At the primary level of analysis both proteins and DNA are one
dimensional sequences of symbols from a finite alphabet. Many
secondary properties, such as gene structure, have a grammatical
structure, and therefore methods from language modelling can often
be
applied to biological sequences. A hidden Markov model (HMM)
is a
probabilistic model developed primarily in speech recognition
research, but it has recently proven very useful also for biological
sequence analysis. In this talk I will describe two applications
of HMMs: prediction of genes in genomic DNA
and prediction of transmembrane helices in membrane proteins.