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Deploying Mass Spectrometric Data Analysis in the Amazon AWS Cloud Computing Environment.
Katz, Jonathan E.
Affiliation
  • Katz JE; Lawrence J. Ellison Institute for Transformative Medicine of USC, Los Angeles, CA, USA. jonathan@proteowizard.org.
Methods Mol Biol ; 2271: 375-397, 2021.
Article in En | MEDLINE | ID: mdl-33908021
There are many advantages for deploying a mass spectrometry workflow to the cloud. While "cloud computing" can have many meanings, in this case, I am simply referring to a virtual computer that is remotely accessible over the Internet. This "computer" can have as many or few resources (CPU, RAM, disk space, etc.) as your demands require and those resources can be changed as you need without requiring complete reinstalls. Systems can be easily "checkpointed" and restored. I will describe how to deploy virtualized, remotely accessible computers on which you can perform your basic mass spectrometry data analysis. This use is a quite restricted microcosm of what is available under the umbrella of "cloud computing" but it is also the (useful!) niche use for which straightforward how-to documentation is lacking.This chapter is intended for people with little or no experience in creating cloud computing instances. Executing the steps in this chapter, will empower you to instantiate a computer with the performance of your choosing with preconfigured software already installed using the Amazon Web Service (AWS) suite of tools. You can use this for use cases that span when you need limited access to high end computing thru when you give your collaborators access to preconfigured computers to look at their data.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Mass Spectrometry / Cloud Computing Type of study: Prognostic_studies Language: En Journal: Methods Mol Biol Journal subject: BIOLOGIA MOLECULAR Year: 2021 Document type: Article Affiliation country: Country of publication:

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Mass Spectrometry / Cloud Computing Type of study: Prognostic_studies Language: En Journal: Methods Mol Biol Journal subject: BIOLOGIA MOLECULAR Year: 2021 Document type: Article Affiliation country: Country of publication: