pHMM-tree: phylogeny of profile hidden Markov models
نویسندگان
چکیده
منابع مشابه
A generalization of Profile Hidden Markov Model (PHMM) using one-by-one dependency between sequences
The Profile Hidden Markov Model (PHMM) can be poor at capturing dependency between observations because of the statistical assumptions it makes. To overcome this limitation, the dependency between residues in a multiple sequence alignment (MSA) which is the representative of a PHMM can be combined with the PHMM. Based on the fact that sequences appearing in the final MSA are written based on th...
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The recent literature on profile hidden Markov model (profile HMM) methods and software is reviewed. Profile HMMs turn a multiple sequence alignment into a position-specific scoring system suitable for searching databases for remotely homologous sequences. Profile HMM analyses complement standard pairwise comparison methods for large-scale sequence analysis. Several software implementations and...
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The “fundamental” states N in a Hidden Markov Model are (especially in bioinformatics) usually called match states. Sequences formed with symbols of a finite alphabet E are the observations on which the HMM will be based. Two different stochastic processes operate on the state set: one process moves from one state to another, and the second process controls the emission of symbols of E with pro...
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The main focus of this work is on developing models for the activity profile of a terrorist group, detecting sudden spurts and downfalls in this profile, and, in general, tracking it over a period of time. Toward this goal, a d-state hidden Markov model (HMM) that captures the latent states underlying the dynamics of the group and thus its activity profile is developed. The simplest setting of ...
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ژورنال
عنوان ژورنال: Bioinformatics
سال: 2017
ISSN: 1367-4803,1460-2059
DOI: 10.1093/bioinformatics/btw779