Discussion of last Friday's exercise
Sequence and structure databanks can be divided into many different categories. One of the most important is: 



One problem in maintaining databanks is "owner ship" of sequences, which in many databanks prevents a continuous update of sequences. Even is errors are detected, they are not easily removed form the databank. E.g. ATP synthase operons in E.coli see http://mic.sgmjournals.org/cgi/contentnw/full/156/7/1909/F1
If you use only a few genomes in your analyses, and if some of these genomes were annotated before 2010, it might be a good idea to reannotate the genomes using the RAST server
Discussion of Take Home Exam 1 (w anwers) (Excel file for extra question)
(homology < significant similarity  one way street from right to left)
Questions on Take Home Exam 2
If you can demonstrate significant similarity using randomization, your sequences are homologous (i.e. related by common ancestry). Convergent evolution has not been shown to lead to sequence similarities between complex sequences detectable through pairwise comparison. When are two similar
sequences significantly similar/homologous? (Note: we will discuss alignment algorithms later, for now it is sufficient to know that given a scoring matrix and two sequences, one can calculate an alignment that has an optimal score) One
way to quantify the similarity between two sequences is to
1.
compare the actual sequences and calculate alignment score
2.
randomize (scramble) one (or both) of the sequences and calculate the alignment
score for the randomized sequences.
3.
repeat step 2 at least 100 times
4.
describe distribution of randomized alignment scores
5.
do a statistical test to determine if the score obtained for the real sequences
is significantly better than the score for the randomized sequences
A
(Skipped: Summary of results is here). There are many other alignment programs. BLAST is a program that is widely used and offered through the NCBI (go here for more info). It also offers to do pairwise comparisons (go here, do example). To force the program to report an alignment increase the Evalue.
Summary of Terminology: Evalues give the expected number of matches with an alignment score this good or better due to chance alone (no shared ancestry, no convergent evolution) Pvalues give the probability of to find a match of this quality or better due to chance alone (no shared ancestry, no convergent evolution). The P value is equal to the probability that the null hypothesis (similarity is due to chance alone) is true. This probability is also known as the significance level a which the null hypothesis can be rejected. P values are [0,1], Evalues are [0,infinity).
BUT: Examples:
Jim Knox (MCBUConn) has studied many
proteins involved in bacterial cell wall biosynthesis and antibiotic binding,
synthesis or destruction. Many of these proteins have identical 3D structure,
and therefore can be assumed to be homologous, however, the above tests fail to
detect this homologies. (for example, enzymes with GRASP nucleotide binding sites
are depicted here.)
DNA
replication involves many different enzymes. Some of the proteins do the same
thing in bacteria, archaea and eukaryotes; they have similar 3D structures (e.g.:
sliding clamp, E. coli dnaN and eukaryotic PCNA, see Edgell and Doolittle,
Cell 89, 995998), but again, the above tests fail to detect homology.
If time, discuss how the P values should be adjusted in case multiple tests are performed. 
Types of Error in a Databank search False positives: The number of false positives are estimated in the Evalue. The Pvalue or significance value gives the probability that a positive identification is made in error (same as with drug tests).
False negatives: Homologous sequences in the databank that are not recognized as such. If there are only 12000 different protein families, an average a sequence should have (size of the databank)/12000 matches. In other words, the number of false negatives is probably very large. 