1. PEDNA Notes Mon, Oct 27, 2008 Marc L. Smith
353 - Bioinformatics Fall 2008
PEDNA Notes
Mon, Oct 27, 2008
Marc L. Smith

2. PEDNA Ch. 13 Understanding Randomness
Don’t be fooled by what appears to be random
Random-looking sequences may not be
Seeming nonrandom sequences might be
Computers don’t do random
Really pseudorandom
Random enough (most of the time)
Eventually random numbers will cycle

3. rand() Built-in perl function (convenient!) Two forms
Without parameter
rand() returns fractional value: 0 <= val <= 1
With parameter
rand(n) returns integer value: 0 <= val <= n

4. Simulations Use rand() to write simulations
programs that simulate nondeterministic systems
nondeterministic doesn’t imply random
still pseudorandom
but nondeterminism lays the foundation for running Computational Experiments!
different runs of program may produce different outputs

5. Examples Roll dice Shuffle a sequence to randomize
Generate sequence of nucleotides
Mutate (evolve) a DNA sequence to simulate evolutionary process
Phylogeny reconstruction: heuristic search of tree space

6. PEDNA Code Let’s look at the code from Ch. 13 Code walks
Run / experiment / repeat

7. Hidden Markov Models (HMMs)
Alternative approach to aligning sequences
Essentially a Finite State Machine (FSM)
Set of states, including start state, end state,
State transitions occur probabilistically -- can use rand() to implement
Some transitions emit residues
Paths through FSM yield sequences
These sequences represent alignments

8. Hidden Markov Models
How would we use rand() to implement probabilistic FSM transitions?
When Jodi returns, we will discuss more about BLAST and HMMs
Read Ch. 6 in UB (especially sec. 6.2)