Virus Poster Assignment

Difficulty
Time RequiredAverage (6-10 days)
PrerequisitesExcellent computer skills. Basic understanding of immunology and protein sequences or willingness to learn about these topics.
Material Availability None required
CostVery Low (under $20)
SafetyNo issues

Abstract

Remember going to the doctor and getting vaccine shots? It is no fun getting poked with a needle, but fortunately, a vaccine gives you protection against a serious illness for years to come. But what about the flu vaccine? How come there is a new one every year? This science fair project will show you why.

Objective

Use free Internet-based computer tools to analyze and estimate the effectiveness of different flu vaccines.

Credits

Author: Kirindi V. Choi; Teisha Rowland, PhD, Science Buddies
Sponsor: The Molecular Sciences Institute, Berkeley, CA
Editor: Ken Hess, Science Buddies

Cite This Page

MLA Style

Science Buddies Staff. "BLASTing Flu Viruses" Science Buddies. Science Buddies, 28 July 2017. Web. 10 Mar. 2018 <https://www.sciencebuddies.org/science-fair-projects/project-ideas/Genom_p003/genetics-genomics/blasting-flu-viruses>

APA Style

Science Buddies Staff. (2017, July 28). BLASTing Flu Viruses. Retrieved March 10, 2018 from https://www.sciencebuddies.org/science-fair-projects/project-ideas/Genom_p003/genetics-genomics/blasting-flu-viruses



Last edit date: 2017-07-28

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Introduction

Influenza, commonly known as the flu, is caused by a virus that attacks the upper respiratory tract (i.e., the nose, the throat and the lungs). Cold and dry weather allows the virus to survive longer outside the body than in warm weather. Therefore, in temperate regions like North America, when we are planning to enjoy Halloween, Thanksgiving, or Christmas, it is also the time when we or our family members have a higher chance of getting the flu.

There are three types of influenza virus: A, B and C. Type A can infect humans, other mammals and birds and can spread fast and affect many people. Types B and C affect only humans and type C causes only a mild infection. Influenza type A viruses are sub-typed into two categories based on proteins, specifically the proteins hemagglutinin and neuraminidase, on the surface of the virus. The virus uses the hemagglutinin protein (often abbreviated "H" or "HA") to latch on to the host's cell and uses the neuramidase protein (often abbreviated "N" or "NA") to spread the infection. Types A and B viruses continually evolve genetically, with changes being made to the amino acid sequence of the H and N proteins. Since hosts recognize the H and N surface proteins to identify and attack the virus, by changing these proteins a little bit the virus prevents the hosts from enjoying any prolonged protection against the virus.

When a person is vaccinated with the influenza vaccine, it should stimulate a protective immune response, particularly against the viral surface proteins in the viral strains used to make the specific vaccine. The influenza vaccine typically contains three virus strains, two are subtypes of type A and one is of type B. Type C is not included in the vaccine because it only causes a mild illness and does not lead to epidemics. To make the influenza vaccine, gene fragments that encode the H and N viral surface proteins are used from each strain. For the vaccine to give a person good protection against the virus, the protein sequences for the H and N proteins that are used in the vaccine should closely match the sequences in the strains the person may be exposed to. Every February, the World Health Organization (WHO), based on the analysis of various laboratories across the globe, will decide what influenza virus strains to include in the vaccine for the new year.

How can scientists check that the protein sequence of the H and N proteins used in the vaccine match the ones in the virus strains they want to protect people against? If you imagine that you can hold the H or N protein with both hands and stretch it out, you will then have a linear protein sequence in your hands. A protein sequence is made up of amino acids. Unlike the English alphabet, which has 26 letters, there are 20 standard amino acids that can be used to "spell" a protein. In English, it is easy to align two words and compare their spellings. Even so, there is often more than one possible alignment, as shown in Figures 1 and 2. In Figure 1, one possible alignment of the words "strawberry" and "blueberry" is shown, where the only matching letter, "r," is highlighted in red.

strawberry
blueberry_

Figure 1. One possible alignment of the words "strawberry" and "blueberry," showing the matching single letter "r" in this alignment highlighted in red.

