scholarly journals Pavian: interactive analysis of metagenomics data for microbiome studies and pathogen identification

2019 ◽  
Author(s):  
Florian P Breitwieser ◽  
Steven L Salzberg
Keyword(s):  
Web Application ◽  
Pathogen Detection ◽  
R Language ◽  
Interactive Analysis ◽  
Alignment Coverage ◽  
Metagenomics Data ◽  
Flow Diagrams ◽  
Interactive Data ◽  
Web App ◽  
Data Tables

Abstract Summary Pavian is a web application for exploring classification results from metagenomics experiments. With Pavian, researchers can analyze, visualize and transform results from various classifiers—such as Kraken, Centrifuge and MethaPhlAn—using interactive data tables, heatmaps and Sankey flow diagrams. An interactive alignment coverage viewer can help in the validation of matches to a particular genome, which can be crucial when using metagenomics experiments for pathogen detection. Availability and implementation Pavian is implemented in the R language as a modular Shiny web app and is freely available under GPL-3 from http://github.com/fbreitwieser/pavian. Contact [email protected]

scholarly journals Pavian: Interactive analysis of metagenomics data for microbiomics and pathogen identification

2016 ◽  
Author(s):  
Florian P. Breitwieser ◽  
Steven L. Salzberg
Keyword(s):  
Web Application ◽  
Disease Diagnosis ◽  
Supplementary Information ◽  
Special Focus ◽  
Web Browser ◽  
R Language ◽  
Interactive Analysis ◽  
Link Type ◽  
Metagenomics Data ◽  
Flow Diagrams

AbstractSummaryPavian is a web application for exploring metagenomics classification results, with a special focus on infectious disease diagnosis. Pinpointing pathogens in metagenomics classification results is often complicated by host and laboratory contaminants as well as many non-pathogenic microbiota. With Pavian, researchers can analyze, display and transform results from the Kraken and Centrifuge classifiers using interactive tables, heatmaps and flow diagrams. Pavian also provides an alignment viewer for validation of matches to a particular genome.Availability and implementationPavian is implemented in the R language and based on the Shiny framework. It can be hosted on Windows, Mac OS X and Linux systems, and used with any contemporary web browser. It is freely available under a GPL-3 license from http://github.com/fbreitwieser/pavian. Furthermore a Docker image is provided at https://hub.docker.com/r/florianbw/[email protected] informationSupplementary data is available at Bioinformatics online.

scholarly journals CRISPRAnalyzeR: Interactive analysis, annotation and documentation of pooled CRISPR screens

2017 ◽  
Author(s):  
Jan Winter ◽  
Marc Schwering ◽  
Oliver Pelz ◽  
Benedikt Rauscher ◽  
Tianzuo Zhan ◽  
...  
Keyword(s):  
Web Application ◽  
Systematic Investigation ◽  
Cellular Processes ◽  
Interactive Analysis ◽  
Link Type ◽  
External Data ◽  
Versatile Tool ◽  
Interactive Visualizations ◽  
Meta Information ◽  
Data Tables

AbstractPooled CRISPR/Cas9 screens are a powerful and versatile tool for the systematic investigation of cellular processes in a variety of organisms. Such screens generate large amounts of data that present a new challenge to analyze and interpret. Here, we developed a web application to analyze, document and explore pooled CRISR/Cas9 screens using a unified single workflow. The end-to-end analysis pipeline features eight different hit calling strategies based on state-of-the-art methods, including DESeq2, MAGeCK, edgeR, sgRSEA, Z-Ratio, Mann-Whitney test, ScreenBEAM and BAGEL. Results can be compared with interactive visualizations and data tables. CRISPRAnalyzeR integrates meta-information from 26 external data resources, providing a wide array of options for the annotation and documentation of screens. The application was developed with user experience in mind, requiring no previous knowledge in bioinformatics. All modern operating systems are supported.Availability and online documentation: The source code, a pre-configured docker application, sample data and a documentation can be found on our GitHub page (http://www.github.com/boutroslab/CRISPRAnalyzeR). A tutorial video can be found at http://www.crispr-analyzer.org.

