Introducing high school students to the Gene Ontology classification system

Overview of gene function with GO

The overall structure of GO is hierarchical and is based on parent-child terms where the parent term is broader and child term is more specialized.

GO terms group genes according to 3 categories, each of which are considered a distinct ontology: Molecular Function (MF, molecular-level activities performed by gene products), Biological Process (BP, the larger processes, or biological programs accomplished by multiple molecular activities), and Cellular Component (CC, the locations relative to cellular structures in which a gene product performs a function).

As an example, consider the GO term classification for the RAB5A gene (Figure 1). RAB5A belongs to a family of genes called RAB GTPases that are key regulators of intracellular membrane trafficking. Rabs are involved in the formation of transport vesicles and their fusion with membranes. They are enzymes and mediate their function by cycling between a GDP bound inactive and a GTP bound active state. Because of their fundamental and ubiquitous role, this family of genes are associated with many biological processes and diseases.

Figure 1. DAVID output for the RAB5A gene.

Screenshot of the DAVID results for the RAB5A gene Top left (blue bar): Gene Symbol identifier. Center (blue bar): full gene name. Labels (left): GO Term BP, GO Term CC, and GO Term MF descriptors. Note that these descriptors are clickable.

The GO term classification for the RAB5A gene gives:

GOTERM_BP: endocytosis, phagocytosis, small GTPase mediated signal transduction, blood coagulation, protein transport, regulation of endocytosis, synaptic vesicle recycling, viral RNA genome replication, early endosome to late endosome transport, positive regulation of exocytosis, regulation of endosome size, regulation of filopodium assembly, receptor internalization involved in canonical Wnt signaling pathway, regulation of synaptic vesicle exocytosis, regulation of autophagosome assembly

GOTERM_CC: ruffle, intracellular, cytoplasm, endosome, early endosome, cytosol, plasma membrane, synaptic vesicle, endosome membrane, actin cytoskeleton, endocytic vesicle, axon, dendrite, phagocytic vesicle membrane, somatodendritic compartment, melanosome, neuronal cell body, terminal bouton, axon terminus, membrane raft, phagocytic vesicle, extracellular exosome, cytoplasmic side of early endosome membrane.

GOTERM_MF: GTPase activity, protein binding, GTP binding, GDP binding

From the RAB5A related GO terms, we get the overall impression that this gene encodes an enzyme that is involved in signaling, transport and vesicle dynamics and is associated with cell membranes. How do we arrive at this description?

In this example, the information obtained from the MF category is that the protein product of the RAB5A gene binds to guanine nucleotides: GTP and GDP (Guanosine tri and di phosphate, respectively) and that it is an enzyme. This is evident by the “GTPase activity” term. Whenever the suffix “ase” is used in the context of a gene or protein, it refers to an enzyme, something that catalyzes a chemical reaction. For the BP category, there are several terms associated with intracellular transport, signaling, and endocytosis. Finally, the terms associated with CC include endosome and endosome-like organelles (melanosomes, synaptic vesicles, phagocytic vesicles), as well as membrane structures (ruffles, rafts).

Gene Analysis in DAVID

DAVID is a database with a suite of analysis tools that groups genes based on different criteria related to GO terms. DAVID also links to other databases that contain primary source information like The Gene Ontology as well as complementary information related to pathways and human disease. In this exercise, students will use the sample gene lists (DEMOLIST1 or DEMOLIST2) that are accessible from the DAVID database to see how the Gene Ontology classification partitions a set of genes based on GO Terms. Screenshots and videos are provided for step by step instruction. We also provide a video to instruct students on analyzing a gene list in DAVID obtained from a random gene list generator.

Protocol

Screenshot 1 (Figure 2). DAVID landing page. The start analysis link is accessed here and is circled in red in this image. (Video 1, Delprato et al., 2019c)

Figure 2. DAVID landing page.

Screenshot of the DAVID landing page containing a brief description of the site and links for available tools. The “start analysis” button is circled in red and is located at the top left side of the page. This is the first step in submitting a geneset for analysis with GO Terms in DAVID.

Screenshot 2 (Figure 3). Submitting a gene list. Select either DEMOLIST 1 or DEMOLIST 2 (left panel). The identifier will come up automatically because this is a demonstration list. If you are submitting your own gene list then, the identifier will have to be specified from the dropdown menu (Video 2, Delprato et al., 2019c). Typically the identifier is the “Official Gene Symbol”. Click “Gene List”, then “Submit List” (Video 1; (Delprato et al., 2019c)).

