ProfileHMM

Profile Hidden Markov Models (Profile HMMs) are statistical models used in bioinformatics to represent a set of related biological sequences, such as protein families or DNA motifs. They are a type of Hidden Markov Model (HMM) specifically adapted for sequence analysis. A Profile HMM captures the statistical properties of a sequence alignment, including the probabilities of observing each type of character (e.g., amino acid or nucleotide) at each position within the alignment, as well as the probabilities of insertions and deletions.

Unlike a standard HMM that models a single sequence, a Profile HMM is built from a multiple sequence alignment (MSA) of related sequences. This MSA provides the training data from which the model's parameters are estimated. The model consists of states that typically represent positions in the alignment. For each position, there are usually three types of states: match states, insertion states, and deletion states. Match states correspond to positions in the alignment where characters are conserved. Insertion states model the possibility of characters being inserted between conserved positions. Deletion states account for the possibility of characters being deleted at specific positions.

The primary applications of Profile HMMs include identifying homologous sequences within large databases, predicting the function of unknown proteins, and constructing multiple sequence alignments. They are particularly useful for detecting distantly related sequences that might be missed by simpler methods. Software packages like HMMER and SAM are commonly used for building and searching with Profile HMMs.