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 spacepartitioning data structures to handle the large size of these datasets. Second, we introduce techniques that combine graphtheoretic 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.kd-tree ͉ orthogonal range query ͉ quantitative proteomics ͉ space partitioning data structures ͉ tandem mass spectrometry

Stoichiometry of Site-specific Lysine Acetylation in an Entire Proteome

“…MS Data Analysis-Data were analyzed using a modified version of the December 25th, 2013, release of open-source, quantitative mass spectrometry analysis tool PVIEW (22,23). We generated a peptide database with cleavage only at arginine, as trypsin did not cleave at acetylated lysines.…”

Global secretome analysis identifies novel mediators of bone metastasis

“…Briefly, Arg-10 and Lys-8 labeled peptides were quantified using area under extracted ion chromatograms (XICs). XICs were found and paired using the previously described methods [71]. The ratio of the areas under the paired XICs was reported as the ratio between heavy and light versions of peptides.…”

“…Peptide identifications were accepted at greater than 95% probability as specified by the Peptide Prophet algorithm [69] or better than 0.01 peptide probability by the Sequest algorithm. False discovery rates were estimated to be 1% by searching a reverse database as previously described [70] Quantitative (SILAC-based) mass spectrometry was performed as previously described [68,71]. Briefly, Arg-10 and Lys-8 labeled peptides were quantified using area under extracted ion chromatograms (XICs).…”

mzDB: A File Format Using Multiple Indexing Strategies for the Efficient Analysis of Large LC-MS/MS and SWATH-MS Data Sets *

“…The general performance gain obtained with the multidimensional indexing model was described in previous studies (36,37). Its application to LC-MS acquisitions was first tested on centroid data (38) and then on profile data (39) by mzRTree, an efficiency oriented data format. Here, the mzRTree structure was implemented as an SQLite file format, taking advantage of the SQLite standardization and its built-in R*Tree index.…”

“…It is written in Cϩϩ language and exploits the ProteoWizard framework (38) to read vendor raw file formats and standards such as mzXML, mzML, and mz5. Two command line interfaces are available: "raw2mzDB.exe" that converts the aforementioned formats to the mzDB, and "mzDB2mzML.exe," which performs the reversed conversion to the mzML standard, and thus also allows to read and manipulate the mzDB files.…”