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lüll Image analysis tools and emerging algorithms for expression proteomics Dowsey AW; English JA; Lisacek F; Morris JS; Yang GZ; Dunn MJProteomics 2010[Dec]; 10 (23): 4226-57Since 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.|*Algorithms[MESH]|*Gene Expression[MESH]|Animals[MESH]|Chromatography, Liquid[MESH]|Data Interpretation, Statistical[MESH]|Electrophoresis, Gel, Two-Dimensional[MESH]|Humans[MESH]|Image Processing, Computer-Assisted/*methods[MESH]|Mass Spectrometry[MESH]|Proteome/*analysis[MESH]|Signal Processing, Computer-Assisted[MESH] |