scholarly journals CrosstalkNet: mining large-scale bipartite co-expression networks to characterize epi-stroma crosstalk

2017 ◽  
Author(s):  
Venkata Manem ◽  
George Adam ◽  
Tina Gruosso ◽  
Mathieu Gigoux ◽  
Nicholas Bertos ◽  
...  
Keyword(s):  
Stromal Cells ◽  
Large Scale ◽  
Network Visualization ◽  
Visualization Tool ◽  
Biological Processes ◽  
Web Based ◽  
Link Type ◽  
A Cell ◽  
Large Scale Networks ◽  
User Friendly

ABSTRACTBackground:Over the last several years, we have witnessed the metamorphosis of network biology from being a mere representation of molecular interactions to models enabling inference of complex biological processes. Networks provide promising tools to elucidate intercellular interactions that contribute to the functioning of key biological pathways in a cell. However, the exploration of these large-scale networks remains a challenge due to their high-dimensionality.Results:CrosstalkNet is a user friendly, web-based network visualization tool to retrieve and mine interactions in large-scale bipartite co-expression networks. In this study, we discuss the use of gene co-expression networks to explore the rewiring of interactions between tumor epithelial and stromal cells. We show how CrosstalkNet can be used to efficiently visualize, mine, and interpret large co-expression networks representing the crosstalk occurring between the tumour and its microenvironment.Conclusion:CrosstalkNet serves as a tool to assist biologists and clinicians in exploring complex, large interaction graphs to obtain insights into the biological processes that govern the tumor epithelial-stromal crosstalk. A comprehensive tutorial along with case studies are provided with the application.Availability:The web-based application is available at the following location: http://epistroma.pmgenomics.ca/app/. The code is open-source and freely available from http://github.com/bhklab/EpiStroma-webapp.Contact:[email protected]

lncRNATargets: A platform for lncRNA target prediction based on nucleic acid thermodynamics

2016 ◽  
Vol 14 (04) ◽  
pp. 1650016 ◽  
Author(s):  
Ruifeng Hu ◽  
Xiaobo Sun
Keyword(s):  
Free Energy ◽  
Nucleic Acid ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Binding Free Energy ◽  
Target Prediction ◽  
Easy Access ◽  
Web Based ◽  
Size Limitation ◽  
User Friendly

Many studies have supported that long noncoding RNAs (lncRNAs) perform various functions in various critical biological processes. Advanced experimental and computational technologies allow access to more information on lncRNAs. Determining the functions and action mechanisms of these RNAs on a large scale is urgently needed. We provided lncRNATargets, which is a web-based platform for lncRNA target prediction based on nucleic acid thermodynamics. The nearest-neighbor (NN) model was used to calculate binging-free energy. The main principle of NN model for nucleic acid assumes that identity and orientation of neighbor base pairs determine stability of a given base pair. lncRNATargets features the following options: setting of a specific temperature that allow use not only for human but also for other animals or plants; processing all lncRNAs in high throughput without RNA size limitation that is superior to any other existing tool; and web-based, user-friendly interface, and colored result displays that allow easy access for nonskilled computer operators and provide better understanding of results. This technique could provide accurate calculation on the binding-free energy of lncRNA-target dimers to predict if these structures are well targeted together. lncRNATargets provides high accuracy calculations, and this user-friendly program is available for free at http://www.herbbol.org:8001/lrt/ .

scholarly journals Cell BLAST: Searching large-scale scRNA-seq databases via unbiased cell embedding

2019 ◽  
Author(s):  
Zhi-Jie Cao ◽  
Lin Wei ◽  
Shen Lu ◽  
De-Chang Yang ◽  
Ge Gao
Keyword(s):  
Real World ◽  
Large Scale ◽  
Web Server ◽  
Reference Database ◽  
Link Type ◽  
One Stop ◽  
User Friendly

AbstractAn effective and efficient cell-querying method is critical for integrating existing scRNA-seq data and annotating new data. Herein, we present Cell BLAST, an accurate and robust cell-querying method. Powered by a well-curated reference database and a user-friendly Web server, Cell BLAST (http://cblast.gao-lab.org) provides a one-stop solution for real-world scRNA-seq cell querying and annotation.

