Question

1. 1. In building a profile for a set of sequences, PSI BLAST weighs sequences differently. Why is this done?
2. 2. What are pseudocounts and why are they used?

Answer 1

BLAST::

BLAST (Basic Local Alignment Search Tool) is a sequence similarity search method, in which a query protein or nucleotide sequence is compared to nucleotide or protein sequences in a target database to identify regions of local alignment and report those alignments that score above a given score threshold

(PSI)-BLAST::

Position-Specific Iterative (PSI)-BLAST is a protein sequence profile search method that builds off the alignments generated by a run of the BLASTp program. The first iteration of a PSI-BLAST search is identical to a run of BLASTp program

PSI-BLAST is most conveniently used on the internet with the help of the graphical user interface provided by the PSI-BLAST search page on National Center for Biotechnology Information (NCBI) website

PSI-BLAST is most conveniently used on the internet with the help of the graphical user interface provided by the PSI-BLAST search page on National Center for Biotechnology Information (NCBI) website

Pseudocounts and their uses::

A pseudocount is an amount (not generally an integer, despite its name) added to the number of observed cases in order to change the expected probability in a model of those data, when not known to be zero. Depending on the prior knowledge, which is sometimes a subjective value, a pseudocount may have any non-negative finite value. It may only be zero (or the possibility ignored) if impossible by definition, such as the possibility of a decimal digit of pi being a letter, or a physical possibility that would be rejected and so not counted, such as a computer printing a letter when a valid program for pi is run, or excluded and not counted because of no interest, such as if only interested in the zeros and ones. Generally, there is also a possibility that no value may be computable or observable in a finite time (see Turing's halting problem). But at least one possibility must have a non-zero pseudocount, otherwise no prediction could be computed before the first observation. The relative values of pseudocounts represent the relative prior expected probabilities of their possibilities. The sum of the pseudocounts, which may be very large, represents the estimated weight of the prior knowledge compared with all the actual observations (one for each) when determining the expected probability

For many of the prediction measures, the optimal prediction on the training data is the mean (the average value). In the case of Boolean data (assuming true is represented as 1, and false as 0), the mean can be interpreted as a probability. However, the empirical mean, the mean of the training set, is typically not a good estimate of the probability of new cases. For example, just because an agent has not observed some value of a variable does not mean that the value should be assigned a probability of zero, which means it is impossible. Similarly, if we are to make predictions for the future grades of a student, the average grade of a student may be appropriate to predict the future grades of the student if the student has taken many courses, but may not be appropriate for a student with just one grade recorded, and is not appropriate for a student with no grades recorded (where the average is undefined).

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