title: 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.affiliation: Department of Computer Science, Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540, USA. zkhan@princeton.edu
authors.forename: Zia
authors.initials: Z
authors.lastname: Khan
authors.forename: Joshua S
authors.initials: JS
authors.lastname: Bloom
authors.forename: Benjamin A
authors.initials: BA
authors.lastname: Garcia
authors.forename: Mona
authors.initials: M
authors.lastname: Singh
authors.forename: Leonid
authors.initials: L
authors.lastname: Kruglyak
mesh_terms: D000465:Algorithms; D000818:Animals; D002853:Chromatography, Liquid; D016208:Databases, Factual; D001330:Electronic Data Processing; D005656:Fungal Proteins / Q000032:analysis; D006801:Humans; D007554:Isotopes; D051379:Mice; D011506:Proteins / Q000032:analysis*; D040901:Proteomics / Q000379:methods* / Q000706:statistics & numerical data*; D053719:Tandem Mass Spectrometry
chemical_list: D005656:Fungal Proteins; D007554:Isotopes; D011506:Proteins