Enhancing the utility of Proteomics Signature Profiling , performance analysis and specialised ontologies

• Published in: BMC genomics 2013-01, Vol.14
• Main Authors: Goh, Wilson Wen Bin, Fan, Mengyuan, Low, Hong Sang, Sergot, Marek, Wong, Limsoon
• Format: Article
• Language: English
• Subjects: Analysis; Liver; Ontology; Proteomics
• ISSN: 1471-2164; 1471-2164
• DOI: 10.1186/1471-2164-14-35

Proteomics Signature Profiling (PSP) is a novel hit-rate based method that proved useful in resolving consistency and coverage issues in proteomics. As a follow-up study, several points need to be addressed: 1/ PSP's generalisability to pathways, 2/ understanding the biological interplay between significant complexes and pathway subnets co-located on the same pathways on our liver cancer dataset, 3/ understanding PSP's false positive rate and 4/ demonstrating that PSP works on other suitable proteomics datasets as well as expanding PSP's analytical resolution via the use of specialised ontologies. 1/ PSP performs well with Pathway-Derived Subnets (PDSs). Comparing the performance of PDSs derived from various pathway databases, we find that an integrative approach is best for optimising analytical resolution. Feature selection also confirms that significant PDSs are closely connected to the cancer phenotype. PSP is a powerful and precise technique, capable of identifying biologically coherent features. It works with biological complexes, network-predicted clusters as well as PDSs. Here, an instance of the interplay between significant PDSs and complexes, possibly significantly involved in liver cancer progression but not well understood as yet, is demonstrated. Also demonstrated is the enhancement of PSP's analytical resolution using specialised ontologies.