A Computer-Based Technique for Automated Spot Detection in Proteomics Images

Abstract

Abstract-This paper introduces a novel computer-based technique for automated detection of protein spots in proteomics images. The proposed technique is based on the localization of regional intensity maxima associated with protein spots and is formulated so as to ignore rectangular-shaped streaks, minimize the detection of false negatives, and allow the detection of multiple overlapping spots. Regional intensity constraints are imposed on the localized maxima in order to cope with the presence of noise and artifacts. The experimental evaluation of the proposed technique on real proteomics images demonstrates that it: 1) achieves a predictive value (PV) and detection sensitivity (DS) which exceed 90%; 2) outperforms Melanie software package in terms of PV, specificity, and DS; 3) ignores artifacts; 4) distinguishes multiple overlapping spots; 5) locates spots within streaks; and 6) is automated and efficient.

Enabling Computational Proteomics by Public and Local Data Management Systems

“…Specific to gel-based proteomics are gel image processing software packages like Melanie, 16 Delta2D, 17 or Phoretix. 18 These packages provide solid algorithms to sort out spot detection and background correction within a single gel image or spot matching across multiple experiments.…”

2D-gel spot detection and segmentation based on modified image-aware grow-cut and regional intensity information

“…Its main drawback is that it underperforms in splitting overlapping spots [26]. Based on morphological operations, Mylona et al [29] proposed a method for spot detection that achieves increased performance in detecting overlapping spots at the expense of occasionally missed spots. Dos Anjos et al [14] proposed "Scimo", a spot segmentation and quantification method that is also based on the watershed algorithm and achieves a more realistic estimation of close proximity spots, as well as partially overlapping non-saturated spots.…”