This adaptation of the sequence similarity resource provides biology students with a basic set of bioinformatics concepts and tools. These concepts and tools are used to explore relationships between Ebola virus strains.
Ebola virus is one of the most devastating human pathogens. It first emerged in 1976 in Zaire (Democratic Republic of the Congo, DRC) and Sudan. It has been responsible for sporadic outbreaks since that time. In most cases, the outbreaks are self-limiting and small in scope. The usual pattern for an outbreak is infection of a small number of individuals followed by infection of family members and health care workers. After the initial outbreak, high mortality and isolated geography leads to a diminishing number of infections until the outbreak ends. Mortality varies between 40% and 95%, making Ebola one of the most deadly infectious diseases in humans. In addition to high mortality, the disease course is devastating. Ebola Virus Disease (EVD) begins with fever, headache, muscle pain, diarrhea and vomiting then progresses to hemorrhagic fever. During hemorrhagic fever, there is major damage to liver, kidney, and gastrointestinal tract in addition to severe bleeding. Fatalities usually occur as a result of multiple organ failure or shock due to lack of fluids.
The West Africa Ebola outbreak of 2014-2015 taught us much about Ebola. We now know that Ebola has unique features that make it a source of worldwide concern. Health agencies around the world, as well as the World Health Organization, responded massively and effectively to the outbreak. This response is credited with reducing the mortality considerably, but still, 40% of those infected died from the disease.
A current Ebola outbreak in DRC has become a major public health concern. The Ebola outbreak that began in August 2018 in North Kivu Province is still ongoing. As of January 2019, with over 600 cases and 360 deaths, the DRC outbreak is now the second largest in history, but pales in comparison to the 28,000 infected and 11,000 dead in the 14-15 West Africa outbreak. An experimental vaccine has been developed and is being deployed, as completely as the difficulties related to the current conflict in the region will allow, in the current DRC outbreak. To develop the vaccine, it is imperative to know the strain of Ebola causing the current outbreak.
As part of our investigation of Ebola virus, we will use bioinformatics analysis to explore the Ebola strain(s) responsible for the most recent outbreak in Democratic Republic of Congo (DRC).
Exercises 1-3 of the Sequence Similarity resource, streamlined in this adaptation provide the bioinformatics concepts and tools that enable students to explore phylogenetic relationships among the known Ebola virus strains. This exploration enables students to test the hypothesis that the current Ebola outbreak in DRC is caused by an known Ebola strain rather than a new strain.
Kleinschmit Resource Learning Goals (all apply)
- Define similarity in a non-biological and biological sense.
- Quantify the similarity between two sequences.
- Explain how a substitution matrix is used to quantify similarity.
- Calculate amino acid similarity scores using various matrices.
- Explain how similarity is used to perform a BLAST search.
- Explain the BLAST search algorithm.
- Evaluate the results of a BLAST search.
- Create a dissimilarly matrix and multiple sequence alignment.
- Create a phylogram based on similarity of amino acid sequences.
- Distinguish between a rooted and unrooted phylogenetic tree.
Tapprich Ebola Inquiry Exercise Adaptation Learning Goals
- Apply bioinformatics skills to conduct inquiry of Ebola virus phylogenetic relationships
- Analyze phylogenetic tree of Ebola strains to guide vaccine development
- Develop new research questions and hypotheses for the exploration of Ebola virus
- Tapprich Sequence Similarity Adaptation Bioinformatics Exercise 1 Worksheet.docx(DOCX | 61 KB)
- Tapprich Sequence Similarity Adaptation Bioinformatics Exercise 1.docx(DOCX | 490 KB)
- Tapprich Sequence Similarity Adaptation Bioinformatics Exercise 2 Worksheet.docx(DOCX | 156 KB)
- Tapprich Sequence Similarity Adaptation Bioinformatics Exercise 2.docx(DOCX | 180 KB)
- Tapprich Sequence Similarity Adaptation Bioinformatics Exercise 3 Worksheet.docx(DOCX | 86 KB)
- Tapprich Sequence Similarity Adaptation Bioinformatics Exercise 3.docx(DOCX | 168 KB)
- Tapprich Sequence Similarity Adaptation Bioinformatics Exercise 4 - Ebola Inquiry.docx(DOCX | 151 KB)
- Tapprich Sequence Similarity Adaptation Exercise 1-3 Answer Key.docx(DOCX | 1 MB)
- Tapprich Sequence Similarity Adaptation TeachingNotesSp19.docx(DOCX | 22 KB)
- Tapprich Sequence Similarity AdaptationEbola Exercise Solution.docx(DOCX | 652 KB)
- Sequence Similarity: An inquiry based and "under the hood" approach for incorporating molecular sequence alignment in introductory undergraduate biology courses | CourseSource
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Cite this work
Researchers should cite this work as follows:
- Tapprich, W. (2019). Sequence Similarity Resource Adaptation: Exploring Ebola Virus. Bring Bioinformatics to Your Biology Classroom, /groups/niblse2019, QUBES Educational Resources. doi:10.25334/Q47X63
- This adaptation was meant to give all students bioinformatics experience. The first three exercises of the Kleinschmit resource were assigned without extensive modification.
- The open-ended Ebola inquiry exercise (fourth exercise) was developed specifically for this adaptation. It explores the phylogenetic relationship between Ebola virus strains. It addresses the hypothesis that the ongoing 2018-2019 Ebola outbreak in the Democratic Republic of Congo (DRC) is caused by a known viral strain rather than a new strain. This information is important for vaccine development. Using skills acquired from working the first three exercises, students are asked to address the hypothesis on their own given reference sequences for the viral glycoprotein (GP) from all of the known viral strains as well as four sequences from individuals infected in the ongoing DRC outbreak.