1. Functional Annotation and Functional Enrichment

2. Annotation Structural Annotation – defining the boundaries of features of interest (coding regions, regulatory elements, functional RNAs, etc). – Ab initio – computationally predicted – Comparative – based on similarity to other genes or genomes – Experimental – transcript sequencing Functional Annotation – attaching meaning to the features (names, product, activity, biological role, etc.) – Sequence homology – Structural similarity or structural features – Experimental data – gene or protein expression patterns

3. Functional Annotation Manual Slow Costly Inconsistent quality Inconsistent coverage across genome Rich content Error correction Automated Fast Cheap? Consistent quality Complete coverage across genome Improving in content Updateable

4. Home many ways are there to say the same thing? Quick survey of GenBank lacI product annotations in 48 bacteria: – Lactose operon repressor (20) – DNA-binding transcriptional repressor (14) – transcriptional regulator LacI family (5) – lac operon repressor (2) – transcriptional repressor of the lac operon (2) – lac repressor (1) – LacI (1) – putative transcriptional regulator (1) – transcriptional repressor of lactose catabolism (1) – transcriptional repressor of lactose catabolism (GalR/LacI family) (1) * Excluding differences in capitalization

5. The Gene Ontology (GO) Goal = consistent annotation of gene products within and between organisms Gene Ontology Consortium began as a collaboration among model organism dbs (FlyBase, SGD, MGD). Now includes larger number of members and interest groups Ontology = A formal representation of concepts and the relationships among them

7. Gene Ontology

8. The 3 GO Ontologies Molecular Function (8,360 terms) Biological Process (14,898 terms) Cellular Component (2,110 terms) GO Term = an entry in an ontology, composed of a unique identifier (GO:000001), definition and “synoynms”

9. CC A cellular component is just that, a component of a cell, but with the proviso that it is part of some larger object; this may be an anatomical structure (e.g. rough endoplasmic reticulum or nucleus) or a gene product group (e.g. ribosome, proteasome or a protein dimer).

10. BP A biological process is series of events accomplished by one or more ordered assemblies of molecular functions. Examples of broad biological process terms are cellular physiological process or signal transduction. Examples of more specific terms are pyrimidine metabolic process or alpha-glucoside transport. It can be difficult to distinguish between a biological process and a molecular function, but the general rule is that a process must have more than one distinct steps. A biological process is not equivalent to a pathway; at present, GO does not try to represent the dynamics or dependencies that would be required to fully describe a pathway.

11. MF Molecular function describes activities, such as catalytic or binding activities, that occur at the molecular level. GO molecular function terms represent activities rather than the entities (molecules or complexes) that perform the actions, and do not specify where or when, or in what context, the action takes place. Molecular functions generally correspond to activities that can be performed by individual gene products, but some activities are performed by assembled complexes of gene products. Examples of broad functional terms are catalytic activity, transporter activity, or binding; examples of narrower functional terms are adenylate cyclase activity or Toll receptor binding. It is easy to confuse a gene product name with its molecular function, and for that reason many GO molecular functions are appended with the word "activity".

12. Annotation File Format

13. Evidence Codes Experimental Evidence Codes – EXP: Inferred from Experiment EXP: Inferred from Experiment – IDA: Inferred from Direct Assay IDA: Inferred from Direct Assay – IPI: Inferred from Physical Interaction IPI: Inferred from Physical Interaction – IMP: Inferred from Mutant Phenotype IMP: Inferred from Mutant Phenotype – IGI: Inferred from Genetic Interaction IGI: Inferred from Genetic Interaction – IEP: Inferred from Expression Pattern IEP: Inferred from Expression Pattern Computational Analysis Evidence Codes – ISS: Inferred from Sequence or Structural Similarity ISS: Inferred from Sequence or Structural Similarity – ISO: Inferred from Sequence Orthology ISO: Inferred from Sequence Orthology – ISA: Inferred from Sequence Alignment ISA: Inferred from Sequence Alignment – ISM: Inferred from Sequence Model ISM: Inferred from Sequence Model – IGC: Inferred from Genomic Context IGC: Inferred from Genomic Context – RCA: inferred from Reviewed Computational Analysis RCA: inferred from Reviewed Computational Analysis

14. Evidence Codes Author Statement Evidence Codes – TAS: Traceable Author Statement TAS: Traceable Author Statement – NAS: Non-traceable Author Statement NAS: Non-traceable Author Statement Curator Statement Evidence Codes – IC: Inferred by Curator IC: Inferred by Curator – ND: No biological Data available ND: No biological Data available Automatically-assigned Evidence Codes – IEA: Inferred from Electronic Annotation IEA: Inferred from Electronic Annotation Obsolete Evidence Codes – NR: Not Recorded NR: Not Recorded

17. What is the source of automated annotations? Integrated automated annotation systems combine a variety of analysis types Comparison to databases protein and/or domain families with defined functions (COGs, NCBI CDD, PFAM, ProSite, etc.) Structural characteristic predictions Sequence characteristic predictions

18. InterPro: www interface

19. InterPro

20. InterPro release 16.0 contains 15045 entries: Active sites34 Binding sites22 Domains4676 Families10060 PTMs18 Repeats235 DatabaseAll SignaturesIntegrated PANTHER301282061 Pfam89578957 PIRSF17481499 PRINTS19001898 ProDom35381041 PROSITE 13191319 SMART724721 TIGRFAMs29492933 Gene3D2147783 SUPERFAMILY 1538463

21. Sample InterPro Family

22. InterPro is one source of IEAs

24. On a genome scale Assign all genes to Interpro families Obtain GO terms (IEA evidence) linked to the Interpro term Use these to find patterns in large gene lists – Experimental ( genes upregulated in array exp) – Comparative (genes with/without orthologs)

25. Enrichment Find categories (InterPro, GO) that are over- represented in a subset of genes relative to the background (genome?) as a whole Example: 40% of the genes that distinguish between two strains of E. coli are mobile elements. Is this more than I expect based on random chance if 10% of the genome as a whole is mobile elements.

26. Hypergeometric Distribution describes the number of successes in a sequence of n draws from a finite population without replacementpopulation Black and white balls in an urn Genes with an ortholog and genes without an ortholog Genes differentially expressed, genes unchanged

29. Comparison of 68 enrichment analysis tools available in 2008