Year: 2020
Type: article
Abstract: BACKGROUND: In today's world of big data, computational analysis has become a key driver of biomedical research. High-performance computational facilities are capable of processing considerable volumes of data, yet often lack an easy-to-use interface to guide the user in supervising and adjusting bioinformatics analysis via a tablet or smartphone. RESULTS: To address this gap we proposed Telescope, a novel tool that interfaces with high-performance computational clusters to deliver an intuitive user interface for controlling and monitoring bioinformatics analyses in real-time. By leveraging last generation technology now ubiquitous to most researchers (such as smartphones), Telescope delivers a friendly user experience and manages conectivity and encryption under the hood. CONCLUSIONS: Telescope helps to mitigate the digital divide between wet and computational laboratories in contemporary biology. By delivering convenience and ease of use through a user experience not relying on expertise with computational clusters, Telescope can help researchers close the feedback loop between bioinformatics and experimental work with minimal impact on the performance of computational tools. Telescope is freely available at https://github.com/Mangul-Lab-USC/telescope.
Source: GigaScience
Language: English
Subfield: Information Systems and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,638
Awards: G1029717563, ABI Innovation: Collaborative Research: Computational framework for inference of metabolic pathway activity from RNA-seq data, QuBBD: Viral Evolution and Spread of Infectious Disease in Complex Network: Big Data Analysis and Modeling, QUBBD: VIRAL EVOLUTION AND SPREAD OF INFECTIOUS DISEASE IN COMPLEX NETWORK: BIG DATA ANALYSIS AND MODELING, CCF-BSF: AF: Small: Collaborative Research: Algorithmic Techniques for Inferring Transmission Networks from Noisy Sequencing Data, G7696340719, G9000170436
OpenAlex ID: W2976147703