Feature Detection Techniques for Preprocessing Proteomic Data

Abstract EN

Numerous gel-based and nongel-based technologies are used to detect protein changes potentially associated with disease.

The raw data, however, are abundant with technical and structural complexities, making statistical analysis a difficult task.

Low-level analysis issues (including normalization, background correction, gel and/or spectral alignment, feature detection, and image registration) are substantial problems that need to be addressed, because any large-level data analyses are contingent on appropriate and statistically sound low-level procedures.

Feature detection approaches are particularly interesting due to the increased computational speed associated with subsequent calculations.

Such summary data corresponding to image features provide a significant reduction in overall data size and structure while retaining key information.

In this paper, we focus on recent advances in feature detection as a tool for preprocessing proteomic data.

This work highlights existing and newly developed feature detection algorithms for proteomic datasets, particularly relating to time-of-flight mass spectrometry, and two-dimensional gel electrophoresis.

Note, however, that the associated data structures (i.e., spectral data, and images containing spots) used as input for these methods are obtained via all gel-based and nongel-based methods discussed in this manuscript, and thus the discussed methods are likewise applicable.