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Proc Natl Acad Sci U S A. 2009 Sep 15;106(37):15544-8. doi: 10.1073/pnas.0904100106. Epub 2009 Aug 26.

Protein quantification across hundreds of experimental conditions.

Author information

1
Department of Computer Science, Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540, USA. zkhan@princeton.edu

Abstract

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.

PMID:
19717460
PMCID:
PMC2732709
DOI:
10.1073/pnas.0904100106
[Indexed for MEDLINE]
Free PMC Article
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