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Functional Annotation and Functional Enrichment

Functional Annotation and Functional Enrichment. 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

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Functional Annotation and Functional Enrichment

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  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 Automated Fast Cheap? Consistent quality Complete coverage across genome Improving in content Updateable • Slow • Costly • Inconsistent quality • Inconsistent coverage across genome • Rich content • Error correction

  4. Home many ways are there to say the same thing? • Quick survey of GenBanklacIproduct annotations in 48 bacteria: • Lactose operon repressor (20) • DNA-binding transcriptional repressor (14) • transcriptional regulator LacI family (5) • lacoperon repressor (2) • transcriptional repressor of the lacoperon (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

  6. Gene Ontology

  7. 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”

  8. 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).

  9. 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.

  10. 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".

  11. Annotation File Format

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

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

  14. 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

  15. InterPro: www interface

  16. InterPro

  17. InterPro release 16.0 contains Database All Signatures Integrated PANTHER 30128 2061 Pfam 8957 8957 PIRSF 1748 1499 PRINTS 1900 1898 ProDom 3538 1041 PROSITE 1319 1319 SMART 724 721 TIGRFAMs 2949 2933 Gene3D 2147 783 SUPERFAMILY 1538 463 15045 entries: Active sites 34 Binding sites 22 Domains 4676 Families 10060 PTMs 18 Repeats 235

  18. Sample InterPro Family

  19. InterPro is one source of IEAs

  20. 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)

  21. 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.

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

  23. Comparison of 68 enrichment analysis tools available in 2008

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