Malaria Dashboard: a digital platform for epidemiological data from India (Preprint)

2021 ◽  
Author(s):  
Chander Prakash Yadav ◽  
Amit Sharma
Keyword(s):  
Web Application ◽  
Epidemiological Data ◽  
Web Page ◽  
Digital Tool ◽  
R Software ◽  
Malaria Epidemiology ◽  
Interactive Analysis ◽  
Data Source ◽  
R Packages ◽  
Excel File

BACKGROUND A digital dashboard on malaria epidemiological data will be an invaluable resource for the research community and the planning of malaria control. OBJECTIVE To develop a digital Malaria Dashboard (MDB) for malaria epidemiological data METHODS We have developed a digital Malaria Dashboard (MDB) using the R software. A total of thirteen different R packages were used in this process, within which shiny and ggplot2 were used more intensively. The MDB is a web application that can work online as well as offline. Presently it is available in offline mode only. The MS Excel file may be used as an input data source and any personal computer may be used for this application. RESULTS The MDB is a highly versatile interface that allows prompt and interactive analysis of malaria epidemiological data. The primary interface of MDB is like a web page that has 14 tabs (or pages), some more tabs may be added or deleted as per requirement and each tab corresponds to a particular analysis. A user may move from one tab to another via tab icons. Each tab thus allows flexibility in correlating various parameters like SPR, API, AFI, ABER, RT, malaria cases, death due to malaria, BSC, and BSE. The data can be analyzed in required granularity (national, state, district), and its enhanced visualization allows for facile usage. Using the MDB, one can quickly assess national or more granular scenarios in a time series manner and then compare the malaria epidemiology in various states and their constituent districts. CONCLUSIONS This MDB is a highly effective digital tool for studying the malaria situation and strategizing for malaria elimination and researcher may use it as a prototype for developing some other dashboards in their own fields.

scholarly journals IDseq—An open source cloud-based pipeline and analysis service for metagenomic pathogen detection and monitoring

GigaScience ◽  
2020 ◽  
Vol 9 (10) ◽  
Author(s):  
Katrina L Kalantar ◽  
Tiago Carvalho ◽  
Charles F A de Bourcy ◽  
Boris Dimitrov ◽  
Greg Dingle ◽  
...  
Keyword(s):  
Data Analysis ◽  
Open Source ◽  
Web Application ◽  
Pathogen Detection ◽  
A Priori ◽  
Virus Detection ◽  
Data Interpretation ◽  
Nasopharyngeal Swab ◽  
Microbial Composition ◽  
Detection And Identification

Abstract Background Metagenomic next-generation sequencing (mNGS) has enabled the rapid, unbiased detection and identification of microbes without pathogen-specific reagents, culturing, or a priori knowledge of the microbial landscape. mNGS data analysis requires a series of computationally intensive processing steps to accurately determine the microbial composition of a sample. Existing mNGS data analysis tools typically require bioinformatics expertise and access to local server-class hardware resources. For many research laboratories, this presents an obstacle, especially in resource-limited environments. Findings We present IDseq, an open source cloud-based metagenomics pipeline and service for global pathogen detection and monitoring (https://idseq.net). The IDseq Portal accepts raw mNGS data, performs host and quality filtration steps, then executes an assembly-based alignment pipeline, which results in the assignment of reads and contigs to taxonomic categories. The taxonomic relative abundances are reported and visualized in an easy-to-use web application to facilitate data interpretation and hypothesis generation. Furthermore, IDseq supports environmental background model generation and automatic internal spike-in control recognition, providing statistics that are critical for data interpretation. IDseq was designed with the specific intent of detecting novel pathogens. Here, we benchmark novel virus detection capability using both synthetically evolved viral sequences and real-world samples, including IDseq analysis of a nasopharyngeal swab sample acquired and processed locally in Cambodia from a tourist from Wuhan, China, infected with the recently emergent SARS-CoV-2. Conclusion The IDseq Portal reduces the barrier to entry for mNGS data analysis and enables bench scientists, clinicians, and bioinformaticians to gain insight from mNGS datasets for both known and novel pathogens.

scholarly journals Data-Driven Intelligent Application for Youtube Video Popularity Analysis using Machine Learning and Statistics

2021 ◽  
Author(s):  
Wenxi Gao ◽  
Ishmael Rico ◽  
Yu Sun
Keyword(s):  
Machine Learning ◽  
Data Processing ◽  
Web Application ◽  
Data Driven ◽  
Accuracy Evaluation ◽  
Video Content ◽  
The Public ◽  
Left Behind ◽  
The World ◽  
Web App

