title: Bioinformatics strategies for proteomic profiling.
abstract: Clinical proteomics is an emerging field that involves the analysis of protein expression profiles of clinical samples for de novo discovery of disease-associated biomarkers and for gaining insight into the biology of disease processes. Mass spectrometry represents an important set of technologies for protein expression measurement. Among them, surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI TOF-MS), because of its high throughput and on-chip sample processing capability, has become a popular tool for clinical proteomics. Bioinformatics plays a critical role in the analysis of SELDI data, and therefore, it is important to understand the issues associated with the analysis of clinical proteomic data. In this review, we discuss such issues and the bioinformatics strategies used for proteomic profiling.
authors.affiliation: Department of Pathology, Johns Hopkins Medical Institutions, Baltimore, MD 21231, USA. cnwhite@jhu.edu
authors.forename: C Nicole
authors.initials: CN
authors.lastname: White
authors.forename: Daniel W
authors.initials: DW
authors.lastname: Chan
authors.forename: Zhen
authors.initials: Z
authors.lastname: Zhang
mesh_terms: D019295:Computational Biology; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D013058:Mass Spectrometry; D040901:Proteomics / Q000379:methods*; D011786:Quality Control; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization