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Abstract

Phosphoproteomic experiments are routinely conducted in laboratories worldwide, and because of the fast development of mass spectrometric techniques and efficient phosphopeptide enrichment methods, life-science researchers frequently end up having lists with tens of thousands of phosphorylation sites for further interrogation. To answer biologically relevant questions from these complex data sets, it becomes essential to apply computational, statistical, and predictive analytical methods. Recently we have provided an advanced bioinformatic platform termed “PhosphoSiteAnalyzer” to the scientific community to explore large phosphoproteomic data sets that have been subjected to kinase prediction using the previously published NetworKIN algorithm. NetworKIN applies sophisticated linear motif analysis and contextual network modeling to obtain kinase-substrate associations with high accuracy and sensitivity. PhosphoSiteAnalyzer provides an algorithm for retrieval of kinase predictions from the public NetworKIN webpage in a semi-automated way and applies hereafter advanced statistics to facilitate a user-tailored in-depth analysis of the phosphoproteomic data sets. The interface of the software provides a high degree of analytical flexibility and is designed to be intuitive for most users. Network biology and in particular kinase-substrate network biology provides an adequate conceptual framework to describe and understand diseases and for designing targeted biomedicine for personalized medicine. Hence network biology and network analysis are absolutely essential to translational medical research. PhosphoSiteAnalyzer is a versatile bioinformatics tool to decipher such complex networks and can be used in the fight against serious diseases such as psychological disorders, cancer and diabetes that arise as a result of dysfunctional cell signalling networks.

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/content/papers/10.5339/qfarf.2013.ICTP-024
2013-11-20
2020-09-19
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http://instance.metastore.ingenta.com/content/papers/10.5339/qfarf.2013.ICTP-024
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