People now prefer to follow trends. Since the time is moving, people can only keep themselves from being left behind if they keep up with the pace of time. There are a lot of websites for people to explore the world, but websites for those who show the public something new are uncommon. This paper proposes an web application to help YouTuber with recommending trending video content because they sometimes have trouble in thinking of the video topic. Our method to solve the problem is basically in four steps: YouTube scraping, data processing, prediction by SVM and the webpage. Users input their thoughts on our web app and computer will scrap the trending page of YouTube and process the data to do prediction. We did some experiments by using different data, and got the accuracy evaluation of our method. The results show that our method is feasible so people can use it to get their own recommendation.

scholarly journals Pixel: a content management platform for quantitative omics data

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6623 ◽  
Author(s):  
Thomas Denecker ◽  
William Durand ◽  
Julien Maupetit ◽  
Charles Hébert ◽  
Jean-Michel Camadro ◽  
...  
Keyword(s):  
Web Application ◽  
Content Management ◽  
Biological Information ◽  
Functional Tests ◽  
Single Experiment ◽  
Code Coverage ◽  
Development Platform ◽  
Management Platform ◽  
Strategic Position ◽  
Web App

Background In biology, high-throughput experimental technologies, also referred as “omics” technologies, are increasingly used in research laboratories. Several thousands of gene expression measurements can be obtained in a single experiment. Researchers are routinely facing the challenge to annotate, store, explore and mine all the biological information they have at their disposal. We present here the Pixel web application (Pixel Web App), an original content management platform to help people involved in a multi-omics biological project. Methods The Pixel Web App is built with open source technologies and hosted on the collaborative development platform GitHub (https://github.com/Candihub/pixel). It is written in Python using the Django framework and stores all the data in a PostgreSQL database. It is developed in the open and licensed under the BSD 3-clause license. The Pixel Web App is also heavily tested with both unit and functional tests, a strong code coverage and continuous integration provided by CircleCI. To ease the development and the deployment of the Pixel Web App, Docker and Docker Compose are used to bundle the application as well as its dependencies. Results The Pixel Web App offers researchers an intuitive way to annotate, store, explore and mine their multi-omics results. It can be installed on a personal computer or on a server to fit the needs of many users. In addition, anyone can enhance the application to better suit their needs, either by contributing directly on GitHub (encouraged) or by extending Pixel on their own. The Pixel Web App does not provide any computational programs to analyze the data. Still, it helps to rapidly explore and mine existing results and holds a strategic position in the management of research data.

scholarly journals A curated compendium of monocyte transcriptome datasets of relevance to human monocyte immunobiology research

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 291 ◽  
Author(s):  
Darawan Rinchai ◽  
Sabri Boughorbel ◽  
Scott Presnell ◽  
Charlie Quinn ◽  
Damien Chaussabel
Keyword(s):  
Gene Expression ◽  
Web Application ◽  
Large Scale ◽  
Contextual Information ◽  
Human Monocyte ◽  
Relevant Group ◽  
Interactive Data ◽  
Public Repositories ◽  
Data Browsing ◽  
Public Datasets

Systems-scale profiling approaches have become widely used in translational research settings. The resulting accumulation of large-scale datasets in public repositories represents a critical opportunity to promote insight and foster knowledge discovery. However, resources that can serve as an interface between biomedical researchers and such vast and heterogeneous dataset collections are needed in order to fulfill this potential. Recently, we have developed an interactive data browsing and visualization web application, the Gene Expression Browser (GXB). This tool can be used to overlay deep molecular phenotyping data with rich contextual information about analytes, samples and studies along with ancillary clinical or immunological profiling data. In this note, we describe a curated compendium of 93 public datasets generated in the context of human monocyte immunological studies, representing a total of 4,516 transcriptome profiles. Datasets were uploaded to an instance of GXB along with study description and sample annotations. Study samples were arranged in different groups. Ranked gene lists were generated based on relevant group comparisons. This resource is publicly available online athttp://monocyte.gxbsidra.org/dm3/landing.gsp.

scholarly journals E-commerce Web App in Azure Cloud: Considerations, Components of Implementation and Schematic Design