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

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.  

scholarly journals BtToxin_Digger: a comprehensive and high-throughput pipeline for mining toxin protein genes from Bacillus thuringiensis

2020 ◽  
Author(s):  
Hualin Liu ◽  
Jinshui Zheng ◽  
Dexin Bo ◽  
Yun Yu ◽  
Weixing Ye ◽  
...  
Keyword(s):  
Bacillus Thuringiensis ◽  
High Throughput ◽  
Large Scale ◽  
Web Based ◽  
Bt Toxin ◽  
Link Type ◽  
Toxin Genes ◽  
Toxin Protein ◽  
Mining Tool ◽  
Downstream Analysis

SummaryBacillus thuringiensis (Bt) which is a spore-forming gram-positive bacterium, has been used as the most successful microbial pesticide for decades. Its toxin genes (cry) have been successfully used for the development of GM crops against pests. We have previously developed a web-based insecticidal gene mining tool BtToxin_scanner, which has been proved to be the most important method for mining cry genes from Bt genome sequences. To facilitate efficiently mining major toxin genes and novel virulence factors from large-scale Bt genomic data, we re-design this tool with a new workflow. Here we present BtToxin_Digger, a comprehensive, high-throughput, and easy-to-use Bt toxin mining tool. It runs fast and can get rich, accurate, and useful results for downstream analysis and experiment designs. Moreover, it can also be used to mine other targeting genes from large-scale genome and metagenome data with the addition of other query sequences.Availability and ImplementationThe BtToxin_Digger codes and instructions are freely available at https://github.com/BMBGenomics/BtToxin_Digger. A web server of BtToxin_Digger can be found at http://bcam.hzau.edu.cn/[email protected]; [email protected].

scholarly journals Cytoscape: the network visualization tool for GenomeSpace workflows

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 151 ◽  
Author(s):  
Barry Demchak ◽  
Tim Hull ◽  
Michael Reich ◽  
Ted Liefeld ◽  
Michael Smoot ◽  
...  
Keyword(s):  
User Experience ◽  
Critical Analysis ◽  
Genomic Analysis ◽  
Network Visualization ◽  
Visualization Tool ◽  
Web Browser ◽  
Web Based ◽  
Data Flows ◽  
Modern Genomic ◽  
Selection Of

Modern genomic analysis often requires workflows incorporating multiple best-of-breed tools. GenomeSpace is a web-based visual workbench that combines a selection of these tools with mechanisms that create data flows between them. One such tool is Cytoscape 3, a popular application that enables analysis and visualization of graph-oriented genomic networks. As Cytoscape runs on the desktop, and not in a web browser, integrating it into GenomeSpace required special care in creating a seamless user experience and enabling appropriate data flows. In this paper, we present the design and operation of the Cytoscape GenomeSpace app, which accomplishes this integration, thereby providing critical analysis and visualization functionality for GenomeSpace users. It has been downloaded over 850 times since the release of its first version in September, 2013.

Abstract 2597: Centralized and Study-specific Phenotyping in a Large-scale Stroke Genetics Study: An Analysis from the NINDS Stroke Genetics Network (SiGN)

Stroke ◽  
2012 ◽  
Vol 43 (suppl_1) ◽  
Author(s):  
Bradford B Worrall ◽  
Alejandro Rabinstein ◽  
Dale M Gamble ◽  
Kevin M Barrett ◽  
Shaneela Malik ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Large Scale ◽  
Association Studies ◽  
Web Based ◽  
Stroke Treatment ◽  
Automated Algorithm ◽  
Genome Association ◽  
Whole Genome Association ◽  
Toast Classification ◽  
User Friendly

