Results 1  10
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35
Hidden Markov models for detecting remote protein homologies
 Bioinformatics
, 1998
"... A new hidden Markov model method (SAMT98) for nding remote homologs of protein sequences is described and evaluated. The method begins with a single target sequence and iteratively builds a hidden Markov model (hmm) from the sequence and homologs found using the hmm for database search. SAMT98 is ..."
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Cited by 306 (12 self)
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A new hidden Markov model method (SAMT98) for nding remote homologs of protein sequences is described and evaluated. The method begins with a single target sequence and iteratively builds a hidden Markov model (hmm) from the sequence and homologs found using the hmm for database search. SAMT98 is also used to construct model libraries automatically from sequences in structural databases. We evaluate the SAMT98 method with four datasets. Three of the test sets are foldrecognition tests, where the correct answers are determined by structural similarity. The fourth uses a curated database. The method is compared against wublastp and against doubleblast, a twostep method similar to ISS, but using blast instead of fasta. Results SAMT98 had the fewest errors in all tests dramatically so for the foldrecognition tests. At the minimumerror point on the SCOPdomains test, SAMT98 got 880 true positives and 68 false positives, doubleblast got 533 true positives with 71 false positives, and wublastp got 353 true positives with 24 false positives. The method is optimized to recognize superfamilies, and would require parameter adjustment to be used to nd family or fold relationships. One key to the performance of the hmm method is a new scorenormalization technique that compares the score to the score with a reversed model rather than to a uniform null model. Availability A World Wide Web server, as well as information on obtaining the Sequence Alignment and PREPRINT to appear in Bioinformatics, 1999
Hidden Markov models for sequence analysis: extension and analysis of the basic method
, 1996
"... Hidden Markov models (HMMs) are a highly effective means of modeling a family of unaligned sequences or a common motif within a set of unaligned sequences. The trained HMM can then be used for discrimination or multiple alignment. The basic mathematical description of an HMM and its expectationmaxi ..."
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Cited by 164 (20 self)
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Hidden Markov models (HMMs) are a highly effective means of modeling a family of unaligned sequences or a common motif within a set of unaligned sequences. The trained HMM can then be used for discrimination or multiple alignment. The basic mathematical description of an HMM and its expectationmaximization training procedure is relatively straightforward. In this paper, we review the mathematical extensions and heuristics that move the method from the theoretical to the practical. Then, we experimentally analyze the effectiveness of model regularization, dynamic model modification, and optimization strategies. Finally it is demonstrated on the SH2 domain how a domain can be found from unaligned sequences using a special model type. The experimental work was completed with the aid of the Sequence Alignment and Modeling software suite. 1 Introduction Since their introduction to the computational biology community (Haussler et al., 1993; Krogh et al., 1994a), hidden Markov models (HMMs...
Approaches to the Automatic Discovery of Patterns in Biosequences
, 1995
"... This paper is a survey of approaches and algorithms used for the automatic discovery of patterns in biosequences. Patterns with the expressive power in the class of regular languages are considered, and a classification of pattern languages in this class is developed, covering those patterns which a ..."
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Cited by 138 (21 self)
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This paper is a survey of approaches and algorithms used for the automatic discovery of patterns in biosequences. Patterns with the expressive power in the class of regular languages are considered, and a classification of pattern languages in this class is developed, covering those patterns which are the most frequently used in molecular bioinformatics. A formulation is given of the problem of the automatic discovery of such patterns from a set of sequences, and an analysis presented of the ways in which an assessment can be made of the significance and usefulness of the discovered patterns. It is shown that this problem is related to problems studied in the field of machine learning. The largest part of this paper comprises a review of a number of existing methods developed to solve this problem and how these relate to each other, focusing on the algorithms underlying the approaches. A comparison is given of the algorithms, and examples are given of patterns that have been discovered...
Scoring Hidden Markov Models
"... Motivation: Statistical sequence comparison techniques, such as hidden Markov models and generalized pro les, calculate the probability that a sequence was generated by a given model. Logodds scoring is a means of evaluating this probability by comparing it to a null hypothesis, usually a simpler s ..."
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Cited by 37 (5 self)
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Motivation: Statistical sequence comparison techniques, such as hidden Markov models and generalized pro les, calculate the probability that a sequence was generated by a given model. Logodds scoring is a means of evaluating this probability by comparing it to a null hypothesis, usually a simpler statistical model intended to represent the universe of sequences as a whole, rather than the group of interest. Such scoring leads to two immediate questions: what should the null model be, and what threshold of logodds score should be deemed a match to the model. Results: This paper experimentally analyses these two issues. Within the context of the Sequence Alignment and Modeling software suite (SAM), we consider a variety ofnull models and suitable thresholds. Additionally, we consider HMMer's logodds scoring and SAM's original Zscoring method. Among the null model choices, a simple looping null model that emits characters according to the geometric mean of the character probabilities in the columns modeled by the HMM performs well or best across all four discrimination experiments.
A flexible motif search technique based on generalized profiles
 COMPUTERS AND CHEMISTRY
, 1996
"... ... generalized profile syntax serving as a motif definition language; and (2) a motif search method specifically adapted to the problem of finding multiple instances of a motif in the same sequence. The new profile structure, which is the core of the generalized profile syntax, combines the functio ..."
