Protein quantification across hundreds of experimental conditions Export

@article{citeulike:5790402, abstract = {10.1073/pnas.0904100106 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.}, author = {Khan, Zia and Bloom, Joshua S. and Garcia, Benjamin A. and Singh, Mona and Kruglyak, Leonid}, citeulike-article-id = {5790402}, citeulike-linkout-0 = {http://dx.doi.org/10.1073/pnas.0904100106}, citeulike-linkout-1 = {http://www.pnas.org/content/106/37/15544.abstract}, citeulike-linkout-2 = {http://www.pnas.org/content/106/37/15544.full.pdf}, citeulike-linkout-3 = {http://view.ncbi.nlm.nih.gov/pubmed/19717460}, citeulike-linkout-4 = {http://www.hubmed.org/display.cgi?uids=19717460}, day = {15}, doi = {10.1073/pnas.0904100106}, journal = {Proceedings of the National Academy of Sciences}, keywords = {proteomics, quantitation}, month = {September}, number = {37}, pages = {15544--15548}, posted-at = {2009-11-06 16:21:18}, priority = {2}, title = {Protein quantification across hundreds of experimental conditions}, url = {http://dx.doi.org/10.1073/pnas.0904100106}, volume = {106}, year = {2009} }

TY - JOUR ID - citeulike:5790402 L3 - citeulike-article-id:5790402 N2 - 10.1073/pnas.0904100106 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. IS - 37 JF - Proceedings of the National Academy of Sciences EP - 15548 TI - Protein quantification across hundreds of experimental conditions VL - 106 SP - 15544 KW - proteomics KW - quantitation AU - Khan, Zia AU - Bloom, Joshua AU - Garcia, Benjamin AU - Singh, Mona AU - Kruglyak, Leonid PY - 2009/09/15/ UR - http://dx.doi.org/10.1073/pnas.0904100106 ER -