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Business intelligence strategies enables rapid analysis of quantitative proteomics data

Malmström, Lars LU ; Nordenfelt, Pontus LU and Malmström, Johan LU (2012) In Journal of Proteome Science and Computational Biology 1(1). p.1-11
Abstract
Integration of high throughput data with online data resources is critical for data analysis and hypothesis generation. Relational databases facilitate the data integration, but larger amounts of data and the growth of the online data resources can slow down the data analysis process. We have developed a proof-of-principle software tool using concepts from the business intelligence field to enable fast, reliable and reproducible quantitative analysis of mass spectrometry data. The software allows the user to apply customizable analysis protocols that aggregates the data and stores it in fast and redundant data structures. The user then interacts with these data structures using web-based viewers to gauge data quality, analyze global... (More)
Integration of high throughput data with online data resources is critical for data analysis and hypothesis generation. Relational databases facilitate the data integration, but larger amounts of data and the growth of the online data resources can slow down the data analysis process. We have developed a proof-of-principle software tool using concepts from the business intelligence field to enable fast, reliable and reproducible quantitative analysis of mass spectrometry data. The software allows the user to apply customizable analysis protocols that aggregates the data and stores it in fast and redundant data structures. The user then interacts with these data structures using web-based viewers to gauge data quality, analyze global properties of the data set and then explore the underlying raw data, which is stored in a tightly integrated relational database. To demonstrate the software we designed an experiment to describe the differentiation of a leukemic cell line, HL-60, to a neutrophil-like phenotype at the molecular level. The concepts described in this paper demonstrates how the new data model enabled rapid overview of the complete experiment in regard of global statistics, statistical calculations of expression profiles and integration with online resources providing deep insight into the data within a few hours. (Less)
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author
organization
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type
Contribution to journal
publication status
published
subject
in
Journal of Proteome Science and Computational Biology
volume
1
issue
1
pages
1 - 11
publisher
Herbert Open Access Journals
ISSN
2050-2273
DOI
10.7243/2050-2273-1-5
language
English
LU publication?
yes
id
258bcf8a-2f15-42d0-81e4-1936ec8036ee
date added to LUP
2017-03-03 10:07:38
date last changed
2017-03-03 10:07:38
@article{258bcf8a-2f15-42d0-81e4-1936ec8036ee,
  abstract     = {Integration of high throughput data with online data resources is critical for data analysis and hypothesis generation. Relational databases facilitate the data integration, but larger amounts of data and the growth of the online data resources can slow down the data analysis process. We have developed a proof-of-principle software tool using concepts from the business intelligence field to enable fast, reliable and reproducible quantitative analysis of mass spectrometry data. The software allows the user to apply customizable analysis protocols that aggregates the data and stores it in fast and redundant data structures. The user then interacts with these data structures using web-based viewers to gauge data quality, analyze global properties of the data set and then explore the underlying raw data, which is stored in a tightly integrated relational database. To demonstrate the software we designed an experiment to describe the differentiation of a leukemic cell line, HL-60, to a neutrophil-like phenotype at the molecular level. The concepts described in this paper demonstrates how the new data model enabled rapid overview of the complete experiment in regard of global statistics, statistical calculations of expression profiles and integration with online resources providing deep insight into the data within a few hours.},
  author       = {Malmström, Lars and Nordenfelt, Pontus and Malmström, Johan},
  issn         = {2050-2273},
  language     = {eng},
  month        = {11},
  number       = {1},
  pages        = {1--11},
  publisher    = {Herbert Open Access Journals},
  series       = {Journal of Proteome Science and Computational Biology},
  title        = {Business intelligence strategies enables rapid analysis of quantitative proteomics data},
  url          = {http://dx.doi.org/10.7243/2050-2273-1-5},
  volume       = {1},
  year         = {2012},
}