Keywords
gene ontology, high school students, genomics
This article is included in the Teaching and communicating science in a digital age collection.
gene ontology, high school students, genomics
Genomics is the branch of biology concerned with the study of genes and their functions (see the National Institutes of Health Frequently Asked Questions about Genetic and Genomic Science). Genomics arose from the acceleration of genetic research which was fueled by the development of rapid and affordable DNA sequencing technologies (Shendure et al., 2017). This opened the door to the sequencing of entire genomes. Presently, the DNA codes for thousands of genomes from diverse species have been sequenced and studied (see the National Center for Biotechnology Information Genome database).
The goals in genomics research are to address all genes and their inter-relationships in order to understand the combined influence on the function of an organism. With this newfound knowledge of the staggering number of genes that make up an organism, the Gene Ontology (GO) classification system was created to organize genes by their similarities and differences (see Gene Ontology Consortium ‘About’ page). “Ontology” is not a commonly encountered term and there are several definitions that are related to philosophical concepts.
In the context of information science, as described here, “ontology” is concerned with the representation, formal naming and classification system with the purpose of describing the relationship categories and properties of the data.
This classification system provides the scientific community with a structured vocabulary for defining genes (Ashburner et al., 2000; du Plessis et al., 2011; Hastings, 2017; Thomas, 2017). GO terms are commonly used in most, if not all, databases and analysis tools relevant to bioinformatics, systems biology (Wanjek, 2011), and genomics studies (du Plessis et al., 2011). GO terms are species specific and are continuously revised and expanded as biological knowledge is obtained (Gaudet et al., 2017).
The importance of the GO term system becomes apparent when analyzing the organization of genomes and coding regions, the distribution of genes involved in specific processes and the conservation of genes across species (Gaudet et al., 2017). This classification system is also quite powerful when analyzing data from large scale gene expression studies (du Plessis et al., 2011) that consider co-expression data from specific tissues obtained under defined circumstances such as treatment with pharmaceutical agents, or with neurodevelopmental disorders, cancer, or diabetes as examples. GO terms are instrumental for understanding the functions of these genes.
Introducing GO terms and the gene classification system to high school students will bring them up to speed on a commonly used research tool in current genomics methods and expose them to the vast amounts of data that have been derived from genomics and systems biology studies.
In the subsequent sections we show an example of how to extract information about a gene from its associated GO terms and then provide instruction for a practical exercise which will enable students to profile a list of genes using GO terms in the bioinformatics resource DAVID, The Database for Annotation, Visualization and Integrated Discovery. This is a protocol that we teach to our high school student interns when they are evaluating gene expression data for their summer projects (Crusio et al., 2017, see BioScience Project student posters).
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.
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).
DAVID is primarily a clustering program that groups genes based on different criteria related to GO terms. DAVID links to other databases that contain complementary information like The Gene Ontology. 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 profiling a gene list in DAVID obtained from a random gene list generator.
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., 2019a))
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., 2019b)). Typically the identifier is the “Official Gene Symbol”. Click “Gene List”, then “Submit List” (Video 1 (Delprato et al., 2019a)).
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., 2019a)).
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., 2019a)).
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., 2019a)).
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., 2019a)). 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.
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., 2019b))
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.
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.
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.
We describe a procedure for students to become acquainted with the Gene Ontology classification systems which is widely used in genomics and systems biology research to characterize gene function. Grouping genes with GO Terms and the DAVID database is based on a protocol that we use with our summer interns to profile gene expression data related to behavioral neuroscience studies (Crusio et al., 2017). Grouping genes in this way identifies genes that function in like processes and also provides information about the overall distribution of a set of genes associated with a particular tissue or process. This tutorial will familiarize early stage students with a biological database and teach them how to mine and extract useful information from a sample list of genes. Entry and exit ticket questions are also included as a formative assessment strategy.
All data underlying the results are available as part of the article and no additional source data are required
Extended data is available from figshare
Figshare: Extended data 1. Video 1: GeneSetProfiling 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.7649225.v1 (Delprato et al., 2019a)
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., 2019b)
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Is the rationale for developing the new method (or application) clearly explained?
Yes
Is the description of the method technically sound?
No
Are sufficient details provided to allow replication of the method development and its use by others?
Yes
If any results are presented, are all the source data underlying the results available to ensure full reproducibility?
No source data required
Are the conclusions about the method and its performance adequately supported by the findings presented in the article?
No
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Molecular microbiology; STEM outreach
Is the rationale for developing the new method (or application) clearly explained?
Yes
Is the description of the method technically sound?
Partly
Are sufficient details provided to allow replication of the method development and its use by others?
Yes
If any results are presented, are all the source data underlying the results available to ensure full reproducibility?
No source data required
Are the conclusions about the method and its performance adequately supported by the findings presented in the article?
Partly
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Neuroscience and pedagogy
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We would like to thank the reviewers for reviewing our article and for providing insightful and constructive feedback. We have taken your recommendations into account and have revised ... Continue reading Response to Reviewers
We would like to thank the reviewers for reviewing our article and for providing insightful and constructive feedback. We have taken your recommendations into account and have revised our article accordingly. We believe that the revised version is much stronger and of more value to the academic community than the originally submitted article.
