BIO/CSC295 2011F, Class 09: Protein Alignments (2)

Overview:
* HIV projects
* HIV Paper
* Overview of lab project (starting next week)

Admin:
* Students who make inappropriate comments shall be smacked.
* Turn in response papers before class starts.
* EC for Volleyball Thursday at 7:00 p.m.
* EC for Family Weekend Poster Session (9:00-11:00 Saturday), provided you visit
your classmates' posters.
* EC for Volleyball Saturday at 11 a.m.
* EC Friday for Bio students talking at noon! Bring your own lunch.
* EC for CS Thursday Extra about CS Grad School. Thursday 4:15PM. Bio majors can come too.
* Friday Bake sale for ISO. CUPCAKES from an unknown source -- buy at your own risk!

Group reports from HIV projects

* NLD
-One patient over time; saw more mutations on later visits.
-Got same rounded scores from their code & ClustalW.
-Diff (1st -> 5th) ~= Diff (2nd -> 5th), but 1st -> 2nd visit had greatest difference. 

* RBKB
-Different results from ClustalW (more divergence after ) & their code
-Large variation from patient-to-patient

*ADK 
-All samples for patient2 ()
-One particular hypervariable nucleotide
-Vaccine would focus on areas that stay same

*B
-Patient1 visit 5 compared with clones
-Variation all across the sequence but not concentrated in one spot.
 -Why vaccines are hard!

*MJK
-First 100 base pairs stable

*JJB
-Surprised about regression on final visit
-Was one data point per visit sufficient?
  Common ancestor not necessarily in data set

*BI
-First four visits of Patient 12
-Not much difference
-Which is not surprising since paper shows nonprogressor

*BMN
Patient: 1 2 3 9 15
-Saw constant of first 100 basepairs too over visits 1->5 (like MJK)


Our code:
Saw limits & strengths of Sam's code.
Couldn't fix it!

Run BLAST as a subroutine for unchanging sequences?



HIV Paper

BATTLE! 

Hard to measure diversity of antibody output  but env (envelope protein) is on the outside, so easy to see. 

*Is environment stable? Best fit would be dominant => not as many strains
* Unstable (more like immune system) => doesn't target dominant form, so highest frequency varies

The paper studies: IV drug users from Baltimore; just infected. 
-Specific point; difficult for anyone to get large sample space
-But what other things affect tcell count?
-No discussion of host genetics. ()
-Not sampled often enough (don't pick up on changes like A->G->A that happen within six months). This changes rapidly!

Disadvantage of rapid/moderate/nonprogressors categorizations
Even Bioinformatics is limited because we may have a lot of data, but not enough that fits into the categories we're researching.


Some Info about Bio: what is PCR, vectors, etc.
Nested PCR
PCR fragment: copies of fragment of DNA

Not all codons are equal! (Synonymous changes can be big [missplice proteins])

Phylogenic trees: visual representation of differences from root seq. 

Don't know what causes what; (does diversity of virus increase in a crashing immune system? Or was the immune system crashing because of high diversity?)


Our Bio lab project:
Worms!
We'll look for Mos-1 Transposon

Ligation: makes some kind of circles with some gene to expose Mos1!

Genetic suppressor screen: 

Mos1 is a transposon: it does what it wants (can jump around to different parts of the DNA).