1. Gene Annotation & Gene Ontology
June 1, 2017

2. Gene lists from RNAseq analysis
What do you do with a list of 100s of genes that contain only the following information?
Gene name or symbol
Ratio between groups (UP or DOWN)
One or more database IDs (accession numbers)
How do you figure out the role of the genes in the model you are studying?

3. Gene annotation
Process of assigning descriptions to a transcript or gene product. Includes:
Official gene symbol & name
Protein features: domains, functional elements such as nuclear localization signals
Predicted molecular function, biological process and cellular location
Experimentally derived information function, process and cellular location
References
....

4. Who does the gene annotation?
Refseq & Gene databases
NCBI staff
Ensemble databases
EMBL & Welcome Trust at Sanger Institute
Uniprot
Staff at European Bioinformatics Institute (EBI), Swiss Institute of Bioinformatics (SIB) and the Protein Information Resource (PIR)
Yeast DB, FlyBase, Mouse Genome Informatics (MGI) & other organism specific databases

5. Gene record for BEST1

6. Ensembl Gene record for BEST1

7. Uniprot record for BEST1

8. Gene, Ensembl or Uniprot?
What information are you looking for?
Comfort level with the interface
All have a little to LOTS of information
Use as a starting point

9. Dealing with gene lists
How can you efficiently categorize the genes in in some biologically meaningful way?
Batch download data from Gene or Uniprot and do a lot of reading?
PubMed?
One approach is to use meta-data in the form of terms assigned to each gene that describe its molecular function, participation in a biological process and its location in a cellular component

10. Gene Ontology
Set of standard biological phrases (terms) which are applied to genes/proteins:
protein kinase
apoptosis
Membrane
Standardize the representation of gene product attributes across species and databases
Maintained by Gene Ontology consortium
Individual groups contribute taxonomic specific terms

11. Cellular Component
Where a gene product acts
Mitochondria

12. Cellular components not all same between organisms

13. Cellular Component Ribosome
Enzyme complexes in the component ontology refer to places, not activities.

14. glucose-6-phosphate isomerase activity
Molecular Function
Activities or “jobs” of a gene product
glucose-6-phosphate isomerase activity

15. insulin receptor activity
Molecular Function
insulin binding
insulin receptor activity

16. Molecular Function A gene product may have several functions
Sets of functions make up a biological process.

17. Biological Process a commonly recognized series of events
cell division

18. Biological Process
transcription

19. regulation of gluconeogenesis
Biological Process
regulation of gluconeogenesis

20. Biological Process
limb development

21. Why use gene ontology?
Allows biologists to make queries across large numbers of genes without researching each one individually
Can find all the PI3 kinases in a given genome or find all proteins involved in oxidative stress response without prior knowledge of every gene

22. TARDBP (TDP-43) GO biological process: GO molecular function:
3’UTR mediated mRNA stabilization
RNA splicing
mRNA processing
GO molecular function:
RNA binding
Double-stranded DNA binding
mRNA 3’-UTR binding
GO cellular component
Cytoplasm
Interchromatin granule
Nuclear speck

23. Gene Ontology for analysis Ontology annotation is NOT complete
Biological process terms are more useful for putting gene lists into a context
More GO terms assigned to process than to function or component
Fewest terms assigned to component
Function in the absence of any process information can imply a biological role
i.e. you are looking for transcription factors responsible for some response
Ontology annotation is NOT complete

24. Ontology Structure Terms are linked by two relationships is-a 
part-of 

25. mitochondrial chloroplast
Ontology Structure
cell
membrane chloroplast
mitochondrial chloroplast
membrane membrane
is-a
part-of

26. Nucleic acid binding is a
GO structure
Nucleic acid binding is a
type of binding.
GO isn’t just a flat list of biological terms
terms are related within a hierarchy
is_a
is_a
DNA binding is a type of
nucleic acid binding.

27. GO structure
gene A
A gene (A) that is associated with a term ‘DNA replication’ is automatically annotated to all that terms parent terms.
A single gene associated with with a particular term is automatically annotated to all of the parent terms

28. GO structure
This means genes can be grouped according to user-defined levels
Allows broad overview of gene set or genome
You can use the level of granularity that makes most sense

30. GO terms term: transcription initiation Each concept has:
id: GO:
definition:
Processes involved in the assembly of the RNA polymerase complex at the promoter region of a DNA template resulting in the subsequent synthesis of RNA from that promoter.
a name
an ID number
a definition

