Corra: A Tool for Analyzing LC-MS Data in Proteomics
Author Information
Author(s): Brusniak Mi-Youn, Bodenmiller Bernd, Campbell David, Cooke Kelly, Eddes James, Garbutt Andrew, Lau Hollis, Letarte Simon, Mueller Lukas N, Sharma Vagisha, Vitek Olga, Zhang Ning, Aebersold Ruedi, Watts Julian D
Primary Institution: Institute for Systems Biology
Hypothesis
Corra aims to provide a user-friendly computational framework for LC-MS-based proteomics that integrates various statistical analyses.
Conclusion
Corra enables researchers to effectively process, analyze, and visualize LC-MS data, addressing the complexities of quantitative proteomics.
Supporting Evidence
- Corra allows for the integration of multiple LC-MS data analysis tools.
- It provides a web-based interface for users to manage data processing.
- Statistical analyses are performed using established Bioconductor packages.
- Corra addresses the need for user-friendly tools in the field of proteomics.
- Pilot studies demonstrated Corra's capability in biomarker discovery.
Takeaway
Corra is a computer program that helps scientists study proteins in blood samples, making it easier to find important differences between healthy and sick people.
Methodology
Corra integrates existing algorithms for LC-MS data analysis and statistical methods from microarray data, using a web-based interface for user interaction.
Potential Biases
Potential biases may arise from sample handling and the inherent variability in biological samples.
Limitations
The study is based on pilot data and the identified proteins require further validation before being considered as biomarkers.
Participant Demographics
The study involved 22 human plasma samples, with 13 from normal glucose tolerance individuals and 9 from newly diagnosed type 2 diabetes patients.
Statistical Information
P-Value
p<0.05
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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