Background: The Stroke Genetics Network (SiGN) funded by the NINDS aims to identify genetic risk factors in ischemic stroke using whole-genome association studies (GWAS). High quality phenotyping is crucial to successful application of GWAS. As a heterogenous disorder, stroke poses specific challenges. The Trial of Org 10172 in Acute Stroke Treatment (TOAST) classification is a broadly used, but its validity is challenged especially when performed by multiple investigators with differing interpretations of the system. The Causative Classification System for Ischemic Stroke (CCS) system is a new, web-based, and computerized algorithm that integrates clinical, diagnostic, and etiologic stroke characteristics in an evidence-based manner ( ccs.mgh.harvard.edu ) to generate subtypes. Methods: In planning the SiGN proposal, a sample of 20 coded charts were collected from a subset of participating studies to assess feasibility of central adjudication and comparability to study-specific TOAST. Two central adjudicators reviewed all records and generated TOAST and CCS subtypes. These were compared to study-specific TOAST subtype and the CCS phenotype generated for SiGN by local trained adjudicators. CCS data is now available for 7134 included cases using both a 5 and a 7 category system as defined in the table . Results: All 4 phenotypes were available for 115 ischemic stroke cases from 6 studies in SiGN. Basic demographics were 54% women, 63% white, and median age between 65-74. Table 1 provides the agreement between the various subtypes. Table 2 describes the types of disagreement. Conclusions: Central adjudication with only two adjudicators and curated medical records yielded more consistent subtyping independent of phenotyping system. The agreement for TOAST was higher than published rates by independent groups (∼0.50). In contrast, the agreement for CCS was lower than previously published (0.85-0.95). Site adjudicators' familiarity with TOAST and inexperience with CCS may contribute. Although CCS is an automated algorithm and has a number of user friendly features, our findings suggest that formal training and certification process before starting to use CCS may be worthwhile to achieve optimal benefit from the system.

scholarly journals A Medicago truncatula Metabolite Atlas Enables the Visualization of Differential Accumulation of Metabolites in Root Tissues

Metabolites ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 238
Author(s):  
Clayton Kranawetter ◽  
Shuai Zeng ◽  
Trupti Joshi ◽  
Lloyd W. Sumner
Keyword(s):  
Medicago Truncatula ◽  
Large Scale ◽  
Spatial Localization ◽  
Plant Roots ◽  
Visualization Tool ◽  
Elongation Zone ◽  
Web Based ◽  
Metabolite Profiles ◽  
Metabolite Accumulation ◽  
Root Tissues

Plant roots are composed of many differentiated tissue types, with each tissue exhibiting differential quantitative and qualitative accumulation of metabolites. The large-scale nontargeted metabolite profiles of these differentiated tissues are complex, which complicates the interpretation and development of hypotheses relative to the biological roles of differentially localized metabolites. Thus, we created a data visualization tool to aid in the visualization and understanding of differential metabolite accumulations in Medicago truncatula roots. This was achieved through the development of the Medicago truncatula Metabolite Atlas based upon an adaptation of the Arabidopsis Electronic Fluorescent Pictograph (eFP) Browser. Medicago truncatula roots were dissected into border cells, root cap, elongation zone, mature root, and root secretions. Each tissue was then analyzed by UHPLC-QTOF-MS and GC-Q-MS. Data were uploaded into a MySQL database and displayed in the Medicago truncatula Metabolite Atlas. The data revealed unique differential spatial localization of many metabolites, some of which are discussed here. Ultimately, the Medicago truncatula Metabolite Atlas compiles metabolite data into a singular, useful, and publicly available web-based tool that enables the visualization and understanding of differential metabolite accumulation and spatial localization.

scholarly journals PsicquicGraph, a BioJS component to visualize molecular interactions from PSICQUIC servers

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 44 ◽  
Author(s):  
Jose M. Villaveces ◽  
Rafael C. Jimenez ◽  
Bianca H. Habermann
Keyword(s):  
Small Molecules ◽  
Molecular Interactions ◽  
Large Scale ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Web Based ◽  
High Throughput Data ◽  
Link Type ◽  
Large Scale Data ◽  
Scale Data

