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Understanding proteome quantification in an interactive learning module on Google Cloud Platform.
O'Connell, Kyle A; Kopchick, Benjamin; Carlson, Thad; Belardo, David; Byrum, Stephanie D.
Affiliation
  • O'Connell KA; Center for Information Technology, National Institutes of Health, 9000 Rockville Pike, Bethesda MD, 20892, United States.
  • Kopchick B; Health Data and AI, Deloitte Consulting LLP, 1919 N Lynn St, Arlington VA, 22209, United States.
  • Carlson T; Center for Information Technology, National Institutes of Health, 9000 Rockville Pike, Bethesda MD, 20892, United States.
  • Belardo D; Health Data and AI, Deloitte Consulting LLP, 1919 N Lynn St, Arlington VA, 22209, United States.
  • Byrum SD; Center for Information Technology, National Institutes of Health, 9000 Rockville Pike, Bethesda MD, 20892, United States.
Brief Bioinform ; 25(Supplement_1)2024 Jul 23.
Article in En | MEDLINE | ID: mdl-39041914
ABSTRACT
This manuscript describes the development of a resource module that is part of a learning platform named 'NIGMS Sandbox for Cloud-based Learning' https//github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers learning materials on protein quantification in an interactive format that uses appropriate cloud resources for data access and analyses. Quantitative proteomics is a rapidly growing discipline due to the cutting-edge technologies of high resolution mass spectrometry. There are many data types to consider for proteome quantification including data dependent acquisition, data independent acquisition, multiplexing with Tandem Mass Tag reporter ions, spectral counts, and more. As part of the NIH NIGMS Sandbox effort, we developed a learning module to introduce students to mass spectrometry terminology, normalization methods, statistical designs, and basics of R programming. By utilizing the Google Cloud environment, the learning module is easily accessible without the need for complex installation procedures. The proteome quantification module demonstrates the analysis using a provided TMT10plex data set using MS3 reporter ion intensity quantitative values in a Jupyter notebook with an R kernel. The learning module begins with the raw intensities, performs normalization, and differential abundance analysis using limma models, and is designed for researchers with a basic understanding of mass spectrometry and R programming language. Learners walk away with a better understanding of how to navigate Google Cloud Platform for proteomic research, and with the basics of mass spectrometry data analysis at the command line. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https//github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Software / Proteome / Proteomics / Cloud Computing Limits: Humans Language: En Journal: Brief Bioinform Journal subject: BIOLOGIA / INFORMATICA MEDICA Year: 2024 Document type: Article Affiliation country: United States Publication country: ENGLAND / ESCOCIA / GB / GREAT BRITAIN / INGLATERRA / REINO UNIDO / SCOTLAND / UK / UNITED KINGDOM

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Software / Proteome / Proteomics / Cloud Computing Limits: Humans Language: En Journal: Brief Bioinform Journal subject: BIOLOGIA / INFORMATICA MEDICA Year: 2024 Document type: Article Affiliation country: United States Publication country: ENGLAND / ESCOCIA / GB / GREAT BRITAIN / INGLATERRA / REINO UNIDO / SCOTLAND / UK / UNITED KINGDOM