Faculty Opinions recommendation of Web-based visual analysis for high-throughput genomics.

2013 ◽  
Author(s):  
Rafael Zardoya ◽  
Diego San Mauro
Keyword(s):  
High Throughput ◽  
Visual Analysis ◽  
Web Based

scholarly journals Web-based visual analysis for high-throughput genomics

BMC Genomics ◽  
2013 ◽  
Vol 14 (1) ◽  
pp. 397 ◽  
Author(s):  
Jeremy Goecks ◽  
Carl Eberhard ◽  
Tomithy Too ◽  
Anton Nekrutenko ◽  
James Taylor ◽  
...  
Keyword(s):  
High Throughput ◽  
Visual Analysis ◽  
Web Based

scholarly journals Intuitive Web-Based Experimental Design for High-Throughput Biomedical Data

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Andreas Friedrich ◽  
Erhan Kenar ◽  
Oliver Kohlbacher ◽  
Sven Nahnsen
Keyword(s):  
Big Data ◽  
Experimental Design ◽  
High Throughput ◽  
Large Scale ◽  
Integrated Design ◽  
Added Value ◽  
Biomedical Data ◽  
Data Generation ◽  
Web Based ◽  
Data Annotation

Big data bioinformatics aims at drawing biological conclusions from huge and complex biological datasets. Added value from the analysis of big data, however, is only possible if the data is accompanied by accurate metadata annotation. Particularly in high-throughput experiments intelligent approaches are needed to keep track of the experimental design, including the conditions that are studied as well as information that might be interesting for failure analysis or further experiments in the future. In addition to the management of this information, means for an integrated design and interfaces for structured data annotation are urgently needed by researchers. Here, we propose a factor-based experimental design approach that enables scientists to easily create large-scale experiments with the help of a web-based system. We present a novel implementation of a web-based interface allowing the collection of arbitrary metadata. To exchange and edit information we provide a spreadsheet-based, humanly readable format. Subsequently, sample sheets with identifiers and metainformation for data generation facilities can be created. Data files created after measurement of the samples can be uploaded to a datastore, where they are automatically linked to the previously created experimental design model.

scholarly journals Panomicon: A web-based environment for interactive, visual analysis of multi-omics data

Heliyon ◽  
2020 ◽  
Vol 6 (8) ◽  
pp. e04618
Author(s):  
Rodolfo S. Allendes Osorio ◽  
Johan T. Nyström-Persson ◽  
Yosui Nojima ◽  
Yuji Kosugi ◽  
Kenji Mizuguchi ◽  
...  
Keyword(s):  
Visual Analysis ◽  
Omics Data ◽  
Web Based ◽  
Interactive Visual Analysis

scholarly journals epiTAD: a web application for visualizing high throughput chromosome conformation capture data in the context of genetic epidemiology

2018 ◽  
Author(s):  
Jordan H. Creed ◽  
Garrick Aden-Buie ◽  
Alvaro N. Monteiro ◽  
Travis A. Gerke
Keyword(s):  
High Throughput ◽  
Genetic Epidemiology ◽  
In Silico ◽  
Web Application ◽  
Chromosome Conformation Capture ◽  
Chromosome Conformation ◽  
Web Based ◽  
Genome Wide ◽  
Public Data ◽  
Complex Scale

AbstractThe increasing availability of public data resources coupled with advancements in genomic technology has created greater opportunities for researchers to examine the genome on a large and complex scale. To meet the need for integrative genome wide exploration, we present epiTAD. This web-based tool enables researchers to compare genomic structures and annotations across multiple databases and platforms in an interactive manner in order to facilitate in silico discovery. epiTAD can be accessed at https://apps.gerkelab.com/epiTAD/.

scholarly journals BioBlocks: Programming protocols in biology made easier

2016 ◽  
Author(s):  
Vishal Gupta ◽  
Jesus Irimia ◽  
Ivan Pau ◽  
Alfonso Rodriguez-Paton
Keyword(s):  
High Throughput ◽  
Reproducibility Of Results ◽  
Visual Development ◽  
Development Environment ◽  
Web Based ◽  
On Demand ◽  
Fluid Handling ◽  
High Throughput Experimentation ◽  
Open Standard ◽  
Experimental Protocols

The methods to execute biological experiments are evolving. Affordable fluid handling robots and on-demand biology enterprises are making automating entire experiments a reality. Automation offers the benefit of high-throughput experimentation, rapid prototyping and improved reproducibility of results. However, learning to automate and codify experiments is a difficult task as it requires programming expertise. Here, we present a web-based visual development environment called BioBlocks for describing experimental protocols in biology. It is based on Google's Blockly and Scratch, and requires little or no experience in computer programming to automate the execution of experiments. The experiments can be specified, saved, modified and shared between multiple users in an easy manner. BioBlocks is open-source and can be customized to execute protocols on local robotic platforms or remotely i.e. in the cloud. It aims to serve as a 'de facto' open standard for programming protocols in Biology.

scholarly journals NASQAR: A web-based platform for high-throughput sequencing data analysis and visualization

2019 ◽  
Author(s):  
Ayman Yousif ◽  
Nizar Drou ◽  
Jillian Rowe ◽  
Mohammed Khalfan ◽  
Kristin C Gunsalus
Keyword(s):  
New York ◽  
Data Analysis ◽  
Open Source ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Web Applications ◽  
Rna Seq ◽  
Sequencing Data ◽  
Web Based ◽  
Link Type

AbstractBackgroundAs high-throughput sequencing applications continue to evolve, the rapid growth in quantity and variety of sequence-based data calls for the development of new software libraries and tools for data analysis and visualization. Often, effective use of these tools requires computational skills beyond those of many researchers. To ease this computational barrier, we have created a dynamic web-based platform, NASQAR (Nucleic Acid SeQuence Analysis Resource).ResultsNASQAR offers a collection of custom and publicly available open-source web applications that make extensive use of a variety of R packages to provide interactive data analysis and visualization. The platform is publicly accessible at http://nasqar.abudhabi.nyu.edu/. Open-source code is on GitHub at https://github.com/nasqar/NASQAR, and the system is also available as a Docker image at https://hub.docker.com/r/aymanm/nasqarall. NASQAR is a collaboration between the core bioinformatics teams of the NYU Abu Dhabi and NYU New York Centers for Genomics and Systems Biology.ConclusionsNASQAR empowers non-programming experts with a versatile and intuitive toolbox to easily and efficiently explore, analyze, and visualize their Transcriptomics data interactively. Popular tools for a variety of applications are currently available, including Transcriptome Data Preprocessing, RNA-seq Analysis (including Single-cell RNA-seq), Metagenomics, and Gene Enrichment.

scholarly journals TACITuS: transcriptomic data collector, integrator, and selector on big data platform

2019 ◽  
Vol 20 (S9) ◽  
Author(s):  
Salvatore Alaimo ◽  
Antonio Di Maria ◽  
Dennis Shasha ◽  
Alfredo Ferro ◽  
Alfredo Pulvirenti
Keyword(s):  
High Throughput ◽  
Rna Seq ◽  
Web Based ◽  
Large Bandwidth ◽  
Specific Analysis ◽  
Galaxy Environment ◽  
Public Repositories ◽  
Microarray Datasets ◽  
User Friendly ◽  
Ngs Data

Abstract Background Several large public repositories of microarray datasets and RNA-seq data are available. Two prominent examples include ArrayExpress and NCBI GEO. Unfortunately, there is no easy way to import and manipulate data from such resources, because the data is stored in large files, requiring large bandwidth to download and special purpose data manipulation tools to extract subsets relevant for the specific analysis. Results TACITuS is a web-based system that supports rapid query access to high-throughput microarray and NGS repositories. The system is equipped with modules capable of managing large files, storing them in a cloud environment and extracting subsets of data in an easy and efficient way. The system also supports the ability to import data into Galaxy for further analysis. Conclusions TACITuS automates most of the pre-processing needed to analyze high-throughput microarray and NGS data from large publicly-available repositories. The system implements several modules to manage large files in an easy and efficient way. Furthermore, it is capable deal with Galaxy environment allowing users to analyze data through a user-friendly interface.

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