Protein image alignment via tensor product cubic splines

Potra, Florian A.; Liu, X.

doi:10.1080/10556780600882116

Cited by 3 publications

(1 citation statement)

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“…For these reasons, advancements in the medical imaging literature are now considering aligning full sets of images together simultaneously 146 as well as generation of a consensus frame of reference that averages out the deformations. While there has been limited research in group‐wise alignment in 2‐DE 147, there has been more development along these lines in LC/MS.…”

Section: Image Analysis In Lc/msmentioning

confidence: 99%

Image analysis tools and emerging algorithms for expression proteomics

et al. 2010

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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-D Gel Electrophoresis (2-DE) technique of protein separation, and by first covering signal analysis for Mass Spectrometry (MS), we also explain the current image analysis workflow for the emerging high-throughput ‘shotgun’ proteomics platform of Liquid Chromatography coupled to MS (LC/MS). The bioinformatics challenges for both methods are illustrated and compared, whilst 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.

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Section: Image Analysis In Lc/msmentioning

confidence: 99%