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312
Hidden Markov models in computational biology: applications to protein modeling
- JOURNAL OF MOLECULAR BIOLOGY
, 1994
"... Hidden.Markov Models (HMMs) are applied t.0 the problems of statistical modeling, database searching and multiple sequence alignment of protein families and protein domains. These methods are demonstrated the on globin family, the protein kinase catalytic domain, and the EF-hand calcium binding moti ..."
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Cited by 655 (39 self)
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Hidden.Markov Models (HMMs) are applied t.0 the problems of statistical modeling, database searching and multiple sequence alignment of protein families and protein domains. These methods are demonstrated the on globin family, the protein kinase catalytic domain, and the EF-hand calcium binding motif. In each case the parameters of an HMM are estimated from a training set of unaligned sequences. After the HMM is built, it is used to obtain a multiple alignment of all the training sequences. It is also used to search the. SWISS-PROT 22 database for other sequences. that are members of the given protein family, or contain the given domain. The Hi " produces multiple alignments of good quality that agree closely with the alignments produced by programs that incorporate three-dimensional structural information. When employed in discrimination tests (by examining how closely the sequences in a database fit the globin, kinase and EF-hand HMMs), the '\ HMM is able to distinguish members of these families from non-members with a high degree of accuracy. Both the HMM and PROFILESEARCH (a technique used to search for relationships between a protein sequence and multiply aligned sequences) perform better in these tests than PROSITE (a dictionary of sites and patterns in proteins). The HMM appecvs to have a slight advantage over PROFILESEARCH in terms of lower rates of false
Unsupervised Learning of Multiple Motifs in Biopolymers Using Expectation Maximization
- Machine Learning
, 1995
"... . The MEME algorithm extends the expectation maximization (EM) algorithm for identifying motifs in unalignedbiopolymer sequences. The aim of MEME is to discover new motifs in a set of biopolymer sequences where little or nothing is known in advance about any motifs that may be present. MEME innovati ..."
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Cited by 278 (8 self)
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. The MEME algorithm extends the expectation maximization (EM) algorithm for identifying motifs in unalignedbiopolymer sequences. The aim of MEME is to discover new motifs in a set of biopolymer sequences where little or nothing is known in advance about any motifs that may be present. MEME innovations expand the range of problems which can be solved using EM and increase the chance of finding good solutions. First, subsequences which actually occur in the biopolymer sequences are used as starting points for the EM algorithm to increase the probability of finding globally optimal motifs. Second, the assumption that each sequence contains exactly one occurrence of the shared motif is removed. This allows multiple appearances of a motif to occur in any sequence and permits the algorithm to ignore sequences with no appearance of the shared motif, increasing its resistance to noisy data. Third, a method for probabilistically erasing shared motifs after they are found is incorporated so tha...
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 174 (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...
Position-based sequence weights
- J. Mol. Biol
, 1994
"... Sequence weighting methods have been used to reduce redundancy and emphasize diversity in multiple sequence alignment and searching applications. Each of these methods is based on a notion of distance between a sequence and an ancestral or generalized sequence. We describe a different approach, whic ..."
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Cited by 139 (4 self)
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Sequence weighting methods have been used to reduce redundancy and emphasize diversity in multiple sequence alignment and searching applications. Each of these methods is based on a notion of distance between a sequence and an ancestral or generalized sequence. We describe a different approach, which bases weights on the diversity observed at each position in the alignment, rather than on a sequence distance measure. These position-based weights make minimal assumptions, are simple to compute, and perform well in comprehensive evaluations. Redundancy is a common feature of sequence databanks, where a typical gene or protein family is represented by a highly non-random sample of sequences. For example, an ancient protein family might be represented by a few highly diverged microbial and invertebrate sequences plus many mammalian sequences that form a closely related subgroup. This situation can be detrimental in sequence alignment and searching applications, where it is usually desirable to represent the diversity among related sequences. Since closely related sequences are largely redundant, they provide less information in a multiple sequence alignment than their distant cousins. Sequence weighting methods have been introduced to compensate for over-representation
Flexible sequence similarity searching with the FASTA3 program package
- Methods Mol. Biol
, 2000
"... Since the publication of the first rapid method for comparing biological sequences 15 years ago (1), DNA and protein sequence comparison have become routine steps in biochemical characterization, from newly cloned proteins to entire genomes. As the DNA and protein sequence databases become more comp ..."
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Cited by 124 (3 self)
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Since the publication of the first rapid method for comparing biological sequences 15 years ago (1), DNA and protein sequence comparison have become routine steps in biochemical characterization, from newly cloned proteins to entire genomes. As the DNA and protein sequence databases become more complete, a sequence similarity search is more likely to reveal
A Neural Network Method For Identification Of Prokaryotic And Eukaryotic Signal Peptides And Prediction Of Their Cleavage Sites
- Int. J. Neural Syst
, 1997
"... this paper we address the organism-specific aspects of the problem and present neural-network based prediction methods to identify signal peptides and their cleavage sites in protein sequences from Gram-positive and Gramnegative bacteria, humans and other eukaryotes. ..."
