DECOD: fast and accurate discriminative DNA motif finding.
Identifieur interne : 000871 ( Ncbi/Curation ); précédent : 000870; suivant : 000872DECOD: fast and accurate discriminative DNA motif finding.
Auteurs : Peter Huggins [États-Unis] ; Shan Zhong ; Idit Shiff ; Rachel Beckerman ; Oleg Laptenko ; Carol Prives ; Marcel H. Schulz ; Itamar Simon ; Ziv Bar-JosephSource :
- Bioinformatics (Oxford, England) [ 1367-4811 ] ; 2011.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- chemical , chemistry : DNA.
- chemical , metabolism : Tumor Suppressor Protein p53.
- Algorithms, Base Sequence, Nucleotide Motifs, Sequence Analysis, DNA.
Abstract
Motif discovery is now routinely used in high-throughput studies including large-scale sequencing and proteomics. These datasets present new challenges. The first is speed. Many motif discovery methods do not scale well to large datasets. Another issue is identifying discriminative rather than generative motifs. Such discriminative motifs are important for identifying co-factors and for explaining changes in behavior between different conditions.
DOI: 10.1093/bioinformatics/btr412
PubMed: 21752801
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pubmed:21752801Le document en format XML
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<term>Nucleotide Motifs</term>
<term>Sequence Analysis, DNA</term>
<term>Tumor Suppressor Protein p53 (metabolism)</term>
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<term>Algorithmes</term>
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<term>Protéine p53 suppresseur de tumeur (métabolisme)</term>
<term>Séquence nucléotidique</term>
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<keywords scheme="MESH" type="chemical" qualifier="chemistry" xml:lang="en"><term>DNA</term>
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</keywords>
<keywords scheme="MESH" xml:lang="en"><term>Algorithms</term>
<term>Base Sequence</term>
<term>Nucleotide Motifs</term>
<term>Sequence Analysis, DNA</term>
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<term>Algorithmes</term>
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<term>Séquence nucléotidique</term>
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<front><div type="abstract" xml:lang="en">Motif discovery is now routinely used in high-throughput studies including large-scale sequencing and proteomics. These datasets present new challenges. The first is speed. Many motif discovery methods do not scale well to large datasets. Another issue is identifying discriminative rather than generative motifs. Such discriminative motifs are important for identifying co-factors and for explaining changes in behavior between different conditions.</div>
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