Abstract:
MOTIVATION:Mass spectrometry is a complex technique used for large-scale protein profiling with clinical and pharmaceutical applications. While individual components in the system have been studied extensively, little work has been done to integrate various modules and evaluate them from a systems point of view. RESULTS:In this work, we investigate this problem by putting together the different modules in a typical proteomics work flow, in order to capture and analyze key factors that impact the number of identified peptides and quantified proteins, protein quantification error, differential expression results, and classification performance. The proposed proteomics pipeline model can be used to optimize the work flow as well as to pinpoint critical bottlenecks worth investing time and resources into for improving performance. Using the model-based approach proposed here, one can study systematically the critical problem of proteomic biomarker discovery, by means of simulation using ground-truthed synthetic MS data.
journal_name
BMC Genomicsjournal_title
BMC genomicsauthors
Sun Y,Braga-Neto U,Dougherty ERdoi
10.1186/1471-2164-13-S6-S2subject
Has Abstractpub_date
2012-01-01 00:00:00pages
S2issn
1471-2164pii
1471-2164-13-S6-S2journal_volume
13 Suppl 6pub_type
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