High-throughput image analysis for proteomics

Abstract

High-throughput image analysis for proteomics The quest for high-throughput proteomics in recent years has revealed a number of critical issues. Whilst improved 2-D gel electrophoresis (2-DE) sample preparation, staining and imaging issues are being actively pursued by industry, reliable high-throughput spot matching and quantification remains a significant bottleneck in the bioinformatics pipeline. The flow of data to mass spectrometry through robotic spot excision and protein digestion so far has been restricted. The purpose of this thesis is to describe a computational framework that is suitable for large-scale image mining and statistical cross-validation of 2-DE. This is in response to the development of a statistical 2-DE ontology that underpins the emerging Human Proteome Organisation’s Proteomics Standards Initiative General Proteomics Format (HUPO PSI GPS). The thesis begins with a comprehensive review of the role of bioinformatics in 2-DE. A platform for Statistical Expression Analysis (SEA) is then proposed, which