Mount Sinai tool analyzes, shares biomedical data

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A cloud-based tool at Mount Sinai’s Icahn School of Medicine can analyze and share large amounts of biomedical data.

The new tool, developed by the organization’s researchers, are said to work in a fraction of the time it takes traditional methods.

When it comes to RNA sequencing, the most common experimental method used to profile cells in biomedical research, it typically requires access to local high-performance computing resources and a time-consuming process.

In addition, the primary way to share biomedical research data has been through print publication in scientific journals.

However, what used to take months or even years to analyze, share and publish RNA sequencing data can now be achieved in just minutes, according to a paper describing the new tool in the November issue of Cell Systems.

The tool, called BioJupies, is freely available as a web-based application that enables researchers with no computer programming skills to perform RNA sequencing analysis without having to collaborate with bioinformaticians.

“Through an intuitive interface, novice users can rapidly generate tailored reports to analyze and visualize their own raw sequencing files, gene expression tables or fetch data from more than 9,000 published studies containing more than 300,000 preprocessed RNA-seq samples,” state the authors.

Developed with partial funding under the National Institutes of Health’s Data Commons pilot phase, BioJupies leverages a “cloud computing pipeline that reduces the cost of RNA-sequencing data processing to less than one cent per sample” and transforms the way researchers can communicate results of their studies, according to Mount Sinai.

Also See: NIH launches ‘Data Commons’ program to aid researchers

“As the amount of biomedical data generated continues to climb exponentially, so should the tools used to analyze and share them,” says Avi Ma’ayan, director of the Mount Sinai Center for Bioinformatics and senior author of the paper. “BioJupies not only accelerates the manner in which we analyze and interpret data, but it also provides a completely new way to share results with the global research community.”

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