S.R. Eddy, Pro l hidden Markov model, Bioinformatics 14 (2002), 755-763.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....method for recognition of homologs with low sequence similarity, and how it fared in the foldrecognition section of the CASP3 experiment. Hmms combine the best aspects of weight matrices and local sequence alignment methods, and can be used to assign probabilities to proteins in database search [6]. Our hmm fold recognition method di ers from protein threading methods [10, 19, 14, 15] in that pairwise interactions are not modeled or used. Instead, we employ Bayesian methods [3, 2, 17] to incorporate prior information in the form of Dirichlet mixture densities [20] over positionspeci c amino ....

S. Eddy. Hidden Markov models. Curr. Opin. Struct. Biol., 6(3):361-365, 1996.

....a#ne gap penalties. Such a score can be formulated as the log likelihood of the data using Hidden Markov Models (HMMs) Durbin et al. 1998, chapter 4) The latter are often explicitly used to generalize the homology concept, and to model alignment against a family of sequences (Krogh et al. 1994; Eddy 1996). Our Contribution We describe here a new method for reconstructing the target sequence, by combining information on a reference sequence with experimental spectrum data obtainable from a standard chip. We call the technique resequencing by hybridization, or spectrum alignment since the ....

....This is a very general setting. Standard literature discussing nucleotide substitution matrices (Jukes Cantor 1969; Kimura 1980) assumes all substitution matrices to be the same, i.e. M (j) M for all j. More recent studies support di#erence between sites for DNA (Yang 1993) and protein (Eddy 1996) sequences. The setting just presented implies a distribution on the space of possible target sequences. This prior distribution for ungapped homology, D u , is explicitly given for each candidate target sequence T by: D u ( T ) P rob( T H) l # j=1 M (j) t j , h j ] 6) One ....

Eddy, S. R. 1996. Hidden markov models. Current Opinions in Structural Biology 6(3):361--365.

....sequence of words. Rabiner (Rabiner 1989) gives a good introduction to the theory of hidden Markov models and their applications to speech recognition. Hidden Markov models were introduced in computational biology in 1989 by Churchill (Churchill 1989) Durbin et al. Durbin et al. 1998) and Eddy (Eddy 1996; 1998) are good overviews of the use of hidden Markov models in computational biology. One of the most popular applications is to use them to characterise sequence families by using so called pro le hidden Markov models introduced by Krogh et al. Krogh et al. 1994) For a pro le hidden Markov ....

Eddy, S. R. 1996. Hidden markov models. Current Opinion in Structurel Biology 6:361-365.

....ane gap penalties. Such a score can be formulated as the log likelihood of the data using Hidden Markov Models (HMMs) Durbin et al. 1998, chapter 4) The latter are often explicitly used to generalize the homology concept, and to model alignment against a family of sequences (Krogh et al. 1994; Eddy 1996). Our Contribution We describe here a new method for reconstructing the target sequence, by combining information on a reference sequence with experimental spectrum data obtainable from a standard chip. We call the technique resequencing by hybridization, or spectrum alignment since the ....

....(5) This is a very general setting. Standard literature discussing nucleotide substitution matrices (Jukes Cantor 1969; Kimura 1980) assumes all substitution matrices to be the same, i.e. M (j) M for all j. More recent studies support di erence between sites for DNA (Yang 1993) and protein (Eddy 1996) sequences. The setting just presented implies a distribution on the space of possible target sequences. This prior distribution for ungapped homology, D u , is explicitly given for each candidate target sequence T by: D u ( T ) P rob( T jH) l Y j=1 M (j) t j ; h j ] 6) ....

Eddy, S. R. 1996. Hidden markov models. Current Opinions in Structural Biology 6(3):361-365.

....in Kestrel s performance occurs at multiples of the 512 array length. Kestrel s programability makes it far more flexible than the other specialized systems. 2. 1 HMM Sequence Analysis Linear hidden Markov models (HMMs, Figure 6) are a powerful extension of standard sequence analysis methods [14, 11, 7, 6]. Instead of comparing or aligning one sequence against another, one compares or aligns one sequence against a statistical model of the family of sequences of interest. There are two parts to this process: training (building and refining) an HMM, and then using it. Taking the simpler part first, ....

S. Eddy, "Hidden Markov models," Curr. Opin. Struct. Biol., vol. 6, no. 3, pp. 361--365, 1996.

....of these alignments contain quite a few sequences, it can be advantageous to fuse them into a consensus pattern or template, just as is done with structures [62] Fig. 3) For this, a variety of probabilistic approaches can be used. A most popular representations is the Hidden Markov Model (HMM) [120 125]. This is a generalization of the sequence profile, and like a profile it gives an explicit probability for each of the 20 amino acids to occur at each position in the model [126] The HMM goes beyond a profile in associating with each position an explicit probability for introducing a gap (either ....

....absolute numbers of fold counts, but the relative values between different folds will usually remain comparable. However, as discussed above, there are other, potentially more sensitive, methods of comparing sequences to structures e.g. profiles, HMMs, and motif analysis, and threading [55, 125, 153 155]. These latter methods find more homologues for certain folds, particularly those for which multiple alignments are available. However, the sensitivity improvement is not consistent for all folds. This is not advantageous for a large scale survey where uniform sampling and treatment of the data is ....

Eddy, S R (1996) Hidden Markov models. Curr. Opin. Struc. Biol. 6, 361-365.

....Markov model (hmm) methods [15, 12] for recognition of remote homologs fared in the fold recognition section of the CASP2 experiment. Hmms combine the best aspects of weight matrices and local sequence alignment methods, and can be used to assign probabilities to proteins in database search [6]. Our hmm fold recognition method differs from protein threading methods [13, 23, 16, 17] in that pairwise interactions are not modeled or used. Instead, we employ Bayesian methods [4, 3, 21] to incorporate prior information in the form of Dirichlet mixture densities [24] over position specific ....

S. Eddy. Hidden Markov models. Curr. Opin. Struct. Biol., 6(3):361--365, 1996.

....method to identify relationships between molecular sequence families by aligning hidden Markov models. Hidden Markov models (HMMs) are probabilistic models that, in computational biology, have been applied to model the primary structure of a sequence family (Krogh et al. 1994; Baldi et al. 1994; Eddy, 1995, 1996; Eddy et al. 1995; Hughey and Krogh, 1996) Given the sequences or a multiple alignment of a sequence family, an HMM can be trained or built to describe the sequence consensus of this family. Therefore, we can study the evolutionary relationship between two sequence families by comparing the ....

....not previously been recognized. 2. Hidden Markov models HMMs of various architectures have been proposed for studying different types of problems in computational biology. We will focus on one type of HMM which has a linear structure and match, insert and delete states, also known as profile HMMs (Eddy, 3 1996). In the following sections, when we use the word hidden Markov model or HMM, we will refer to only this class of HMM. Figure 1 illustrates the structure of a simple profile HMM with 5 sites. Figure 1. A simple HMM. This model has 5 sites. The match states are shown as squares. The insert states ....

Eddy, S.R. (1996) Hidden Markov models. Curr. Opin. Struc. Biol., 6, 361-365.

....to estimate the emission probabilities for the amino acids in each state based on the training data (see Sections refsec:target and 2. 3) Hmms combine the best aspects of weight matrices and local sequence alignment methods, and can be used to assign probabilities to proteins in database search [6]. Our hmm fold recognition method differs from protein threading methods [13, 23, 16, 17] in that pairwise (residue residue) interactions are not modeled or used. Instead, we employ Bayesian methods [4, 3, 21] to incorporate prior information in the form of Dirichlet mixture densities [24] over ....

S. Eddy. Hidden Markov models. Curr. Opin. Struct. Biol., 6(3):361--365, 1996.

.... is one of the most successful modeling approaches for acoustic events in speech recognition (Rabiner 1989; Juang Rabiner 1991) and more recently they have proven useful for several problems in biological sequence analysis like protein modeling and gene finding, see e.g. Durbin et al. 1998; Eddy 1996; Krogh et al. 1994) Although the HMM is good at capturing the temporal nature of processes such as speech it has a very limited capacity for recognizing complex patterns involving more than first order dependencies in the observed data. This is due to the first order state process and the ....

Eddy, S. R. 1996. Hidden Markov models. Current Opinion in Structural Biology 6:361--365.

