Management, visualisation & mining of quantitative proteomics data

Exponential data growth in life sciences demands cross discipline work that brings together computing and life sciences in a usable manner that can enhance knowledge and understanding in both fields. High throughput approaches, advances in instrumentation and overall complexity of mass spectrometry data have made it impossible for researchers to manually analyse data using existing market tools. By applying a user-centred approach to effectively capture domain knowledge and experience of biologists, this thesis has bridged the gap between computation and biology through software, PepTracker (http://www.peptracker.com). This software provides a framework for the systematic detection and analysis of proteins that can be correlated with biological properties to expand the functional annotation of the genome. The tools created in this study aim to place analysis capabilities back in the hands of biologists, who are expert in evaluating their data. Another major advantage of the PepTracker suite is the implementation of a data warehouse, which manages and collates highly annotated experimental data from numerous experiments carried out by many researchers. This repository captures the collective experience of a laboratory, which can be accessed via user-friendly interfaces. Rather than viewing datasets as isolated components, this thesis explores the potential that can be gained from collating datasets in a “super-experiment” ideology, leading to formation of broad ranging questions and promoting biology driven lines of questioning. This has been uniquely implemented by integrating tools and techniques from the field of Business Intelligence with Life Sciences and successfully shown to aid in the analysis of proteomic interaction experiments. Having conquered a means of documenting a static proteomics snapshot of cells, the proteomics field is progressing towards understanding the extremely complex nature of cell dynamics. PepTracker facilitates this by providing the means to gather and analyse many protein properties to generate new biological insight, as demonstrated by the identification of novel protein isoforms.

Name dc:type.qualificationname

Doctor of Philosophy

Level dc:type.qualificationlevel

Doctoral Thesis

Grantor dc:publisher.institution

University of Dundee

Year dc:date.issued

2012

Author dc:creator

• Ahmad, Yasmeen

Advisor dc:contributor.advisor

• Lamond, Angus

• Mass Spectrometry
• Proteomics
• Data Management
• Visualisation
• Data Mining
• Business Intelligence
• Analytics
• Data Science

Language dc:language

eng

Identifier

oai:discovery.dundee.ac.uk:studenttheses/6ed071fc-e43b-410c-898d-50529dc298ce

OAI identifier oai:identifier

oai:discovery.dundee.ac.uk:studenttheses/6ed071fc-e43b-410c-898d-50529dc298ce