Information System for Bee Gene Annotation

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