In Figure 2, another possible alignment of these words is shown, where several matching letters, spelling "berry,"

strawberry
_blueberry

Figure 2. A second possible alignment of the words "strawberry" and "blueberry," showing the matching letters "berry" in this alignment highlighted in red.

For the words "strawberry" and "blueberry," the alignment in Figure 2 clearly gives us a greater number of matched letters between these words. Similarly, you can take two protein sequences and compare if their spelling is alike; this is called sequence alignment in bioinformatics.

The alignment example is simple enough that we can do it manually. However, when we want to align two protein sequences, they can be over 100 letters long and consequently it is much more difficult and more time consuming to do it manually. Luckily, bioinformatics comes to the rescue. Bioinformatics is the collection and analysis of large amount of biological data using computers and computational/statistical methods.

A powerful Internet-based bioinformatics tool for aligning sequences is BLAST, which stands for Basic Local Alignment Search Tool. It aligns your query sequence of interest to a collection of sequences stored in the database, or to a specific second sequence you are interested in. It compares the results, telling you which sequences or segments are similar to your query sequence.

All else being equal, we would expect that a strong match between the protein sequences for the H and/or N proteins used in the vaccine virus and the corresponding sequences in the "wild" virus to result in good protection against that virus. On the other hand, a poor match would result in weak protection against the virus. But to create a strong match, the WHO would need to accurately predict which strains people should be vaccinated against for the upcoming flu season. Is the prediction always accurate? How often is there a good match, and how often does the prediction fail and the vaccine does not give good protection against the common strains of the season? In this genetics and genomics science project, you will use BLAST to measure the quality of the match and estimate the effectiveness of a vaccine against different viruses.

Terms and Concepts

  • Influenza (or flu)
  • Virus
  • Influenza virus type
  • Surface proteins
  • Vaccine
  • Virus strains
  • Epidemic
  • Protein sequence
  • Amino acids
  • Sequence alignment
  • Bioinformatics
  • BLAST
  • Flu notation

Questions

  • How are flu viruses named?
  • BLAST can be used to align and compare both DNA (nucleotide) sequences and protein (amino acid) sequences. What are some reasons for using a protein alignment instead of a DNA alignment?
  • There are many H and N subtypes for influenza A. Why is it that in recent years the annual vaccine has only included influenza A subtypes H1N1 and H3N2? What is happening with the other subtypes? Under what conditions might they be included in the annual vaccine?
  • How does a vaccine help prevent the spread of a disease?

Bibliography

To do this science project you will need to use this database.
  • National Center for Biotechnology Information (NCBI). (August 27, 2012). GenBank Overview. NCBI GenBank, U.S. National Library of Medicine. Retrieved January 18, 2013, from http://www.ncbi.nlm.nih.gov/genbank
This article from the Centers for Disease Control and Prevention describes how strains of influenza are selected for vaccines. This website has BLAST, as well as a BLAST tutorial. This website has sequence information as well as a BLAST tool. You can use the strain information from the CDC reports (from the CDC resource) to search this database. Table 1 in the Procedure was created using data from this database.

