GNPS Dashboard: Collaborative Analysis of Mass Spectrometry Data in the Web Browser

Access to web-based platforms has enabled scientists to perform research remotely. A critical aspect of mass spectrometry data analysis is the inspection, analysis, and visualization of the raw data to validate data quality and confirm statistical observations. We developed the GNPS Dashboard, a web-based data visualization tool, to facilitate synchronous collaborative inspection, visualization, and analysis of private and public mass spectrometry data remotely.

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Facilitating Collaborative Analysis in SWAN

EPJ Web of Conferences ◽

10.1051/epjconf/201921407022 ◽

2019 ◽

Vol 214 ◽

pp. 07022

Author(s):

Enrico Bocchi ◽

Diogo Castro ◽

Hugo Gonzalez ◽

Massimo Lamanna ◽

Pere Mato ◽

...

Keyword(s):

Data Analysis ◽

Special Kind ◽

Service Model ◽

Web Browser ◽

Web Based ◽

Single File ◽

Cloud Storage Service ◽

Collaborative Analysis ◽

Interactive Data ◽

The Ideal

SWAN (Service for Web-based ANalysis) is a CERN service that allows users to perform interactive data analysis in the cloud, in a “software as a service” model. It is built upon the widely-used Jupyter notebooks, allowing users to write - and run - their data analysis using only a web browser. By connecting to SWAN, users have immediate access to storage, software and computing resources that CERN provides and that they need to do their analyses. Besides providing an easier way of producing scientific code and results, SWAN is also a great tool to create shareable content. From results that need to be reproducible, to tutorials and demonstrations for outreach and teaching, Jupyter notebooks are the ideal way of distributing this content. In one single file, users can include their code, the results of the calculations and all the relevant textual information. By sharing them, it allows others to visualize, modify, personalize or even re-run all the code. In that sense, this paper describes the efforts made to facilitate sharing in SWAN. Given the importance of collaboration in our scientific community, we have brought the sharing functionality from CERNBox, CERN’s cloud storage service, directly inside SWAN. SWAN users have available a new and redesigned interface where theycan share “Projects”: a special kind of folder containing notebooks and other files, e.g., like input datasets and images. When a user shares a Project with some other users, the latter can immediately see andwork with the contents of that project from SWAN.

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A bioinformatics approach for mass spectrometry data processing: Applications to proteomics and small molecule analysis

Spectroscopy An International Journal ◽

10.1155/2002/529268 ◽

2002 ◽

Vol 16 (2) ◽

pp. 81-87

Author(s):

Martin Sonderegger ◽

Kristin Staniszewski ◽

Andrew Meyers ◽

Gary Siuzdak

Keyword(s):

Mass Spectrometry ◽

Data Processing ◽

Portable Document Format ◽

Mass Spectrometry Data ◽

Web Browser ◽

Mass Spectrometers ◽

Web Based ◽

Mass Spectral ◽

Microsoft Access ◽

To Receive

We have developed a web‒based software system,JULIAN, that simplifies the process of relaying mass spectral information for chemists, protein chemists, biochemists and all others performing mass spectrometry experiments through a centralized mass spectrometry laboratory.JULIANallows for relative ease in submitting compound information as well as instant access to analysis results from any networked computer equipped with a web browser. Compound information is centralized in a Microsoft Access database and results are available in Adobe's portable document format (PDF) from an NT4 server. This gives researchers the ability to easily obtain data and allows the analysts in the mass spectrometry lab to browse analysis results when assisting researchers with their inquiries. Due to this web‒based designJULIANis independent of the mass spectrometers' hardware and operating system. Approximately seven hundred on‒site and off‒site users have utilizedJULIANtransmitting over 40,000 analyses. The conversion from paper to electronic mass spectrometry data processing has enabled our Center to receive compound information, perform analysis, and relay the results four times faster than required previously.

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GNPS Dashboard: collaborative exploration of mass spectrometry data in the web browser

Nature Methods ◽

10.1038/s41592-021-01339-5 ◽

2021 ◽

Author(s):

Daniel Petras ◽

Vanessa V. Phelan ◽

Deepa Acharya ◽

Andrew E. Allen ◽

Allegra T. Aron ◽

...

Keyword(s):

Mass Spectrometry ◽

Mass Spectrometry Data ◽

Web Browser ◽

Collaborative Exploration ◽

The Web

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mzServer: Web-based Programmatic Access for Mass Spectrometry Data Analysis

Molecular & Cellular Proteomics ◽

10.1074/mcp.m110.003988 ◽

2011 ◽

Vol 10 (5) ◽

pp. M110.003988 ◽

Cited By ~ 7

Author(s):

Manor Askenazi ◽

James T. Webber ◽

Jarrod A. Marto

Keyword(s):

Mass Spectrometry ◽

Data Analysis ◽

Mass Spectrometry Data ◽

Web Based ◽

Programmatic Access

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CLMSVault: A Software Suite for Protein Cross-Linking Mass-Spectrometry Data Analysis and Visualization

Journal of Proteome Research ◽

10.1021/acs.jproteome.7b00205 ◽

2017 ◽

Vol 16 (7) ◽

pp. 2645-2652 ◽

Cited By ~ 11

Author(s):

Mathieu Courcelles ◽

Jasmin Coulombe-Huntington ◽

Émilie Cossette ◽

Anne-Claude Gingras ◽

Pierre Thibault ◽

...

Keyword(s):

Mass Spectrometry ◽

Data Analysis ◽

Mass Spectrometry Data ◽

Cross Linking ◽

Software Suite ◽

Protein Cross Linking

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Advancements in capturing and mining mass spectrometry data are transforming natural products research

Natural Product Reports ◽

10.1039/d1np00040c ◽

2021 ◽

Author(s):

Scott A. Jarmusch ◽

Justin J. J. van der Hooft ◽

Pieter C. Dorrestein ◽

Alan K. Jarmusch

Keyword(s):

Mass Spectrometry ◽

Data Mining ◽

Natural Products ◽

Data Analysis ◽

Community Participation ◽

Mass Spectrometry Data ◽

Metabolomics Data ◽

Analysis Tools ◽

Public Data ◽

Potential Use

This review covers the current and potential use of mass spectrometry-based metabolomics data mining in natural products. Public data, metadata, databases and data analysis tools are critical. The value and success of data mining rely on community participation.

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