Informatics Tool for Proteomic Biomarker Detection using large-scale nanoLC-FT Mass Spectrometry Data
EMSL Project ID
16711
Abstract
Efficiently identifying and quantifying disease- or treatment-related changes in the abundance of proteins in easy accessible body fluids such as serum is an important area of research for biomarker discovery. Currently, cancer diagnosis and management are hampered by lack of discriminatory and easy obtainable biomarkers. In order to improve disease management more sensitive and specific biomarkers need to be identified. In this light, the simultaneous detection and identification of multiple biomarkers ("molecular signatures") may be more accurate than single marker detection. Therefore great promise holds in combining global profiling methods, such as proteomics with powerful bioinformatics tools that allow for marker identification. The additional dimension of separation provided by coupling nanoliquid chromatography to high-performance Fourier transform mass spectrometry (nanoLC-FTMS) allows for profiling large numbers of peptides and proteins in complex biological samples at great resolution, sensitivity and dynamic range. NanoLC-FTMS applied to a sample yields a large matrix of (time, m/z intensity) measurements, each indicating that, at a particular time (at about one second interval), an ion with a particular mass-to-charge (m/z) ratio was detected with a particular intensity. Analysis and interpretability of these large scale datasets requires application of automatated methods applied to the complete datasets, thus going beyond detection and comparison of peak areas or features. The aim of the present proposal is to introduce a novel fully automatic approach for the comparative analysis of large-scale nanoLC-FTMS data sets based on ensemble machine learning that will allow for classification of complex serum samples of cancer patients, speeding discovery of diagnostic, prognostic and drug-response biomarkers. The final product of this project will be a first prototype of a user-friendly tool incorporating advanced bioinformatic techniques for automated analysis of multiple large-scale LC-MS datasets. The combination of cutting edge technology and an integrated advanced bioinformatics tool to be developed during this project will no doubt boost biomarker discovery and will yield applications with high clinical relevance.
To this end we would like to optimize our tool first with well characterized label-free LC-FTMS datasets from Shewanella grown under normoxic and suboxic conditions (or any other suitable dataset).
Project Details
Project type
Exploratory Research
Start Date
2005-11-21
End Date
2006-12-11
Status
Closed
Released Data Link
Team
Principal Investigator