The most probable annotation problem in HMMs and its application to bioinformatics

Hidden Markov models (HMMs) are often used for biological sequence annotation. Each sequence feature is represented by a collection of states with the same label. In annotating a new sequence, we seek the sequence of labels that has highest probability. Computing this most probable annotation was shown NP-hard by Lyngsø and Pedersen [15]. We improve their result by showing that the problem is NP-hard for a specific HMM, and present efficient algorithms to compute the most probable on for a large class of HMMs, including abstractions of models previously used for transmembrane protein topology prediction and coding region detection. We also present a small experiment showing that the maximum probability annotation is more accurate than the labeling that results from simpler heuristics.

• Type: Journal
• Year: 2007
• Where: JCSS
• Authors: Brona Brejová, Daniel G. Brown 0001, Tomás Vinar
• Keywords: Biological Sequence Annotation, Hidden Markov Models, JCSS 2007, Probable Annotation