A big mathematical challenge
The underlying assumption of Hidden Markov Models that
DNA sequences are emitted by a Markov Model is
obviously far removed from biological reality. So the
question is: How can we construct gene finding tools that
are based on biologically more meaningful assumptions?
This has practical consequences for evaluating the
probability that a predicted gene is actually a gene, or
estimating the fraction of actual genes that have been
identified as such by a given gene-finding algorithm.