Contents
What are HMMs used for?
A hidden Markov model (HMM) is a statistical model that can be used to describe the evolution of observable events that depend on internal factors, which are not directly observable. We call the observed event a `symbol’ and the invisible factor underlying the observation a `state’.
What kind of alignment is used for searching a protein sequence database?
Basic Local Alignment Search Tool
Today, one of the most common tools used to examine DNA and protein sequences is the Basic Local Alignment Search Tool, also known as BLAST (Altschul et al., 1990).
How is Blast used for sequence alignment?
The Basic Local Alignment Search Tool (BLAST) finds regions of local similarity between protein or nucleotide sequences. The program compares nucleotide or protein sequences to sequence in a database and calculates the statistical significance of the matches.
Which is the best type of hmm for sequence analysis?
After reviewing the basic concept of HMMs, we introduce three types of HMM variants, namely, profile-HMMs, pair-HMMs, and context-sensitive HMMs, that have been useful in various sequence analysis problems.
One model that is especially useful for representing the profile of a multiple sequence alignment is the profile hidden Markov model (profile-HMM) [24, 25]. Profile-HMMs are HMMs with a specific architecture that is suitable for modeling sequence profiles.
How are HMMs used in the biological world?
As we mentioned earlier, HMMs can be effectively used for representing biological sequences. As a simple example, let us consider an HMM that models protein-coding genes in eukaryotes. It is well known that many protein-coding regions display codon bias.
What are the hidden states of a HMM?
An HMM consists of two stochastic processes, namely, an invisible process of hidden states and a visible process of observable symbols. The hidden states form a Markov chain, and the probability distribution of the observed symbol depends on the underlying state.