Image analysis tools and emerging algorithms for expression proteomics

Since 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.