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.
Institute of Biomedical Engineering, Imperial College London, South Kensington, London, UK. a.w.dowsey@imperial.ac.uk
Andrew W
AW
Dowsey
Jane A
JA
English
Frederique
F
Lisacek
Jeffrey S
JS
Morris
Guang-Zhong
GZ
Yang
Michael J
MJ
Dunn
D000465:Algorithms; D000818:Animals; D002853:Chromatography, Liquid; D003627:Data Interpretation, Statistical; D015180:Electrophoresis, Gel, Two-Dimensional; D015870:Gene Expression; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D013058:Mass Spectrometry; D020543:Proteome / Q000032:analysis*; D012815:Signal Processing, Computer-Assisted
D020543:Proteome