This outline is also available in PDF.
Held: Tuesday, 27 September 2011
Summary:
We consider the various techniques that are used to build the large
DNA databases that we rely upon.
Related Pages:
Notes:
- Sam is back. Sorry for the extended absence, but citizenship requires it. He is likely to remain discombobulated for a few days.
- Thursday, we will meet downstairs in the Molecular Lab, Science 1104.
- Project 2.5 returned. General comments via email. BLAST papers will take a little more time.
- The lab instructions and protocol for the biology lab are now available.
- Programming assignment for chapter 5 (due Tuesday): Write a procedure,
generate_fragments(sequence, minlen, maxlen, coverage) that generates a bunch of random fragments from the sequence, with fragments between minlen and maxlen. You should generate enough fragments to get the average coverage specified. Then, write a procedure, determine_coverage(sequence, fragments), that aligns each fragment to the sequence and indicates how much each position is covered. Make sure to include sample runs of your program.
- CS Picnic, Friday, October 7. Sam should have the signup tickets on Thursday.
- EC: From Eternity to Here: Shrinkage in American Thinking about Higher Education. Today, 4:15, JRC 101.
- EC: Thursday Extra; Max Kaufmann '12 on generating parallel corpora. Thursday, 4:30, SCI 3821.
- EC: Biology Seminar Friday at noon. Unknown topic.
- EC: Men's Soccer vs. St. Norbert, Saturday at 2:00 p.m.
Overview:
- Simliarity Matrices.
- Lab.
- Sequencing DNA.
Disclaimer: I could not track down the original PAM paper, and the
variety of online resources are surprisingly inconsistent in their
descriptions.
- One of the standard (and perhaps oldest) substitution matrices.
- PAM1
- 1% mutation - closely related species
- Fill in basic matrix with frequencies (e.g., the position indexed by (A,R) is the probability of seeing R in the mutant given A in the wild type
- Scale!
- Convert to probabilities
- Take log
- Do other funky stuff
- That is, the value at position (i,j), representing a mutation from amino acid
i to amino acid j is something like
log((f(j)*M(i,j))/(f(i)*f(j)))
= log(M(i,j)/f(i))
- Where f(i) is the frequency of amino acid i occuring.
- And M(i,j) is the probability that amino acid i transforms into amino acid j
- No, we won't do it by hand, but we'll talk about the design of the
formula.
- To handle more than 1% mutation, we multiply the base matrix by itself
k times. So PAM250 is PAM1 multiplied by itself 250 times.
- Problems with PAM? Here are some of my answers
- No indels used in analysis.
- Every position treated as equally likely. In practice, mutations seems
more likely at some positions than others.
- Choice of PAM250 (or whatever) is primarily heuristic
- ...
- It's time to step back a bit and look at the biological (and
bioinformatical) production of data.
- How do we get all the wonderful sequence data that we've
been using, at least for DNA sequences?
- The Sanger method is used for short DNA
sequences.
- The book tells us it's really the only strategy used.
- We'll look at how it works and some of the data it produces
- Unlike the book, we'll use Ridom TraceEdit to explore data
(TraceEdit is available for all three platforms.)
- Note: You may have wondered why the official FASTA format permits
more that A, C, G, T, U, and N for DNA/RNA sequences. Hopefully, the
sequencing data we looked at gives you a sense as to why.
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