@article {49548,
	title = {Quantitative measurement of allele-specific protein expression in a diploid yeast hybrid by LC-MS.},
	volume = {8},
	year = {2012},
	month = {2012},
	pages = {602},
	abstract = {<p>Understanding the genetic basis of gene regulatory variation is a key goal of evolutionary and medical genetics. Regulatory variation can act in an allele-specific manner (cis-acting) or it can affect both alleles of a gene (trans-acting). Differential allele-specific expression (ASE), in which the expression of one allele differs from another in a diploid, implies the presence of cis-acting regulatory variation. While microarrays and high-throughput sequencing have enabled genome-wide measurements of transcriptional ASE, methods for measurement of protein ASE (pASE) have lagged far behind. We describe a flexible, accurate, and scalable strategy for measurement of pASE by liquid chromatography-coupled mass spectrometry (LC-MS). We apply this approach to a hybrid between the yeast species Saccharomyces cerevisiae and Saccharomyces bayanus. Our results provide the first analysis of the relative contribution of cis-acting and trans-acting regulatory differences to protein expression divergence between yeast species.</p>},
	keywords = {Alleles, Chromatography, Liquid, Fungal Proteins, Gene Expression Profiling, Gene Expression Regulation, Fungal, HUMANS, Mass Spectrometry, proteomics, Regression Analysis, Saccharomyces, Saccharomyces cerevisiae, Saccharomyces cerevisiae Proteins, Species Specificity},
	issn = {1744-4292},
	doi = {10.1038/msb.2012.34},
	author = {Khan, Zia and Bloom, Joshua S and Amini, Sasan and Singh, Mona and Perlman, David H and Caudy, Amy A and Kruglyak, Leonid}
}
@article {49747,
	title = {Protein quantification across hundreds of experimental conditions.},
	journal = {Proc Natl Acad Sci U S A},
	volume = {106},
	year = {2009},
	month = {2009 Sep 15},
	pages = {15544-8},
	abstract = {<p>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.</p>},
	keywords = {algorithms, Animals, Automatic Data Processing, Chromatography, Liquid, Databases, Factual, Fungal Proteins, HUMANS, Isotopes, Mice, Proteins, proteomics, Tandem Mass Spectrometry},
	issn = {1091-6490},
	doi = {10.1073/pnas.0904100106},
	author = {Khan, Zia and Bloom, Joshua S and Garcia, Benjamin A and Singh, Mona and Kruglyak, Leonid}
}
