Sequence Annotation with HMMs: New Problems and Their Complexity

TL;DR

This paper explores the computational complexity of three HMM decoding criteria, proving their NP-hardness, and demonstrates their practical usefulness in HIV recombination detection.

Contribution

It introduces and analyzes new HMM decoding criteria, establishing their NP-hardness and showing their application in biological sequence analysis.

Findings

Proved NP-hardness of three HMM decoding criteria

Demonstrated effectiveness in HIV recombination detection

Provided insights into sequence annotation complexity

Abstract

Hidden Markov models (HMMs) and their variants were successfully used for several sequence annotation tasks. Traditionally, inference with HMMs is done using the Viterbi and posterior decoding algorithms. However, recently a variety of different optimization criteria and associated computational problems were proposed. In this paper, we consider three HMM decoding criteria and prove their NP hardness. These criteria consider the set of states used to generate a certain sequence, but abstract from the exact locations of regions emitted by individual states. We also illustrate experimentally that these criteria are useful for HIV recombination detection.