....paper, we introduce the probabilistic framework of the hidden Markov model (HMM) to transmembrane helix prediction. Hidden Markov models have been used successfully in computational biology to model e.g. the statistical structure of genomes (Churchill 1992) protein families (Krogh et al. 1994; Eddy 1996) and gene structure (Kulp et al. 1996; Krogh 1997) The basic principle is to define a set of states, each corresponding to a region or specific site in the proteins being modelled. In the simplest case, a model for a transmembrane protein may consist of three states: one for inside loops, one for ....

Eddy, S. R. 1996. Hidden Markov models. Current Opinion in Structural Biology 6:361--365.

.... association with the values for other sequences in the training set and a regularizer or Dirichlet mixture prior (Sj olander et al. 1996) The notation has been simplified; the reader is referred to the literature for a more detailed treatment (Rabiner, 1989; Krogh et al. 1994) and an HMM review (Eddy, 1996). The simplest approach to computing these O(nm) dynamic programs is to create a large, n Theta m table in memory to store values. Unfortunately, this table will not fit entirely in a workstation s memory for large model and sequence lengths. For example, if a family of long molecules is to be ....

Eddy, S. (1996). Hidden Markov models. Current Opinion in Structural Biology, 6 (3), 361--365.

.... CASPII prediction contest, the full protein folding problem is far from solved [20] Improved methods for sequence data analysis have resulted from a probabilistic modelling approach [5] In particular, the use of HMM s for sequence alignment tasks and protein modelling has been quite successful [6]. In a similar spirit, this work approaches the protein structure problem in terms of generative probabilistic modelling. The focus is on problems where the statistical correlations present in the data prompt the use of models which can capture long range correlations. For such problems, the ....

Eddy, Sean R. "Hidden Markov Models." Current Opinion in Structural Biology 6:361-365.

....model, one can use standard methods like maximum likelihood to determine the model parameters. Introductions to HMMs can be found in (Rabiner 1989; Krogh 1998; Durbin et al. 1998) In computational biology the most commonly used HMM type is probably the profile HMM (Krogh et al. 1994; Eddy 1996), which has a structure inspired by profiles (Gribskov, McLachlan, Eisenberg 1987) However, HMMs are more general, and the model structures used in this work are not of the profile type. One of the advantages of HMMs is that it is usually very easy to build biological knowledge into the model ....

Eddy, S. R. 1996. Hidden Markov models. Current Opinion in Structural Biology 6:361--365.

.... 1996) PositionSpecific Scoring Matrices (Henikoff et al. 1990) and hidden Markov models (HMMs) Churchill, 1989; White et al. 1994; Stultz et al. 1993; Krogh et al. 1994; Hughey and Krogh, 1996; Baldi et al. 1992; Baldi and Chauvin, 1994; Asai et al. 1993; Eddy, 1995; Eddy et al. 1995; Eddy, 1996) . We address this problem by incorporating prior information about amino acid distributions that typically occur in columns of multiple alignments into the process of building a statistical model. We present a method to condense the information in databases of multiple alignments into a mixture ....

Eddy, S.R. 1996. Hidden markov models. Current Opinions in Structural Biology.

....tutorial introduction to their use has been written by Rabiner (Rabiner, 1989) Throughout, I will often use HMM to refer to the specific case of profile HMMs as described by Krogh et al. Krogh et al. 1994) This shorthand usage is for convenience only. For a review of profile HMMs, see (Eddy, 1996), and for a complete book on the subject of probabilistic modeling in computational biology, see (Durbin et al. 1998) 2.2 Primary changes from HMMER 1.x HMMER 2 is an almost complete rewrite of the original 1992 1996 HMMER code. A list of the major changes follows. Plan7 The model ....

Eddy, S. R. (1996). Hidden Markov models. Curr. Opin. Struct. Biol., 6:361--365.

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S.R. Eddy, Pro l hidden Markov model, Bioinformatics 14 (2002), 755-763.

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Eddy, S.R. 1996. Hidden markov models. Current Opinions in Structural Biology.

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: 2360--2365. Eddy, S.R. 1996. Hidden Markov models. Curr. Opin. Struct. Biol.

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Eddy,S.R. (1996) Hidden Markov models. Curr. Opin. Struct. Biol., 6, 361--365.

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Eddy, S.R. (1996). Hidden Markov models. Curr. Opin. Struct. Biol. 6, 361-365.

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Eddy, S.R. Hidden Markov models. Curr. Opin. Struct,. Biol. 6:361--365, 1996.

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