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

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

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

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

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

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

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

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)