31. GO terms assigned to TARDBP

32. Types of evidence codes
Experimental:
Inferred from Experiment (EXP)
Inferred from Direct Assay (IDA)
Inferred from Physical Interaction (IPI)
Inferred from Mutant Phenotype (IMP)
Inferred from Genetic Interaction (IGI)
Inferred from Expression Pattern (IEP)

33. Types of evidence codes
Computational:
Inferred from Sequence or structural Similarity (ISS)
Inferred from Sequence Orthology (ISO)
Inferred from Sequence Alignment (ISA)
Inferred from Sequence Model (ISM)
Inferred from Genomic Context (IGC)
Inferred from Biological aspect of Ancestor (IBA)
Inferred from Biological aspect of Descendant (IBD)
Inferred from Key Residues (IKR)
Inferred from Rapid Divergence(IRD)
Inferred from Reviewed Computational Analysis (RCA)

34. Types of evidence codes
Other:
Author Statement Evidence Codes
Traceable Author Statement (TAS)
Non-traceable Author Statement (NAS)
Curator Statement Evidence Codes
Inferred by Curator (IC)
No biological Data available (ND)
Automatically-assigned
Inferred from Electronic Annotation (IEA)

35. Manual annotation Molecular function Biological process
In this study, we report the isolation and molecular characterization of the B. napus PERK1 cDNA, that is predicted to encode a novel receptor-like kinase. We have shown that like other plant RLKs, the kinase domain of PERK1 has serine/threonine kinase activity, In addition, the location of a PERK1-GTP fusion protein to the plasma membrane supports the prediction that PERK1 is an integral membrane protein…these kinases have been implicated in early stages of wound response…
This is an example of how a curator might approach a paper to find GO terms.
Biological process
Cellular component

36. Electronic Annotation
Annotation derived without human validation
mappings file e.g. interpro2go, ec2go.
Blast search ‘hits’
Lower ‘quality’ than manual codes
Used in non-model organisms
Define a similarity cut-off (E-value of 10-25)
So electronic annotation is where a human hasn’t looked at an annotation, it’s been done entirely automatically.
This can be from a mappings file e.g. InterPro2go, spkw2go, from non-validated sequence similarity, or from a combination of different methods.
These electronic methods produce very large numbers of annotations, but because they are not individually validated by a curator, can be thought of as having a lower quality than curator approved annotations.

37. Quality of annotation varies by organism

38. GO & analysis of gene lists
Maintains the databases of GO terms, serves a clearing house for terms as they are assigned in new organisms
Tools for exploring gene lists using GO:
WebGestalt, gProfiler, Onto-Express, and GSEA to name a few
DAVID is a suite of tools for gene enrichment analysis that also includes GO.
We’ll use both DAVID and WebGestalt to explore our gene list
insert slides from sorin’s talk

39. Gene Ontology tools input a gene list
shows which GO categories have most genes associated with them or are “enriched”
provides a statistical measure to determine whether enrichment is significant

40. Using GO in practice statistical measure
how likely your differentially regulated genes fall into that category by chance
mitosis – 80/100
apoptosis – 40/100
Cell proliferation – 30/100
glucose transport – 20/100
The better ones include an statistical measure of how likely your differentially regulated genes fall into that category by chance
So why is that necessary
So imagine you do a microarray with a 1000 genes, and you find that 100 are differentially regualted
And these are the GO processes that those differentially regualted genes fall into - it looks like mitosis is overrepresented….
microarray
1000 genes
100 genes differentially regulated
experiment

41. Using GO in practice
However, when you look at the distribution of all genes on the microarray:
Proportions analysis
Chi-squared or Fisher’s exact test
Process
Genes on array
# genes expected
(out of 100)
# genes observed
Mitosis
800/1000 80
Apoptosis
400/1000 40
Cell proliferation
100/1000 10 30
Glucose transport
50/1000 5 20
you can see that 80% of them were involved in mitosis, so the number upregulated is what you’d expect by chance.
The category positive regulation of cell proliferation actually contains more differentially regualted genes than you would expect by chance
Need a statistical test e.g. Chi-squared to see if this overrepresentation or enrichment of a certain class is statistically significant.

42. Other sources of annotation
Uniprot (Swiss-Prot) keywords
Protein domain databases
PFAM, Panther, PDB, PROSITE, ect
GeneDB summaries from NCBI
Protein-protein interactions databases
Pathway databases
KEGG, BioCarta, BBID, Reactome
DAVID incorporates annotation from all of these and clusters the redundant terms

43. Today in computer lab Tutorial on using DAVID
Tutorial on using WebGestalt
Analysis of gene lists using DAVID and WebGestalt