Summary: Protein interaction networks have become an essential tool in large-scale data analysis, integration, and the visualization of high-throughput data in the context of complex cellular networks. Many individual databases are available that provide information on binary interactions of proteins and small molecules. Community efforts such as PSICQUIC aim to unify and standardize information emanating from these public databases. Here we introduce PsicquicGraph, an open-source, web-based visualization component for molecular interactions from PSIQUIC services. Availability: PsicquicGraph is freely available at the BioJS Registry for download and enhancement. Instructions on how to use the tool are available here http://goo.gl/kDaIgZ and the source code can be found at http://github.com/biojs/biojs and DOI:10.5281/zenodo.7709.

scholarly journals A repository of web-based bioinformatics resources developed in India

2020 ◽  
Author(s):  
Abhishek Agarwal ◽  
Piyush Agrawal ◽  
Aditi Sharma ◽  
Vinod Kumar ◽  
Chirag Mugdal ◽  
...  
Keyword(s):  
Scientific Community ◽  
Biological Databases ◽  
Web Interface ◽  
Web Based ◽  
Link Type ◽  
Complete Detail ◽  
User Friendly

AbstractIndiaBioDb (https://webs.iiitd.edu.in/raghava/indiabiodb/) is a manually curated comprehensive repository of bioinformatics resources developed and maintained by Indian researchers. This repository maintains information about 543 freely accessible functional resources that include around 258 biological databases. Each entry provides a complete detail about a resource that includes the name of resources, web link, detail of publication, information about the corresponding author, name of institute, type of resource. A user-friendly searching module has been integrated, which allows users to search our repository on any field. In order to retrieve categorized information, we integrate the browsing facility in this repository. This database can be utilized for extracting the useful information regarding the present scenario of bioinformatics inclusive of all research labs funded by government and private bodies of India. In addition to web interface, we also developed mobile to facilitate the scientific community.

scholarly journals Biblio-MetReS for user-friendly mining of genes and biological processes in scientific documents

2014 ◽  
Author(s):  
Anabel Usie ◽  
Hiren Karathia ◽  
Ivan Teixidó ◽  
Francesc Solsona ◽  
Rui Alves
Keyword(s):  
Text Mining ◽  
Network Reconstruction ◽  
Biological Processes ◽  
Web Based ◽  
New Strategy ◽  
Programming Skills ◽  
Run Time ◽  
Active Research ◽  
The Cost ◽  
User Friendly

One way to initiate the reconstruction of molecular circuits is by using automated text-mining techniques. Developing more efficient methods for such reconstruction is a topic of active research, and those methods are typically included by bioinformaticians in pipelines used to mine and curate large literature datasets. Nevertheless, experimental biologists have a limited number of available user-friendly tools that use text-mining for network reconstruction and require no programming skills to use. One of these tools is Biblio-MetReS. Originally, this tool permitted an on-the-fly analysis of documents contained in a number of web-based literature databases to identify co-occurrence of proteins/genes. This approach ensured results that were always up-to-date with the latest live version of the databases. However, this “up-to-dateness” came at the cost of large execution times. Here we report an evolution of the application Biblio-MetReS that permits constructing co-occurrence networks for genes, GO process, Pathways, or any combination of the three types of entities and graphically represent those entities. We show that the performance of Biblio-MetReS in identifying gene co-occurrence is as least as good as that of other comparable applications (STRING and iHOP). In addition, we also show that the identification of GO processes is on par to that reported in the latest BioCreAtIvE challenge. Finally, we also report the implementation of a new strategy that combines on-the-fly analysis of new documents with preprocessed information from documents that were encountered in previous analyses. This combination simultaneously decreases program run time and maintains “up-to-dateness” of the results. Availability: http://metres.udl.cat/index.php/downloads Contact: [email protected]

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