Non: Week 6

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Course Work
Assignments Journal Pages Shared Class Journal Pages
BIOL368/S20:Week 1 BIOL368/S20:Class Journal Week 1
BIOL368/S20:Week 2 Non: Week 2 BIOL368/S20:Class Journal Week 2
BIOL368/S20:Week 3 Non: Week 3 BIOL368/S20:Class Journal Week 3
BIOL368/S20:Week 4 Non: Week 4 BIOL368/S20:Class Journal Week 4
BIOL368/S20:Week 5 Non: Week 5 BIOL368/S20:Class Journal Week 5
BIOL368/S20:Week 6 Non: Week 6 BIOL368/S20:Class Journal Week 6
BIOL368/S20:Week 8 Non: Week 8 BIOL368/S20:Class Journal Week 8
BIOL368/S20:Week 10 Non: Week 10 BIOL368/S20:Class Journal Week 10
BIOL368/S20:Week 11 Non: Week 11 BIOL368/S20:Class Journal Week 11
BIOL368/S20:Week 13 Non: Week 13 BIOL368/S20:Class Journal Week 13
BIOL368/S20:Week 14 Non: Week 14 BIOL368/S20:Class Journal Week 14


The purpose of today's lab is to answer our hypothesis from last week: How does the location and identity of nucleotide differences observed in HIV clones in subjects with a similar slope of divergence affect virulence?

Combined Methods/Results

Subjects 2, 3, 5, 11, 13, 14 (2 from each progressor group; all have relatively similar slopes of divergence (less than 1))

four random clones from each subject's visit #1 and #4

For my portion of the presentation, I focused mainly on using Excel to analyze sequence data.

  1. I entered all of the sequence data from the noted subjects above from their first and fourth visits into
  2. I used the clustal alignment data and imported it into Microsoft Word.
  3. Using the find and replace tool in Microsoft Word, I added commas after every nucleotide and dash (which signified a deletion) to delimit the data.
  4. The delimited data was then transferred to Microsoft Excel.

The data was then analyzed in Microsoft Excel in a variety of steps.

  1. Each nucleotide base was color-coded for easy identification.
  2. Each subject was color coded based on their progressor group.
  3. I then found the "mode" of each column/base pair location using this website in order to find the consensus for each column.
  4. I calculated the ratio of # consensus:total number of clones for each column to get an idea of the hotspots.
  5. Then I calculated the percentage of nucleotide for each base pair location using the COUNTIF function.
  6. Conditional formatting rules were then used to color code the percentages with the 100% consensus base pairs marked in red.
  7. The previous two steps were repeated using only the progressor groups in each base pair location.
  8. Hotspots were found using a formula rule that analyzed the nucleotide percentages; if all the nucleotides had less than 80% of total, meaning there was no consensus, they were marked in purple on the base pair location number.


Scientific Conclusion

We found that certain base pair locations had preferences for certain nucleotides.


  • I worked with Carolyn as my partner for the project by helping each other analyze data and create the presentation.
  • I worked with Maya in class by helping each other with small tasks in Excel.
  • I emailed Dr. Dahlquist for guidance in our analysis.
  • I used and modified the Week 6 Protocol.
  • Except for what is noted above, this individual journal entry was completed by me and not copied from another source.

Non (talk) 00:00, 27 February 2020 (PST)


  • OpenWetWare. (2020). BIOL368/S20:Week 6. Retrieved February 26, 2020, from
  • Markham, R.B., Wang, W.C., Weisstein, A.E., Wang, Z., Munoz, A., Templeton, A., Margolick, J., Vlahov, D., Quinn, T., Farzadegan, H., & Yu, X.F. (1998). Patterns of HIV-1 evolution in individuals with differing rates of CD4 T cell decline. Proc Natl Acad Sci U S A. 95, 12568-12573. doi: 10.1073/pnas.95.21.12568 (PubMed ID: 98445411)
  • How to find the mode for text value from a list/column in Excel? ExtendOffice. Retrieved February 26, 2020, from