making, obtaining large group response plans within a limited amount of time plays a crucial role under emergency of local energy shortage. An energy network dispatch optimization under emergency of local energy shortage has been found to possess broad applications in energy system; however, the existing mechanism did not consider and failed to provide an approach to deal with the large group decision makers taking part in the energy network dispatch optimization. In this paper, the main contribution is that an energy network dispatch optimization under emergency of local energy shortage with web tool for automatic large group decision-making is proposed, in which anytime and anywhere, each expert can login in the system and provide his/her personal preference to the emergency of local energy shortage alternatives, and then the large group response plans to emergency of local energy shortage will be obtained within a limited amount of time. A case study and simulation results are presented that prove that the proposed model is feasible, and achieves better performance of energy network dispatch optimization under emergency of local energy shortage than the method proposed in the literature.

With the arrival of ICT's in the classroom, the teacher must introduce new ways of teaching; one of them is the use of digital tools, which help the student in his academic performance. The area of mathematics is distinguished by being one of the most complex for students, so it is necessary to look for new teaching - learning strategies. In this paper we present a web tool with the implementation of gamification, responsive and didactic techniques that provide the student with a learning environment for Numerical Methods, specifically for the calculation of roots of nonlinear equations by means of approximations; being this an innovative tool. The experience obtained with the use of the tool in the classroom is also presented.

Phage-microbe interactions not only are appealing systems to study coevolution but also have been increasingly emphasized due to their roles in human health, diseases, and novel therapeutic development. Meanwhile, their interactions leave diverse signals in bacterial and phage genomic sequences, defined as phage-host interaction signals (PHISs), such as sequence composition, CRISPR targeting, prophage, and protein-protein interaction signals. We infer that proper detection and integration of these diverse PHISs will allow us to predict phage-host interactions. Here, we developed PHISDetector, a novel tool to predict phage-host interactions by detecting and integrating diverse in silico PHISs and scoring the probability of phage-host interactions using machine-learning models based on PHIS features. PHISDetector is available as a one-stop web service version for general users to study individual inputs. A stand-alone software version is also provided to process massive phage contigs from virome studies. PHISDetector is freely available at http://www.microbiome-bigdata.com/PHISDetector/ and https://github.com/HIT-ImmunologyLab/PHISDector.

The AIA (Atmospheric Imaging Assembly) instrument, on-board the SDO (Solar Dynamics Observatory) satellite, provides high-resolution and high-cadence solar images since 2010. To extract scientific knowledge from those high-resolution images there is a need for efficient automatic web tools to detect and/or track the coronal bright points (CBPs). Identifying and tracking CBPs is essential for successfully calculate the solar corona rotation rate at different latitudes. Over the last years this topic has been an area of research in solar physics and some effective methods have been developed. The purpose of this work is to design an automatic and near real-time web tool that detects and tracks CBPs on solar images and publishes the results online, thus, allowing search and visualization functionalities to support astrophysicists perform improved solar analysis. The detection process uses a gradient based segmentation algorithm that has proved to provide accurate data about CBPs' dynamics. The paper propose to extend that work by using SunPy and OpenCV in Python, together with the Gradient Path Labeling (GPL) segmentation algorithm. The results obtained display good approximations, when compared with the ones from other authors, and the tool also seems reliable over long testing periods.

In this paper we describe a web-based tool for creating and publishing interactive presentations with 2D and 3D visualizations of mathematical objects. It provides ways for enhancing students ability to understand mathematical concepts by means of direct manipulation of functions attributes. Our prototype is realized using Javascript and WebGL and consists of some solved exercises which represent a learning unit. It offers an innovative look at one of possible ways of preparing better teaching material.

eCollection 2019; In order to automatically process qPCR raw data, we present the tool "Do my qPCR calculation". We offer a website to automatically calculate the data normalization and represent the different samples graphically in an Excel file. This tool is also available on Github for installation and local use with or without web interface.

Molecular target prediction can provide a starting point to understand the efficacy and side effects of phenotypic screening hits. Unfortunately, the vast majority of in silico target prediction methods are not available as web tools. Furthermore, these are limited in the number of targets that can be predicted, do not estimate which target predictions are more reliable and/or lack comprehensive retrospective validations. We present MolTarPred ( http://moltarpred.marseille.inserm.fr/), a user-friendly web tool for predicting protein targets of small organic compounds. It is powered by a large knowledge base comprising 607,659 compounds and 4,553 macromolecular targets collected from the ChEMBL database. In about 1 min, the predicted targets for the supplied molecule will be listed in a table. The chemical structures of the query molecule and the most similar compounds annotated with the predicted target will also be shown to permit visual inspection and comparison. Practical examples of the use of MolTarPred are showcased. MolTarPred is a new resource for scientists that require a more complete knowledge of the polypharmacology of a molecule. The introduction of a reliability score constitutes an attractive functionality of MolTarPred, as it permits focusing experimental confirmatory tests on the most reliable predictions, which leads to higher prospective hit rates.

