Protein quantification across hundreds of experimental conditions.

TitleProtein quantification across hundreds of experimental conditions.
Publication TypeJournal Articles
Year of Publication2009
AuthorsKhan Z, Bloom JS, Garcia BA, Singh M, Kruglyak L
JournalProc Natl Acad Sci U S A
Date Published2009 Sep 15
Keywordsalgorithms, Animals, Automatic Data Processing, Chromatography, Liquid, Databases, Factual, Fungal Proteins, HUMANS, Isotopes, Mice, Proteins, proteomics, Tandem Mass Spectrometry

Quantitative studies of protein abundance rarely span more than a small number of experimental conditions and replicates. In contrast, quantitative studies of transcript abundance often span hundreds of experimental conditions and replicates. This situation exists, in part, because extracting quantitative data from large proteomics datasets is significantly more difficult than reading quantitative data from a gene expression microarray. To address this problem, we introduce two algorithmic advances in the processing of quantitative proteomics data. First, we use space-partitioning data structures to handle the large size of these datasets. Second, we introduce techniques that combine graph-theoretic algorithms with space-partitioning data structures to collect relative protein abundance data across hundreds of experimental conditions and replicates. We validate these algorithmic techniques by analyzing several datasets and computing both internal and external measures of quantification accuracy. We demonstrate the scalability of these techniques by applying them to a large dataset that comprises a total of 472 experimental conditions and replicates.

Alternate JournalProc. Natl. Acad. Sci. U.S.A.
PubMed ID19717460
PubMed Central IDPMC2732709
Grant List / / Howard Hughes Medical Institute / United States