Figure 3. Submitting a geneset.

Screenshot of the page where a geneset can be submitted “Step1: Enter Gene List”, there are options to copy and paste a geneset or upload a file. There is also an option to use either of two sample lists provided by the DAVID site. Under the pull down menu, “Step 2 Select Identifier”, there are many types of identification designations for the same gene. For the sample lists, the identifier and species will be recognized automatically. When submitting a gene list from the geneset generator, the identifier is “Official Gene Symbol” which is used in most cases. If the identifier that you choose does not match the identifiers in the submitted gene set, you will receive a message stating this and an option to convert to the correct identifier.

Screenshot 3 (Figure 4). Species selection. You will see a notice: “Multiple Species, have been Detected”, Highlight “Homo Sapiens” in the window, Select “Homo Sapiens” below the window (Example - DEMOLIST 1: 149 genes, highlighted in grey, left panel). Next, you will see the message “Submission Successful” (Video 1; Delprato et al., 2019c).

Figure 4. Species selection.

Screenshot of a successfully submitted gene set. Here it is necessary to select the species. Again, note that in this instance for the sample genesets provided by DAVID, “Homo sapiens” is already highlighted along with the number of genes that are recognized by the site and for which information is available. Once the species is highlighted, it is necessary to click the “Select Species” button in order to limit the output to just the desired species.

Screenshot 4 (Figure 5). Obtaining the results. Select “Functional Annotation Tool”, beneath the blue arrow. Next, select “Functional Annotation Table”, Bottom of the page (Video 1, Delprato et al., 2019c).

Figure 5. Obtaining the results.

Screenshot of the different types of analysis options provided by DAVID for a given geneset. For the purpose of this tutorial, the relevant output is the “Functional Annotation Table” located at the bottom of the page.

Screenshot 5 (Figure 6). Reading the output. The gene ID and the full gene name are shown in the blue bars above each entry. The GO Term BP (Biological Process), GO Term CC (Cellular Component), and GO Term MF (Molecular Function), terms are clickable descriptors and link to the Gene Ontology website. See above for a complete description of the GO categories (Video 1, Delprato et al., 2019c).

Figure 6. Interpreting the output.

Screenshot of the DAVID “Functional Annotation Table” results for a geneset Top left: (blue bar): Gene Symbol identifier. Center (blue bar): full gene name. Labels (left): GO Term BP, GO Term CC, and GO Term MF descriptors. Note that these descriptors are clickable.

Screenshot 6 (Figure 7). Keyword search. When selecting terms for a keyword search, a more complete outcome is achieved if just a few letters are specified. For example, -”neur” will capture terms both starting with neuro and neural (Video 1, Delprato et al., 2019c). DAVID output can be searched for genes related to other process and diseases as well. Have students evaluate the gene list based on their interest. They can identify genes related to a particular process. Students may work individually or in groups.

Figure 7. Keyword search.

Screenshot of a keyword search of the DAVID output to identify genes that are relevant to a given biological function. In this case the search term is “neur” (highlighted in yellow), to identify genes related to neurological processes. The keyword search is done via the general search function on your computer. For larger gene sets, a computer program may be used.

Optional exercise

Students may wish to try this with their own gene lists. This online gene list generator will enable students to generate a random list of genes for evaluation. (See also Video 2 for instruction; Delprato et al., 2019c)

Protocol

Step 1. Specify species: Human is the default

Step 2. Specify list length: 200–500 is a good representative number. Note that DAVID will not evaluate lists with more than 2000 genes. An error message stating this will be received.

Step 3. Select “Generate”

Step 4. Copy the gene list using the “Select All” option and paste the list directly into DAVID for evaluation as described above. Make sure to select “Official Gene Symbol” as the identifier when submitting the gene list.

Optional exercise 2

Students may perform a gene enrichment analysis exercise based on GO annotation of their gene list directly at the Gene Ontology site. (Video 3). The results can be viewed as a pie chart which makes for a great visual representation of the GO categories (Figure 8). Clicking on the individual sections of the pie will result in more inclusive and specific annotation. Clicking on the legends will redirect to a table that contains the gene names associated with a specific GO category (For a more in depth explanation of the results page, see http://geneontology.org/docs/go-enrichment-analysis/).

Figure 8. Gene enrichment.

Screenshot of a pie chart generated at the Gene Ontology website showing a gene enrichment analysis for a sample gene list along with the associated category legends. The enrichment analysis finds GO terms that are over-represented (or under-represented) for a gene set. In this example, the annotation for Biological Process is shown for 72 genes.