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Materials and Equipment

  • Computer with an Internet connection
  • Lab notebook

Remember Your Display Board Supplies

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Experimental Procedure

  1. First, study the Terms and Concepts in the Background tab. It is especially important that you research and understand flu notation.
  2. Next, pick an influenza season that you would like to investigate from Table 1. The seasons are listed in the far left column, under "Influenza Season."
    1. Note that each influenza season spans two years. This is from October of the first year to May of the second year.
      1. For example, if the influenza season is listed as "2002 - 2003" this means that the data is from October 2002 to May 2003.
    2. If you want to study more recent flu data for your science project (data that are not included in Table 1), go to the Flu Activity & Surveillance webpage at The U.S. Centers for Disease Control and Prevention (CDC) website: http://www.cdc.gov/flu/weekly/fluactivitysurv.htm.
      1. Click on the link for "Past Weekly Surveillance Reports" and choose the influenza season you want to investigate.
      2. You will want to investigate a season that has ended so that there is a complete influenza season summary available.
      3. Click on "Go!" next to the influenza season you want to investigate. Make sure that the middle column has the season summary selected, and not a weekly report.
      4. To find the most common influenza strains subtyped that season, read the section titled "Antigenic Characterization."
      5. To find information on the strains in that season's influenza vaccine, you will need to go back to the "Past Weekly Surveillance Reports" webpage and select the previous influenza season. For example, if you are investigating the 2012–2013 influenza season, to find information about the 2012–2013 vaccine you will need to look at the 2011–2012 influenza season summary and read the section titled "Composition of the 2012–2013 Influenza Vaccine."
    3. In your lab notebook, record the years of the influenza season that you chose to investigate.
Influenza SeasonInfluenza TypeMost Common Influenza Strains Subtyped That SeasonStrains in That Season's Influenza Vaccine
2000–2001A (H1N1)A/New Caledonia/20/99A/New Caledonia/20/99
 A/Bayern/07/95 
A (H3N2)A/Panama/2007/99A/Moscow/10/99
B B/Beijing/184/93B/Beijing/184/93
 B/Sichuan/379/99 
2001–2002 A (H1N1) A/New Caledonia/20/99A/New Caledonia/20/99
A (H3N2)A/Panama/2007/99A/Moscow/10/99
B B/Yamagata/16/88B/Sichuan/379/99
 B/Victoria/2/87 
2002–2003 A (H1N1) A/New Caledonia/20/99 A/New Caledonia/20/99
A (H3N2) A/Panama/2007/99A/Moscow/10/99
B B/Hong Kong/330/01 B/Hong Kong/330/2001
 B/Sichuan/379/99 
2003–2004 A (H1N1) A/New Caledonia/20/99 A/New Caledonia/20/99
A (H3N2) A/Fujian/411/2002A/Moscow/10/99
 A/Panama/2007/99 
B B/Hong Kong/330/01 B/Hong Kong/330/2001
 B/Sichuan/379/99 
2004–2005 A (H1N1) A/New Caledonia/20/99 A/New Caledonia/20/99
A (H3N2) A/Wyoming/3/2003A/Fujian/411/2002
 A/California/7/2004 
B B/Yamagata/16/88B/Shanghai/361/2002
 B/Victoria/2/87 
2005–2006 A (H1N1) A/New Caledonia/20/99 A/New Caledonia/20/99
A (H3N2) A/California/7/2004 A/California/7/2004
 A/Wisconsin/67/2005 
B B/Shanghai/361/2002 B/Shanghai/361/2002
 B/Florida/07/2004 
 B/Ohio/1/2005 
2006–2007 A (H1N1) A/New Caledonia/20/99 A/New Caledonia/20/99
 A/Solomon Islands/3/2006 
A (H3N2) A/Wisconsin/67/2005 A/Wisconsin/67/2005
BB/Ohio/1/2005B/Malaysia/2506/2004
2007–2008 A (H1N1) A/Solomon Islands/3/2006 A/Solomon Islands/3/2006
 A/Brisbane/59/2007 
A (H3N2) A/Wisconsin/67/2005 A/Wisconsin/67/2005
 A/Brisbane/10/2007 
B B/Yamagata/16/88B/Malaysia/2506/2004
 B/Florida/4/2006 
 B/Ohio/1/2005 
2008–2009 A (H1N1) A/Brisbane/59/2007 A/Brisbane/59/2007
A (H3N2) A/Brisbane/10/2007 A/Brisbane/10/2007
B B/Yamagata/16/88B/Florida/4/2006
  B/Victoria/2/87 
2009–2010 A (H1N1) A/Brisbane/59/2007 A/Brisbane/59/2007
  A/California/07/2009 
A (H3N2) A/Brisbane/10/2007 A/Brisbane/10/2007
B B/Brisbane/60/2008 B/Brisbane/60/2008
2010–2011 A (H1N1) A/California/07/2009 A/California/07/2009
A (H3N2)A/Perth/16/2009 A/Perth/16/2009
B B/Brisbane/60/2008 B/Brisbane/60/2008

Table 1. This table lists the most commonly subtyped (characterized) strains of influenza from different influenza seasons, as well as the influenza strains used in the influenza vaccine for that season. If a common strain was different from any strains used in the vaccine for that year, the common strain's name has been bolded. The information used to generate this table was collected from the Flu Activity & Surveillance webpage at the CDC website at http://www.cdc.gov/flu/weekly/fluactivitysurv.htm.

