Michael Schroeder
Professor BIOTEC TU Dresden, DE.http://www.biotec.tu-dresden.de/research/schroeder/
Extraction and reuse of mutations and annotations from literature using ontologies
Textmining has proven successful in numerous applications ranging from drug discovery to search and annotation. We will review some of these applications in particular how to answer questions with GoPubMed, how generate ontologies and gene annotations, and how to predict drug-targets. We will focus in particular on the problem of ambiguity, which is important in mutation mining. We introduce three approaches to disambiguation and discuss their strenghts and weaknesses.
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Joost Schymkowitz
Professor VIB Switch Laboratory, Vrije Universiteit Brussel.http://www.vib.be/Research/EN/Research+Departments/Switch+Laboratory/
A knowledgebase for phenotyping of human SNPs and disease mutations
Linking structural effects of mutations to functional outcomes is a major issue in structural bioinformatics, and many tools and studies have shown that specific structural properties such as stability and residue burial can be used to distinguish neutral variations and disease associated mutations. The SNPeffect database uses sequence- and structure-based bioinformatics tools to predict the effect of non-synonymous SNPs on the molecular phenotype of proteins. SNPeffect analyses the effect of SNPs on three categories of functional properties: (1) structural and thermodynamic properties affecting protein dynamics and stability (2) the integrity of functional and binding sites and (3) changes in posttranslational processing and cellular localization of proteins. We have investigated 39 structural properties on a set of SNPs and disease mutations from the Uniprot Knowledge Base that could be mapped on high quality crystal structures and show that none of these properties can be used as a sole classification criterion to separate the two data sets. Furthermore, we have reviewed the annotation process from mutation to result and identified the liabilities in each step. Although excellent annotation results of various research groups underline the great potential of using structural bioinformatics to investigate the mechanisms underlying disease, the interpretation of such annotations cannot always be extrapolated to proteome wide variation studies. Difficulties for large-scale studies can be found both on the technical level, i.e. the scarcity of data and the incompleteness of the structural tool suites, and on the conceptual level, i.e. the correct interpretation of the results in a cellular context.