Protocol

Step 1. Paste a gene list in the window of the Gene Ontology landing page (http://geneontology.org/).

Step 2. Specify the ontology (MF, CC, BP) that you would like to analyze from the drop down menu. This can be specified later as well on the results page. (Biological Process is the default).

Step 3. Select species (Homo sapiens is the default).

Step 4. Select “Launch”.

Step 5: Select the clickable number that represents your list of genes relative to the reference list to which is compared.

Step 6. Click on the pie chart above the table and specify the desired ontology from the drop down menu. Pie chart personalization and options for extended information are described in the text above the pie chart. Pie chart and legends may be saved as screenshots.

Learning assessment

We polled 12 student interns from our summer program for feedback on their experience working with the GO system and DAVID. The survey consists of 5 direct yes or no response questions and two open ended questions. The responses are show in Table 1, Table 2, and Table 3.

For the direct response questions, 100% of the students had not worked or heard of the DAVID database (Ques1, Table 1.) 83% of the students had not heard of the GO system. Two students had heard of the GO system in an advanced placement biology class but had not explored it further (Ques2, Table1). 100% of the students responded that they benefited intellectually from working with the GO system and DAVID tools (Ques3, Table1) and also that they had enjoyed the experience (Ques 4, Table 1). 92% of the students thought that the experience would better prepare them for future database use. One student indicated that this was not applicable (Quest 5, Table 1).

For the open ended survey questions 3a and 4a (Table 2 and Table 3), students were asked to explain how they benefited from working with the databases and tools and what about the experience they had enjoyed. A two step process was used to analyze their answers. First, the responses to each question were grouped and an online text analyzer text analyzer was used to assess the words occurring with the highest frequency (Workbooks 1 and 2) (Delprato et al., 2019a; Delprato et al., 2019b). Words of 2 or fewer characters were not considered in the analysis. In a subsequent step, a spreadsheet for coding open ended survey questions was used to organize the results from the text analysis (Workbooks 1 and 2).

Response category words chosen represent replies to the question asked. Words containing the same root such as learn, learned, learnt, learning were grouped. The response categories selected for question 3a that may provide insight into why students believe they have benefited intellectually are: expose (5), analyze (4) understand (3), help (3), experience (2), and gained (2) (Workbook 1).

For question 4a, the response categories chosen which may provide insight into why students enjoyed the experience are: interesting (6), gained (2), new (2) and explore (2). Other adjectives used in single responses were: interactive, rewarding, and refreshing (Workbook 2).

Entry and exit tickets

A basic entry and exit ticket method is suggested to determine what students know about genomics and genes before the lesson as well as what they have learned: main points, questions they may have and what they found most interesting. Sample questions are provided in what follows.

Entry ticket questions and answers

A sequence of DNA or RNA which codes for a molecule that has a function. A gene is the basic physical and functional unit of heredity

Study of the full set of the genes and DNA in an organism

~20,000

Yes, but people have different alleles. Alleles are the variation of a gene resulting from mutations. As an example consider eye color. We all have the gene for eye color but some of us have brown, blue or green eyes and there are different shades and hues within those categories.

Exit ticket questions

Underlying data

Figshare: Workbook 1. Coding of student responses to question 3a. https://doi.org/10.6084/m9.figshare.8166611.v1 (Delprato et al., 2019a)

This project contains the following underlying data:

Figshare: Workbook 2. Coding of student responses to question 4a. https://doi.org/10.6084/m9.figshare.8166650.v1 (Delprato et al., 2019b)

This project contains the following underlying data:

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

Extended data

Extended data is available from figshare

Figshare: Extended data 1. Video 1: GeneSetProfiling Analysis Instructional video for using DAVID to obtain Gene Ontology classifiers for a sample geneset which is provided by the DAVID site https://doi.org/10.6084/m9.figshare.8166650.v1 (Delprato et al., 2019c)

Figshare: Extended data 2. Video 2. UploadGeneSet Instructional video for generating a random geneset and submitting this geneset to DAVID for Gene Ontology classification, https://doi.org/10.6084/m9.figshare.7649231.v1 (Delprato et al., 2019d)

Figshare: Extended data 3. Video 3. GeneEnrichment Instructional video for performing a gene enrichment exercise at the Gene Ontology site (http://geneontology.org/) https://doi.org/10.6084/m9.figshare.9172121 (Delprato et al., 2019e)