A fully automated 2-DE gel image analysis pipeline for high throughput proteomics

摘要

Image analysis is still considered as the bottleneck in 2D-gel based expression proteomics analysis for biomarkers discovery. We are presenting a new end-to-end image analysis pipeline of operations that can be fully automated. The pipeline includes image denoising and enhancement based on contourlets, image segmentation into Regions of Interest (ROIs) based on active contours, followed by the analysis of the extracted ROIs for spot detection and quantification using mixture modeling, model selection and unsupervised machine learning methods. The proposed system is shown to match the sensitivity and exceed the precision of commercial spot detection software when analyzing real 2D gel images. It is suitable for high throughput proteomics analysis of image stacks since, unlike commercial software, it does not require any manual re-calibration of parameters every time a new image is to be processed.