Protein quantification across hundreds of experimental conditions.

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.

authors

Khan Z,Bloom JS,Garcia BA,Singh M,Kruglyak L

doi

10.1073/pnas.0904100106

subject

Has Abstract

pub_date

2009-09-15 00:00:00

pages

15544-8

issue

37

eissn

0027-8424

issn

1091-6490

pii

0904100106

journal_volume

106

pub_type

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