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Cited by 35 (7 self)
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... generalized profile syntax serving as a motif definition language; and (2) a motif search method specifically adapted to the problem of finding multiple instances of a motif in the same sequence. The new profile structure, which is the core of the generalized profile syntax, combines the functions of a variety of motif descriptors implemented in other methods, including regular expressionlike patterns, weight matrices, previously used profiles, and certain types of hidden Markov models (HMMs). The relationship between generalized profiles and other biomolecular motif descriptors is analyzed in detail, with special attention to HMMs. Generalized profiles are shown to be equivalent to a particular class of HMMs, and conversion procedures in both directions are given. The conversion procedures provide an interpretation for local alignment in the framework of stochastic models, allowing for clear, simple significance tests. A mathematical statement of the motif search problem defines the new method exactly without linking it to a specific algorithmic solution. Part of the definition includes a new definition of disjointness of alignments.
The emergence of pattern discovery techniques in computational biology
 Metabolic Engineering
, 2000
"... In the past few years, pattern discovery has been emerging as a generic tool of choice for tackling problems from the computational biology domain. In this presentation, and after defining the problem in its generality, we review some of the algorithms that have appeared in the literature and descri ..."
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Cited by 28 (4 self)
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In the past few years, pattern discovery has been emerging as a generic tool of choice for tackling problems from the computational biology domain. In this presentation, and after defining the problem in its generality, we review some of the algorithms that have appeared in the literature and describe several applications of pattern discovery to problems from computational biology. 2000 Academic Press 1.
Fast and simple character classes and bounded gaps pattern matching, with application to protein searching
 Journal of Computational Biology
, 2001
"... The problem of fast exact and approximate searching for a pattern that contains classes of characters and bounded size gaps (CBG) in a text has a wide range of applications, among which a very important one is protein pattern matching (for instance, one PROSITE protein site is associated with the CB ..."
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Cited by 23 (4 self)
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The problem of fast exact and approximate searching for a pattern that contains classes of characters and bounded size gaps (CBG) in a text has a wide range of applications, among which a very important one is protein pattern matching (for instance, one PROSITE protein site is associated with the CBG [RK]  x(2,3)  [DE]  x(2,3)  Y, where the brackets match any of the letters inside, and x(2,3) a gap of length between 2 and 3). Currently, the only way to search for a CBG in a text is to convert it into a full regular expression (RE). However, a RE is more sophisticated than a CBG, and searching for it with a RE pattern matching algorithm complicates the search and makes it slow. This is the reason why we design in this article two new practical CBG matching algorithms that are much simpler and faster than all the RE search techniques. The first one looks exactly once at each text character. The second one does not need to consider all the text characters, and hence it is usually faster than the first one, but in bad cases may have to read the same text character more than once. We then propose a criterion based on the form of the CBG to choose a priori the fastest between both. We also show how to search permitting a few mistakes in the occurrences. We performed many practical experiments using the PROSITE database, and all of them show that our algorithms are the fastest in virtually all cases.
Weighting Hidden Markov Models For Maximum Discrimination
 Bioinformatics
, 1998
"... 1.1 Motivation Hidden Markov models can efficiently and automatically build statistical representations of related sequences. Unfortunately, training sets are frequently biased toward one subgroup of sequences, leading to an insufficiently general model. This work evaluates sequence weighting metho ..."
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Cited by 20 (3 self)
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1.1 Motivation Hidden Markov models can efficiently and automatically build statistical representations of related sequences. Unfortunately, training sets are frequently biased toward one subgroup of sequences, leading to an insufficiently general model. This work evaluates sequence weighting methods based on the maximumdiscrimination idea. 1.2 Results One good method scales sequence weights by an exponential that ranges between 0.1 for the best scoring sequence and 1.0 for the worst. Experiments with a curated data set show that while training with one or two sequences performed worse than singlesequence Probabilistic SmithWaterman, training with five or ten sequences reduced errors by 20% and 51%, respectively. This new version of the SAM HMM suite outperforms HMMer (17% reduction over PSW for 10 training sequences), MetaMEME (28% reduction), and unweighted SAM (31% reduction). 1.3 Availability A WorldWide Web server, as well as information on obtaining the Sequence Alignme...
In silico prediction of the peroxisomal proteome in fungi, plants and animals
 Journal of Molecular Biology
, 2003
"... In an attempt to improve our abilities to predict peroxisomal proteins, we have combined machinelearning techniques for analyzing peroxisomal targeting signals (PTS1) with domainbased crossspecies comparisons between eight eukaryotic genomes. Our results indicate that this combined approach has a ..."
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Cited by 10 (1 self)
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In an attempt to improve our abilities to predict peroxisomal proteins, we have combined machinelearning techniques for analyzing peroxisomal targeting signals (PTS1) with domainbased crossspecies comparisons between eight eukaryotic genomes. Our results indicate that this combined approach has a significantly higher specificity than earlier attempts to predict peroxisomal localization, without a loss in sensitivity. This allowed us to predict 430 peroxisomal proteins that almost completely lack a localization annotation. These proteins can be grouped into 29 families covering most of the known steps in all known peroxisomal pathways. In general, plants have the highest number of predicted peroxisomal proteins, and fungi the smallest number.