Arbeitspapier
Detecting correlations among functional sequence motifs
Sequence motifs are words of nucleotides in DNA with biological functions, e.g. gene regulation. Identification of such words proceeds through rejection of Markov models on the expected motif frequency along the genome. Additional biological information can be extracted from the correlation structure among patterns of motif occurrences. In this paper a log-linear multivariate intensity Poisson model is estimated via expectation maximization on a set of motifs along the genome of E. coli K12. The proposed approach allows for excitatory as well as inhibitory interactions among motifs and between motifs and other genomic features like gene occurrences. Our findings confirm previous stylized facts about such types of interactions and shed new light on genome-maintenance functions of some particular motifs. We expect these methods to be applicable to a wider set of genomic features.
- Language
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Englisch
- Bibliographic citation
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Series: LEM Working Paper Series ; No. 2012/07
- Classification
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Wirtschaft
Mathematical and Quantitative Methods: General
Mathematical Methods
- Event
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Geistige Schöpfung
- (who)
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Pirino, Davide
Rigosa, Jacopo
Ledda, Alice
Ferretti, Luca
- Event
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Veröffentlichung
- (who)
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Scuola Superiore Sant'Anna, Laboratory of Economics and Management (LEM)
- (where)
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Pisa
- (when)
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2012
- Handle
- Last update
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10.03.2025, 11:41 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Pirino, Davide
- Rigosa, Jacopo
- Ledda, Alice
- Ferretti, Luca
- Scuola Superiore Sant'Anna, Laboratory of Economics and Management (LEM)
Time of origin
- 2012
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Algorithms for finding RNA sequence-structure motifs
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Detecting correlations and triangular arbitrage opportunities in the Forex by means of multifractal detrended cross-correlations analysis
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Transferring functional annotations of membrane transporters on the basis of sequence similarity and sequence motifs
Parametric bootstrapping for biological sequence motifs
Functional quantum abstract detecting systems
Effect of the sequence data deluge on the performance of methods for detecting protein functional residues
Algorithms for finding RNA sequence-structure motifs
Detecting epigenetic motifs in low coverage and metagenomics settings
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Detecting false positive sequence homology: a machine learning approach
Sequence Bundles: a novel method for visualising, discovering and exploring sequence motifs
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