An expectation maximization (EM) algorithm for the identification and characterization of common sites in unaligned biopolymer sequences

Lawrence, Charles E.; Reilly, Andrew

doi:10.1002/prot.340070105

Cited by 448 publications

(337 citation statements)

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“…The development of these methods has been motivated by the current rapid increase in sequence data because relatively large sets (containing, for example, more than 15 sequences) are needed for weakly conserved patterns to reach statistical significance. Lawrence et al (1993) describe a Gibbs sampling strategy for detecting conserved patterns in multiple sequences that is a stochastic analog of earlier expectation-maximization methods (Lawrence & Reilly, 1990;Cardon & Stormo, 1992) and that is closely related to (EM-based) hidden Markov model multiple sequence alignment methods (Baldi et al, 1994;Krogh et al, 1994), which, unlike the Gibbs sampler, permit gaps anywhere in the sequences. This Gibbs sampler (which is referred to here as the site sampler) addresses the problem of finding motifs when the number of occurrences of each motif in each sequence is assumed.…”

Section: 'mentioning

confidence: 99%

Gibbs motif sampling: Detection of bacterial outer membrane protein repeats

1995

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The detection and alignment of locally conserved regions (motifs) in multiple sequences can provide insight into protein structure, function, and evolution. A new Gibbs sampling algorithm is described that detects motif-encoding regions in sequences and optimally partitions them into distinct motif models; this is illustrated using a set of immunoglobulin fold proteins. When applied to sequences sharing a single motif, the sampler can be used to classify motif regions into related submodels, as is illustrated using helix-turn-helix DNA-binding proteins. Other statistically based procedures are described for searching a database for sequences matching motifs found by the sampler. When applied to a set of 32 very distantly related bacterial integral outer membrane proteins, the sampler revealed that they share a subtle, repetitive motif. Although BLAST (Altschul SF et al., 1990, J Mol Biol 215:403-410) fails to detect significant pairwise similarity between any of the sequences, the repeats present in these outer membrane proteins, taken as a whole, are highly significant (based on a generally applicable statistical test for motifs described here). Analysis of bacterial porins with known trimeric 0-barrel structure and related proteins reveals a similar repetitive motif corresponding to alternating membrane-spanning 0-strands. These &strands occur on the membrane interface (as opposed to the trimeric interface) of the &barrel. The broad conservation and structural location of these repeats suggests that they play important functional roles.

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Section: 'mentioning

confidence: 99%

Gibbs motif sampling: Detection of bacterial outer membrane protein repeats

1995

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“…Motif discovery methods usually fall in one of two main categories: (i) enumerative, which examine the frequency of all DNA strings and compute overrepresented strings to form a PWM [76][77][78][79] and (ii) probabilistic, which tackle the problem by creating a multiple local alignment of all sequences while simultaneously learning the PWM parameters using methods like expectation-maximization [80][81][82][83], Gibbs sampling [84][85][86][87][88][89] or greedy approaches [90]. Each category has certain advantages over the other.…”

Section: Identification Of Tf Binding Specificitiesmentioning

confidence: 99%

Identifying regulatory elements in eukaryotic genomes

Narlikar

Ovcharenko²

2009

Briefings in Functional Genomics and Proteomics

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Proper development and functioning of an organism depends on precise spatial and temporal expression of all its genes. These coordinated expression-patterns are maintained primarily through the process of transcriptional regulation. Transcriptional regulation is mediated by proteins binding to regulatory elements on the DNA in a combinatorial manner, where particular combinations of transcription factor binding sites establish specific regulatory codes. In this review, we survey experimental and computational approaches geared towards the identification of proximal and distal gene regulatory elements in the genomes of complex eukaryotes. Available approaches that decipher the genetic structure and function of regulatory elements by exploiting various sources of information like gene expression data, chromatin structure, DNA-binding specificities of transcription factors, cooperativity of transcription factors, etc. are highlighted. We also discuss the relevance of regulatory elements in the context of human health through examples of mutations in some of these regions having serious implications in misregulation of genes and being strongly associated with human disorders.

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“…The major advantage of EM over k-Means is its ability to model a much richer set of cluster shapes. This generality has made EM (and its many variants and extensions) the clustering algorithm of choice in data mining [7] and bioinformatics [17]. …”

Section: I-em With Expectation Maximization (Em)mentioning

confidence: 99%

Iterative Incremental Clustering of Time Series

Lin

Vlachos

Keogh

et al. 2004

Advances in Database Technology - EDBT 2004

133

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Abstract. We present a novel anytime version of partitional clustering algorithm, such as k-Means and EM, for time series. The algorithm works by leveraging off the multi-resolution property of wavelets. The dilemma of choosing the initial centers is mitigated by initializing the centers at each approximation level, using the final centers returned by the coarser representations. In addition to casting the clustering algorithms as anytime algorithms, this approach has two other very desirable properties. By working at lower dimensionalities we can efficiently avoid local minima. Therefore, the quality of the clustering is usually better than the batch algorithm. In addition, even if the algorithm is run to completion, our approach is much faster than its batch counterpart. We explain, and empirically demonstrate these surprising and desirable properties with comprehensive experiments on several publicly available real data sets. We further demonstrate that our approach can be generalized to a framework of much broader range of algorithms or data mining problems.

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An expectation maximization (EM) algorithm for the identification and characterization of common sites in unaligned biopolymer sequences

Cited by 448 publications

References 20 publications

Gibbs motif sampling: Detection of bacterial outer membrane protein repeats

Gibbs motif sampling: Detection of bacterial outer membrane protein repeats

Identifying regulatory elements in eukaryotic genomes

Iterative Incremental Clustering of Time Series

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