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Liquid chromatography coupled to mass spectrometry is a complicated technique used for large-scale protein profiling. While individual components in the system have been studied extensively, little work has been done to integrate various modules and evaluate them from a system point of view. In this work, we put together different modules in a typical proteomics work flow, capture and analyze key factors that may impact the number of identified peptides and quantified proteins, protein quantification error, and differential expression results. The proposed proteomics pipeline model can be used to optimize the work flow as well as to pinpoint critical steps worth to sinking resource into.