Functional annotation of DEGs

1. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
Gene Ontology (Ashburner et al., 2000)
Genomic sequencing has made it clear that a large fraction of the genes
specifying the core biological functions are shared by all eukaryotes.
Knowledge of the biological role of such shared proteins in one organism
can often be transferred to other organisms.
Three independent ontologies - biological process, molecular function and
cellular component.

2. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
Functional annotation of Commonly
differentially expressed genes

3. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
Commonly differentially expressed genes
DAVID, AmiGO2, g:Profiler, PROSITE, PRINTS, Pfam, ProDom, SMART, TIGRFAMs, PIR
superfamily, SUPERFAMILY, Gene3D, PANTHER, BLAST2GO and HAMAP
Differentially expressed genes identified by Cuffdiff, EBSeq,DESeq2 and edgeR
Functional annotation
Tools

4. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
Finding the Gene symbols for Bos taurus ensembl IDs
using g:Convert in g:Profiler:

5. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
Identifying common differentially expressed genes predicted
across different packages using venny (http://
bioinfogp.cnb.csic.es/tools/venny/)

6. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
• A total of 4246 commonly differentially expressed genes have been identified
by all the packages.
• These genes are further used for downstream analysis. Functional
annotation is to find out the gene ontology terms enriched in the commonly
differentially expressed genes.
• Gene ontology (GO) is a major bioinformatics initiative to unify the
representation of gene and gene product attributes across all species.
• Gene products are described in terms of their
• associated biological processes,
• cellular components and
• molecular functions in a species-independent manner in the process of
assigning the annotations.
• There are several databases for performing the functional annotation -
DAVID, AmiGO2, g:profiler, PROSITE, PRINTS, Pfam, ProDom, SMART,
TIGRFAMs, PIR superfamily, SUPERFAMILY, Gene3D, PANTHER,
BLAST2GO and HAMAP. Here we will be discussing about g:profiler and
DAVID.
Functional Annotation

7. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
Functional annotation using g:Profiler
Step 1. Open http://biit.cs.ut.ee/gprofiler/ , paste the gene list, select Bos taurus
as the organism and the output type as excel spread sheet

8. Computational Biology and Genomics Facility, Indian Veterinary Research Institute

9. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
Step 2. Download the excel file to check for the annotations enriched in
the differentially expressed genes
The output shows the significance of terms and the genes associated in
the query (Q) in the term (T).
For the first term - response to abiotic stimulus (Biological process - BP (t
type)) has a term ID of GO:0009628 with the p - value of 1.79E-07. The
term has 625 genes associated with it out of which only 210 are enriched
in the gene list out of a total of 4133 genes considered.

10. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
Step 4. Representing the functional terms graphically
• Most common way of representing the functional terms is by choosing the
top ten terms (by sorting on the basis of p-value) in each category
(Biological process - BP; Molecular process - MP and cellular component -
CC).
• The terms can be represented on y- axis and significance which is the -
Log10P is represented on the x-axis as shown below for the biological
process. The same can be done for all the categories.

11. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
Biological Process
0 15 30 45 60
Cellular metabolic process
Metabolic process
Positive regulation of biological process
Organic substance metabolic process
Response to stress
Cellular macromolecule metabolic process
Primary metabolic process
Cellular protein metabolic process
Positive regulation of metabolic process
Immune system process
Significance (-log10P)
Step 5: Interpretation of the data, completely the researchers purview

12. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
Functional annotation using DAVID
(Database for Annotation, Visualization and Integrated Discovery)
Step 1. Open https://david.ncifcrf.gov and upload a multilist file if you have >
3000 genes to be annotated. The multilist file should be a list1 and list2
separated by a tab. Call this list into DAVID by uploading, select official gene
symbol from the drop down menu (as identifier), check the radio button against
the genelist and submit.

13. Computational Biology and Genomics Facility, Indian Veterinary Research Institute

14. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
Step 2. Select an appropriate background (Here it is Bos taurus) against
which you wish to test your gene list. Create a combined list by clicking
combine after selecting both the lists and select the combined list to get the
functional annotations

15. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
Step 3. Click on the functional annotation tool in the window to get the
annotation summary results

16. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
Step 4. The + button can be clicked in the window to get the results.
• For getting the gene ontology terms click the + button by the side of
the gene ontology and then proceed to any specific category viz.
BP,MP or CC. Clicking on the chart option opens up a window with
all the specific terms.
• Here we click on BP to get all the gene ontology terms enriched for
biological processes in our differentially expressed genes.
• The detail containing the genes associated with each gene can be
downloaded and opened in excel for further use.
• The same can be done to visualize pathways enriched in the DEGs

17. Computational Biology and Genomics Facility, Indian Veterinary Research Institute

18. Computational Biology and Genomics Facility, Indian Veterinary Research Institute

19. Computational Biology and Genomics Facility, Indian Veterinary Research Institute

20. Computational Biology and Genomics Facility, Indian Veterinary Research Institute

21. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
Step 1. Open the ClueGO app in cytoscape, paste genes in the window
(Here we copy pasted the DEHC genes identified in Chapter 8)
Functional annotation using ClueGO

22. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
Step 2. Select a gene ontologies or pathways and start - Here we selected
immune system processes as seen in figure above
Step 3. Represent a network with GO term as node label,%associated genes
as node colour and P-Value Corrected with Bonferroni step down as node size
(There parameters are selected as per the requirements of the researcher)
Functional annotation using ClueGO

23. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
regulation of defense
response to virus by host
T cell activation involved
in immune response
antigen processing and
presentation of peptide
antigen via MHC class II
antigen processing and
presentation of
exogenous peptide
antigen via MHC class II
T−helper cell
differentiation
hemopoiesis
regulation of antigen
receptor−mediated
signaling pathway
antigen
receptor−mediated
signaling pathway
T cell proliferation
positive regulation of
lymphocyte activation
negative regulation of T
cell activation
cytoplasmic pattern
recognition receptor
signaling pathway
T cell receptor signaling
pathway
negative regulation of
leukocyte activation
regulation of T cell
receptor signaling
pathway
regulation of T cell
activation
leukocyte differentiation
myeloid cell activation
involved in immune
response
positive thymic T cell
selection
positive regulation of
myeloid leukocyte
differentiation
negative T cell selection
myeloid leukocyte
mediated immunity
regulation of myeloid
leukocyte differentiation
T cell mediated immunity
thymic T cell selection
alpha−beta T cell
activation involved in
immune response
CD4−positive, alpha−beta
T cell activation
CD4−positive, alpha−beta
T cell differentiation
somatic diversification of
immunoglobulins
CD4−positive, alpha−beta
T cell differentiation
involved in immune
response
regulation of alpha−beta T
cell activation
positive regulation of
leukocyte differentiation
T cell activation
regulation of B cell
proliferation
alpha−beta T cell
differentiation involved in
immune response
T cell selection
B cell differentiation
thymocyte aggregation
lymphocyte differentiation
positive regulation of T
cell activationalpha−beta T cell activation
negative regulation of
antigen
receptor−mediated
signaling pathway
somatic diversification of
immune receptors via
germline recombination
within a single locus
B cell activation involved
in immune response
leukocyte activation
involved in immune
response
regulation of leukocyte
differentiation
regulation of osteoclast
differentiation
T cell differentiation
activation of immune
response
innate immune
response−activating signal
transduction
pattern recognition
receptor signaling
pathway
negative thymic T cell
selection
TRIF−dependent toll−like
receptor signaling
pathway
regulation of innate
immune response
MyD88−dependent
toll−like receptor signaling
pathway
toll−like receptor 5
signaling pathway
positive regulation of T
cell differentiation
T cell differentiation
involved in immune
response
positive regulation of
hemopoiesis
lymphocyte activation
involved in immune
response
leukocyte mediated
cytotoxicity
alpha−beta T cell
differentiation
T cell differentiation in
thymus
myeloid leukocyte
differentiation
positive T cell selection
regulation of lymphocyte
differentiation
osteoclast differentiation
positive regulation of
myeloid cell
differentiation
regulation of neutrophil
migration
neutrophil migration
granulocyte chemotaxis
regulation of production
of molecular mediator of
immune response
positive regulation of
leukocyte migration
positive regulation of
leukocyte chemotaxis
positive regulation of
myeloid leukocyte
cytokine production
involved in immune
response
regulation of granulocyte
chemotaxis
positive regulation of
granulocyte chemotaxis
positive regulation of
neutrophil migration
neutrophil chemotaxis
toll−like receptor 2
signaling pathway
toll−like receptor 3
signaling pathway
MyD88−independent
toll−like receptor signaling
pathway
toll−like receptor
TLR6:TLR2 signaling
pathway
toll−like receptor
TLR1:TLR2 signaling
pathway
toll−like receptor 10
signaling pathway
positive regulation of
innate immune response
regulation of lymphocyte
activation
positive regulation of B
cell activation positive regulation of B
cell proliferation
regulation of B cell
activation negative regulation of B
cell activation
B cell proliferation
B cell homeostasis
lymphocyte proliferation
negative regulation of T
cell receptor signaling
pathway
regulation of toll−like
receptor signaling
pathway
regulation of toll−like
receptor 4 signaling
pathway
positive regulation of
neutrophil chemotaxis
positive regulation of
production of molecular
mediator of immune
response
negative regulation of
innate immune response
negative regulation of
immune effector process
toll−like receptor 9
signaling pathway
negative regulation of
immune response
positive regulation of
toll−like receptor signaling
pathway
positive regulation of
toll−like receptor 4
signaling pathway
B cell activation
regulation of cytokine
production involved in
immune response
positive regulation of
lymphocyte differentiation
positive regulation of
cytokine production
involved in immune
response
negative regulation of T
cell proliferation
regulation of neutrophil
chemotaxis
myeloid leukocyte
cytokine production
T cell homeostasis
granulocyte migration
macrophage activation
regulation of macrophage
activation
activation of innate
immune response
toll−like receptor 4
signaling pathway
immune
response−activating signal
transduction
positive regulation of
leukocyte activation
immune
response−regulating
signaling pathway
positive regulation of
lymphocyte proliferation
toll−like receptor signaling
pathway
negative regulation of
lymphocyte activation
regulation of T cell
proliferation
nucleotide−binding
oligomerization domain
containing signaling
pathway
regulation of lymphocyte
proliferation
negative regulation of
lymphocyte proliferation

24. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
Step 4. The attributes of the network can be exported in a table format