Abstract
Mass spectrometry-based proteomics holds great promise as a discovery tool for biomarker candidates in the early detection of diseases. Recently much emphasis has been placed upon producing highly reliable data for quantitative profiling for which highly reproducible methodologies are indispensable. The main problems that affect experimental reproducibility stem from variations introduced by sample collection, preparation, and storage protocols and LC-MS settings and conditions. On the basis of a formally precise and quantitative definition of similarity between LC-MS experiments, we have developed Chaorder, a fully automatic software tool that can assess experimental reproducibility of sets of large scale LC-MS experiments. By visualizing the similarity relationships within a set of experiments, this tool can form the basis of systematic quality control and thus help assess the comparability of mass spectrometry data over time, across different laboratories, and between instruments. Applying Chaorder to data from multiple laboratories and a range of instruments, experimental protocols, and sample complexities revealed biases introduced by the sample processing steps, experimental protocols, and instrument choices. Moreover we show that reducing bias by correcting for just a few steps, for example randomizing the run order, does not provide much gain in statistical power for biomarker discovery.
Highlights
Mass spectrometry-based proteomics holds great promise as a discovery tool for biomarker candidates in the early detection of diseases
An Approach to Measure Similarity in LC-MS Experiments—Here we present a practical, quantitative measure of experimental reproducibility and, more generally, similarity in LC-MS-based proteomics
Building upon an existing alignment algorithm to compute this measure of similarity between two LC-MS experiments [9], we present a tool Chaorder1 that produces a visual representation of the similarity relationships in a set of experiments
Summary
Mass spectrometry-based proteomics holds great promise as a discovery tool for biomarker candidates in the early detection of diseases. Many approaches go beyond the straightforward use of CID for large scale protein identification These range from the accurate mass and time tag approach [7], clustering [8], complete workflow solutions for LC-MS data sets (9 –12), alignment algorithms for LC-MS [13, 14], and feature detection approaches for SELDI platforms [15,16,17]. These approaches rely heavily on high quality LC-MS profiles for peak alignment, peptide identification, and quantitation and require a high degree of reproducibility in the sample collection, processing, and analytical run conditions [12].
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