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48
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
Nucleotides of Transcription Factor Binding Sites Exert Interdependent Effects on the Binding Affinities of Transcription Factors
, 2002
"... We can determine the effects of many possible sequence variations in transcription factor binding sites using microarray binding experiments. Analysis of wildtype and mutant Zif268 (Egr1) zinc fingers bound to microarrays containing all possible central 3 bp triplet binding sites indicates that the ..."
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Cited by 84 (4 self)
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We can determine the effects of many possible sequence variations in transcription factor binding sites using microarray binding experiments. Analysis of wildtype and mutant Zif268 (Egr1) zinc fingers bound to microarrays containing all possible central 3 bp triplet binding sites indicates that the nucleotides of transcription factor binding sites cannot be treated independently. This indicates that the current practice of characterizing transcription factor binding sites by mutating individual positions of binding sites one base pair at a time does not provide a true picture of the sequence specificity. Similarly, current bioinformatic practices using either just a consensus sequence, or even mononucleotide frequency weight matrices to provide more complete descriptions of transcription factor binding sites, are not accurate in depicting the true binding site specificities, since these methods rely upon the assumption that the nucleotides of binding sites exert independent effects on binding affinity. Our results stress the importance of complete reference tables of all possible binding sites for comparing protein binding preferences for various DNA sequences. We also show results suggesting that microarray binding data using particular subsets of all possible binding sites can be used to extrapolate the relative binding affinities of all possible fulllength binding sites, given a known binding site for use as a starting sequence for site preference refinement.
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.
Separating Real Motifs From Their Artifacts
, 2001
"... The typical output of many computational methods to identify binding sites is a long list of motifs containing some real motifs (those most likely to correspond to the actual binding sites) along with a large number of random variations of these. We present a statistical method to separate real moti ..."
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Cited by 22 (3 self)
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The typical output of many computational methods to identify binding sites is a long list of motifs containing some real motifs (those most likely to correspond to the actual binding sites) along with a large number of random variations of these. We present a statistical method to separate real motifs from their artifacts. This produces a short list of high quality motifs that is sufficient to explain the overrepresentation of all motifs in the given sequences. Using synthetic data sets, we show that the output of our method is very accurate. On various sets of upstream sequences in S. cerevisiae, our program identifies several known binding sites, as well as a number of significant novel motifs. Contact: fblanchem,saurabhg@cs.washington.edu
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...
Sequence alignment kernel for recognition of promoter regions
 Bioinformatics
, 2003
"... In this paper we propose a new method for recognition of prokaryotic promoter regions with startpoints of transcription. The method is based on Sequence Alignment Kernel, a function reflecting the quantitative measure of match between two sequences. This kernel function is further used in Dual SVM, ..."
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Cited by 18 (0 self)
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In this paper we propose a new method for recognition of prokaryotic promoter regions with startpoints of transcription. The method is based on Sequence Alignment Kernel, a function reflecting the quantitative measure of match between two sequences. This kernel function is further used in Dual SVM, which performs the recognition. Several recognition methods have been trained and tested on positive data set, consisting of 669 σ 70promoter regions with known transcription startpoints of Escherichia coli and two negative data sets of 709 examples each, taken from coding and noncoding regions of the same genome. The results show that our method performs well and achieves 16.5 % average error rate on positive & coding negative data and 18.6% average error rate on positive & noncoding negative data. Availability: The demo version of our method is accessible from our website
Statistical Significance of Probabilistic Sequence Alignment and Related Local Hidden Markov Models
 J. COMP. BIOL
, 2001
"... The score statistics of probabilistic gapped local alignment of random sequences is investigated both analytically and numerically. The full probabilistic algorithm (e.g., the “local” version of maximumlikelihood or hidden Markov model method) is found to have anomalous statistics. A modified “semi ..."
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Cited by 17 (2 self)
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The score statistics of probabilistic gapped local alignment of random sequences is investigated both analytically and numerically. The full probabilistic algorithm (e.g., the “local” version of maximumlikelihood or hidden Markov model method) is found to have anomalous statistics. A modified “semiprobabilistic” alignment consisting of a hybrid of Smith–Waterman and probabilistic alignment is then proposed and studied in detail. It is predicted that the score statistics of the hybrid algorithm is of the Gumbel universal form, with the key Gumbel parameter l taking on a fixed asymptotic value for a wide variety of scoring systems and parameters. A simple recipe for the computation of the “relative entropy,” and from it the finite size correction to l, is also given. These predictions compare well with direct numerical simulations for sequences of lengths between 100 and 1,000 examined using various PAM substitution scores and affine gap functions. The sensitivity of the hybrid method in the detection of sequence homology is also studied using correlated sequences generated from toy mutation models. It is found to be comparable to that of the Smith–Waterman alignment and significantly better than the Viterbi version of the probabilistic alignment.
Familybased Homology Detection via Pairwise Sequence Comparison
, 1998
"... The function of an unknown biological sequence can often be accurately inferred by identifying sequences homologous to the original sequence. Given a query set of known homologs, there exist at least three general classes of techniques for #nding additional homologs: pairwise sequence comparisons, m ..."
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Cited by 17 (2 self)
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The function of an unknown biological sequence can often be accurately inferred by identifying sequences homologous to the original sequence. Given a query set of known homologs, there exist at least three general classes of techniques for #nding additional homologs: pairwise sequence comparisons, motif analysis, and hidden Markov modeling. Pairwise sequence comparisons are typically employed when only a single query sequence is known. Hidden Markov models #HMMs#, on the other hand, are usually trained with sets of more than 100 sequences. Motifbased methods fall in between these two extremes. The currentwork compares the performance of representative examples of these three homology detection techniquesusing the BLAST, MEME and HMMER softwareacross a wide range of protein families, using query sets of varying sizes. A linear combination of multiple pairwise sequence comparisons outperforms motifbased and HMM methods for all query set sizes. Furthermore, heuristic pairwise com...
Homology Detection via Family Pairwise Search
 Journal of Computational Biology
, 1998
"... The function of an unknown biological sequence can often be accurately inferred by identifying sequences homologous to the original sequence. Given a query set of known homologs, there exist at least three general classes of techniques for finding additional homologs: pairwise sequence comparisons, ..."
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Cited by 13 (2 self)
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The function of an unknown biological sequence can often be accurately inferred by identifying sequences homologous to the original sequence. Given a query set of known homologs, there exist at least three general classes of techniques for finding additional homologs: pairwise sequence comparisons, motif analysis, and hidden Markov modeling. Pairwise sequence comparisons are typically employed when only a single query sequence is known. Hidden Markov models (HMMs), on the other hand, are usually trained with sets of more than 100 sequences. Motifbased methods fall in between these two extremes. The current work introduces a straightforward generalization of pairwise sequence comparison algorithms to the case when when multiple query sequences are available. This algorithm, called Family Pairwise Search (FPS), combines pairwise sequence comparison scores from each query sequence. A BLAST implementation of FPS is compared to representative examples of hidden Markov modeling...