  1. Take a moment to look at the different strains listed in Table 1 for the season you selected. Look at both the strains that were the most common ones subtyped that season as well as the strains that were in that season's vaccine.
    1. The influenza type of each specific virus strain is listed in the column labeled "Influenza Type," on the same row as the virus strain's name.
      1. For example, in the 2010–2011 season, the A/California/07/2009 strain is listed as a type A virus, specifically an H1N1 virus. The "H" and "N" refer to the type of hemagglutinin and neuraminidase surface proteins on the virus.
      2. You may notice that in some seasons there were multiple common strains of the same type (or subtype) of influenza virus. For example, in the 2009–2010 season, there were two common types of Type A (H1N1) strains, specifically A/Brisbane/59/2007 and A/California/07/2009.
    2. Note how the strains are written in flu notation. What do the notations tell you about the strains?
      1. For example, in the 2010–2011 season, the strain "A/California/07/2009" is listed. The notation means that this viral strain is a type A virus and it was the 7th influenza virus isolated in 2009 in California.
  2. In your lab notebook, record all of the data listed in Table 1 for your season of interest. To do this you may want to make a small data table similar to Table 1.
  3. Next you will compare the sequences for the strains that were common that season to the strains that were used in that season's vaccine. This will show you how good of a match the vaccine was to the strains that were prevalent. But before you do this, check the data table in your lab notebook to see if any of the common strains have the same name as the strains in the vaccine. If they are the same, you will not need to compare their sequences since they should be identical.
    1. To make this easier for you to spot, in Table 1, if a common strain was different than the ones used in the vaccine, the common strain's name has been bolded.
    2. For example, in Table 1, all of the common strains for the 2010–2011 influenza season are the same as the strains that were used in the vaccine that year (none of the strains are bolded).
    3. As another example, in the 2009–2011 influenza season, the A/Brisbane/59/2007 strain was both common and included in the vaccine. However, another Type A (H1N1) strain, A/California/07/2009, was also common but was not included in the vaccine that season. (The A/California/07/2009 strain has consequently been bolded there.)
    4. If, in your data table, any of the strains common that season were the same as the strains used in the vaccine, do not use that common strain in the next steps of the Procedure.
  4. Compare the sequences of one of the common virus strains to the same type of virus that was included in the vaccine that season. You will only be analyzing the sequences for the hemagglutinin and neuraminidase proteins. (If you are unsure of why this is, reread the Introduction in the Background tab.) Obtain the sequences for these strains from the NCBI GenBank website: http://www.ncbi.nlm.nih.gov/Genbank/.
    1. On the top of the webpage next to the search bar, select "Protein" from the drop-down menu.
    2. Type in the name of one of the common influenza strains (e.g., A/California/07/2009) in the search box from your data table.
    3. Look for the full-length protein sequence of the hemagglutinin protein. When you find this result, click on it.
      1. For example, for the A/California/07/2009 strain, the desired result is listed as "hemagglutinin [Influenza A virus (A/California/07/2009(H1N1))]" and on the next line says "566 aa protein," meaning this protein contains 566 amino acids. Do not select a result that says "partial" in its title, as this is not the full protein.
      2. If there is no data on the hemagglutinin protein for this strain (the protein is not listed in the results), skip this strain. Start step 6 over using a different common strain from your data table.
    4. You should now be on a webpage for the hemagglutinin protein for the strain. It should look similar to this example.
    5. Copy the accession number for the full-length hemagglutinin protein, listed at the top of the webpage. Record this number in your lab notebook. You may want to add to your existing data table so that it can easily include this information.
      1. For example, for the A/California/07/2009 strain's hemagglutinin protein, the accession number is AFM72832.1.
    6. Repeat steps 6a to 6e but this time in step 6b type in the name of the vaccine strain that is the same type of influenza as the common strain you just searched for.
      1. For example, if you are investigating the 2009–2010 influenza season and just searched for the A/California/07/2009 strain, you will now want to search for the A/Brisbane/59/2007 strain (the Type A, H1N1, strain used in the vaccine that season).
      2. Do not forget to record the accession number for hemagglutinin protein for this strain in your lab notebook.
    7. Click on the "Run BLAST" link (column on right side of webpage, at the top).
    8. Click the box next to "Align two or more sequences."
    9. In the query box at the top, enter the accession number for the hemagglutinin protein of either the common strain or the vaccine strain that is the same type of influenza. In the query box below that, enter the accession number for the other strain's hemagglutinin protein.
    10. The database should be set automatically to use the algorithm "blastp."
    11. Click the BLAST button at the bottom. You may need to wait a few seconds for your results to appear.
    12. Look at your BLAST results. How similar are the two sequences to each other? In your lab notebook, record the percentage that is identical between the two sequences.
  5. Repeat step 6 until you have compared the hemagglutinin protein of each common strain in your data table with the same type of influenza virus that was used in the vaccination for that season.
    1. If you want a more advanced challenge, print your BLAST results each time. When you do comparisons between the different strains, see if there is a region of the hemagglutinin protein that tends to be different from the strain used in the vaccination.
      1. Tip: Differences between BLASTed sequences show up on the line of text that is between the query and subject lines of text.
  6. Repeat steps 6 to 7 but this time use the neuraminidase protein sequences for the strains instead of the hemagglutinin protein sequences.
  7. Repeat steps 2–8 four more times using different influenza seasons (from Table 1) so that you have analyzed a total of five influenza seasons. Do you notice any trends?
    1. Overall, how similar are the hemagglutinin and neuraminidase protein sequences in the common influenza strains to the same type of influenza virus that was used in the vaccination for that season?
    2. Based on how similar the sequences are, how well do you think the vaccine protected a vaccinated person from the different strains in a given season?
    3. How well does it seem that an influenza vaccine from one year will protect a person against the common influenza strains one, two, three, or more years later?
    4. Do you notice any other trends in your data?