SATQPCR is a web tool providing statistical analysis of real-time quantitative PCR data including all MIQE rules (gene efficiency, selection of reference genes and normalization with them). Our application is a quick tool that provides to the biologist, graphs as well as statistical tables summarizing their results with the chosen methods (t-test or ANOVA with Tukey test). The application is available at http://satqpcr.sophia.inra.fr with a demo dataset. Source code can be found at https://framagit.org/. SUPPLEMENTARY INFORMATION: Tutorials at http://satqpcr.sophia.inra.fr/cgi/help.cgi.

The main objective of this chapter is to provide an introduction to the IndoorLoc Platform, a web tool to support the comparison and the evaluation of indoor positioning algorithms. The proposed web platform can be used to download datasets, learn how some well-known algorithms work, study the implementation of those algorithms, test the methods, and even upload indoor positioning estimations of the user’s methods to check their accuracy in the same experimental conditions than other methods already included in a ranking, among other functionalities. This chapter also shows a comparative study of the accuracy of two well-known fingerprinting-based indoor localization algorithms using the datasets included in the platform. This comparative study can be performed using the tools included in the platform.

PHI-Base is a database that it is accessible through the web and contains a catalogue of experimentally verified pathogen-host interactions from fungal, oomycete and bacterial pathogens and animal, plant, fungal and insect hosts. In 2016, researchers from Centre for Plant Biotechnology and Genomics at Universidad Politecnica de Madrid, Spain, released a semantic-based version of part of this database (named Semantic PHI) focusing on those interactions where the affected host are plants. This semantic version was created and published using FAIR methodology and was available through a SPARQL endpoint. In this paper is presented a proof of concept done to create an interface for Semantic PHI-Base dataset. A web tool that allows interacting and visualizing the interactions contained in Semantic PHI database using an easy interface was created. This tool represents a case study of interacting and visualizing the knowledge contained within a semantic dataset allowing the use of such dataset by users with no expertise in semantic technologies.

Gene Ontology (GO) is one of the most popular Bioinformatics resources for studying the functional relationships between genes. GO enrichment analysis has become a routine method for gene function analysis, and it is of great significance for revealing the molecular mechanism of biology. However, existing GO enrichment analysis tools do not take into account tissue specificity and cannot distinguish gene sets that users interested. In this paper, we propose an easy-to-use web application, termed as TSGOE, which allows users to perform tissue specific-based GO enrichment analysis conveniently. Besides, TSGOE also provides rigorous statistical testing to improve the reliability of analytical result. To evaluate its performance, we apply TSGOE on several cancer-related gene lists. The experimental results show that our tool perform better than traditional GO enrichment analysis method. TSGOE is available at http://120.77.47.2:5678.

Clustered heat maps are the most frequently used graphics for visualization and interpretation of genome-scale molecular profiling data in biology. Construction of a heat map generally requires the assistance of a biostatistician or bioinformatics analyst capable of working in R or a similar programming language to transform the study data, perform hierarchical clustering, and generate the heat map. Our web-based Interactive Heat Map Builder can be used by investigators with no bioinformatics experience to generate high-caliber, publication quality maps. Preparation of the data and construction of a heat map is rarely a simple linear process. Our tool allows a user to move back and forth iteratively through the various stages of map generation to try different options and approaches. Finally, the heat map the builder creates is available in several forms, including an interactive Next-Generation Clustered Heat Map that can be explored dynamically to investigate the results more fully.

The seminars are a learning mechanism that serve to complement regular education with more specialized content. Different learning materials are generated from the seminars activity, such as documentation, presentations, videos, exercises… These materials can also be used for teaching purposes in other contexts. The management and distribution of these materials is normally done by hand or stored in a digital repository. The problem with these tools is that they facilitate storage management but are not focused to support their formative application. This article describes the experience of creating a web tool that allows to take profit of the materials generated in a seminar called Zaragoza Linguistics for later teaching use. The functionality has been implemented using web scraping techniques and the YouTube web services API.

