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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.

Evaluation for computational platforms of LC-MS based label-free quantitative proteomics: A global view