Methods, algorithms and tools in computational proteomics: A practical point of view.

@article{citeulike:1652000, abstract = {Computational MS-based proteomics is an emerging field arising from the demand of high throughput analysis in numerous large-scale experimental proteomics projects. The review provides a broad overview of a number of computational tools available for data analysis of MS-based proteomics data and gives appropriate literature references to detailed description of algorithms. The review provides, to some extent, discussion of algorithms and methods for peptide and protein identification using MS data, quantitative proteomics, and data storage. The hope is that it will stimulate discussion and further development in computational proteomics. Computational proteomics deserves more scientific attention. There are far fewer computational tools and methods available for proteomics compared to the number of microarray tools, despite the fact that data analysis in proteomics is much more complex than microarray analysis.}, address = {Bioinformatics Group, CIC bioGUNE, CIBER-HEPAD, Technology Park of Bizkaia, Derio, Bizkaia, Spain.}, author = {Matthiesen, R. }, citeulike-article-id = {1652000}, doi = {10.1002/pmic.200700116}, issn = {1615-9853}, journal = {Proteomics}, month = {August}, number = {16}, pages = {2815--2832}, posted-at = {2007-09-13 15:23:19}, priority = {2}, title = {Methods, algorithms and tools in computational proteomics: A practical point of view.}, url = {http://dx.doi.org/10.1002/pmic.200700116}, volume = {7}, year = {2007} }

TY - JOUR ID - citeulike:1652000 L3 - citeulike-article-id:1652000 TI - Methods, algorithms and tools in computational proteomics: A practical point of view. JF - Proteomics VL - 7 IS - 16 SP - 2815 EP - 2832 AD - Bioinformatics Group, CIC bioGUNE, CIBER-HEPAD, Technology Park of Bizkaia, Derio, Bizkaia, Spain. SN - 1615-9853 N2 - Computational MS-based proteomics is an emerging field arising from the demand of high throughput analysis in numerous large-scale experimental proteomics projects. The review provides a broad overview of a number of computational tools available for data analysis of MS-based proteomics data and gives appropriate literature references to detailed description of algorithms. The review provides, to some extent, discussion of algorithms and methods for peptide and protein identification using MS data, quantitative proteomics, and data storage. The hope is that it will stimulate discussion and further development in computational proteomics. Computational proteomics deserves more scientific attention. There are far fewer computational tools and methods available for proteomics compared to the number of microarray tools, despite the fact that data analysis in proteomics is much more complex than microarray analysis. AU - Matthiesen, R PY - 2007/08// UR - http://dx.doi.org/10.1002/pmic.200700116 ER -