Business intelligence strategies enables rapid analysis of quantitative proteomics data

Research output: Contribution to journalArticle


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.


External organisations
  • ETH Zürich
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Bioinformatics (Computational Biology)
Original languageEnglish
Pages (from-to)1-11
JournalJournal of Proteome Science and Computational Biology
Issue number1
Publication statusPublished - 2012 Nov 20
Publication categoryResearch