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Variations

  • Pick an influenza season from Table 1 in the Procedure. If you could travel back in time and redesign the influenza vaccine for the year you pick, which influenza strains would you use for the vaccine? Do you think you could make the vaccine more effective than it was? Based on sequence alignment, if the choice of virus strains you suggest are not available, are there any alternative strains you can use that you think are similar enough to still make an effective vaccine?
  • When designing an influenza vaccine, is it important to make sure the vaccine targets certain types of influenza virus more than other types? To figure this out, you can look into how common the different types of influenza virus are each season. The following resource, which is listed in the Bibliography in the Background tab, contains this information. To find the information, follow step 2b in the Procedure. You will need to carefully read through the influenza summary webpage for the season you are interested in. Which types of influenza virus are most common? Does this change from season to season, or does it stay fairly constant?
    1. Centers for Disease Control and Prevention (CDC). (2009). Flu Activity & Surveillance. Retrieved January 18, 2013, from http://www.cdc.gov/flu/weekly/fluactivitysurv.htm

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Evidence of student learning

Student learning was assessed at each stage of the project via a set of rubrics and evaluation materials: ADRP rubric, poster session quiz, and the pre- and post-project surveys (see Appendix 2, 4, 5, and 6).

Rubrics are discussed in the Activity Explanation and are found in the Supplementary Materials. The pre/post survey consisted of eight prompts to which students responded “Agree”, “Disagree”, or “Don’t Know”. Students were also asked to explain their response to each prompt. Two of the eight prompts were designed to evaluate students learning of principles targeted by the ADRP. Six of the eight prompts targeted students’ perceptions of the research process. We used mixed-methods analysis to interpret the results. Responses to the open-ended questions were analyzed qualitatively using an inductive approach (11), in which related responses were grouped into subcategories that could be quantified. A team of faculty and graduate students, including a science education faculty member, categorized the responses separately and then discussed their categories until they came to agreement. Their interrater agreement was 90%. The quantitative data was obtained from the Likert-scale (“Agree”, “Disagree”, “Don’t Know”) questions.