Reviewer 1
1) The use of Entry Ticket and Exit Ticket questions is an excellent pedagogical idea. The questions, however, seem a bit simplistic.
Response: We agree with the reviewer’s comment that the Entry and Exit Tickets were simplistic. To address this we have added additional questions that we think are more challenging.
2) A bigger problem with the article is that there are no data showing how using the DAVID tool in teaching has produced changes in measurable ways. Do students do better on tests of content knowledge? Are students more sophisticated in their thinking as a function of using the DAVID tool? Are they better able to navigate other genomic tools? Do they simply enjoy the lessons more than with traditional instruction? Many of these questions can be answered with a simple pre- and posttest quiz or even comparing current scores on tests/evaluations to historical scores. This addition would make this article of much more value to educators who could then see if incorporating this tool into their pedagogy is worthwhile.
Response: To address this point, we have conducted and provided the results of a student survey designed to determine the viability of the method. The survey questions and responses are provided in the newly added Tables, 1, 2, and 3. To summarize the results, students report that they benefited intellectually from this tutorial, they found it enjoyable and feel better prepared for future database work.
Reviewer 2
1) This article presents an interesting way to introduce gene ontologies and related vocabulary to high school students to enhance biological education. While the proposal describes the method in detail, there is no data presented to show that the method actually works. While this is an interesting protocol, it is not a fully fleshed out methods article. The authors will need to present data to show the viability of the method, or resubmit potentially as a protocol.
Response: This point has been addressed in our response to Reviewer 1.
A few more specific comments about the article are listed below:
1) The abstract is worded as more of a proposal rather than a paper.
Response: The abstract has been revised to make it sound less like a proposal and we have incorporated the information obtained from the survey responses.
2) The protocol seems very much like following a set of directions (not very thought-provoking or inquiry-based), without relating it to the neuroscience. I think the paper would be much stronger if the authors were to relate it back to the behavioral neuroscience projects that are mentioned in the abstract.
Response: The method presented in this article is intended to introduce students to an unfamiliar and sophisticated topic. This instruction may serve as a stepping stone for inquiry-based projects.
We expanded the set of Exit Ticket questions to stimulate ideas on how these databases and tools may be used in a research project.
A paragraph to relate the tutorial back to the behavioral neuroscience has been added.
We also provide a suggestion for expanding the scope of the tutorial into an inquiry-based project.
We would like to thank the reviewers for reviewing our article and for providing insightful and constructive feedback. We have taken your recommendations into account and have revised our article accordingly. We believe that the revised version is much stronger and of more value to the academic community than the originally submitted article.
Reviewer 1
1) The use of Entry Ticket and Exit Ticket questions is an excellent pedagogical idea. The questions, however, seem a bit simplistic.
Response: We agree with the reviewer’s comment that the Entry and Exit Tickets were simplistic. To address this we have added additional questions that we think are more challenging.
2) A bigger problem with the article is that there are no data showing how using the DAVID tool in teaching has produced changes in measurable ways. Do students do better on tests of content knowledge? Are students more sophisticated in their thinking as a function of using the DAVID tool? Are they better able to navigate other genomic tools? Do they simply enjoy the lessons more than with traditional instruction? Many of these questions can be answered with a simple pre- and posttest quiz or even comparing current scores on tests/evaluations to historical scores. This addition would make this article of much more value to educators who could then see if incorporating this tool into their pedagogy is worthwhile.
Response: To address this point, we have conducted and provided the results of a student survey designed to determine the viability of the method. The survey questions and responses are provided in the newly added Tables, 1, 2, and 3. To summarize the results, students report that they benefited intellectually from this tutorial, they found it enjoyable and feel better prepared for future database work.
Reviewer 2
1) This article presents an interesting way to introduce gene ontologies and related vocabulary to high school students to enhance biological education. While the proposal describes the method in detail, there is no data presented to show that the method actually works. While this is an interesting protocol, it is not a fully fleshed out methods article. The authors will need to present data to show the viability of the method, or resubmit potentially as a protocol.
Response: This point has been addressed in our response to Reviewer 1.
A few more specific comments about the article are listed below:
1) The abstract is worded as more of a proposal rather than a paper.
Response: The abstract has been revised to make it sound less like a proposal and we have incorporated the information obtained from the survey responses.
2) The protocol seems very much like following a set of directions (not very thought-provoking or inquiry-based), without relating it to the neuroscience. I think the paper would be much stronger if the authors were to relate it back to the behavioral neuroscience projects that are mentioned in the abstract.
Response: The method presented in this article is intended to introduce students to an unfamiliar and sophisticated topic. This instruction may serve as a stepping stone for inquiry-based projects.
We expanded the set of Exit Ticket questions to stimulate ideas on how these databases and tools may be used in a research project.
A paragraph to relate the tutorial back to the behavioral neuroscience has been added.
We also provide a suggestion for expanding the scope of the tutorial into an inquiry-based project.