卫资所生物资讯组陈俊宇April0703市公开课金奖市赛课一等奖课件.pptx

衛資所 生物資訊組 陳俊宇April 07,03第1页第1页graphgraphnodeedgeChromosomegenepositional correlationsPathwayenzymefunctional correlationsGene expressiongenecoexpressedProtein interactionproteinprotein-protein interactionProtein structureprotein3D structural similarity第2页第2页What questions they want to answer?Ci:correlated gene cluster (correlated cluster)hi:hyperedgeTo extract a set of correlated genes with respect to multiple biological features.Provide biological information to classify genes.第3页第3页MethodClustering of hyperedges!Input datasets:graph G=G1,Gnhyperedges H=h1,hmDistance between hyperedges:第4页第4页E.coli correlated gene clustersE.coli genome dataset(G1:4,396 nodes and 4,396 edges)E.coli pathway dataset(G2:761 nodes and 1,223 edges)E.coli structure similarity dataset(G3:538 nodes and 3,823 edges)917 hyperedgesthreshold parameters p1=2,p2=3,p3=0第5页第5页Screening the two-hybrid protein-protein interaction dataset.(yeast protein interaction)Compared this dataset with the following datasets:S.cerevisiae coexpression datasetS.cerevisiae pathway datasetE.coli genome datasetIf an interaction or a relation is also observed in biological attributes other than protein-protein interactions,we judge the interaction is relevant.第6页第6页S.cerevisiae two-hybrid vs.E.coli genome第7页第7页Discussionsgraphs being compared really can provide biological information to classify genes.Its not clear whether the inclusion of the yeast genome dataset can improve the confidence of screening the two-hybrid dataset.Deriving sub-networks that indicate how genes are connected in a correlated gene cluster.edge-weight(normalization of edge weights among graphs for comparison!)第8页第8页