Proteomics computational methods

Computational methods have been applied to determine the connections in systems that are not well-defined by canonical pathways. This is either done by semi-automated and/or curated literature causal modeling [1] or by statistical methods based on large-scale data from expression or proteomic studies (a mostly theoretical approach is given by reference [2] and a more applied approach is in reference [3]). Many methods, including clustering, Bayesian analysis and principal component analysis have been used to find relationships and "fingerprints" in gene expression data [4]. 

Wang, Y, Hanley, R., Klemke, R. L. (2006b). Computational methods for comparison of large genomic and proteomic datasets reveal protein markers of metastatic cancer. Journal of Proteome Research, 5, 907—915. 

Listgarten J, Emili A. Statistical and computational methods for comparative proteomic profiling using liquid chromatography—tandem mass spectrometry. Mol Cell Pro-... 

Nesvizhskii AL A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics. J Proteomics. 2010 73 2092-123. doi 10.1016/j.jprot.2010.08.009. 

X. Gao, Reeent Advances in Computational Methods for Nuclear Magnetic Resonance Data Processing, Genomics, Proteomics Bioinf., 2013,11, 29. 

Park SKet al (2009) A computational approach to correct arginine-to-proline conversion in quantitative proteomics. Nat Methods 6 184-185... 

The statistical degree of overlapping (SDO) and 2D autocovariance function (ACVF) methods have been applied to 2D-PAGE maps (Marchetti et al., 2004 Pietrogrande et al., 2002, 2003, 2005, 2006a Campostrini et al., 2005) the means for extracting information from the experimental data and their relevance to proteomics are discussed in the following. The procedures were validated on computer-simulated maps. Their applicability to real samples was tested on reference maps obtained from literature sources. Application to experimental maps is also discussed. 

This is a reflection of the increasing knowledge of the complexity of in viva systems, and also the current powerful computational resources to capture, analyze, interpret and model those systems. In view of this complexity, not only the development of genomics, proteomics and metabonomics databases, but also the development of systems biology methods helps us to understand the underlying mechanisms in any given ADME process [80]. 

ECVAM is the leading international center for alternative test method validation. Hartung et al. (29) summarized the modular steps necessary to accomplish stage 3 (test validation). The seven modular steps are (I) test definition, (2) within-laboratory variability, (3) transferability, (4) between-laboratory variability, (5) predictive capacity, (6) applicability domain, and (7) performance standards (29). Steps 2-4 evaluate the test s reliability steps 5 and 6 evaluate the relevance of the test. Successful completion of all seven steps is necessary to proceed to stage 4 (independent assessment or peer review). This modular approach allows flexibility for the validation process where information on the test method can be gathered either prospectively or retrospectively. The approach is applicable not only to in vitro test methods but also to in silico approaches (e.g., computer-based approaches such as quantitative structure-activity relationships or QSAR) and pattern-based systems (e.g., genomics and proteomics). 

The second step in proteomic research is to identify the separated proteins. This can be achieved by MS. Here, proteins are differentiated based on their mass-to-charge ratio (m/z). At first, the protein molecule is ionized. The resultant ion is propelled into a mass analyzer by charge repulsion in an electric field. Ions are then resolved according to their m/z ratio. Information is collected by a detector and transferred to a computer for analysis. The most commonly used ionization methods are... 

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