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Information System for Bee Gene Annotation

Information System for Bee Gene Annotation. Xin He Beespace Grouping Meeting Nov 30, 2005. Motivation. Analysis of bee microarray expression data requires an information system that provides functions not available elsewhere No public database dedicated to honey bee

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Information System for Bee Gene Annotation

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  1. Information System for Bee Gene Annotation Xin He Beespace Grouping Meeting Nov 30, 2005

  2. Motivation • Analysis of bee microarray expression data requires an information system that provides functions not available elsewhere • No public database dedicated to honey bee • Non-traditional queries. Example: EST queries, find similarly expressed genes, etc.

  3. Tasks • Gene  homologs • Gene  GO terms • GO term  genes • Gene  genes with similar expression • Gene  genes with similar GO annotation

  4. Database Design: Basic Entities • Ids: biological sequences. Three subtypes • Gene • Protein • EST • Gonames: GO terms

  5. Database Design: Basic Relationship • Homologs: pairwise sequence similarity • Gos: gene annotation • Gosims: pairwise similarity of GO annotations • Exprsims: pairwise simiarity of gene expression pattern

  6. Implementation of Tasks • Gene  homologs: BLAST all pairs of genes. Choose E-value threshold 10E-10 • Gene  GO terms • Fly: downloaded from Gene Ontology • Bee: from bee biologists • GO term  genes

  7. Implementation of Tasks • Gene  genes with similar expression: compute pairwise Pearson correlation. Choose threshold 0.5 • Gene  genes with similar GO annotation

  8. GO-based Similarity • Idea: two genes are similar if they share some GO terms. Favor specific GO terms • View each gene as a document and a GO term as a term • Vector-space model: let t be a term, g be a gene, then • TF(t,g) = 1 if g is annotated with t; 0 o/w • IDF(t) = log[n/n(t)] n(t): #genes annotated with t • Cosine similarity

  9. Demonstration…

  10. For Discussion • Internal database, shared by all Beespace projects. Include: Genes, Proteins, GO Terms, Expression • Ontology-based similarity: applications? • “Candidate genes” retrieval. Example: find all genes involved in segmentation clock

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