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STAMP: a web tool for exploring DNA-binding motif similarities.” Nucleic Acids Res, 35(Web Server issue
, 2007
"... doi:10.1093/nar/gkm272 ..."
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TJ: NestedMICA: sensitive inference of over-represented motifs in nucleic acid sequence
- Nucleic Acids Res
"... NestedMICA is a new, scalable, pattern-discovery system for finding transcription factor binding sites and similar motifs in biological sequences. Like several previous methods, NestedMICA tackles this problem by optimizing a probabilistic mixture model to fit a set of sequences. However, the use of ..."
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Cited by 55 (1 self)
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NestedMICA is a new, scalable, pattern-discovery system for finding transcription factor binding sites and similar motifs in biological sequences. Like several previous methods, NestedMICA tackles this problem by optimizing a probabilistic mixture model to fit a set of sequences. However, the use of a newly developed inference strategy called Nested Sampling means NestedMICA is able to find optimal solutions without the need for a problematic initialization or seeding step. We investigate the performance of NestedMICA in a range scenario, on synthetic data and a well-characterized set of muscle regulatory regions, and compare it with the popular MEME program. We show that the new method is significantly more sensitive than MEME: in one case, it successfully extracted a target motif from background sequence four times longer than could be handled by the existing program. It also performs robustly on synthetic sequences containing multiple significant motifs. When tested on a real set of regulatory sequences, NestedMICA produced motifs which were good predictors for all five abundant classes of annotated binding sites.
On the immortality of television sets: ‘function’ in the human genome according to the evolution-free gospel of ENCODE.
- Genome Biology and Evolution
, 2013
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MotifCut: regulatory motif finding with maximum density subgraphs bioinformatics
- In Proceedings of International Conference on Intelligent Systems and Molecular Biology
, 2006
"... doi:10.1093/bioinformatics/btl243BIOINFORMATICS ..."
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Efficient and Accurate Discovery of Patterns in Sequence Data Sets
- IEEE Transactions On Knowledge and Data Engineering
, 2011
"... Abstract — Existing sequence mining algorithms mostly focus on mining for subsequences. However, a large class of applications, such as biological DNA and protein motif mining, require efficient mining of “approximate ” patterns that are contiguous. The few existing algorithms that can be applied to ..."
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Cited by 13 (0 self)
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Abstract — Existing sequence mining algorithms mostly focus on mining for subsequences. However, a large class of applications, such as biological DNA and protein motif mining, require efficient mining of “approximate ” patterns that are contiguous. The few existing algorithms that can be applied to find such contiguous approximate pattern mining have drawbacks like poor scalability, lack of guarantees in finding the pattern, and difficulty in adapting to other applications. In this paper, we present a new algorithm called FLAME (FLexible and Accurate Motif DEtector). FLAME is a flexible suffix tree based algorithm that can be used to find frequent patterns with a variety of definitions of motif (pattern) models. It is also accurate, as it always find the pattern if it exists. Using both real and synthetic datasets, we demonstrate that FLAME is fast, scalable, and outperforms existing algorithms on a variety of performance metrics. Using FLAME, it is now possible to mine datasets that would have been prohibitively difficult with existing tools. I.
MoD Tools: regulatory motif discovery in nucleotide sequences from co-regulated or homologous genes
- Nucleic Acids Res
, 2006
"... sequences from co-regulated or homologous genes ..."
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Localized motif discovery in gene regulatory sequences
, 2010
"... Motivation: Discovery of nucleotide motifs that are localized with respect to a certain biological landmark is important in several appli-cations, such as in regulatory sequences flanking the transcription start site, in the neighborhood of known transcription factor binding sites, and in transcript ..."
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Cited by 10 (0 self)
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Motivation: Discovery of nucleotide motifs that are localized with respect to a certain biological landmark is important in several appli-cations, such as in regulatory sequences flanking the transcription start site, in the neighborhood of known transcription factor binding sites, and in transcription factor binding regions discovered by massively parallel sequencing (ChIP-Seq). Results: We report an algorithm called LocalMotif to discover such localized motifs. The algorithm is based on a novel scoring function, called spatial confinement score, which can determine the exact interval of localization of a motif. This score is combined with other existing scoring measures including over-representation and relative entropy to determine the overall prominence of the motif. The approach successfully discovers biologically relevant motifs and their intervals of localization in scenarios where the motifs cannot be discovered by general motif finding tools. It is especially useful for discovering multiple co-localized motifs in a set of regulatory sequences, such as those identified by ChIP-Seq. Availability and Implementation: The LocalMotif software is available at
SPACER: Identification of cis-regulatory elements with noncontiguous
"... critical residues ..."
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Evolutionary divergence and limits of conserved non-coding sequence detection
, 2010
"... in plant genomes ..."
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