In this article a web tool which contributes to the visual analysis of the Differential Evolution (DE) algorithms is presented. The tool provides a graphic interface with 8 views that allows understanding the underlying process of the algorithm. The tool has a library which extracts data from DE algorithms and its main feature is that the functions of the library can be embedded in the code of any DE algorithm to be analyzed. To validate the tool, three DE algorithms: DE/Rand/1/bin, DE/best/1/bin, and JADE and three test functions: Sphere, Rosenbrock, and Rastrigin have been used, which produced a total of 234 different tests, all of them performed successfully. The tool can allow to experts to analyze algorithms, particularly DE algorithms, and it can contribute to improve such algorithms or in generating new strategies that can emerge from the analysis of the extracted information.

Drug repurposing aims to find novel indications of clinically used or experimental drugs. Because drug data already exist, drug repurposing may save time and cost, and bypass safety concerns. Polypharmacology, one drug with multiple targets, serves as a basis for drug repurposing. Large-scale databases have been accumulated in recent years, and utilization and integration of these databases would be highly helpful for polypharmacology and drug repurposing. The Connectivity Map (CMap) is a database collecting gene-expression profiles of drug-treated human cancer cells, which has been widely used for investigation of polypharmacology and drug repurposing. In this study, we integrated the next-generation L1000-based CMap and an analytic Web tool, the L1000FWD, for systematic analyses of polypharmacology and drug repurposing. Two different types of anti-cancer drugs were used as proof-of-concept examples, including histone deacetylase (HDAC) inhibitors and topoisomerase inhibitors. We identified KM-00927 and BRD-K75081836 as novel HDAC inhibitors and mitomycin C as a topoisomerase IIB inhibitor. Our study provides a prime example of utilization and integration of the freely available public resources for systematic polypharmacology analysis and drug repurposing.

We present GranatumX, a next-generation software environment for single-cell data analysis. GranatumX is inspired by the interactive web tool Granatum. It enables biologists to access the latest single-cell bioinformatics methods in a web-based graphical environment. It also offers software developers the opportunity to rapidly promote their own tools with others in customizable pipelines. The architecture of GranatumX allows for easy inclusion of plugin modules, named Gboxes, that wrap around bioinformatics tools written in various programming languages and on various platforms. GranatumX can be run on the cloud or private servers and generate reproducible results. It is a community-engaging, flexible, and evolving software ecosystem for scRNA-Seq analysis, connecting developers with bench scientists. GranatumX is freely accessible at http://garmiregroup.org/granatumx/app.

The analysis of tumor growth curves is standard practice in experimental oncology including tumor immunology. In experimental oncology, cancer cells are inoculated into rodents (mostly mice) and their growth is monitored by measuring tumor diameter, surface or volume over time as a function of distinct treatments. Then, different groups of tumors/treatments are compared among each other for their evolution and possible responses to treatment. The R package TumGrowth has been created as a software tool allowing to carry out a series of statistical comparisons across or between groups of tumor growth curves obtained in a standard laboratory, for experimenters with limited knowledge in statistics. TumGrowth is freely available online at https://kroemerlab.shinyapps.io/TumGrowth/ and can be downloaded into any computer. It offers an exhaustive panoply of tools to visualize and analyze complex data sets including longitudinal, cross-sectional and time-to-endpoint measurements.

Background Gene fusions often occur in cancer cells and in some cases are the main driver of oncogenesis. Correct identification of oncogenic gene fusions thus has implications for targeted cancer therapy. Recognition of this potential has led to the development of a myriad of sequencing-based fusion detection tools. However, given the same input, many of these detectors will find different fusion points or claim different sets of supporting data. Furthermore, the rate at which these tools falsely detect fusion events in data varies greatly. This discrepancy between tools underscores the fact that computation algorithms still cannot perfectly evaluate evidence; especially when provided with small amounts of supporting data as is typical in fusion detection. We assert that when evidence is provided in an easily digestible form, humans are more proficient in identifying true positives from false positives. Results We have developed a web tool that, given the genomic coordinates of a candidate fusion breakpoint, will extract fusion and non-fusion reads adjacent to the fusion point from partner transcripts, and color code reads by transcript origin and read orientation for ease of intuitive inspection by the user. Fusion partner transcript read alignments are performed using a novel variant of the Smith-Waterman algorithm. Conclusions Combined with dynamic filtering parameters, the visualization provided by our tool introduces a powerful new investigative step that allows researchers to comprehensively evaluate fusion evidence. Additionally, this allows quick identification of false positives that may deceive most fusion detectors, thus eliminating unnecessary gene fusion validation. We apply our visualization tool to publicly available datasets and provide examples of true as well as false positives reported by open source fusion detection tools.

This article describes the creation, development and practical application of a prototype version of a web tool called Student Play, an educational module of Agent SocialMetric, whose main objective is to establish interactive games with students to educate in values, through different conversational interface software agents.