Based on the pre- and post-project surveys and the assessment of the students’ proposal development, the following observations regarding the achievement of our learning objectives were made (Table 2 and Fig. 3). Based on the instructors’ evaluation of in-class discussion, the one-on-one student evaluation during the poster session and the overall quality of the students posters, the students demonstrated an understanding of the overall research process involved in antiviral drug development (Learning Objective #1). Comparison of student responses to the knowledge-based survey questions in the pre-project survey and the post-project survey and the overall team scores for the posters indicated that specific gains were made in student understanding of the use of clinical trials in antiviral drug development and the concept that viruses can acquire drug resistance (Table 2 and Fig. 3). When prompted with, “Prior to releasing an antiviral drug as a treatment for disease, the drug is first tested in one animal model (for example in a mouse model or in a rabbit model), if no complications are observed, the drug is then tested in humans (clinical trials),” 40% of the class answered correctly before the ADRP and 74% answered correctly after the project. Additionally, the precision of the correct answers improved. Prior to the project, one student wrote, “More than one animal is used” and following the project another student wrote, “It is my understanding that at least two rodent models and one non-human primate model must be tested on before proceeding to clinical trials on humans.” Similar results were seen with the second knowledge-based question. When prompted with, “Viruses rapidly develop resistance to antiviral drugs,” 42% answered correctly before and 78% answered correctly after the ADRP. The precision of the correct answers to these questions also improved. Before the project a student wrote, “They mutate.” After the project a student wrote, “Viruses mutate quickly and can develop resistance in response to selective pressure.”

TABLE 2

Proposal development assessment. Each virus team’s proposal development was assessed in eleven categories (within section 4 of the ADRP rubric). The possible points for each category are listed with the category in the Rubric categories column...

FIGURE 3

Knowledge-based survey responses. Students were given statements that pertained to the development of antiviral drugs prior to and following the learning activity. The students were given the prompts: agree, disagree and don’t know and asked to...

One area of the antiviral drug development that several groups struggled with was the concept of using high throughput screening to identify candidate compounds. Only three out of the ten virus teams accurately explained the use of high throughput screening to identify candidate compounds (Table 2). Several students reported having difficulty finding information about this specific topic, suggesting that the instructors may need to play a more active role in addressing this concept either through the group discussion or in lecture.

Learning Objective #2 was to improve/build upon the student’s ability to utilize library and journal databases to find credible and relevant information in the form of data. We found that most students were successful in finding data and graphs relevant to their assigned topics, as was evident by the quality of their submitted research reports, in-class discussions and data presented in the poster, which were assessed using the ADRP rubric. While most students were able to identify primary literature, several students referenced review articles or even popular literature in their specialty research reports (Appendix 9). This suggests that not all the student were capable of differentiating between primary and popular literature and research reviews at onset of the project. The inclusion of a lecture that discussed finding primary literature and methods to discern it from other forms of communication prior to discussion #1 may improve the relevancy of information submitted for the initial specialty research report. In addition, several groups failed to include citations for all of the figures and graphs that were presented in the final poster (Table 2, ADRP rubric section 4b). To address this issue, the lecture on distinguishing primary literature should also include a review of how to properly cite references and the importance of acknowledging the proper source of information used in the project.

Learning Objective #3 focused on helping students to comprehend primary literature. Evaluation of the final poster project suggested that several of the groups struggled in this area. As evident from the primary comprehension score on the final poster project, there was substantial variability among the viral teams in this area (Table 2, see ADRP rubric 4b for assessment criteria). The majority of virus teams were penalized for either the misinterpretation of the graphs and figures that were included in the final submission of the poster and/or the misuse of terminology. The inclusion of more examples of primary literature during lecture may help the student to gain familiarity with terminology commonly used in virology. In addition, requiring students to interpret graphs and figures either in class or as a take-home assignment may improve their ability to interpret and therefore comprehend data.

The overall design of the project provided the students with multiple opportunities to practice both individual and team oral and written communication/presentation skills (Learning Objective #4). Written communication was assessed in the research report, as well as the poster project, while oral communication was assessed during discussion sessions and the poster presentations (see ADRP rubric sections 2, 3, and 4). The high scores for the organization of the posters reflect the fact that the students’ posters were well organized and flowed smoothly between the assigned topics, indicating that the students working in teams effectively communicated with each other (assessed in ADRP rubric section 4c). Similarly, the written questions submitted by most virus teams were well written and insightful, as reflected the average score of 1.9 out of 2.

