Background Info:

http://dwb4.unl.edu/Chem/CHEM869N/CHEM869NLinks/www.dur.ac.uk/~dbl0www/Staff/Croy/cDNAfigs.htm

mix:

water

reaction mix

RNA

enzyme (keep on ice)

add RNA

tap it

PCR

## Thursday, May 27, 2010

### Protein Extraction

http://www.piercenet.com/browse.cfm?fldID=FA97D803-6953-48E4-A7BD-6947D35FE83B

Basic Protocol:

Mix:

Lysis Buffer

Protease Inhibitor (comes in pellet --> vortex with buffer)

Phosphorase Inhibitor

add to pulverized tumor samples

homogenize while keeping tube on ice

spin down using vortex

Basic Protocol:

Mix:

Lysis Buffer

Protease Inhibitor (comes in pellet --> vortex with buffer)

Phosphorase Inhibitor

add to pulverized tumor samples

homogenize while keeping tube on ice

spin down using vortex

## Wednesday, May 26, 2010

## Tuesday, May 25, 2010

## Monday, May 24, 2010

### BLAST Algorithm and Substitution Matrix

http://en.wikipedia.org/wiki/BLAST#Algorithm

http://en.wikipedia.org/wiki/Substitution_matrix

http://en.wikipedia.org/wiki/Substitution_matrix

### Sequence Alignment

http://en.wikipedia.org/wiki/Sequence_alignment

*know global vs local alignment, various programs: FASTA, genebank, dot matrix-hits on the main diagonal determines sequence similarity

*know global vs local alignment, various programs: FASTA, genebank, dot matrix-hits on the main diagonal determines sequence similarity

### Poisson Distribution

http://en.wikipedia.org/wiki/Poisson_distribution

http://bioinfo.mbb.yale.edu/course/classes/c4/c4-p1.html

"Having introduced the idea of approximating the binomial distribution with two distributions, each of which is applicable in a different regime of the value of p, lets consider the case where p is small (p0.1). First, let us perform the substitution l =np. The binomial distribution then becomes,

(7)

Now, consider the case where n grows to infinity and p shrinks to zero. Hopefully you appreciate the utility of the substitution that we made above, since we can force n to grow and p to shrink such that l =np remains constant. This is nice since nothing in the above expression will "blow up" for large n and/or small p. In this limit we get,

(8) for k=0,1,2,3,...

This expression is the Poisson distribution, and is useful in the situations where the probability of an occurrence is small and the number of "trials" (n) is large. For example, we might consider the probability of k adverse reactions to a test drug in a given sample of the population or the probability of registering k complaints about a particular product in a 1-hour period or the probability of finding k point mutations in a given stretch of nucleotides. Though the Poisson distribution is essential to application and you will doubtless see it again, we will leave it now to discuss the other binomial-approximating continuous distribution. "

http://bioinfo.mbb.yale.edu/course/classes/c4/c4-p1.html

"Having introduced the idea of approximating the binomial distribution with two distributions, each of which is applicable in a different regime of the value of p, lets consider the case where p is small (p0.1). First, let us perform the substitution l =np. The binomial distribution then becomes,

(7)

Now, consider the case where n grows to infinity and p shrinks to zero. Hopefully you appreciate the utility of the substitution that we made above, since we can force n to grow and p to shrink such that l =np remains constant. This is nice since nothing in the above expression will "blow up" for large n and/or small p. In this limit we get,

(8) for k=0,1,2,3,...

This expression is the Poisson distribution, and is useful in the situations where the probability of an occurrence is small and the number of "trials" (n) is large. For example, we might consider the probability of k adverse reactions to a test drug in a given sample of the population or the probability of registering k complaints about a particular product in a 1-hour period or the probability of finding k point mutations in a given stretch of nucleotides. Though the Poisson distribution is essential to application and you will doubtless see it again, we will leave it now to discuss the other binomial-approximating continuous distribution. "

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