Background Genetic association studies (GAS) aims to evaluate the association between genetic variants and phenotypes. In the last few years, the number of this type of study has increased exponentially, but the results are not always reproducible due to experimental designs, low sample sizes and other methodological errors. In this field, meta-analysis techniques are becoming very popular tools to combine results across studies to increase statistical power and to resolve discrepancies in genetic association studies. A meta-analysis summarizes research findings, increases statistical power and enables the identification of genuine associations between genotypes and phenotypes. Meta-analysis techniques are increasingly used in GAS, but it is also increasing the amount of published meta-analysis containing different errors. Although there are several software packages that implement meta-analysis, none of them are specifically designed for genetic association studies and in most cases their use requires advanced programming or scripting expertise. Results We have developed MetaGenyo, a web tool for meta-analysis in GAS. MetaGenyo implements a complete and comprehensive workflow that can be executed in an easy-to-use environment without programming knowledge. MetaGenyo has been developed to guide users through the main steps of a GAS meta-analysis, covering Hardy-Weinberg test, statistical association for different genetic models, analysis of heterogeneity, testing for publication bias, subgroup analysis and robustness testing of the results. Conclusions MetaGenyo is a useful tool to conduct comprehensive genetic association meta-analysis. The application is freely available at http://bioinfo.genyo.es/metagenyo/. Electronic supplementary material The online version of this article (10.1186/s12859-017-1990-4) contains supplementary material, which is available to authorized users.

Summary GCPred is a webserver for the prediction of guanylyl cyclase (GC) functional centres from amino acid sequence. GCs are enzymes that generate the signalling molecule cyclic guanosine 3’, 5’-monophosphate from guanosine-5’-triphosphate. A novel class of GC centres (GCCs) has been identified in complex plant proteins. Using currently available experimental data, GCPred is created to automate and facilitate the identification of similar GCCs. The server features GCC values that consider in its calculation, the physicochemical properties of amino acids constituting the GCC and the conserved amino acids within the centre. From user input amino acid sequence, the server returns a table of GCC values and graphs depicting deviations from mean values. The utility of this server is demonstrated using plant proteins and the human interleukin-1 receptor-associated kinase family of proteins as example. Availability and implementation The GCPred server is available at http://gcpred.com. Supplementary information Supplementary data are available at Bioinformatics online.

Protein disorders such as neurodegenerative diseases are characterized by neuronal death resulting in progressive decline in memory. This owes to protein aggregation or misfolding due to the deposition of insoluble plaques known as amyloid. A group of bio-physico-chemical (BPC) characteristics of amino acids constituting proteins are evidenced to aid in the study of protein macroscopic properties like folding/aggregation. It has been verified that identifying underlying causes due to the deposits of amyloid plaques, established on the basis of BPC properties is fundamental in designing therapeutic targets against amyloid-related disorders including Alzheimer's disease. The information about BPC properties has been organized in multiple databases. In fact, the BPC properties identified in-vitro and in-vivo are distributed across databases in diverse formats. Moreover, as the volume of biological data including protein sequences and their characteristics is huge, data analytics in Bioinformatics becomes critically in need. Consequently, how to store, manage, mine, and visualize the required data and their correlations with disease is clearly a challenge to the research community. This research work focuses on implementing an online web tool comprising two databases in a nutshell namely ABPCBase: Amino acid Bio-Physico-Chemical properties Database and APPDBase: Amino acid Properties Protein Disorder Database, for mining relevant details of BPC properties in a single format and their association with protein disorders that fills the lacuna in extracting knowledge from various sources scattered around the web. This web interface is freely accessible at http://www.bpc-property.890m.com/index.php

The forthcoming European Higher Education Area implies an important change in the teaching/learning process: it is necessary to get students more involved as well as to promote their independence and active participation. To achieve this objective, the new teaching methodologies aimed at more effective and appropriate learning for professional practice involve the use of audiovisual, computer and multimedia tools on the part of lecturers. Therefore, a web tool, based on a content management system, has been developed for the teaching in Power Electronics Laboratory. Moreover, the use of these multimedia tools makes possible to promote the students independence. Finally, the use of this web tool results in a very significant increase in the motivation students.

The Proteopathogen database was the first proteomics online resource focused on experiments related to Candida albicans and other fungal pathogens and their interaction with the host. Since then, the HUPO-PSI standards were implemented and settled, and the first large scale C. albicans proteomics resource appeared as a C. albicans PeptideAltas. This has enabled the remodeling of Proteopathogen to take advantage and benefit from the use of the HUPO-PSI adopted format for peptide and protein identification mzIdentML and continue offering a centralized resource for C. albicans , other fungal pathogens and different cell lines proteomics data.