Image analysis tools and emerging algorithms for expression proteomics

Andrew W. Dowsey*, Jane A. English, Frederique Lisacek, Jeffrey S. Morris, Guang Zhong Yang, Michael J. Dunn

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

44 Citations (Scopus)


their origins in academic endeavours in the 1970s, computational analysis tools have matured into a number of established commercial packages that underpin research in expression proteomics. In this paper we describe the image analysis pipeline for the established 2-DE technique of protein separation, and by first covering signal analysis for MS, we also explain the current image analysis workflow for the emerging high-throughput 'shotgun' proteomics platform of LC coupled to MS (LC/MS). The bioinformatics challenges for both methods are illustrated and compared, whereas existing commercial and academic packages and their workflows are described from both a user's and a technical perspective. Attention is given to the importance of sound statistical treatment of the resultant quantifications in the search for differential expression. Despite wide availability of proteomics software, a number of challenges have yet to be overcome regarding algorithm accuracy, objectivity and automation, generally due to deterministic spot-centric approaches that discard information early in the pipeline, propagating errors. We review recent advances in signal and image analysis algorithms in 2-DE, MS, LC/MS and Imaging MS. Particular attention is given to wavelet techniques, automated image-based alignment and differential analysis in 2-DE, Bayesian peak mixture models, and functional mixed modelling in MS, and group-wise consensus alignment methods for LC/MS.

Original languageEnglish
Pages (from-to)4226-4257
Number of pages32
Issue number23
Publication statusPublished - Dec 2010


  • 2-DE
  • Bioinformatics
  • Image analysis
  • Imaging MS
  • LC
  • Proteome informatics


Dive into the research topics of 'Image analysis tools and emerging algorithms for expression proteomics'. Together they form a unique fingerprint.

Cite this