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1.	Hayes, Catherine A, et al. (författare) Statistical analysis of glycosylation profiles to compare tissue type and inflammatory disease state. 2012 Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4811 .- 1367-4803. ; 28:13, s. 1669-76 Tidskriftsartikel (refereegranskat)abstract Motivation: Glycosylation is one of the most important post-translational modifications of proteins and explains some aspects of the diversification of higher organisms not explained by template-driven synthesis. For glycomics to mature as much as genomics and proteomics, the necessary tools need to be developed and tested. Liquid chromatography-mass spectrometry is one of the gold standards for oligosaccharide analysis and leads to large amounts of data, not easily interpreted manually. We present a study on the testing and validation of statistical analysis tools to aid the structural elucidation of these analyses as well as using the results to answer biologically relevant questions. Results: We show the usefulness of data reduction and statistical analysis in the interpretation of complex glycosylation data. The reduction does not result in the loss of importance of the glycosylation information as shown by comparison of control and disease samples in two tissue types.

Hayes, Catherine A, et al. (författare)
Statistical analysis of glycosylation profiles to compare tissue type and inflammatory disease state.
2012
Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4811 .- 1367-4803. ; 28:13, s. 1669-76
Tidskriftsartikel (refereegranskat)abstract
- Motivation: Glycosylation is one of the most important post-translational modifications of proteins and explains some aspects of the diversification of higher organisms not explained by template-driven synthesis. For glycomics to mature as much as genomics and proteomics, the necessary tools need to be developed and tested. Liquid chromatography-mass spectrometry is one of the gold standards for oligosaccharide analysis and leads to large amounts of data, not easily interpreted manually. We present a study on the testing and validation of statistical analysis tools to aid the structural elucidation of these analyses as well as using the results to answer biologically relevant questions. Results: We show the usefulness of data reduction and statistical analysis in the interpretation of complex glycosylation data. The reduction does not result in the loss of importance of the glycosylation information as shown by comparison of control and disease samples in two tissue types.

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