2021 ◽  
Vol 14 (4) ◽  
pp. 32
Author(s):  
Rasha Al-Mahrouqi ◽  
Khalsa Al Siyabi ◽  
Amani Al Nabhani ◽  
Salma Al-Hashemi ◽  
Shoukath Ali Muhammed
Keyword(s):  
Web Application ◽  
Transition Period ◽  
Cost Effective ◽  
Development Project ◽  
Cloud Services ◽  
Application Development ◽  
Careful Planning ◽  
Digital Strategy ◽  
Web App ◽  
Schematic Design

Consumers shifted their spending to the web due to the coronavirus (Covid-19) outbreak. Businesses and organizations that once mapped digital strategy with careful planning over a transition period, now forced to scale their initiatives in a matter of days. In this regard, we are motivated by the need to develop a scalable, highly available, resilient, secure, and cost-effective e-commerce web application for demonstrating how cloud services can be leveraged for implementing such applications. This paper is a part of the aforementioned web application development project, titled “A cloud-based e-commerce storefront prototype for SMEs in Oman”. In this paper, we discuss the system considerations, components of implementation, and the schematic design of the proposed software solution. This paper provides meaningful guidelines for companies that want to adopt cloud-based E-commerce web application to bring their products and services online without much upfront cost or initial investment.

scholarly journals COVIDrugNet: a network-based web tool to investigate the drugs currently in clinical trial to contrast COVID-19

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Luca Menestrina ◽  
Chiara Cabrelle ◽  
Maurizio Recanatini
Keyword(s):  
Clinical Trial ◽  
Web Application ◽  
Drug Target ◽  
Unmet Need ◽  
Target System ◽  
Holistic View ◽  
Online Tool ◽  
Clinical Drug Research ◽  
Web App ◽  
Clinical Drug

AbstractThe COVID-19 pandemic poses a huge problem of public health that requires the implementation of all available means to contrast it, and drugs are one of them. In this context, we observed an unmet need of depicting the continuously evolving scenario of the ongoing drug clinical trials through an easy-to-use, freely accessible online tool. Starting from this consideration, we developed COVIDrugNet (http://compmedchem.unibo.it/covidrugnet), a web application that allows users to capture a holistic view and keep up to date on how the clinical drug research is responding to the SARS-CoV-2 infection. Here, we describe the web app and show through some examples how one can explore the whole landscape of medicines in clinical trial for the treatment of COVID-19 and try to probe the consistency of the current approaches with the available biological and pharmacological evidence. We conclude that careful analyses of the COVID-19 drug-target system based on COVIDrugNet can help to understand the biological implications of the proposed drug options, and eventually improve the search for more effective therapies.

scholarly journals DOSCHEDA: a web application for interactive chemoproteomics data analysis

2017 ◽  
Vol 3 ◽  
pp. e129 ◽  
Author(s):  
Bruno Contrino ◽  
Eric Miele ◽  
Ronald Tomlinson ◽  
M. Paola Castaldi ◽  
Piero Ricchiuto
Keyword(s):  
Data Analysis ◽  
Protein Interactions ◽  
Web Application ◽  
Main Tool ◽  
Bioinformatics Analyses ◽  
User Input ◽  
R Language ◽  
Link Type ◽  
Input Dataset ◽  
Programming Knowledge

Background Mass Spectrometry (MS) based chemoproteomics has recently become a main tool to identify and quantify cellular target protein interactions with ligands/drugs in drug discovery. The complexity associated with these new types of data requires scientists with a limited computational background to perform systematic data quality controls as well as to visualize the results derived from the analysis to enable rapid decision making. To date, there are no readily accessible platforms specifically designed for chemoproteomics data analysis. Results We developed a Shiny-based web application named DOSCHEDA (Down Stream Chemoproteomics Data Analysis) to assess the quality of chemoproteomics experiments, to filter peptide intensities based on linear correlations between replicates, and to perform statistical analysis based on the experimental design. In order to increase its accessibility, DOSCHEDA is designed to be used with minimal user input and it does not require programming knowledge. Typical inputs can be protein fold changes or peptide intensities obtained from Proteome Discover, MaxQuant or other similar software. DOSCHEDA aggregates results from bioinformatics analyses performed on the input dataset into a dynamic interface, it encompasses interactive graphics and enables customized output reports. Conclusions DOSCHEDA is implemented entirely in R language. It can be launched by any system with R installed, including Windows, Mac OS and Linux distributions. DOSCHEDA is hosted on a shiny-server at https://doscheda.shinyapps.io/doscheda and is also available as a Bioconductor package (http://www.bioconductor.org/).

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