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Abstract
Label-free shotgun proteomics is a promising semi-quantitative protein profi ling method with capability of comparing a large number of samples in a single experiment. One of the key challenges in this proteomics approach is the high requirement of computational capability for tasks such as feature detection and LC-MS alignment due to the complexity of proteomics systems. Many software tools have been developed in recent years to aid these processes, yet it is often not clear to users whether these tools extract information from raw data correctly and comprehensively. In this paper, we described a comprehensive procedure to provide a fast and global view for performances of LC-MS label-free computational software. Two high quality mass spectrometry datasets with carefully controlled QC samples and spikedin proteins were also provided as benchmark datasets for such evaluations.
Highlights
LC-MS based quantitative shotgun proteomics has gradually replaced traditional two-dimensional gel electrophoresis to become a method of choice for profiling protein composition in a given biological system
As the retention time, precursor mass/ charge ratio and charge state are usually the only parameters by which a “feature” is defined, the requirements for reproducible LC retention time, high resolution and mass accuracy for mass spectrometers are critical to ensure that signals are well separated (Norbeck, et al, 2005) and the same peptides are compared across experiments
Features with 2-4 charge will be considered in the following analysis as the charge state of most tryptic digested peptides are likely between +2 and +4
Summary
LC-MS based quantitative shotgun proteomics has gradually replaced traditional two-dimensional gel electrophoresis to become a method of choice for profiling protein composition in a given biological system. With increased availability of high resolution and accuracy MS instruments, label-free shotgun quantitative proteomics has gained great popularity in recent years due to the capability of comparing a large number of samples without resource intensive and potentially biased labeling steps. Such a capability is critical for clinical proteomics as inter-individual variation can be substantial and experiments with large sample sizes are required. The principle of label-free quantitative proteomics is based on comparison of precursor ion intensities across all experiments after all features (defined as isotopic clusters) are aligned according to their LC retention time, m/z and charge states.
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