One of the major goals for the design of the ADRP was to engage students in higher-order thinking. Higher-order thinking, defined as the use of cognitive skills rated within the upper levels of Blooms taxonomy, was evaluated throughout the project (Fig. 1). The successful completion of the ADRP required students to analyze data both individually and as a group, integrate information from various resources, evaluate information presented by other students, synthesize a coherent proposal, and effectively communicate findings in a written presentation (poster) and an oral presentation (poster presentation). As indicated by team scores for the final project (Table 2), the majority of the students were successfully engaged in higher-order thinking. For those students who struggled with the expectations of the project, the authors are currently testing the implementation of the minor changes suggested above. One surprising observation not mentioned previously was that several students took personal ownership of the data presented in their team poster. This was evident in the one-on-one evaluation of students during the poster session. Instructors observed that students presented data from primary research articles as if they personally performed the experiments. By doing so, they failed to give appropriate attribution to the source of the information or recognizing the investigators actually responsible for the work.

Student perceptions

The second goal of the development of the ADRP was to enhance the students understanding of the scientific process. The responses to the pre- and post-project surveys suggest that students’ perception of the research process was altered by participation in the ADRP (Fig. 4). Furthermore, we saw a shift in students’ awareness and understanding of research on our campus and the overall research process. Students were asked to circle their level of agreement with several statements (agree, disagree, and don’t know) and explain their response choice. When prompted with, “I have an understanding of how the research process is conducted by scientific researchers working in a research lab,” there was a 20% increase in students who agreed with this statement. Prior to the course only 73% of the students agreed with this statement while, by the end of the course, almost all of the students (93%) reported that they were aware of the research process. Of the 51 students who completed the post survey, seven explained that their understanding was a direct result of this course. One student wrote, “After participating in the antiviral project, I have a better understanding how research is conducted and the processes behind it.”

FIGURE 4

Self-reported awareness survey responses. Students were asked to respond to prompts regarding their awareness of ongoing research

When prompted with, “Team work (collaborative work) is valuable for scientific advances,” almost all of the students responded before (95%) and after (96%) the course that teamwork was important, indicating that students continue to have positive views of teamwork. Prior to the project, students claimed that teamwork is important in general; in the post-survey, they commented on specific attributes of working with a team. One student wrote, “It takes a whole team of researchers and assistants and collaboration from other fields for success in science.”

When asked what they would change about the project, 11 students specifically stated that they thought the project was good and one stated, “I actually think that it was set up good. I liked how we had virus teams and specialty teams. That was a good idea. I didn’t feel like the workload was too much because there were six people in our team. But with that many people, it was hard to find a meeting time between all of us. I liked how class time was allocated to talk about the project rather than just having everything done and talked about outside of class.”

In the group of students who felt there was room for improvement, ten students felt that they needed more direction and five students requested that the antiviral lecture be given prior to the start of the project. The authors have modified the original student instruction handout to more clearly define the expectations of the project (available upon request) and are in the process of assessing whether these modifications alleviate some of the students’ concerns. Additionally, more of an effort has been made to emphasize the usefulness of student-directed learning at the beginning of the project.

Potential modifications

This project can be altered to fit classes of varying sizes. The number of team members can be reduced to five by redistributing Specialty Six duties amongst the other five team members. This modification is currently being field-tested and does not appear to substantially increase the workload on the individual students. The number of virus teams can be increased or decreased to match the size of the class. For institutions without an online Course Management System, reports could be submitted via email or hard copies.

In order to make the discussions and poster session more comfortable, rooms other than a lecture hall would be preferable. Use of a conference room or a classroom with tables is preferable for discussion sessions, while the use of a large open room is recommended for the poster session.

Depending on the size of the class, it may not be feasible for the instructors to assess all the students during the poster session. The rubric can be altered to replace the instructor’s evaluation of the poster session with a peer- review system.

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