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Cited by 120 (2 self)
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this paper we address the organism-specific aspects of the problem and present neural-network based prediction methods to identify signal peptides and their cleavage sites in protein sequences from Gram-positive and Gramnegative bacteria, humans and other eukaryotes.
Empirical statistical estimates for sequence similarity searches
- J. Mol. Biol
, 1998
"... Sequence similarity searches today are the most effective method for exploiting the information in the rapidly growing DNA and protein sequence databases. One of the most dramatic improvements ..."
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Cited by 119 (3 self)
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Sequence similarity searches today are the most effective method for exploiting the information in the rapidly growing DNA and protein sequence databases. One of the most dramatic improvements
MetaMEME: motif-based hidden Markov models of protein families
- Comput Appl Biosci
, 1997
"... Motivation: Modeling families of related biological sequences using Hidden Markov models (HMMs), although increasingly widespread, faces at least one major problem: because of the complexity of these mathematical models, they require a relatively large training set in order to accurately recognize a ..."
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Cited by 95 (10 self)
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Motivation: Modeling families of related biological sequences using Hidden Markov models (HMMs), although increasingly widespread, faces at least one major problem: because of the complexity of these mathematical models, they require a relatively large training set in order to accurately recognize a given family. For families in which there are few known sequences, a standard linear HMM contains too many parameters to be trained adequately. Results: This work attempts to solve that problem by generating smaller HMMs which precisely model only the conserved regions of the family. These HMMs are constructed from motif models generated by the EM algorithm using the MEME software. Because motif-based HMMs have relatively few parameters, they can be trained using smaller data sets. Studies of short chain alcohol dehydrogenases and 4Fe-4S ferredoxins support the claim that motif-based HMMs exhibit increased sensitivity and selectivity in database searches, especially when training sets contain few sequences.
Valenzia A: Effective use of sequence correlation and conservation in fold recognition
- J Mol Biol
, 1999
"... Protein families are a rich source of information; sequence conservation and sequence correlation are two of the main properties that can be derived from the analysis of multiple sequence alignments. Sequence conservation is related to the direct evolutionary pressure to retain the chemical characte ..."
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Cited by 73 (7 self)
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Protein families are a rich source of information; sequence conservation and sequence correlation are two of the main properties that can be derived from the analysis of multiple sequence alignments. Sequence conservation is related to the direct evolutionary pressure to retain the chemical characteristics of some positions in order to maintain a given function. Sequence correlation is attributed to the small sequence adjustments needed to maintain protein stability against constant mutational drift. Here, we showed that sequence conservation and correlation were each frequently informative enough to detect incorrectly folded proteins. Furthermore, combining conservation, correlation, and polarity, we achieved an almost perfect discrimination between native and incorrectly folded proteins. Thus, we made use of this information for threading by evaluating the models suggested by a threading method according to the degree of proximity of the corresponding correlated, conserved, and apolar residues. The results showed that the fold recognition capacity of a given threading approach could be improved almost fourfold by selecting the alignments that score best under the three different sequencebased approaches.
A generalized profile syntax for biomolecular sequences motifs and its function in automatic sequence interpretation
- In Altman,R., Brutlag,D., Karp,P., Lathrop,R. and Searls,D. (eds), ISMB-94; Proceedings 2nd International Conference on Intelligent Systems for Molecular Biology. AAAIPress, Menlo Park
, 1994
"... bairoch @ cmu.unige.ch A general syntax for expressing biomolecular sequence motifs is described, which will be used in future releases of the PROSITE data bank and in a similar collection of nucleic acid sequence motifs currently under development. The central part of the syntax is a regular struct ..."
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Cited by 71 (2 self)
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bairoch @ cmu.unige.ch A general syntax for expressing biomolecular sequence motifs is described, which will be used in future releases of the PROSITE data bank and in a similar collection of nucleic acid sequence motifs currently under development. The central part of the syntax is a regular structure which can be viewed as a generalization of the profiles intro-duced by Gribskov and coworkers. Accessory features implement specific motif search strategies and provide information helpful for the interpretation of predicted matches. Two contrasting examples, representing E. coil promoters and SH3 domains respectively, are shown to demonstrate the versati.lity of the syntax, and its compati-bility with diverse motif search methods. It is argued, that a comprehensive machine-readable motif collection based on the new syntax, in conjunction with a standard search program, can serve as a general-purpose sequence interpretation and function prediction tool.
