Combining bioinformatics and MS-based proteomics: clinical implications.
Clinical proteomics research aims at i) discovery of protein biomarkers for screening, diagnosis and prognosis of disease, ii) discovery of protein therapeutic targets for improvement of disease prevention, treatment and follow-up, and iii) development of mass spectrometry (MS)-based assays that could be implemented in clinical chemistry, microbiology or hematology laboratories. MS has been increasingly applied in clinical proteomics studies for the identification and quantification of proteins. Bioinformatics plays a key role in the exploitation of MS data in several aspects such as the generation and curation of protein sequence databases, the development of appropriate software for MS data treatment and integration with other omics data and the establishment of adequate standard files for data sharing. In this article, we discuss the main MS approaches and bioinformatics solutions that are currently applied to accomplish the objectives of clinical proteomic research.
Department of Human Protein Sciences, Faculty of Medicine, Geneva University, Geneva, Switzerland.
Adelina Elena
AE
Acosta-Martin
Lydie
L
Lane
D000818:Animals; D015415:Biomarkers / Q000032:analysis; D019295:Computational Biology; D030562:Databases, Protein; D055808:Drug Discovery; D006801:Humans; D013058:Mass Spectrometry / Q000379:methods*; D011379:Prognosis; D011506:Proteins / Q000032:analysis; D040901:Proteomics / Q000379:methods*
D015415:Biomarkers; D011506:Proteins
biocuration; bioinformatics; biomarker discovery; clinical chemistry proteomics; clinical proteomics; database; mass spectrometry; systems biology